Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products’ and Foods’ Origin

Mazarakioti, Eleni C.; Zotos, Anastasios; Thomatou, Anna-Akrivi; Kontogeorgos, Achilleas; Patakas, Angelos; Ladavos, Athanasios

doi:10.3390/foods11223705

Open AccessReview

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products’ and Foods’ Origin

¹

Department of Food Science and Technology, University of Patras, 30100 Agrinio, Greece

²

Department of Sustainable Agriculture, University of Patras, 30100 Agrinio, Greece

³

Department of Agriculture, International Hellenic University, 57001 Thessaloniki, Greece

^*

Authors to whom correspondence should be addressed.

Foods 2022, 11(22), 3705; https://doi.org/10.3390/foods11223705

Submission received: 11 October 2022 / Revised: 11 November 2022 / Accepted: 15 November 2022 / Published: 18 November 2022

(This article belongs to the Special Issue Foods: 10th Anniversary)

Download

Browse Figures

Versions Notes

Abstract

:

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human’s health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

Keywords:

ICP-MS; geographical origin; authenticity; traceability; agricultural products; foods; beverages

1. Introduction

Food safety management (or food forensics) focuses on the strengthening of food safety and quality control procedures [1]. Effective control protocols shield health and well-being of people, and subsequently support financially domestic, regional, and international markets and producers. The design of food safety and quality control systems has become more essential than ever due to the tendency to low-cost products. Global financial crisis, COVID-19 pandemic and plenty of other social issues have resulted in economic fallout. Due to lockdowns millions of people suffer a lot of hardship among others unemployment, shortages in supply chain, higher transportations expenses which directly rise product’s final price. The increased cost of living due to inflation has influenced the purchasing decision of consumers, most of the times against the quality. The chart flow in Figure 1 presents the change in purchasing behavior of consumers in United States [2].

According to World Health Organization (WHO), more than 200 diseases are spread around the world, through food contaminated with infectious microorganisms, viruses or chemical substances [3]. Food contamination could be caused in any stage of the supply chain process; however, knowing the geographical origin of edible products consists the front line of quality control. Due to this fact, consumers have become more cautious about the origin of foodstuff hence, they require for supply chain transparency. In addition, given the tremendous global demand for food, some producers debase the quality of their products by either altering the components or by mislabeling the products for economic gain. Frequently, authentic products are partially or totally substituted by undeclared ones, with the intention of reducing the cost production [4]. Noticeable fraudulent activity has been reported in agri-food industry focusing in many cases on rice [5,6], wine [7], milk [8] and olive oil [9,10].

All developed countries have enacted food laws laying down the basic principles and procedures for safety and quality. American Viticultural Areas in United States, Wine of Origin in South Africa, Denominación de Origen in Spain and Denominazione di Origine Controllata in Italy are few of the standardization organizations around the world supervising the production process and certifying the origin of agricultural products, foods and beverages. In an attempt to minimize and/or prevent food adulteration, in 2002, European Union (EU) adopted the Regulation (EC) No 178/2002 (amended by Regulations (EU) No. 652/2014, No. 2017/228 and 2019/1381) and established the European Food Safety Authority (EFSA). The latter is an independent scientific agency which is responsible for examining novel issues and hazards, and simultaneously developing the evaluation methodologies providing scientific advice upon request of EC and EU Member States. Particularly, EU has established regulations which define the obligation of indicating the origin of primary ingredients (Regulation (EU) No 1169/2011 and Implementing Regulation (EU) 2018/775), the origin of meat (Regulation (EU) No 1337/2013), and the origin of fishery and aquaculture products (Regulation (EU) No 1379/2013). Additionally, the EU traceability regulations (Regulation (EC) No 178/2002, Regulation (EU) 1151/2012 amended by Regulation (EU) 2017/625) protects the names and the reputation of agri-food products from specific geographical provenance as follows:

(a): Protected designation of origin (PDO/food, agricultural products, and wines). All the production stages are taken place in specific geographical region.
(b): Protected geographical indication (PGI/food, agricultural products, and wines). At least one of the production stages are taken place in specific geographical region.
(c): Geographical indication (GI/Spirit drinks, and aromatized wines). In this category, at least one stage of the production should be in a specific area.

It is more than obvious that authenticity and traceability provide the base on quality control and safety of agricultural products. The last decades, much effort has been focused on developing highly sophisticated biological and chemical scientific methods for certifying the origin and authentic nature of agri-food [11]. Generally, biological methods aim to evaluate the organic part (i.e., DNA, sugars, lipids, and proteins) while determination of multi-elemental compositions and/or isotopic fingerprint of an agricultural product reflects the growth conditions on a particular geographical area. Among the traceability methods, Nuclear Magnetic Resonance (NMR) is typically used for analyzing the chemical and molecular food composition. NMR spectroscopy or in combination with other techniques have been applied to determine the origin of foodstuffs [11]. Trace and rare earth elements have also been shown to play an important role on geographical origin and authenticity of a variety of products including, among others, wheat grains, wines, dairies, olive oils, legumes and meats. Currently, atomic and mass spectrometry which comprise Atomic Absorption Spectrometry (AAS), Atomic Fluorescence Spectrometry (AFS), Flame Atomic Absorption Spectrometry (FAAS), Inductively Plasma Optical Emission Spectrometry (ICP-OES), Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) are the most common analytical methods for the elemental or multi-elemental determination of food samples [12].

Isotope-Ratio Mass Spectrometry (IRMS) is able to provide a database of stable isotope ratios of C (¹³C/¹²C), N (¹⁵N/¹⁴N), O (¹⁸O/¹⁶O), H (²H/¹H) and S (³⁴S/³²S). These elements are directly affected by the growth environment of the product, as they participate in all significant biological and ecological pathways. Hence, they can provide a stable isotope fingerprinting representative of a specific geographical provenance which has obvious advantages [13]. Nonetheless, it has been noticed that concentrations of light elements (C, O, N, H and S) are occasionally influenced by their involvement in biological and ecological cycles which is a negative aspect of traceability [14,15]; therefore, isotope ratios of heavy elements (Sr and Pb) are also investigated. Sr and Pb could be considered as good candidates for trace markers as the isotope ratios of each element (⁸⁷Sr/⁸⁶Sr, and ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁶Pb/²⁰⁷Pb and ²⁰⁸Pb/²⁰⁶Pb) are associated with the age of the substratum [16,17,18]. Assuming that the amount of these elements on the soil are proportionally related to their concentrations absorbed by the plant, they could be excellent markers to predict the geographical origin. In case of Lead, the anthropogenic source (fertilizers, pesticides etc.), which leads to environmental pollution, should also be considered.

As an alternative or complementary to C, N, O, H and S stable isotope analysis, multi-elemental composition analysis is increasingly being investigated in identifying the geographical origin of edible products. In the same manner as with the latter elements, the composition of metals in agri-food substances, is directly related to their availability and mobility in the soil of the growing area. Considering the elemental diversity of the different substrata, multi-elemental analysis of agricultural products can lead to provenance recognition. Several research studies utilize ICP-MS and/or IRMS techniques to trace the origin and certify the authenticity of food products. Comparing with other analytical methods ICP-MS is a precise and accurate technique with wide measurement range (for more than 65 elements), low detection limits (ppt), large capacity sample, and developed methods which can minimize the possible interferences. Furthermore, it is able to perform isotopic analyses of heavier elements than the previously mentioned, broadening the employment of isotope ratio method [19]. Ignacio Garcia Alonso and co-workers published in 2022, an excellently written and well explained tutorial review about the use of ICP-MS for isotopic measurements [20]. Overall, ICP-MS finds application in disciplinary areas of research, including pharmaceuticals, medicine, food chemistry, environmental science, and semiconductors.

The present systematic review aims to gather the research work has been done to date, regarding the application of ICP-MS as unique technique or as complementary to stable isotope analysis in authenticity of geographical origin of agri-food products.

2. ICP-MS: Understanding the Basic Principles

ICP-MS is a robust analytical technique for the determination of multi-elemental composition (qualitatively), concentration (quantitatively) and isotopic abundances of various matrices. Generally, an ICP-MS instrument consists of (i) a sample introduction system, (ii) an ion source (Inductively Coupled Plasma, ICP), (iii) the electrostatic lenses, (iv) an interface, (v) a mass spectrometer and (vi) a detector. Figure 2 depicts a typical schematic representation of the basic components of ICP-MS.

In food chemistry, prior to analysis, sample preparation procedure comprises various steps depending on material’s physical state which could include washing, lyophilization, crushing, extraction, and homogenization. In all cases a thermally-assisted digestion in acidic conditions (HNO₃, HNO₃/H₂O₂, HNO₃/HCl, or HNO₃/HF) is essential. According to the literature, samples are commonly digested with pure HNO₃ (65–70%) on microwave oven, followed by dilution of the resulting solution with ultra-pure water [22]. It is an essential step of the analysis leading to the dissociation of sample’s matrix and the simultaneous release of the elements (analytes).

Due to the fact that initially the ICP-MS analysis was designed for liquids, samples are ordinarily pumped into a solution nebulizing system in liquid phase. Although, gases and solids are able to be inserted by using diverse introduction systems including chemical gas generation, electrothermal vaporization, high performance liquid chromatography (HPLC) or laser ablation [23]. In a typical arrangement, samples are placed into an autosampler and a peristaltic pump guides them to the nebulizer. Each sample is mixed with argon (Ar) gas creating an aerosol which enters to the spray chamber. The later extracts the large aerosol droplets, due to the inefficiency of the plasma to dissociates them, and subsequently normalizes the flow of the liquid coming from the peristaltic pumps [24,25]. Thereafter, the final fine aerosol traverses the main channel of the argon plasma. The high temperature plasma fosters the ionization of the sample by vaporizing, decomposing, atomizing and finally ionizing the droplets of the aerosol. It should be noted that the ionization potential and the temperature of the plasma define the degree of ionization of the elements [26]. Argon has much higher ionization potential than the majority of the elements, leading to their efficient ionization and creating singly charged positive ions. The generated positively charged ions are separated from the plasma into the interface region through the orifice (~1 mm) at the tip of the sample cone, which, in turn passes through the next orifice (~0.45 mm) of the skimmer cone. Between the two cones the interface pressure is constant at 150–300 Pa. Skimmer cone is the entrance of the high vacuum area (~77 × 10⁻⁵–1 × 10⁻³ Pa) where the ion lenses and the mass analyzer are located. The electrostatic lenses or ion optics focus and direct the ion beam toward the mass spectrometer, and simultaneously redirect unwanted species (i.e., non-ionized substances and photons) which are the main reason of background noise and signal instability when they are observed by the detector. The fundamental operation of the lenses depends on the manufacturer company of the ICP-MS instrument (i.e., Agilent, and Perkin Elmer). Following the procedure, the ion beam reaches the mass analyzer, the type of which is depended on the ICP-MS system. Generally, in food analysis, quadrupole or octupole type mass analyzer is used. The function of all mass analyzers is based on the determination of ion’s mass/charge (m/z) ratio, which is responsible for the division of ions in a sample [27,28]. At the end of mass analyzer, the positively charged ions, separated according to their m/z ratio, strike the detector, which is largely an electron multiplier (EM). The resulting culminated signal pulse is referred as ion ‘count’ (with units counts per second (CPS)) and corresponds to ions with specific m/z ratio. Due to this fact, ICP-MS is a highly sensitive analytical technique with detection limits being in ng/L for most of the elements.

As most analytical techniques, ICP-MS utilizes a calibration curve to convert the CPS signal to concentration. Calibration curves are constructed, for each element which is needed to be investigated, by measuring solutions of known concentrations. Figure 3 depicts an example of a calibration curve for calcium (⁴⁴Ca) measured in different concentrations (2, 10, 50, 100, and 600 ppb). Moreover, in an attempt to correct variations in instrument’s sensitivity and/or matrix effects internal standardization (IS) technique is performed. One or more internal standard (IS) elements with similar masses and ionization potentials with the measured analytes are used in order to monitor the analyte-IS signal ratio [28,29,30]. When this ratio is relatively constant the (±20% referring to IS recovery, Figure 4) alterations in operational conditions and matrix effects are minimized, improving the sensitivity and precision of the ICP-MS analysis. Depending on the material which is analyzed other correction methods have been reported including matrix-mached calibration [31,32], method of standard additions [33] and isotope dilution [34,35].

Interferences in ICP-MS analysis are divided into two main categories: (a) spectroscopic, when atomic or molecular ions have equal m/z ratio as the analyte, and (b) non-spectroscopic, which occurred by sample matrix and/or instrument drift. Spectroscopic interferences are potentially a result of isobaric elements (i.e., ¹¹⁴Cd⁺ and ¹¹⁴Sn⁺), double charged (i.e., ⁸⁸Sr²⁺ and ⁴⁴Ca⁺), or polyatomic ions (i.e., ⁴⁴CO₂⁺ and ⁴⁴Ca⁺), and/or tailing interferences due to overlapping of two adjacent peaks in the spectra. Table 1 lists a number of the most common spectroscopic interferences [28,36]. On the other hand, analytical errors may be occurred by the non-spectroscopic interferences. The latter arise from matrix effects or instrument drifts leading to suppression or enhancement of the signal. Malfunctions at introduction system (Sample Introduction Effects), ionization in the plasma (Plasma Effects), and electrostatic phenomena among the ions in the interface and ion lenses (Space-Charge Effects) constitute the origin of matrix effects. On top of that, gradually by the use of instrument salts dissolved in the aerosol deposit around cones. The fact that decreases the size of the orifice of the cones suppressing the analyte’s signal. In-depth study about ICP-MS interferences and various strategies to reduce or eliminate them can be found on the review article written by Wilschefski and Baxter, published in 2019 [28].

According to the literature various methods of data analysis are used for building analytical models in order to accurately determine the geographical origins of the samples based on the concentrations of measured analytes. Principal component analysis (PCA), Cluster analysis (CA), Linear Discriminant Analysis (LDA), and Canonical Discriminant Analysis (CDA) consist the most common analytical models [5].

3. Database and Literature Search

Two online databases, Web of Science (Clarivate) and Scopus (Elsevier), were used for the literature searching. The terms were used, initially, on each of the above databases, are the following: ICP-MS AND authentication origin, ICP-MS AND geographical origin, ICP-MS AND geographical fingerprints, ICP-MS AND protected geographical indication, ICP-MS AND protected designation of origin, ICP-MS AND traceability, ICP-MS AND geographical origin AND agricultural products, ICP-MS AND geographical origin AND foods, ICP-MS AND protected geographical indication AND agricultural products, ICP-MS AND protected geographical indication AND foods, ICP-MS AND protected designation of origin AND agricultural products, ICP-MS AND protected designation of origin AND foods, ICP-MS AND traceability AND agricultural products, and ICP-MS AND traceability AND foods. As a second step, the type of each individual product (X), resulting from the initial literature investigation, was added as a term as follows: ICP-MS AND authentication origin AND (X), ICP-MS AND geographical origin AND (X), ICP-MS AND geographical fingerprints AND (X), ICP-MS AND protected geographical indication AND (X), ICP-MS AND protected designation of origin AND (X), ICP-MS AND traceability AND (X). All the included records are screened to be relevant to our topic of study, which includes the employment of ICP-MS analysis for the geographical origin authentication of agricultural products and foods. Moreover, Zotero software was used to prepare the references and simultaneously to avoid duplicated publications. The final 173 articles were classified according to the food group investigated for the geographical discrimination with the use of ICP-MS technique (Figure 5).

4. Discussion

Interpretation of ICP-MS analysis to authentication and traceability would be easier in groups of food samples. Therefore, the products were divided into classes with particular features. Our aim is to provide and discuss the most important information for each group of products. Table 2 lists the published studies up to date (August 2022) referring to ICP-MS analysis as a method for geographical origin authentication of agricultural products and foods.

4.1. Alcoholic Beverages

Wine belongs to the most strictly protected products worldwide. Fast and accurate analytical methodology for authenticity and traceability has become apparent. It has been shown that elemental analysis (trace and rare elements, and stable isotope ratios) is commonly used for the geographical origin of wines [37].

In 1997, Greenough et al. [38], performed multi-elemental analysis with the use of ICP-MS, in an attempt to build element ‘fingerprinting’ of different varieties of wine from the Okanagan Valley (British Columbia, Canada). The same year, Baxter and coworkers [39] analyzed the trace element composition of wines from England and Spain. According to our knowledge, the forementioned studies were the first endeavors to distinguish diverse varieties and origins of wines with the employment of ICP-MS. Since then, several studies [40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59] (Table 2) have been carried out on the elemental determination and correlation among various wine varieties, grapes, and soils aiming to trace elements which would be excellent markers for the authentication of geographical origin. In 2021, Wang’s group compared the elemental profile and the variations from soil to grapes and wines from diverse Chinese regions. They claimed that K, Sr, and Li could potentially be used as tracers for geographical origin of Chinese wine; those elements show significant correlation among all the samples [60]. Remarkable results have been shown when multi-elemental analysis is combined with the ⁸⁷Sr/⁸⁶Sr isotope ratio [61,62,63]. ⁸⁷Sr/⁸⁶Sr ratio makes an exceptional marker for the origin determination as it is directly related to the geological age of the bedrock of a territory. Detailed studies by Bora et al. [63], came to the conclusion that there was a relevant geographical origin discrimination of wines based on their elemental composition and ²⁰⁷Pb/²⁰⁶Pb, ²⁰⁸Pb/²⁰⁶Pb, ²⁰⁴Pb/²⁰⁶Pb, and ⁸⁷Sr/⁸⁶Sr isotope ratio. Another notable group of elements which has engaged the scientific attention in food forensics is Lanthanides (Ln) or Rare Earth Elements (REE): Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu including La, and Y. Samples were analyzed focusing on the REE, claiming that traceability is feasible through soil to grape and must, however changes occurred possibly after clarification process of wine with bentonites [7,64,65,66]. Figure 6 is reproduced by the publication of Aceto et al. [7]; it depicts the variation of lanthanides concentrations measured in pulp, skin, and seed of the grapes and the must. As it is shown only Eu did not follow the stable tendency of the rest lanthanide elements.

Other alcoholic beverages which have been studied by means of multi-element distribution are whiskey [67,68,69], cider [70,71], beer [72,73,74,75] and Chinese liquors [76]. Gajek and co-workers [69] studied extensively diverse varieties and ages of whiskey from different geographical regions and production procedures. Their investigations demonstrated that there were discrepancies in the metals Al, Cr, Cu, Fe, K, Mg, Mn, P, S, Ti, Tl, Zn, and V between single malt and blended Scotch whiskies. Furthermore, it was noteworthy that homemade whiskies from Poland are composed with the highest concentrations of Sr, K, S, and P. They were also observed that Cu, Mn, Zn, and P exhibit alterations in their concentrations during the aging of the samples. In case of beer, earlier this year, Lafontaine et al. [74], published in Food Chemistry their studies on the elemental profile of a wide variety of hops (Humulus lupulus) from various territories in German and USA, assuming that the authentication of hops might also be related to the quality and authentication of beer. They concluded that the concentrations of the elements Ba, Ca, Cd, Co, Ni, Mg, Sb, Sr, and U were impacted by the variety and the region of hops. The correlation between the elemental profile of hops and the dry-hopped beer is under investigation. Cider is the less studied alcoholic beverage by means of elemental analysis. In 2007, the group of J. Ignacio García Alonso [70] analyze 67 samples from various regions in order to authenticate their geographical origin. For this purpose, ⁸⁷Sr/⁸⁶Sr isotope ratio and multi-elemental analysis were determined by the combination of ICP-MS and ICP-AES. Fluctuations in the concentration of Na, Mg, Al, K, Ca, Ti, V, Mn, Zn, As, Rb, Sr, Mo, Ba and ⁸⁷Sr/⁸⁶Sr isotope ratio allowed the classification of cider samples originating from Spain, France, England, and Switzerland.

4.2. Dairy Products: Milk and Cheese

Trace element analysis in dairy products may derived from the metabolic pathways of the animals and the geographical regions of the farms. In 2008, Benincasa and co-workers [77] investigated the multi-elemental profile of 12 cow and 6 water buffalo milk samples. All the animals were treated equally in the same farm to identify elemental discrimination between the two animal species. Indeed, the authors achieved to differentiate the two types of milk based on their multi-elemental profile. Fernando’s group [8] published, lately, a study which investigated the geographical origin authentication of cow milk from different territories in Sri Lanka by determining the stable isotope ratios of C, H, N, and O as well as the elemental composition with the IRMS and ICP-MS techniques, respectively. It was proposed that a combination of stable isotope ratio of δ¹⁵N, δ¹³C (in milk casein), δ¹⁵N, δ¹⁸O, and δ²H (in whole milk), and the concentration of the metals Li, Al, Cr, Mn, Fe, Co, Ba, and Sr could be promising markers to identify the geographical region of samples which were collected by four different agroclimatic zones. According to the authors the latter elements were possibly related to intrinsic factors of the animal (breed, lactation etc.), dietary habits and supplements. In similar studies, Tedesco et al. (2021) [78], and Aceto et al. (2017) [79] investigated the role of trace and rare earth elements in milk samples, throughout the production chain, from various regions of Italy. It was observed that the concentration of lanthanides remains nearly constant during the milk production and therefore were classified as suitable tracers.

In case of cheese and generally of dairy products, the research is based on the assumption that the metals are not affected by the production procedure; consequently, the resulting records reflect the elemental profile of the geographical region. In 2003, Pillonel et al. [80] analyzed Emmental cheese samples from six European countries for stable isotope ratios (¹³C/¹²C, ¹⁵N/¹⁴N, ¹⁸O/¹⁶O, ²H/¹H and ⁸⁷Sr/⁸⁶Sr), elemental composition (Ca, Mg, Na, K, Cu, Mn, Mo, I) and radioactive elements (⁹⁰Sr, ²³⁴U, ²³⁸U). The concentration of Mo and Na showed interesting variations related to the origin of cheese. In an international collaborative study Camin and co-workers [81] investigated the elemental profile (H, C, N and S stable isotope ratios, and multi-elemental analysis) of seven samples of the hard cheeses Grana Padano and Parmigiano Reggiano according to the IUPAC protocol and ISO Standards 5725/2004 and 13528/2005. Thirteen different laboratories were involved in performing isotope and/or elemental analysis. The determination of Li, Na, Mn, Fe, Cu, Se, Rb, Sr, Mo, Ba, Re, Bi, and U and the H, C, N and S stable isotope ratios were able to verify the origin of both cheeses.

4.3. Meats

Soil, feed supplements and environmental pollution are the main sources of elements in meat. In 2005, in a literature review study, Franke and co-workers [82] discussed the methods for the authentication of geographical origin of raw meat by determining the stable isotope ratios and trace elements, concluding that Se and Rb could possibly be used as tracers. In an attempt to authenticate the geographical origin of beef, in 2008, Heaton et al. [83] collected samples produced in various countries worldwide, and analyzed them by using IRMS and ICP-MS techniques. According to the results, they stated that it would be possible to verify the origin of beef particularly based on six key variables δ¹³C‰ (defatted dry mass), Sr, Fe, δ²H‰ (lipid), Rb and Se. Applying Canonical Discriminant Analysis (CDA) a broad classification of samples into European, South American, and Australasian was achieved. Since then, analytical studies, following by the appropriate statistical analysis, have been carried out for examining elements as markers of the geographical origin of yak [84], rabbit [85,86], pork [87], mutton [88], and cured ham [89]. It is worth mentioning that in the latter, the ratio ⁸⁷Sr/⁸⁶Sr has been measured in addition to the elemental composition of the cured ham, since it could be considered as a tracer of the regional geological components. An interesting work has been performed by Meisel’s group in which aimed at developing a method for labeling via REE, unlabeled eggs and poultry products [90], and lamb meat and goat milk [91] by selectively enrich animals feeding with Tb and Tm; those elements have a single stable isotope, and are relatively low-cost. Naturally the composition of lanthanides in food is remarkably low allowing the REE spiking, and simultaneously to secure human’s health. In both cases, the REE labeling method was succeeded allowing the origin determination of the products.

4.4. Fish and Seafood

Fish and seafood belong to the most mislabeled foodstuffs around the world. Due to the challenging traceability throughout their production chain, new more effective control methods of origin authentication are developed. ICP-MS tend to become a leading, among others, analytical method for the classification of fish and seafood. The scientific literature revealed that several studies for geographical discrimination and elemental fingerprinting have been performed for sea cucumber [92,93], mussels [94,95], salmon [96], cuttlefish [97], clams [98], crabs [99], sea bass [100], shrimps [101], caviar [102], anchovy products [103] and various marine species from China regions [104].

In 2010, Costas-Rondríguez et al. [94] classified Galician (Spain) and non-Galician mussels from different cultivated areas, by determining their elemental composition combined with different multivariate qualitative methods. Mussels were the first seafood products recognized by European PDO. Sea cucumber has been studied by Liu et al. [92] (2012) and Kang et al. [93] (2018) in an attempt to classify samples from various regions of China. Linear discriminant analysis gave better classification and cross-validation rates on both studies suggested as good tracers the elements Zn, Al, As, Co, Fe, Se and Li, Na, Al, K, Co, Cu, Cd, Sc, respectively. Stable isotope ratio of ¹³C/¹²C, and ¹⁵N/¹⁴N and REE (La, Eu, Ho, Er, Lu, and Tb) were investigated as possible markers for the discrimination of different production methods as well as the authentication of geographical origin (Western, Central, and Eastern Mediterranean Sea) of sea bass by Varrà et al. [100], in 2019. Indeed, the combination of the abovementioned analytical methods led to the classification of sea bass samples in relation to production methods and sea regions. In particular, La and Ho resulted to be the elements which were differentiated most in geographical origin classification.

Recently, Varrà et al. [105] reviewed the fish and seafood authentication by inorganic elemental composition.

4.5. Vegetables, Mushrooms and Fruits

Vegetables and fruits are products which are directly linked with the soil; thus, it could be an unambiguous correlation between their elemental fingerprints and the geological setting of a region. Traceability and authenticity of vegetable and fruit foodstuffs can be a more straightforward procedure by comparing and relating the elemental distributions of samples and the soils. These days, the authentication of geographical origin of fruits and vegetables is a crucial requirement for food safety, due to the heavy demand for those products which has led to fraudulent labeling practices. Stable isotope analysis (C, N, H, O, S, and Sr) is considered as a pioneer in the field of food forensics. Multi-elemental analysis has been also used, in combination with stable isotope ratio or by itself. Trace elements and lanthanides represent the mineralization characteristics of a particular region.

Reviewing the literature, vegetables such as onion [106,107], garlic [108,109], soybean [110], eggplant [111], various types of pepper [111,112,113], tomato [114,115,116], Chinese cabbage [117], and truffles [118] have been investigated by means of elemental composition for their geographical origin authentication with the use of ICP-MS. Since 2005, Bettinelli et al. [115] investigated the concentration of lanthanide elements of tomato plants and soils from their growing area. They had stated that ICP-MS seems a promising technique for traceability. Almost five years later, Lo Feudo et al. [114] studied tomatoes from Italian farms and tomato paste originating from Italy, California, Greece, and China, resulting in geographical discrimination based on 32 elements as variables. In a similar study, in 2011, the group of Tagarelli determined the elemental fingerprint (25 elements) of the Tropea red onion (PGI brand since 2002), in order to categorize the samples into Tropea and non-Tropea [106]. A noteworthy observation was the contribution of REE and especially of Dy in the authentication of geographical origin of onions. Recently, Segelke et al. [118] published their endeavors to develop an accurate methodology for the geographical discrimination of truffles. Truffle is one of the most expensive products; however, it belongs among to the most misdescribed foodstuffs due to its different varieties which are not easily distinguishable.

In case of fruits, a series of studies have examined the elemental composition for the authentication of geographical origin of lemons [119], apples [120], mango [121], avocado [122], pears [123], jackfruit [124], and clementines [125]. In 2012, PGI brand Clementine of Calabria were geographically distinguished from non-PGI samples from Spain, Tunisia, and Algeria by Benabdelkamel et al. [125] who developed a multi-element analytical method with the employment of ICP-MS. Similar studies were performed on Italian PGI and non-PGI Turkish lemons by Giorgia Potortì et al. [119]. Muñoz-Redondo et al. published two research projects on the stable isotope and multi-element analysis of avocado [122] and mango [121] for geographical origin authentication purposes. The findings on both reports confirmed that the combination of two techniques lead to more accurate geographical discrimination (Figure 7).

4.6. Oils and Olives

The elemental determination of edible oils is a challenging procedure due to their lipophilic matrix, rich in carbon. The low concentration levels of trace and rare earth elements can be detected by the highly sensitive ICP-MS technique.

Particularly, olive oil is considered the basis of the Mediterranean diet and simultaneously belongs among the most traded products. Consequently, the assurance of authenticity, high quality and purity of olive oil is a matter of great importance. For this purpose, investigating olive oil by means of ICP-MS analysis gains widespread scientific attention [9,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140]. A considerable amount of literature has been published on Spanish, Italian, Tunisian, and Greek olive oil varieties. Preliminary work on multi-elemental analysis of olive oil was undertaken by Benincasa (2007) [126] who presented a simple and rapid ICP-MS method for the geographical origin discrimination of olive oil from various Italian regions. The final records revealed that Fe, Mg, Sr, Ca, and As led to discrimination of the samples. Systematic examination of geographical discrimination of Italian extra-virgin olive oils (EVOO) [127] and European EVOO [128] was reported in 2010 by Camin et al. The authors explored both C, H, and O isotopic ratios (IRMS) and elemental composition (ICP-MS) of olive oil samples. In case of the different European olive oils, the combination of the three isotopic ratios and the selected elements Mg, Al, K, Ca, V, Mn, Ni, Zn, Rb, Sr, Ce, Sm, Cs, La, Eu, and U showed an adequate geographical classification. Similarly, Gumus et al. [131] found that the δ¹³C isotope ratio and the elements Fe, Zn, Ca, Cu, and Mn were effective tracers for determining the origin of VOO from different locations of western Turkey. In a comprehensive study Aceto and co-workers [133] investigated the mineral composition of EVOO and especially the importance of lanthanides. The researchers carried out a number of experiments investigating the elemental composition of soil, olive leaves, olive seeds, olive pericarps, and olive oil Taggiasca variety in order to examine the effect of the production chain. The Taggiasca olive oils was further distinguished from EVOO of different regions. It was shown that Tm and Y were the primary discrimination elements. Similarly, Nasr et al. [9] concluded that Cu, Cr, Fe, Mn, Sr, V, and Zn facilitated to the accurately prediction of country of origin between European and Tunisian olive oils. In an attempt to certify the purity of the edible oils and to detect possible contaminations by mixing oils Llorent-Martínez et al. [139] (2011) and de Souza et al. [140] (2022) investigated the mineral composition of different types of edible oils and fats. Llorent-Martínez and co-workers observed correlations between Cr, Cu, Fe, and Mn and the different investigated oils. In the study of de Souza et al. [140], the authors achieved discrimination of the different types of oils and fat based on 9 elements: Cd, Cr, Cu, Fe, Mn, Ni, Ti, V, and Zn.

Despite the fact that olive oil is obtained from olives, there is a relatively small number of studies on elemental characterization and geographical discrimination [133,134,137,138]. In a recent study, Pucci and co-workers [138] suggested that the elements Sr, Cu, Rb, Ti, Ni, Cr, V, and Co were the most sufficient variables in the discrimination of diverse olive cultivars in Italy.

Recently, Amit and co-workers [141] identified the geographical origin of virgin coconut oil (VCO) produced in various regions. It was suggested that the combination of ICP-MS analysis with multivariate chemometrics were able to authenticate the origins of the VCO.

4.7. Honey

Honey is considered nature’s sweet superfood due to its beneficial properties and can find multiple applications in cooking, baking, and beverages. Because of its raw form the adulteration is easier by adding cheaper sweeteners (corn, sugar, and rice cane syrups), aiming to financial gain. Developing advanced, accurate and sensitive analytical methodologies for testing and authenticating the purity and the origin of honey is a necessity.

Literature review revealed that research has been focused on the classification of honey botanically and geographically. Carbon stable isotope ratio and elemental analysis are commonly used for the determination of the authenticity and tracing of honey [142,143,144,145,146,147,148,149,150,151]. In 2011, Chudzinska and Baralkiewicz [142] investigated the elemental composition of 140 honey samples of three types (honeydew, buckwheat, and rape) from 16 regions of Poland. They suggested Al, Mg, and Zn as best tracers for the geographical classification of samples. A key study of Zhou et al. [146], in Scientific Reports (2018), measured both C stable isotope ratio and trace elements produced in several countries worldwide. The additional sweeteners in an adulterated honey can be identified by determining the C stable isotope ratio. The sugar of these additives is produced by the C-4 metabolic pathway of plants (C-4 plants) while sugar of pure honey by C-3 metabolic pathway (C-3 plants). As a consequence, there is a disagreement between the δ¹³C values for C-4 (−10‰ to −20‰) and C-3 (−22‰ to −33‰) plants [152,153]. As second step, the authors further examined the mineral composition of pure honey samples in order to authenticate their origin. Generally, it was found that Ba, Ca, Fe, Mg, Mn, P, Na, and Sr exhibit variations in their concentrations according to production area of honey. Notwithstanding, further analysis and comparison of specific elements and countries led to more accurate classifications.

A comprehensive review on analytical techniques of honey authentication was published by Tsagkaris et al., in 2021 [154]. Among other analytical techniques, ICP-MS is primarily used for the determination of multi-elemental composition of honey samples from different geographical origins.

4.8. Cereals

Cereals, including wheat [155,156,157,158,159,160], corn (maize) [161], rice [162,163,164,165,166,167,168,169,170,171,172,173,174,175,176] and others, are the most important class of plants contributing essential nutrients and energy to human diet. Cereal production ranks among the largest in the food market due to the high demand globally. Authenticity problem is the main concern of the cereal grain trade, particularly in case of rice in which the percentage of mislabeled products is continuously increasing.

Preliminary work on geographical authenticity of wheat was performed by Branch et al., in 2002 [155]. The authors investigated the isotope analysis of Cd, Pb, Se and Sr, with the use of ICP-MS, on wheat samples from certain geographical origins. A detailed study of Podio and co-workers [157], reported the elemental and isotopic fingerprint of Argentinian wheat and correlated them with the soil and water of the certain studied regions. They demonstrated that Ba, ⁸⁷Sr/⁸⁶Sr, Co, Mo, Zn, Mn, Eu, δ¹³C, and Na were efficient variables for geographical discrimination of wheat. In a similar study, Liu et al. [159] came to the conclusion that Mn, Sr, Mo, and Cd led to correct classification of wheat samples from various regions of China. In 2017, Wang et al. [161] determined the elemental fingerprinting of maize samples by using ICP-MS. The origin of samples was certified based on the differentiations between the concentrations of the elements Na, Cr, Rb, Sr, Mo, Cs, Ba, and Pb.

Respecting the rice, it was constantly being the subject of authentication studies. According to literature, there is a high number of publications about the verification of geographical origin of rice samples from all over the world. In 1999, the group of Kokot [162,163] studied the elemental composition of Vietnamese rice by combining different analytical techniques including ICP-MS, ICP-AES, and FAAS creating an element profile for the studied samples. A successful geographical discrimination was achieved by comparing the Vietnamese rice with the Australian one, based on Mn, and Mo elements. In 2002, Kelly et al. [164] investigated the C, and O stable isotope ratios and multi-elemental (B, Ho, Gd, Mg, Rb, Se, and W) analysis for the determination of the geographical origin of long grain rice, with the employment of IRMS and ICP-MS, respectively. It was suggested that B and Mg could be used as discriminative tracers. An interesting study was published by Qian et al. [170], in which the authors investigated how the fertilizers and pesticides affected the elemental composition of rice and what was the impact on the origin determination. In 2021, Xu et al. [172] developed an accurate analytical method for the authentication of the geographical origin of Chinese GI rice samples by combining ICP-MS and principal component analysis (PCAs). Figure 8a depicts the separation between the different types of GI rice; separation is clear for few samples while the rest could not be classified. In Figure 8b was shown the loading plot 1st and 2nd principal components; the authors claimed that Al, Ga, Nb, V, and Ti contributed to the first two PCAs.

The same year, Kongsri et al. [174] studied the tracing and authenticity of Thai Hom Mali rice combining stable isotope and elemental analysis. Classification of the geographical origin of samples was achieved based on Mn, Rb, Co, Mo, and δ¹⁸O.

Three extensive reviews with respect to traceability and authenticity of rice have been published by Qian et al. [177] and Maione and Barbosa [5], in 2019, and by Quinn et al. [6], in 2022.

4.9. Seeds and Nuts

Seeds and nuts constitute an essential part of human diet with a wide variety of products. As all the aforementioned foodstuffs, seeds and nuts are also examined for fraudulent practices possessing not only financial impact but also a high human health risk due to allergic ingredients some of them contain.

The existing literature on authentication of coffee [178,179,180,181,182,183] and cocoa beans [184,185,186] focuses mostly on the isotopic composition and multi-elemental concentrations measurements. In 2011, Rodriguez et al. [179] supported that the use of S, O, C, N, and Sr isotope ratios and elemental composition could lead to the geographical discrimination of green coffee beans originated from Hawaiian islands. In a similar work, Santato and co-workers [182] used IRMS and ICP-MS techniques to examine and classify samples of green coffee beans from different places of the world. Recently, Albals et al. [180] investigated the elemental composition by means of essential and toxic metals in green coffee beans from Brazil, Ethiopia, Kenya, Columbia, and India. As regards cocoa beans, there are relatively few authentication studies with the employment of ICP-MS technique. In 2016, Bertoldi et al. [184] determined, for the first time, the elemental profile of cocoa beans (from Africa, Asia, Central and South America) for tracing the geographical origin. The resulting records and the statistical approach, they followed, led to the selection of Ag, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Hg, K, Li, Mg, Mn, Na, Ni, P, Rb, Se, Sr, Th, Tl, U, Y and Zn as efficient tracers. A similar investigation of Acierno et al. [185] revealed Fe, Cr, and Cd as potential geographical tracers.

A critical review in Food Science and Nutrition for traceability, authenticity and sustainability of cocoa beans and their derivatives was published in 2022 by Perez and co-workers [187].

Legumes are prominent members in human diet due to their nutritional value containing high protein and mineral element concentrations. They are also considered as the base of the vegetarian and vegan diet since they can substitute meat. This reason leads to a higher demand of legumes, the last decades. There are few studies on authentication and traceability of legumes [160,188,189,190,191,192] with the most studies being on fava beans. In particular, in 2014, Drivelos et al. [189] suggested the use of REE or their combination with trace elements for the geographical discrimination of the PDO “Fava Santorinis” from different Greek varieties of split peas. The results revealed that the combination of all elements (lanthanides and trace elements) provided the best geographical classification. Two years later, Drivelos and co-workers [190] examined the variations on elemental composition of PDO “Fava Santorinis” through three harvesting years. The study revealed that, in case of “Fava Santorinis”, there is discrimination of the fava beans samples from different harvesting years, while fluctuations were not observed on REEs composition throughout the years.

Similarly, nuts (almonds, hazelnuts etc.) are well-known superfoods; they are an excellent source of protein, fiber, fats, vitamins, and minerals. Literature research showed an increasing interest in studying the elemental composition of nuts for geographical origin authentication [193,194,195,196,197,198] the last five years. The first attempt to classify hazelnuts according to their geographical origin was accomplished by Oddone et al. [193], in 2009. The authors investigated the elemental concentrations (trace elements and lanthanides) on hazelnuts and the soils from their grown region. The results confirmed the correlation of lanthanide distribution between hazelnuts and soil samples. Recently, Chen and co-workers [195] examined the concentrations of macro (K, Ca, Mg, Na, and Al), micro (Fe, Zn, Mn, Ni, Sr, Mo, Cu, Se, V, and Co), and toxic (As, Cd, Cr, and Pb) elements, with ICP-MS, in peanuts from different regions of China. Linear discriminant analysis (LDA) on all 19 elements resulted the geographical discrimination of 97% for all regions. Moreover, the authors performed radar plotting to display the elemental distribution among the different origins (Figure 9).

4.10. Spices and Herbs

In the spices/herbs branch fraudulent activities can occur in the form of mislabeling, and adding fillers (i.e., flour, chalk etc.) and tend to become the most vulnerable food class [199]. More recent attention has focused on the development of novel methodologies for tracking the spices/herbs supply chain. In particular, the last five years, ICP-MS has a dynamic appearance in the field of geographical origin authentication of spices and herbs [200,201,202,203,204,205,206,207,208,209,210,211]. Special attention has been paid to saffron due to its relatively high value. In 2019, D’Archivio et al. [207] examined saffron samples produced in different Italian territories. Geographical discrimination was achieved based on the analysis of the most efficient variables which were: Li, B, Na, Ga, Rb, Sr, Zr, Nb, Cs, Ba, Sm, and Hf. Perini and co-workers [206] worked on similar project by combining stable isotope ratio and multi-elemental analysis. The authors analyzed 67 saffron samples from Italy, Iran and Morocco succeeded the geographical discrimination based on the elements δ¹³C, δ³⁴S, δ¹⁵N, δ¹⁸O, δ²H, K, Cr, Mn, Ni, Zn, Rb, Sr, Mo, Cs, Nd, Eu, Pb, and Ni.

An interesting review has been written, few years ago, by Galvin-King et al. [212] about herbs and spices industry. The authors clearly described the global spice and herb production chain, mentioning the consequences of adulteration on economy and public health. All the analytical methods for the safety control of spice and herbs were also referred.

Above all herbs, tea consists unique sector since it is the most popular beverage worldwide following the fresh water. A growing body of literature has focused on the tracing of tea origins with the use of ICP-MS technique [213,214,215,216,217,218,219,220,221] to further improve the accuracy of their measurements. A detailed study has been performed, in 2020, by Liu et al. [214,215], who examined the stable isotope ratios of C, N, H, O and various elements, through EA-IRMS and ICP-MS, of Chongqing tuo and Pu’er teas. In case of Chongqing tuo teas, the authors concluded to δ²H, δ¹⁸O, ⁹⁸Mo/⁹⁵Mo, ⁹⁶Mo/⁹⁵Mo, and ⁹⁸Mo/⁹⁶Mo as the most sufficient tracers while in Pu’er tea project the geographical discrimination was achieved based on the stable isotope ratio of δD, δ¹³C, and ¹⁵⁴Sm/¹⁵²Sm.

Currently, a comprehensive literature review was published by Shuai et al. [222], summarizing the analytical techniques for the authentication of tea and the factors that influence the content of these measurements.

Table 2. Table containing the overview of the literature regarding the authenticity and traceability of agricultural products, foods and beverages.

	Product	Measured Elements	Region	References
1.	Alcoholic Beverages (Wine)	Li, Be, Na, Mg, Al, K, Ca, Sc, Ti, V, Mn, Co, Ni, Ga, As, Se, Rb, Sr, Mo, Cs, Ba, La, Ce, W, and Pb	Australia	[40]
2.	Alcoholic Beverages (Wine)	Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Ni, Pb, Rb, Se, Na, Ag, Sr, Tl, V, and U	Romania	[41]
3.	Alcoholic Beverages (Wine)	Al, Cd, Co, Cr, Cu, Li, Mn, Ni, P, Pb, Rb, Sr, and Zn	California (USA)	[42]
4.	Alcoholic Beverages (Wine)	B, Ba, Ca, Co, Cr, Cs, Cu, Fe, Ga, K, Li, Mg, Mn, Na, Ni, P, Rb, S, Sc, Si, Sr, Ti, Zn, Zr, Al, As, Cd, Ce, La, Mo, Nd, Pb, Sb, Sn, U, V, W, and Y	Spain	[43]
5.	Alcoholic Beverages (Wine)	Al, As, B, Ba, Ca, Co, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr, and Zn	Portuguese	[44]
6.	Alcoholic Beverages (Wine)	⁸⁷Sr/⁸⁶Sr	Romania	[61]
7.	Alcoholic Beverages (Wine)	Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, Rb, Sr, Li, Cd, Cs, and Ba	China	[60]
8.	Alcoholic Beverages (Wine)	Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Re, Sm, Ta, Tb, Tm, V, Y, and Yb	Italy	[64]
9.	Alcoholic Beverages (Wine)	As, Be, Bi, Co, Cr, Cu, K, Li, In, Tl, Se, Rb, V, U, Mg, Ni, Ba, Al, Cd, Fe, Ag, Ni and Zn	Romania	[45]
10.	Alcoholic Beverages (Wine)	Na, Mg, P, K, Ca, Cu, Co, Cr, Zn, Sn, Fe, Mn, Li, Be, B, V, Sr, Ba, Al, Ag, Ni, As, Sn, Hg, Pb, Sb, Cd, Ti, Ga, Zr, Nb, Pd, Te, La, Sm, Ho, Tm, Yb, W, Os, Au, Tl, Th, and U	Greece	[46]
11.	Alcoholic Beverages (Wine)	Li, V, Co, Ni, Ga, Mo, Cd, Sb, Cs, Ba, Ce, Nd, Ta, W, Tl, Pb, P, B, Si, Ca, Mn, Sr, K, and Rb	California (USA)	[47]
12.	Alcoholic Beverages (Wine)	Mg, K, Ca, V, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Mo, Cd, Ba, Pb, and U. ²⁰⁷Pb/²⁰⁶Pb, ²⁰⁸Pb/²⁰⁶Pb, ²⁰⁴Pb/²⁰⁶Pb, and ⁸⁷Sr/⁸⁶Sr	Argentina	[62]
13.	Alcoholic Beverages (Wine)	Li, Be, V, Mn, Co, Ni, Cu, Ge, As, Rb, Sr, Mo, Cd, Ba, Hg, Tl, Pb, and Bi	Argentina	[48]
14.	Alcoholic Beverages (Wine)	Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, V, U, Zn, and Hg	Romania	[63]
15.	Alcoholic Beverages (Wine)	Cu, Ni, Ca, Fe, B, Mg, As, Sb, Mn, Sn, P, Al, Zn, U, Sr, Cr, S, Co, Ba, La, Mo, Ti, Pb, Ce and V	Okanagan Valley (B.C., Canada)	[38]
16.	Alcoholic Beverages (Wine)	Li, Be, Mg, Al, P, Cl, Ca, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sb, I, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U	Okanagan Valley, and Niagara Region (Canada)	[49]
17.	Alcoholic Beverages (Wine)	Al, As, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Se, Sr, Ti, Tl, V, Zn, U, Sn, Sb, and Ga	Croatia	[50]
18.	Alcoholic Beverages (Wine)	Sr, Rb, Ni, Co, Pb, Mn, Cd, Ga and Cs	New Zealand	[65]
19.	Alcoholic Beverages (Wine)	Na, Mg, P, K, Ca, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Cs, Ba, and Pb	Spain	[51]
20.	Alcoholic Beverages (Champagne)	K, Ca, Mg, Na, B, Fe, Al, Mn, Sr, Rb, Ba, Cu, Ni, Pb, Cr, and Li	6 different brands of different vintages between 1983 and 2016	[52]
21.	Alcoholic Beverages (Wine)	Al, As, B, Ba, Ca, Ce, Cs, Co, Cr, Cu, Er, Eu, Fe, Ga, K, Mg, Mn, Mo, Na, Ni, P, Pb, Rh, Rb, Sb, Sn, Sr, Ti, Tl, Zn, and V	West coast of the USA	[53]
22.	Alcoholic Beverages (Wine)	Ag, Al, As, B, Ba, Be, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Hf, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, P, Pb, Pr, Rb, Re, Rh, Sb, Se, Sn, Sr, Ti, Tl, Tm, U, V, W, Yb, and Zn	California (USA)	[54]
23.	Alcoholic Beverages (Wine)	Ag, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sb, Sn, Sr, Te, Tl, U, and Zn	Italy, France, Poland, Spain, Slovakia, Australia, Portugal, Bulgaria, Germany, Hungary, Moldova, Chile, Austria, South Africa, New Zealand, Ukraine, Argentina, Czech Republic, Greece, UK, Armenia, and USA	[55]
24.	Alcoholic Beverages (Wine)	Li, Be, Al, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, Br, Rb, Sr, Y, Zr, Nb, Pd, Ag, Cd, Ba, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re, Pt, Tl, Pb, Bi, U, Mo, Sn, Sb, La, and Ce	Portuguese	[56]
25.	Alcoholic Beverages (Wine)	Na, K, P, Mg, and Ca	Greece	[57]
26.	Alcoholic Beverages (Wine)	La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm and Yb	Italy	[7]
27.	Alcoholic Beverages (Wine)	Ag, Al, As, B, Ba, Bi, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Mo, Na, Ni, Pb, Sb, Se, Sn, Sr, Ti, Tl, V, Zn, and Zr	Poland	[58]
28.	Alcoholic Beverages (Wine)	Li, Al, V, Cr, Mn, Co, Ni, Cu, Rb, Sr, Mo, Ag, Cd, Ba, Tl, Pb, Bi, U, Be, Fe, As, Se, and Zn	Poland, Hungary, Moldova, and Bulgaria	[59]
29.	Alcoholic Beverages (Wine)	La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu.	Italy	[66]
30.	Alcoholic Beverages (Wine)	Li, Be, A1, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Rh, Pd, Ag, Cd, In, Sn, Sb Te, I, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re, Os, Ir, Pt, Au, Hg, Tl, Pb, Bi, Th, and U	Spain, and England	[39]
31.	Alcoholic Beverages (Whiskey)	Al, Ti, V, Cr, Mn, Fe, Ni, Co, CU, Zn, Ga, As, Se, Rb, Sr, Zr, Mo, Nb, Ru, Rh, Pd, Ag, Cd, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re, Ir, Pt, Au, Tl, Pb, Th, and U	USA	[67]
32.	Alcoholic Beverages (Whiskey)	Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Sr, Te, Tl, U, V, and Zn	Scotland, Ireland, and USA	[68]
33.	Alcoholic Beverages (Whiskey)	Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, and V	Scotland, the USA, Ireland, Poland, Japan, the United Kingdom, India, Azerbaijan, Slovakia, Wales, and Bulgaria	[69]
34.	Alcoholic Beverages (Cider)	⁸⁷Sr/⁸⁶Sr, Li, Be, B, Al, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Y, Mo, Cd, Sn, Sb, Cs, Ba, La, Ce, W, Tl, Pb, Bi, Th, and U	England, Switzerland, France, and Spain	[70]
35.	Alcoholic Beverages (Cider)	Li, Be, B, Al, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Y, Mo, Cd, Sn, Sb, Cs, Ba, La, Ce, W, Tl, Pb, Bi, Th and U	Spain, England, France, and Switzerland	[71]
36.	Alcoholic Beverages (Beer)	V, Cr, Co, Ni, As, Se, Mo, Cd, In, Sb, Cs, Pb, Bi, and U	USA	[72]
37.	Alcoholic Beverages (Beer)	Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mo, Mn, Ni, Se, Sr, Pb, and Zn	Hungary, Belgium, the Czech Republic, Germany and Austria	[73]
38.	Alcoholic Beverages (Beer)	K, Ca, Mg, Fe, Al, Mn, Zn, Na, Sr, Cu, Ti, Ba, Ni, Mo, V, Cr, Pb, Co, As, Se, Sn, Sb, U, and Cd	USA and Germany	[74]
39.	Alcoholic Beverages (Beer)	Nb, Fe, Rb, Zr, Mg, Ni, and Zn	Cavalese, and Imér	[75]
40.	Alcoholic Beverages (Liquor)	V, Cr, Mn, Ni, Co, As, Se, Sr, Mo, Cd, Sb, Ba, Pb, Bi, Al, Fe, and K	China	[76]
41.	Milk and Dairy (Cow and Buffalo Milk)	P, S, K, Ca, V, Cr, Mn, Fe, Co, Zn, Ga, Rb, Sr, Mo, Cs and Ba	Italy	[77]
42.	Milk and Dairy (Cow Milk)	Li, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Ba, Pb, and Bi	Sri Lanka	[8]
43.	Milk and Dairy (Cow and Goat Milk)	Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Cs, Ba, Pb, U, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Yb, Lu, and Y	Italy	[78]
44.	Milk and Dairy (Cow Milk)	Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Sn, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Pb, Th, and U	Italy	[79]
45.	Milk and Dairy (Cheese)	Ca, Mg, Na, K, Cu, Mn, Mo, and I	Finland, England, Germany, Austria, France, and Switzerland	[80]
46.	Milk and Dairy (Cheese)	Li, Na, Mn, Fe, Cu, Se, Rb, Sr, Mo, Ba, Re, Bi, and U	Italy	[81]
47.	Meat (Yak)	Na, Mg, Al, K, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Y, Mo, Ru, Rh, Pd, Ag, Cd, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Lu, Yb, Hf, Ir, Pt, Au, Tl, Pb, Th, and U	Qinghai-Tibetan	[84]
48.	Meat (Rabbit)	As, Be, Ca, Cd, Co, Cr, Cu, Fe, Li, Mg, Mn, Mo, Ni, Pb, Sb, Se, Sr, Ti, Tl, V, Zn, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb, Sc, Y, Th and U	Lemnos (Greece)	[85]
49.	Meat (Pork)	Ba, Be, Bi, Cd, Co, Cr, Cu, Cs, Ga, Li, Mn, Ni, Pb, Rb, Se, Sr, U, and V	Korea, USA, Germany, Austria, Netherlands, and Belgium	[87]
50.	Meat (Mutton)	Be, Na, Al, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Ag, Sb, Ba, Tl, Pb, Th, and U	China	[88]
51.	Meat (Cured Ham)	Zn, Fe, Rb, Cu, Sr, Al, Mn, Se, Ni, Cs, Cr, Ba, Li, As, Pb, V, Cd, Sc, Co, Ga, Tl, Y, Nd, Gd, Pr, Be, U, Sm, Dy, Yb, Eu, Ho, Tb, Tm, Rb/Sr, and ⁸⁷Sr/⁸⁶Sr	Europe	[89]
52.	Meat (Rabbit)	Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sc, Sm, Tb, Tm, Y, Yb, Th, and U	Lemnos (Greece)	[86]
53.	Meat (Beef)	Na, Al, K, V, Cr, Mn, Fe, Ni, Cu, Rb, Sr, Mo, Cs, and Ba	Europe, USA, South America, Australia, and New Zealand	[83]
54.	Fish and Seafood (Sea Cucumber)	Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg, and Pb	Bohai Sea, Yellow Sea, and East China Sea (China)	[92]
55.	Fish and Seafood (Mussels)	Ag, As, Ba, Cd, Co, Cr, Cu, Ga, Mn, Mo, Nb, Ni, Pb, Rb, Sb, Se, Sn, Sr, Te, Tl, V, Zn, Ta, Zr; Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Th, Tm, U, Y, and Yb	Spain, and France	[94]
56.	Fish and Seafood (Salmon)	B, Ba, Fe, K, Mg, Mn, Na, Pb, S, Sr, U and Zn	Norway	[96]
57.	Fish and Seafood (various Marine Species)	Cr, Mn, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ni, Cd, Sn, I, Ti, Ba, Hg, Pb, and Bi	China	[104]
58.	Fish and Seafood (Mussels)	Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, Sn, V, and Zn	Mediterranean Sea (Venice Lagoon)	[95]
59.	Fish and Seafood (Cuttlefish)	Na, Mg, K, Ca, P, Cu, Zn, Cr, Fe, Mo, Co, V, Ni, Mn, As, Cd, Pb, and Hg	Mediterranean Sea (Sicilian Coasts)	[97]
60.	Fish and Seafood (Manila Clam)	Na, Mg, Al, K, V, Mn, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Pd, Cd, Sn, Sb, Cs, Ba, La, Ce, Pb, and U	China	[98]
61.	Fish and Seafood (Mitten Crab)	Na, Mg, Al, K, Ca, Mn, Cu, Zn, Sr, and Ba	China	[99]
62.	Fish and Seafood (Sea Bass)	La, Eu, Ho, Er, Lu, and Tb	Mediterranean Sea	[100]
63.	Fish and Seafood (Sea Cucumber)	Li, V, Cr, Mn, Co, Ni, Cu, As, Sn, Sr, Ag, Cd, Se, Ba, Pb, Bi, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Sc	China	[93]
64.	Fish and Seafood (Shrimps)	Pb, Cd, As, P, and S	Senegal, Mozambique, North Atlantic, Argentina, and Nigeria	[101]
65.	Fish and Seafood (Caviar)	Cl, Na, P, S, K, Mg, Ca, Zn, Br, Fe, Mn, Si, Sr, Rb, Cu, I, Se, As, Ba, Al, B, Co, Pb, Ag, Mo, Li, Ti, Hg, Cs, Ni, Ge, Sn, Cd, V, Cr, Sb, Pb, Te, U, Tl, Zr, Nd, Ga, Rh, La, Y, Ce, W, Be, Ta, Bi, Gd, Ru, Pr, Se, Sm, Th, Eu, Re, Dy, Au, Nb, Er, Yb, Hf, Tb, Ho, Pt, Tm, Os, Lu, and Ir	Sweeden, and Finland	[102]
66.	Fish and Seafood (Anchovy)	Li, Be, B, Al, V, Cr, Fe, Mn, Ni, Cu, Zn, Co, Ga, Ge, As, Se, Rb, Sr, Zr, Mo, Ru, Cd, In, Sn, Sb, Te, Cs, Ba, Hf, Ta, Re, Pt, Tl, Pb, Bi, Th, La, Ce, Pr, Nd, U, Y, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Na, Mg, P, K, Ca, Mn, Cu, Zn	Cantabria, Tunisia, and Croatia	[103]
67.	Vegetables (Onion)	Al, Ba, Ca, Cd, Ce, Cr, Dy, Eu, Fe, Ga, Gd, Ho, La, Mg, Mn, Na, Nd, Ni, Pr, Rb, Sm, Sr, Tl, Y, and Zn	Calabria (Italy)	[106]
68.	Vegetables (Onion)	Co, Ni, Cu, Rb, Mo, Cd, and Cs	Japan, China, the United States, New Zealand, Thailand, Australia, and Chile	[107]
69.	Vegetables (Garlic)	Cr, Ni, Cu, As, Se, Sb, Ba, Pb, Zn, Fe, Mg, Ca, Al, Na, K, Mn and Cd	Spain, Tunisia, and Italy	[108]
70.	Vegetables (Garlic)	Li, B, Na, Mg, P, S, Ca, Ti, Mn, Fe, Cu, Ni, Zn, Rb, Sr, Mo, Cd, and Ba	Argentine, Canada, Chile, Korea, Mexico, Pakistan, Thailand, United States, and Vietnam	[109]
71.	Vegetables (Soybean)	Ag, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, Pb, Sb, Se, Sn, Sr, Ti, Tl, V and Zn	Zhejiang, Heilongjiang, Hebei, Inner Mongolia, Henan, Hainan, and Fujian (China)	[110]
72.	Vegetables (Tomato, Pepper, Eggplant)	Mn, Fe, Cu, Zn, Cr, Ni, Cd, and Pb	Romania	[111]
73.	Vegetables (Peper Capsicum annuum L.)	Ar, Ba, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, Mn, Mg, P, Mo, Ni, Na, Pb, Rb, Sb, Sn, Tl, Y, Sr, and Zn	Xiazi, Huaxi, and Hezhang (China)	[112]
74.	Vegetables (Chili peppers)	Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, and Zn	Calabria (Italy)	[113]
75.	Vegetables (Tomato)	Al, As, Ba, Be, Ca, Cd, Ce, Cu, Dy, Fe, K, La, Lu, Mg, Mn, Na, Nd, Pb, Rb, Sm, Sr, Th, U, V, and Zn	Italy, China, Greece and California	[114]
76.	Vegetables (Tomato)	Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, and Yb	Italy	[115]
77.	Vegetables (Tomato)	Li, Be, B, Na, Mg, Al, P, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Ag, Cd, Sn, Sb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Tm, Yb, Ir, Tl, Pb, U	Italy	[116]
78.	Vegetables (Chinese cabbage)	Mn, Cu, Sr, Ba, S, Co, Cr, Li, Ni, Ti, V, and Zn	China, and Korea	[117]
79.	Mushrooms (Truffles)	Li, Na, Mg, Al, K, V, Cr, Mn, Co, Ni, Cu, Ga, Rb, Sr, Mo, Ag, Cd, Te, Ba, Tl, Pb, Bi, U, Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Th, Be, B, Fe, Zn, As, and Se	Bulgaria, Romania, Croatia, Hungary, Iran, Slovenia, Italy, Spain, Australia, and China	[118]
80.	Fruits (Lemon)	K, Ca, Mg, Na, Fe, Zn, B, Cu, Al, Mn, Ni, Cr, Pb, Co, As, Se, Cd, Sb, V, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, and Lu	Italy, and Turkey	[119]
81.	Fruits (Apples)	Ag, Al, As, Be, Bi, Cd, Co, Cr, Cu, Fe, Ga, In, Mn, Mo, Ni, Pb, Rb, Se, Sn, Tl, U, V, Zn, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, and and Lu	Italy	[120]
82.	Fruits (Mango)	Li, Be, B, Na, Mg, Al, P, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Pd, Ag, Cd, In, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Er, Tm, Yb, Re, Ir, Pt, Au, Hg, Pb, Th and U	Spain, Senegal, Ivory Coast, Equatorial Guinea, Peru, Mexico, and Brazil	[121]
83.	Fruits (Avocado)	Li, Be, B, Na, Mg, Al, P, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Pd, Ag, Cd, Sn, Sb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm, Yb, Hg, Pb, and U	Spain, Brazil, Chile, Colombia, Kenya, Mexico, Peru, and South Africa	[122]
84.	Fruits (Pear)	Al, As, B, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Se, Sn, Sr, Tl and Zn	Portuguese	[123]
85.	Fruits (Jackfruits)	Ba, Al, Ca, Co, Cs, Cr, Cu, Fe, Ga, S, K, Li, Mg, Mn, Na, Ni, Mo, Rb, Ti, U, B, Zn, Si, and Xe	India	[124]
86.	Fruits (Clementine)	Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, V, U, Zn, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Sc, Tb, Th, Tm, Y, and Yb	Calabria (Italy)	[125]
87.	Oil (EVOO)	Be, Mg, Ca, Sc, Cr, Mn, Fe, Co, Ni, As, Se, Sr, Y, Cd, Sb, Sm, Eu, and Gd	Italy	[126]
88.	Oil (EVOO)	Li, B, Na, Mg, K, Ca, Mn, Co, Cu, Ga, Se, Rb, Sr, Mo, Cd, Cs, Ba, La, Ce, Nd, Sm, Eu, Yb, Tl, Pb, and U	Italy	[127]
89.	Oil (EVOO)	Mg, K, Ca, V, Mn, Zn, Rb, Sr, Cs, La, Ce, Sm, Eu, U	European Region	[128]
90.	Oil (VOO)	Y, La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Th	Zakynthos, Iraklio, Lakonia, and Messinia (Greece)	[129]
91.	Oil (VOO)	Al, As Ba, Ca, Co, Cr, Cs, Cu, Fe, Ga, Hf, K, Li, Mo, Mn, Mg Na, Sr, Nb, Ni, Pb, Rb, Sc, Se, Sn, and Ta	Spain	[130]
92.	Oil (VOO)	V, Mn, Ni, Cu, Ba, Na, K, Ca, Fe, Mg, Pb, As, Co, Cr, and Zn	İzmir, Manisa, Aydın, Muğla, Bursa, and Edremit Bay (Turkey)	[131]
93.	Oil (EVOO)	Na, Mg, V, Fe, Mn, Zn, As, Rb, Sr, Ba, and Pb	Tunisia	[132]
94.	Oil (EVOO)	Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Rb, Sr, Y, Cd, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, W, Tl, Pb, Th, and U	Italy (different varieties)	[133]
95.	Oil (EVOO)	B, Na, P, Ca, Li, Mg, Fe, Cu, and As	Tunisia	[134]
96.	Oil (EVOO)	Li, Be, B, Na, Mg, Al, Si, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Zr, Nb, Mo, Ag, Cd, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Tb, Dy, W, Tl, Pb, Bi, and U	Tuscany, Umbria, Apulia, Sardinia, Sicily, Abruzzo, Campania, and Marche (Italy)	[135]
97.	Oil (EVOO)	Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, Sb, Se, Si, Sn, Sr, Ti, V, W, Zn, Zr, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Sc, Tb, Tm, Yb, and Y	Tunisia	[136]
98.	Oil (VOO) and Olives	Al, As, Cd, Co, Cr, Cu, Fe, Ni, Pb, Sb, and V	Spain	[137]
99.	Olives	Sr, Cu, Rb, Ti, Ni, Sn, Cr, V, Co, Sb Cd, Pb, As, and Zr	Italy	[138]
100.	Oil (EVOO)	As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, Rb, Sr, Pb, V, and Zn	Tunisia, and Europe	[9]
101.	Different types of oil	Ag, As, Ba, Be, Cd, Co, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, Ti, Tl, and V	Spain	[139]
102.	Oil (VCO)	Na, Mg, Al, P, Ca, Cr, Mn, Fe, Ni, Cu, Zn, Se, Rb, Sr, Mo, Cs, and Pb	Kerala, Karnataka, Andhra, Tamil Nadu, Goa (India)	[141]
103.	Honey	Al, B, Ba, Ca, Cd, Cr, Cu, K, Mg, Mn, Na, Ni, Pb, Sr, and Zn	Poland	[142]
104.	Honey	Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, and Cr	Brazil	[143]
105.	Honey	Na, Mg, P, K, Ca, Mn, Fe, Cu, Zn, Rb, Sr, and Ba	China	[144]
106.	Honey	Mn, Cu, Cr, Ni, Se, Pb, Cd, and As	Sicily, and Calabria (Italy)	[145]
107.	Honey	As, Cd, Cr, Cu, Hg, Fe, Mn, Ni, Pb, and Zn	Romania	[147]
108.	Honey	Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Cs, Cr, Co, Cu, Dy, Er, Eu, Fe, Ga, Gd, Ge, Hg, Hf, Ho, Rb, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Nd, Ni, Os, P, Pb, Pd, Pt, Pr, Re, Ru, Se, Sb, Sr, Sm, Sn, Ta, Tb, Te, Th, Tl, Tm, Ti, U, V, W, Y, Yb, Zn and Zr.	Africa, Asia, Europe, North America, and Oceania	[146]
109.	Honey	Al, As, Ba, Ca, Cd, Co, Cr, Cu, Mg, Mn, Na, Ni, K, Pb, Sr, Ti, V and Zn	Romania	[148]
110.	Honey	Al, As, Au, Ba, Co, Cr, Cs, Cu, Fe, In, Ir, Mg, Mn, Pb, Pd, Pt, Rb, Sb, Se, Te, Th, Tl, U, V, Zn, Ce, Dy, Eu, Er, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb, and Zn	Santa Catarina, Paraná, and Rio Grande do Sul (Brazil)	[149]
111.	Honey	Li, Mg, Mn, Ni, Co, Cu, Sr, Ba, Pb, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu	Greece, Bulgaria, Romania, Italy, Thailand, and Poland	[150]
112.	Honey	Ag, As, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, V, and Zn	Sardinia (Italy)	[151]
113.	Cereals (Wheat)	Cd, Pb, Se, and Sr	USA, Canada, and Europe	[155]
114.	Cereals (Wheat)	Be, Na, Mg, Al, K, Ca, V, Mn, Fe, Cu, Zn, Mo, Cd, Ba, and Th)	China	[156]
115.	Cereals (Wheat)	K/Rb, Ca/Sr, Ba, ⁸⁷Sr/⁸⁶Sr, Co, Mo, Zn, Mn, Eu, δ13C, and Na	Buenos Aires, Córdoba, and Entre Ríos	[157]
116.	Cereals (Wheat)	Be, Na, Mg, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, Ga, Se, Rb, Sr, Y, Zr, Cd, Cs, and Pb	China	[158]
117.	Cereals (Wheat)	Mg, Al, Ca, Mn, Fe, Cu, Zn, As, Sr, Mo, Cd, Ba, and Pb	China	[159]
118.	Cereals (Maize)	B, Na, Mg, Al, P, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Cs, Ba, Pb, and U	China	[161]
119.	Cereals (Wheat, Barley), and Legumes (Faba Bean)	Li, Be, B, Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Rh, Pd, Ag, Cd, In, Sn, Sb, Te, I, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re, Os, Ir, Pt, Au, Hg, Tl, Pb, Bi, Th, and U	Zealand, Central Jutland, and South Jutland	[160]
120.	Rice	Ni, Mo, As, and Cd	Vietnam	[162]
121.	Rice	Ni, Mo, As, and Cd	Vietnam	[163]
122.	Rice	B, Ho, Gd, Mg, Rb, Se, and W	India, Pakistan, USA, France, Italy, Spain,	[164]
123.	Rice	Al, Fe, Co, Ni, Cu, Rb, Sr, and Ba	Japan, USA, China, and Thailand	[165]
124.	Rice	Mg, K, Ca, Na, Be, Mn, Ni, Cu, Cd, Fe, Al, Cr, Zn, Sb, and Pb	China	[166]
125.	Rice	B, Co, Sr, Mo, Cd, Cs, Ba, Pb, Ti, V, As, Se, Mn, Cu, Rb, Mg, Al, Cr, Fe, Ni, and Zn	Thailand, France, Japan, India, Italy, and Pakistan	[167]
126.	Rice	As, B, Ba, Ca, Cd, Ce, Co, Cr, Cu, Fe, K, La, Mg, Mn, Mo, P, Pb, Rb, Se and Zn	Brazil	[168]
127.	Rice	B, Mg, Al, Ti, V, Cr, Mn, Fe, Ni, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Pb, Bi, and Tl	China	[169]
128.	Rice	Al, As, Ba, Bi, Cd, Ca, Cr, Co, Cu, Fe, Pb, Li, Mg, Mn, Mo, Ni, K, Se, Na, Sr, Tl, Ti, Zn, La, Ce, Pr, Nd, Sm, Eu, Ho, Er and Yb	China	[170]
129.	Rice	⁸⁷Sr/⁸⁶Sr, ²⁰⁷Pb/²⁰⁶Pb, ²⁰⁸Pb/²⁰⁷Pb, Li, Be, Na, Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Mo, Ag, Cd, Sn, Sb, Ba, Pb, and Bi	China, Thailand, and Malaysia	[171]
130.	Rice	B, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Nb, Mo, Ag, Cd, Cs, Ba, Hg, and Pb	Heilongjiang, Liaoning, Jiangsu, Hubei, and Guangxi	[172]
131.	Rice	Na, Mg, Al, Si, P, S, K, Ca, Sc, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Mo, Ba, La, Ce, Au, and Pb	India, and Pakistan	[173]
132.	Rice	Mn, Rb, Co, and Mo	Thailand	[174]
133.	Rice	B, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Nb, Mo, Ag, Cd, Cs, Ba, Hg, and Pb	China, India, Vietnam, and Ghana	[175]
134.	Rice	Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, U, V, Zn, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sc, Sm, Tb, Th, Tm, and Y	Vietnam	[176]
135.	Coffee	Ba, Ca, Cu, Fe, Mg, Mn, P, Si, K, and S	Ethiopia	[178]
136.	Coffee	B, Na, Mg, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Rb, Sr, Mo, Ba, Pb, Bi, Y, La, Ce, Pr, Sm, Nd, Eu, Dy, Th, Sc, Ho, and Gd	Hawaii, Kauai, Maui, Molokai, and Oahu	[179]
137.	Coffee	K, Mg, Ca, Fe, Al, Mn, Cu, Ba, Sr, Zn, Cr, Pb, Ni, V, Co, Ga, U, Cd, Ag, Li, In, Bi, Th, and Tl	Brazil, Ethiopia, Kenya, Columbia, and India	[180]
138.	Coffee	Na, Ca, K, Mg, Al, As, Pb, Ni, Zn, Cu, Mn, Cd, Fe, Hg, and Cr	Germany, Netherlands, Italy, Austria, Slovenia, France, Romania, Brazil, and Greece	[181]
139.	Coffee	Li, Be, B, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Pd, Ag, Cd, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Er, Tm, Yb, Re, Ir, Pt, Au, Hg, Tl, Pb, Bi and U	Central/South America, Africa, and Asia	[182]
140.	Coffee	Ba, Ca, Cu, Fe, K, Mg, Mn, P, Sr, Sc, and Zn	Espirito Santo, Minas Gerais, and São Paolo (Brazil)	[183]
141.	Cocoa	Li, Be, B, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Pd, Ag, Cd, In, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Er, Tm, Yb, Re, Ir, Pt, Au, Hg, Tl, Pb, Bi, Th, and U	Africa, Asia, Central and South America	[184]
142.	Cocoa	Na, Cr, La, Ce, Mo, Cs, Ga, Ti, Y, Ba, Rb, Zn, Sr, Fe, Mg, Al, Co, Cu, Cd, Mn, Ni, As, Pb, and V	Congo, Mexico, Ecuador, Venezuela, Costa Rica, Vanuatu, and Trinidad	[185]
143.	Cocoa	B, Ba, Ca, Cd, Co, Cr, Cs, Cu, K, Ni, Mg, Mn, Mo, P, S, Sr, V, and Zn	Africa, Asia Pasific, Central/South America	[186]
144.	Legumes (Cowpeas)	Ag, As, Ba, Be, Cd, Cs, Co, Cr, Cu, Mo, Ni, Pb, Sb, Se, Sn, Sr, Tl, Rb, V, and Zn	Argentina	[188]
145.	Legumes (Yellow Split Pea)	Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Yb, Th, Sc, B, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Se, Cd, Ba, and Tl	Santorini, different places of Greece, India, Canada, USA, Iran, and Australia	[189]
146.	Legumes (Faba Beans)	Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Be, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Ba	Santorini (Greece)	[190]
147.	Legumes (Faba Beans)	Li, B, Na, Mg, Al, P, S, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Ba, La, Hg, and Pb	Manitoba and Saskatchewan (Canada)	[191]
148.	Legumes (Common Beans)	N, Mg, P, S, K, Ca, Mn, Fe, Cu, Na, Cr, Co, Zn, and Mo	Slovenia	[192]
149.	Nuts (Hazelnuts)	La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, 165Ho, Er, Tm, Yb, and Lu	Italy, and Turkey	[193]
150.	Nuts (Pistachio)	La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, and Yb	Greece, and Turkey	[194]
151.	Nuts (Peanuts)	K, Ca, Mg, Na, Al, Fe, Zn, Mn, Ni, Sr, Mo, Cu, Se, V, Co, As, Cd, Cr, and Pb	China	[195]
152.	Nuts (Walnuts)	Li, Be, B, Na, Mg, Al, K, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Y, Mo, Ag, Cd, Te, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Tl, Pb, Bi, Th, and U	Switzerland, Chile, China, Germany, France, Hungary, Italy, Pakistan, Turkey, and USA	[196]
153.	Nuts (Almonds)	Li, B, Al, Ti, Mn, Fe, Ni, Cu, Zn, Rb, Y, Ag, Cd, Ba, Ce, Tl, and U	Australia, Spain, Iran, Italy, Morocco, USA	[197]
154.	Sesame seeds	Mg, Al, K, Ca, Cr, Mn, Co, Ni, Cu, Zn, Rb, Sr, Cd, Ba, and Pb	Korean, Chinese, and Indian	[198]
155.	Spices	Cr, Co, Ni, Cu, Hg, Cd, Pb, and As	17 Different Countries	[200]
156.	Spices (red pepper flake)	Al, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, and Pb	Southeast Anatolia Region, the Mediterranean Region, and the Central Anatolia Region (Turkey)	[201]
157.	Spices (Black pepper)	Mg, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Se, Sr, Y, Mo, Sb, Ba, Pt, and Pb	Vietnam, Pakistan, and India	[202]
158.	Spices (Chili)	Ba, Be, Co, Cr, Cu, Fe, Ga, Li, Mn, Ni, Rb, Se, Sr, V, Zn, As, Cd, In, Pb, and Tl	South Korea, China, and Vietnam	[203]
159.	Spices (Hot/Sweet Paprika)	K, Mg, Fe, Zn, Cu, Mn, B, Al, Co, Ni, Se, Mo, As, Pb, and Cd	Serbia, and Hungry (Comparison with literature samples from Spain, Turkey, and Poland)	[204]
160.	Spices	⁸⁷Sr/⁸⁶Sr, Rb, Sr, Y, Zr, Mo, Cd, Ba, Pb, Th, U, Mg, Ca, Sc, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, and REE	Hungary, Spain, Romania, France, Senegal, China, and Germany	[205]
161.	Saffron	Li, Be, B, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Dy, Ho, Er, Hf, Re, Pb	Italy, Morocco and Iran	[206]
162.	Saffron	Fe, Ca, Na, Mg, Sr, Ag, Al, As, Ba, Be, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Tl, V, Zn, and U	Italy, and Iran	[207]
163.	Herbs	Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Sb, Se, Si, Sn, Sr, Ti, V, Zn, Zr	China	[208]
164.	Spices, and Herbs	Fe, Zn, Cr, Ni, Cu, Se, Pb, As, K, Ca, Mg, Na, Co, Mn, Hg and Cd	Italy, and Tunisia	[209]
165.	Spices, and Herbs	Na, Mg, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Cd, Hg, and Pb	Algeria	[210]
166.	Herbs	Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K	China	[211]
167.	Tea	La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc, and Y	China	[213]
168.	Tea	¹⁰⁹Ag/¹⁰⁷Ag, ¹³⁸Ba/¹³⁷Ba, ⁸¹Br/⁷⁹Br, ¹¹²Cd/¹¹¹Cd, ¹¹⁴Cd/¹¹²Cd, ¹¹⁴Cd/¹¹¹Cd, ⁵³Cr/⁵²Cr, ⁷²Ge/⁷⁰Ge, ⁷⁴Ge/⁷²Ge, ⁷⁴Ge/⁷⁰Ge, ²⁰²Hg/²⁰⁰Hg, ⁷Li/⁶Li, ⁹⁶Mo/⁹⁵Mo, ⁹⁸Mo/⁹⁶Mo, ⁹⁸Mo/⁹⁵Mo, ⁶⁰Ni/⁵⁸Ni, ²⁰⁷Pb/²⁰⁶Pb, ²⁰⁸Pb/²⁰⁷Pb, ²⁰⁸Pb/²⁰⁶Pb, ¹²³Sb/¹²¹Sb, ⁸⁰Se/⁷⁸Se, ¹²⁰Sn/¹¹⁸Sn, ⁸⁸Sr/⁸⁶Sr, ⁴⁷Ti/⁴⁶Ti, and ⁴⁸Ti/⁴⁷Ti, ⁴⁸Ti/⁴⁶Ti, ²⁰⁵Tl/²⁰³Tl, ⁶⁶Zn/⁶⁴Zn, ⁶⁸Zn/⁶⁶Zn, ⁶⁸Zn/⁶⁴Zn, ⁷¹Ga/⁶⁹Ga, ¹⁵³Eu/¹⁵¹Eu, ¹⁵⁴Sm/¹⁵²Sm, ¹⁵⁸Gd/¹⁵⁶Gd, ¹⁶⁰Gd/¹⁵⁸Gd, ¹⁶⁰Gd/¹⁵⁶Gd, ¹⁶⁴Dy/¹⁶²Dy, ¹⁶⁸Er/¹⁶⁶Er, ¹⁷⁴Yb/¹⁷²Yb, ¹⁷⁶Lu/¹⁷⁵Lu	China	[214]
169.	Tea	¹⁰⁹Ag/¹⁰⁷Ag, ¹³⁸Ba/¹³⁷Ba, ⁸¹Br/⁷⁹Br, ¹¹²Cd/¹¹¹Cd, ¹¹⁴Cd/¹¹²Cd, ¹¹⁴Cd/¹¹¹Cd, ⁵³Cr/⁵²Cr, ⁷²Ge/⁷⁰Ge, ⁷⁴Ge/⁷²Ge, ⁷⁴Ge/⁷⁰Ge, ²⁰²Hg/²⁰⁰Hg, ⁷Li/⁶Li, ⁹⁶Mo/⁹⁵Mo, ⁹⁸Mo/⁹⁶Mo, ⁹⁸Mo/⁹⁵Mo, ⁶⁰Ni/⁵⁸Ni, ²⁰⁷Pb/²⁰⁶Pb, ²⁰⁸Pb/²⁰⁷Pb, ²⁰⁸Pb/²⁰⁶Pb, ¹²³Sb/¹²¹Sb, ⁸⁰Se/⁷⁸Se, ¹²⁰Sn/¹¹⁸Sn, ⁸⁸Sr/⁸⁶Sr, ⁴⁷Ti/⁴⁶Ti, and ⁴⁸Ti/⁴⁷Ti, ⁴⁸Ti/⁴⁶Ti, ²⁰⁵Tl/²⁰³Tl, ⁶⁶Zn/⁶⁴Zn, ⁶⁸Zn/⁶⁶Zn, ⁶⁸Zn/⁶⁴Zn, ⁷¹Ga/⁶⁹Ga, ¹⁵³Eu/¹⁵¹Eu, ¹⁵⁴Sm/¹⁵²Sm, ¹⁵⁸Gd/¹⁵⁶Gd, ¹⁶⁰Gd/¹⁵⁸Gd, ¹⁶⁰Gd/¹⁵⁶Gd, ¹⁶⁴Dy/¹⁶²Dy, ¹⁶⁸Er/¹⁶⁶Er, ¹⁷⁴Yb/¹⁷²Yb, ¹⁷⁶Lu/¹⁷⁵Lu	China	[215]
170.	Tea	V, Cr, Co, Ga, Sr, Mo, Cd, Pb, Na, Al, Fe, Ni, Cu, Zn, Rb, and Ba	China	[216]
171.	Tea	86 Mineral elements	China	[217]
172.	Tea	Ag, As, Ba, Be, Bi, Br, Cd, Co, Cr, Ge, Hg, Li, Mo, Nb, Ni, Pb, Rb, Sb, Se, Sn, Sr, Ti, Tl, V, Zn, Cs, Hf, Y, La, Ce, Pr, Nd, Eu, Sm, Gd, Dy, Ho, Er, Tm, Yb, Lu, Sc, and Tb	China	[221]
173.	Tea	Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, U, V, Zn, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sc, Sm, Tb, Th, Tm, and Y	China	[218]
174.	Tea	Ti, Cr, Co, Ni, Cu, Zn, Rb, Cd, Cs, Ba, Sr, Ca, Mg, and Mn	China	[219]
175.	Tea	Al, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, K, Li, Mn, Ni, P, Pb, Rb, Sr, Tl, U, Y, and Zn	China	[220]

5. Conclusions

The present systematic review summarizes the research and development on inductively coupled plasma mass spectrometry (ICP-MS) in geographical origin authentication of agricultural products, food, and beverages. In addition to multi-elemental analysis, C, H, O, S, and Sr stable isotope ratio analysis is often utilized complementary, providing a more complete data source for confirming the research objective. The reader is able to understand the fundamentals of the ICP-MS technique in a brief tutorial presentation in Section 2, while Section 3 offers a complete overview of the research about ICP-MS in authenticity of geographical origin of food. Notwithstanding the fact that, when origin authentication and traceability of agri-food products is the main issue, ICP-MS has been the first option, confirming the superiority of the technique in the field. This is, also, confirmed by the huge number of publications on the topic and even more by the representative works of Zhou et al. [146] and Quinn et al. [6], who successfully determined the geographical origin of global honeys and Asian rice samples, respectively. Hence, it can be safely concluded that ICP-MS analysis is mature with the highest possible accuracy and precision in multi-elemental measurements with low uncertainty. Novel instrumental developments allow researchers to avoid and/or correct possible drawbacks of the technique including the interferences (spectroscopic and non-spectroscopic), Plasma Effects, Space-Charge Effects and Sample Introduction Effects which result in false data. Edible products from diverse areas have been shown to differentiate on their matrix compositions which affect the inorganic components of food substances. Exploiting this, ICP-MS is highly beneficial providing information about the geographical origin of food products due to the variety of matrices and consequently the inorganic components.

Author Contributions

Conceptualization, E.C.M., A.Z., A.P. and A.L.; methodology, E.C.M., A.Z., A.-A.T. and A.K.; validation, E.C.M., A.Z. and A.K.; investigation, E.C.M. and A.Z.; resources, A.P. and A.L.; data curation, E.C.M., A.Z. and A.-A.T.; writing—original draft preparation, E.C.M.; writing—review and editing, E.C.M., A.Z., A.P. and A.L.; visualization, E.C.M.; supervision, A.P. and A.L.; project administration, A.L.; funding acquisition, A.P. and A.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research has been financed by the Public Investment Programme/General Secretariat for Research and Innovation, under the call “YPOERGO 3, code 2018ΣΕ01300000: project title: ‘Elaboration and implementation of methodology for authenticity and geographical origin assessment of agricultural products’.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

Crandall, P.G.; Mauromoustakos, A.; O’Bryan, C.A.; Thompson, K.C.; Yiannas, F.; Bridges, K.; Francois, C. Impact of the Global Food Safety Initiative on Food Safety Worldwide: Statistical Analysis of a Survey of International Food Processors. J. Food Prot. 2017, 80, 1613–1622. [Google Scholar] [CrossRef] [PubMed]
How Inflation is Changing Consumer Behavior. Available online: https://www.ipsos.com/en-us/knowledge/consumer-shopper/How-Inflation-is-Changing-Consumer-Behavior (accessed on 2 August 2022).
Food Safety. Available online: https://www.who.int/news-room/fact-sheets/detail/food-safety (accessed on 1 August 2022).
Sharma, A. Food Adulteration: A Review. Sci. Eng. Technol. 2017, 5, 686–689. [Google Scholar] [CrossRef]
Maione, C.; Barbosa, R.M. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Crit. Rev. Food Sci. Nutr. 2019, 59, 1868–1879. [Google Scholar] [CrossRef] [PubMed]
Quinn, B.; McCarron, P.; Hong, Y.; Birse, N.; Wu, D.; Elliott, C.T.; Ch, R. Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam. Food Chem. 2022, 386, 132738. [Google Scholar] [CrossRef]
Aceto, M.; Bonello, F.; Musso, D.; Tsolakis, C.; Cassino, C.; Osella, D. Wine Traceability with Rare Earth Elements. Beverages 2018, 4, 23. [Google Scholar] [CrossRef] [Green Version]
Kalpage, M.; Dissanayake, C.; Diyabalanage, S.; Chandrajith, R.; Frew, R.; Fernando, R. Stable Isotope and Element Profiling for Determining the Agroclimatic Origin of Cow Milk within a Tropical Country. Foods 2022, 11, 275. [Google Scholar] [CrossRef]
Nasr, E.G.; Epova, E.N.; de Diego, A.; Souissi, R.; Hammami, M.; Abderrazak, H.; Donard, O.F. X Trace Elements Analysis of Tunisian and European Extra Virgin Olive Oils by ICP-MS and Chemometrics for Geographical Discrimination. Foods 2022, 11, 82. [Google Scholar] [CrossRef]
Nasr, E.G.; Epova, E.N.; Sebilo, M.; Larivière, D.; Hammami, M.; Souissi, R.; Abderrazak, H.; Donard, O.F.X. Olive Oil Traceability Studies Using Inorganic and Isotopic Signatures: A Review. Molecules 2022, 27, 2014. [Google Scholar] [CrossRef]
Dimitrakopoulou, M.-E.; Vantarakis, A. Does Traceability Lead to Food Authentication? A Systematic Review from A European Perspective. Food Rev. Int. 2021. [Google Scholar] [CrossRef]
Patriarca, M.; Barlow, N.; Cross, A.; Hill, S.; Robson, A.; Taylor, A.; Tyson, J. Atomic spectrometry update: Review of advances in the analysis of clinical and biological materials, foods and beverages. J. Anal. At. Spectrom. 2022, 37, 410–473. [Google Scholar] [CrossRef]
Katerinopoulou, K.; Kontogeorgos, A.; Salmas, C.E.; Patakas, A.; Ladavos, A. Geographical Origin Authentication of Agri-Food Products: A Review. Foods 2020, 9, 489. [Google Scholar] [CrossRef] [PubMed]
Aceto, M. Chapter 9—Food Forensics. In Comprehensive Analytical Chemistry; Picó, Y., Ed.; Elsevier: Amsterdam, The Netherlands, 2015; Volume 68, pp. 441–514. [Google Scholar] [CrossRef]
Rodrigues, C.; Maia, R.; Lauteri, M.; Brugnoli, E.; Máguas, C. Chapter 4—Stable Isotope Analysis. In Comprehensive Analytical Chemistry; de la Guardia, M., Gonzálvez, A., Eds.; Elsevier: Amsterdam, The Netherlands, 2013; Volume 60, pp. 77–99. [Google Scholar] [CrossRef]
Laursen, K.H.; Hansen, T.H.; Persson, D.P.; Schjoerring, J.K.; Husted, S. Multi-elemental fingerprinting of plant tissue by semi-quantitative ICP-MS and chemometrics. J. Anal. At. Spectrom. 2009, 24, 1198–1207. [Google Scholar] [CrossRef]
Fortunato, G.; Mumic, K.; Wunderli, S.; Pillonel, L.; Bosset, J.O.; Gremaud, G. Application of strontium isotope abundance ratios measured by MC-ICP-MS for food authentication. J. Anal. At. Spectrom. 2004, 19, 227–234. [Google Scholar] [CrossRef]
Barbaste, M.; Halicz, L.; Galy, A.; Medina, B.; Emteborg, H.; Adams, F.C.; Lobinski, R. Evaluation of the accuracy of the determination of lead isotope ratios in wine by ICP MS using quadrupole, multicollector magnetic sector and time-of-flight analyzers. Talanta 2001, 54, 307–317. [Google Scholar] [CrossRef]
Rodushkin, I.; Engström, E.; Baxter, D.C. Isotopic analyses by ICP-MS in clinical samples. Anal. Bioanal. Chem. 2013, 405, 2785–2797. [Google Scholar] [CrossRef] [PubMed]
Penanes, P.A.; Galán, A.R.; Huelga-Suarez, G.; Rodríguez-Castrillón, J.Á.; Moldovan, M.; Alonso, J.I.G. Isotopic measurements using ICP-MS: A tutorial review. J. Anal. At. Spectrom. 2022, 37, 701–726. [Google Scholar] [CrossRef]
Li, Q.; Wang, Z.; Mo, J.; Zhang, G.; Chen, Y.; Huang, C. Imaging gold nanoparticles in mouse liver by laser ablation inductively coupled plasma mass spectrometry. Sci. Rep. 2017, 7, 2965. [Google Scholar] [CrossRef] [Green Version]
da Silva, I.J.S.; Lavorante, A.F.; Paim, A.P.S.; da Silva, M.J. Microwave-assisted digestion employing diluted nitric acid for mineral determination in rice by ICP OES. Food Chem. 2020, 319, 126435. [Google Scholar] [CrossRef]
Bulska, E.; Wagner, B. Quantitative aspects of inductively coupled plasma mass spectrometry. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2016, 374, 20150369. [Google Scholar] [CrossRef] [Green Version]
Sharp, B.L. Pneumatic nebulisers and spray chambers for inductively coupled plasma spectrometry. A review. Part 1. Nebulisers. J. Anal. At. Spectrom. 1988, 3, 613–652. [Google Scholar] [CrossRef]
Sharp, B.L. Pneumatic nebulisers and spray chambers for inductively coupled plasma spectrometry. A review. Part 2. Spray chambers. J. Anal. At. Spectrom. 1988, 3, 939–963. [Google Scholar] [CrossRef]
Formation of Doubly Charged Atomic Ions in the Inductively Coupled Plasma—ScienceDirect. Available online: https://www.sciencedirect.com/science/article/pii/S0584854701003019 (accessed on 5 August 2022).
Miller, P.E.; Denton, M.B. The quadrupole mass filter: Basic operating concepts. J. Chem. Educ. 1986, 63, 617. [Google Scholar] [CrossRef]
Wilschefski, S.; Baxter, M. Inductively Coupled Plasma Mass Spectrometry: Introduction to Analytical Aspects. Clin. Biochem. Rev. 2019, 40, 115–133. [Google Scholar] [CrossRef] [PubMed]
Eickhorst, T.; Seubert, A. Germanium dioxide as internal standard for simplified trace determination of bromate, bromide, iodate and iodide by on-line coupling ion chromatography-inductively coupled plasma mass spectrometry. J. Chromatogr. A 2004, 1050, 103–109. [Google Scholar] [CrossRef]
Carter, J.A.; Barros, A.I.; Nóbrega, J.A.; Donati, G.L. Traditional Calibration Methods in Atomic Spectrometry and New Calibration Strategies for Inductively Coupled Plasma Mass Spectrometry. Front. Chem. 2018, 6, 504. [Google Scholar] [CrossRef]
Palmer, C.D.; Lewis, M.E.; Geraghty, C.M.; Barbosa, F.; Parsons, P.J. Determination of lead, cadmium and mercury in blood for assessment of environmental exposure: A comparison between inductively coupled plasma–mass spectrometry and atomic absorption spectrometry. Spectrochim. Acta Part B At. Spectrosc. 2006, 61, 980–990. [Google Scholar] [CrossRef]
Praamsma, M.L.; Arnason, J.G.; Parsons, P.J. Monitoring Mn in whole blood and urine: A comparison between electrothermal atomic absorption and inorganic mass spectrometry. J. Anal. At. Spectrom. 2011, 26, 1224–1232. [Google Scholar] [CrossRef]
Huang, C.; Beauchemin, D. Direct multielemental analysis of human serum by ICP-MS with on-line standard addition using flow injection. J. Anal. At. Spectrom. 2003, 18, 951–952. [Google Scholar] [CrossRef]
Haldimann, M.; Zimmerli, B.; Als, C.; Gerber, H. Direct determination of urinary iodine by inductively coupled plasma mass spectrometry using isotope dilution with iodine-129. Clin. Chem. 1998, 44, 817–824. [Google Scholar] [CrossRef] [Green Version]
Keyes, W.R.; Turnlund, J.R. Determination of molybdenum and enriched Mo stable isotope concentrations in human blood plasma by isotope dilution ICP-MS. J. Anal. At. Spectrom. 2002, 17, 1153–1156. [Google Scholar] [CrossRef]
May, T.W.; Wiedmeyer, R.H. A table of polyatomic interferences in ICP-MS. At. Spectrosc. 1998, 19, 6. [Google Scholar]
Drivelos, S.A.; Georgiou, C.A. Multi-element and multi-isotope-ratio analysis to determine the geographical origin of foods in the European Union. TrAC Trends Anal. Chem. 2012, 40, 38–51. [Google Scholar] [CrossRef]
Greenough, J.D.; Longerich, H.P.; Jackson, S.E. Element fingerprinting of Okanagan Valley wines using ICP-MS: Relationships between wine composition, vineyard and wine colour. Aust. J. Grape Wine Res. 1997, 3, 75–83. [Google Scholar] [CrossRef]
Baxter, M.J.; Crews, H.M.; Dennis, M.J.; Goodall, I.; Anderson, D. The determination of the authenticity of wine from its trace element composition. Food Chem. 1997, 60, 443–450. [Google Scholar] [CrossRef]
Ranaweera, R.K.R.; Gilmore, A.M.; Capone, D.L.; Bastian, S.E.P.; Jeffery, D.W. Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling. Food Chem. 2021, 335, 127592. [Google Scholar] [CrossRef]
Feher, I.; Magdas, D.A.; Dehelean, A.; Sârbu, C. Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: A new chemometric approach. J. Food Sci. Technol. 2019, 56, 5225–5233. [Google Scholar] [CrossRef] [PubMed]
da Costa, N.L.; Ximenez, J.P.B.; Rodrigues, J.L.; Barbosa, F.; Barbosa, R. Characterization of Cabernet Sauvignon wines from California: Determination of origin based on ICP-MS analysis and machine learning techniques. Eur. Food Res. Technol. 2020, 246, 1193–1205. [Google Scholar] [CrossRef]
Granell, B.; Izquierdo-Llopart, A.; Sahuquillo, À.; López-Sánchez, J.F.; Saurina, J. Characterization of Musts, Wines, and Sparkling Wines Based on Their Elemental Composition Determined by ICP-OES and ICP-MS. Beverages 2022, 8, 3. [Google Scholar] [CrossRef]
Rodrigues, S.M.; Otero, M.; Alves, A.A.; Coimbra, J.; Coimbra, M.A.; Pereira, E.; Duarte, A.C. Elemental analysis for categorization of wines and authentication of their certified brand of origin. J. Food Compos. Anal. 2011, 24, 548–562. [Google Scholar] [CrossRef]
Geana, I.; Iordache, A.; Ionete, R.; Marinescu, A.; Ranca, A.; Culea, M. Geographical origin identification of Romanian wines by ICP-MS elemental analysis. Food Chem. 2013, 138, 1125–1134. [Google Scholar] [CrossRef]
Pasvanka, K.; Kostakis, M.; Tarapoulouzi, M.; Nisianakis, P.; Thomaidis, N.S.; Proestos, C. ICP–MS Analysis of Multi-Elemental Profile of Greek Wines and Their Classification According to Variety, Area and Year of Production. Separations 2021, 8, 119. [Google Scholar] [CrossRef]
Tanabe, C.K.; Nelson, J.; Boulton, R.B.; Ebeler, S.E.; Hopfer, H. The Use of Macro, Micro, and Trace Elemental Profiles to Differentiate Commercial Single Vineyard Pinot noir Wines at a Sub-Regional Level. Molecules 2020, 25, 2552. [Google Scholar] [CrossRef] [PubMed]
Azcarate, S.M.; Martinez, L.D.; Savio, M.; Camiña, J.M.; Gil, R.A. Classification of monovarietal Argentinean white wines by their elemental profile. Food Control 2015, 57, 268–274. [Google Scholar] [CrossRef]
Taylor, V.F.; Longerich, H.P.; Greenough, J.D. Multielement Analysis of Canadian Wines by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Multivariate Statistics. J. Agric. Food Chem. 2003, 51, 856–860. [Google Scholar] [CrossRef]
Kruzlicova, D.; Fiket, Ž.; Kniewald, G. Classification of Croatian wine varieties using multivariate analysis of data obtained by high resolution ICP-MS analysis. Food Res. Int. 2013, 54, 621–626. [Google Scholar] [CrossRef]
Pérez-Álvarez, E.P.; Garcia, R.; Barrulas, P.; Dias, C.; Cabrita, M.J.; Garde-Cerdán, T. Classification of wines according to several factors by ICP-MS multi-element analysis. Food Chem. 2019, 270, 273–280. [Google Scholar] [CrossRef]
Cellier, R.; Berail, S.; Epova, E.; Barre, J.; Claverie, F.; van Leeuwen, C.; Milcent, S.; Ors, P.; Donard, O.F.X. The stability of major and trace element concentrations from musts to Champagne during the production process. OENO One 2022, 56, 29–40. [Google Scholar] [CrossRef]
Lima, M.M.M.; Hernandez, D.; Yeh, A.; Reiter, T.; Runnebaum, R.C. Reproducibility of elemental profile across two vintages in Pinot noir wines from fourteen different vineyard sites. Food Res. Int. 2021, 141, 110045. [Google Scholar] [CrossRef]
Grainger, C.; Yeh, A.; Byer, S.; Hjelmeland, A.; Lima, M.M.M.; Runnebaum, R.C. Vineyard site impact on the elemental composition of Pinot noir wines. Food Chem. 2021, 334, 127386. [Google Scholar] [CrossRef]
Gajek, M.; Pawlaczyk, A.; Szynkowska-Jozwik, M.I. Multi-Elemental Analysis of Wine Samples in Relation to Their Type, Origin, and Grape Variety. Molecules 2021, 26, 214. [Google Scholar] [CrossRef]
Rocha, S.; Pinto, E.; Almeida, A.; Fernandes, E. Multi-elemental analysis as a tool for characterization and differentiation of Portuguese wines according to their Protected Geographical Indication. Food Control 2019, 103, 27–35. [Google Scholar] [CrossRef]
Pasvanka, K.; Tzachristas, A.; Kostakis, M.; Thomaidis, N.; Proestos, C. Geographic characterization of Greek wine by inductively coupled plasma–mass spectrometry macroelemental analysis. Anal. Lett. 2019, 52, 2741–2750. [Google Scholar] [CrossRef]
Płotka-Wasylka, J.; Frankowski, M.; Simeonov, V.; Polkowska, Ż.; Namieśnik, J. Determination of Metals Content in Wine Samples by Inductively Coupled Plasma-Mass Spectrometry. Molecules 2018, 23, 2886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Karasinski, J.; Elguera, J.C.T.; Ibarra, A.A.G.; Wrobel, K.; Bulska, E.; Wrobel, K. Comparative Evaluation of Red Wine from Various European Regions Using Mass Spectrometry Tools. Anal. Lett. 2018, 51, 2645–2659. [Google Scholar] [CrossRef]
Hao, X.; Gao, F.; Wu, H.; Song, Y.; Zhang, L.; Li, H.; Wang, H. From Soil to Grape and Wine: Geographical Variations in Elemental Profiles in Different Chinese Regions. Foods 2021, 10, 3108. [Google Scholar] [CrossRef] [PubMed]
Geană, E.-I.; Sandru, C.; Stanciu, V.; Ionete, R.E. Elemental Profile and 87Sr/86Sr Isotope Ratio as Fingerprints for Geographical Traceability of Wines: An Approach on Romanian Wines. Food Anal. Methods 2017, 10, 63–73. [Google Scholar] [CrossRef]
Di Paola-Naranjo, R.D.; Baroni, M.V.; Podio, N.S.; Rubinstein, H.R.; Fabani, M.P.; Badini, R.G.; Inga, M.; Ostera, H.A.; Cagnoni, M.; Gallegos, E.; et al. Fingerprints for Main Varieties of Argentinean Wines: Terroir Differentiation by Inorganic, Organic, and Stable Isotopic Analyses Coupled to Chemometrics. J. Agric. Food Chem. 2011, 59, 7854–7865. [Google Scholar] [CrossRef]
Bora, F.D.; Donici, A.; Rusu, T.; Bunea, A.; Popescu, D.; Bunea, C.I. Elemental Profile and 207Pb/206Pb, 208Pb/206Pb, 204Pb/206Pb, 87Sr/86Sr Isotope Ratio as Fingerprints for Geographical Traceability of Romanian Wines. Not. Bot. Horti Agrobot. Cluj-Napoca 2018, 46, 223–239. [Google Scholar] [CrossRef] [Green Version]
Bronzi, B.; Brilli, C.; Beone, G.M.; Fontanella, M.C.; Ballabio, D.; Todeschini, R.; Consonni, V.; Grisoni, F.; Parri, F.; Buscema, M. Geographical identification of Chianti red wine based on ICP-MS element composition. Food Chem. 2020, 315, 126248. [Google Scholar] [CrossRef]
Angus, N.S.; O’keeffe, T.J.; Stuart, K.R.; Miskelly, G.M. Regional classification of New Zealand red wines using inductively-coupled plasma-mass spectrometry (ICP-MS). Aust. J. Grape Wine Res. 2006, 12, 170–176. [Google Scholar] [CrossRef]
Aceto, M.; Robotti, E.; Oddone, M.; Baldizzone, M.; Bonifacino, G.; Bezzo, G.; Di Stefano, R.; Gosetti, F.; Mazzucco, E.; Manfredi, M.; et al. A traceability study on the Moscato wine chain. Food Chem. 2013, 138, 1914–1922. [Google Scholar] [CrossRef] [PubMed]
Hopfer, H.; Gilleland, G.; Ebeler, S.E.; Nelson, J. Elemental Profiles of Whisk(e)y Allow Differentiation by Type and Region. Beverages 2017, 3, 8. [Google Scholar] [CrossRef] [Green Version]
Pawlaczyk, A.; Gajek, M.; Jozwik, K.; Szynkowska, M.I. Multielemental Analysis of Various Kinds of Whisky. Molecules 2019, 24, 1193. [Google Scholar] [CrossRef] [Green Version]
Gajek, M.; Pawlaczyk, A.; Jóźwik, K.; Szynkowska-Jóźwik, M.I. The Elemental Fingerprints of Different Types of Whisky as Determined by ICP-OES and ICP-MS Techniques in Relation to Their Type, Age, and Origin. Foods 2022, 11, 1616. [Google Scholar] [CrossRef] [PubMed]
García-Ruiz, S.; Moldovan, M.; Fortunato, G.; Wunderli, S.; García Alonso, J.I. Evaluation of strontium isotope abundance ratios in combination with multi-elemental analysis as a possible tool to study the geographical origin of ciders. Anal. Chim. Acta 2007, 590, 55–66. [Google Scholar] [CrossRef]
Gajek, M.; Pawlaczyk, A.; Wysocki, P.; Szynkowska-Jozwik, M.I. Elemental Characterization of Ciders and Other Low-Percentage Alcoholic Beverages Available on the Polish Market. Molecules 2021, 26, 2186. [Google Scholar] [CrossRef]
Mahmood, N.; Petraco, N.; He, Y. Elemental fingerprint profile of beer samples constructed using 14 elements determined by inductively coupled plasma-mass spectrometry (ICP-MS): Multivariation analysis and potential application to forensic sample comparison. Anal. Bioanal. Chem. 2012, 402, 861–869. [Google Scholar] [CrossRef]
Alexa, L.; KÃ¡ntor, A.; KovÃ¡cs, B.; Czipa, N. Determination of micro and trace elements of commercial beers. J. Microbiol. Biotechnol. Food Sci. 2018, 7, 432–436. [Google Scholar] [CrossRef] [Green Version]
Lafontaine, S.; Thomson, D.; Schubert, C.; Müller, I.; Kyle, M.; Biendl, M.; Conn, S.; Schüll, F.; Lutz, A.; Ligare, M.; et al. How deviations in the elemental profile of Humulus lupulus grown throughout the U.S. and Germany influence hop and beer quality. Food Chem. 2022, 395, 133543. [Google Scholar] [CrossRef]
Pepi, S.; Chicca, M.; Telloli, C.; Di Roma, A.; Grisenti, P.; Tessari, U.; Vaccaro, C. Discrimination of geographical origin of hop (Humulus lupulus L.) using geochemical elements combined with statistical analysis. Environ. Geochem. Health 2019, 41, 1559–1576. [Google Scholar] [CrossRef]
Xiong, Q.; Lin, Y.; Wu, W.; Hu, J.; Li, Y.; Xu, K.; Wu, X.; Hou, X. Chemometric intraregional discrimination of Chinese liquors based on multi-element determination by ICP-MS and ICP-OES. Appl. Spectrosc. Rev. 2021, 56, 115–127. [Google Scholar] [CrossRef]
Benincasa, C.; Lewis, J.; Sindona, G.; Tagarelli, A. The use of multi element profiling to differentiate between cowand buffalo milk. Food Chem. 2008, 110, 257–262. [Google Scholar] [CrossRef] [PubMed]
Tedesco, R.; del CarmenVilloslada Hidalgo, M.; Vardè, M.; Kehrwald, N.M.; Barbante, C.; Cozzi, G. Trace and rare earth elements determination in milk whey from the Veneto region, Italy. Food Control 2021, 121, 107595. [Google Scholar] [CrossRef]
Aceto, M.; Musso, D.; Calà, E.; Arieri, F.; Oddone, M. Role of Lanthanides in the Traceability of the Milk Production Chain. J. Agric. Food Chem. 2017, 65, 4200–4208. [Google Scholar] [CrossRef]
Pillonel, L.; Badertscher, R.; Froidevaux, P.; Haberhauer, G.; Hölzl, S.; Horn, P.; Jakob, A.; Pfammatter, E.; Piantini, U.; Rossmann, A.; et al. Stable isotope ratios, major, trace and radioactive elements in emmental cheeses of different origins. LWT Food Sci. Technol. 2003, 36, 615–623. [Google Scholar] [CrossRef]
Camin, F.; Bertoldi, D.; Santato, A.; Bontempo, L.; Perini, M.; Ziller, L.; Stroppa, A.; Larcher, R. Validation of methods for H, C, N and S stable isotopes and elemental analysis of cheese: Results of an international collaborative study. Rapid Commun. Mass Spectrom. 2015, 29, 415–423. [Google Scholar] [CrossRef] [PubMed]
Franke, B.M.; Gremaud, G.; Hadorn, R.; Kreuzer, M. Geographic origin of meat—Elements of an analytical approach to its authentication. Eur. Food Res. Technol. 2005, 221, 493–503. [Google Scholar] [CrossRef]
Heaton, K.; Kelly, S.D.; Hoogewerff, J.; Woolfe, M. Verifying the geographical origin of beef: The application of multi-element isotope and trace element analysis. Food Chem. 2008, 107, 506–515. [Google Scholar] [CrossRef]
Hao, L.; Yang, X.; Huang, Y.; Hocquette, J.-F.; Bryant, R.H.; Xun, W.; Niu, J.; Sun, L.; Chai, S.; Ding, L.; et al. Yak Etinin Coğrafi Kökenini Doğrulamak Amacıyla Mineral Elementlerin Kullanılması. Kafkas. Univ. Vet. Fak. Derg. 2019, 25, 93–98. [Google Scholar] [CrossRef]
Danezis, G.P.; Pappas, A.C.; Zoidis, E.; Papadomichelakis, G.; Hadjigeorgiou, I.; Zhang, P.; Brusic, V.; Georgiou, C.A. Game meat authentication through rare earth elements fingerprinting. Anal. Chim. Acta 2017, 991, 46–57. [Google Scholar] [CrossRef]
Danezis, G.P.; Zoidis, E.; Zhang, P.; Pappas, A.C.; Tsagkaris, A.S.; Papachristidis, C.A.; Papadomichelakis, G.; Hadjigeorgiou, I.; Georgiou, C.A. Tissue distribution of rare earth elements in wild, commercial and backyard rabbits. Meat Sci. 2019, 153, 45–50. [Google Scholar] [CrossRef] [PubMed]
Kim, J.S.; Hwang, I.M.; Lee, G.H.; Park, Y.M.; Choi, J.Y.; Jamila, N.; Khan, N.; Kim, K.S. Geographical origin authentication of pork using multi-element and multivariate data analyses. Meat Sci. 2017, 123, 13–20. [Google Scholar] [CrossRef]
Sun, S.; Guo, B.; Wei, Y.; Fan, M. Multi-element analysis for determining the geographical origin of mutton from different regions of China. Food Chem. 2011, 124, 1151–1156. [Google Scholar] [CrossRef]
Epova, E.N.; Bérail, S.; Zuliani, T.; Malherbe, J.; Sarthou, L.; Valiente, M.; Donard, O.F.X. 87Sr/86Sr isotope ratio and multielemental signatures as indicators of origin of European cured hams: The role of salt. Food Chem. 2018, 246, 313–322. [Google Scholar] [CrossRef] [PubMed]
Bandoniene, D.; Walkner, C.; Zettl, D.; Meisel, T. Rare Earth Element Labeling as a Tool for Assuring the Origin of Eggs and Poultry Products. J. Agric. Food Chem. 2018, 66, 11729–11738. [Google Scholar] [CrossRef] [PubMed]
Bandoniene, D.; Walkner, C.; Ringdorfer, F.; Meisel, T. Authentication of meat and dairy products using rare earth element labeling and detection by solution based and laser ablation ICP-MS. Food Res. Int. 2020, 132, 109106. [Google Scholar] [CrossRef] [PubMed]
Liu, X.; Xue, C.; Wang, Y.; Li, Z.; Xue, Y.; Xu, J. The classification of sea cucumber (Apostichopus japonicus) according to region of origin using multi-element analysis and pattern recognition techniques. Food Control 2012, 23, 522–527. [Google Scholar] [CrossRef]
Kang, X.; Zhao, Y.; Shang, D.; Zhai, Y.; Ning, J.; Sheng, X. Elemental analysis of sea cucumber from five major production sites in China: A chemometric approach. Food Control 2018, 94, 361–367. [Google Scholar] [CrossRef]
Costas-Rodríguez, M.; Lavilla, I.; Bendicho, C. Classification of cultivated mussels from Galicia (Northwest Spain) with European Protected Designation of Origin using trace element fingerprint and chemometric analysis. Anal. Chim. Acta 2010, 664, 121–128. [Google Scholar] [CrossRef]
Cubadda, F.; Raggi, A.; Coni, E. Element fingerprinting of marine organisms by dynamic reaction cell inductively coupled plasma mass spectrometry. Anal. Bioanal. Chem. 2006, 384, 887–896. [Google Scholar] [CrossRef]
Flem, B.; Moen, V.; Finne, T.E.; Viljugrein, H.; Kristoffersen, A.B. Trace element composition of smolt scales from Atlantic salmon (Salmo salar L.), geographic variation between hatcheries. Fish. Res. 2017, 190, 183–196. [Google Scholar] [CrossRef]
Bua, G.D.; Albergamo, A.; Annuario, G.; Zammuto, V.; Costa, R.; Dugo, G. High-Throughput ICP-MS and Chemometrics for Exploring the Major and Trace Element Profile of the Mediterranean Sepia Ink. Food Anal. Methods 2017, 10, 1181–1190. [Google Scholar] [CrossRef]
Zhao, H.; Zhang, S. Effects of sediment, seawater, and season on multi-element fingerprints of Manila clam (Ruditapes philippinarum) for authenticity identification. Food Control 2016, 66, 62–68. [Google Scholar] [CrossRef]
Luo, R.; Jiang, T.; Chen, X.; Zheng, C.; Liu, H.; Yang, J. Determination of geographic origin of Chinese mitten crab (Eriocheir sinensis) using integrated stable isotope and multi-element analyses. Food Chem. 2019, 274, 1–7. [Google Scholar] [CrossRef] [PubMed]
Varrà, M.O.; Ghidini, S.; Zanardi, E.; Badiani, A.; Ianieri, A. Authentication of European sea bass according to production method and geographical origin by light stable isotope ratio and rare earth elements analyses combined with chemometrics. Ital. J. Food Saf. 2019, 81, 7872. [Google Scholar] [CrossRef] [PubMed]
Ortea, I.; Gallardo, J.M. Investigation of production method, geographical origin and species authentication in commercially relevant shrimps using stable isotope ratio and/or multi-element analyses combined with chemometrics: An exploratory analysis. Food Chem. 2015, 170, 145–153. [Google Scholar] [CrossRef]
Rodushkin, I.; Bergman, T.; Douglas, G.; Engström, E.; Sörlin, D.; Baxter, D.C. Authentication of Kalix (N.E. Sweden) vendace caviar using inductively coupled plasma-based analytical techniques: Evaluation of different approaches. Anal. Chim. Acta 2007, 583, 310–318. [Google Scholar] [CrossRef]
Varrà, M.O.; Husáková, L.; Patočka, J.; Ghidini, S.; Zanardi, E. Classification of transformed anchovy products based on the use of element patterns and decision trees to assess traceability and country of origin labelling. Food Chem. 2021, 360, 129790. [Google Scholar] [CrossRef]
Guo, L.; Gong, L.; Yu, Y.; Zhang, H. Multi-element Fingerprinting as a Tool in Origin Authentication of Four East China Marine Species. J. Food Sci. 2013, 78, C1852–C1857. [Google Scholar] [CrossRef]
Varrà, M.O.; Ghidini, S.; Husáková, L.; Ianieri, A.; Zanardi, E. Advances in Troubleshooting Fish and Seafood Authentication by Inorganic Elemental Composition. Foods 2021, 10, 270. [Google Scholar] [CrossRef]
Furia, E.; Naccarato, A.; Sindona, G.; Stabile, G.; Tagarelli, A. Multielement Fingerprinting as a Tool in Origin Authentication of PGI Food Products: Tropea Red Onion. J. Agric. Food Chem. 2011, 59, 8450–8457. [Google Scholar] [CrossRef] [PubMed]
Ariyama, K.; Aoyama, Y.; Mochizuki, A.; Homura, Y.; Kadokura, M.; Yasui, A. Determination of the Geographic Origin of Onions between Three Main Production Areas in Japan and Other Countries by Mineral Composition. J. Agric. Food Chem. 2007, 55, 347–354. [Google Scholar] [CrossRef] [PubMed]
Vadalà, R.; Mottese, A.F.; Bua, G.D.; Salvo, A.; Mallamace, D.; Corsaro, C.; Vasi, S.; Giofrè, S.V.; Alfa, M.; Cicero, N.; et al. Statistical Analysis of Mineral Concentration for the Geographic Identification of Garlic Samples from Sicily (Italy), Tunisia and Spain. Foods 2016, 5, 20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Smith, R.G. Determination of the Country of Origin of Garlic (Allium sativum) Using Trace Metal Profiling. J. Agric. Food Chem. 2005, 53, 4041–4045. [Google Scholar] [CrossRef] [PubMed]
Nguyen-Quang, T.; Bui-Quang, M.; Truong-Ngoc, M. Rapid Identification of Geographical Origin of Commercial Soybean Marketed in Vietnam by ICP-MS. J. Anal. Methods Chem. 2021, 2021, e5583860. [Google Scholar] [CrossRef]
Voica, C.; Roba, C.; Feher, I.; Magdas, D.A. Elemental Analysis and Metal Intake of Romanian Vegetables. Anal. Lett. 2017, 50, 2755–2771. [Google Scholar] [CrossRef]
Zhang, J.; Yang, R.; Chen, R.; Li, Y.C.; Peng, Y.; Wen, X. Geographical origin discrimination of pepper (Capsicum annuum L.) based on multi-elemental concentrations combined with chemometrics. Food Sci. Biotechnol. 2019, 28, 1627–1635. [Google Scholar] [CrossRef]
Naccarato, A.; Furia, E.; Sindona, G.; Tagarelli, A. Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper. Food Chem. 2016, 206, 217–222. [Google Scholar] [CrossRef]
Lo Feudo, G.; Naccarato, A.; Sindona, G.; Tagarelli, A. Investigating the Origin of Tomatoes and Triple Concentrated Tomato Pastes through Multielement Determination by Inductively Coupled Plasma Mass Spectrometry and Statistical Analysis. J. Agric. Food Chem. 2010, 58, 3801–3807. [Google Scholar] [CrossRef]
Bettinelli, M.; Spectroscopy, A. ICP-MS determination of REEs in tomato plants and related products: A new analytical tool to verify traceability. At. Spectrosc. 2005, 26, 41–50. [Google Scholar]
Bontempo, L.; Camin, F.; Manzocco, L.; Nicolini, G.; Wehrens, R.; Ziller, L.; Larcher, R. Traceability along the production chain of Italian tomato products on the basis of stable isotopes and mineral composition. Rapid Commun. Mass Spectrom. 2011, 25, 899–909. [Google Scholar] [CrossRef] [PubMed]
Bong, Y.-S.; Shin, W.-J.; Gautam, M.K.; Jeong, Y.-J.; Lee, A.-R.; Jang, C.-S.; Lim, Y.-P.; Chung, G.-S.; Lee, K.-S. Determining the geographical origin of Chinese cabbages using multielement composition and strontium isotope ratio analyses. Food Chem. 2012, 135, 2666–2674. [Google Scholar] [CrossRef]
Segelke, T.; von Wuthenau, K.; Neitzke, G.; Müller, M.-S.; Fischer, M. Food Authentication: Species and Origin Determination of Truffles (Tuber spp.) by Inductively Coupled Plasma Mass Spectrometry and Chemometrics. J. Agric. Food Chem. 2020, 68, 14374–14385. [Google Scholar] [CrossRef] [PubMed]
Potortì, A.G.; Di Bella, G.; Mottese, A.F.; Bua, G.D.; Fede, M.R.; Sabatino, G.; Salvo, A.; Somma, R.; Dugo, G.; Lo Turco, V. Traceability of Protected Geographical Indication (PGI) Interdonato lemon pulps by chemometric analysis of the mineral composition. J. Food Compos. Anal. 2018, 69, 122–128. [Google Scholar] [CrossRef]
Squadrone, S.; Brizio, P.; Stella, C.; Mantia, M.; Pederiva, S.; Giordanengo, G.; Pistone, G.; Abete, M.C. Distribution and bioaccumulation of trace elements and lanthanides in apples from Northwestern Italy. J. Trace Elem. Med. Biol. 2020, 62, 126646. [Google Scholar] [CrossRef]
Muñoz-Redondo, J.M.; Bertoldi, D.; Tonon, A.; Ziller, L.; Camin, F.; Moreno-Rojas, J.M. Tracing the geographical origin of Spanish mango (Mangifera indica L.) using stable isotopes ratios and multi-element profiles. Food Control 2021, 125, 107961. [Google Scholar] [CrossRef]
Muñoz-Redondo, J.M.; Bertoldi, D.; Tonon, A.; Ziller, L.; Camin, F.; Moreno-Rojas, J.M. Multi-element and stable isotopes characterization of commercial avocado fruit (Persea americana Mill) with origin authentication purposes. Food Control 2022, 137, 108975. [Google Scholar] [CrossRef]
Coelho, I.; Matos, A.S.; Teixeira, R.; Nascimento, A.; Bordado, J.; Donard, O.; Castanheira, I. Combining multielement analysis and chemometrics to trace the geographical origin of Rocha pear. J. Food Compos. Anal. 2019, 77, 1–8. [Google Scholar] [CrossRef]
Debbarma, N.; Manivannan, S.; Muddarsu, V.R.; Umadevi, P.; Upadhyay, S. Ionome signatures discriminates the geographical origin of jackfruits (Artocarpus heterophyllus Lam.). Food Chem. 2021, 339, 127896. [Google Scholar] [CrossRef]
Benabdelkamel, H.; Di Donna, L.; Mazzotti, F.; Naccarato, A.; Sindona, G.; Tagarelli, A.; Taverna, D. Authenticity of PGI “Clementine of Calabria” by Multielement Fingerprint. J. Agric. Food Chem. 2012, 60, 3717–3726. [Google Scholar] [CrossRef]
Benincasa, C.; Lewis, J.; Perri, E.; Sindona, G.; Tagarelli, A. Determination of trace element in Italian virgin olive oils and their characterization according to geographical origin by statistical analysis. Anal. Chim. Acta 2007, 585, 366–370. [Google Scholar] [CrossRef] [PubMed]
Camin, F.; Larcher, R.; Perini, M.; Bontempo, L.; Bertoldi, D.; Gagliano, G.; Nicolini, G.; Versini, G. Characterisation of authentic Italian extra-virgin olive oils by stable isotope ratios of C, O and H and mineral composition. Food Chem. 2010, 118, 901–909. [Google Scholar] [CrossRef]
Camin, F.; Larcher, R.; Nicolini, G.; Bontempo, L.; Bertoldi, D.; Perini, M.; Schlicht, C.; Schellenberg, A.; Thomas, F.; Heinrich, K.; et al. Isotopic and Elemental Data for Tracing the Origin of European Olive Oils. J. Agric. Food Chem. 2010, 58, 570–577. [Google Scholar] [CrossRef] [PubMed]
Farmaki, E.G.; Thomaidis, N.S.; Minioti, K.S.; Ioannou, E.; Georgiou, C.A.; Efstathiou, C.E. Geographical Characterization of Greek Olive Oils Using Rare Earth Elements Content and Supervised Chemometric Techniques. Anal. Lett. 2012, 45, 920–932. [Google Scholar] [CrossRef]
Beltrán, M.; Sánchez-Astudillo, M.; Aparicio, R.; García-González, D.L. Geographical traceability of virgin olive oils from south-western Spain by their multi-elemental composition. Food Chem. 2015, 169, 350–357. [Google Scholar] [CrossRef]
Gumus, Z.P.; Celenk, V.U.; Tekin, S.; Yurdakul, O.; Ertas, H. Determination of trace elements and stable carbon isotope ratios in virgin olive oils from Western Turkey to authenticate geographical origin with a chemometric approach. Eur. Food Res. Technol. 2017, 243, 1719–1727. [Google Scholar] [CrossRef]
Damak, F.; Asano, M.; Baba, K.; Suda, A.; Araoka, D.; Wali, A.; Isoda, H.; Nakajima, M.; Ksibi, M.; Tamura, K. Interregional traceability of Tunisian olive oils to the provenance soil by multielemental fingerprinting and chemometrics. Food Chem. 2019, 283, 656–664. [Google Scholar] [CrossRef] [Green Version]
Aceto, M.; Calà, E.; Musso, D.; Regalli, N.; Oddone, M. A preliminary study on the authentication and traceability of extra virgin olive oil made from Taggiasca olives by means of trace and ultra-trace elements distribution. Food Chem. 2019, 298, 125047. [Google Scholar] [CrossRef]
Wali, A.; Damak, F.; Kawada, K.; Isoda, H.; Tamura, K.; Ksibi, M. The effects of geographic region and cultivar on oxidative stability and elemental analysis of Tunisian extra virgin olive oil. Eur. Food. Res. Technol. 2021, 247, 1401–1409. [Google Scholar] [CrossRef]
Astolfi, M.L.; Marini, F.; Frezzini, M.A.; Massimi, L.; Capriotti, A.L.; Montone, C.M.; Canepari, S. Multielement Characterization and Antioxidant Activity of Italian Extra-Virgin Olive Oils. Front. Chem. 2021, 9, 769620. [Google Scholar] [CrossRef]
Damak, F.; Bougi, M.S.M.; Araoka, D.; Baba, K.; Furuya, M.; Ksibi, M.; Tamura, K. Soil geochemistry, edaphic and climatic characteristics as components of Tunisian olive terroirs: Relationship with the multielemental composition of olive oils for their geographical traceability. Eur. Mediterr. J. Environ. Integr. 2021, 6, 37. [Google Scholar] [CrossRef]
Llorent-Martínez, E.J.; Fernández-de Córdova, M.L.; Ortega-Barrales, P.; Ruiz-Medina, A. Quantitation of Metals During the Extraction of Virgin Olive Oil from Olives Using ICP-MS after Microwave-assisted Acid Digestion. J. Am. Oil Chem. Soc. 2014, 91, 1823–1830. [Google Scholar] [CrossRef]
Pucci, E.; Palumbo, D.; Puiu, A.; Lai, A.; Fiorani, L.; Zoani, C. Characterization and Discrimination of Italian Olive (Olea europaea sativa) Cultivars by Production Area Using Different Analytical Methods Combined with Chemometric Analysis. Foods 2022, 11, 1085. [Google Scholar] [CrossRef] [PubMed]
Llorent-Martínez, E.J.; Ortega-Barrales, P.; Fernández-de Córdova, M.L.; Domínguez-Vidal, A.; Ruiz-Medina, A. Investigation by ICP-MS of trace element levels in vegetable edible oils produced in Spain. Food Chem. 2011, 127, 1257–1262. [Google Scholar] [CrossRef] [PubMed]
de Souza, R.M.; Toloza, C.A.T.; Aucélio, R.Q. Fast determination of trace metals in edible oils and fats by inductively coupled plasma mass spectrometry and ultrasonic acidic extraction. J. Trace Elem. Miner. 2022, 1, 100003. [Google Scholar] [CrossRef]
Amit; Jamwal, R.; Kumari, S.; Kelly, S.; Cannavan, A.; Singh, D.K. Assessment of geographical origin of virgin coconut oil using inductively coupled plasma mass spectrometry along with multivariate chemometrics. Curr. Res. Food Sci. 2022, 5, 545–552. [Google Scholar] [CrossRef]
Chudzinska, M.; Baralkiewicz, D. Application of ICP-MS method of determination of 15 elements in honey with chemometric approach for the verification of their authenticity. Food Chem. Toxicol. 2011, 49, 2741–2749. [Google Scholar] [CrossRef]
Batista, B.L.; da Silva, L.R.S.; Rocha, B.A.; Rodrigues, J.L.; Berretta-Silva, A.A.; Bonates, T.O.; Gomes, V.S.D.; Barbosa, R.M.; Barbosa, F. Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques. Food Res. Int. 2012, 49, 209–215. [Google Scholar] [CrossRef]
Chen, H.; Fan, C.; Chang, Q.; Pang, G.; Hu, X.; Lu, M.; Wang, W. Chemometric Determination of the Botanical Origin for Chinese Honeys on the Basis of Mineral Elements Determined by ICP-MS. J. Agric. Food Chem. 2014, 62, 2443–2448. [Google Scholar] [CrossRef]
Di Bella, G.; Lo Turco, V.; Potortì, A.G.; Bua, G.D.; Fede, M.R.; Dugo, G. Geographical discrimination of Italian honey by multi-element analysis with a chemometric approach. J. Food Compos. Anal. 2015, 44, 25–35. [Google Scholar] [CrossRef]
Zhou, X.; Taylor, M.P.; Salouros, H.; Prasad, S. Authenticity and geographic origin of global honeys determined using carbon isotope ratios and trace elements. Sci. Rep. 2018, 8, 14639. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Oroian, M.; Prisacaru, A.; Hretcanu, E.C.; Stroe, S.-G.; Leahu, A.; Buculei, A. Heavy Metals Profile in Honey as a Potential Indicator of Botanical and Geographical Origin. Int. J. Food Prop. 2016, 19, 1825–1836. [Google Scholar] [CrossRef]
Voica, C.; Iordache, A.M.; Ionete, R.E. Multielemental characterization of honey using inductively coupled plasma mass spectrometry fused with chemometrics. J. Mass Spectrom. 2020, 55, e4512. [Google Scholar] [CrossRef]
Silva, B.; Gonzaga, L.V.; Maltez, H.F.; Samochvalov, K.B.; Fett, R.; Costa, A.C.O. Elemental profiling by ICP-MS as a tool for geographical discrimination: The case of bracatinga honeydew honey. J. Food Compos. Anal. 2021, 96, 103727. [Google Scholar] [CrossRef]
Drivelos, S.A.; Danezis, G.P.; Halagarda, M.; Popek, S.; Georgiou, C.A. Geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics. Food Chem. 2021, 338, 127936. [Google Scholar] [CrossRef]
Mara, A.; Deidda, S.; Caredda, M.; Ciulu, M.; Deroma, M.; Farinini, E.; Floris, I.; Langasco, I.; Leardi, R.; Pilo, M.I.; et al. Multi-Elemental Analysis as a Tool to Ascertain the Safety and the Origin of Beehive Products: Development, Validation, and Application of an ICP-MS Method on Four Unifloral Honeys Produced in Sardinia, Italy. Molecules 2022, 27, 2009. [Google Scholar] [CrossRef]
Padovan, G.J.; Rodrigues, L.P.; A.Leme, I.; Jong, D.D.; S.Marchini, J. Presence of C4 Sugars in Honey Samples Detected by The Carbon Isotope Ratio Measured by IRMS. Eurasian J. Anal. Chem. 2007, 2, 134–141. [Google Scholar]
Detection of Adulteration of Commercial Honey Samples by the 13C/12C Isotopic Ratio—ScienceDirect. Available online: https://www.sciencedirect.com/science/article/pii/S0308814602005046?via%3Dihub (accessed on 17 September 2022).
Tsagkaris, A.S.; Koulis, G.A.; Danezis, G.P.; Martakos, I.; Dasenaki, M.; Georgiou, C.A.; Thomaidis, N.S. Honey authenticity: Analytical techniques, state of the art and challenges. RSC Adv. 2021, 11, 11273–11294. [Google Scholar] [CrossRef]
Branch, S.; Burke, S.; Evans, P.; Fairman, B.; Briche, C.S.J.W. A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with 13C, 15N mass spectrometry. J. Anal. At. Spectrom. 2003, 18, 17–22. [Google Scholar] [CrossRef]
Zhao, H.; Guo, B.; Wei, Y.; Zhang, B.; Sun, S.; Zhang, L.; Yan, J. Determining the Geographic Origin of Wheat Using Multielement Analysis and Multivariate Statistics. J. Agric. Food Chem. 2011, 59, 4397–4402. [Google Scholar] [CrossRef]
Podio, N.S.; Baroni, M.V.; Badini, R.G.; Inga, M.; Ostera, H.A.; Cagnoni, M.; Gautier, E.A.; García, P.P.; Hoogewerff, J.; Wunderlin, D.A. Elemental and Isotopic Fingerprint of Argentinean Wheat. Matching Soil, Water, and Crop Composition to Differentiate Provenance. J. Agric. Food Chem. 2013, 61, 3763–3773. [Google Scholar] [CrossRef] [PubMed]
Zhao, H.; Guo, B.; Wei, Y.; Zhang, B. Multi-element composition of wheat grain and provenance soil and their potentialities as fingerprints of geographical origin. J. Cereal Sci. 2013, 57, 391–397. [Google Scholar] [CrossRef]
Liu, H.; Wei, Y.; Zhang, Y.; Wei, S.; Zhang, S.; Guo, B. The effectiveness of multi-element fingerprints for identifying the geographical origin of wheat. Int. J. Food Sci. Technol. 2017, 52, 1018–1025. [Google Scholar] [CrossRef]
Laursen, K.H.; Schjoerring, J.K.; Olesen, J.E.; Askegaard, M.; Halekoh, U.; Husted, S. Multielemental Fingerprinting as a Tool for Authentication of Organic Wheat, Barley, Faba Bean, and Potato. J. Agric. Food Chem. 2011, 59, 4385–4396. [Google Scholar] [CrossRef]
Wang, F.; Zhao, H.; Yu, C.; Tang, J.; Wu, W.; Yang, Q. Determination of the geographical origin of maize (Zea mays L.) using mineral element fingerprints. J. Sci. Food Agric. 2020, 100, 1294–1300. [Google Scholar] [CrossRef] [PubMed]
Phuong, T.D.; Kokot, S.; Chuong, P.V.; Khiem, D.T. Elemental content of Vietnamese ricePart 1. Sampling, analysis and comparison with previous studies. Analyst 1999, 124, 553–560. [Google Scholar] [CrossRef]
Kokot, S.; Phuong, T.D. Elemental content of Vietnamese ricePart 2.† Multivariate data analysis. Analyst 1999, 124, 561–569. [Google Scholar] [CrossRef]
Kelly, S.; Baxter, M.; Chapman, S.; Rhodes, C.; Dennis, J.; Brereton, P. The application of isotopic and elemental analysis to determine the geographical origin of premium long grain rice. Eur Food Res Technol 2002, 214, 72–78. [Google Scholar] [CrossRef]
Ariyama, K.; Shinozaki, M.; Kawasaki, A. Determination of the Geographic Origin of Rice by Chemometrics with Strontium and Lead Isotope Ratios and Multielement Concentrations. J. Agric. Food Chem. 2012, 60, 1628–1634. [Google Scholar] [CrossRef]
Shen, S.; Xia, L.; Xiong, N.; Liu, Z.; Sun, H. Determination of the geographic origin of rice by element fingerprints and correlation analyses with the soil of origin. Anal. Methods 2013, 5, 6177–6185. [Google Scholar] [CrossRef]
Cheajesadagul, P.; Arnaudguilhem, C.; Shiowatana, J.; Siripinyanond, A.; Szpunar, J. Discrimination of geographical origin of rice based on multi-element fingerprinting by high resolution inductively coupled plasma mass spectrometry. Food Chem. 2013, 141, 3504–3509. [Google Scholar] [CrossRef] [PubMed]
Borges, E.M.; Gelinski, J.M.L.N.; de Oliveira Souza, V.C.; Barbosa, F., Jr.; Batista, B.L. Monitoring the authenticity of organic rice via chemometric analysis of elemental data. Food Res. Int. 2015, 77, 299–309. [Google Scholar] [CrossRef]
Du, M.; Fang, Y.; Shen, F.; Mao, B.; Zou, Y.; Li, P.; Pei, F.; Hu, Q. Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components. Int. J. Food Sci. Technol. 2018, 53, 2088–2096. [Google Scholar] [CrossRef]
Qian, L.; Zhang, C.; Zuo, F.; Zheng, L.; Li, D.; Zhang, A.; Zhang, D. Effects of fertilizers and pesticides on the mineral elements used for the geographical origin traceability of rice. J. Food Compos. Anal. 2019, 83, 103276. [Google Scholar] [CrossRef]
Liu, Z.; Zhang, W.; Zhang, Y.; Chen, T.; Shao, S.; Zhou, L.; Yuan, Y.; Xie, T.; Rogers, K.M. Assuring food safety and traceability of polished rice from different production regions in China and Southeast Asia using chemometric models. Food Control 2019, 99, 1–10. [Google Scholar] [CrossRef]
Xu, F.; Kong, F.; Peng, H.; Dong, S.; Gao, W.; Zhang, G. Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice. npj Sci. Food. 2021, 5, 18. [Google Scholar] [CrossRef]
Arif, M.; Chilvers, G.; Day, S.; Naveed, S.A.; Woolfe, M.; Rodionova, O.Y.; Pomerantsev, A.L.; Kracht, O.; Brodie, C.; Mihailova, A.; et al. Differentiating Pakistani long-grain rice grown inside and outside the accepted Basmati Himalayan geographical region using a ‘one-class’ multi-element chemometric model. Food Control 2021, 123, 107827. [Google Scholar] [CrossRef]
Kongsri, S.; Sricharoen, P.; Limchoowong, N.; Kukusamude, C. Tracing the Geographical Origin of Thai Hom Mali Rice in Three Contiguous Provinces of Thailand Using Stable Isotopic and Elemental Markers Combined with Multivariate Analysis. Foods 2021, 10, 2349. [Google Scholar] [CrossRef]
McGrath, T.F.; Shannon, M.; Chevallier, O.P.; Ch, R.; Xu, F.; Kong, F.; Peng, H.; Teye, E.; Akaba, S.; Wu, D.; et al. Food Fingerprinting: Using a Two-Tiered approach to Monitor and Mitigate Food Fraud in Rice. J. AOAC Int. 2021, 104, 16–28. [Google Scholar] [CrossRef]
Bui, M.Q.; Quan, T.C.; Nguyen, Q.T.; Tran-Lam, T.-T.; Dao, Y.H. Geographical origin traceability of Sengcu rice using elemental markers and multivariate analysis. Food Addit. Contam. Part B 2022, 15, 177–190. [Google Scholar] [CrossRef]
Qian, L.; Zuo, F.; Zhang, C.; Zhang, D. Geographical Origin Traceability of Rice: A Study on the Effect of Processing Precision on Index Elements. Food Sci. Technol. Res. 2019, 25, 619–624. [Google Scholar] [CrossRef]
Mehari, B.; Redi-Abshiro, M.; Chandravanshi, B.S.; Combrinck, S.; McCrindle, R. Characterization of the Cultivation Region of Ethiopian Coffee by Elemental Analysis. Anal. Lett. 2016, 49, 2474–2489. [Google Scholar] [CrossRef]
Rodrigues, C.; Brunner, M.; Steiman, S.; Bowen, G.J.; Nogueira, J.M.F.; Gautz, L.; Prohaska, T.; Máguas, C. Isotopes as Tracers of the Hawaiian Coffee-Producing Regions. J. Agric. Food Chem. 2011, 59, 10239–10246. [Google Scholar] [CrossRef] [PubMed]
Albals, D.; Al-Momani, I.F.; Issa, R.; Yehya, A. Multi-element determination of essential and toxic metals in green and roasted coffee beans: A comparative study among different origins using ICP-MS. Sci. Prog. 2021, 104, 00368504211026162. [Google Scholar] [CrossRef] [PubMed]
Voica, C.; Feher, I.; Iordache, A.M.; Cristea, G.; Dehelean, A.; Magdas, D.A.; Mirel, V. Multielemental Analysis of Coffee by Inductively Coupled Plasma-Mass Spectrometry. Anal. Lett. 2016, 49, 2627–2643. [Google Scholar] [CrossRef]
Santato, A.; Bertoldi, D.; Perini, M.; Camin, F.; Larcher, R. Using elemental profiles and stable isotopes to trace the origin of green coffee beans on the global market. J. Mass Spectrom. 2012, 47, 1132–1140. [Google Scholar] [CrossRef]
de Gois, J.S.; Lucena, I.O.; Paulo, S.D.O.; da Silva, A.P.; Lima, I.C.A.; Luna, A.S. A high-throughput method for multi-element determination in green coffee beans using diluted nitric acid and ultrasound energy. Anal. Methods 2018, 10, 1656–1661. [Google Scholar] [CrossRef]
Bertoldi, D.; Barbero, A.; Camin, F.; Caligiani, A.; Larcher, R. Multielemental fingerprinting and geographic traceability of Theobroma cacao beans and cocoa products. Food Control 2016, 65, 46–53. [Google Scholar] [CrossRef]
Acierno, V.; de Jonge, L.; van Ruth, S. Sniffing out cocoa bean traits that persist in chocolates by PTR-MS, ICP-MS and IR-MS. Food Res. Int. 2020, 133, 109212. [Google Scholar] [CrossRef]
Vanderschueren, R.; Montalvo, D.; De Ketelaere, B.; Delcour, J.A.; Smolders, E. The elemental composition of chocolates is related to cacao content and origin: A multi-element fingerprinting analysis of single origin chocolates. J. Food Compos. Anal. 2019, 83, 103277. [Google Scholar] [CrossRef]
Perez, M.; Lopez-Yerena, A.; Vallverdú-Queralt, A. Traceability, authenticity and sustainability of cocoa and chocolate products: A challenge for the chocolate industry. Crit. Rev. Food Sci. Nutr. 2022, 62, 475–489. [Google Scholar] [CrossRef] [PubMed]
Pérez-Rodríguez, M.; Gaiad, J.E.; Hidalgo, M.J.; Avanza, M.V.; Pellerano, R.G. Classification of cowpea beans using multielemental fingerprinting combined with supervised learning. Food Control 2019, 95, 232–241. [Google Scholar] [CrossRef]
Drivelos, S.A.; Higgins, K.; Kalivas, J.H.; Haroutounian, S.A.; Georgiou, C.A. Data fusion for food authentication. Combining rare earth elements and trace metals to discriminate “Fava Santorinis” from other yellow split peas using chemometric tools. Food Chem. 2014, 165, 316–322. [Google Scholar] [CrossRef] [PubMed]
Drivelos, S.A.; Danezis, G.P.; Haroutounian, S.A.; Georgiou, C.A. Rare earth elements minimal harvest year variation facilitates robust geographical origin discrimination: The case of PDO “Fava Santorinis”. Food Chem. 2016, 213, 238–245. [Google Scholar] [CrossRef] [PubMed]
Khazaei, H.; Vandenberg, A. Seed Mineral Composition and Protein Content of Faba Beans (Vicia faba L.) with Contrasting Tannin Contents. Agronomy 2020, 10, 511. [Google Scholar] [CrossRef] [Green Version]
Pipan, B.; Sinkovič, L.; Pipan, B. Multi-elemental composition, nutrients and total phenolics in seeds of Phaseolus vulgaris L. breeding material. J. Elem. 2021, 26, 613–623. [Google Scholar] [CrossRef]
Oddone, M.; Aceto, M.; Baldizzone, M.; Musso, D.; Osella, D. Authentication and Traceability Study of Hazelnuts from Piedmont, Italy. J. Agric. Food Chem. 2009, 57, 3404–3408. [Google Scholar] [CrossRef]
Kalogiouri, N.P.; Manousi, N.; Klaoudatos, D.; Spanos, T.; Topi, V.; Zachariadis, G.A. Rare Earths as Authenticity Markers for the Discrimination of Greek and Turkish Pistachios Using Elemental Metabolomics and Chemometrics. Foods 2021, 10, 349. [Google Scholar] [CrossRef]
Chen, L.; Ding, M.; Li, Z.; Li, X.; Deng, L. Determination of macro, micro and toxic element concentrations in peanuts from main peanut producing areas of China by ICP-MS: A pilot study on the geographical characterization. RSC Adv. 2022, 12, 16790–16797. [Google Scholar] [CrossRef]
Segelke, T.; von Wuthenau, K.; Kuschnereit, A.; Müller, M.-S.; Fischer, M. Origin Determination of Walnuts (Juglans regia L.) on a Worldwide and Regional Level by Inductively Coupled Plasma Mass Spectrometry and Chemometrics. Foods 2020, 9, 1708. [Google Scholar] [CrossRef]
von Wuthenau, K.; Segelke, T.; Müller, M.-S.; Behlok, H.; Fischer, M. Food authentication of almonds (Prunus dulcis mill.). Origin analysis with inductively coupled plasma mass spectrometry (ICP-MS) and chemometrics. Food Control 2022, 134, 108689. [Google Scholar] [CrossRef]
Choi, Y.H.; Hong, C.K.; Kim, M.; Jung, S.O.; Park, J.; Oh, Y.H.; Kwon, J.-H. Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry. Food Sci. Biotechnol. 2017, 26, 375–379. [Google Scholar] [CrossRef] [PubMed]
Herbs and Spices (2019–2021). Available online: https://food.ec.europa.eu/safety/agri-food-fraud/eu-coordinated-actions/coordinated-control-plans/herbs-and-spices-2019-2021_en (accessed on 21 September 2022).
Voica, C.; Nechita, C.; Iordache, A.M.; Roba, C.; Zgavarogea, R.; Ionete, R.E. ICP-MS Assessment of Essential and Toxic Trace Elements in Foodstuffs with Different Geographic Origins Available in Romanian Supermarkets. Molecules 2021, 26, 7081. [Google Scholar] [CrossRef]
Tokalıoğlu, Ş.; Dokan, F.K.; Köprü, S. ICP-MS multi-element analysis for determining the origin by multivariate analysis of red pepper flakes from three different regions of Turkey. LWT 2019, 103, 301–307. [Google Scholar] [CrossRef]
Ahmad, R.; Ahmad, N.; Amir, M.; Aljishi, F.; Alamer, M.H.; Al-Shaban, H.R.; Alsadah, Z.A.; Alsultan, B.M.; Aldawood, N.A.; Chathoth, S.; et al. Quality variation and standardization of black pepper (Piper nigrum): A comparative geographical evaluation based on instrumental and metabolomics analysis. Biomed. Chromatogr. 2020, 34, e4772. [Google Scholar] [CrossRef]
Hwang, I.M.; Moon, E.W.; Lee, H.-W.; Jamila, N.; Su Kim, K.; Ha, J.-H.; Kim, S.H. Discrimination of Chili Powder Origin Using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES), and Near Infrared (NIR) Spectroscopy. Anal. Lett. 2019, 52, 932–947. [Google Scholar] [CrossRef]
Poór, P.; Poór, P.; Ördög, A.; Tari, I.; Bátori, Z.; Štajner, D.; Popović, B. Comparison of the mineral content of processed spice samples of sweet and hot paprika from the Szeged region. J. Elem. 2018, 23, 521–530. [Google Scholar] [CrossRef]
Brunner, M.; Katona, R.; Stefánka, Z.; Prohaska, T. Determination of the geographical origin of processed spice using multielement and isotopic pattern on the example of Szegedi paprika. Eur. Food Res. Technol. 2010, 231, 623–634. [Google Scholar] [CrossRef]
Perini, M.; Pianezze, S.; Ziller, L.; Ferrante, M.; Ferella, F.; Nisi, S.; Foschi, M.; D’Archivio, A.A. Stable isotope ratio analysis combined with inductively coupled plasma-mass spectrometry for geographical discrimination between Italian and foreign saffron. J. Mass Spectrom. 2020, 55, e4595. [Google Scholar] [CrossRef]
D’Archivio, A.A.; Di Vacri, M.L.; Ferrante, M.; Maggi, M.A.; Nisi, S.; Ruggieri, F. Geographical discrimination of saffron (Crocus sativus L.) using ICP-MS elemental data and class modeling of PDO Zafferano dell’Aquila produced in Abruzzo (Italy). Food Anal. Methods 2019, 12, 2572–2581. [Google Scholar] [CrossRef]
Zhu, L.; Xu, J.; Dou, P.; Dou, D.; Huang, L. The rhizosphere soil factors on the quality of wild-cultivated herb and its origin traceability as well as distinguishing from garden-cultivated herb: Mountainous forest cultivated ginseng for example. Ind. Crops Prod. 2021, 172, 114078. [Google Scholar] [CrossRef]
Potortì, A.G.; Bua, G.D.; Lo Turco, V.; Ben Tekaya, A.; Beltifa, A.; Ben Mansour, H.; Dugo, G.; Di Bella, G. Major, minor and trace element concentrations in spices and aromatic herbs from Sicily (Italy) and Mahdia (Tunisia) by ICP-MS and multivariate analysis. Food Chem. 2020, 313, 126094. [Google Scholar] [CrossRef] [PubMed]
Potortì, A.G.; Lo Turco, V.; Di Bella, G. Chemometric analysis of elements content in Algerian spices and aromatic herbs. LWT 2021, 138, 110643. [Google Scholar] [CrossRef]
Xu, L.; Shi, Q.; Yan, S.-M.; Fu, H.-Y.; Xie, S.; Lu, D. Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins. J. Anal. Methods Chem. 2019, 2019, e2796502. [Google Scholar] [CrossRef] [Green Version]
Galvin-King, P.; Haughey, S.A.; Elliott, C.T. Herb and spice fraud; the drivers, challenges and detection. Food Control 2018, 88, 85–97. [Google Scholar] [CrossRef] [Green Version]
Ni, Z.; Ren, C.; Cheng, J.; Tang, F. Determination of Rare Earth Elements in Some Flower Herb Teas and their Infusions. J. Braz. Chem. Soc. 2017, 28, 1960–1965. [Google Scholar] [CrossRef]
Liu, H.; Zeng, Y.; Yan, J.; Huang, R.; Zhao, X.; Zheng, X.; Mo, M.; Tan, S.; Tong, H. C N H O and mineral element stable isotope ratio analysis for authentication in tea. J. Food Compos. Anal. 2020, 91, 103513. [Google Scholar] [CrossRef]
Liu, H.; Zeng, Y.; Zhao, X.; Tong, H. Chemometric Authentication of Pu’er Teas in Terms of Multielement Stable Isotope Ratios Analysis by EA-IRMS and ICP-MS. J. Food Drug Anal. 2020, 28, 248–260. [Google Scholar] [CrossRef]
Liu, Z.; Yuan, Y.; Zhang, Y.; Shi, Y.; Hu, G.; Zhu, J.; Rogers, K.M. Geographical traceability of Chinese green tea using stable isotope and multi-element chemometrics. Rapid Commun. Mass Spectrom. 2019, 33, 778–788. [Google Scholar] [CrossRef]
Liu, H.; Zeng, Y.; Zhao, X.; Tong, H. Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemometric approach. J. Sci. Food Agric. 2020, 100, 3507–3516. [Google Scholar] [CrossRef]
Zhang, J.; Yang, R.; Chen, R.; Li, Y.C.; Peng, Y.; Liu, C. Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China. Molecules 2018, 23, 3013. [Google Scholar] [CrossRef] [PubMed]
Lou, Y.; Fu, X.; Yu, X.; Ye, Z.; Cui, H.; Zhang, Y. Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea). J. Anal. Methods Chem. 2017, 2017, e5454231. [Google Scholar] [CrossRef] [Green Version]
Zhang, J.; Yang, R.; Li, Y.C.; Wen, X.; Peng, Y.; Ni, X. Use of mineral multi-elemental analysis to authenticate geographical origin of different cultivars of tea in Guizhou, China. J. Sci. Food Agric. 2020, 100, 3046–3055. [Google Scholar] [CrossRef] [PubMed]
Liu, H.-L.; Zeng, Y.-T.; Zhao, X.; Ye, Y.-L.; Wang, B.; Tong, H.-R. Monitoring the authenticity of pu’er tea via chemometric analysis of multielements and stable isotopes. Food Res. Int. 2020, 136, 109483. [Google Scholar] [CrossRef] [PubMed]
Shuai, M.; Peng, C.; Niu, H.; Shao, D.; Hou, R.; Cai, H. Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review. Food Chem. 2022, 374, 131713. [Google Scholar] [CrossRef]

Figure 1. Impact of purchasing decision of consumers due to inflation or higher prices. A higher percentage (80%) of the consumers would choose at least one of the above. Source: Ipsos Coronavirus Consumer Tracker, fielded 4–5 January 2022, among 1600 U.S. adults [2].

Figure 2. Schematic diagram of the main components of an ICP-MS (Figure adapted with permission and modified from Ref. [21]).

Figure 3. Example of a typical calibration standard curve (⁴⁴Ca in helium mode ([He]) with Rh internal standard). y = 0.0414x + 0.8600: The output equation extracting after fitting the data to a linear regression, R: Standard Deviation, DL: Detection Limits, BEC: Background Equivalent Concentration. Units of Ratio between the signal of ⁴⁴Ca and ¹⁰³Rh (y-axis): counts per second (CPS).

Figure 4. Plot of internal standard stability in an ICP-MS analysis of agri-food samples. Y-axis is referred to the percentage recovery of internal standard (Rh) solution.

Figure 5. Diagrammatic representation of the number of publications with regards to the type of product.

Figure 6. The diagram represents the lanthanide distribution in the various parts of Primitivo grapes. Figure is reprinted with permission from Ref. [7]. Promethium (Pm) is not measured as all of its isotopes are radioactive.

Figure 7. Plots of the PLS-DA for the geographical discrimination of (a) mangoes; (b) avocados. Figures are reprinted with permission from Refs. [121,122], respectively.

Figure 8. Plots with the results of PCA analysis of the measured analytes. (a) Resulting scoring diagram of the 1st and 2nd PCs; (b) Loading diagram of all analytes for the first two PCs. Figure is reprinted with permission from Ref. [172].

Figure 9. Radar plots of the elements Cu, Zn, Ni, Na, Mo and Mn in six different regions of China. Figure is reprinted with permission from Ref. [195].

Table 1. Selected typical spectroscopic interferences.

Isotope	Isobaric Interference
⁴⁰K	⁴⁰Ca
⁵⁰V	⁵⁰Cr
⁶⁴Ni	⁶⁴Zn
⁹⁴Zr	⁹⁴Mo
¹¹³In	¹¹³Cd
Isotope	Double Charge Ion
⁴⁴Ca	⁸⁸Sr²⁺
⁶⁹Ga	¹³⁸Ba²⁺
⁷⁰Ge	¹⁴⁰Ce²⁺
⁸⁵Rb	¹⁷⁰Er²⁺
¹¹⁹Sn	²³⁸U²⁺
Isotope	Polyatomic Ions
²⁸Si	¹⁴N₂⁺, ¹²C¹⁶O⁺
³¹P	¹⁴N¹⁶O¹H⁺
⁴⁴Ca	¹²C¹⁶O₂⁺
³⁶Fe	⁴⁰Ar¹⁶O⁺
⁷⁵As	⁴⁰Ar³⁵Cl⁺

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Mazarakioti, E.C.; Zotos, A.; Thomatou, A.-A.; Kontogeorgos, A.; Patakas, A.; Ladavos, A. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products’ and Foods’ Origin. Foods 2022, 11, 3705. https://doi.org/10.3390/foods11223705

AMA Style

Mazarakioti EC, Zotos A, Thomatou A-A, Kontogeorgos A, Patakas A, Ladavos A. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products’ and Foods’ Origin. Foods. 2022; 11(22):3705. https://doi.org/10.3390/foods11223705

Chicago/Turabian Style

Mazarakioti, Eleni C., Anastasios Zotos, Anna-Akrivi Thomatou, Achilleas Kontogeorgos, Angelos Patakas, and Athanasios Ladavos. 2022. "Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products’ and Foods’ Origin" Foods 11, no. 22: 3705. https://doi.org/10.3390/foods11223705

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products’ and Foods’ Origin

Abstract

1. Introduction

2. ICP-MS: Understanding the Basic Principles

3. Database and Literature Search

4. Discussion

4.1. Alcoholic Beverages

4.2. Dairy Products: Milk and Cheese

4.3. Meats

4.4. Fish and Seafood

4.5. Vegetables, Mushrooms and Fruits

4.6. Oils and Olives

4.7. Honey

4.8. Cereals

4.9. Seeds and Nuts

4.10. Spices and Herbs

5. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Conflicts of Interest

References

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI