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Analytical Chemical Methods Combined with Multivariate Analysis for Food Authenticity Assessment and Quality Evaluation

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A topical collection in Foods (ISSN 2304-8158). This collection belongs to the section "Food Quality and Safety".

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Editors

Dr. Francesco Longobardi

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Guest Editor

Department of Chemistry, University of Bari, Via Orabona 4, I-70126, Bari, Italy
Interests: analytical chemistry; food chemistry; food analysis; food athentication; food safety; food quality; chemiometrics

Dr. Ana María Jiménez Carvelo

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Guest Editor

Department of Analytical Chemistry, University of Granada, c/ Fuentenueva, s.n., E-18071 Granada, Spain
Interests: olive oil; food authentication; food quality; chromatographic; fingerprinting; multivariate analysis; chemometrics

Topical Collection Information

Dear Colleagues,

Quality and authenticity of food are essential for food industry and consumers, as well as are important for trade relationships in a global market. Moreover, food frauds related to mislabeling or adulteration are of major concern for both regulatory authorities and industry due to economic and public health implications. All these aspects highlight why it is necessary to have approaches for identifying adulterations or mislabeling, and more generally to asses authenticity and quality of food. Analytical methods in combination with multivariate analysis have been successfully used for these purposes. Despite the large number of studies that have been published during the past years, real applications have remained limited due to a lack of validation and international harmonization of the developed methods. This Special Issue will publish recent researches based on the combination of analytical chemical methods with multivariate analysis for food authenticity assessment and quality evaluation, with an emphasis on method development and validation. Both review and original research articles are welcome.

Kinds regards,

Dr. Francesco Longobardi
Dr. Ana María Jiménez Carvelo
Guest Editors

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Keywords

food quality
food authenticity
food adulteration
food fraud
chemometrics
multivariate analysis
foodomics
method validation

Published Papers (14 papers)

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2023

Jump to: 2022, 2021, 2020

19 pages, 5990 KiB

Open AccessArticle

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

by Zongxiu Bai, Rongguang Zhu, Dongyu He, Shichang Wang and Zhongtao Huang

Foods 2023, 12(19), 3594; https://doi.org/10.3390/foods12193594 - 27 Sep 2023

Viewed by 832

Abstract

To achieve accurate detection the content of multiple parts pork adulterated in mutton under the effect of mutton flavor essence and colorant by RGB images, the improved CBAM-Invert-ResNet50 network based on the attention mechanism and the inversion residual was used to detect the content of pork from the back, front leg, and hind leg in adulterated mutton. The deep features of different parts extracted by the CBAM-Invert-ResNet50 were fused by feature, stitched, and combined with transfer learning, and the content of pork from mixed parts in adulterated mutton was detected. The results showed that the R² of the CBAM-Invert-ResNet50 for the back, front leg, and hind leg datasets were 0.9373, 0.8876, and 0.9055, respectively, and the RMSE values were 0.0268 g·g⁻¹, 0.0378 g·g⁻¹, and 0.0316 g·g⁻¹, respectively. The R² and RMSE of the mixed dataset were 0.9264 and 0.0290 g·g⁻¹, respectively. When the features of different parts were fused, the R² and RMSE of the CBAM-Invert-ResNet50 for the mixed dataset were 0.9589 and 0.0220 g·g⁻¹, respectively. Compared with the model built before feature fusion, the R² of the mixed dataset increased by 0.0325, and the RMSE decreased by 0.0070 g·g⁻¹. The above results indicated that the CBAM-Invert-ResNet50 model could effectively detect the content of pork from different parts in adulterated mutton as additives. Feature fusion combined with transfer learning can effectively improve the detection accuracy for the content of mixed parts of pork in adulterated mutton. The results of this study can provide technical support and a basis for maintaining the mutton market order and protecting mutton food safety supervision. Full article

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23 pages, 8302 KiB

Open AccessArticle

Determination of Metal Content by Inductively Coupled Plasma-Mass Spectrometry in Polish Red and White Wine Samples in Relation to Their Type, Origin, Grape Variety and Health Risk Assessment

by Dorota Jakkielska, Ioannis Dasteridis, Maciej Kubicki, Marcin Frankowski and Anetta Zioła-Frankowska

Foods 2023, 12(17), 3205; https://doi.org/10.3390/foods12173205 - 25 Aug 2023

Cited by 1 | Viewed by 886

Abstract

The main objective of the research was to assess the influence of selected factors (type of wine, grape variety, origin, alcohol content and daily consumption) on the concentration levels of 26 elements in 53 Polish wine samples, also using chemometric analysis tools. Concentration of Al, As, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb, Sb, Se, Sr, Ti, V, Zn and Zr was analyzed by ICP-MS, while concentration of Ca, Na, K and Mg was determined by ICP-OES. White wines were characterized by higher concentrations of Al, As, Be, Ca, Co, Cu, Fe, Hg, Li, Mg, Na, Pb, Sb, Ti, V, Zn and Zr (mean values: 0.075–86,403 μg·L⁻¹ in white wines, 0.069–81,232 μg·L⁻¹ in red wines). Red wines were characterized by higher concentrations of Ba, Cd, Cr, K, Mn, Se and Sr (mean values: 0.407–1,160,000 μg·L⁻¹ in white wines, 0.448–1,521,363 μg·L⁻¹ in red wines). The results obtained for the health risk assessment indices, including the Target Hazard Quotient (THQ, mean values per glass of wine: 2.097 × 10⁻⁵ (Cr)—0.041 (B) in all wines), indicate that the analyzed elements do not show a potential toxic effect resulting from wine consumption. The chemometric analysis confirmed that elements such as Li, Ti, Ca, Mn, Sr, Ba, Zn, Mg, Cu, Se and B were closely related to local conditions and soil properties, and the presence of Fe, Cr, V and Pb was related to contamination of the soil. Full article

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2022

Jump to: 2023, 2021, 2020

14 pages, 1373 KiB

Open AccessArticle

QTRAP LC/MS/MS of Garlic Nanoparticles and Improving Sunflower Oil Stabilization during Accelerated Shelf Life Storage

by Nouara Abdelli, Enas Mekawi, Mohammed Ebrahim Abdel-Alim, Nesreen Saad Salim, Mahran El-Nagar, Sati Y. Al-Dalain, Ridab Adlan Abdalla, Ganesan Nagarajan, Emad Fadhal, Rashid I. H. Ibrahim, Eman Afkar and Mohamed K. Morsy

Foods 2022, 11(24), 3962; https://doi.org/10.3390/foods11243962 - 07 Dec 2022

Cited by 2 | Viewed by 1479

Abstract

The purpose of this research was to assess and utilize the bioactive compounds of garlic nanoparticles (Ga-NPs) as a natural antioxidant in sunflower oil (SFO) stored at 65 ± 1 °C for 24 days. The garlic nanoparticles (Ga-NPs) from the Balady cultivar were prepared, characterized, and added to SFO at three concentrations: 200, 600, and 1000 ppm (w/v), and they were compared with 600 ppm garlic lyophilized powder extract (Ga-LPE), 200 ppm BHT, 200 ppm α-tocopherol, and SFO without Ga-NPs (control). The QTRAP LC/MS/MS profile of Ga-NPs revealed the presence of four organosulfur compounds. Ga-NPs exhibited the highest capacity for phenolic, flavonoid, and antioxidant compounds. In Ga-NP SFO samples, the values of peroxide, p-anisidine, totox, conjugated dienes, and conjugated trienes were significantly lower than the control. The antioxidant indices of SFO samples containing Ga-NPs were higher than the control. The Ga-NPs enhanced the sensory acceptability of SFO treatments up to day 24 of storage. The shelf life of SFO treated with Ga-NPs was substantially increased (presuming a Q₁₀ amount). The results show that Ga-NPs are a powerful antioxidant that improves SFO stability and extends the shelf life (~384 days at 25 °C). Full article

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11 pages, 1000 KiB

Open AccessArticle

Effects of Fertilizer on the Quality and Traceability of Tibet highland Barley (Hordeum vulgare L.): A Diagnosis Using Nutrients and Mineral Elements

by Shanshan Zhao, Cheng Qiu, Tangwei Zhang, Xiangyu Hu, Yan Zhao, Xiyu Cheng, Yuxuan Ma, Mengjie Qie and Chang Chen

Foods 2022, 11(21), 3397; https://doi.org/10.3390/foods11213397 - 27 Oct 2022

Cited by 1 | Viewed by 1071

Abstract

Production areas influence the quality of highland barley (Hordeum vulgare L.), and fertilization levels may be associated with the origin traceability of highland barley. As the main object of the study, a collection of highland barley was planted in different areas in Tibet, China, to explore the effect of fertilizer on the quality and traceability of highland barley. We carried out field experiments with and without fertilizer treatment (using urea and diamine phosphate). Highland barley was distinguished by nutrient and mineral element contents in combination with chemometric methods. The results indicated that fertilizer treatment significantly affected some mineral element contents in highland barley and improved the accuracy of highland barley traceability. The combination of nutrients and mineral elements could distinguish highland barley from those raised in other areas due to influence of growing environment. P, K, Fe, and Cu provided a great contribution to the classification of highland barley. Thus, the combination of nutrients and mineral elements can be used as a powerful tool to track highland barley, indicating that fertilization treatment should be considered when tracing highland barley. Full article

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2021

Jump to: 2023, 2022, 2020

13 pages, 1486 KiB

Open AccessArticle

Comprehensive Evaluation of 17 Qualities of 84 Types of Rice Based on Principal Component Analysis

by Shijie Shi, Enting Wang, Chengxuan Li, Hui Zhou, Mingli Cai, Cougui Cao and Yang Jiang

Foods 2021, 10(11), 2883; https://doi.org/10.3390/foods10112883 - 22 Nov 2021

Cited by 28 | Viewed by 3149

Abstract

Rice quality is a complex indicator, and people are paying more and more attention to the quality of rice. Therefore, we used seven rice varieties for twelve nitrogen fertilizer treatments and obtained eighty-four rice types with seventeen qualities. It was found that 17 quality traits had different coefficients of variation. Among them, the coefficient of variation of chalkiness and protein content was the largest, 44.60% and 17.89% respectively. The cluster analysis method was used to define four categories of different rice qualities. The principal component analysis method was used to comprehensively evaluate 17 qualities of 84 rice. It was found that rice quality was better under low nitrogen conditions, Huanghuazhan and Lvyinzhan were easier to obtain better comprehensive rice quality during cultivation. Future rice research should focus on reducing protein content and increasing peak viscosity. Full article

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15 pages, 2800 KiB

Open AccessArticle

Electronic Tongue as a Correlative Technique for Modeling Cattle Meat Quality and Classification of Breeds

by József Surányi, John-Lewis Zinia Zaukuu, László Friedrich, Zoltan Kovacs, Ferenc Horváth, Csaba Németh and Zoltán Kókai

Foods 2021, 10(10), 2283; https://doi.org/10.3390/foods10102283 - 26 Sep 2021

Cited by 10 | Viewed by 2546

Abstract

Discrimination and species identification of meat has always been of paramount importance in the European meat market. This is often achieved using different conventional analytical methods but advanced sensor-based methods, such as the electronic tongue (e-tongue), are also gaining attention for rapid and reliable analysis. The aim of this study was to discriminate Angus, domestic buffalo, Hungarian Grey, Hungarian Spotted cattle, and Holstein beef meat samples from the chuck steak part of the animals, which mostly contained longissimus dorsi muscles, using e-tongue as a correlative technique with conventional methods for analysis of pH, color, texture, water activity, water-holding capacity, cooking yield, water binding activity, and descriptive sensory analysis. Analysis of variance (ANOVA) was used to determine significant differences between the measured quality traits of the five-meat species after analysis with conventional analytical methods. E-tongue data were visualized with principal component analysis (PCA) before classifying the five-meat species with linear discriminant analysis (LDA). Significant differences were observed among some of the investigated quality parameter. In most cases, Hungarian Grey was most different from the other species. Using e-tongue, separation patterns could be observed in the PCA that were confirmed with 100% recognition and 97.5% prediction of all the different meat species in LDA. Full article

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15 pages, 1987 KiB

Open AccessArticle

Authentication of Geographical Origin in Hainan Partridge Tea (Mallotus obongifolius) by Stable Isotope and Targeted Metabolomics Combined with Chemometrics

by Jiashun Fu, Hai-Dong Yu, Long Wu, Chenghui Zhang, Yong-Huan Yun and Weimin Zhang

Foods 2021, 10(9), 2130; https://doi.org/10.3390/foods10092130 - 09 Sep 2021

Cited by 11 | Viewed by 2725

Abstract

Partridge tea (Mallotus oblongifolius (Miq.) Müll.Arg.) is a local characteristic tea in Hainan, the southernmost province of China, and the quality of partridge tea may be affected by the producing areas. In this study, stable isotope and targeted metabolomics combined chemometrics were used as potential tools for analyzing and identifying partridge tea from different origins. Elemental analysis—stable isotope ratio mass spectrometer and liquid chromatography-tandem mass spectrometrywas used to analyze the characteristics of C/N/O/H stable isotopes and 54 chemical components, including polyphenols and alkaloids in partridge tea samples from four regions in Hainan (Wanning, Wenchang, Sanya and Baoting). The results showed that there were significant differences in the stable isotope ratios and polyphenol and alkaloid contents of partridge tea from different origins, and both could accurately classify partridge tea from different origins. The correct separation and clustering of the samples were observed by principal component analysis and the cross-validated Q² values by orthogonal partial least squares discriminant analysis (OPLS-DA) were 0.949 (based on stable isotope) and 0.974 (based on polyphenol and alkaloid), respectively. Potential significance indicators for origin identification were screened out by OPLS-DA and random forest algorithm, including three stable isotopes (δ¹³C, δ D, and δ¹⁸O) and four polyphenols (luteolin, protocatechuic acid, astragalin, and naringenin). This study can provide a preliminary guide for the origin identification of Hainan partridge tea. Full article

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18 pages, 2515 KiB

Open AccessArticle

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

by Hongzhe Jiang, Yi Yang and Minghong Shi

Foods 2021, 10(9), 2127; https://doi.org/10.3390/foods10092127 - 09 Sep 2021

Cited by 10 | Viewed by 2111

Abstract

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC₁ and PC₂ were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn’t influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls. Full article

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23 pages, 4082 KiB

Open AccessArticle

Vibrational Spectroscopy Combined with Chemometrics as Tool for Discriminating Organic vs. Conventional Culture Systems for Red Grape Extracts

by Cristiana Radulescu, Radu Lucian Olteanu, Cristina Mihaela Nicolescu, Marius Bumbac, Lavinia Claudia Buruleanu and Georgeta Carmen Holban

Foods 2021, 10(8), 1856; https://doi.org/10.3390/foods10081856 - 11 Aug 2021

Cited by 9 | Viewed by 2109

Abstract

Food plants provide a regulated source of delivery of functional compounds, plant secondary metabolites production being also tissue specific. In grape berries, the phenolic compounds, flavonoids and non-flavonoids, are distributed in the different parts of the fruit. The aim of this study was to investigate the applicability of FTIR and Raman screening spectroscopic techniques combined with multivariate statistical tools to find patterns in red grape berry parts (skin, seeds and pulp) according to grape variety and vineyard type (organic and conventional). Spectral data were acquired and processed using the same pattern for each different berry part (skin, seeds and pulp). Multivariate analysis has allowed a separation between extracts obtained from organic and conventional vineyards for each grape variety for all grape berry parts. The innovative approach presented in this work is low-cost and feasible, being expected to have applications in studies referring to the authenticity and traceability of foods. The findings of this study are useful as well in solving a great challenge that producers are confronting, namely the consumers’ distrust of the organic origin of food products. Further analyses of the chemical composition of red grapes may enhance the capability of the method of using both vibrational spectroscopy and chemometrics for discriminating the hydroalcoholic extracts according to grape varieties. Full article

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15 pages, 2110 KiB

Open AccessArticle

Quantification of the Geranium Essential Oil, Palmarosa Essential Oil and Phenylethyl Alcohol in Rosa damascena Essential Oil Using ATR-FTIR Spectroscopy Combined with Chemometrics

by Nur Cebi

Foods 2021, 10(8), 1848; https://doi.org/10.3390/foods10081848 - 11 Aug 2021

Cited by 7 | Viewed by 2845

Abstract

Rosa damascena essential oil is an essential oil that has the greatest industrial importance due to its unique quality properties. The study used ATR-FTIR (attenuated total reflectance-Fourier transform infrared) spectroscopy coupled with chemometrics of PLSR (partial least squares regression) and PCR (principal component regression) for quantification of probable adulterants of geranium essential oil (GEO), palmarosa essential oil (PEO) and phenyl ethyl alcohol (PEOH). Hierarchical cluster analysis was performed to observe the classification pattern of Rosa damascena essential oil, spiked samples and adulterants. Rosa damascena essential oil was spiked with each adulterant at concentrations of 0–100% (v/v). Excellent R² (regression coefficient) values (≥0.96) were obtained in all PLSR and PCR cross-validation models. The SECV (standard error of cross-validation) values ranged between 0.43 and 4.15. The lowest SECV and bias values were observed in the PLSR and PCR models, which were built by using the raw FTIR spectra of all samples. Hierarchical cluster analysis through Ward’s algorithm and Euclidian distance had high potential to observe the classification pattern of all adulterated and authentic samples. In conclusion, the combination of ATR-FTIR spectroscopy with multivariate analysis can be used for rapid, cost-effective, easy, reliable and high-throughput detection of GEO, PEO and PEOH in Rosa damascena essential oil. Full article

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15 pages, 3636 KiB

Open AccessArticle

Optimization of an Untargeted DART-HRMS Method Envisaging Identification of Potential Markers for Saffron Authenticity Assessment

by Elisabetta De Angelis, Rosa Pilolli, Alice Bejjani, Rocco Guagnano, Cristiano Garino, Marco Arlorio and Linda Monaci

Foods 2021, 10(6), 1238; https://doi.org/10.3390/foods10061238 - 29 May 2021

Cited by 7 | Viewed by 2837

Abstract

Saffron is one of the most expensive agricultural products in the world and as such, the most commonly adulterated spice, with undeclared plant-based surrogates or synthetic components simulating color and morphology. Currently, saffron quality is certificated in the international trade market according to specific ISO guidelines, which test aroma, flavor, and color strength. However, it has been demonstrated that specific adulterants such as safflower, marigold, or turmeric up to 20% (w/w) cannot be detected under the prescribed approach; therefore, there is still a need for advanced and sensitive screening methods to cope with this open issue. The current investigation aims to develop a rapid and sensitive untargeted method based on an ambient mass spectrometry ionization source (DART) and an Orbitrap™high-resolution mass analyzer to discriminate pure and adulterated saffron samples with either safflower or turmeric. The metabolic profiles of pure and adulterated model samples prepared at different inclusion levels were acquired. Unsupervised multivariate analysis was carried out based on hierarchical cluster analysis and principal component analysis as first confirmation of the discriminating potential of the metabolic profile acquired under optimized DART-HRMS conditions. In addition, a preliminary selection of potential markers for saffron authenticity was accomplished, identifying compounds able to discriminate the type of adulteration down to a concentration level of 5%. Full article

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14 pages, 11825 KiB

Open AccessArticle

Rapid Screening of Mentha spicata Essential Oil and L-Menthol in Mentha piperita Essential Oil by ATR-FTIR Spectroscopy Coupled with Multivariate Analyses

by Osman Taylan, Nur Cebi and Osman Sagdic

Foods 2021, 10(2), 202; https://doi.org/10.3390/foods10020202 - 20 Jan 2021

Cited by 28 | Viewed by 3857

Abstract

Mentha piperita essential oil (EO) has high economic importance because of its wide usage area and health-beneficial properties. Besides health-beneficial properties, Mentha piperita EO has great importance in the flavor and food industries because of its unique sensory and quality properties. High-valued essential oils are prone to being adulterated with economic motivations. This kind of adulteration deteriorates the quality of authentic essential oil, injures the consumers, and causes negative effects on the whole supply chain from producer to the consumer. The current research used fast, economic, robust, reliable, and effective ATR-FTIR spectroscopy coupled chemometrics of hierarchical cluster analysis(HCA), principal component analysis (PCA), partial least squares regression (PLSR) and principal component regression (PCR) for monitoring of Mentha spicata EO and L-menthol adulteration in Mentha piperita EOs. Adulterant contents (Mentha spicata and L-menthol) were successfully calculated using PLSR and PCR models. Standard error of the cross-validation SECV values changed between 0.06 and 2.14. Additionally, bias and press values showed alteration between 0.06 and1.43 and 0.03 and 41.15, respectively. Authentic Mentha piperita was successfully distinguished from adulterated samples, Mentha spicata and L-menthol, by HCA and PCA analysis. The results showed that attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometrics could be effectively used for monitoring various adulterants in essential oils. Full article

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2020

Jump to: 2023, 2022, 2021

14 pages, 1404 KiB

Open AccessArticle

Detection of Orange Essential Oil, Isopropyl Myristate, and Benzyl Alcohol in Lemon Essential Oil by FTIR Spectroscopy Combined with Chemometrics

by Nur Cebi, Osman Taylan, Mona Abusurrah and Osman Sagdic

Foods 2021, 10(1), 27; https://doi.org/10.3390/foods10010027 - 24 Dec 2020

Cited by 29 | Viewed by 4361

Abstract

Essential oils are high-valued natural extracts that are involved in industries such as food, cosmetics, and pharmaceutics. The lemon essential oil (LEO) has high economic importance in the food and beverage industry because of its health-beneficial characteristics and desired flavor properties. LEO, similar to other natural extracts, is prone to being adulterated through economic motivations. Adulteration causes unfair competition between vendors, disruptions in national economies, and crucial risks for consumers worldwide. There is a need for cost-effective, rapid, reliable, robust, and eco-friendly analytical techniques to detect adulterants in essential oils. The current research developed chemometric models for the quantification of three adulterants (orange essential oil, benzyl alcohol, and isopropyl myristate) in cold-pressed LEOs by using hierarchical cluster analysis (HCA), principal component regression (PCR), and partial least squares regression (PLSR) based on FTIR spectra. The cold-pressed LEO was successfully distinguished from adulterants by robust HCA. PLSR and PCR showed high accuracy with high R² values (0.99–1) and low standard error of cross-validation (SECV) values (0.58 and 5.21) for cross-validation results of the raw, first derivative, and second derivative FTIR spectra. The findings showed that FTIR spectroscopy combined with multivariate analyses has a considerable capability to detect and quantify adulterants in lemon essential oil. Full article

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12 pages, 2450 KiB

Open AccessArticle

Application of Ultraviolet-Visible Absorption Spectroscopy with Machine Learning Techniques for the Classification of Cretan Wines

by Aggelos Philippidis, Emmanouil Poulakis, Renate Kontzedaki, Emmanouil Orfanakis, Aikaterini Symianaki, Aikaterini Zoumi and Michalis Velegrakis

Foods 2021, 10(1), 9; https://doi.org/10.3390/foods10010009 - 22 Dec 2020

Cited by 17 | Viewed by 4325

Abstract

The present study was aimed at the identification, differentiation and characterization of red and white Cretan wines, which are described with Protected Geographical Indication (PGI), using ultraviolet–visible absorption spectroscopy. Specifically, the grape variety, the wine aging process and the role of barrel/container type were investigated. The combination of spectroscopic results with machine learning-based modelling demonstrated the use of absorption spectroscopy as a facile and low-cost technique in wine analysis. In this study, a clear discrimination among grape varieties was revealed. Moreover, a grouping of samples according to aging period and container type of maturation was accomplished, for the first time. Full article

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Analytical Chemical Methods Combined with Multivariate Analysis for Food Authenticity Assessment and Quality Evaluation

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Published Papers (14 papers)

2023

Jump to: 2022, 2021, 2020

2022

Jump to: 2023, 2021, 2020

2021

Jump to: 2023, 2022, 2020

2020

Jump to: 2023, 2022, 2021

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