Search Results (160)

While pH possesses inherent chemical significance and potential forensic value in Scotch whisky authentication, it has previously lacked a statistically rigorous exclusionary standard. This study addressed this gap by performing statistical distribution fitting analysis on the pH of 32 authentic single malt and 33 authentic blended Scotch whiskies, utilizing the three-parameter lognormal distribution to establish the 99.7% authentic pH ranges for the first time: 3.47–4.46 for single malt and 3.73–4.67 for blended whisky. Validation using seized counterfeit samples confirmed that an abnormally elevated pH serves as critical evidence of adulteration. Consequently, this research proposes the chemically fundamental pH threshold as a rapid, non-destructive, and cost-effective forensic exclusionary criterion. Although the pH value feature alone is insufficient to confirm authenticity, its high operational applicability significantly enhances the efficiency of rapid field screening for counterfeit whisky, strongly supporting law enforcement and food safety efforts in Taiwan. Full article

Nuts such as pecans, almonds, peanuts, pistachios, and walnuts are nutrient-dense foods rich in unsaturated fatty acids and antioxidant compounds. Their regular consumption has been linked to significant health benefits, including reduced risks of cardiovascular disease, diabetes, and high cholesterol. With increasing global demand, ensuring the quality of nuts before they reach consumers is critical. Conventional quality assessment methods dominate the industry but are often subjective, destructive, time-intensive, environmentally burdensome, and laborious. Therefore, there is an urgent need for rapid, non-destructive, and objective alternatives capable of meeting modern quality standards. In this systematic review, we summarize traditional approaches for evaluating nut quality parameters and introduce hyperspectral imaging as a novel technique with promising applications. We examine its use in detecting nut adulteration, assessing chemical composition, identifying defects, and evaluating other quality traits. Limitations of hyperspectral imaging in industrial settings are also discussed, along with potential solutions and future directions. Given the relatively limited research area, approximately 44 relevant studies were critically reviewed. This work provides valuable insights for researchers and industry stakeholders developing innovative technologies for nut quality assessment. Full article

This review synthesizes current knowledge on chemical biomarker panels in the honeybee in a tissue-specific and factor-oriented framework. We show that these panels undergo predictable shifts under endogenous factors (age, caste) and environmental stressors, including mites, bacteria, fungi, viruses, pesticides, antibiotics, adulterated wax, nutritional deficits, and monodiets. These changes are particularly evident in the hemolymph and fat body and are assessed via markers of energy metabolism, enzymatic activities, oxidative stress, and lipid homeostasis. Because insects lack established clinical reference intervals, emphasis is placed on general trends and tissue interrelationships. Moreover, in the honeybee, patterns can at times be inverted relative to vertebrates for example, for enzymatic activities marker, where increased activity may indicate a beneficial effect on the organism. Research in bee ecophysiology is gaining prominence and aligns with contemporary understandings of global challenges. Full article

Nanotechnology offers powerful new tools to enhance food quality monitoring and safety assurance. In the food industry, nanoscale materials (e.g., metal, metal oxide, carbon, and polymeric nanomaterials) are being integrated into sensory systems to detect spoilage, contamination, and intentional food tampering with unprecedented sensitivity. Nanosensors can rapidly identify foodborne pathogens, toxins, and chemical changes that signal spoilage, overcoming the limitations of conventional assays that are often slow, costly, or require expert operation. These advances translate into improved food safety and extended shelf-life by allowing early intervention (for example, via antimicrobial nano-coatings) to prevent spoilage. This review provides a comprehensive overview of the types of nanomaterials used in food sensory applications and their mechanisms of action. We examine current applications in detecting food spoilage indicators and adulterants, as well as recent innovations in smart packaging and continuous freshness monitoring. The advantages of nanomaterials—including heightened analytical sensitivity, specificity, and the ability to combine sensing with active preservative functions—are highlighted alongside important toxicological and regulatory considerations. Overall, nanomaterials are driving the development of smarter food packaging and sensor systems that promise safer foods, reduced waste, and empowered consumers. However, realizing this potential will require addressing safety concerns and establishing clear regulations to ensure responsible deployment of nano-enabled food sensing technologies. Representative figures of merit include Au/AgNP melamine tests with LOD 0.04–0.07 mg L⁻¹ and minute-scale readout, a smartphone Au@carbon-QD assay with LOD 3.6 nM, Fe₃O₄/DPV detection of Sudan I at 0.001 µM (linear 0.01–20 µM), and a reusable Au–Fe₃O₄ piezo-electrochemical immunosensor for aflatoxin B1 with LOD 0.07 ng mL⁻¹ (≈15 × reuse), alongside freshness labels that track TVB-N/amine in near-real time and e-nose arrays distinguishing spoilage stages. Full article

Ensuring the quality and safety of agricultural and food products is crucial for protecting consumer health, meeting market expectations, and complying with regulatory requirements. Quality and safety parameters are commonly assessed using chemical and microbiological analyses, which are time-consuming, impractical, and involve the use of toxic solvents, often disrupting the material’s original structure. An alternative technique, infrared spectroscopy, including near-infrared (NIR), mid-infrared (MIR), and short-wave infrared (SWIR), has emerged as a rapid, powerful, and minimally destructive technique for evaluating the quality and safety of food and agricultural products. This review focuses on discussing MIR spectroscopy, particularly Fourier transform infrared (FTIR) techniques, with emphasis on the attenuated total reflectance (ATR) measurement mode (globar infrared light source is commonly used) and on the use of synchrotron radiation (SR) as an alternative high-brightness light source. Both approaches enable the extraction of detailed spectral data related to molecular and functional attributes concerning quality and safety, thereby facilitating the assessment of crop disorders, food chemical composition, microbial contamination (e.g., mycotoxins, bacteria), and the detection of food adulterants, among several other applications. In combination with advanced chemometric techniques, FTIR spectroscopy, whether employing ATR as a measurement mode or SR as a high-brightness light source, is a powerful analytical tool for classification based on attributes, variety, nutritional and geographical origins, with or without minimal sample preparation, no chemical use, and short analysis time. However, limitations exist regarding calibrations, validations, and accessibility. The objective of this review is to address recent technological advancements and existing constraints of FTIR conducted in ATR mode and using SR as a light source (not necessarily in combination). It defines potential pathways for the comprehensive integration of FTIR and chemometrics for real-time quality and safety monitoring systems into the global food supply chain. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article

Poor-quality milk and the use of chemicals in it can lead to serious health problems, including various diseases and, in some cases, even death for those who consume it. In our society, using such products or contaminated milk that contains chemicals or is of bad quality, often with water or other adulterants, is very common. Based on previous research and existing models, we have improved the process to better and more accurately predict milk quality by using a voting system. This system uses four different algorithms: KNN (K Nearest Neighbour), Naïve Bayes, Random Forest, and Decision Tree. We applied these models to a dataset with almost 1000 samples. To enhance performance, we used brute-force feature selection and a voting process to make accurate decisions. All these procedures were implemented in RapidMiner Studio, resulting in an overall accuracy of 99.69%. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Schefflera oleifera honey (SH) is produced from the nectar of S. Oleifera by worker bees. Due to its unique properties and potential biological activities, this winter honey has attracted much attention. In this study, the physicochemical characteristics, antioxidant and antibacterial activities, antitumor effect against HepG2 cells, and its potential mechanisms of SH were systematically evaluated. The results showed that different SH samples differed significantly in their physicochemical characteristics. The 910 chemical components, including 52 kinds of phenols, phenolic acids, and flavonoids, were detected in the methanol extract of SH using UHPLC-MS/MS by non-targeted metabolomics. Based on our limited knowledge, solanine and soyasaponin I are the first determined components in honey, and they may be used as characteristic substances of SH for identification and adulteration. SH had a weaker inhibitory effect against Salmonella typhimurium and Staphylococcus aureus than MH (UMF 10+), analyzed by MBC and MIC assays. Network pharmacology analysis showed that 95 overlapping targets were found between the active ingredients of SH and liver cancer cells (HepG2), which were enriched in KEGG of the PI3K-Akt pathway, Lipid and atherosclerosis, Proteoglycans in cancer, etc. The IC₅₀ of SH against HepG2 cells was 5.07% (dw/v), which is lower than the glucose, fructose, and sucrose contents in SH on HepG2 cells, of 16.24%, 9.60% dw/v, and 9.94% dw/v, respectively. SH significantly down-regulated the expression of EGFR, AKT1, and SRC in HepG2 cells (p < 0.05), determined by an enzyme-linked immunosorbent assay kit, and induced cell cycle arrest and apoptosis by multiple pathways. These results provide a theoretical basis for its potential application in developing functional foods and additives. Full article

Background: Food safety remains a global concern due to biological and chemical contaminants, including adulterants, pathogens, antibiotic residues, and pesticides. Traditional detection methods are accurate but limited by time requirements, complex sample preparation, high costs, and poor field applicability. Surface-Enhanced Raman Spectroscopy (SERS) offers non-destructive analysis with low detection limits and high specificity, yet conventional SERS substrates face challenges with reproducibility, nanoparticle aggregation, and sensitivity in food matrices. Hydrogels have emerged as supporting materials for SERS due to their water content, tunable porosity, flexibility, and ability to entrap plasmonic nanostructures. Scope and Approach: This review examines recent advances in hydrogel-integrated SERS platforms for food safety applications. The three-dimensional structure of hydrogels enables homogeneous distribution of metal nanoparticles, prevents aggregation, and offers analyte enrichment. We analyze material design, functionalization strategies, and how hydrogel properties—crosslinking density, porosity, surface charge, and nanoparticle distribution—influence SERS performance in food matrices. Key Findings and Conclusions: Hydrogel-integrated SERS platforms demonstrate superior performance in detecting various food contaminants—including pesticides, adulterants, and additives—in real food matrices, often achieving detection limits in the nanomolar to picomolar range, depending on the analyte and substrate design. Current limitations include storage stability concerns, batch-to-batch variability, and regulatory acceptance hurdles. Future research directions should focus on multiplex detection capabilities, integration with smart sensing technologies, and industrial scalability to facilitate practical deployment in global food safety monitoring across diverse supply chains. Full article

Novel methods of coffee processing, including animal-assisted fermentation, are gaining popularity—among them, elephant dung coffee stands out for its rarity and high price, making it a likely target for adulteration. This study aims to discover candidate biomarkers for elephant coffee by comparing the chemical composition, antioxidant activity, and volatile profiles of Arabica coffee processed by three methods: conventional, civet-derived, and elephant-derived (all originated from Southeast Asia, medium roast). Analytical methods included HPLC-UV and GC-SPME-MS, along with in vitro antioxidant assays (DPPH, ORAC, ABTS, total phenolics, and total flavonoids). Principal Component Analysis (PCA) was used to evaluate differences between the samples. While elephant coffee showed lower caffeine (0.93%) and antioxidant capacity across all assays, it was richer in selected volatile compounds, such as pyrazines (e.g., 3-ethyl-2,5-dimethylpyrazine; 3.73% RPA), 2- and 3-methybutanal (1.18 and 0.19% RPA), and furfuryl acetate (18.00% RPA; p < 0.05). These changes are likely to be due to fermentation in the gastrointestinal tract. Despite differences, no definitive biomarker of elephant coffee was found, suggesting that discrimination from other coffee samples may not be as simple as previous studies indicated. More studies with a higher number of samples that employ an extensive analytical approach (e.g., omics or NMR) to thoroughly analyze the phytochemical profile of coffee beans before and after digestion by the elephant are needed. Full article

In this review article, the production of extra neutral alcohol was explored, a specific segment in the alcohol industry, aiming to fill knowledge gaps and provide information on the practical implementation of a Food Defense plan. The understanding of the physical–chemical specifications of alcohol, its applications in food, and the manufacturing and fractionation processes were comprehensively addressed. The critical importance of extra neutral alcohol in various industries, from the production of distilled beverages to food preservation through innovative technologies, was highlighted. When discussing hazards related to food safety, the possibility of system security failures is recognized, emphasizing the need for a Food Defense plan. Additionally, we explored potential contamination methods in the production of extra neutral alcohol, analyzing its miscibility with harmful substances, which is crucial for understanding the implications of chemical contamination. The possibility of deliberate adulteration of beverages and foods requires constant attention to enhance security measures, implement advanced technologies, and proactive monitoring strategies. Ultimately, this article contributes to advancing knowledge at the intersection of extra neutral alcohol production and food safety. Full article

Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it is challenging to collect representative samples from all over the world. Moreover, it is also very hard to evaluate the representativeness of collected samples. In this study, we blazed a new trail to propose an authentication method to identify adulterated edible oils without building a prediction model beforehand. An authentication method developed by real-time one-class classification modeling, and model population analysis was designed to identify adulterated oils in the market without building a classification model beforehand. The underlying philosophy of the method is that the sum of the absolute centered residual (ACR) of the good model built by only authentic samples is higher than that of the bad model built by authentic and adulterated samples. In detail, a large number of OCC models were built by selecting partial samples out of inspected samples using Monte Carlo sampling. Then, adulterated samples involved in the test of these good models were identified. Taking the inspected samples of avocado oils as an example, as a result, 6 out of 40 avocado oils were identified as adulterated and then validated by chemical markers. The successful identification of avocado oils adulterated with soybean oil, corn oil, or rapeseed oil validated the effectiveness of our method. The proposed method provides a novel idea for oils as well as other high-value food adulteration detection. Full article

Background: Toxic chemical adulteration of food is harmful to human health and a major global risk to healthy food consumption. The United Nations declared 2021 as the “International Year of Fruits and Vegetables in an effort to raise public awareness of the nutritional and health benefits of including more fruits and vegetables in a balanced diet”. Although consumers are aware of organic food products, their understanding of the concept is still restricted. Hence, it is paramount to understand their level of awareness and consumption behavior. Methods: Data were captured from 578 samples using a structured questionnaire. Samples were drawn from four districts in Karnataka state of India using a purposive sampling technique. “IBM-SPSS” was used for descriptive analysis, and Smart PLS 4 was adopted to assess the measurement model. Findings: Indian consumers are significantly influenced by health and concern for the environment when buying organic food. Its natural ingredients positively impact customers’ willingness to spend more for organic food. The idea that the natural content of organic food influences millennials’ purchase habits more indirectly than directly is supported by empirical data. Conclusions: With an emphasis on how health concerns influence millennials’ decisions to buy organic fruits and vegetables, this study offers insightful information about customers’ intentions to buy organic food. As the organic food industry develops and fills in current knowledge gaps, the findings are intended to help researchers, food producers, and marketers create focused marketing strategies. Full article

The illicit production and distribution of amphetamines present significant challenges to public health and law enforcement, particularly in Europe, where these substances dominate the stimulant market. This study aimed to profile amphetamines consumed within a Polish community by employing gas chromatography–mass spectrometry (GC-MS) and chemometric techniques to analyze their chemical composition and associated impurities. The optimized GC-MS methodology facilitated the identification of synthesis markers, precursor origins, and distribution patterns. Impurity profiling provided critical insights into regional production trends, including the use of specific precursors and adulterants. Chemometric analysis further enabled the classification of samples into distinct groups, shedding light on their origins and distribution chains. These findings underscore the potential of extending amphetamine profiling to include distribution-related compounds, offering a powerful tool for tracking production trends and enhancing forensic investigations in the fight against drug trafficking. Full article