Search Results (560)

Cooked-rice aroma strongly affects consumer choice, yet the chemical traits distinguishing glutinous rice from normal-amylose japonica rice remain underexplored because earlier studies targeted only a few dozen volatiles using one-dimensional gas chromatography–mass spectrometry (GC-MS). In this study, four glutinous and seven normal Japanese cultivars were cooked under identical conditions, their headspace volatiles trapped with MonoTrap and qualitatively profiled by comprehensive GC × GC-TOFMS. The two-dimensional platform resolved 1924 peaks—about ten-fold previous coverage—and, together with hierarchical clustering, PCA, heatmap visualization and volcano plots, cleanly separated the starch classes (78.3% cumulative PCA variance; Euclidean distance >140). Volcano plots highlighted 277 compounds enriched in the glutinous cultivars and 295 in Koshihikari, including 270 compounds that were not previously documented in rice. Normal cultivars were dominated by ethers, aldehydes, amines and other nitrogenous volatiles associated with grainy, grassy and toasty notes. Glutinous cultivars showed abundant ketones, furans, carboxylic acids, thiols, steroids, nitro compounds, pyrroles and diverse hydrocarbons and aromatics, yielding sweeter, fruitier and floral accents. These results expand the volatile library for japonica rice, provide molecular markers for flavor-oriented breeding and demonstrate the power of GC × GC-TOFMS coupled with chemometrics for grain aroma research. Full article

In this paper, an in-depth characterization of the composition of extra-virgin olive oil (EVOO) from different cultivars was performed, with the aim of obtaining the fingerprint profile of bioactive constituents and studying the oxidative stability of the samples, both by an accelerated stability test and after four months of storage at room temperature. Among the investigated cultivars, some were typical of Umbria (Central Italy), namely Moraiolo, Frantoio, and Dolce Agogia, others of Apulia (Southern Italy), Coratina, Peranzana, and Bella di Cerignola, and others were typical Spanish cultivars cultivated in Umbria (Arbequina and Arbosana). The comparison of the chemical parameters among oils from the different cultivars allowed for their discrimination by multivariate statistical analysis. Some phenolic compounds were mainly responsible for the sample group’s differentiation, with the oils from the Spanish cultivars clearly distinguished from the Umbrian and Apulian sample groups. The processing of the results by chemometric analysis during oil storage and stability tests again allowed the discrimination of the samples analyzed at different storage times. This study contributes to increasing knowledge on olive oils—chemical and nutraceutical properties from specific cultivars, particularly some less studied so far, such as the Bella di Cerignola cultivar, and their changes in their nutraceutical properties during storage. Full article

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

The quality and authenticity of honey are of crucial importance for food safety and consumer confidence. Given the increasing interest in enriched honey and potential fraud, rapid and non-destructive analytical methods for quality assessment, such as Near-Infrared Spectroscopy (NIRS), are needed. Therefore, the aim of this work was to investigate the applicability of NIR spectroscopy coupled with chemometric methods to assess the quality change in honey from three different countries, after addition of five different aromatic plants (lavender, rosemary, oregano, sage, and white pine oil) in three different concentrations (0.5%, 0.8% and 1%). Measurements of basic physicochemical properties, color, antioxidant activity, and NIR spectra were performed for all samples (pure honey and honey with added aromatic plants). Chemometric models, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, were applied to analyze spectral data, correlate spectra with physicochemical properties, color and antioxidant activity measurements, and develop classification and prediction models. Spectral changes in the NIR region, as expected, showed the ability to distinguish samples depending on the type and concentration of added aromatic plants. Chemometric models enabled efficient discrimination between pure and enriched honey samples, as well as assessment of the influence of different additives on antioxidant activity and color. The results highlight the potential of NIRS as a rapid, non-destructive and environmentally friendly method for quality monitoring and detection of specific additives in honey, offering technical support for quality control and food safety regulation. Full article

Polygonati Rhizoma, a renowned edible homologous material, encompasses an array of widely distributed species. Despite their morphological and medicinal similarities, their overlapping distribution and evolving varieties present challenges for their classification and identification. This study provides a comprehensive characterization of the physicochemical and antioxidant properties of polysaccharides extracted from three common species: P. sibiricum, P. cyrtonema, and P. kingianum. An analysis of their monosaccharide composition reveals distinct profiles, with P. kingianum polysaccharides (PKPs) demonstrating a significantly higher glucose content compared to P. sibiricum polysaccharides (PSPs) and P. cyrtonema polysaccharides (PCPs). Infrared (IR) spectroscopy and derivative spectral processing affirm both structural similarities and quantitative differences in functional groups among the species. Multivariate analyses, including HCA, PCA, and OPLS-DA, confidently classify the 12 batches of polysaccharides into three distinct groups (PSPs, PCPs, and PKPs), exhibiting strong model robustness (PCA: R²X = 0.951, Q² = 0.673; OPLS-DA: R²Y = 0.953, Q² = 0.922). Importantly, PKPs from number S11 show exceptional in vitro antioxidant activity (DPPH scavenging), which directly correlates with their high monosaccharide content and distinctive spectral features. These findings establish a robust foundation for the quality assessment of Polygonatum polysaccharides as potential natural antioxidants in functional foods, positioning PKPs as leading candidates for dietary supplement development. Full article

Sheep and goat milk authenticity is of great importance, especially for countries like Greece, where these products are connected to the country’s rural economy and cultural heritage. The aim of the study is to evaluate the effectiveness of Fourier Transform Infrared Attenuated Total Reflectance (ATR-FTIR) spectroscopy in combination with chemometric techniques for the classification of cow, sheep, and goat milk and consequently support fraud identification. A total of 178 cow, sheep and goat milk samples were collected from livestock farms in Thessaly, Greece. Sheep and goat milk samples were confirmed as authentic by applying a validated Enzyme Linked Immunosorbent Assay (ELISA), while all samples were analyzed using ATR-FTIR spectroscopy in both raw and freeze-dried form. Freeze-dried samples exhibited clearer spectral characteristics, particularly enhancing the signals from triglycerides, proteins, and carbohydrates. Partial Least Squares Discriminant Analysis (PLS-DA) delivered robust discrimination. By using the spectral range between 600 and 1800 cm⁻¹, 100% correct classification of all milk types was achieved. These findings highlight the potential of FTIR spectroscopy as a fast, non-destructive, and cost-effective tool for milk identification and species differentiation. This method is particularly suitable for industrial and regulatory applications, offering high efficiency. Full article

The authentication of high-value spices such as paprika and cinnamon is critical due to increasing food fraud. This study explored the potential of a multi-analytical approach, combined with chemometric tools, to differentiate 45 paprika and 46 cinnamon samples from the Slovenian market based on their geographic origin, production methods, and possible adulteration. The applied techniques included stable isotope ratio analysis (δ¹³C, δ¹⁵N, δ³⁴S), multi-elemental profiling, FTIR, and antioxidant compound analysis. Distinct isotopic and elemental markers (e.g., δ¹³C, δ³⁴S, Rb, Cs, V, Fe, Al) contributed to classification by geographic origin, with preliminary classification accuracies of 90% for paprika (Hungary, Serbia, Spain) and 89% for cinnamon (Sri Lanka, Madagascar, Indonesia). Organic paprika samples showed higher values of δ¹⁵N, δ³⁴S, and Zn, whereas conventional ones had more Na, Al, V, and Cr. For cinnamon, a 95% discrimination accuracy was achieved between production practice using δ³⁴S and Ba, as well as As, Rb, Na, δ¹³C, S, Mg, Fe, V, Al, and Cu. FTIR differentiated Ceylon from cassia cinnamon and suggested possible paprika adulteration, as indicated by spectral features consistent with oleoresin removal or azo dye addition, although further verification is required. Antioxidant profiling supported quality assessment, although the high antioxidant activity in cassia cinnamon may reflect non-phenolic contributors. Overall, the results demonstrate the promising potential of the applied analytical techniques to support spice authentication. However, further studies on larger, more balanced datasets are essential to validate and generalize these findings. Full article

Maize seed variety classification has become essential in agriculture, driven by advancements in non-destructive sensing and machine learning techniques. This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. To enhance feature selection, the competitive adaptive reweighted sampling (CARS) algorithm was applied to reduce redundant information, identifying 100 key wavelengths from an initial set of 774. This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. These results showed that the CNN-LSTM model excelled in extracting complex spectral features and offering strong generalization and classification capabilities. Therefore, the model proposed in this study served as an effective tool for maize variety identification. Full article

Post-harvest processing (PHP) is a key determinant of coffee quality, flavor profile, and market classification, yet verifying PHP claims remains a significant challenge in the specialty coffee industry. This study introduces near-infrared spectroscopy (NIRS) coupled with chemometrics as a rapid, non-destructive approach to classify green coffee beans based on PHP. For the first time, seven distinct PHP categories—Alchemy, Anaerobic Processing (Deep Fermentation), Dry-Hulled, Honey, Natural, Washed, and Wet-Hulled—were discriminated using NIRS, encompassing 20 different processing protocols under varying environmental and fermentation conditions. The NIR spectra (350–2500 nm) of 524 green Arabica coffee samples were analyzed using PCA-LDA models (750–2450 nm), achieving classification accuracies up to 100% for underrepresented categories and strong performance (91–95%) for dominant PHP groups in an independent test set. These results demonstrate that NIRS can detect subtle chemical signatures associated with diverse PHP techniques, offering a scalable tool for quality assurance, fraud prevention, and traceability in global coffee supply chains. While limited sample sizes for some PHP categories may influence model generalization, this study lays the foundation for future work involving broader datasets and integration with digital traceability systems. The approach has direct implications for producers, traders, and certifying bodies seeking reliable, real-time PHP verification. Full article

Camellia oil, rich in monounsaturated fatty acids, squalene, tocopherols, and polyphenols, is highly valued for its nutritional benefits. However, its high market value and regional variations have led to frequent adulteration, highlighting the need for rapid, non-destructive methods for authentication, geographical traceability, and quality assessment. This study employed portable Raman spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) and Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) to differentiate camellia oil from other edible oils and evaluate its thermal and photo-oxidative stability. PLS-DA, based on VIP-selected spectral variables, effectively distinguished camellia oil, with Raman bands near 1250 cm⁻¹ and 1650 cm⁻¹ contributing significantly. A unique peak at 1525 cm⁻¹, observed in samples from Gongcheng, Guangxi, was associated with carotenoids and served as a potential marker for geographical traceability. MCR-ALS modeling revealed significant reductions in the 1650 cm⁻¹ and 1525 cm⁻¹ peaks when temperatures exceeded 150 °C, indicating degradation of unsaturated fatty acids and carotenoids. Under UV exposure, the 1525 cm⁻¹ peak declined sharply and nearly disappeared after 24 h, suggesting rapid carotenoid degradation via photooxidation. Extended UV treatment also affected the 1650 cm⁻¹ peak and led to oxidative product accumulation. Overall, this study demonstrates the feasibility of integrating Raman spectroscopy with chemometric analysis for efficient oil classification, traceability, and stability monitoring, offering a valuable tool for food quality control and market supervision. Full article

The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and chemometrics. The E-nose results demonstrated that the volatile characteristics of Tongcheng Xiaohua tea exhibit distinct geographical signatures, confirming the regional specificity of its aroma. HS-SPME-GC-MS identified 66 volatile metabolites across samples, with 18 key odorants (OAV > 1) including linalool, geraniol, (Z)-jasmone, and β-ionone driving aroma profiles. The partial least squares–discriminant analysis (PLS-DA) model, combined with variable importance in projection (VIP) scores and OAV, identified seven compounds that effectively differentiate the origins, among which α-pinene and β-cyclocitral emerged as novel markers imparting unique regional characteristics. Further comparative analysis between standard and premium grades revealed 2-methyl butanal, 3-methyl butanal, and dimethyl sulfide as main differential metabolites. Notably, the influence of geographical origin on metabolite profiles was found to be more significant than batch effects. These findings establish a robust analytical framework for origin traceability, quality standardization, and flavor optimization in tea production, providing valuable insights for the tea industry. Full article

This study presents an innovative approach to differentiate Southern Greek yellow prickly pear samples according to geographical origin based on physicochemical parameters, mineral composition, and bioactive compounds using advanced chemometrics. A total of 56 yellow prickly pear samples were collected from four distinct Greek regions (Crete, Paros, Symi, Peloponnese) during the 2019 and 2020 harvest seasons. A multi-platform analytical strategy was employed, combining classical physicochemical analyses and UV spectrophotometry for total antioxidant activity with cutting-edge techniques such as UPLC-MS/MS for precise quantification of vitamins and antioxidants, and ICP-MS for mineral profiling. In total, thirteen physicochemical parameters, nineteen macro-, micro-, and trace elements, nine vitamins, and seven antioxidants were identified and quantified. Application of MANOVA and Linear discriminant analysis (LDA) revealed that eight physicochemical parameters, ten mineral elements, and sixteen bioactive compounds played a crucial role in sample geographical differentiation. The classification success rates using the cross-validation method were 82.1% for physicochemical parameters, 75.0% for minerals, and an impressive 96.4% for vitamins and antioxidants highlighting the robust tool for the geographical differentiation of Southern Greek yellow prickly pears. Full article

The fermentation of prune juice significantly enhances its nutritional profile, antioxidant capacity, and flavor characteristics. In this study, Non-Saccharomyces cerevisiae and Lactiplantibacillus plantarum were used to co-ferment prune juice to systematically investigate the dynamic changes in physicochemical properties and antioxidant activity during fermentation. The evolution of volatile compounds across fermentation stages was analyzed using gas chromatography–ion mobility spectroscopy (GC-IMS) combined with chemometric methods, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that after fermentation, the total acidity (TA), total phenolic content (TPC), and total flavonoid content (TFC) increased by 37.35%, 20.28%, and 28.95%, respectively. Meanwhile, the pH, total soluble solids (TSS), and reducing sugars (RS) decreased by 16.87%, 23.36%, and 39.94%, respectively. Additionally, the DPPH radical scavenging capacity and ABTS radical scavenging capacity improved by 76.16% and 57.25% during fermentation process. A total of 37 volatile compounds were identified across the four fermentation stages of prune juice (PJ). These compounds included 14 esters, 8 alcohols, 7 aldehydes, 4 terpenoids, 3 ketones, and 1 amine. Considerable quantities of organic acids and free amino acids were detected in samples from all fermentation phases. Among these, lactic acid, citric acid, and D-glucuronic acid exhibited significant increases in their concentration (p < 0.05). In the free amino acid profile of fermented prune juice (FPJ), asparagine was the most abundant component, followed by glutamine and proline. Full article