Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses

Metabolome of different cultivars of peas, lentils, faba beans and lupins - An 1H NMR spectroscopic exploration of their sensory attributes and potential biofunctionality

The transition to a more plant-based diet embraces a higher consumption of diversified pulses. Understanding the chemical composition of pulses is crucial to decipher their biofunctionality. This study analyzed 14 different cultivars of 4 types of pulses (pea, lentil, faba bean, and lupin) using NMR-based metabolomics. Sucrose, glutamate, and citrate were the metabolites representing the most abundant polar chemical classes (carbohydrates, amino acids, and organic acids). Lupin had a higher content of carbohydrates, and a lower content of free amino acids than the other species. Differences among the cultivars related to carbohydrates were found for peas and lentils, which was reflected in variations in their metabolic pathway potential. Faba beans showed highest concentrations of phenolic compounds. Correlation with data from descriptive sensory profiling enabled pinpointing several amino acids and some organic acids that contributed to explain variations in perceived smell and taste among the cultivars.

Identification of lentils (Lens culinaris Medik) from Eglouvi (Lefkada, Greece) based on rare earth elements profile combined with chemometrics

An analytical approach has been developed to verify the authenticity of premium lentils originating from Eglouvi, Lefkada, Greece. The method relies on the digestion of samples followed by the analysis of their rare earth elements (REEs) content. Lentils originating from Eglouvi exhibit higher content in most REEs compared to lentils from other regions as well as distinct Sc/Y and Sc/Yb concentration ratios. Principal component analysis effectively segregates "Eglouvi" lentils into a distinct cluster. Soft Independent Modelling of Class Analogy (SIMCA) successfully models "Eglouvi" lentils. Significant enhancement in model specificity was achieved upon inclusion of Sc/Y and Sc/Yb concentration ratios as additional variables. The model is capable of detecting adulteration in blends of Eglouvi lentils, with a minimum rejection threshold of 4.6% w/w for Greek lentil adulterants and 6.0% w/w for imported lentil adulterants.

Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.

Application of segmented analysis via multivariate curve resolution with alternating least squares to 1H-nuclear magnetic resonance spectroscopy to identify different sugar sources

The different types of sugar employed in the food industry exhibit chemical similarity and are mostly dominated by sucrose. Owing to the sugar origin of and differences in production, the presence of certain minor organic compounds differs. To differentiate between sugars based on their botanical source, geographical origin, or storage conditions, commercial brown sugars and sugar beet extracts were analyzed by ¹H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS). Principal component analysis and partial least squares-discriminant analysis yielded excellent differentiation between sugars from different sources after the application of this preprocessing strategy; without loss of chemical information and with direct interpretation of the results. By applying a segmented analysis via MCR-ALS to ¹H NMR sugar data, similar spectroscopic profiles could be differentiated. This improved the selectivity of ¹H NMR spectroscopy for sugar source differentiation which can be useful for industrial sugar authentication purposes.

Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam

Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, ¹H NMR combined intelligent "rotation-invariant uniform local binary pattern" identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of ¹H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

NMR Tracing of Food Geographical Origin: The Impact of Seasonality, Cultivar and Production Year on Data Analysis

The traceability of typical foodstuffs is necessary to protect high quality of traditional products. It is well-known that several factors could influence metabolites content in certified foods, but soil composition, altitude, latitude and coded production protocols constitute the territorial conditions responsible for the peculiar organoleptic and nutritional properties of labelled foods. Instead, regardless of origin, seasonality, cultivar, collection year can affect all agricultural products, so it is appropriate to include them in data analysis in order to obtain a correct interpretation of the differences linked to growing areas alone. Therefore, it is useful to use a flexible all-round technique, and NMR spectroscopy coupled with multivariate statistical analysis is considered a powerful means of assessing food authenticity. The purpose of this review is to investigate the relevance of year, cultivar, and seasonal period in the determination of food geographical origin using NMR spectroscopy. The strategy for testing these three factors may differ from author to author, but a preliminary study of cultivar or collection year effects on NMR spectra is the most popular method before starting the geographical characterization of samples. In summary, based on the available literature, the most significant influence is due to cultivar, followed by harvesting year, however seasonality is not considered a source of variability in data analysis.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review

In the last decades, the demand for molecular tools for authenticating and tracing agri-food products has significantly increased. Food safety and quality have gained an increased interest for consumers, producers, and retailers, therefore, the availability of analytical methods for the determination of food authenticity and the detection of major adulterations takes on a fundamental role. Among the different molecular approaches, some techniques such as the molecular markers-based methods are well established, while some innovative approaches such as isothermal amplification-based methods and DNA metabarcoding have only recently found application in the agri-food sector. In this review, we provide an overview of the most widely used molecular techniques for fresh and processed agri-food authentication and traceability, showing their recent advances and applications and discussing their main advantages and limitations. The application of these techniques to agri-food traceability and authentication can contribute a great deal to the reassurance of consumers in terms of transparency and food safety and may allow producers and retailers to adequately promote their products.

Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics

With the increase in soybean trade between countries, the intentional mislabeling of the origin of soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%–100% specificity, and 94.4%–100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.

Compared digestibility of plant protein isolates by using the INFOGEST digestion protocol

The use of ingredients based on plant protein isolates is being promoted due to sustainability and health reasons. However, it is necessary to explore the behaviour of plant protein isolates during gastrointestinal digestion including the profile of released free amino acids and the characterization of resistant domains to gastrointestinal digestion. The aim of the present study was to monitor protein degradation of four legume protein isolates: garden pea, grass pea, soybean and lentil, using the harmonized Infogest in vitro digestion protocol. In vitro digests were characterized regarding protein, peptide and free amino acid content. Soybean was the protein isolate with the highest percentage of insoluble nitrogen at the end of the digestion (12%), being this fraction rich in hydrophobic amino acids. Free amino acids were mainly released during the intestinal digestion, comprising 21-24% of the total nitrogen content, while the percentage of nitrogen corresponding to peptides ranged from 66 to 76%. Legume globulins were resistant to gastric digestion whereas they were hydrolysed into peptides and amino acids during the intestinal phase. However, the molecular weight (MW) distribution demonstrated that all intestinal digests, except those from soybean, contained peptides with MW > 4 kDa at the end of gastrointestinal digestion. The profile of free amino acids released during digestion supports legume protein isolates as an excellent source of essential amino acids to be used in protein-rich food products. Peptides released during digestion matched with previously reported epitopes from the same plant species or others, explaining the ability to induce allergic reactions and cross-linked reactivity.

Varietal classification of white wines by fluorescence spectroscopy

The Slovak Tokaj region is one of the producers of high-quality white wine having protected designations of origin. The main grape varieties of this region are Furmint, Lipovina and Muscat blanc, which have specific sensory characteristics. This research work presents a strategy for the classification of three mentioned varieties of white wines using fluorescence spectroscopy with chemometrics. Emission and synchronous fluorescence spectra were obtained for bulk as well as diluted samples, principal component analysis (PCA) was applied for exploratory analysis and the scores of the selected PCs were used in linear discriminant analysis (LDA). For undiluted samples, the best PCA-LDA models based on either emission spectra excited at 370 nm or synchronous fluorescence spectra obtained at wavelength difference of 40 and 100 nm provided total correct classifications of 100, 100 and 93% for the calibration, validation and prediction steps, respectively. For diluted samples, the best PCA-LDA models (excitation at 280 nm; wavelength difference of 40 nm) again provided total correct classifications as mentioned above.

Bulk and compound-specific stable isotope ratio analysis for authenticity testing of organically grown tomatoes

Until now, there has been a lack of analytical methods that can reliably verify the authenticity of organically grown plants and derived organic food products. In this study, stable isotope ratio analysis of hydrogen (H, δ²H), carbon (C, δ¹³C), nitrogen (N, δ¹⁵N), oxygen (O, δ¹⁸O) and sulfur (S, δ³⁴S) was conducted along the tomato passata production process using organic and conventionally grown tomatoes from two Italian regions over two years. A gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) based method was developed and applied for analysis of C and N isotope ratios in amino acids derived from tomatoes. Of the bulk isotope ratios, δ¹⁵N was the most significant parameter for discriminating organic from conventional products. The classification power was improved significantly by compound-specific isotope analysis regardless of the production years and regions. We conclude that isotope analysis of amino acids is a novel analytical tool for complementing existing certification and control procedures in the organic tomato sector.

Enhancing the nutritional profile and digestibility of lentil flour by solid state fermentation with Pleurotus ostreatus

Lentils (Lens culinaris) present an excellent nutrient profile.

Particle morphomics by high-throughput dynamic image analysis

A novel omics-like method referred to as "particle morphomics" has been proposed in the present study. The dynamic images of >2,000,000 particles per sample in sediments, soils and dusts were collected by a Sympatec GmbH QICPIC particle size and shape analyzer, and the morphological descriptors of each particle including equivalent diameter, sphericity, aspect ratio and convexity were extracted as the "particle morphome". Various multivariate analyses were adopted to process the high-throughput data of particle morphome including analyses of alpha and beta diversities, similarity, correlation, network, redundancy, discretion and principal coordinate. The outcome of particle morphomics could estimate the morphological diversity and sketch the profile of morphological structure, which aided to develop a morphological fingerprint for specific particle samples. The distribution and properties of particle assemblages of specific morphology could also be evaluated by selecting particles with respect to filter criteria. More importantly, the particle morphomics may be extended to investigate and explain the biogeochemical and environmental processes involved with particle morphology if linked with external variables.

Comparison and application of fluorescence EEMs and DRIFTS combined with chemometrics for tracing the geographical origin of Radix Astragali

Selection of the appropriate method for traceability may be of great interest for the characterization of food authenticity and to reveal falsifications. The possibility of tracing the geographical origins of Radix Astragali based on diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) technique and fluorescence fingerprints (EEMs) technique was investigated in this work. DRIFTS technique combined with PCA and PLS-DA and EEMs technique combined with M-PCA and N-PLS-DA were used to determine the geographical origin of Radix Astragali samples, respectively. DRIFTS-PLS-DA provided total recognition rates of 98.4% for all Radix Astragali samples in the training sets and 94.6% in the predicted sets. Compared with the DRIFTS, EEMs combined with chemometrics obtained more accurate recognition results. The total recognition rates (RRs) of the training sets and prediction sets obtained with EEMs-N-PLS-DA were all 100%. The good classification results of fluorescence fingerprints technique should be attributed mainly to two reasons. One reason is that three-dimensional fluorescence spectrum can provide more information than two-dimensional DRIFTS, and the other reason is that fluorescence spectrum has higher sensitivity and selectivity than the DRIFTS. Therefore, fluorescence fingerprint (EEMs) technique combined with chemometrics results more adequate for tracing the food geographical origin. It should be noted that the more the analysis target contains fluorescent substances, the more accurate results are obtained by using the fluorescent fingerprint method. Conversely, if the classification object contains very few fluorescent substances, the classification result may not be as good as the DRIFTS method. Furthermore, due to relatively cumbersome operation of fluorescence method, EEMs fluorescence method is unsuitable for rapid analysis as compared to infrared method.

Food fingerprints - A valuable tool to monitor food authenticity and safety

In recent years, food frauds and adulterations have increased significantly. This practice is motivated by fast economical gains and has an enormous impact on public health, representing an important issue in food science. In this context, this review has been designed to be a useful guide of potential biomarkers of food authenticity and safety. In terms of food authenticity, we focused our attention on biomarkers reported to specify different botanical or geographical origins, genetic diversity or production systems, while at the food safety level, molecular evidences of food adulteration or spoilage will be highlighted. This report is the first to combine results from recent studies in a format that allows a ready overview of metabolites (<1200 Da) and potentially molecular routes to monitor food authentication and safety. This review has therefore the potential to unveil important aspects in food adulteration and safety, contributing to improve the current regulatory frameworks.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

Altered Serum Metabolite Profiling and Relevant Pathway Analysis in Rats Stimulated by Honeybee Venom: New Insight into Allergy to Honeybee Venom

To improve our understanding of the disturbed metabolic pathways and cellular responses triggered by honeybee venom stimulation, we compared the changes in serum metabolites in rats, either stimulated or not by honeybee venom, by performing ¹H nuclear magnetic resonance (NMR) spectrometry-based metabonomics to identify potential biomarkers. In this study, 65 metabolites were structurally confirmed and quantified and the following results were obtained. First, by pattern recognition analysis, 14 metabolites were selected as potential biomarkers 3 h after venom stimulation. Second, metabolic pathway analysis showed that methane metabolism, glyoxylate and dicarboxylate metabolism, tricarboxylic acid cycle, glycine, serine, and threonine metabolism, arginine and proline metabolism were affected. Finally, the time-dependent metabolic modifications indicated that rats could recover without medical treatment 24 h after venom stimulation. In summary, this new insight into the changes in serum metabolites in rats after honeybee venom stimulation has enhanced our understanding of the response of an organism to honeybee venom.