Geographical discrimination and authentication of lentils (Lens culinaris Medik.) by ICP-OES elemental analysis and chemometrics

Geographical differentiation between South Korean and Chinese onions using stable isotope ratios and mineral content analysis

Geographical origin authentication of onions has become significant owing to origin labelling fraud in South Korea. Various analytical techniques based on stable isotope ratios, organic and inorganic constituents, or their combinations, can distinguish agricultural products geographically. However, studies on the geographical classification of South Korean and Chinese onions using stable isotopes and minerals remain scarce. This study aimed to discriminate geographically between South Korean and Chinese onions using stable isotope ratios (δ13C, δ15N, and δ34S) and mineral contents (K, Ca, Mg, Na, P, Fe, Zn, Mn, Cu, and Sr) combined with multivariate statistical analysis. Fifty-eight onion samples cultivated in South Korea and China were collected in 2023. The two stable isotope ratios (δ15N and δ34S) and six minerals (K, Ca, Na, Fe, Zn, and Sr) significantly differed between these onions. These variables were applied in orthogonal partial least squares discriminant analysis to classify the onion samples regionally. The predictive ability and goodness-of-fit parameters (R2X and R2Y) were 0.671, 0.383, and 0.677, respectively. K, Sr, δ34S, and Na served as potential markers contributing to the classification. Therefore, stable isotopes and mineral elements may serve as effective indicators for the geographical discrimination of South Korean and Chinese onion samples using multivariate analyses.

Physiological and molecular mechanisms of silicon and potassium on mitigating iron-toxicity stress in Panax ginseng

Iron plays a crucial role in plant chlorophyll synthesis, respiration, and plant growth. However, excessive iron content can contribute to ginseng poisoning. We previously discovered that the application of silicon (Si) and potassium (K) can mitigate the iron toxicity on ginseng. To elucidate the molecular mechanism of how Si and K alleviate iron toxicity stress in ginseng. We investigated the physiological and transcriptional effects of exogenous Si and K on Panax ginseng. The results suggested that the leaves of ginseng with Si and K addition under iron stress increased antioxidant enzyme activity or secondary metabolite content, such as phenylalanine amino-lyase, polyphenol oxidase, ascorbate peroxidase, total phenols and lignin, by 6.21%-25.94%, 30.12%-309.19%, 32.26%-38.82%, 7.81%-23.66%, and 4.68%-48.42%, respectively. Moreover, Si and K increased the expression of differentially expressed genes (DEGs) associated with resistance to both biotic and abiotic stress, including WRKY (WRKY1, WRKY5, and WRKY65), bHLH (bHLH35, bHLH66, bHLH128, and bHLH149), EREBP, ERF10 and ZIP. Additionally, the amount of DEGs of ginseng by Si and K addition was enriched in metabolic processes, single-organism process pathways, signal transduction, metabolism, synthesis and disease resistance. In conclusion, the utilization of Si and K can potentially reduce the accumulation of iron in ginseng, regulate the expression of iron tolerance genes, and enhance the antioxidant enzyme activity and secondary metabolite production in both leaves and roots, thus alleviating the iron toxicity stress in ginseng.

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.

Geographical identification of strawberries based on stable isotope ratio and multi-elemental analysis coupled with multivariate statistical analysis: A Slovenian case study

The geographical classification and authentication of strawberries were attempted using discriminant and class-modelling methods applied to stable isotopes of light elements and elemental composition. The work involved creating a database of 92 authentic Slovenian strawberry samples and 32 imported samples. All samples were harvested between 2018 and 2020. A good geographical classification of Slovenian and non-Slovenian strawberries was obtained despite different production years using discriminant approaches. However, for verifying compliance with a given specification (geographical indications), a class-modelling approach was used to build an unbiased verification model. Class models generated by data-driven soft independent modelling of class analogy (DD-SIMCA) had high sensitivity (96% to 97%) and good specificity (81% to 91%) on a yearly basis, while a more generalised model combining total yearly data gave a lower specificity (63%). Of the 33 commercially available samples (test samples) with declared Slovenian origin, 39% were from outside of Slovenia.

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.

Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses

The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin.