Geographical Origin Differentiation of Rice by LC-MS-Based Non-Targeted Metabolomics

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Untargeted Metabolomics Reveals the Metabolic Characteristics and Biomarkers of Antioxidant Properties of Gardeniae Fructus from Different Geographical Origins in China

Background/Objectives: Gardeniae Fructus (GF) has been widely used as both food and medicinal purposes for thousands of years, but their antioxidant properties and potential metabolite biomarkers remain unclear. Methods: The purposes of this study were to examine antioxidant activities of 21 GF varieties from different geographical origins in China and identify potential biomarkers of antioxidant properties using an untargeted LC-MS-based metabolomics approach. Results: The results demonstrate that metabolomics had the ability to trace the geographical origins of GF. We found that antioxidant activities varied with different varieties of GF, which was dependent on their chemical compositions. The key chemical categories were obtained as the primary contributors of the antioxidant activity, including prenol lipids, flavonoids, coumarins and derivatives, as well as steroids and steroid derivatives. In addition, adouetine Y, coagulin R 3-glucoside and epicatechin 3-glucoside were identified as potential biomarkers for the antioxidant activity of GF. Conclusions: Therefore, our study sheds light on the metabolic characteristics and biomarkers of the antioxidant properties of GF, contributing to the selection and cultivation of a high antioxidant variety.

Identification and quality evaluation of Lushan Yunwu tea from different geographical origins based on metabolomics

The relationship between the chemical composition and quality of Lushan Yunwu tea (LYT) from different geographical origins is not clear. Sensory evaluation, metabolomics analyses combined with chemometrics were conducted on LYT from 8 different geographical origins, and altitude was identified as the main factor responsible for the differences among LYT. A total of 32 non-volatile and 27 volatile compounds were identified as marker metabolites to distinguish the origins of high altitudes from those of low altitudes. LYT samples from higher altitude areas contained more free amino acids, sugars, and organic acids, and less catechins, which may contribute to the reduction of bitterness and astringency and the enhancement of umami. The contents of geranylacetone, ethyl hexanoate, ethyl caprylate, 3-carene, d-cadinene, linalool, nerol, and nerolidol in high altitude areas were higher than those in low altitude areas, indicating that LYT from high altitude had strong floral and fruity aroma. The altitudes were positively correlated with pH value, total flavonoids, soluble protein, total free amino acids, and the antioxidant capacities of the LYT. This study provided a theoretical basis for the study of the effect of altitude on tea quality.

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

Identification of Tartary Buckwheat (Fagopyrum tataricum (L.) Gaertn) and Common Buckwheat (Fagopyrum esculentum Moench) Using Gas Chromatography-Mass Spectroscopy-Based Untargeted Metabolomics

Tartary buckwheat has attracted more attention than common buckwheat due to its unique chemical composition and higher efficacy in the prevention of various diseases. The content of flavonoids in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) is higher than that in common buckwheat (Fagopyrum esculentum Moench). However, the processing process of Tartary buckwheat is complex, and the cost is high, which leads to the frequent phenomenon of common buckwheat counterfeiting and adulteration in Tartary buckwheat, which seriously damages the interests of consumers and disrupts the market order. In order to explore a new and simple identification method for Tartary buckwheat and common buckwheat, this article uses metabolomics technology based on GC-MS to identify Tartary buckwheat and common buckwheat. The results show that the PLS-DA model can identify Tartary buckwheat and common buckwheat, as well as Tartary buckwheat from different regions, without an over-fitting phenomenon. It was also found that ascorbate and aldarate metabolism was the main differential metabolic pathway between Tartary buckwheat and common buckwheat, as well as the amino acids biosynthesis pathway. This study provides a new attempt for the identification of Tartary buckwheat and common buckwheat for the quality control of related agricultural products.