Tracing the origin of Taiping Houkui green tea using 1H NMR and HS-SPME-GC-MS chemical fingerprints, data fusion and chemometrics

Temporal analysis of non-targeted flavor component variation in green tea storage and rapid prediction of storage duration

The precise changes in the flavor quality of green tea before and after the optimal consumption period remain elusive. This study used HS-SPME-GC-MS to carefully examine the volatile compounds of green tea samples from multiple storage periods and simultaneously detected changes in taste and color during tea infusion. Notably, 16 compounds with VIP > 1.5 were identified, highlighting their significance in the overall analysis profile. Among these components, nonanoic acid, octanoic acid and hexanal were identified as potential contributors to off-flavors and rancid oil flavors in tea after storage. The color of the tea broth becomes darker. Bitter flavor is associated with increased amino acid content. The PSO-SVM model showed excellent accuracy for predicting the storage duration of green tea. These findings provide critical theoretical understandings of green tea manufacturing and quality control.

Recent advances of machine learning in the geographical origin traceability of food and agro-products: A review

The geographical origin traceability of food and agro-products has been attracted worldwide. Especially with the rise of machine learning (ML) technology, it provides cutting-edge solutions to erstwhile intractable issues to identify the origin of food and agro-products. By utilizing advanced algorithms, ML can extract feature information of food and agro-products that is closely related to origin and, more accurately, identify and trace their origins, which is of great significance to the entire food and agriculture industry. This paper provides a comprehensive overview of the state-of-the-art applications of ML in the geographical origin traceability of food and agro-products. First, commonly used ML methods are summarized. The paper then outlines the whole process of preparation for modeling, model training as well as model evaluation for building traceability models-based ML. Finally, recent applications of ML combined with different traceability techniques in the field of food and agro-products are revisited. Although ML has made many achievements in solving the geographical origin traceability problem of food and agro-products, it still has great development potential. For example, the application of ML is yet insufficient in the geographical origin traceability using DNA or computer vision techniques. The ability of ML to predict the geographical origin of food and agro-products can be further improved, for example, by increasing model interpretability, incorporating data fusion strategies, and others.

Online Comprehensive Two-Dimensional Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry-Based Metabolic Profiling and Comparison Enabling the Characterization of 1146 Ginsenosides and More Explicit Differentiation of Ginseng

This work was designed for the in-depth characterization and holistic comparison of up to 12 ginseng varieties, which can benefit the development of functional foods and ensure their authenticity in the food industry. An online comprehensive two-dimensional liquid chromatography/quadrupole time-of-flight mass spectrometry (2D-LC/QTOF-MS) approach was established by configurating the XCharge C18 and HSS Cyano columns. Under the optimal conditions, we characterized a total of 1146 ginsenosides (including 876 potentially new compounds) from 12 ginseng varieties by reference to an in-house library of 573 known ginsenosides and 70 reference compounds. The online 2D-LC/QTOF-MS-based untargeted metabolomics workflows were developed, by which 126 potential ginsenoside markers were unveiled and utilized to establish the key identification points for each ginseng species. Compared with the conventional liquid chromatography/mass spectrometry metabolomics, our multidimensional chromatography approach performed better in discriminating multiple ginseng varieties. This work demonstrates a potent and practical methodology to identify easily confused functional plants.

1H NMR Spectroscopy Combined with Machine-Learning Algorithm for Origin Recognition of Chinese Famous Green Tea Longjing Tea

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks

This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis - linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.