Geographical origin traceability and identification of refined sugar using UPLC-QTof-MS analysis

Quantitative NMR analysis of sugars in natural sweeteners: Profiling in honey, jaggery, date syrup, and coconut sugar

Due to the high demand for natural sweeteners and their perceived health benefits, it is crucial to use analytical techniques for accurately profiling natural sweeteners. The present study describes a simple and fast approach for the analysis of sweeteners using 1D - 1H NMR spectroscopy. This method is based on the direct detection of protons in sugar molecules with an internal standard, without the need for complex derivatization steps. The presented approach offers a faster and more convenient way of quantifying mono-saccharides mainly glucose and fructose and di-saccharides like sucrose in various selected sweeteners. These includes honey, jaggery, coconut sugar, and date syrup. The direct 1D - 1H NMR method with an internal standard yields accurate and precise quantification results with good reproducibility and minute analysis times. This information is of increasing importance to both consumers and the food industry, as it provides a reliable and accurate method for characterizing and verifying natural sweeteners. Overall, 1D - 1H NMR spectroscopy can be a valuable tool for the rapid and easy analysis of sugar content in food products, and it may have potential applications in the food industry.

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.

Tracing the geographical origin of endangered fungus Ophiocordyceps sinensis, especially from Nagqu, using UPLC-Q-TOF-MS

Ophiocordyceps sinensis (OS), known as "soft gold", played an important role in local economic development. OS from different producing areas was difficult to be discriminated by the appearance. Nagqu OS, a distinguished and safeguarded geographical indication product, commands a premium price in market. The real claim of OS geographical origins is urgently required. Here, 81 OS samples were collected from Tibetan Plateau in China to explore markers for tracing origins. OS from Xigazê can be distinguished by dark color of head of caterpillar. Then 57 samples, a fully representative training-sample set, were used to set up OPLS-DA models by nontargeted metabolomics from UPLC-QTOF-MS. Certain markers were successfully identified and validation using 21 blind test samples confirmed that the markers can trace the geographical origin of OS, especially Nagqu samples. It was affirmed that UPLC-QTOF-MS-based untargeted metabolomics coupled with OPLS-DA was a reliable strategy to trace the geographical origins of OS.

Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.

Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis

In addressing the generalization issue faced by data-driven methods in food origin traceability, especially when encountering diverse input variable sets, such as elemental contents (C, N, S), stable isotopes (C, N, S, H and O) and 43 elements measured under varying laboratory conditions. We introduce an innovative, versatile deep learning-based framework incorporating explainable analysis, adept at determining feature importance through learned neuron weights. Our proposed framework, validated using three rice sample batches from four Asian countries, totaling 354 instances, exhibited exceptional identification accuracy of up to 97%, surpassing traditional reference methods like decision tree and support vector machine. The adaptable methodological system accommodates various combinations of traceability indicators, facilitating seamless replication and extensive applicability. This groundbreaking solution effectively tackles generalization challenges arising from disparate variable sets across distinct data batches, paving the way for enhanced food origin traceability in real-world applications.

Pilot study on provenance tracing of cocoons via strontium isotopes

Isotopic analysis has seen significant use in archaeological sciences to date objects, determine their origin, and depict ancient human dietary habits. However, the potential of this technique for provenance studies of ancient silks remains underdeveloped. In this study, we applied Sr isotopic ratios to the provenance tracing of silkworm cocoons. We investigated the ⁸⁷Sr/⁸⁶Sr ratios of cocoons from seven Chinese provinces to determine their regional differences. The ⁸⁷Sr/⁸⁶Sr ratios of mulberry leaves and cocoons in Shandong and Sichuan were analyzed and silkworms were cultured at four sampling locations in Hangzhou, Zhejiang, to determine isotopic signature relationships between mulberry leaves and cocoons. Those results showed that the ⁸⁷Sr/⁸⁶Sr signature of cocoons not only reflected regional differences, but also was related to the type of soil in each sampling location from which the samples were collected. It is suggested that the Sr isotope ratios was able to be an effective tool for the provenance tracing of cocoons. The Mann-Whitney test result indicated no significant differences in isotopic ratios between mulberry leaves and cocoons. In other words, mulberry leaves may predict mean isotopic values in the cocoons. No clear evidence of Sr isotopic fractionation was found in our control experiments. However, mulberry leaves and cocoons from Sichuan did not show significant correlation between them, overall reducing the predictive power of the ⁸⁷Sr/⁸⁶Sr ratios of mulberry leaf for provenance studies of cocoons. Finally, in order to improve the accuracy of Sr isotope ratios for the provenance tracing of cocoons, more ⁸⁷Sr/⁸⁶Sr data should be complemented and the relationship needs to be established between Sr isotope information in more kinds of proxies and cocoons.

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.