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Citri Reticulatae Pericarpium (CRP) has both culinary and medicinal uses, with high medicinal and economic value. However, CRP fraud such as mislabeling of the geographical origin, passing off, and aging time cheating has occurred frequently, which damages consumer interests. In order to ensure the safety and quality of CRP, it is urgent to establish effective techniques for its geographical origin traceability and authentication. In this article, we review recent progress on the application of metabolomics, spectroscopy, DNA analysis, elemental analysis and intelligent sensory analysis in identifying the geographical origin, variety, harvest period and aging time of CRP. Meanwhile, we compare and analyze the advantages and disadvantages of each technique, and propose the existing problems and future research priorities in the field of geographical origin traceability and authentication of CRP.

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Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea. [ABSTRACT FROM AUTHOR]

This study aimed to improve the traceability of rice-producing areas to address the increasing demand for accurate methods to confirm food quality and safety. Compound-specific δ 13C of fatty acids, δ 13C of starch and bulk of rice were measured. PCA, PLS-DA and VIP value analysis of the obtained data were performed to track the source of rice from the six regions. The PLS-DA model established with bulk δ 13C, starch δ 13C, and fatty acid δ 13C, which clearly separated the rice from six regions. The VIP graph showed the value of starch, C18:0 and C18:2 δ 13C values (VIP > 1) were important to distinguish the origin of rice. Also, according to loading plots the contribution of starch δ 13C was the largest. The findings indicate that the introduction of starch δ 13C improves the precision of rice traceability and provides an effective method for identifying rice origin. • Stable isotope analysis is explored for rice traceability in China. • Various multivariate modeling methods were applied for origin traceability purpose. • The introduction of starch-δ13C was beneficial to trace the origin of rice. • This strategy is an efficient method for combating origin mislabeling in rice trade. [ABSTRACT FROM AUTHOR]

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

• Successful differentiation of PGI Hongyuan yak milk by using stable isotopes. • δ 13C and δ 18O was the main indication to identify the PGI Hongyuan yak milk. • Sampling dates affect stable isotope ratios of PGI Hongyuan yak milk. • Sampling locations do not affect stable isotope ratios of PGI Hongyuan yak milk. Hongyuan yak milk is a protected geographical indication (PGI) product of rich nutritional value, which is popular among consumers. Stable isotope ratio analysis (SIRA) is an effective way to protect the authenticity of the geographical origin of PGI products, and it is crucial to study the factors affecting stable isotopes. Firstly, we proved that the SIRA could be used to identify the geographical origin of Hongyuan yak milk, and that the identification accuracy in combination with δ 13C and δ 18O was 100 %. Secondly, we analyzed the effect of sampling selection on the stable isotopes of Hongyuan yak milk in practical applications, which showed that sampling time influenced the δ 13C, δ 2H, and δ 18O, while the sampling locations did not. There were interactions between the effect of sampling time and location on δ 2H and δ 18O. These results provide a reliable method for identifying PGI products and also provide new guidance on sampling models. [ABSTRACT FROM AUTHOR]

Pingwu Fuzhuan brick tea (PWT), a distinctive post-fermentation tea originated from Pingwu region of Sichuan province in China, is a popular “western road” border-selling tea. In this study, we investigated characteristics and chemical compositions of PWT, together with comparing its physicochemical properties with other regions of Fuzhuan brick teas (FBTs). Firstly, inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the geographical origin of FBTs, and the profiles of mineral elements were subjected to principal component analysis (PCA) to evaluate the differences among FBTs. Results from ICP-MS showed that the compositions of mineral elements from PWT, Hunan FBT (HNT), Shaanxi FBT (SNT), and Guizhou FBT (GZT) were distinctly different. The four different types of FBTs were clearly distinguished according to the results of PCA. Furthermore, all FBT samples were assessed for physicochemical properties and heatmap method was applied to data analysis. It was found that the total colour difference was highly distinct when compared PWT with HNT, SNT and GZT. Moreover, most of contents of biochemical compounds (gallic acid, catechins, free amino acids, alkaloids and volatile components) in PWT were lower compared to others except for total essential amino acids, GABA, aldehydes and acids.

Pingwu Fuzhuan brick tea (PWT), a distinctive post-fermentation tea originated from Pingwu region of Sichuan province in China, is a popular "western road" border-selling tea. In this study, we investigated characteristics and chemical compositions of PWT, together with comparing its physicochemical properties with other regions of Fuzhuan brick teas (FBTs). Firstly, inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the geographical origin of FBTs, and the profiles of mineral elements were subjected to principal component analysis (PCA) to evaluate the differences among FBTs. Results from ICP-MS showed that the compositions of mineral elements from PWT, Hunan FBT (HNT), Shaanxi FBT (SNT), and Guizhou FBT (GZT) were distinctly different. The four different types of FBTs were clearly distinguished according to the results of PCA. Furthermore, all FBT samples were assessed for physicochemical properties and heatmap method was applied to data analysis. It was found that the total colour difference was highly distinct when compared PWT with HNT, SNT and GZT. Moreover, most of contents of biochemical compounds (gallic acid, catechins, free amino acids, alkaloids and volatile components) in PWT were lower compared to others except for total essential amino acids, GABA, aldehydes and acids. [ABSTRACT FROM AUTHOR]