[Comparison of multi-type chemical compounds in fruits of Lycium and correlations between compounds and traits].

To comprehensively reveal and utilize the plant resources of Lycium in China, this study determined and compared the content of monosaccharides, polysaccharides, proteins, carotenoids, organic acids, and phenols in the dried fruits of 8 different Lycium species. Furthermore, the traits including the hundred-fruit weight, shape index, and the ratio of seed to fruit were measured, and the correlations between the content of chemical compounds and fruit traits were assessed. The results showed that L. truncatum, L. barbarum var. auranticarpum, and L. dasystemum var. rubricaulium were the species with high content of monosaccharides. L. barbarum and L. barbarum var. auranticarpum were the species with high content of total polysaccharides, and L. barbarum was the species with high content of carotenoids. L. yunnanense and L. chinense var. potaninii had high content of soluble proteins. L. truncatum, L. dasystemum, and L. barbarum showed high content of organic acids and phenols. L. barbarum and L. barbarum var. auranticarpum demonstrated high fruit weight, while L. yunnanense and L. chinense had high ratios of seed to fruit. The multivariate statistical analysis indicated that polysaccharides, carotenoids, hundred-fruit weight, ratio of seed to fruit, scopolamine, fructose, 5-O-feruloylquinic acid, kaempferol-3-O-rutinoside, scopoletin, cryptochlorogenic acid, and caffeic acid were the main differential compounds in the fruits among different species of Lycium. Moreover, the results of correlation ananysis showed strong correlations between fruit traits and compound content. Specifically, the hundred-fruit weight had positive correlations with the content of total polysaccharides and scopola-mine. The ratio of seed to fruit was negatively correlated with the content of rutin, kaempferol-3-O-rutinoside, fructose, and glucose and positively correlated with the content of succinic acid, soluble proteins, and zeaxanthin. The results implied that chemical compounds presented different distribution patterns in the fruits of 8 Lycium species. This study provides a basis for the comprehensive development and utilization, targeted breeding, and value-added application of Lycium plants.

[Application and prospect of stable isotope technology in tracing geographical origin of Chinese herbal medicines].

As a unique medical resource in China, Chinese herbal medicine plays a key role in the prevention and treatment of human diseases. With the gradual expansion of applications, the quality of Chinese herbal medicine has become the focus of attention. The quality of Chinese herbal medicines depends largely on their source authenticity. Tracing the origin of Chinese herbal medicines plays an important role in ensuring their quality and efficacy and reducing the mixing and adulteration of Chinese herbal medicines from different regions. Stable isotope technology, as a key technology for origin tracing of agricultural products and food, has been used in the research of Chinese herbal medicines from multiple sources and origins in recent years. This new technological means contributes to standardizing the origin of Chinese herbal medicines and controlling their quality from the source. Apart from introducing the basic principles of stable isotope technology and the characteristics of common stable isotopes, this study reviewed the application status of light and heavy stable isotopes in the origin tracing of Chinese herbal medicines and their correlation with ecological factors, and forecasted the application prospect of this technology in the authentication of Chinese herbal medicines, aiming to provide reference for the geographical origin tracing of Chinese herbal medicines and promote the sustainable development of traditional Chinese medicine.

[Rapid identification of geographic origins of Zingiberis Rhizoma by NIRS combined with chemometrics and machine learning algorithms].

In this study, 280 batches of Zingiberis Rhizoma samples from nine producing areas were analyzed to obtain infrared spectral information based on near-infrared spectroscopy(NIRS). Pluralistic chemometrics such as principal component analysis(PCA), partial least squares-discriminant analysis(PLS-DA), orthogonal partial least squares-discriminant analysis(OPLS-DA), K-nearest neighbors(KNN), support vector machine(SVM), random forest(RF), artificial neural network(ANN), and gradient boosting(GB) were applied for tracing of origins. The results showed that the discriminative accuracy of the spectral preprocessing by standard normal variate transformation coupled with the first derivative was 93.9%, which could be used for the construction of the discrimination model. PCA and PLS-DA score plots showed that samples from Shandong, Sichuan, Yunnan, and Guizhou could be effectively distinguished, but the remaining samples were partially overlapped. As revealed by the analysis results by machine learning algorithms, the AUC values of KNN, SVM, RF, ANN, and GB algorithms were 0.96, 0.99, 0.99, 0.99, and 0.98, respectively, with overall prediction accuracies of 83.3%, 89.3%, 90.5%, 91.7%, and 89.3%. It indicated that the developed model was reliable and the machine learning algorithm combined with NIRS for origin identification was sufficiently feasible. OPLS-DA showed that Zingiberis Rhizoma from Sichuan(genuine producing areas) could be significantly distinguished from other regions, with good discriminative accuracy, suggesting that the NIRS established in this study combined with chemometrics can be used for the identification of Zingiberis Rhizoma from Sichuan. This study established a rapid and nondestructive identification and reliable data analysis method for origin identification of Zingiberis Rhizoma, which is expected to provide a new idea for the origin tracing of Chinese medicinal materials.

[Content and distribution of inorganic elements in Laminaria japonica based on ICP-MS and Micro-XRF].

In order to evaluate the composition and distribution characteristics of inorganic elements in Laminaria japonica, this study employed inductively coupled plasma mass spectrometry(ICP-MS) to detect the inorganic elements and used high performance liquid chromatography tandem ICP-MS(HPLC-ICP-MS) to determine the content of different arsenic species in L. japonica from diffe-rent origins. Micro X-ray fluorescence(Micro-XRF) was used to determine micro-area distribution of inorganic elements in L. japonica. The results showed that the average content of Mn, Fe, Sr, and Al was high, and that of As and Cr exceeded the limits of the national food safety standard. According to the results of HPLC-ICP-MS, arsenobetaine(AsB) was the main species of As contained in L. japonica. The more toxic inorganic arsenic accounts for a small proportion, whereas its content was 1-4 times of the limit in the national food safety standard. The results of Micro-XRF showed that As, Pb, Fe, Cu, Mn, and Ni were mainly distributed on the surface of L. japonica. Among them, As and Pb had a clear tendency to diffuse from the surface to the inside. The results of the study can provide a basis for the processing as well as the medicinal and edible safety evaluation of L. japonica.

Combining stable isotope, multielement and untargeted metabolomics with chemometrics to discriminate the geographical origins of ginger (Zingiber officinale Roscoe).

Ginger (Zingiber officinale Roscoe) is a high-value food and herb worldwide. The quality of ginger is often related to its production regions. In this study, stable isotopes, multiple elements, and metabolites were investigated together to realize ginger origin traceability. Chemometrics showed that ginger samples could be preliminarily separated, and 4 isotopes (δ13C, δ2H, δ18O, and δ34S), 12 mineral elements (Rb, Mn, V, Na, Sm, K, Ga, Cd, Al, Ti, Mg, and Li), 1 bioelement (%C), and 143 metabolites were the most important variables for discrimination. Furthermore, three algorithms were introduced, and the fused dataset based on VIP features led to the highest accuracies for origin classification, with predictive rates of 98% for K-nearest neighbor and 100% for support vector machine and random forest. The results demonstrated that isotopic, elemental, and metabolic fingerprints were useful indicators for the geographical origins of Chinese ginger.

Headspace GC/MS and fast GC e-nose combined with chemometric analysis to identify the varieties and geographical origins of ginger (Zingiber officinale Roscoe).

Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC-MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC-MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.

Comparison of Different Drying Methods on the Volatile Components of Ginger (Zingiber officinale Roscoe) by HS-GC-MS Coupled with Fast GC E-Nose.

Ginger (Zingiber officinale Roscoe) is one of the most popular spices in the world, with its unique odor. Due to its health benefits, ginger is also widely used as a dietary supplement and herbal medicine. In this study, the main flavor components of gingers processed by different drying methods including hot air drying, vacuum drying, sun-drying, and vacuum-freeze drying, were identified on the basis of headspace-gas chromatography coupled with mass spectrometry (HS-GC-MS) and fast gas chromatography electronic-nose (fast GC e-nose) techniques. The results showed that the ginger dried by hot air drying exhibited high contents of volatile compounds and retained the richest odor in comparison with those dried by other methods, which indicated that hot air drying is more suitable for the production of dried ginger. Sensory description by fast GC e-nose exhibited that ginger flavor was mainly concentrated in the spicy, sweet, minty, fruity, and herbaceous odor. The relative content of the zingiberene was significantly higher in the hot air drying sample than those by other methods, suggesting that dried ginger by hot air drying can retain more unique spicy and pungent odorants. Furthermore, the results of chemometrics analyses showed that the main variance components among the samples by different drying methods were α-naginatene, (+)-cyclosativene, and sulcatone in HS-GC-MS analysis, and α-terpinen-7-al, dimethyl sulfide, and citronellal in fast GC e-nose analysis. For comparison of fresh and dried gingers, terpinolene, terpinen-4-ol, 2,4-decadienal, (E, Z)-, and linalool were considered the main variance components. This study generated a better understanding of the flavor characteristics of gingers by different drying methods and could provide a guide for drying and processing of ginger.

Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics.

Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.