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.

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

Fruit of Citrus wilsonii Tanaka called as “Xiang yuan” in Chinese, which means fragrant and round. It was widely used in the pharmaceutical and food industries. This fruit has well-known health benefits such as antioxidant, radical scavenging, and anti-inflammatory. Naringin, deacetylnomilin, citric acid, limonin, and nomilin were the characteristic components of Citrus wilsonii Tanaka. Although the fruit of Citrus wilsonii Tanaka possessed many applications, there was a lack of research on the growth period and drying process. In this study, plant metabolomics was used to analyze the biomarkers of the growth period, and appearance indicators and metabolites abundance were combined for the analysis of change regularities of the growth period. The representative differential metabolites of naringin, citric acid, and limonin were screened out, and the abundance of these components was relatively highest in the middle of the growth period. Therefore, the fruit of Citrus wilsonii Tanaka should be harvested before it turned yellow completely, which could effectively ensure the content of potential active ingredients. In the comparison of different drying methods, citric acid and naringin were considered to be representative differential components, but limonoids were relatively stable and not easily affected by drying methods. Naringin was an index component that could not only be reflected the maturity but also related to different drying methods. Considering its physical and chemical properties and its position, naringin had the potential to be a biomarker of Citrus wilsonii Tanaka.

Geographic origins play a vital role in traditional Chinese medicinal materials. Using the geo-authentic crude drug can improve the curative effect. The main producing areas of Chinese wolfberry are Ningxia, Gansu, Qinghai, and so on. The geographic origin of Chinese wolfberry can affect its texture, shape, color, smell, nutrients, etc. However, the traditional method for identifying the geographic origin of Chinese wolfberries is still based on human eyes. To efficiently identify Chinese wolfberries from different origins, this paper presents an intelligent identification method for Chinese wolfberries based on color space transformation and texture morphological features. The first step is to prepare the Chinese wolfberry samples and collect the image data. Then the images are preprocessed, and the texture and morphology features of single wolfberry images are extracted. Finally, the random forest algorithm is employed to establish a model of the geographic origin of Chinese wolfberries. The proposed method can accurately predict the origin information of a single wolfberry image and has the advantages of low cost, fast recognition speed, high recognition accuracy, and no damage to the sample.

Angelica sinensis is a medicinal plant widely used to treat multiple diseases in Asia and Europe, which contains numerous active components with therapeutic value. The interaction between root and rhizosphere microorganisms is crucial for the growth and quality formation of medicinal plants. But the micro-plant-metabolite regulation patterns for A. sinensis remain largely undetermined. Here, we collected roots and rhizosphere soils from A. sinensis in seedling stage (M) and picking stage (G), respectively cultivated for one year and two years, generated metabolite for roots, microbiota data for rhizospheres, and conducted a comprehensive analysis. Changes in metabolic and microbial communities of A.sinensis over growth were distinct. The composition of rhizosphere microbes in G was dominated by proteobacteria, which had a strong correlation with the synthesis of organic acids, while in M was dominated by Actinobacteria, which had a strong correlation with the synthesis of phthalide and other organoheterocyclic compounds, flavonoids, amines, and fatty acid. Additionally, co-occurrence network analysis identified that Arthrobacter was found to be strongly correlated with the accumulation of senkyunolide A and n-butylidenephthalide. JGI 0001001.H03 was found to be strongly correlated with the accumulation of chlorogenic acid. Based on rhizosphere microorganisms, this study investigated the correlation between root metabolism and rhizosphere microbiota of A. sinensis at different growth stages in traditional geoherb region, which could provide references for exploring the quality formation mechanism of A. sinensis in the future.