The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky–Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

In the presented study, natural rice containing high resistant starch content was used as a raw material to produce rice resistant starch (RRS) through enzymatic hydrolysis with heat-stable α-amylase and glucoamylase. The chemical composition, structural characteristics and in vitro glycemic index (GI) of RRS were evaluated. The effects of RRS at different doses on the body weight, serum biochemical levels, pathological indexes, production of short-chain fatty acids (SCFAs) in the gut and the intestinal microbial composition in T2DM mice were investigated. The results of physiochemical characterization indicated that, relative to rice flour, RRS mainly comprising resistant starch had higher crystallinity (25.85%) and a more stable structure, which contributed to its lower digestibility and decreased GI in vitro. Compared with the model control group, 1 g per kg BW and 2 g per kg BW oral gavage dosages of RRS effectively enhanced the SCFA productivity in the T2DM mouse gut, as well as alleviating T2DM symptoms, involving an increase in body weight, reduction in fasting blood glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, alanine transaminase and aspartate aminotransferase, and an increase in serum insulin and high-density lipoprotein cholesterol. Besides, 1 g per kg BW and 2 g per kg BW dosages of RRS mitigated T2DM-induced pancreas damage. Furthermore, up-regulation in the abundance of probiotics (Lactobacillus, Ruminococcus, etc.) and down-regulation in the number of harmful bacteria (Desulfovibrio, Prevotella, etc.) were observed in all RRS-treated groups. In summary, this work suggested that RRS prepared using heat-stable α-amylase and glucoamylase could be a potential functional component for amelioration of T2DM applied in the fields of food and pharmaceutics.

In this paper, rhamnolipids are investigated, for the first time, for their feasibility for inhibiting dimorphic fungi. Rhamnolipids were found to effectively inhibit a dimorphic fungus isolated from tomato plants which was identified as Mucor circinelloides according to characterizations by morphologies as well as 28S rDNA sequences. Rhamnolipids markedly reduced growth of this fungus in both the yeast-like form and the filamentous form. Such an inhibitive effect was similarly obtained with Verticillium dahliae, a representative member of dimorphic fungi, confirming the effectiveness of rhamnolipids in the two growth forms of dimorphic fungi. Interestingly, rhamnolipids showed a greater inhibitive function in the case of the pathogenic growth mode of dimorphic fungi, such as the mycelium growth for M. circinelloides and the yeast-like growth for V. dahliae, than their non-pathogenic modes. The use of rhamnolipids might greatly reduce the frequently-reported drugresistance to the common anti-fungal agents by deterring the possible switch between the two modes of dimorphic fungi. Overall, rhamnolipids as environment-friendly biocontrol agents have a potential use in protecting plants from dimorphic fungi infections, and could also offer guidance toward future research into controlling dimorphic disease infection in humans.