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.

Pear fruits have been reported to contain abundant bioactive compounds and exhibit antidiabetic activity. In this study, Pingguoli pear (Pyrus pyrifolia cv.‘Pingguoli’) fermentation broth was sequentially extracted by five solvents with increasing polarity (petroleum ether, chloroform, ethyl acetate, n-butanol, and water) to evaluate its antioxidant and hypothermic activities, and then the main compounds of the fraction with the highest activity were assessed, which might be responsible for such activities. The results showed that the ethyl acetate fraction (EAF) exhibited the highest antioxidant activity according to DPPH (IC₅₀ = 0.238 mg/mL), ABTS (IC₅₀ = 0.293 mg/mL), and FRAP (IC₅₀ = 0.193 mg/mL) assays. The in vitro hypoglycemic activity assay showed that EAF exhibited the strongest inhibitory effect, with IC₅₀ values of 0.34 and 0.95 mg/mL for α-amylase and α-glucosidase, respectively. The glucose consumption in HepG2 cells treated with EAF was significantly increased to 252%, compare with control group. Liquid chromatography–mass spectrometry analysis implied that the main compounds, 3′-C-glucosylisoliquiritigenin, robustside D, caffeic acid, and chlorogenic acid may be potential candidates for the antioxidant and hypoglycemic activities of the EAF. This study suggested that EAF of Pingguoli pear fermentation broth could be utilized for development of potential functional food and antidiabetic agents.

Different pollutants often coexist in natural environments, making it crucial to monitor and study the ecotoxicological effects of composite pollutants in aquatic environments. Nanoplastics and heavy metals are emerging environmental pollutants that can affect the health of aquatic organisms and threaten human health via the food chain. In this study, zebrafish was employed as a model organism to explore the effects of short-term exposure to polystyrene nanoplastics (PS-NPs) and heavy metal copper ions (Cu²⁺) either individually or in combination on fish behavior. First, the single and combined toxicity of Cu²⁺ and PS-NPs to adult zebrafish was investigated to obtain the LC₅₀ values of the two pollutants at 24, 48, 72, and 96 h. Then, the effects of sub-lethal concentrations of Cu²⁺ (0.06, 0.15, and 0.3 mg/L), PS-NPs (5, 10, and 15 mg/L) and binary mixtures containing Cu²⁺ and PS-NPs (0.06 mg/L + 10 mg/L, 0.15 mg/L + 10 mg/L, and 0.3 mg/L + 10 mg/L) on the swim speed and individual distance of zebrafish within 4 h were studied. The results show that the LC₅₀ value for single exposure of zebrafish to Cu²⁺ decreased with the increase in the exposure time, while PS-NPs showed no significant acute toxicity to zebrafish when the concentration was less than 20 mg/L and the exposure time was less than 96 h. The combined exposure of zebrafish to Cu²⁺ and PS-NPs resulted in a 3.1–32.2% reduction in the LC₅₀ value at different time points compared with Cu²⁺ alone. In the behavioral study, both single and combined exposure to Cu²⁺ and PS-NPs induced hyperactivity and aggregation phenomena in the zebrafish at different levels; the duration of these two phenomena was correlated with the concentration of the pollutants. The combined exposure to Cu²⁺ and PS-NPs exacerbated the behavioral changes in zebrafish compared with exposure to Cu²⁺ alone, reducing their hyperactivity time, average swim speed and aggregation time by 30.7–41.0%, 13.6–15.4%, and 28.3–28.8%, respectively. Therefore, this study indicates that the combined short-term exposure to PS-NPs and Cu²⁺ can exacerbate the toxicity of pollutants, and also proves the feasibility for early warning of combined NPs and heavy metals pollution based on adult zebrafish behavioral indicators.