Rapid prediction method of α-Glycosidase inhibitory activity of Coreopsis tinctoria extract from different habitats by near infrared spectroscopy

A review on the in vitro and in vivo screening of α-glucosidase inhibitors

As a global metabolic disease, the control and treatment of diabetes have always been the focus of medical research. α-Glucosidase is a key enzyme in regulating blood glucose levels and has important applications in the treatment of diabetes. This review aims to explore the enzyme activity of α-glucosidase and its inhibition mechanism and evaluate the efficacy and limitations of existing inhibitor screening methods. First, the chemical structure, biological activity, and influencing factors of α-glucosidase on diabetes are discussed in detail. Then, the various methods that have been used to screen α-glucosidase inhibitors in recent years are reviewed, including in vivo animal experiments, in vitro experiments, and virtual molecular docking. The experimental principles, advantages, and limitations of each method and their application in discovering new inhibitors are also discussed. Finally, this review emphasizes the importance of developing efficient and safe α-glucosidase inhibitors, summarizes the advantages and disadvantages of various screening models, and proposes future research directions. This review comprehensively examines the enzyme activity of α-glucosidase and the screening methods for α-glucosidase inhibitors, provides an important perspective in the field of diabetes drug discovery and development, and provides a reference for future research.

Rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy

Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV-Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample's enzyme activity data were linked with each sample's original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong relationship between NIRs and CRL/ZIF-8 enzyme activity. As a result, the established model could be implemented to quantify the enzyme activity of CRL/ZIF-8 quickly by including more variations of natural samples. The prediction method is simple, rapid, and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in enzymology and spectroscopy.

Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation

The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system.

Assessment of the Bioaccessibility of Carotenoids in Goji Berry (Lycium barbarum L.) in Three Forms: In Vitro Digestion Model and Metabolomics Approach

Goji berry (Lycium barbarum L., LBL) is a good source of carotenoids, while the bioaccessibility of various types of LBL carotenoids has not been explored. In the study, eight carotenoids, three carotenoid esters and two carotenoid glycosylated derivatives were identified by a non−targeted metabolomics approach. The dried LBL (DRI), LBL in water (WAT), and LBL in “Baijiu” (WIN) were used to recreate the three regularly chosen types of utilization, and the in vitro digestion model showed that the bioaccessibility of the carotenoids increased significantly from the oral to the gastric and intestinal phase (p < 0.05). The bioaccessibility of LBL carotenoids was the most elevated for DRI (at 28.2%), followed by WIN and WAT (at 24.9% and 20.3%, respectively). Among the three carotenoids, zeaxanthin dipalmitate showed the highest bioaccessibility (51.8−57.1%), followed by β−carotene (51.1−55.6%) and zeaxanthin (45.2−56.3%). However, the zeaxanthin from DRI exhibited significantly higher bioaccessibility (up to 58.3%) than WAT and WIN in both the gastric and intestinal phases (p < 0.05). Results of antioxidant activity tests based on DPPH, FRAP, and ABTS showed that the addition of lipids improved the bioaccessibility of the carotenoids. (p < 0.05).

Characterization of selected Chinese soybean paste based on flavor profiles using HS-SPME-GC/MS, E-nose and E-tongue combined with chemometrics

Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic nose (E-nose) and electronic tongue (E-tongue) was applied for flavor characterization of traditional Chinese fermented soybean paste. Considering geographical distribution and market representation, twelve kinds of samples were selected to investigate the feasibility. A total of 57 volatile organic compounds (VOCs) were identified, of which 8 volatiles were found in all samples. Linear discrimination analysis (LDA) of fusion data exhibited a high discriminant accuracy of 97.22%. Compared with partial least squares regression (PLSR), support vector machine regression (SVR) analysis exhibited a more satisfying performance on predicting the content of esters, total acids, reducing sugar, salinity and amino acid nitrogen, of which correlation coefficients for prediction (Rp) were about 0.803, 0.949, 0.960, 0.896, 0.923 respectively. This study suggests that intelligent sensing technologies combined with chemometrics can be a promising tool for flavor characterization of fermented soybean paste or other food matrixes.