Youngcline4871

Weighted multiscale support vector regression combined with ultraviolet-visible (UV-Vis) spectra for quantitative analysis of edible blend oil is proposed. In the approach, UV-Vis spectra of the training set are decomposed into a certain number of intrinsic mode functions (IMFs) and a residue by empirical mode decomposition (EMD) at first. Then support vector regression (SVR) sub-models are built on each IMF and residue. For prediction set, the spectra are decomposed as done on the training set and the final predictions are obtained by integrating SVR sub-model predictions by weighted average. The weight of the sub-model is the reciprocal of the fourth power of the root mean square error of cross-validation (RMSECV). For predicting peanut oil in binary blend oil and sesame oil in ternary blend oil, the proposed method has superiority in root mean square error of prediction (RMSEP) and correlation coefficient (R) compared with SVR and partial least squares (PLS).The inhibition effect of urea on ovalbumin (OVA) glycation was investigated, and the mechanism was evaluated through the changes in protein structure as well as glycation sites and average degree of substitution per peptide molecule (DSP) by conventional spectrometry and liquid chromatography-high resolution mass spectrometry (LC-HRMS). Ivacaftor supplier A urea concentration of 3 M was chosen as the optimum condition. Ultraviolet and fluorescence spectra suggested that both glycation and urea treatment could unfold the OVA, but urea inhibited the glycation-induced protein unfolding. Circular dichroism spectra showed that urea treatment could increase the β-sheet content and reduce the α-helix content of OVA. LC-HRMS indicated that the number of glycation sites was reduced from 15 to 3, and DSP values decreased with urea treatment. In conclusion, urea could significantly inhibit the OVA-glucose glycation, and the sites competition as well as structure unfolding inhibition resulted from urea could be the main factors.Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.Caryota urens L. has long been valued as a traditional food, the edible fruits being eaten raw and the inflorescences commonly used on sweet sap and flour production. In the current work, the phenolic profile of methanol extracts obtained from the inflorescences and fruits was unveiled for the first time, nine caffeic acid derivatives being identified and quantified. Since kitul products have been reported for their antidiabetic properties, extracts radical scavenging activity and α-amylase, α-glucosidase and aldose reductase inhibitory activity were assessed. The inflorescences' extract was particularly active against yeast α-glucosidase (IC50 = 1.53 μg/mL), acting through a non-competitive inhibitory mechanism. This activity was also observed in enzyme-enriched homogenates obtained from human Caco-2 cells (IC50 = 64.75 µg/mL). Additionally, the extract obtained from the inflorescences showed no cytotoxicity on HepG2, AGS and Caco-2 cell lines. Our data suggest that C. urens inflorescences can support the development of new functional foods with α-glucosidase inhibitory activity.Vancomycin and norvancomycin are glycopeptide antibiotics for gram-positive bacteria infection, but indiscriminately used in aquaculture. In this study, a QuEChERS (quick, easy, cheap, effective, rugged, and safe)/96-well solid-phase extraction (SPE) plate method was used to extract vancomycin and norvancomycin in fish meat samples, and the drugs were further analyzed by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The parameters, such as the sorbent of cation exchange resin, the proportion of acetonitrile (15%) in extractant, the mobile phase of water (0.1% formic acid)/acetonitrile, were optimized. The method was validated in terms of linearity (0.9990-0.9994), LOD (0.51 μg·kg-1), LOQ (1.73 μg·kg-1), intra-dayprecision ( less then 5.19%), inter-day precision ( less then 6.30%), and recovery (86.7-98.6%). Finally, the method was successfully applied to contaminated and randomly collected samples. The results indicated that the proposed method meet the daily monitoring requirements for vancomycin and norvancomycin.The purpose of this study was to construct a fusion model using probe-based and non-probe-based fluorescence spectroscopy and low-field nuclear magnetic resonance spectroscopy (Low-field NMR) for rapid quality evaluation of frying oil. Iron tetraphenylporphyrin (FeTPP) was selected as the probe to detect polar compounds in frying oil samples. Non-probe-based fluorescence spectroscopy and low-field NMR were employed to determine the fluorescence changes of antioxidants, triglycerides and fatty acids in frying oil samples. Compared to the models constructed using non-fusion data, the fusion-data models achieved a better regression prediction performance and correlation coefficients with values of 0.9837 and 0.9823 for the training and test sets, respectively. This study suggested that the multiple data fusion method was capable to construct better regression models to rapidly evaluate the quality of frying oil and other food with high oil contents.