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https://www.selleckchem.com/TGF-beta.html This study aimed to assess the feasibility of identifying multiple chemical constituents in matcha using visible-near infrared hyperspectral imaging (VNIR-HSI) technology. Regions of interest (ROIs) were first defined in order to calculate the representative mean spectrum of each sample. Subsequently, the standard normal variate (SNV) method was applied to correct the characteristic spectra. Competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) were used to optimize the models. They were built based on partial least squares (PLS), creating two models referred to as CARS-PLS and BOSS-PLS. The BOSS-PLS models achieved best predictive accuracy, with coefficients of determination predicted to be 0.8077 for caffeine, 0.7098 for tea polyphenols (TPs), 0.7942 for free amino acids (FAAs), 0.8314 for the ratio of TPs to FAAs, and 0.8473 for chlorophyll. These findings highlight the potential of VNIR-HSI technology as a rapid and nondestructive alternative for simultaneous quantification of chemical constituents in matcha.In this study, the ochratoxin A (OTA) interaction with some metal cations exhibits a quenching effect of the Cu2+, Co2+, and Ni2+ in the OTA intrinsic fluorescence intensity. The property of the OTA fluorescence change in complexation with the Cu2+ has been applied in developing a label-free and selective fluorimetric sensor for fast detection of the OTA trace amounts in wheat flour samples. The achieved color difference map (CDM) based on the spectral profile provides the technical support for the rapid visual sensing of OTA in the wheat flour samples. The feasibility of the hybrid fluorimetric sensor and visual OTA detection in wheat flour samples has been confirmed with the high-performance liquid chromatography (HPLC) method as a standard method. The calculated LOD (0.4 ng g-1) and LOQ (1.2 ng g-1) values of the proposed method are much lower than the maximum permissible limit of

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