RESUMO
BACKGROUND: Proteins and anionic octenyl succinic anhydride (OSA)-modified starch (OSA-starch) are common ingredients in food systems. The interactions between OSA-starch and protein are found to alter the structural and functional properties of the protein-OSA-starch complexes. In this regard, the close understanding of the relationship among the molecular interactions between whey protein isolate (WPI) and OSA-high amylose corn starch (HAS), structure changes and rheological, digestibility and release properties of WPI-OSA-HAS was investigated. RESULTS: The molecular interactions of WPI-OSA-HAS were significant for increasing the surface rough, solubility, storage modulus and loss modulus, but decreasing the R1047/1022 values. For the nutritional evaluation, the anti-digestibility of WPI-OSA-HAS was enhanced with increased resistant starch + slowly digestible starch contents and decreased equilibrium hydrolysis percentage and kinetic constant. During the digestion, part of the starch granule, OSA groups and WPI were lost, but the loss was lower than for OSA-HAS. Furthermore, the results of curcumin-loaded WPI-OSA-HAS in simulated gastrointestinal fluids demonstrated that curcumin could be gradually released to simulate colonic fluid. Notably, the interaction between WPI and OSA-HAS depended on the WPI concentration with the stronger molecular interactions obtained at 35% concentration. CONCLUSION: These results provided important information concerning how to adjust the rheological, anti-digestibility and release properties of WPI-OSA-HAS through altering the electrostatic interactions and hydrophobic interactions of WPI-OSA-HAS. © 2024 Society of Chemical Industry.
RESUMO
In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = -36 > FZA = -55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.