Guangmei Deng , Honggao Liu , Jieqing Li , Yuanzhong Wang
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引用次数: 0
Abstract
Boletus bainiugan has a unique flavor profile, its quality is correlated with metabolites. Herein, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) is utilized to characterize the free amino acid and organic acid of Boletus bainiugan at different drying temperatures. Attenuated total internal reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is employed to identify Boletus bainiugan with various treatment and to predicted compounds. The metabolome includes 72 amino acids and 64 organic acids, wherein, 11 important taste components are analyzed the changes with drying temperatures. The residual convolutional neural network (ResNet) model achieves 100 % accuracy for Boletus bainiugan with distinct treatment. The partial least squares regression (PLSR) model accurately predicted the contents of 11 compounds with an optimal R2P of 0.975 and a best residual predictive deviation (RPD) of 4.404. The ATR-FTIR spectroscopy coupled with metabolomics can be used as a good tool to estimate the taste enhancers of Boletus bainiugan.
期刊介绍:
Food Chemistry: X, one of three Open Access companion journals to Food Chemistry, follows the same aims, scope, and peer-review process. It focuses on papers advancing food and biochemistry or analytical methods, prioritizing research novelty. Manuscript evaluation considers novelty, scientific rigor, field advancement, and reader interest. Excluded are studies on food molecular sciences or disease cure/prevention. Topics include food component chemistry, bioactives, processing effects, additives, contaminants, and analytical methods. The journal welcome Analytical Papers addressing food microbiology, sensory aspects, and more, emphasizing new methods with robust validation and applicability to diverse foods or regions.