Quantitative analysis of multi-component adulteration in camellia oil by near-infrared spectroscopy combined with long short-term memory neural networks algorithm

Leave a Comment / Procesos Alimentarios / By
Bookmark (0)
Please login to bookmark Close

Publication date: December 2025

Source: Journal of Food Composition and Analysis, Volume 148, Part 2

Author(s): Jing Zhao, Ruoni Wang, Ziqi Zhang, Yue Yu, Zhongyang Ren, Yue Huang, Zhanming Li

Publication date: December 2025Source: Journal of Food Composition and Analysis, Volume 148, Part 2Author(s): Jing Zhao, Ruoni Wang, Ziqi Zhang, Yue Yu, Zhongyang Ren, Yue Huang, Zhanming Li Read More

Related posts:

Default ThumbnailColorimetric detection of metronidazole in animal-derived food using a silver-coated glass slide aptasensor Default ThumbnailMulti-level characterization and comparison of polysaccharides and small-molecule metabolites enabling the differentiation of three Codonopsis species Default ThumbnailOptimization of convenient infrared spectroscopy detection methods and infrared-Raman fusion spectroscopic detection for milk powder adulteration Default ThumbnailA customized enzyme-like FeMOF@PtNPs aptasensor for rapid and sensitive colorimetric detection of aflatoxin B1 in feed Default ThumbnailPreparation of TP-PA-coated needle trap device for determination of polychlorinated biphenyls in milk combined with gas chromatography Default ThumbnailIdentification of 37 kinds of herbs containing oligosaccharides by combining data fusion and machine learning