数字农科院2.0

Novel decision-level fusion strategies combined with hyperspectral imaging for the detection of soybean protein content

文献类型： 外文期刊

作者： Jing Zhang;Zhen Guo;Chengye Ma;Chengqian Jin;Liangliang Yang;Dongliang Zhang;Xiang Yin;Juan Du;Peng Fu

作者机构：

关键词： Data fusion;Hyperspectral imaging;Protein content;Short-wave infrared;Visible-near infrared

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年 469 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Soybeans are used for human consumption or animal feed due to their abundant protein content. In this study, visible-near infrared (VNIR) hyperspectral imaging (HSI) and short-wave infrared HSI combined with three-levels data fusion methods were employed to detect the protein content of soybean seeds, including measurement-level fusion, feature-level fusion, and decision-level fusion. Additionally, three novel decision-level fusion methods were proposed, including binary linear regression, feature-based multiple linear regression (MLR), and model-based MLR. An IVISSA-SPA-MLR model based on decision-level fusion demonstrated the best predictive performance, with a residual prediction deviation value of 3.6796. The results suggested that the IVISSA-SPA-MLR achieved accurate predictions, effectively enabling precise detection of soybean seeds protein content. Decision-level fusion proved to be an accurate and efficient quantitative detection technique, enhancing the predictive performance of regression models. This research provides a novel method for protein content detection in food products and introduces new strategies for data fusion.

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