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

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.