外文摘要：This research aims to accurately forecast the freshness indicators (TVB-N) of skinned and skinless grass carp fillets by integrating near-infrared spectroscopy (NIR) with machine learning algorithms. By comparing the predictive accuracy of machine learning models for the two types of grass carp fillets, the most effective modeling method is identified. Methodologically, the study first applies orthogonal signal correction (OSC) and the first derivative among other algorithms for spectral pre-processing. Subsequently, competitive adaptive reweighted sampling (CARS), moving window partial least squares (MWPLS), and random frog (RF) are used for the selection of variables. Lastly, partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural networks (BPNN), and particle swarm optimization-enhanced BP neural networks (PSO-BP) are employed to quantitatively analyze the NIR data. The most relevant results reveal that the (OSC+D1)-CARSPSO-BP model exhibits superior predictive capabilities. Especially when applied to skin-on fish fillets (R2P =0.988, RMSEP=0.092), this model surpasses that for skinless fish fillet data (R2P =0.987, RMSEP=0.096). Therefore, combining near-infrared with machine learning to predict the freshness (TVB-N) of grass carp fillets based on skin-on samples is a more effective non-destructive testing method.
外文关键词：machine learning;Grass carp fillets;Near -Infrared spectroscopy;Rapid non-destructive detection;Total volatile basic nitrogen;Orthogonal signal correction (OSC);Particle swarm optimization algorithm;optimized BP neural network (PSO-BP)
作者：Zhang, Qinyu;Hu, Zhigang;Xu, Zijian;Zhang, Peilin;Jiang, Yajun;Fu, Dandan;Chen, Yan
作者单位：Wuhan Polytech Univ;Hubei Prov Cereal & Oil Machinery Engn Technol Res;Hubei Prov Aquat Prod Proc Equipment Engn Technol
期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS
期刊影响因子：0.0
出版年份：2024
出版刊次：126
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台