高光谱成像
预处理器
特征(语言学)
残余物
相关系数
均方误差
模式识别(心理学)
人工智能
数学
计算机科学
统计
算法
语言学
哲学
作者
Jiewen Zuo,Yankun Peng,Yongyu Li,Wenlong Zou,Yahui Chen,Daoyu Huo,Kuanglin Chao
出处
期刊:Meat Science
[Elsevier]
日期:2023-08-01
卷期号:202: 109204-109204
被引量:1
标识
DOI:10.1016/j.meatsci.2023.109204
摘要
Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.
科研通智能强力驱动
Strongly Powered by AbleSci AI