肌红蛋白
偏最小二乘回归
高光谱成像
最小二乘支持向量机
数学
模式识别(心理学)
计算机科学
化学
人工智能
支持向量机
统计
有机化学
作者
Lijuan Cheng,Guishan Liu,Junfeng He,Guoling Wan,Chao Ma,Jingjing Ban,Limin Ma
出处
期刊:Meat Science
[Elsevier]
日期:2020-09-01
卷期号:167: 107988-107988
被引量:34
标识
DOI:10.1016/j.meatsci.2019.107988
摘要
This study aimed to develop simplified models for rapid and nondestructive monitoring myoglobin contents (DeoMb, MbO2 and MetMb) during refrigerated storage of Tan sheep based on a hyperspectral imaging (HSI) system in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were applied to correlate the spectral data with the reference values of myoglobin contents measured by a traditional method. In order to simplify the LSSVM models, competitive adaptive reweighted sampling (CARS) and Interval variable iterative space shrinkage approach (iVISSA) were used to select key wavelengths. The new CARS-LSSVM models of DeoMb and MbO2 yielded good results, with R2p of 0.810 and 0.914, RMSEP of 1.127 and 2.598, respectively. The best model of MetMb was new iVISSA-CARS-LSSVM, with an R2p of 0.915 and RMSEP of 2.777. The overall results from this study indicated that it was feasible to predict myoglobin contents in Tan sheep using HSI.
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