高光谱成像
主成分分析
偏最小二乘回归
主成分回归
相关系数
均方误差
水分
含水量
数学
化学
生物系统
统计
遥感
生物
岩土工程
有机化学
工程类
地质学
作者
Qingyi Wei,Chaoying Pan,Hongbin Pu,Da‐Wen Sun,Xiaobin Tang,Zhe Wang
标识
DOI:10.1016/j.foodchem.2024.139868
摘要
The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P21) and a positive correlation with the proportion of immobilized water (P22). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (RP2) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P21 with RP2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P22 with RP2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method.
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