利用高光谱成像技术预测经过两次冻融循环的牛肉的冰点和水分分布。
Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging.
机构信息
School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
出版信息
Food Chem. 2024 Oct 30;456:139868. doi: 10.1016/j.foodchem.2024.139868. Epub 2024 May 27.
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 (P) and a positive correlation with the proportion of immobilized water (P). 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 (R) 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 P with R of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P with R 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.
冰点（FP）是超冷鲜肉的一个重要质量指标。目前，探讨了高光谱成像（HSI）在预测多次冻融（F-T）循环影响下牛肉 FP 的潜力。相关分析表明，FP 与结合水比例（P）呈负相关，与固定水比例（P）呈正相关。此外，通过主成分分析（PCA）选择最佳波长。基于最佳波长，成功地建立了主成分回归（PCR）和偏最小二乘回归（PLSR）模型，用于预测 FP，预测（R）分别为 0.76 和 0.76，预测均方根误差（RMSEP）分别为 0.12 和 0.12。此外，建立了基于全波长的 PLSR 来预测 P，R 为 0.80，RMSEP 为 0.67，以及基于最佳波长的 PLSR 来预测 P，R 为 0.87，RMSEP 为 0.66。结果表明，高光谱技术有望成为一种无损预测肉类 FP 和水分分布的方法。
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