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Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging.

Authors :
Wei, Qingyi
Pan, Chaoying
Pu, Hongbin
Sun, Da-Wen
Shen, Xiaolei
Wang, Zhe
Source :
Food Chemistry. Oct2024, Vol. 456, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

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 21) and a positive correlation with the proportion of immobilized water (P 22). 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 P 2) 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 21 with R P 2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P 22 with R P 2 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. • Effect of freeze-thawing on the freezing point were studied. • The freezing point of beef was successfully predicted by HSI combining PLSR. • HSI successfully revealed the proportion of bound water and immobilized water. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03088146
Volume :
456
Database :
Academic Search Index
Journal :
Food Chemistry
Publication Type :
Academic Journal
Accession number :
178503057
Full Text :
https://doi.org/10.1016/j.foodchem.2024.139868