The surface structure of beef during refrigerated storage undergoes dynamic changes driven by moisture migration and tissue degradation;however, current evaluation approaches largely rely on destructive physicochemical measurements, with limited systematic quantification of surface texture information.This study investigated the evolution of surface texture characteristics of beef Longissimus thoracis (LT) during early refrigerated storage using a machine vision approach combined with gray-level co-occurrence Matrix analysis, based on 30 independent beef samples collected at 10 storage time points (0-30 h), with three samples analyzed at each time point.Standardized image processing procedures were applied to obtain surface images of beef samples stored at 4 ℃ for different durations.After image segmentation to extract effective muscle regions, multiple GLCM texture features, including contrast, dissimilarity, homogeneity, energy, angular second moment, and entropy, were calculated under multi-distance (1, 3, and 5 pixels) and directions (0°, 45°, 90° and 135°).The results showed that contrast increased significantly from 37.82 to 54.31 and entropy from 6.65 to 7.52 (P<0.05), whereas homogeneity decreased from 0.486 to 0.434, energy from 0.314 to 0.245, and angular second moment from 0.132 to 0.050 (P<0.05), indicating a transition of beef surface texture from uniform and smooth to heterogeneous and complex.Principal component analysis revealed that the first principal component explained 59.76% of the total variance and displayed a clear time-dependent shift, effectively discriminating samples at different storage stages.These findings demonstrate that GLCM-based texture features are sensitive to refrigerated storage-induced surface structural changes in beef and provide a non-destructive and quantitative approach for evaluating beef quality evolution during cold storage.
CHEN Yixuan
,
YOU Lu
,
HE Zhiyong
,
CHEN Qiuming
,
WANG Zhaojun
,
ZENG Maomao
,
CHEN Jie
. Surface texture evolution of beef during early refrigerated storage based on gray-level co-occurrence matrix analysis[J]. Food and Fermentation Industries, 2026
, 52(12)
: 125
-132
.
DOI: 10.13995/j.cnki.11-1802/ts.045655
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