Freeze-thaw accelerated the colour deterioration of beef with the increase of colour b* and the decrease of colour a* values (P < 0.05). The maximum exudate loss reached 22 % after the seventh freeze–thaw. A strong correlation between the transversal relaxation time T₂₁ and thawing loss may mean that T₂₁ water contributed to the exudate loss during freeze–thaw. Afterwards, competitive adaptive reweighted sampling-partial least square (CARS-PLS) has the best prediction in thawing loss of frozen/thawed beef with correlation coefficients of prediction (R_p) of 0.971, and root mean square error of prediction (RMSEP) of 1.436. Besides, Uninformative variable elimination-partial least squares (UVE-PLS) showed good prediction effects on colour values (R_p = 0.932 – 0.994) and water content (R_p = 0.928, RMSEP = 0.582) of frozen/thawed beef. Therefore, this work demonstrated that Raman spectroscopy coupled with multivariate calibration has a good ability for non-destructive prediction in colour and water-related properties of frozen/thawed beef.

冻融加速牛肉变色，颜色b*值增加，颜色a*值降低（P < 0.05）。第七次冻融后最大渗出液损失达到 22%。横向弛豫时间 T ₂₁与解冻损失之间的强相关性可能意味着 T ₂₁水有助于冻融过程中的渗出物损失。之后，竞争性自适应加权抽样-偏最小二乘法 (CARS-PLS) 对冷冻/解冻牛肉的解冻损失的预测最好，预测相关系数 (R p ) 为 0.971，预测均方根误差 (RMSEP ₎为1.436。此外，无信息变量消除-偏最小二乘法（UVE-PLS）对颜色值（R _p = 0.932 – 0.994) 和冷冻/解冻牛肉的含水量 (R _p  = 0.928, RMSEP = 0.582)。因此，这项工作表明，拉曼光谱结合多变量校准对冷冻/解冻牛肉的颜色和水相关特性具有良好的无损预测能力。