基于多光谱图像分割算法的冷冻猪肉色泽无损表征技术

Non-destructive Characterization of Frozen Pork Color Based on Multispectral ImageSegmentation Algorithm

色泽是冻藏肉质评价第一感官指标,构建原位评价冻肉色泽方法对稳定消费市场十分关键。本文提出一种以图像阈值分割算法为核心的冷冻肉色实时检测方法,即:利用典型判别分析和全局阈值分割两种算法对120个冻肉多光谱图像进行分割处理,并提取两类像素光谱,随后采用连续投影、偏最小二乘等算法建立光谱与肉色(L*、a*和b*)之间的关联性模型。结果显示:全局阈值分割算法结合连续投影和偏最小二乘3种计量学方法建立最佳L*、a*和b*值的预测模型,其决定系数(R2)分别为0.9563,0.9593和0.9570,相应的剩余预测偏差(RPD)分别为4.7745,4.6265和4.2126,表现出较好的精度与鲁棒性。研究结果为冷冻肉色无损、快速检测的工业实践提供了理论基础。

Color is the first perceptual indicator for evaluating frozen meat quality,and establishing an in-situ method for evaluating the color of frozen pork will be critical to stabilize the consumer market.This work proposed a real-time method for detecting the color of frozen pork based on the image threshold segmentation algorithms including canonical discriminant analysis(CDA)and global threshold segmentation(GTS).A total of 120 multispectral images of frozen pork were segmented.These segmented images were then converted into corresponding spectra,which were applied to establish calibration models for predicting the color characteristics(L*,a*and b*)of frozen pork by using various algorithms such as successive projections algorithm(SPA)and partial least squares regression(PLSR).The results showed that three optimal models for predicting L*,a*and b*values were built by combining the GTS with SPA and PLSR,and their corresponding determination coefficients(R2)and residual prediction deviations(RPD)were 0.9565 and 4.7745,0.9593 and 4.6265,and 0.9570 and 4.2126,respectively.Results of high accuracy and robustness would provide a theoretical basis for non-destructive and rapid detection of the color of frozen pork in industrial practice.