基于NIR高光谱技术检测滩羊冷鲜肉颜色

Color Detection of Chilled Meat of Tan Sheep Based on NIR Hyperspectral Technique

试验评估了利用近红外高光谱成像系统(900~1 700 nm)快速预测滩羊冷鲜肉颜色的可行性。采集具有代表性的120个滩羊外脊肌肉样品的高光谱数据,并测量其颜色参数(L*,a*和b*)。对光谱数据进行标准正态变量变换(SNV)预处理,并用正自适应加权算法(CARS)对光谱数据进行降维处理,最后应用偏最小二乘回归对光谱数据建模。结果表明,建立的偏最小二乘回归模型能很好地预测冷鲜滩羊肉的颜色参数,利用高光谱技术预测颜色是可行的。

Near infrared（NIR）（900-1 700 nm） hyperspectral imaging technology can be used to do nondestructive testing on cold meat. A total number of 120 representative samples of Tan sheep are collected as the test material in the study; Their hyperspectral imaging information is obtained, and the color parameters（L^＊, a^＊ and b^＊） of these samples are also determined. Then, pretreat with the spectral data, and select characteristic wavelengths by application adaptive weighted algorithm（CARS）.Finally, the spectral data is modeled by partial least squares regression. The results show that the proposed partial least squares regression model can predict the color parameters of cold fresh Tan sheep, and that using hyperspectral techniques to predict color is feasible.