挥发性盐基氮（TVB-N）含量是评价肉制品新鲜度的重要指标。尝试采用遗传联合区间偏最小二乘（GA-Si-PLS）从高光谱数据之光谱信息中筛选出最优波长。再提取各波长所对应的灰度图像的纹理特征，纹理特征变量经主成分优化后，作为输入层，运用反向传播神经网络（BP-ANN）构建鸡肉的TVB-N含量的定量模型。实验表明，模型对训练集和预测集的均方根误差分别6.61和9.84，相关系数分别为0.9054和0.8030。研究表明可以利用高光谱中的图像信息对鸡肉TVB-N含量进行快速无损检测。

Total volatile basic nitrogen (TVB-N) content is an important index in evaluating the chicken′s freshness. We attempt to use synergy interval partial least square coupled with genetic algorithm to select the best wavelengths. Texture features of gray images of the corresponding wavelengths are extracted. Principal component analysis (PCA) is implemented on these feature variables from image information. We take the best principal component factor numbers as the input layer. And a prediction model of the TVB-N content is developed by the back-propagation artificial neural network (BP-ANN). The results of the model are achieved as root-mean-square error (RMSE) of 6.61 and 9.84, and correlation coefficient r of 0.9054 and 0.8030 in training and prediction sets, respectively. This work demonstrates that hyperspectral imaging technique is a valid means for quick and nondestructive detection of TVB-N content in chicken.