肉鸡产业统计监测数据优化研究

木质化鸡胸肉（wooden breast，WB）制约肉鸡行业发展，传统触诊检测方法耗时且效率低，为提升高光谱成像（hyperspectral imaging，HSI）技术检测鸡胸肉木质化程度的效果，本论文以白羽鸡鸡胸肉为研究对象，将其划分4个木质化等级，采集其在400~1000和1000~2000 nm内的HSI信息，通过不同光谱预处理算法和特征波段筛选方法，建立基于全波段、特征波段和HSI数据融合的偏最小二乘判别分析（Partial least squares-discriminant analysis，PLS-DA）模型和支持向量机（Support vector machine，SVM）模型。结果显示，SVM模型比PLS-DA模型更适于判别鸡胸肉木质化程度，基于1000~2000 nm内全波段和特征波段的最佳模型预测集总体正确率均高于400~1000 nm内的模型，基于两波段HSI数据融合的木质化判别模型优于基于单一波段（包括全波段和特征波段）的模型，最佳模型预测集总体正确率为96.7%，能较好地区分出4个木质化等级，且对4个等级的判别准确率均可达90%以上。研究结果为HSI实现木质化鸡胸肉的准确无损检测提供技术支持。

Study on the optimization of statistical monitoring data for the broiler industry

Wooden breast barriers the development of broiler industry, and traditional detection methods are time-consuming and inefficient. To investigate the feasibility of the hyperspectral imaging (HSI) technique for the detection of wooden breasts, four different grades of white feather chicken breast were selected and their HSI information of 400~1000 and 1000~2000 nm was collected. After spectral preprocessing and spectral variable selection, partial least squares discriminant (PLS-DA) models and support vector machine (SVM) models were developed based on full wavelength and characteristic spectral variables, as well as their fused HSI data. The results showed that SVM models showed better results than PLS-DA models to discriminate woody grades of chicken breasts. The overall discrimination rates based on the full HSI bands and selected spectral variables in 1000~2000 nm were higher than those of models in 400~1000 nm. Besides, the discrimination models based on fused HSI data of HSI bands and selected spectral variables provided the best results, with the overall discrimination rate of 96.7% for four different woody grades, and the accuracy of the four grades could reach more than 90%. The research results provided technical support for HSI to achieve rapid and non-destructive detection of wooden chicken breasts.