应用近红外漫反射光谱对猪肉肉糜进行定性定量检测研究

利用傅里叶变换近红外漫反射光谱结合不同数学建模算法对不同部位取样的猪肉肉糜进行定性判别建模,并建立猪肉肉糜品质指标脂肪、蛋白质和水分含量的定量检测模型。结果表明:不同部位取样猪肉肉糜判别分析PLSDA模型性能良好,最优模型校正集判别正确率为100%,预测集判别正确率为96%;比较两种方法结合,不同光谱预处理建立各品质指标的定量模型,LS-SVM模型性能优于PLSR模型,脂肪和水分含量最佳预测模型校正及预测相关系数r均高于0.9,蛋白质含量最优模型校正及预测相关系数r,RMSEC,RMSEP和RMSECV分别为0.722,0.593,1.595,1.550和1.888,模型精度需进一步提高。研究表明利用傅里叶变换近红外漫反射光谱快速判别不同部位猪肉肉糜的方法是可行的,脂肪和水分含量定量分析模型从预测精度、稳定性及适应性考虑均具一定的通用性,具有良好的市场应用前景。

Qualitative and quantitative detection of minced pork quality by near infrared reflectance spectroscopy

The present study is concerning qualitative and quantitative detection of minced pork quality based on FT-near infrared (FT-NIR) spectroscopy and achieving the rapid approach to detecting the minced pork quality. Firstly, FT-NIR spectroscopy combined with partial least squares (PLS) and least squares-support vector machine (LS-SVM) was used for minced pork quality prediction including discrimination of the different muscle type of pig and quantitative detection of the fat, protein and moisture content of pork. The result indicated that 100% recognition ratio for calibration and 96% recognition ratio for validation were achieved by PLSDA for 4 different muscle types of pig. These two methods for chemical composition detection both have good performances in predicting fat and moisture content, the correlation coefficient for calibration and validation was all more than 0.9, but the models for protein content prediction were of less well performances, the correlation coefficients for calibration and validation, RMSEC, RMSEP and RMSECV respectively were 0.722, 0.593, 1.595, 1.550 and 1.888, respectively. The LS-SVM method is more accurate in predicting each quality index than the PLSR method. The result shows that the prediction models for fat and moisture content based on LS-SVM have a better performance with high precision, good stability and adaptability and can be used to predict the fat and moisture content of minced pork rapidly, and provide a fast approach to discrimination of the different muscle type of pig.