基于粒子群聚类的牛肉含水率光谱检测技术

采用市场当日上架的生鲜牛肉外侧最长肌制作样本,在波长900~2300 nm内进行光谱检测和分析。利用基于粒子群算法(PSO)的聚类分析方法,对光谱信息进行优化以减少计算量,提高回归模型精度。该算法以经过多元散射校正(MSC)、变量标准化(SNV)等方法预处理后的光谱信息作为目标矩阵,以波长为目标进行聚类,根据聚类结果对不同波段进行重新组合,并建立偏最小二乘回归(PLSR)模型。结果表明,利用PSO聚类分析方法在900~1 400 nm波段内获得的生鲜牛肉含水率预测模型最优,Rc=0.920 5,Rv=0.919 1。该方法能够有效减少光谱的数量,提升回归模型的预测结果。

Water Content Detecting of Beef Based on Spectral Analysis and Clustering Analysis of PSO Algorithm

Total water content is an important quality attribute for consumer satisfaction, and a more accurate pre-detecting method is necessary. The conventional method of partial least squares regression (PLSR) has been widely used in meat water content forecasting. In this study, the cluster analysis of particle swarm optimization algorithm was carried out and calibrated as one of the optimization methods of PLSR with the goal of reducing computation complexity and enhancing the prediction precision. Based on the novel method above, a predicting model of beef water content was developed in wavelength range of 900~2 300 nm, and the best predicting result with Rc=0.920 5 and Rv=0.919 1 was obtained in wavebands of 900~1 400 nm. The samples used in the experiment were beef longissimus collected from the supermarket in that day, and the water contents of samples were detected according to the national standard. Spectra of samples were acquired in reflectance spectral detection system and pre-treated procedure was carried out by means of multiplication scatter correlation method before model construction.