近红外结合Si-ELM检测食醋品质指标

摘要

To address the performance of NIR predicted model in measurement of soluble salt-free solid content (SSFSC) in vinegar, synergy interval partial least square( Si-PLS) was employed to select efficient spectral regions, and then extreme learning machine(ELM) algorithm was employed to develop the non-linear regression model. The relevant parameters of the model were optimized by cross-validation. The performance of the model was evaluated according to the correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Experimental results showed that the model based on Si-PLS and ELM(I. E. Si-ELM model) was superior to others, and the optimum results were achieved as follows: Rt = 0. 973 9, RMSEP = 1. 232 g/100 mL. The work demonstrated that NIR spectroscopy can be applied in rapid measurement of SSFSC in vinegar, and Si-PLS and ELM algorithms has the potentials in increasing the performance of NIR prediction model.%为了提高近红外光谱技术检测食醋中可溶性无盐固形物含量(SSFSC)的精度和稳定性,提出采用联合区间偏最小二乘(Si-PLS)筛选光谱特征区间,再利用极限学习机(ELM)算法建立非线性回归模型,并对该方法的优越性进行系统比较；试验通过交互验证优化模型相关参数,以预测时的相关系数(Rp)和预测均方根误差(RMSEP)作为模型的评价指标.结果表明,Si-PLS结合ELM算法(Si-ELM)所建模型最佳,预测结果:Rp=0.973 9,RMSEP=1.232 g/100 mL.说明利用近红外光谱技术可以快速准确检测食醋中的SSF-SC,Si- ELM的应用可以适当提高该预测模型的精度.