光谱预处理结合模拟退火算法的小麦粉面筋含量检测

为得到可靠的小麦粉中面筋含量定量分析模型,基于光谱预处理及模拟退火算法(simulated annealing algorithm,SAA)对近红外光谱(near infrared spectroscopy,NIR)进行优化处理。偏最小二乘(partial least squares,PLS)回归用于建立预测模型,以决定系数R2、校正均方根误差(root mean square error of calibration,RMSEC)、预测均方根误差(root mean square error of prediction,RMSEP)为指标,对比在不同光谱预处理条件下建立的回归模型与光谱预处理结合模拟退火算法优化处理条件下的回归模型。结果表明光谱预处理结合SAA-PLS模型能够有效提高模型的稳定性和预测能力,将R2从0.763 7提高到0.949 1、RMSEC从1.371 2降低到0.589 8、RMSEP从1.450 2降低到0.534 1。结果说明,光谱预处理结合模拟退火算法对光谱进行优化处理是可行的,模型预测能力和稳定性均优于未处理模型和仅进行光谱预处理的模型。

Near Infrared Spectroscopic Detection of Gluten Content in Wheat Flour Based on Spectral Pretreatment and Simulated Annealing Algorithm

This study aimed to establish a reliable predictive model for quantitative analysis of gluten in wheat flour using near infrared (NIR) spectroscopy. The optimal spectral pretreatment method combined with simulated annealing algorithm (SAA) was obtained by comparison of the partial least squares (PLS) regression models developed after different spectral pretreatments alone and combined with SAA based on their coefficient of determination (R2), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The results indicated that the stability and prediction performance of the PLS model were greatly improved by using spectral pretreatment combined with SAA, as demonstrated by an increase in R2 from 0.763 7 to 0.949 1, a reduction in RMSEC from 1.371 2 to 0.589 8, and a decrease in RMSEP from 1.450 2 to 0.534 1. The combination of spectral pretreatment and SAA was feasible for the development of a predictive model for quantitative analysis of gluten. Moreover, the optimized model exhibited better stability and prediction performance than the unoptimized model and the one developed with spectral pretreatment alone.