基于GA-PLS算法的小麦粉灰分含量快速检测

为得到噪声小、准确性强、自适应性良好的模型,对实验所得的近红外光谱进行预处理及优化波长是十分必要的.实验确定小麦粉中灰分含量为研究对象,提出将光谱预处理及遗传算法(GA)优化波长用于提高偏最小二乘法(PLS)定量建模的稳健性.对比在不同预处理方法下相关系数R2、矫正标准差(RMSEC)、预测标准偏差(RMSEP)3个指标,随机选择130份样本,建立预处理+GA+PLS定量分析模型.实验结果为:R2从70.31%提到了97.46%,RMSEC从0.0775降低到了0.0226,RMSEP从0.0996降低到了0.0213,表明基于光谱预处理结合GA优化谱区定量分析小麦粉中灰分含量可行,模型预测能力和精度更高.

Fast detection of wheat flour ash content based on GA-PLS algorithm

To obtain a low-noise,high-accuracy and good self-adaptvity model,it is essential to preprocess near infrared(NIR)spectrum and optimize wave length. Experiment ensures ash content in wheat flour as research object and improve robustness of partial least squares method(PLS)model by combining genetic algorithm(GA)-wavelength-optimization with spectral pretreatment. Contrast the three indexes,R2,RMSEC and RMSEP,by different data preprocessing methods,130 samples are selected randomly to establish spectral data pretreatment+GA+PLS model.According to the result,R2is increased from 70.31% to 97.46%,RMSEC is reduced from 0.077 5 to 0.022 6,RMSEP is reduced from 0.0996 to 0.021 3.It is feasible to quantitatively analyze on wheat flour ash content by GA and spectral preprocessing,prediction ability and precision of the obtained model is much more higher.