BP神经网络结合变量选择方法在牛奶蛋白质含量检测中的应用

Application of BP neural network and variable selection method in protein content detection of milk

牛奶中的蛋白质含量会影响牛奶的品质,利用高光谱图像的光谱特征信息研究对牛奶蛋白质含量预测的可行性。本文提出一种基于竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)和连续投影算法(successive projections algorithm, SPA)结合多层前馈神经网络(back propagation, BP)的预测建模方法,实验以含有不同浓度蛋白质的牛奶为对象,利用可见光/近红外高光谱成像系统共采集到5种牛奶共计250组高光谱数据,通过实验对比选择采用标准化方法对获取到的吸收光谱预处理,然后采用CARS结合SPA筛选特征波长,得到18个特征波长,建立CARS-SPA-BP模型,经过试验,CARS-SPA-BP模型的训练集决定系数和测试集决定系数R;和R;分别达到0.971和0.968,训练集均方根误差(root mean square error of calibration,RMSEC)和测试集均方根误差(root mean square error of prediction,RMSEP)达到了0.033和0.034。研究发现,采用CARS结合SPA筛选的牛奶特征波长建立的多层前馈神经网络模型,其模型预测结果与全波长建模相比并没有明显降低,因此将CARS结合SPA用于波长筛选并且结合BP神经网络基本可以完成对牛奶蛋白质含量的预测。为验证CARS-SPA-BP模型的预测能力,在相同数据环境下,使用较为传统的偏最小二乘回归(partial least squares regression, PLSR)进行建模,实验结果表明,CARS-SPA-BP相较于PLSR,R;和RMSEP均有明显提升。研究表明,CARS-SPA-BP可充分利用牛奶光谱特征信息实现较高精度的牛奶蛋白质含量检测。

The protein content of milk will affect the quality of milk.The feasibility of predicting the protein content of milk is studied by using the spectral feature information of hyperspectral image.In this paper, a prediction modeling method(CARS-SPA-BP) based on competitive adaptive reweighted sampling(CARS) and successive projections algorithm(SPA) combined with multilayer feedforward neural network(back propagation, BP) is proposed.In the experiment, 250 groups of hyperspectral data of five kinds of milk were collected by the visible/near infrared hyperspectral imaging system.Through the experimental comparison, the standardized method was used to preprocess the obtained absorption spectrum, and then the CARS combined with SPA was used to select the characteristic wavelength, 18 characteristic wavelengths are obtained.Through experiments, the determination coefficients R;and R;of training set and test set of CARS-SPA-BP model reach 0.971 and 0.968 respectively, and the root mean square error of calibration(RMSEC) and root mean square error of prediction(RMSEP) reach 0.033 and 0.034,respectively.It is found that the prediction results of multilayer back propagation(BP) neural network model based on CARS and SPA are not significantly lower than that of full wavelength model, Therefore, the CARS combined with SPA for wavelength screening and BP neural network can basically complete the prediction of milk protein content.In order to verify the prediction ability of CARS-SPA-BP model, the traditional partial least squares regression(PLSR) is used to model under the same data environment.The experimental results show that CARS-SPA-BP has significantly improved R;and RMSEP compared with PLSR.The results show that CARS-SPA-BP can make full use of the spectral characteristics of milk to achieve high-precision detection of milk protein content.