基于FastICA和神经网络的红酒主要品质参数红外检测

为实现红酒中酒精含量、pH值以及残糖量的快速检测，对44个红酒样品的红外光谱数据进行了分析。使用快速独立分量分析(FastICA)算法对光谱数据矩阵进行分解，得到独立成分和相应的混合系数矩阵，再利用误差反向传播算法(back-propagation, BP)构造了三层的神经网络结构，建立了ICA-NNR模型。利用此模型对红酒样品的酒精含量、pH值以及残糖量进行预测，根据预测相关系数(r)和预测标准偏差(RMSEP)来评价预测模型的性能，结果表明该模型对红酒酒精含量、pH值以及残糖量测定的相关系数r分别为0.953，0.983和0.994，RMSEP分别为0.161，0.017，0.181。此外，预测样品集中的22个样品ICA-NNR模型预测值与参考值相比，酒精含量、pH值以及残糖量的最大相对偏差均小于4%。这为进一步开发红酒成分红外在线分析仪奠定了基础。

Detection of the Main Quality Indicators in Red Wine with Infrared Spectroscopy Based on FastICA and Neural Network

For the rapid detection of the ethanol,pH and rest sugar in red wine,infrared (IR) spectra of 44 wine samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of IR spectra,and their independent components and the mixing matrix were obtained. Then,the ICA-NNR calibration model with three-level artificial neural network (ANN) structure was built by using back-propagation (BP) algorithm. The models were used to estimate the contents of ethanol,pH and...