为探究一种快速、 可靠的化橘红检测方法, 本实验分别采用傅里叶变换衰减全反射红外光谱法和荧光光谱成像技术结合多层感知器(MLP)神经网络所构建的模式识别方法, 对化橘红进行鉴别, 并对两种方法进行了比较。 实验以81个正毛化橘红, 37个其他品种橘红共118个样品为研究对象, 采集所有样品的红外光谱和荧光光谱图像。 根据光谱曲线中不同样品间的差异, 取红外光谱中550-1800 cm-1区段范围内的光谱数据和荧光光谱曲线中的400～720 nm区段的光谱数据进行分析, 应用主成分分析法(PCA)对化橘红的光谱数据进行降维处理, 再结合MLP神经网络对化橘红样品进行判别分析。 实验中分别使用多元散射校正(MSC)、 标准正态变量校正(SNV)、 一阶导(FD)、 二阶导(SD)以及Savitzky-Golay(SG)平滑数据预处理方法, 并比较他们对鉴别模型的影响。 分析结果表明: 利用红外光谱法(FTIR/ATR), 经由Savitzky-Golay(SG)平滑预处理得到的数据, 通过隐层函数为sigmoid的三层MLP模型, 能够得到最优正毛化橘红识别率, 其结果训练集和测试集的识别率都为100%; 利用荧光光谱成像技术, 由多元散射(MSC)预处理的结果是最理想的。 经过预处理的数据, 通过隐层函数为sigmoid函数的三层MLP模型, 训练集识别率达到100%, 测试集识别率达到96.7%。 由此可见, 衰减全反射红外光谱法(FTIR/ATR)和荧光光谱成像技术分别与MLP神经网络构建的识别模式, 均可对化橘红的判别达到快速、 可靠的效果。

To explore rapid reliable methods for detection of Epicarpium citri grandis(ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy(FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected.According to the differences in tspectrum, the spectra data in the 550～1 800 cm-1 wavenumber range and 400～720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction(SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that:after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.