An Anthocyanin Prediction Model of Blueberry Pomace Based on Stacked Supervised Autoencoders
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摘要: 基于可见近红外光谱技术，采用深度学习中的堆叠监督自编码器（stacked supervised autoencoders，SSAE）对蓝莓果渣的花青素含量进行了建模。首先对光谱数据进行预处理和特征筛选处理，以预设SSAE模型的预测集均方根误差（RMSEP）最低为标准，选择出178个特征波长；以选择出的特征波长处的吸光值作为SSAE模型的输入，以蓝莓果渣中的花青素含量为输出，讨论SSAE模型激活参数、节点数、训练次数和学习率，得到SSAE最优参数，即激活函数rule、结构178-60-5-1、训练次数70、学习率0.01。选取训练集均方根误差（RMSEC）、预测集均方根误差（RMSEP）、预测集相关系数（Rp）为评价标准，获得所建立模型的RMSEC、RMSEP、Rp分别为1.0500、0.3835、0.9042。最后通过与经典回归预测模型极限学习机（extreme learning machine，ELM）、最小二乘支持向量机回归（least squares support vector regression，LSSVR）和偏最小二乘回归（partial least squares regression，PLSR）算法进行对比，发现本研究所建SSAE模型的预测精度更高，表明SSAE模型与可见近红外光谱结合能有效预测蓝莓果渣中的花青素含量。Abstract: Based on the visible and near-infrared reflectance spectroscopy technique, stacked supervised autoencoders (SSAE) in deep learning were used to model the anthocyanin content of blueberry pomace. First, preprocessing and feature screening for spectral data were performed. With the minimum value of prediction set root mean square error (RMSEP) of the preset SSAE model as the standard, 178 characteristic wavelengths were selected. The absorbance of the selected characteristic wavelength was used as the input to the SSAE model. The anthocyanin content of blueberry pomace was used as the output. By exploring the activation parameters, node number, training times and learning rate of the SSAE model, the optimal parameters of SSAE were obtained, namely, the activation function of rule, the structure of 178-60-5-1, the training times of 70, and the learning rate of 0.01. The training set root mean square error (RMSEC), prediction set root mean square error (RMSEP), and prediction set correlation coefficient (Rp) were selected as the evaluation criteria. The RMSEC, RMSEP, and Rp of the established model were 1.0500, 0.3835, and 0.9042, respectively. Compared with the classic regression prediction model extreme learning machine (ELM), least squares support vector regression (LSSVR) and partial least squares regression (PLSR) algorithm, the prediction accuracy of the SSAE model was higher. Therefore, the combination of the SSAE model with visible and near-infrared reflectance spectroscopy proved to be effective in predicting anthocyanin content of blueberry pomace.
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图 3 不同训练次数和学习率对模型的影响
注：（a）不同训练次数和学习率对RMSEP的影响；（b）不同训练次数和学习率对Rp的影响。
Figure 3. Effect of different training times and learning rates on the model
表 1 不同预处理和特征选择方式下SSAE模型结果
Table 1. SSAE model results under different preprocessing and feature selection methods
预处理方式 特征选择方式 RMSEC RMSEP Rp SG CARS 4.6994 0.6466 0.6940 Pearson 1.1321 0.4435 0.8696 不使用降维 1.8665 0.4302 0.8778 MSC CARS 1.5124 0.6942 0.6344 Pearson 1.7429 0.7063 0.6177 不使用降维 1.7991 0.6540 0.6854 SNV CARS 5.1595 0.6002 0.7439 Pearson 5.2719 0.6342 0.7081 不使用降维 5.8429 0.6692 0.6669 1st-D CARS 1.4947 0.8981 0.0088 Pearson 0.9399 0.8980 0.0148 不使用降维 1.1199 0.8982 0.0088 DT CARS 1.4034 0.7954 0.4645 Pearson 1.8946 0.6792 0.6543 不使用降维 1.6619 0.5357 0.8027 源数据 CARS 1.5709 0.4717 0.8510 Pearson 1.1095 0.4154 0.8866 不使用降维 2.0287 0.4267 0.8799 
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表 2 不同激活函数下SSAE模型结果
Table 2. SSAE model results under different activation functions
激活函数 RMSEC RMSEP Rp tanh 1.0191 0.4814 0.8442 rule 1.1095 0.4154 0.8866 未使用激活函数 0.8755 0.4493 0.8659
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表 3 不同神经元配置的SSAE建模结果
Table 3. SSAE modeling results for different neuron configurations
神经元配置 RMSEC RMSEP Rp （90，30） 0.9451 0.4155 0.8866 （90，15） 1.0142 0.3929 0.8992 （90，10） 1.0493 0.3852 0.9034 （60，30） 0.9890 0.4240 0.8816 （60，15） 1.0980 0.4107 0.8893 （60，10） 1.1207 0.3915 0.9000 （60，5） 1.0500 0.3835 0.9042 （30，15） 1.1805 0.4162 0.8861 （30，10） 1.2605 0.4403 0.8716 （30，5） 1.1095 0.4154 0.8866
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表 4 不同模型的建模预测结果
Table 4. Modeling prediction results of different models
模型 输入模型的数据维数 RMSEP Rp O+Pearson+SSAE 178 0.3835 0.9042 SG+O+ELM 2000 0.4025 0.8916 O+Pearson+LSSVR 178 0.4479 0.8746 1st-D+CARS+PLSR 136 0.4012 0.8939 注：表中的O代表不进行预处理或特征筛选。
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