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。
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.
作者
刘思岐
冯国红
刘中深
朱玉杰
LIU Siqi;FENG Guohong;LIU Zhongshen;ZHU Yujie(College of Engineering and Technology,Northeast Forestry University,Harbin 150040,China;Heilongjiang Agricultural Engineering Vocational College,Harbin 150025,China)
出处
《食品工业科技》
CAS
北大核心
2023年第10期304-310,共7页
Science and Technology of Food Industry
基金
中央高校基本科研业务费专项资金项目(2572020BL01)
黑龙江省自然科学基金项目(LH2020C050)。
关键词
蓝莓果渣
花青素
可见近红外光谱技术
堆叠监督自编码器
无损检测
blueberry pomace
anthocyanin
visible near infrared spectroscopy(VNIS)
stacked supervised autoencoders(SSAE)
nondestructive testing(NDT)
