基于近红外光谱数据的李子可溶性固形物含量预测模型研究

Research on Prediction Model of Soluble Solid Content in Plums Based on Near-Infrared Spectroscopy Data

针对李子可溶性固体物含量预测方法存在前期数据预处理较复杂、预测精度不高、预测模型泛化能力不强的问题,提出针对李子品种的一维卷积神经网络(1D Convolutional Neural Network,1D-CNN)模型。首先针对300个三华李和三月李样本果品构建可溶性固体物含量的近红外光谱数据,并分别设计输入层、一维卷积层、池化层与全连接层和输出层等多层结构构建1D-CNN模型。模型决定系数为0.980、均方根误差为0.192,表现均优于支持向量回归、随机森林等传统机器学习方法,并且作为轻量级模型,具有建模过程简便、泛化能力强的特点,可满足实际场景需求。

To address the issues of complex data preprocessing,low prediction accuracy,and poor generalization capability of existing methods for predicting the soluble solid content(SSC)of plums,a one-dimensional convolutional neural network(1D-CNN)model specifically for plum varieties is proposed.Near-infrared(NIR)spectral data were constructed for 300 samples of Sanhua and Sanyue plums to predict their SSC.And the multi-layer structures such as input layer,one-dimensional convolution layer,pooling layer and full connection layer and output layer were designed to construct 1D-CNN model.Comparative experiments demonstrated that the model achieved a coefficient of determination(R2)of 0.980 and a root mean square error(RMSE)of 0.192,outperforming traditional machine learning methods such as support vector regression and random forest.As a lightweight model,it offers simplicity in the modeling process and strong generalization capability,making it suitable for practical applications.