摘要
可溶性固形物含量(SSC)是决定鲜桃风味和品质的重要成分。高光谱影像的特征提取为无损检测可溶性固形物含量提供了数据基础和方法路径。先前的研究表明,基于多光谱、荧光谱、近红外光谱、电子鼻的水果内部品质评估取得较好的结果。但是,由于缺少多特征融合,从而限制了水果品质的精准估测。为此,提出了一种基于堆栈自动编码器-粒子群优化支持向量回归(SAE-PSO-SVR)模型预测鲜桃可溶性固形物含量。首先,利用高光谱影像提取光谱信息、空间信息及空-谱融合信息。其次,设置普适性堆栈自动编码器(SAE)提取光谱信息、空间信息及空-谱融合信息的深层特征。最后,将深层特征作为粒子群优化支持向量回归(PSO-SVR)模型的输入数据进行鲜桃可溶性固形物含量的预测。其中,对于光谱信息作为输入的SAE模型,设计了453-300-200-100-40, 453-350-250-150-50, 453-350-250-100-60的三个隐含层结构。对于空间信息作为输入的SAE模型,设计了894-700-500-300-50, 894-650-350-200-80, 894-800-700-500-100的三个隐含层结构。对于融合信息作为输入的SAE模型,设计了1347-800-400-200-40, 1347-750-550-400-100, 1347-700-500-360-150的三个隐含层结构。实验结果表明,对于输入数据分别为光谱信息、空间信息及融合信息的SAE模型,结构为453-300-200-100-40, 894-800-700-500-100和1347-750-550-400-100的模型效果较好,而且基于融合信息的模型预测精度明显优于基于光谱信息或者图像信息的模型。为了验证模型的普适性,利用结构为1347-750-550-400-100的SAE模型提取融合信息的深层特征估测不同品种鲜桃的可溶性固形物含量并进行可视化。结果表明,基于结构为1237-650-310-130的SAE-PSO-SVR模型预测效果最好(R2=0.873 3, RMSE=0.645 1)。因此,所提出的SAE-PSO-SVR模型提高了鲜桃可溶性固形物含量的估计精度,为鲜桃的其他成分检测提供了技术支撑。
Soluble solid content(SSC) is a key factor to evaluate the flavor and quality of fruits. The feature extraction of hyperspectral images provides the data basis and method path for the non-destructive estimation of the solid soluble content. Previous studies have shown that fruit internal quality evaluation based on multi-spectrum, fluorescence spectrum, near-infrared spectrum, and electronic nose has achieved good results. However, the lack of multi-feature fusion limits the accurate estimation of fruit quality. Therefore, this study proposed a model based on stacked autoencoder-particle swarm optimization-support vector regression(SAE-PSO-SVR) to predict the solid soluble content of fresh peaches. Firstly, hyperspectral images extracted spectral information, image pixel information corresponding to different bands, and fusion information. Secondly, a universal stacked autoencoder(SAE) was set up to extract the deep features of spectral information, spatial information, and space-spectrum fusion information. Finally, the deep features were used as the input data of the particle swarm optimization-support vector regression(PSO-SVR) model to predict the solid soluble content of fresh peaches.Among them, three hidden layer network structures were designed for the SAE model with spectral information as input data, including 453-300-200-100-40, 453-350-250-150-50 and 453-350-250-100-60. Three network structures of hidden layer nodes were designed forthe SAE model with image information as input data, including 894-700-500-300-50, 894-650-350-200-80 and 894-800-700-500-100. Three hidden layer network structures were designed forthe SAE model with fusion information as input data, including 1347-800-400-200-40, 1347-750-550-400-100 and 1347-700-500-360-150.The experimental results show that the models with SAE structures of 453-300-200-100-40, 894-800-700-500-100 and 1347-750-550-400-100 have the better estimation effect for spectral information, image information and fusion information as input data of the SAE model, and the prediction accuracy of the model based on the deep features of the fusion information was significantly better than that of the model based on spectral features or image features. The SAE model with the structure of 1347-750-550-400-100 was used to extract the deep features of the fusion information to estimate and visualize the solid soluble content of different varieties of fresh peaches. The results show that the prediction performance based on the SAE-PSO-SVR model was the best(R2=0.873 3, RMSE=0.645 1). Therefore, the SAE-PSO-SVR model proposed can improve the estimation accuracy of solid soluble content of fresh peaches, which provide technical support for detecting other components of fresh peaches.
作者
杨宝华
高志伟
齐麟
朱月
高远
YANG Bao-hua;GAO Zhi-wei;QI Lin;ZHU Yue;GAO Yuan(School of Information and Computer,Anhui Agricultural University,Hefei 230036,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2021年第11期3559-3564,共6页
Spectroscopy and Spectral Analysis
基金
安徽省自然科学基金项目(1808085MF195)
安徽农业大学茶树生物学与资源利用国家重点实验室开放基金项目(SKLTOF20200116)资助。
