title: [Effectively predicting soluble solids content in apple based on hyperspectral imaging].
abstract: It is very important to extract effective wavelengths for quantitative analysis of fruit internal quality based on hyperspectral imaging. In the present study, genetic algorithm (GA), successive projections algorithm (SPA) and GA-SPA combining algorithm were used for extracting effective wavelengths from 400-1 000 nm hyperspectral images of Yantai "Fuji" apples, respectively. Based on the effective wavelengths selected by GA, SPA and GA-SPA, different models were built and compared for predicting soluble solids content (SSC) of apple using partial least squares (PLS), least squared support vector machine (LS-SVM) and multiple linear regression (MLR), respectively. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Among all the models, the SPA-MLR achieved the best results, where Rp(2), RMSEP and RPD were 0.950 1, 0.308 7 and 4.476 6 respectively. Results showed that SPA can be effectively used for selecting the effective wavelengths from hyperspectral data. And, SPA-MLR is an optimal modeling method for prediction of apple SSC. Furthermore, less effective wavelengths and simple and easily-interpreted MLR model show that the SPA-MLR model has a great potential for online detection of apple SSC and development of a portable instrument.
authors.affiliation: Beijing Research Center of Intelligent Equipment for Agriculture, National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China. huangwenqian@iea.ac.cn
authors.forename: Wen-Qian
authors.initials: WQ
authors.lastname: Huang
authors.affiliation: Beijing Research Center of Intelligent Equipment for Agriculture, National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
authors.forename: Jiang-Bo
authors.initials: JB
authors.lastname: Li
authors.affiliation: Beijing Research Center of Intelligent Equipment for Agriculture, National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
authors.forename: Li-Ping
authors.initials: LP
authors.lastname: Chen
authors.affiliation: Beijing Research Center of Intelligent Equipment for Agriculture, National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
authors.forename: Zhi-Ming
authors.initials: ZM
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D002138:Calibration; D005638:Fruit; D016018:Least-Squares Analysis; D016014:Linear Models; D027845:Malus; D008962:Models, Theoretical; D015999:Multivariate Analysis; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine