title: [Identification of varieties of black bean using ground based hyperspectral imaging].
abstract: In the present study, hyperspectral imaging combined with chemometrics was successfully proposed to identify different varieties of black bean. The varieties of black bean were defined based on the three different colors of the bean core. The hy-perspectral images in the spectral range of 380-1,030 nm of black bean were acquired using the developed hyperspectral imaging system, and the reflectance spectra were extracted from the region of interest (ROD) in the images. The average spectrum of a ROI of the sample in the images was used to represent the spectrum of the sample and build classification models. In total, 180 spectra of 180 samples were extracted. The wavelengths from 440 to 943 nm were used for analysis after the removal of the spec- tral region with absolute noises, and 440-943 nm spectra were preprocessed by multiplicative scatter correction (MSC). Five classification methods, including partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and extreme learning machine (ELM), were used to build discriminant models using the preprocessed full spectra, the feature information extracted by principal component analysis (PCA) and the feature information extracted by wavelet transform (WT) from the preprocessed spectra, respectively. Among all the classification models using the preprocessed full spectra, ELM models obtained the best performance; among all the classification models using the feature information extracted from the preprocessed spectra by PCA, ELM model also obtained the best classification accuracy; and among all the classification models using the feature information extracted from the preprocessed spectra by WT, ELM models obtained the best classification performance with 100% accuracy in both the calibration set and the prediction set. Among all classification models, WT-ELM model obtained the best classification accuracy. The overall results indicated that it was feasible to identify black bean varieties nondestructively by using hyperspectral imaging, and WT could effectively extract feature information from spectra and ELM algorithm was effective to build high performance classification models.
authors.forename: Chu
authors.initials: C
authors.lastname: Zhang
authors.forename: Fei
authors.initials: F
authors.lastname: Liu
authors.forename: Hai-Liang
authors.initials: HL
authors.lastname: Zhang
authors.forename: Wen-Wen
authors.initials: WW
authors.lastname: Kong
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002138:Calibration; D016002:Discriminant Analysis; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D027805:Phaseolus / Q000145:classification*; D025341:Principal Component Analysis; D013057:Spectrum Analysis; D060388:Support Vector Machine; D058067:Wavelet Analysis