Li Xl

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds.

Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

Quantitative analysis of the varieties of apple using near infrared spectroscopy by principal component analysis and BP model

Artificial neural networks (ANN) combined with PCA are being used in a growing number of applications. In this study, the fingerprint wavebands of apple were got through principal component analysis (PCA). The 2-dimensions plot was drawn with the scores of the first and the second principal components. It appeared to provide the best clustering of the varieties of apple. The several variables compressed by PCA were applied as inputs to a back propagation neural network with one hidden layer. This BP model had been used to predict the varieties of 15 unknown samples; the recognition rate of 100% was achieved. This model is reliable and practicable. So a PCA-BP model can be used to exactly distinguish the varieties of apple.

Textural feature extraction and optimization in wavelet sub-bands for discrimination of green tea brands

This study aimed to discriminate green tea brands with textural feature from wavelet sub-bands based on multi-spectral image. Firstly, 250 multi-spectral images of five brands tea were obtained from a common-aperture multi-spectral charged coupled device camera with three channels (550, 660 and 800 nm). Secondly, each image was converted into seven wavelet sub-bands images by wavelet pyramidal decomposition at second level. Then statistic textural features such as contrast, homogeneity, energy, correlation and entropy were calculated from grey level co-occurrence matrix (GLCM) of wavelet sub-bands image. 105 textural features were obtained by feature extraction way combined by wavelet transform and GLCM. Thirdly, statistical feature selection was used to optimize the number of textural feature. 11 characteristic features were selected from 105 original features through STEPDISC of SAS with high statistic significance. Discriminant functions were generated based on these 11 characteristic features. Perfect classification performance (100%) was obtained for samples both in training and prediction sets. It can be concluded that green tea brands can be effectively discriminated by texture analysis based on multi-spectral image.

Estimating Nitrogen Status of Plant by Vis/NIR Spectroscopy and Mathematical Model

This paper investigated the potential of Vis/NIR spectroscopy and chemometrics to estimate N status of plant. Chemometrics was used as Vis/NIR spectroscopy analysis method to establish models to estimate N status of rapeseed and tea plant. In the research of rapeseed plant, a hybrid estimation model, artificial neural network (ANN) combined with partial least square regression (PLS) method, has been developed for diagnosis of nitrogen nutrition of rapeseed plant. 5 optimal PLS principal components were were selected as the input of BP neural network to establish the prediction model. The result showed that the prediction performance was excellent with r=0.95405, and the accuracy of prediction reached 95%. In the research of tea plant, PLS method was used to look for the fingerprint wavelengths (488, 695 and 931 nm). The PLS model for predicting the N status with r=0.908, SEP=0.21 and bias=0.138, showed an excellent prediction performance. Thus, it was concluded that chemometrics was a good tool for the spectroscopic estimation of plant N status based on Vis/NIRS.

Tea category classification using morphological characteristics and support vector machines

Tea categories classification is an importance task for quality inspection. And traditional way for doing this by human is time-consuming, requirement of too much manual labor. This study proposed a method for discriminating green tea categories based on multi-spectral images technique. Four tea categories were selected for this study, and total of 243 multi-spectral images were collected using a common-aperture multi-spectral charged coupled device camera with three channels (550, 660 and 800 nm). A compound image which has the clearest outline of samples was process by combination of the three monochrome images (550, 660 and 800 nm). After image preprocessing, 18 morphometry parameters were obtained for each samples. The 18 parameters used including area, perimeter, centroid and eccentricity et al. To better understanding these parameters, principal component analysis was conducted on them, and score plot of the first three independent components was obtained. The first three components accounted for 99.02% of the variation of original 18 parameters. It can be found that the four tea categories were distributed in dense clusters respectively in score plot. But the boundaries among them were not clear, so a further discrimination must be developed. Three algorithms including support vector machines, artificial neural network and linear discriminant analysis were adopted for developed classification models based on the optimized 9 features. Wonderful result was obtained by support vector machines model with accuracy of 93.75% for prediction unknown samples in testing set. It can be concluded that it is an effective method to classification tea categories based on computer vision, and support vector machines is very specialized for development of classification model.

Identification of monosodium glutamate by visible and near infrared reflectance spectroscopy

Visible (VIS) and near infrared reflectance spectroscopy (NIRS) was used to identify and authenticate different monosodium glutamate species. Samples from Xihu, Linhua, Taitaile and Foshou monosodium glutamate were homogenised and scanned in the visible (VIS) and near infrared (NIR) region (325-1075 nm) in reflectance. Partial least squares (PLS) models were developed to identify different monosodium glutamate species. The model correctly classified more than 99% of the monosodium glutamate sample. The results showed the potential of VIS and NIR spectra as an objective and rapid method for authentication and identification of monosodium glutamate species