Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver

Wu, Di; Chen, Xiaojing; Zhu, Xiaodong; Guan, Xiaochun; Wu, Guichu

doi:10.1039/c1ay05075c

Cited by 59 publications

(27 citation statements)

References 30 publications

(34 reference statements)

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“…Each PC image is a linear sum of the original images at individual wavelengths multiplied by corresponding (spectral) weighting coefficients [17]. Although multivariate data analysis can sometimes be applied directly to data of continuous spectra, its calibration process is often time-consuming [18]. Loadings resulting from PCA (weighting coefficients) can be used to identify important variables that are responsible for the specific features appearing in the corresponding scores.…”

Section: Methodsmentioning

confidence: 99%

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

Zhang

Liu

et al. 2012

Sensors

141

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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.

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Section: Methodsmentioning

confidence: 99%

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

Zhang

Liu

et al. 2012

Sensors

141

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“…According to previous research (Wu et al, 2011), effective wavelengths may be more efficient than full wavelengths, because they contain the most important information relevant to the discrimination. In this study, SPA was applied to select effective wavelengths from an original spectral matrix (X) and a preprocessed spectral matrix (X M ).…”

Section: Spectral Data Preprocessingmentioning

confidence: 99%

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

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This paper presents a study that was performed for rapid and noninvasive detection of waxed chestnuts using hyper-spectral imaging. A visual near-infrared (400 _ 1026 nm) hyper-spectral imaging system was assembled to acquire scattering images from two groups of chestnuts (waxed and non-waxed chestnuts). The spectra of the samples were extracted from the hyper-spectral images using image segmentation process. Then multiplicative scatter correction (MSC) was conducted to preprocess the original spectra. Effective wavelengths were selected to reduce the computational burden of the hyper-spectral data. Using the seven effective wavelengths that were obtained from a successive projections algorithm (SPA), three calibration algorithms were compared: partial least squares regression (PLSR), multiple linear regression (MLR) and linear discriminant analysis (LDA). The best model for discriminating between waxed and non-waxed chestnuts was found to be the MSC-SPA-MLR model.Keywords: hyper-spectral imaging, waxed chestnut, effective wavelength, pear IntroductionThe chestnut (Castanea mollissima Bl.) is an important edible fruit in the northern hemisphere, which has been consumed as extensively as the potato in the past (Ferreira-Cardoso et al., 1999).and has become increasingly important in human nutrition because of its nutrient composition and potential beneficial health effects, for example, in reducing coronary heart disease and cancer rates (Sabaté et al., 2001). Fried chestnut is the most popular preparation method for chestnuts. However, some unscrupulous traders add industrial wax while frying chestnuts to ensure that the chestnuts are brighter and more attractive. Generally, industrial wax is extracted directly from oil, which will also extract polycyclic aromatic hydrocarbons and polycyclic aromatic hydrocarbons which are present in the industrial process, both of which are highly carcinogenic (Bonser et al., 1963). Mixed industrial wax will infiltrate into the pulp in the process of frying chestnuts which seriously endangers human health. Hence, a sorting process is necessary to identify and screen out waxed chestnuts.Traditionally, screening waxed chestnuts is usually dependent on subjective criteria to distinguish between waxed and non-waxed chestnuts, taking into consideration that brighter and more attractive chestnuts are mixed with industrial wax. Although industrial wax will make chestnuts brighter and more attractive, it is not a reliable way to identify waxed chestnuts. Hyper-spectral imaging (also called imaging spectrometry or imaging spectroscopy) is an emerging analytical technology which is receiving growing attention as a rapid, efficient, and nondestructive analytical tool for quantitative determination of the quality and safety of agricultural goods. By combining the advantages of computer vision and spectroscopy technology into one system, hyper-spectral imaging can generate a spatial image of the spectral variation, which provides structural, chemical, physical and functional information abou...

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“…Although variables with no more information for modeling than noise were eliminated after UVE calculation, the remaining informative variables might have the characteristics of collinearity and redundancy. Therefore, SPA was further calculated based on the informative variables with high S/N selected by UVE in some previous works, and the results show that better prediction and robustness were achieved (Wu et al 2011;. In this study, the strategy was also applied and the results show that compared with the UVE models, the corresponding UVE-SPA models were more robust (mean AB_RMSE 0.083 vs. 0.129).…”

Section: Model Optimization By Wavelength Variable Selectionmentioning

confidence: 84%

Feasibility Study on Quantitative Pixel-Level Visualization of Internal Quality at Different Cross Sections Inside Postharvest Loquat Fruit

Zhu

Nie

et al. 2016

Food Anal. Methods

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Visualizing the quality variation inside loquat fruit at pixel level is important to deeply understand its ripening process and further optimize its preharvest planting pattern and postharvest storage strategy. Total soluble solids (TSS) is a major quality attribute of loquat fruit and detecting changes in its content can be used to monitor the ripening and postharvest senescence of loquat fruit. The refractometer method cannot provide detailed TSS distribution within loquat flesh, because the analysis focuses on measuring the mean TSS of only a flesh cube, which is used to make juice for the refractometer measurement. In this study, hyperspectral imaging was used to measure and visualize internal TSS distribution within loquat fruit. Loquat fruits with different TSS contents were selected and cut at different cross sections for imaging. Different calibration and wavelength selection algorithms were applied and compared. The uninformative variable elimination by partial least square regression model with preprocessing from spectral set I (468-1026 nm) was identified as the best model for the TSS determination of loquat flesh, which had a high prediction ability with a correlation coefficient of 0.960 and residual predictive deviation of 3.513. On the basis of the best model, the quantitative TSS distribution at different cross sections inside loquat fruit was visualized at pixel level. The results showed that hyperspectral imaging is a feasible way of visualizing the spatial changes of TSS distribution inside loquat fruit at pixel level, which would be helpful to understand the detailed TSS change inside postharvest loquat fruit.

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Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver

Cited by 59 publications

References 30 publications

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

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

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Feasibility Study on Quantitative Pixel-Level Visualization of Internal Quality at Different Cross Sections Inside Postharvest Loquat Fruit

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