Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models

Abstract We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imagi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hongyan Zhu, Bingquan Chu, Yangyang Fan, Xiaoya Tao, Wenxin Yin, Yong He
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/75222954063f455481066c3c390382c1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:75222954063f455481066c3c390382c1
record_format	dspace
spelling	oai:doaj.org-article:75222954063f455481066c3c390382c12021-12-02T12:32:00ZHyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models10.1038/s41598-017-08509-62045-2322https://doaj.org/article/75222954063f455481066c3c390382c12017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08509-6https://doaj.org/toc/2045-2322Abstract We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm–partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre = 0.9812, RPD = 5.17) and SSC (R pre = 0.9523, RPD = 3.26) at 380–1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874–1734 nm for predicting pH (R pre = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.Hongyan ZhuBingquan ChuYangyang FanXiaoya TaoWenxin YinYong HeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongyan Zhu Bingquan Chu Yangyang Fan Xiaoya Tao Wenxin Yin Yong He Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models
description	Abstract We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm–partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre = 0.9812, RPD = 5.17) and SSC (R pre = 0.9523, RPD = 3.26) at 380–1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874–1734 nm for predicting pH (R pre = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.
format	article
author	Hongyan Zhu Bingquan Chu Yangyang Fan Xiaoya Tao Wenxin Yin Yong He
author_facet	Hongyan Zhu Bingquan Chu Yangyang Fan Xiaoya Tao Wenxin Yin Yong He
author_sort	Hongyan Zhu
title	Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models
title_short	Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models
title_full	Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models
title_fullStr	Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models
title_full_unstemmed	Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models
title_sort	hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/75222954063f455481066c3c390382c1
work_keys_str_mv	AT hongyanzhu hyperspectralimagingforpredictingtheinternalqualityofkiwifruitsbasedonvariableselectionalgorithmsandchemometricmodels AT bingquanchu hyperspectralimagingforpredictingtheinternalqualityofkiwifruitsbasedonvariableselectionalgorithmsandchemometricmodels AT yangyangfan hyperspectralimagingforpredictingtheinternalqualityofkiwifruitsbasedonvariableselectionalgorithmsandchemometricmodels AT xiaoyatao hyperspectralimagingforpredictingtheinternalqualityofkiwifruitsbasedonvariableselectionalgorithmsandchemometricmodels AT wenxinyin hyperspectralimagingforpredictingtheinternalqualityofkiwifruitsbasedonvariableselectionalgorithmsandchemometricmodels AT yonghe hyperspectralimagingforpredictingtheinternalqualityofkiwifruitsbasedonvariableselectionalgorithmsandchemometricmodels
_version_	1718394222966472704

Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models

Ejemplares similares