Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce

•Vis-NIR HSI was used to detect compound heavy metals content in lettuce leaves.•WT-SCAE is proposed to obtain the deep spectral features.•Deep learning has a great potential for the identification of compound heavy metals content. The aim of this research was to develop a deep learning method which...

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Vydané v:	Food chemistry Ročník 321; s. 126503
Hlavní autori:	Zhou, Xin, Sun, Jun, Tian, Yan, Lu, Bing, Hang, Yingying, Chen, Quansheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 15.08.2020
Predmet:	cadmium Cadmium - analysis Compound heavy metals Deep learning heavy metals hyperspectral imagery Lactuca - chemistry Lactuca sativa lead Lead - analysis Least-Squares Analysis leaves Lettuce Nondestructive testing Plant Leaves - chemistry prediction Stack convolution auto encoder Support Vector Machine support vector machines wavelet Wavelet Analysis Wavelet transform Deep learning Lettuce Wavelet transform Compound heavy metals Nondestructive testing Stack convolution auto encoder
ISSN:	0308-8146, 1873-7072, 1873-7072
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Abstract	•Vis-NIR HSI was used to detect compound heavy metals content in lettuce leaves.•WT-SCAE is proposed to obtain the deep spectral features.•Deep learning has a great potential for the identification of compound heavy metals content. The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68–1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rp2) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rp2 of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm.
AbstractList	The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68-1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (R ) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with R of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm. •Vis-NIR HSI was used to detect compound heavy metals content in lettuce leaves.•WT-SCAE is proposed to obtain the deep spectral features.•Deep learning has a great potential for the identification of compound heavy metals content. The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68–1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rp2) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rp2 of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm. The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68–1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rₚ²) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rₚ² of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm. The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68-1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rp2) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rp2 of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm.The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68-1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rp2) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rp2 of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm.
ArticleNumber	126503
Author	Sun, Jun Zhou, Xin Hang, Yingying Tian, Yan Lu, Bing Chen, Quansheng
Author_xml	– sequence: 1 givenname: Xin surname: Zhou fullname: Zhou, Xin organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 2 givenname: Jun orcidid: 0000-0002-6600-3699 surname: Sun fullname: Sun, Jun email: sun2000jun@sina.com organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 3 givenname: Yan surname: Tian fullname: Tian, Yan organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 4 givenname: Bing surname: Lu fullname: Lu, Bing organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 5 givenname: Yingying surname: Hang fullname: Hang, Yingying organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 6 givenname: Quansheng orcidid: 0000-0003-2498-3278 surname: Chen fullname: Chen, Quansheng email: qschen@ujs.edu.cn organization: School of Food and Biological Engineering of Jiangsu University, Zhenjiang 212013, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32240914$$D View this record in MEDLINE/PubMed
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Keywords	Deep learning Lettuce Wavelet transform Compound heavy metals Nondestructive testing Stack convolution auto encoder
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Snippet	•Vis-NIR HSI was used to detect compound heavy metals content in lettuce leaves.•WT-SCAE is proposed to obtain the deep spectral features.•Deep learning has a... The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting...
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SubjectTerms	cadmium Cadmium - analysis Compound heavy metals Deep learning heavy metals hyperspectral imagery Lactuca - chemistry Lactuca sativa lead Lead - analysis Least-Squares Analysis leaves Lettuce Nondestructive testing Plant Leaves - chemistry prediction Stack convolution auto encoder Support Vector Machine support vector machines wavelet Wavelet Analysis Wavelet transform
Title	Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce
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