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

Zhou, Xin; Sun, Jun; Tian, Yan; Lu, Bing; Hang, Yingying; Chen, Quansheng

doi:10.1016/j.foodchem.2020.126503

Cited by 88 publications

(39 citation statements)

References 14 publications

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“…By extending the dataset with more samples, respectively, more parameters, state of the art deep learning algorithms, or support vector machine regression (SVR) could be used in assessing the heavy metal concentrations [ 98 ].…”

Section: Resultsmentioning

confidence: 99%

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A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Petrea

Costache²,

Moga

et al. 2020

Molecules

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Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.

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Section: Resultsmentioning

confidence: 99%

“…Thus, heavy metals in turbot were determined in several scientific studies that were based on FAAS and GFAAS methods [ 22 , 72 , 75 , 98 , 102 , 107 , 117 ], followed by ICP-MS [ 73 , 74 , 106 , 108 ] and ICP-AES [ 100 ], and the collected dataset was used in the development of the present paper analytical framework.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Petrea

Costache²,

Moga

et al. 2020

Molecules

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“…It has been found that when crops absorb excessive amount of heavy metal, the cell structure and biochemical components such as chlorophyll will be affected (Liu, Liu, Chen, Xu, & Ding, 2010; Yingqiong et al, 2003), and thus will cause the variation in crop spectrum (Elliott et al, 2012; Zhao, Wang, Ouyang, & Chen, 2011). The reflection peak observed in 550 nm is formed by the strong reflection of chlorophyll a and chlorophyll b, and the absorption valleys observed in 480 nm and 670 nm are formed by the strong absorption of the pigment (Zhou et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Hyperspectral image technology is a popular nondestructive testing technology in recent years (Khulal, Zhao, Hu, & Chen, 2016; Sunli et al, 2018; Wu, Fu, Tian, Wu, & Sun, 2017), which is an effective method to analyze the change information of spectrum for detection of heavy metals (Zhou et al, 2020). It has been applied in the researches on heavy metal in agricultural products.…”

Section: Introductionmentioning

confidence: 99%

Detection for lead pollution level of lettuce leaves based on deep belief network combined with hyperspectral image technology

Sun

Cao

Zhou

et al. 2020

Journal of Food Safety

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Fast detection for heavy metal in vegetables is one of the most important steps to ensure the food safety. A novel method to identify lead pollution levels of lettuce based on hyperspectral image technology was proposed in this study. Firstly, hyperspectral images of lettuce samples cultivated under four lead stress levels (0 mg/L, 50 mg/L, 100 mg/L and 200 mg/L) were collected using hyperspectral image system. Then, a total of 240 spectra were calculated from region of interest (ROI) in the range of 478–978 nm covering 399 bands. Besides, chemical test showed that the excessive level of lead residues content in lettuce leaves were none, slight, moderate and severe. Moreover, conventional models and deep belief network (DBN) were established to determine the best identification model. The discriminant DBN model reached the highest accuracy with the training set of 100% and test set of 96.67%. Finally, t‐distribution stochastic neighbor embedding (t‐SNE) was successful to visualize the feature values in DBN's last hidden layer. This study indicates that it is viable to detect the lead pollution levels of lettuce leaves based on hyperspectral image technology coupled with DBN discriminant model.

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“…Hyperspectral imaging technology (HSI) is a new technique of conventional spectroscopy and computer vision (Cao et al, 2020;Khulal et al, 2016;Zhou et al, 2020). HSI can provide spatial and 2 | MATERIAL S AND ME THODS…”

Section: Introductionmentioning

confidence: 99%

Classification of tea varieties based on fluorescence hyperspectral image technology and ABC‐SVM algorithm

Ahmad

Sun

Nirere

et al. 2021

J. Food Process. Preserv.

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In this study, a rapid and non-destructive method for the classification of tea varieties based on fluorescence hyperspectral imaging technology was proposed in the wavelength range of 400.6797-1001.612 nm. Multiplication Scatter Correction (MSC) was used to preprocess the spectral data of tea samples. For optimal feature selection, variable iterative space shrinkage approach (VISSA) and competitive adaptive reweighed sampling (CARS) were established and CARS achieved good results on tea spectral data. Four linear and non-linear classification models, Naïve Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Bee Colony Support Vector Machine (ABC-SVM) were established and then performances of classification models were compared according to classification accuracy. The classification accuracy of the ABC-SVM model coupled with CARS was achieved 100% which was the highest classification accuracy. The results of this study demonstrated that fluorescence hyperspectral image technology combined with the CARS-ABC-SVM model is feasible to classify tea varieties. Novelty Impact Statement: Traditional methods for the classification of tea varieties mainly focus on the appearance of tea and depend on human sensory evaluation, which is expensive and time-consuming. In this study, a method involving fluorescence hyperspectral image technology with the CARS-ABC-SVM algorithm successfully was used for precise and non-destructive classification of tea varieties. 2 of 9 | AHMAD et Al. spectral information of an object by integrating conventional spectroscopic and imaging techniques (Kaliramesh et al., 2013;

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

Cited by 88 publications

References 14 publications

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Detection for lead pollution level of lettuce leaves based on deep belief network combined with hyperspectral image technology

Classification of tea varieties based on fluorescence hyperspectral image technology and ABC‐SVM algorithm

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