Non-destructive detection of heavy metals in vegetable oil based on nano-chemoselective response dye combined with near-infrared spectroscopy

Hao, Lin; Jiang, Hao; He, Peihuan; Haruna, Suleiman A.; Chen, Quansheng; Xue, Zhaoli; Chan, Chenming; Ali, Shujat

doi:10.1016/j.snb.2021.129716

Cited by 33 publications

(6 citation statements)

References 37 publications

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“…Linear discriminant analysis of different degrees of copper stress on the stems and roots of Jatropha curcas L. was studied using an optimal band based on PCA analysis, with a classification accuracy of 83.93% [ 41 ]. Some studies have improved the PLS method and combined it with other methods for feature band selection, such as the synergistic interval PLS (siPLS) algorithm, which enhances modeling predictive accuracy [ 42 , 43 ]. Furthermore, a recent study combined siPLS with the Monte Carlo method and proposed MC-siPLS, which was superior to similar wavelength interval selection methods.…”

Section: Spectral Features For Plant Heavy Metal Stress Inspectionmentioning

confidence: 99%

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Zhai,

Zhou,

et al. 2023

Plant Phenomics

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Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants’ absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

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Section: Spectral Features For Plant Heavy Metal Stress Inspectionmentioning

confidence: 99%

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Zhai,

Zhou,

et al. 2023

Plant Phenomics

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“…It is oen performed to complete spectrum variables in order to screen and choose relevant variables with appropriate information based on the goal components being studied. 24,25 The three essential features for the operation of this model are selection, crossover, and mutation. The selection process involves choosing certain parent chromosomes within their group for crossover based on preset parameters.…”

Section: Spectral Signal Pretreatmentsmentioning

confidence: 99%

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Haruna

Wei

et al. 2022

Anal. Methods

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“…Scholars at home and abroad have conducted several studies on the application of this field. Imaging technology based on chemo-responsive dyes has been proposed to evaluate the states of some commodities or agro-products such as detection of cereal (Lin, Kang, et al, 2019;Lin, Wang, et al, 2020), wine and vinegar flavored foods (Guan et al, 2013(Guan et al, , 2014Lin et al, 2018;Lin, Jiang, et al, 2021), poultry meat (Alimelli et al, 2007;Huang et al, 2015), aquatic products, fruits and vegetables, and tea.…”

Section: Application In Sensing Food Quality and Safetymentioning

confidence: 99%

Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Kang

Hao

Jiang

et al. 2021

Comp Rev Food Sci Food Safe

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Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemoresponsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application:The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

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Non-destructive detection of heavy metals in vegetable oil based on nano-chemoselective response dye combined with near-infrared spectroscopy

Cited by 33 publications

References 37 publications

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

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