Use of Near-Infrared hyperspectral images to identify moldy peanuts

Jiang, Jinbao; Qiao, Xuejun; He, Renjie

doi:10.1016/j.jfoodeng.2015.09.013

Cited by 68 publications

(35 citation statements)

References 19 publications

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“…Together with 3 representative wavelengths (617, 675, and 818nm) that were selected by SPA, the BR values were suitable to detect fungal diseases in peaches with an accuracy of 98.75%. Additionally, multispectral imaging was considered as a very practical method to detect different levels of aflatoxin B 1 on maize and peanuts kernels with PCA, SVM and factorial discriminant analysis (FDA) (Wang and others ; Jiang and others ; Chu and others ).…”

Section: Determination Of Quality Parameters Of Plant Foodsmentioning

confidence: 99%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

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The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

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Section: Determination Of Quality Parameters Of Plant Foodsmentioning

confidence: 99%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

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“…Those wavelengths with larger absolute coe±cients in each PC were considered to play an important role in representing the corresponding PC. 33 All the peaks and valleys (1067, 1195, 1279, 1369, 1459, 1694, 1834 and 1929 nm) of the loading line plots of PC 2 and PC 3 were selected as characteristic wavelengths. Among them, 1067 nm was near 1065 nm which was ascribed to the O-H structure of water.…”

Section: Feature Parameter Acquisition Of a Parasiticus Inoculated Omentioning

confidence: 99%

Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared (SWIR) hyperspectral imaging

Chu

Wang

et al. 2018

J. Innov. Opt. Health Sci.

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The growth characteristics of Aspergillus parasiticus incubated on two culture media were examined using shortwave infrared (SWIR, 1000–2500[Formula: see text]nm) hyperspectral imaging (HSI) in this work. HSI images of the A. parasiticus colonies growing on rose bengal medium (RBM) and maize agar medium (MAM) were recorded daily for 6 days. The growth phases of A. parasiticus were indicated through the pixel number and average spectra of colonies. On score plot of the first principal component [Formula: see text] and PC2, four growth zones with varying mycelium densities were identified. Eight characteristic wavelengths (1095, 1145, 1195, 1279, 1442, 1655, 1834 and 1929[Formula: see text]nm) were selected from PC1 loading, average spectra of each colony as well as each growth zone. Furthermore, support vector machine (SVM) classifier based on the eight wavelengths was built, and the classification accuracies for the four zones (from outer to inner zones) on the colonies on RBM were 99.77%, 99.35%, 99.75% and 99.60% and 99.77%, 99.39%, 99.31% and 98.22% for colonies on MAM. In addition, a new score plot of PC2 and PC3 was used to differentiate the colonies incubated on RBM and MAM for 6 days. Then characteristic wavelengths of 1067, 1195, 1279, 1369, 1459, 1694, 1834 and 1929[Formula: see text]nm were selected from the loading of PC2 and PC3. Based on them, a new SVM model was developed to differentiate colonies on RBM and MAM with accuracy of 100.00% and 99.99%, respectively. In conclusion, SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A. parasiticus incubated in different culture media.

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“…The PLS-DA model has been created to arrange control and contaminated kernels and was discovered that most elevated general arrangement exactness yielded of the created model was 96.9% [70]. In their study, Jiang et al [71] focused to identify the moldy peanuts using near-infrared (NIR) hyperspectral images, and NIR hyperspectral images were obtained at the wavelength ranging from 970 to 2570 nm. In order to select sensitive bands, principle component analysis (PCA) in the spectral dimension was used as well as the spectral vector was employed to identify the moldy information [71].…”

Section: Harvest Control Strategiesmentioning

confidence: 99%

The Control of Aflatoxin Contamination at Harvest, Drying, Pre‐ Storage and Storage Periods in Peanut: The New Approach

Lavkor¹,

Var²

2017

Aflatoxin-Control, Analysis, Detection and Health Risks

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Aflatoxins produced by Aspergillus flavus and Aspergillus parasiticus are contaminants of peanut (Arachis hypogea L.). Aflatoxin contamination is a serious concern given their hepatotoxic properties and their widespread occurrence during cultivation, harvest, drying and storage. Management of aflatoxin contamination of peanut is very important using cultural practice such as habitat management, soil amendments and pre-and post-harvest managements, using physical control methods, biological control methods and chemical control methods at harvest, drying, pre-storage and storage periods. Some procedures such as upkeep of low temperature and relative humidity (RH) in storage, keeping away the pod-and seed-feeding insects, doing the harvest and post-harvest procedure control, fast post-harvest drying, optimal timing of digging and harvest, providing optimum water to the crop through irrigation, avoiding mechanical damage during cultivation and optimal timing of digging and harvest might prevent the contamination of aflatoxin. In this review, various strategies for control of aflatoxin in peanuts in all periods are discussed.

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Use of Near-Infrared hyperspectral images to identify moldy peanuts

Cited by 68 publications

References 19 publications

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared (SWIR) hyperspectral imaging

The Control of Aflatoxin Contamination at Harvest, Drying, Pre‐ Storage and Storage Periods in Peanut: The New Approach

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