Non‐destructive technique for determining the viability of soybean (<scp><i>Glycine max</i></scp>) seeds using FT‐NIR spectroscopy

Kusumaningrum, Dewi; Lee, Hoonsoo; Lohumi, Santosh; Mo, Changyeun; Kim, Moon S.; Cho, Byoung‐Kwan

doi:10.1002/jsfa.8646

Cited by 70 publications

(50 citation statements)

References 35 publications

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“…Lohumi, Mo, Kang, Hong, and Cho () explored the feasibility of using Fourier transform near‐infrared spectroscopy (FT‐NIR) combined with partial least squares‐discriminant analysis (PLS‐DA) to identify the viability of watermelon seeds. Moreover, FT‐NIR technology combined with PLS‐DA model was applied for the viability detection of crop seeds such as corn (Ambrose, Lohumi, Lee, & Cho, ) and soybean (Kusumaningrum et al, ), and the satisfactory results were achieved. In addition, HSI technology coupled with suitable model was successfully applied in the identification of viable and nonviable muskmelon seeds (Kandpal, Lohumi, Kim, Kang, & Cho, ), wheat (Zhang et al, ), and other seeds.…”

Section: Introductionmentioning

confidence: 99%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

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In this study, the feasibility of the fluorescence hyperspectral imaging (FHSI) technology to detect the viability of soybean seeds was investigated. Viable and nonviable seed samples were obtained by artificial aging method. Hyperspectral images of samples were collected by the FHSI device and then the spectral data were collected. Characteristic wavelengths were respectively selected by three variable selection methods, eliminating a large number of redundant information irrelevant to the viability of soybean seeds. Support vector machine (SVM) models based on the full spectra and the optimal spectral data were developed to identify the viability of soybean seeds. To further improve the accuracy of the model, the adaptive boosting (AdaBoost) algorithm was used. The results showed that the accuracy of the calibration and validation sets in the CARS‐SVM‐AdaBoost model (22 characteristic wavelengths) reached 100%, indicating that the combination of FHSI technology and the optimization model can greatly improve the recognition accuracy. Practical applications A rapid and accurate nondestructive identification method of viability of soybean seeds can contribute to the construction of the online seed viability detection system. FHSI technology has the advantages of high sensitivity and comprehensive analysis of sample information. Combined with the optimization model proposed in this paper, the recognition accuracy can be greatly improved. It can be applied to the online seed viability detection by seed companies, seed quality inspection departments, and soybean breeding units.

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Section: Introductionmentioning

confidence: 99%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

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“…This indicated that the new hyperplane in the characteristic spectral space could no longer classify the samples as the hyperplace managed to do in the full-range spectral space. The results in previous literature [12,23] showed that the performances of those models established on the selected variables had decreased slightly. However, Yang et al, (2015) showed the opposite result, where the classification rates of the SVM model based on feature wavelengths increased while using SPA methods to select the feature wavelength variables [6].…”

Section: Discriminant Models Based On Feature Wavelength Variablesmentioning

confidence: 86%

“…Applying appropriate methods to select feature wavelengths for modeling would obtain the same performance as the full-range wavelength models in seed quality detection [23,35]. In this study, the genetic algorithm (GA) was utilized to identify the feature wavelengths.…”

Section: Feature Wavelengths Selectionmentioning

confidence: 99%

“…It has been widely studied in seed quality detection because of its advantages of a high speed and low cost, and its non-contact detection features give it considerable potential for online detection. Thus far, FT-NIR spectroscopy has been studied in detecting the composition contents [16][17][18], viability [19][20][21][22][23], and contaminations [24][25][26] in various varieties of grains or seeds. Attaviroj et al, (2011) applied FT-NIR spectroscopy to classify five varieties of rice and achieved a high accuracy of 99.22% using the partial least-squares discriminant analysis (PLS-DA) modeling method [27].…”

Section: Introductionmentioning

confidence: 99%

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Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

et al. 2019

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Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

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“…Цвет является довольно устойчивым генетически и наиболее простым в измерении биофизическими мето-дами феномаркером для контроля качества семян. Оптическая технология сепарации семян, согласно обширным исследованиям [25,28,31,32,33,34,36,38,39,40,42,43,44,45,46,47,49], способна определять происхождение и жизнеспособность репродуктивного материала. При необходимости нетрудно реализовать и классификацию семян по количественному признаку, и элиминирование примесей.…”

Section: технологии машины и оборудование --------------------------unclassified

Analysis of Operational Mechanized Technologies of Seed Separation Under Artificial Forest Restoration

Драпалюк¹,

Drapalyuk

Новиков³

et al. 2018

Forestry Engineering Journal

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Сведения об авторах Бартенев Игорь Иванович-заведующий отделом семеноводства и семеноведения сахарной свеклы с элементами механизации ФГБНУ «Всероссийский научно-исследовательский институт сахарной свеклы и сахара имени А.Л. Мазлумова», кандидат технических наук, п.

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Non‐destructive technique for determining the viability of soybean (Glycine max) seeds using FT‐NIR spectroscopy

Cited by 70 publications

References 35 publications

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

Analysis of Operational Mechanized Technologies of Seed Separation Under Artificial Forest Restoration

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