Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds

Kandpal, Lalit Mohan; Lohumi, Santosh; Kim, Moon S.; Kang, Jum-Soon; Cho, Byoung‐Kwan

doi:10.1016/j.snb.2016.02.015

Cited by 128 publications

(89 citation statements)

References 42 publications

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“…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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“…The highest absolute values of the beta coefficient considered the most important wavebands responsible for the prediction and interpretation of the model (Kandpal et al, 2016). In this study, the major absolute peaks in Table 4.…”

Section: Partial Least Square Regression Analysismentioning

confidence: 63%

Study on Rapid Measurement of Wood Powder Concentration of Wood-Plastic Composites using FT-NIR and FT-IR Spectroscopy Techniques

Cho¹,

Lohoumi²,

Choi³

et al. 2016

Journal of the Korean Wood Science and Technology

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Wood-plastic composite (WPC) is a promising and sustainable material, and refers to a combination of wood and plastic along with some binding (adhesive) materials. In comparison to pure wood material, WPCs are in general have advantages of being cost effective, high durability, moisture resistance, and microbial resistance. The properties of WPCs come directly from the concentration of different components in composite; such as wood flour concentration directly affect mechanical and physical properties of WPCs. In this study, wood powder concentration in WPC was determined by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra from WPC in both powdered and tableted form with five different concentrations of wood powder were collected and preprocessed to remove noise caused by several factors. To correlate the collected spectra with wood powder concentration, multivariate calibration method of partial least squares (PLS) was applied. During validation with an independent set of samples, good correlations with reference values were demonstrated for both FT-NIR and FT-IR data sets. In addition, high coefficient of determination (R 2 p ) and lower standard error of prediction (SEP) was yielded for tableted WPC than powdered WPC. The combination of FT-NIR and FT-IR spectral region was also studied. The results presented here showed that the use of both zones improved the determination accuracy for powdered WPC; however, no improvement in prediction result was achieved for tableted WPCs. The results obtained suggest that these spectroscopic techniques are a useful tool for fast and nondestructive determination of wood concentration in WPCs and have potential to replace conventional methods.

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“…Based on multispectral imaging systems, PLSDA models were also constructed to effectively detect sizes, germination abilities, and germ lengths of spinach and muskmelon seeds. In their studies, PLSDA prediction results demonstrated that larger seeds had higher viability and vigor compared with smaller seeds (Shetty and others ; Kandpal and others ). Also, the prediction of castor seed viability by using normalized canonical discriminant analysis (nCDA) resulted in a 96% classification accuracy, and it confirmed the feasibility to apply multispectral technology in seed viability testing (Olesen and others ).…”

Section: Determination Of Quality Parameters Of Plant Foodsmentioning

confidence: 95%

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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Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds

Cited by 128 publications

References 42 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

Study on Rapid Measurement of Wood Powder Concentration of Wood-Plastic Composites using FT-NIR and FT-IR Spectroscopy Techniques

Multispectral Imaging for Plant Food Quality Analysis and Visualization

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