Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli

Lee, Hoonsoo; Kim, Moon S.; Qin, Jianwei; Park, Eunsoo; Song, Yu-Rim; Oh, Chang‐Sik; Cho, Byoung‐Kwan

doi:10.3390/s17102188

Cited by 34 publications

(26 citation statements)

References 48 publications

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“…A larger F-value indicates that the statistical mean separation between two groups is more significant. Referring to the research results of Lee [ 48 ], this study selects the local maximum F-value to determine the characteristic wavelength for detecting water core apple. Figure 5 A–C shows the F-values of all single wavebands for O1, O2, and O3 detection orientations, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Zhang

Cai

Fan

et al. 2021

Foods

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Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

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Section: Resultsmentioning

confidence: 99%

“…Band ratio method was widely applied for inspecting fruit quality [ 45 , 46 , 47 ]. Lee et al [ 48 ] identified the watermelon seeds infected by bacteria Acidovoraxcitrulli based on the optimal Raman hyperspectral band ratio determined by ANOVA. The success rates of healthy seeds and infected seeds were 75% and 86%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Zhang

Cai

Fan

et al. 2021

Foods

View full text Add to dashboard Cite

show abstract

“…A high accuracy was obtained using the method and is thus recommended for classification. PLS-DA has been used with different purposes for the classification of seeds, for example, Raman hyperspectral imaging has been used for the classification of watermelon seeds infected with Acidovorax citrulli [41], which presented a high accuracy. In another study, hyperspectral imaging was used for the classification of Arabic and Robusta green coffee based on PLS-DA [42].…”

Section: Pls-da Model Resultsmentioning

confidence: 99%

Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds

et al. 2020

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Almonds are nutrient-rich nuts. Due to their high level of consumption and relatively high price, their production is targeted for illegal practices, with the intention of earning more profit. The most common adulterants are based on superficial matching, and as an adulterant, the apricot kernel is comparatively inexpensive and almost identical in color, texture, odor, and other physicochemical characteristics to almonds. In this study, a near-infrared hyperspectral imaging (NIR-HSI) system in the wavelength range of 900–1700 nm synchronized with a conveyor belt was used for the online detection of added apricot kernels in almonds. A total of 448 samples from different varieties of almonds and apricot kernels (112 × 4) were scanned while the samples moved on the conveyor belt. The spectral data were extracted from each imaged nut and used to develop a partial least square discrimination analysis (PLS-DA) model coupled with different preprocessing techniques. The PLS-DA model displayed over a 97% accuracy for the validation set. Additionally, the beta coefficient obtained from the developed model was used for pixel-based classification. An image processing algorithm was developed for the chemical mapping of almonds and apricot kernels. Consequently, the obtained model was transferred for the online sorting of seeds. The online classification system feedback had an overall accuracy of 85% for the classification of nuts. However, the model presented a relatively low accuracy when evaluated in real-time for online application, which might be due to the rough distribution of samples on the conveyor belt, high speed, delaying time in suction, and lighting variations. Nevertheless, the developed online prototype (NIR-HSI) system combined with multivariate analysis exhibits strong potential for the classification of adulterated almonds, and the results indicate that the system can be effectively used for the high-throughput screening of adulterated almond nuts in an industrial environment.

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“…reported that Raman hyperspectral imaging was an excellent candidate to substitute conventional methods for identification of bacteria in infected watermelon seeds as it provides high accuracy, rapid and non-destructive detection tools. Although this technique is recommended for detection of A. citrulli, it is however, requires extremely expensive equipment and skilled manpower (Lee et al 2017).…”

Section: Raman Hyperspectral Imagingmentioning

confidence: 99%

Etiology, diagnostic approaches and management strategies of Acidovorax citrulli, a bacterial fruit blotch pathogen of cucurbits

Husni¹,

Ismail²,

Jaafar³

et al. 2021

Plant Prot. Sci.

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Bacterial fruit blotch (BFB) caused by Acidovorax citrulli, represents one of the most destructing diseases of cucurbits, especially to watermelon- and melon producing-regions. This disease has been spread sporadically to many countries globally, due to the unintentionally dispersal of contaminated commercial seeds. The BFB causes massive yield losses up to 100% under conducive conditions. Once infected, all parts of the host plants are extremely susceptible to this bacterium, especially the seedlings and fruits parts. In recent years, various management approaches and detection tools have been employed to control A. citrulli. Genotypic characterization methods revealed two distinct groups of A. citrulli strains; (i) group I strains primarily isolated from non-watermelon cucurbits and consist of moderate to highly aggressive strains from wide range of cucurbit hosts, and (ii) group II strains isolated from watermelon which are highly aggressive on watermelon, but mildly aggressive on non-watermelon hosts. In this paper, an attempt has been made to review research findings where the impact of diverse methods and management approaches were applied in detection and controlling of A. citrulli infection. A better understanding of this devastating bacterium will serve as guidelines for agricultural practitioners in developing the most efficient and sustainable BFB control strategies.

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Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli

Cited by 34 publications

References 48 publications

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds

Etiology, diagnostic approaches and management strategies of Acidovorax citrulli, a bacterial fruit blotch pathogen of cucurbits

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