Rapid and nondestructive method for identification of molds growth time in wheat grains based on hyperspectral imaging technology and chemometrics

Sun, Yuan; Ye, Zhumiao; Zhong, Menghan; Wei, Kaidong; Shen, Fei; Li, Guanglei; Yuan, Jian; Xing, Changrui

doi:10.1016/j.infrared.2022.104532

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Besides Fusarium , another mold, namely Aspergillus , also easily causes wheat mildew ( Mohapatra et al, 2017 ). The growth time of four Aspergillus including A. flavus , A. glaucus , A. parasiticu , and A. ochraceus in wheat grains was respectively quantified and distinguished by HIT using different number of characteristic wavelengths (13–17) with good performance (R 2 P , 0.882–0.932; RMSEP, 0.682–0.895) ( Sun et al, 2023 ), which enables HIT to inspect the mold growth in wheat grains at early stage.…”

Section: Application Of Hit In Wheat Quality Evaluationmentioning

confidence: 99%

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang,

Ou,

et al. 2024

Food Chemistry: X

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Section: Application Of Hit In Wheat Quality Evaluationmentioning

confidence: 99%

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang,

Ou,

et al. 2024

Food Chemistry: X

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“…However, identifying the disease in bananas at an early stage is still challenging. Several laboratory methods, such as microbiological and/or physicochemical techniques, are accurate but they are usually slow, labor-intensive, costly and need complicated sample pretreatment (Yao et al, 2008;Alander et al, 2013;Sun et al, 2023). These compelled non-destructive and economic methods for the early detection of fungal infection (Brosnan and Sun, 2004).…”

Section: Introductionmentioning

confidence: 99%

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

et al. 2023

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IntroductionAnthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.MethodsThis study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.ResultsThe PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.DiscussionThese results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

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A Method for Non-destructive Detection of Moisture Content in Oilseed Rape Leaves Using Hyperspectral Imaging Technology

Liu,

Zhou,

Sun

et al. 2024

J Nondestruct Eval

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Rapid and nondestructive method for identification of molds growth time in wheat grains based on hyperspectral imaging technology and chemometrics

Cited by 7 publications

References 34 publications

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

A Method for Non-destructive Detection of Moisture Content in Oilseed Rape Leaves Using Hyperspectral Imaging Technology

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