Detection of early bruises in jujubes based on reflectance, absorbance and Kubelka-Munk spectral data

Yuan, Ruirui; Guo, Mei; Li, Chengyang; Chen, Shoutao; Liu, Guishan; He, Junfeng; Wan, Guoling

doi:10.1016/j.postharvbio.2021.111810

Cited by 38 publications

(20 citation statements)

References 52 publications

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“…It is worth noting that, unlike the R and A spectra, most of the characteristic wavelengths in the K–M spectrum were in the range of 450 nm to 550 nm. This is consistent with the findings of Yuan et al 58 …”

Section: Resultssupporting

confidence: 94%

“…spectrum were in the range of 450 nm to 550 nm. This is consistent with the ndings of Yuan et al 58 The characteristic wavelengths of the raw K-M spectra and the GF preprocessed spectra were selected by UVE algorithm, and the selection process was referred to in Section 3.4.1. For the original spectrum, the numbers of characteristic wavelengths corresponding to the damage area, absorbed energy and maximum force were 98, 68 and 114, representing 55.7%, 38.6% and 64.8% of the total wavelengths, respectively.…”

Section: Absorbance Spectral Characteristic Variable Selectionmentioning

confidence: 75%

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Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

Li¹,

Zhang²,

Liu³

et al. 2022

RSC Adv.

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Section: Resultssupporting

confidence: 94%

Section: Absorbance Spectral Characteristic Variable Selectionmentioning

confidence: 75%

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

Li¹,

Zhang²,

Liu³

et al. 2022

RSC Adv.

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“…Finally, a support vector machine (SVM) model based on the full waveband and selected wavelengths was used for nondestructive detection of mechanical parameters. Yuan et al (2022) obtained the reflection (R), absorption (A) and Kubelka-Munk (K-M) spectra of Lingwu jujube after bruising by hyperspectral imaging technology, and the feasibility of detecting the early damage degree of Lingwu jujube based on absorption spectra using different preprocessing methods and modeling approaches was demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method

Li¹,

Yin²,

Liu³

et al. 2022

J Anal Sci Technol

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To deduce the process of bruise and reduce the number of bruised fruits from the source, the storage time of yellow peaches after bruise should be identified. In order to distinguish the different storage times of mild bruise’s yellow peaches more effectively than current detection methods, the combined hyperspectral imaging and machine learning method was proposed. Firstly, the sample bruise region spectrum was extracted as spectral features, and then, the hyperspectral images were processed by Principal Component Analysis (PCA), and eight single-wavelength images were selected according to the weight coefficient curve of PC1 images, and the gray values of the selected images were calculated as image features. Finally, in order to find the optimal discriminative model, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were built based on spectral features, image features, and spectral features combined with image features, respectively. The results show that the XGBoost models based on spectral features, image features, and spectral features combined with image features are the optimal models with the overall accuracy of 77.50%, 87.50% and 90.00%, respectively. To simplify the model, Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to screen the wavelength of the normalized spectral data, and then they were fused with the image feature data again and the XGBoost model with an overall model accuracy of 95.00% was built. To sum up, the combined hyperspectral imaging and machine learning method can be used to distinguish the different storage times (2 h, 8 h, 24 h and 48 h) of mild bruise’s yellow peaches effectively. It provides a certain theoretical basis for hyperspectral imaging technology in fruit bruise detection.

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“…In the process of loquat picking and postharvest handling, bruised loquats are identi ed through the operator's naked eye that it is a ected by personal habits, light intensity, and subjective psychological factors, resulting in misclassi cation that mild bruised loquat as normal loquat [4], so it is crucial to nd a method by which the high-precision, rapid, and nondestructive detection of the bruised loquats can come true. In recent years, the spectroscopic techniques have been used in agriculture, including preharvest and postharvest product quality and safety detection and sorting [5].…”

Section: Introductionmentioning

confidence: 99%

Detection Storage Time of Mild Bruise’s Loquats Using Hyperspectral Imaging

Han¹,

Li²,

Wang³

et al. 2022

Journal of Spectroscopy

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Bruise may cause spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, it is crucial to detect whether a loquat is bruised and when it is bruised to save storage and transportation costs. At present, the bruise of loquats is mainly discriminated by the operator’s naked eye, which is affected by personal habits, light intensity, and subjective psychological factors. The detection method is time-consuming, inaccurate, inefficient, and difficult to identify the bruise’s time of loquats. Due to the fact that the color features can be used to perform the conditions of the darkened and brownish regions in bruise’s loquats, the combined spectral information and the color features method is proposed to accurately detect the storage time of mild bruise’s loquats in this study. In order to reduce economic losses, different methods are used to deal with the loquats at the corresponding bruise’s time. Loquats with four types of bruise’s time, including 6, 12, 24, and 36 h, are studied. Models with four types of characteristics, including spectral information, RGB features combined with spectral information, HSI features combined with spectral information, and mixed color features combined with spectral information (mixed-spectral), are established based on linear discriminant analysis (LDA), support vector machine (SVM), and least-squares support vector machine (LS-SVM). The investigated 400 independent samples with four bruise’s time conditions are utilized to assess the classification ability of the proposed methods. The results indicate that the Mixed-RBF-LS-SVM model has the lowest errors, and the accuracies of storage time of mild bruise’s loquats at 6, 12, 24, and 36 h are 100%, 92%, 92%, and 100%, respectively. The overall accuracy of the LS-SVM model based on mixed-spectral is 96%, and it demonstrates that the combined spectral information and color features method can be used to accurately detect the bruise’s time of loquats. Finally, the LS-SVM model based on mixed-spectral is optimized by UVE, SPA, CARS, and GA, respectively; it is found that the UVE-LS-SVM model based on mixed-spectral is the best, and the overall accuracy is 92%. It also lays a foundation for future studies about detecting the bruise’s time of fruits with a high-precision, rapid, and nondestructive measurement.

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Detection of early bruises in jujubes based on reflectance, absorbance and Kubelka-Munk spectral data

Cited by 38 publications

References 52 publications

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method

Detection Storage Time of Mild Bruise’s Loquats Using Hyperspectral Imaging

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