Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Hu, Can; Zhu, Xiaotong; Yi, Xiaokang; Dong, Xiaoguang; Lu, Bing; Tang, Xiuying

doi:10.1080/00387010.2019.1648295

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Vis-NIR spectroscopy is an effective method to realize the rapid and non-destructive detection of jujube quality [ 40 , 41 , 42 ]. Most of these studies have focused on fresh jujubes.…”

Section: Discussionmentioning

confidence: 99%

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

et al. 2022

Foods

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Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/near-infrared spectroscopy was proposed. For this, the diffuse reflectance spectra in the range of 400–1100 nm of dried Hami jujubes were obtained. Borderline synthetic minority oversampling technology (BL-SMOTE) was applied to solve the problem of imbalanced sample distribution, and its effectiveness was demonstrated compared to other methods. Then, the feature variables selected by competitive adaptive reweighted sampling (CARS) were used as the input to establish the support vector machine (SVM) classification model. The parameters of SVM were optimized by the modified reptile search algorithm (MRSA). In MRSA, Tent chaotic mapping and the Gaussian random walk strategy were used to improve the optimization ability of the original reptile search algorithm (RSA). The final results showed that the MRSA-SVM method combined with BL-SMOTE had the best classification performance, and the detection accuracy reached 97.22%. In addition, the recall, precision, F1 and kappa coefficient outperform other models. Furthermore, this study provided a valuable reference for the detection of defective fruit in other fruits.

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“…Vis-NIR spectroscopy is an effective method to realize the rapid and non-destructive detection of jujube quality [ 40 , 41 , 42 ]. Most of these studies have focused on fresh jujubes.…”

Section: Discussionmentioning

confidence: 99%

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

et al. 2022

Foods

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“…The calculation of the Kappa coefficient is based on the confusion matrix and can be used to measure the classification accuracy. The higher the Kappa value, the better the classification result and the more stable the model (Hu et al, 2019). From the analysis of reverse and ventral spectral data, the performance of the three models based on the feature wavelengths selected by SPA had been slightly improved.…”

Section: Modeling Analysis Based On Feature Wavelengthsmentioning

confidence: 99%

Nondestructive identification of barley seeds varieties using hyperspectral data from two sides of barley seeds

Sun

Zhang

et al. 2021

J Food Process Engineering

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The identification of seed varieties is of significance to ensure seed purity. In this study, 1,600 barley seeds (400 per variety) were selected as the samples. By using a near-infrared hyperspectral imaging system, the hyperspectral cube data on the reverse and ventral sides of each barley seed were obtained. Three preprocessing methods (standard normal variate, multivariate scatter correction, and Savitzky-Golay first derivative) and three discriminant models (k-nearest neighbors, support vector machine [SVM], and random forest) were selected for preprocessing and modeling, respectively. The results indicated that the reverse spectral data preprocessed by multivariate scatter correction and the ventral spectral data preprocessed by Savitzky-Golay first derivative had the best modeling results. In order to further simplify the model, feature selection algorithm (successive projections algorithm) was used for feature wavelength selection. After multivariate data analysis, the combination of knearest neighbors and successive projections algorithm based on the reverse spectral data was selected as the best discriminant model. The accuracy of the calibration set and the prediction set were 95.52% and 93.71%, and the Kappa value were 0.9424 and 0.9156, respectively. The reverse spectral data of barley seeds of the validation set were used to verify the accuracy of the discriminant model and visualize the classification results. Finally, considering the practical application, the mixed spectral data containing different ratios of the reverse spectral data was analyzed, and satisfactory results were obtained. It can be concluded from this study that hyperspectral imaging technology was a very promising method for identification of barley seed varieties. Practical applicationsWith the circulation of the seed market, the traditional methods used for variety identification can no longer meet the needs of large-scale, high-quality production. As a nondestructive determination method, hyperspectral imaging technology combined machine vision and near-infrared spectroscopy. In this study, hyperspectral imaging technology was used to identify barley seed varieties, and satisfactory results were obtained. It can provide a theoretical reference for future multispectral online detection technology. | INTRODUCTIONHitherto, cereals are still the most important source of food in the world, which provide us with daily food and are the foundation of human nutrition (Kearney, 2010). Among them, barley (Hordeum vulgare L.) is the fourth largest cereal crop in terms of planting area (Han et al., 2017). Barley has a wide range of usage which can be used not only as feed, food, but also as a raw material for liquor brewing,

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“…It has an important role in enhancing the body's immunity and inhibiting cell cancer. In recent years, the jujube industry has developed rapidly in China, accounting for more than 98% of the world's jujube planting area and output (Hu, Zhu, & Yi, 2019). With the improvement of quality of life, people have higher requirements for the appearance quality and internal nutrients of jujube fruit.…”

Section: Introductionmentioning

confidence: 99%

Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

Guo

Zheng

et al. 2020

J Food Process Engineering

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As an important link in the processing of jujube products, the qualities classification of jujubes have an important impact on improving the value of commodities. In this study, jujube target was extracted based on the RGB color space characteristics and then put into a black background through a mask. The data augmentation method combined deep convolutional generative adversarial networks and rigid transformation (RT) was used to improve the data richness of defective jujubes, effectively solve the imbalance problem between different types of jujube data. A composite convolutional neural network (CNN) method based on residual networks was designed to effectively solve the problem of misjudgment between jujubes with subtle defects and healthy jujubes. The overall results illustrated that the defect detection accuracy of the proposed scheme was 99.2%, which was superior to the widely used support vector machine and CNN methods. This work could be applied to the actual processing site and greatly improved the quality classification effect of jujubes.Practical ApplicationsCracks, peeling, wrinkles, and other defects have seriously affected the quality and value of jujubes, and the quality classification of jujubes is imperative. This paper proposes a set of deep learning schemes from three aspects of improving data quality, enhancing data richness, and designing more accurate and effective classification models. Experimental results show that this scheme can significantly improve the accuracy of jujube quality grading.

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Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Cited by 7 publications

References 15 publications

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

Nondestructive identification of barley seeds varieties using hyperspectral data from two sides of barley seeds

Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

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