Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine

Ji, Yamin; Sun, Laijun; Li, Yingsong; Liu, Shuangcai; Xie, Xu; Xu, Yuantong

doi:10.1016/j.infrared.2019.04.007

Cited by 62 publications

(36 citation statements)

References 26 publications

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“…SVM, based on the statistical learning theory and structural risk minimization, is a supervised classification method which can deal with both linear and nonlinear data efficiently with good generalization ability [25,26]. Compared with other methods, SVM has such advantages as it does not need a large number of training samples for developing a model and is not affected by the presence of outliers [27] and has been proven as a reliable and efficient method for the spectral data analysis of agricultural products [28][29][30][31][32]. Artificial neural networks have been widely used in agriculture areas, including surface inspection of fruits [17,[33][34][35][36][37][38], since regaining their popularity in the early 1980s.…”

Section: Classification Methodsmentioning

confidence: 99%

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Pham

Liou

2020

Applied Sciences

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A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.

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Section: Classification Methodsmentioning

confidence: 99%

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Pham

Liou

2020

Applied Sciences

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“…Support vector regression is a classical modeling method with excellent generalization capability and high prediction accuracy, it uses mapping relationships to transform the data with non-linear relationship in low-dimensional space into high-dimensional space, so that a linear function can be constructed to describe the relationship between these data. In general, it can transform non-linear data into linear, data with regression [33].…”

Section: Model Establishment and Evaluationmentioning

confidence: 99%

Predicting the moisture content of Daqu with hyperspectral imaging

Chen

Tian

et al. 2020

International Journal of Food Engineering

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Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging.

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“…These studies all showed the feasibility of characterizing water content and chromaticity by HSI. As for the quality assessment of potatoes, HSI has been used in the following studies: prediction of pigment content in purple-fleshed sweet potato tuber slices [15], sugar content detection [16], determination of starch content [17], prediction of starch, soluble sugars and amino acids [18], identification of sliced organic potatoes [19], prediction of sprouting potato eyes [20], evaluation of optimal cooking time for boiled potatoes [21], classification of defective potatoes [22], as well as detection of blackspot [23]. Moreover, Sun et al [24] utilized HSI to predict the moisture content and freezable water content of purple-fleshed sweet potato slices during drying process.…”

Section: Introductionmentioning

confidence: 99%

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

Xiao

Bai

2020

Foods

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Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2c, R2p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2c, R2p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.

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Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine

Cited by 62 publications

References 26 publications

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Predicting the moisture content of Daqu with hyperspectral imaging

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

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