Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging

Longguo, Wu; He, Junfeng; Liu, Guishan; Wang, Songlei; He, Xiyun

doi:10.1016/j.postharvbio.2015.09.003

Cited by 85 publications

(31 citation statements)

References 24 publications

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“…Although the image correction in Section 2.3 reduced the effect of dark currents on spectra, the phenomenon of noise and baseline drift could not be eliminated . To reduce the influence of this phenomenon, it is necessary to preprocess the spectra data before modeling.…”

Section: Methodsmentioning

confidence: 99%

“…2.6 | Bruising degree classification 2.6.1 | Data processing Although the image correction in Section 2.3 reduced the effect of dark currents on spectra, the phenomenon of noise and baseline drift could not be eliminated. 14 To reduce the influence of this phenomenon, it is necessary to preprocess the spectra data before modeling. By comparing standard normal variate (SNV), multivariate scattering correction, firstorder and second-order derivative, Savitzky-Golay smoothing, and other pretreatment methods, the experimental results showed that SNV could provide a better bruising degree identification model than the other three methods.…”

Section: Image Processing and Average Spectral Data Acquisitionmentioning

confidence: 99%

“…The acquired hyperspectral image provides a spectral image for each spectral band and a spectral curve for each pixel in the image in a three‐dimension form called a “hypercube”; therefore, hyperspectral imaging technology is more reliable than traditional machine vision or spectroscopy techniques in analyzing the characteristics of objects . Currently, hyperspectral imaging technology is widely used in the quality inspection of agricultural products such as identification of hidden bruises on kiwi fruit, internal injury in almond nuts, common defects in jujube, and black spots in potatoes . An increasing amount of research on the detection of different types of apple bruises or diseases using hyperspectral imaging technology has also been reported .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Tan

Sun

Yang

et al. 2018

Journal of Chemometrics

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The ability to determine if an apple is bruised and to provide quantitative and objective descriptions of the degree of bruising is not the only important basis for assessing apple quality but also has significance for improving the postharvest handling of apples. In this study, segmented principal component analysis for hyperspectral images in the spectral range of 401 to 1037 nm was carried out, and seven characteristic wavelengths were selected based on the weight coefficients of the principal component images. By using the principal component analysis operations with the selected wavelengths and image processing methods, an accurate recognition algorithm for apple bruises was proposed. For 40 intact samples and 160 bruised samples, the average correct recognition rate was 99.1%. Moreover, this paper obtained the average spectra of 157 segmented bruised regions by applying a binary mask. A characteristic wavelength selection method that combines competitive adaptive reweighted sampling with correlation coefficient methods and supports vector machine modeling methods based on grid parameter optimization was put forward for the classification and identification of the bruising degrees of apples. The results showed that the classification accuracy was as high as 97.5% for the test set. Overall, this study demonstrated that hyperspectral imaging technology can be used to accurately and effectively identify early bruises and determine the bruising degree of apples, which provides a new method for on‐line, nondestructive detection, and grading of early bruises in apples.

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Section: Methodsmentioning

confidence: 99%

Section: Image Processing and Average Spectral Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Tan

Sun

Yang

et al. 2018

Journal of Chemometrics

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“…The highest classification accuracy of 82% was obtained for insect‐infested apples (Rady and others ). The multispectral imaging technique has also been widely investigated to detect various types of defects (such as insect damage, bruising, decay, cold injury, black heart, puncture injury, and cracks) on various plant foods (such as peach, radish, sunflower seed, citrus, and jujube) (Ma and others ; Zhang and others ; Folch‐Fortuny and others ; Li and others , ; Liu and others ; Pan and others ; Song and others ; Wu and others ). Based on feature wavelengths associated with corresponding defects, simplified models (such as soft independent modeling of class analogy (SIMCA), PCA, ANN, LS‐SVM, FLDA, and MNF) were conducted for nondestructively assessing defects on such plant foods with classification accuracies of over 90%.…”

Section: Determination Of Quality Parameters Of Plant Foodsmentioning

confidence: 99%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

103

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The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

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“…[21,22] A typical hyperspectral image consists of a series of images of different wavelengths, and each pixel of image is a spectrum on this position, which covers the Vis/NIR range. HSI has been used to detect many important quality attributes of agricultural materials, such as color of sausage, [23] defect on jujube, [24] allicin, and soluble solid content of garlic, [25] chilling injury of peaches, [26,27] SSC and firmness of pear, [28] contaminants on wheat [29] and internal qualities of apples. [30] Therefore, the use of hyperspectral imaging has great potential for quality assessments of agricultural materials.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

Weng

Dong

et al. 2020

International Journal of Food Properties

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In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (R p 2 ) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with R p 2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries. ARTICLE HISTORY

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Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging

Cited by 85 publications

References 24 publications

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

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