Hyperspectral imaging with different illumination patterns for the hollowness classification of white radish

Pan, Leiqing; Sun, Yi; Xiao, Hui; Gu, Xuefan; Hu, Pengcheng; Wei, Yingying; Tu, Kang

doi:10.1016/j.postharvbio.2016.12.006

Cited by 52 publications

(24 citation statements)

References 38 publications

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“…. First, to reduce the influence of camera quantum efficiency and different configurations of HSI system, dark and white reference images are used for obtaining relative corrected images according to

R (λ) (%) = [(R_{0} (λ) - B_{normald}) / (B_{normalw} - B_{normald})] \times 100

…”

Section: Methodsmentioning

confidence: 99%

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Non‐destructive classification of apple bruising time based on visible and near‐infrared hyperspectral imaging

Pan

Sun

et al. 2018

J Sci Food Agric

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R (λ) (%) = [(R_{0} (λ) - B_{normald}) / (B_{normalw} - B_{normald})] \times 100

…”

Section: Methodsmentioning

confidence: 99%

“…It can be observed that wavelengths ranging from 733 to 900 nm get relatively high importance scores. Also, there are 15…”

Section: Pretreatment Of Spectral Datamentioning

confidence: 99%

Non‐destructive classification of apple bruising time based on visible and near‐infrared hyperspectral imaging

Pan

Sun

et al. 2018

J Sci Food Agric

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“…Moreover, transmittance measurements are influenced by product size and shape, and the long light pathlength in the product could complicate quality assessment. In addition, transmittance mode can be implemented in a semi-transmittance setup [35], by placing the lamp horizontally at one side of the sample, which is able to acquire the information inside the sample with less light power supply. In interactance mode (Figure 3c), the detector and light source are positioned in the same side of the sample with a certain source-detector separation, and a light barrier is placed between them to ensure that the detected light has gone through a minimum distance, which is conducive to assessing the properties of sublayer tissues.…”

Section: Commen Sensing Modesmentioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

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“…The successive projections algorithm (SPA) was used to identify the optimal wavelengths from the three patterns of spectra to detect the hollowness in white radishes with a prediction accuracy of 98%. [11] This algorithm was also used to select the characteristic wavelengths in the classification of black beans.- [12] The competitive adaptive reweighted sampling (CARS) was applied to obtain optimal wavelengths for detecting blueberry internal bruising from 30 min to 12 h after mechanical impact. [13] The Monte Carlo-uninformative variable elimination (MC-UVE) and successful projections algorithm (SPA) was conducted in the spectral domain for the discriminant wavelength (DW) selection to detect common defects on peaches.…”

Section: Introductionmentioning

confidence: 99%

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Zhang

2018

International Journal of Food Properties

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Hyperspectral imaging technique (400-1000 nm) was used for rapid and nondestructive recognition of bruises of apples. A total of 324 hyperspectral images were collected from 108 Fuji apples and the average spectral reflectance was extracted from the region of interest (ROI) of each image. The classification results of AdaBoost for the data pretreated by various existing methods were compared. Then, the correlation-based feature selection (CFS) algorithm was used to obtain characteristic wavelengths for reducing data redundancy. After pretreating with multiplicative scatter correction (MSC) and CFS, the average accuracy of the selected wavelengths was 97.63%. Then, an image processing algorithm based on the characteristic wavelengths selected before was proposed for the visual discrimination of bruises. This algorithm performed independent component analysis (ICA) transformation of the selected wavelengths, and chose the third component image of the ICA transform, then used adaptive threshold segmentation to obtain the bruise region of apples. The results showed that hyperspectral imaging technology could discriminate apple bruise, and this study can help to develop an online apple bruises detection system. ARTICLE HISTORY

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Hyperspectral imaging with different illumination patterns for the hollowness classification of white radish

Cited by 52 publications

References 38 publications

Non‐destructive classification of apple bruising time based on visible and near‐infrared hyperspectral imaging

Non‐destructive classification of apple bruising time based on visible and near‐infrared hyperspectral imaging

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

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