Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning

Kamruzzaman, Mohammed; Makino, Yoshio; Oshita, Seiichi

doi:10.1016/j.jfoodeng.2015.08.023

Cited by 168 publications

(81 citation statements)

References 57 publications

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“…Also, the results in [7][8][9] showed that an HSI system is able to provide significant information for performing classification in a plurality of applications for meat, such as detection of adulteration of minced meat [7], detection of chicken adulteration in minced beef [8], and lamb muscle discrimination [9]. In all of these studies, the models produced misclassification of pixels in pixel-based prediction, although they performed well in the case of sample-based prediction.…”

Section: Literature Reviewmentioning

confidence: 96%

“…In this case, it is more practical and reliable to perform a pixel-wise classification (i.e., local) than a sample-wise classification [16]. For dealing with spectral variations, several methods were established and used such as spectral derivatives [5], unit-vector normalization [16], standard normal variates (SNV) [8], and multiplicative scatter correction (MSC) [8]. In [16], SNV and unit-vector normalization were evaluated with regard to the problem of classifying red-meat; SNV performed better than unit-vector normalization in this classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, hyperspectral imaging (HSI) systems have gained attention in a plethora of research areas such as medical applications, remote sensing imagery [1][2][3], food and meat processing [4][5][6][7][8][9], etc. The key advantage of HSI systems is that they provide a combination of spectroscopy technologies [10] (i.e., availability of spectral information) and conventional imaging systems (i.e., availability of spatial information).…”

Section: Introductionmentioning

confidence: 99%

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Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

et al. 2018

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This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).

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Section: Literature Reviewmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

et al. 2018

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“…To minimize the signal noises due to the disturbance of instrument structure and detector sensitivity, the raw hyperspectral reflectance images were normalized into relative hyperspectral reflectance images using white reference and dark reference images, referring to the following formula [18]:…”

Section: Hyperspectral Image Calibration and Region Of Interest (Roi)mentioning

confidence: 99%

“…Then, in order to eliminate the undesired impact, such as random noise, light scattering, and baseline shifts [18], three spectral preprocessing techniques, including standard normal variate (SNV), Savitzky-Golay first derivative (1st Der) (with a second-order polynomial and a five-point window), de-trending (Det) (with a second-order polynomial), two combinations of SNV with 1st Der (SNV + 1st Der) and SNV with Det (SNV + Det), were separately adopted and compared to deal with the absorption spectra prior to the model establishment. SNV is normally applied to remove scatter effect.…”

Section: Spectral Preprocessingmentioning

confidence: 99%

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

Zhao

Wang

et al. 2018

Applied Sciences

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Featured Application: Near-infrared hyperspectral imaging was employed for inspection of contaminant of peanut or walnut powders in whole wheat flour. A multispectral prediction model and visualization maps demonstrated the potential of the technique for the application of rapid quality control. Abstract:The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility of utilizing near-infrared hyperspectral imaging to inspect peanut and walnut powder in whole wheat flour was evaluated herein. Hyperspectral images at wavelengths 950-1700 nm were acquired. A standard normal variate combined with the Savitzky-Golay first derivative spectral transformation was adopted for the development of a partial least squares regression (PLSR) model to predict contamination concentrations. A successive projection algorithm (SPA) and uninformative variable elimination (UVE) for feature wavelength selection were compared. Two individual prediction models for peanut or walnut-contaminated flour, and a general multispectral model for both peanut-contaminated flour and walnut-contaminated flour, were developed. The optimal general multispectral model had promising results, with a determination coefficient of prediction (R p 2 ) of 0.987, and a root mean square error of prediction (RMSEP) of 0.373%. Visualization maps based on multispectral PLSR models reflected the contamination concentration variations in a spatial manner. The results demonstrated that near-infrared hyperspectral imaging has the potential to inspect peanut and walnut powders in flour for rapid quality control.

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Visible Light Imaging

Ali

Hashim

2021

Electromagnetic Technologies in Food Science

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Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning

Cited by 168 publications

References 57 publications

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

Visible Light Imaging

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