Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system

ElMasry, Gamal; Iqbal, Abdullah; Sun, Da‐Wen; Allen, Paul; Ward, Paddy

doi:10.1016/j.jfoodeng.2010.10.031

Cited by 152 publications

(53 citation statements)

References 42 publications

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“…employed to interpret spectral data by identifying the greatest directions of variability in a multivariate data space (ElMasry et al, 2011). In this study, PCA was applied to identify variations between tested samples that could be attributed to differences in their spectral information.…”

Section: Spectral Variation Of Chestnut Samples Pca Is a Techniquementioning

confidence: 99%

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

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This paper presents a study that was performed for rapid and noninvasive detection of waxed chestnuts using hyper-spectral imaging. A visual near-infrared (400 _ 1026 nm) hyper-spectral imaging system was assembled to acquire scattering images from two groups of chestnuts (waxed and non-waxed chestnuts). The spectra of the samples were extracted from the hyper-spectral images using image segmentation process. Then multiplicative scatter correction (MSC) was conducted to preprocess the original spectra. Effective wavelengths were selected to reduce the computational burden of the hyper-spectral data. Using the seven effective wavelengths that were obtained from a successive projections algorithm (SPA), three calibration algorithms were compared: partial least squares regression (PLSR), multiple linear regression (MLR) and linear discriminant analysis (LDA). The best model for discriminating between waxed and non-waxed chestnuts was found to be the MSC-SPA-MLR model.Keywords: hyper-spectral imaging, waxed chestnut, effective wavelength, pear IntroductionThe chestnut (Castanea mollissima Bl.) is an important edible fruit in the northern hemisphere, which has been consumed as extensively as the potato in the past (Ferreira-Cardoso et al., 1999).and has become increasingly important in human nutrition because of its nutrient composition and potential beneficial health effects, for example, in reducing coronary heart disease and cancer rates (Sabaté et al., 2001). Fried chestnut is the most popular preparation method for chestnuts. However, some unscrupulous traders add industrial wax while frying chestnuts to ensure that the chestnuts are brighter and more attractive. Generally, industrial wax is extracted directly from oil, which will also extract polycyclic aromatic hydrocarbons and polycyclic aromatic hydrocarbons which are present in the industrial process, both of which are highly carcinogenic (Bonser et al., 1963). Mixed industrial wax will infiltrate into the pulp in the process of frying chestnuts which seriously endangers human health. Hence, a sorting process is necessary to identify and screen out waxed chestnuts.Traditionally, screening waxed chestnuts is usually dependent on subjective criteria to distinguish between waxed and non-waxed chestnuts, taking into consideration that brighter and more attractive chestnuts are mixed with industrial wax. Although industrial wax will make chestnuts brighter and more attractive, it is not a reliable way to identify waxed chestnuts. Hyper-spectral imaging (also called imaging spectrometry or imaging spectroscopy) is an emerging analytical technology which is receiving growing attention as a rapid, efficient, and nondestructive analytical tool for quantitative determination of the quality and safety of agricultural goods. By combining the advantages of computer vision and spectroscopy technology into one system, hyper-spectral imaging can generate a spatial image of the spectral variation, which provides structural, chemical, physical and functional information abou...

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Section: Spectral Variation Of Chestnut Samples Pca Is a Techniquementioning

confidence: 99%

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

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“…To meet the quality requirements and to improve competitiveness of the fruit production industry, rapid, nondestructive, and automated inspection techniques and grading systems are essential. Among them, spectroscopic and hyperspectral imaging systems have many advantages compared to classical chemical and physical analytical methods in the assessment of fruit internal qualities (Nicolaï et al 2007;Lorente et al 2012;Magwaza et al 2012;Cen et al 2014;Yu et al 2014), in particular hyperspectral imaging has recently been extensively investigated for food quality evaluation (ElMasry et al 2011;Barbin et al 2012a;Wu and Sun 2013;Wu et al 2012;Elmasry et al 2012;Barbin et al 2012b). These advantages include short measuring time with limited sample preparation and allowing several constituents to be evaluated at the same time.…”

Section: Introductionmentioning

confidence: 99%

Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

Liú

Wang

et al. 2015

Food Anal. Methods

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Hyperspectral imaging (HSI) technique has shown promise as a rapid and nondestructive tool to evaluate various internal quality attributes of fruits and vegetables. The objective of this study was to investigate the nondestructive prediction of soluble solids content (SSC) and pH of lychees and maturity discrimination. Two hyperspectral imaging systems of visible/short-wave near infrared range (600-1000 nm, Spectral Set I) and long-wave near infrared range (1000-2500 nm, Spectral Set II) were employed. Results showed that Spectral Set II (SSC: r p =0.877, RMSEP=0.911°Brix; pH: r p =0.745, RMSEP=0.291) performed better than Spectral Set I (SSC: r p =0.790, RMSEP=1.279°Brix; pH: r p =0.701, RMSEP=0.308) for the internal quality prediction of litchi and maturity discrimination. The partial least square discriminant analysis (PSL-DA) model had a discrimination rate of 90.63 % for Spectral Set I and 96.88 % for Spectral Set II. β-Coefficients of partial least squares regression (PLSR) models were used to choose optimal wavelengths for quality predictions. The performance of optimized PLSR in both spectral sets were comparable to the models developed using the whole spectral range.

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“…10,14,[42][43][44] While the majority reported quite high accuracies, it is difficult to compare these asresultsweretypicallynotreportedonthepixellevel.…”

Section: Discussionmentioning

confidence: 99%

Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

Roy

Wouters

Ketelaere

et al. 2018

J. Spectral Imaging

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Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system

Cited by 152 publications

References 42 publications

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

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