Application of NIR hyperspectral imaging for discrimination of lamb muscles

Kamruzzaman, Mohammed; ElMasry, Gamal; Sun, Da‐Wen; Allen, Paul

doi:10.1016/j.jfoodeng.2010.12.024

Cited by 219 publications

(122 citation statements)

References 43 publications

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“…It decomposed the spectral data into several principal components, which were orthogonal to each other and could keep maximum variation of the data points (pixels in case of hyperspectral data) in the original spectral space [24]. In particular, the loading consisted of coefficients that multiplied each variable, which can be used to identify variables that are highly correlated with each PC [25], while the scores of PCA represent the weighted sums of the original variables without significant loss of useful information. PC score images can be got by multiplying image in each band and their loadings matrix [26].…”

Section: Explanatory Methodsmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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Visible/near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA) was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC 2 to PC 6 ) gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM) classifier were applied to the full spectral range. SVM models built by PC 2 to PC 6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm) and seven (461, 538, 542, 742, 753, 756, 919 nm) wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength's SVM models on every pixel of the hyperspectral image.

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Section: Explanatory Methodsmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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“…Similarly, hyperspectral imaging (900 to 1700 nm) was applied for non-destructive determination of the tenderness of lamb meat (Kamruzzaman et al, 2012b). The same group of researchers also reported the potential use of NIR hyperspectral imaging (900 to 1700 nm) for discrimination of three different lamb muscles, i.e., semitendinosus (ST), longissimus dorsi (LD), and psoas major (PM), of Charollais breed (Kamruzzaman et al, 2011).…”

Section: Lambmentioning

confidence: 99%

Optical Methods and Techniques for Meat Quality Inspection

Peng¹,

Dhakal²

2015

Trans.ASABE

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“…Recently, hyperspectral imaging has entered wide use for evaluating the quality and safety of food and agricultural products [16][17][18]. Characterized as a rapid, nondestructive, and chemical-free method, hyperspectral imaging can simultaneously offer spatial information and spectral signals from one object, with the combination of conventional imaging and spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Song

Sun

et al. 2016

Applied Sciences

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Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%-100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.

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Application of NIR hyperspectral imaging for discrimination of lamb muscles

Cited by 219 publications

References 43 publications

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Optical Methods and Techniques for Meat Quality Inspection

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

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