Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms

Sanz, José Antonio; Fernandes, Armando; Barrenechea, Edurne; Silva, Severiano; Santos, Virgínia; Gonçalves, Norberto Jorge; Paternain, Daniel; Jurío, Aránzazu; Melo‐Pinto, Pedro

doi:10.1016/j.jfoodeng.2015.11.024

Cited by 55 publications

(51 citation statements)

References 25 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: 98%

“…Thus, it provides a powerful model which takes the local variation in the image into account. In HSI for meat processing [4][5][6][7][8][9], the used strategy is averaging all pixels in the region of interest (ROI) as a spectral signature of the ROI. In this case, the resulting models consider only the spectral features to be used, while the spatial features were ignored.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As shown in Figure 3, the total number of prepared frames is 6 frames: 4 frames [including 57 samples of lamb (12), beef (9), pork (9), and fat (27)] were used for training purposes and one special frame for testing purposes (18 samples of lamb (6), beef (4), pork (4), and fat (4)). In the frames, the meat and fat samples preserved their original meat textural characteristics, similar to retail-ready products commonly found at supermarkets.…”

Section: Spectral Data Analysis and Visualizationmentioning

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: 98%

Section: Literature Reviewmentioning

confidence: 99%

Section: Spectral Data Analysis and Visualizationmentioning

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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“…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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Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

Meng,

Feng,

Tan

et al. 2024

Food Science & Nutrition

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Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time‐consuming. To investigate the feasibility of rapid and non‐destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200–1000 nm was used in this study. The full spectra were pre‐processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back‐propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS‐PLSR model performed relatively better than the other models for predicting the MC, with R2P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS‐SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

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Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms

Cited by 55 publications

References 25 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

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

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

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