Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods

Prevolnik, M.; Čandek-Potokar, Marjeta; Škorjanc, Dejan

doi:10.1016/j.jfoodeng.2009.11.022

Cited by 55 publications

(29 citation statements)

References 28 publications

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“…The former has been used for the rapid detection of total viable counts in pork (Barbin, Sun, & Su, 2013;Huang, Zhao, Chen, & Zhang, 2013) and of the water-holding capacity of fresh beef (ElMasry, Sun, & Allen, 2011) and pork (Prevolnik, Čandek-Potokar, & Škorjanc, 2010). Meanwhile, multispectral image analysis has high potency for the evaluation of food quality systems during handling, processing and storage (Løkke et al, 2013), and it has been previously used for the conversion of meat color in L*, a*, b* values (Sharifzadeh, Clemmensen, Borggaard, Støier, & Ersbøll, 2014) and for quality assessment of beef (Dissing et al, 2013;Panagou, Papadopoulou, Carstensen, & Nychas, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

Ropodi

Pavlidis

Mohareb

et al. 2015

Food Research International

130

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The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ±10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate.

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Section: Introductionmentioning

confidence: 99%

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

Ropodi

Pavlidis

Mohareb

et al. 2015

Food Research International

130

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show abstract

“…Leroy et al [9] reported the R 2 cv range of 0.25 to 0.47 for beef CL prediction using NIR spectroscopy, while an R 2 value of 0.001 for young cattle meat and 0.138 for oxen meat was obtained in Prieto et al [11]. Prevolnik et al [12] reported unsuccessful prediction of pork CL with NIR spectroscopy by showing the R p 2 of 0.26-0.39, and in the study of Liu et al [10], an R 2 value of 0.49 for chicken meat CL was obtained. Despite the meat species and discrepancies in experimental designs, the performance in our study was better than in the published studies using NIR spectroscopy.…”

Section: Spectral Featuresmentioning

confidence: 97%

“…Therefore, in this work, the established PLS-DA model based on full spectra was selected for further evaluation. In the literature there are few reports concerning the qualitative or quantitative prediction of meat CL using the spectroscopic method [9][10][11][12]. Leroy et al [9] reported the R 2 cv range of 0.25 to 0.47 for beef CL prediction using NIR spectroscopy, while an R 2 value of 0.001 for young cattle meat and 0.138 for oxen meat was obtained in Prieto et al [11].…”

Section: Spectral Featuresmentioning

confidence: 99%

Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets

et al. 2018

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Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400-1000 nm) range. Mean spectra were extracted from regions of interest (ROIs) determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1) of VNIR full spectra, performed best with correct classification rates (CCRs) of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS). Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories.

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“…Besides water content, another attribute of concern for consumers purchasing meat, is water holding capacity (WHC). WHC (indicated as drip loss) is an important meat quality indicator because decrease in WHC correlates with reduced processing quality (Prevolnik et al, 2010). In the recent years scientists have also applied HSI techniques in pork, beef, and lamb for determining WHC (ElMasry et al, 2011b;Barbin et al, 2012b;Kamruzzaman et al, 2012a).…”

Section: Prediction Of Water Content and Water Holding Capacity (Whc)mentioning

confidence: 99%

Hyperspectral imaging (hsi): applications in animal and dairy sector

Kumar¹,

Saxena²,

Shrivastava³

et al. 2016

JEBAS

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Hyperspectral imaging (HSI), also known as imaging spectroscopy or 3D spectroscopy, combines imaging and spectroscopy into a single system. With a high resolution measurement of spectral signatures, HSI is able to provide critical information of the target. Thus it is useful for various scientific and industrial applications, including food safety and disease diagnosis. Due to constantly increasing demands for safe animal products, there is pressure on the processing sector for applications of advanced, high throughput methods for non-destructive quality analysis of animal products. In this context, HSI finds its applications for grading, classification, quality & composition analysis of animal products including meat, egg, milk etc. Further, the technique is also a useful tool in poultry sector for assessment of wholesomeness and quality control of chicken carcasses, as well as, chicken meat products. In fish industry also, the technique has established its potential for determining freshness and quality attributes of sea-foods. Apart from quality control of animal products, HSI has also demonstrated its usefulness for disease diagnosis in animal models and for detection of mammary cancers in dogs. Thus, the future of HSI technology in animal industry is promising and associated with multivariate analysis, HSI technique will further dominate in animal products authentication and analysis in the future also.

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Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods

Cited by 55 publications

References 28 publications

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets

Hyperspectral imaging (hsi): applications in animal and dairy sector

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