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

Abstract

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.

Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

“…Compared with hyperspectral imaging, less processing time make MSI easer to meet the speed requirement of industrial production lines used for online high-throughput screening to assess meat quality, nutrition, safety, and authenticity. In recent years, MSI technology as a rapid and nondestructive analysis method has been wildly applied in fishes (Dissing, Nielsen, Ersbøll, & Frosch, 2011), meats (Ma et al, 2014a;Ropodi, Pavlidis, Mohareb, Panagou, & Nychas, 2015), fruits (Lunadei, Galleguillos, Diezma, Lleó, & Ruiz-Garcia, 2011), vegetables (Løkke, Seefeldt, Skov, & Edelenbos, 2013), etc. However, almost all of these studies only utilized spectral data without incorporating information on spatial data, which has been successfully used to improve the accuracy of model prediction in hyperspectral imaging studies (He, Wu, & Sun, 2014;Pu, Sun, Ma, & Cheng, 2015).…”

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

“…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.…”

“…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.…”

“…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).…”

Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat

“…Meat and meat products are important food commodities that have been targets for adulteration in the past and, whether deliberately or accidentally, undeclared admixtures and previously unknown and unpredictable adulterants have been observed. While some of these cases include substitution or partial substitution of high commercial value food commodities with cheaper ones, such as beef adulteration with pork or offal or by adding proteins from several origins (Kamruzzaman, Makino, & Oshita, 2015;Ropodi, Pavlidis, Mohareb, Panagou, & Nychas, 2015;Tian, Wang, & Cui, 2013;Zhao, Downey, & O'Donnell, 2014), non-compliance to label has not only economic, but also quality, safety and socio-religious consequences (Alamprese, Casale, Sinelli, Lanteri, & Casiraghi, 2013).…”