A novel method for discrimination of beef and horsemeat using Raman spectroscopy

Boyaci, İsmail Hakkı; Temiz, Havva Tümay; Uysal, Reyhan Selin; Velioğlu, Hasan Murat; Yadegari, Reza Jafarzadeh; Rishkan, Mojtaba Mahmoudi

doi:10.1016/j.foodchem.2013.10.006

Cited by 146 publications

(58 citation statements)

References 22 publications

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“…beef vs. kangaroo (Ding & Xu, 1999), pork vs. beef vs. lamb (Kamruzzaman, Barbin, ElMasry, Sun, & Allen, 2012), chicken vs. turkey (Ellis, Broadhurst, Clarke, & Goodacre, 2005), and beef vs. horsemeat (Boyacı et al, 2014;Ebrahim, Sowoidnich, & Kronfeldt, 2013). Few studies has been published on the adulteration of poultry with pork (Soares et al, 2010), pork in minced mutton (Tian et al, 2013), pork in beef meatball (Rohman, Sismindari, Erwanto, & Che Man, 2011), beef offal in fresh and frozen beefburger (Zhao, Downey, & O'Donnell, 2014), pork meat in raw beef burger (Giaretta, Di Giuseppe, Lippert, Parente, & Di Maro, 2013), minced lamb (Kamruzzaman et al, 2013), gelatin in prawn , different types of caviar in Caspian caviar (Mohamadi Monavar et al, 2013).…”

Section: Discussionmentioning

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

123

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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: Discussionmentioning

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

123

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“…Larger loadings of the principal component analysis indicate greater importance of the corresponding wavelength variable, with more information contained [18–19]. To select the characteristic wavelengths by PCA loadings, the contribution rate of different principal components (PCs) was determined, and then the cumulative contribution rate of analyzed PCs and the number of PCs were selected.…”

Section: Methodsmentioning

confidence: 99%

Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries

et al. 2017

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Near-infrared (874–1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972–1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.

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“…PLS was used to quantitatively analyse the olive oil samples adulterated with different rates of olive pomace oil. 190 In a similar study reported by the same research group, extracted fat samples were used to differentiate meat species namely, cattle, sheep, pig, sh, poultry, goat and buffalo. 178 PCA and stepwise linear discriminant analysis (SLDA) methods were performed to classify oils and fats by conducting cluster and discriminant analyses.…”

Section: Fermentation Productsmentioning

confidence: 98%

Dispersive and FT-Raman spectroscopic methods in food analysis

et al. 2015

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Raman spectroscopy is a powerful technique for molecular analysis of food samples. A fingerprint spectrum can be obtained for a target molecule by using Raman technology, as specific signals are obtained for chemical bonds in the target. In this way, food components, additives, processes and changes during shelf life, adulterations and numerous contaminants such as microorganisms, chemicals and toxins, can also be determined with or without the help of chemometric methods. The studies included in this review show that Raman spectroscopy has great potential for food analyses. In this review, we aim to bring together Raman studies on components, contaminants, raw materials, and adulterations of various food, and attempt to prepare a database of Raman bands obtained from food samples.

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A novel method for discrimination of beef and horsemeat using Raman spectroscopy

Cited by 146 publications

References 22 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

Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries

Dispersive and FT-Raman spectroscopic methods in food analysis

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