Article

Identification of Pig Adulterant in Mixture of Fat Samples and Selected Foods based on FTIR-PCA Wavelength Biomarker Profile

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Authenticity is an important issue in food industry. Tampering the authenticity of food product involves the adulteration of products with certain material. Various authentication techniques for detection of adulteration have been developed in line with the advent of current technology. Of particular interest, Infrared (IR) spectroscopy; a rapid and non-destructive technique allowing the screening of a large number of samples has been shown to be able to detect pig derivatives in meat products. Following this, the present study aims to identify pig adulteration in different mixture of fat samples and some selected food; based on wavelength biomarker obtained from FTIR coupled with PCA analysis. Twenty-six fats at two frequencies along the graph (1236 and 3007 nm) were studied including samples representing Non Halal Food A (NHFA) fat, Halal Food A(HFA) fat and Non Halal Food B (NHFB) fat. At wavelength 1236 and 3007 nm along the spectrum; NHFA, HA and NHFB fat samples were easily identified at visibly good distance compared to other fat samples. The first two samples; NHFA and NHFB were located very close to PF (Pig Fat) indicating that NHFA and NHFB samples contained pork fat while HA was located closer to CF, indicating that the sample possibly contained chicken fat. To this end, FTIR coupled with PCA has been shown to be a powerful tool to detect adulteration in meat products and as such can be recommended for authentication purposes.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... ATR-FTIR has required little or no sample preparation. Previous studies reporting on meat speciation with FTIR mainly deploy the ATR-FTIR spectral analysis on lipid extracts from meat samples [9,12,13]. The classification of different meat species is therefore solely based on triacylglyceride and derived structures and other fat-soluble compounds present in the lipid extracts. ...
... In previous studies, different multivariate methods such as partial least squares (PLS), support vector machine (SVM), K-nearest neighbor (KNN) and soft independent modelling of class analogy (SIMCA) were used in combination with an appropriate (combination of) data pre-processing method(s) for successful meat speciation. In these studies, there isn't a suitable comparison between OCC approach on the one hand and linear and non-linear discriminant approach on the other hand, as a tool for halal species certification [2, 9,12,13]. As a plausible and more efficient chemometric modelling strategy for finding fraud, one-class classification (OCC) can be considered. ...
Article
Full-text available
In the present contribution, the feasibility of portable Fourier transform infrared spectroscopy (FTIR) combined with multivariate classification techniques is assessed for classification of minced beef, lamb, chicken and pork samples. In this regard, both attenuated total reflectance-FTIR (ATR-FTIR) and diffuse reflectance-FTIR (DR-FTIR) methods are evaluated. First, principal component analysis (PCA) was used for exploring FT-IR spectra of four meat species to find similarities and dissimilarities among samples. Additionally, one-class classification (OCC) was utilized as a new approach for halal meat species certification. For OCC, two scenarios were defined: (i) 100% correct classification for pork, and (ii) a most favorable overall classification rate for all species investigated simultaneously. With the OCC approach, both ATR and DR methods were found to produce high false-positive scores in scenario (i), whilst the DR method scored the best in scenario (ii) with an overall score of 89% correct classification. In the next step, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) with radial basis function (RBF) as kernel function were evaluated for meat speciation. On this matter, SVM showed better classification performance in terms of total accuracy for both ATR-FTIR (98%) and DR-FTIR (100%) datasets over PLS-DA (90% and 98%, respectively). The promising results of both portable ATR-FTIR and DR-FTIR combined with OCC approach and discriminant analysis indicated for the first time their use as successful non-destructive, cost-effective and rapid routine screening methods for on-site analysis of meat speciation and halal meat species certification which could be useful for quality control officers to manage and control meat authenticity at various stages of the supply chain.
... Every Muslim is forbidden from using non-halal ingredients or eating non-halal food (Denyingyhot et al., 2022;Halimi et al., 2021). Meanwhile, it is not easy to distinguish non-halal mixed meat from beef, so developing a reliable detection technique for mixing ingredients needs is necessary (Prachugsorn et al., 2022;Saputra et al., 2018). ...
Article
Full-text available
Adulterating meat products with several species, including non-halal species, is often found in commercial products. Therefore, non-halal ingredients are a major source of concern for Muslims. Multiplex polymerase chain reaction (PCR) with multiple primers can detect contamination of meat components from non-halal species in a single reaction process, making it more effective and efficient. Multiplex PCR is a PCR technique that uses multiple primers and DNA samples in one reaction to amplify multiple target regions. In this study, a pair of species-specific primers encoding the Cytochrome c oxidase subunit I (CO1) gene were designed to amplify bovine DNA, tested for specificity, and applied in multiplex PCR technique together with D-loop primers for pigs, Cyt-b for rats, and 12S rRNA for dogs. The CO1 primers, along with D-loop primers for porcine, Cyt-b primers for rats, and 12S rRNA primers for dogs, can be used to detect specific bovine DNA with a size of 279 bp and sequence similarity of 96%.
... FTIR spectroscopy has wide applications in food analysis including milk, butter, cheese, fat, and oil [11,12]. For instance, FTIR spectroscopy using attenuated total reflectance (ATR) technique has been used for the detection and quantification of urea in milk. ...
Article
Full-text available
This research was aimed to develop Fourier transform infrared spectroscopy (FTIR) combined with chemometrics of linear discriminant analysis (LDA), partial least square (PLS), and principal component regression (PCR) for authentication of milk fat from palm oil adulterant. FTIR spectroscopy and LDA have been successfully used to detect the presence of palm oil in MF. All the adulterated samples were clearly separated with authentic MF shown by the Cooman’s plot. Chemometrics of PLS at the wavenumber of 3033-692 cm ⁻¹ using first derivative spectra was successfully applied for the quantification of palm oil in MF. The suitability of the model was presented by its high R ² value both for calibration and validation models, accounting for 1 and 0.9998 respectively and its lower RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction) value, accounting for 0.154 and 0.743 respectively. Quantification of palm oil was also successfully performed using chemometrics of PCR. The model showed high R ² in both calibration (0.9998) and validation (0.9997) values with lower RMSEC (0.671) and RMSEP (0.905) values. It can be concluded that a combination of FTIR spectroscopy with chemometrics could be used for the authentication of milk fat adulteration.
... Normal spectra at 1/λ 1236 and 3007 cm −1 using ATR technique Classification using PCA Combination of FTIR spectra and chemometrics could classify lard in chicken fat, pure lard, food containing lard, palm oil, and chicken fat [57] Classification using SIMCA, quantification using PLS Pure ghee and the one adulterated with lard could be classified using PCA. Using SIMCA, 90% of the samples were classified into their respective class. ...
Article
Full-text available
Halal is an Arabic term used to describe any components allowed to be used in any products by Muslim communities. Halal food and halal pharmaceuticals are any food and pharmaceuticals which are safe and allowed to be consumed according to Islamic law (Shariah). Currently, in line with halal awareness, some Muslim countries such as Indonesia, Malaysia, and Middle East regions have developed some standards and regulations on halal products and halal certification. Among non-halal components, the presence of pig derivatives (lard, pork, and porcine gelatin) along with other non-halal meats (rat meat, wild boar meat, and dog meat) is typically found in food and pharmaceutical products. This review updates the recent application of molecular spectroscopy, including ultraviolet-visible, infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies, in combination with chemometrics of multivariate analysis, for analysis of non-halal components in food and pharmaceutical products. The combination of molecular spectroscopic-based techniques and chemometrics offers fast and reliable methods for screening the presence of non-halal components of pig derivatives and non-halal meats in food and pharmaceutical products.
Article
Full-text available
The activity of antibacterial material is conventionally estimated by using an indirect method-a bacteria suspension is inoculated onto a surface, and then the bacteria are collected from the surface and examined as to whether they can form colonies on the agar plate [1]. In the present study, the presence of bacteria was examined by direct detection. Our study is based on FTIR PAS with an interferometer cantilever detector [2]. Our work discusses the possibility of identifying and distinguishing the presence of different bacteria (Staphylococcus epidermidis and Pseudomonas aeruginosa) and the possibility to evaluate the crystallization processes on the pressed calcium phosphate surface.
Book
Full-text available
The origin of PCA is confounded with that of linear regression. In 1870, Sir Lord Francis Galton worked on the measurement of the physical features of human populations. He assumed that many physical traits are transmitted by heredity. From theoretical assumptions, he supposed that the height of children with exceptionally tall parents will, eventually, tend to have a height close to the mean of the entire population. This assumption greatly disturbed Lord Galton, who interpreted it as a "move to mediocrity," in other words as a kind of regression of the human race. This led him in 1889 to formulate his law of universal regression which gave birth to the statistical tool of linear regression. Nowadays, the word "regression" is still in use in statistical science (but obviously without any connotation of the regression of the human race). Around 30 years later, Karl Pearson,[1] - who was one of Galton’s disciples, exploited the statistical work of his mentor and built up the mathematical framework of linear regression. In doing so, he laid down the basis of the correlation calculation, which plays an important role in PCA. Correlation and linear regression were exploited later on in the fields of psychometrics, biometrics and, much later on, in chemometrics. Usually, physicochemical analysis - or else the more generally parametric monitoring of a number of physicochemical properties of a set of samples - resulted in the construction of a data matrix with samples in rows and physicochemical properties in columns. This data matrix is a mathematical representation of the characteristics of the sample set at time t in preparatory and analytical conditions attached to the instant action. Incidentally, the fact of working on a matrix which is a two-dimensional array allows us to speak of two modes or 2-way data. Now, imagine that you reproduce these measurements on several dates t1, t2,..., tn. You no longer have a matrix X (n, p) but the N matrices Xi (n, p) of the same size where i is the number of the matrix corresponding to the time ti. This is known as 3-mode data or three-way data or a "data cube". Figure 8 below illustrates what has been said.
Article
Full-text available
Adulterated food can be defined as food incompatible with the declaration of the seller. In the case of meat and meat articles, adulterations refer not only to the replacement of ingredients but also to inappropriate information concerning the origin of raw materials. Methods aiming at investigating meat and meat product authenticity may be based either on the analysis of protein composition or on the analysis of nucleic acids. At the present time, meat and meat product authenticity investigations based on protein analysis employ electrophoretic, enzymic, and chromatographic methods, sometimes supported by the mass spectrometry technique. On the other hand, species identification is often based on polymerase chain reaction (PCR). Biochips present a promising technology.
Article
Full-text available
Four types of animal fats, namely lard (LD) and body fats of lamb (LBF), cow (Cow-BF) and chicken (Ch-BF), in quaternary mixtures were quantitatively analyzed using FTIR spectroscopy in combination with multivariate calibration of partial least square (PLS). The animal fats, either individual or in quaternary mixtures, were subjected to horizontal total attenuated total reflectance (HATR) as sample handling technique and scanned at mid-infrared region (4000–650 cm–1) with resolution of 4 cm–1 and with 32 interferograms. PLS calibration revealed that the first derivative FTIR spectrum was well suited for the correlation between actual value of LD and FTIR calculated value. The other animal fats (LBF, Cow-BF and Ch-BF) were better determined using normal FTIR spectra. The coefficient of determination (R2) obtained using the optimized spectral treatments was higher than 0.99. The root mean standard error of calibration (RMSEC) values obtained were in the range of 0.773–1.55. Analysis of animal fats using FTIR spectroscopy allows rapid, no excessive sample preparation, and can be regarded as “green analytical technique” due to the absence of solvent and chemical reagent used during the analysis.
Article
Full-text available
Basically, this article focuses on the theory of istihalah from both Islamic and modern science perspectives. It involves discussions on the application of istihalah in several food products, whether it is classified as istihalah sahihah (acceptable change) or istihalah fasidah (unacceptable change). In order to achieve the objective, samples of animal-based food products are used to analyse the theory of istihalah. The research finding shows that istihalah is relevant as a purification alternative to solve the recent issues on food product.
Article
Full-text available
Fourier transform infrared (FTIR) spectroscopy provides a simple and rapid means of detecting lard blended with chicken, lamb, and cow body fats. The spectral bands associated with chicken, lamb, and cow body fats and their lard blends were recorded, interpreted, and identified. Qualitative differences between the spectra are proposed as a basis for differentiating between the pure animal fats and their blends. A semiquantitative approach is proposed to measure the percent of lard in blends with lamb body fat (LBF) on the basis of the frequency shift of the band in the region 3009–3000 cm−1, using the equation y=0.1616x+3002.10. The coefficient of determination (R 2) was 0.9457 with a standard error (SE) of 1.23. The percentage of lard in lard/LBF blends was also correlated to the absorbance at 1417.89 and 966.39 cm−1 by the equations y=0.0061x+0.1404 (R 2=0.9388, SE=0.018) and y=0.004x+0.1117 (R 2=0.9715, SE=0.009), respectively. For the qualitative determination of lard blended with chicken body fat (CF), the FTIR spectral bands in the frequency ranges of 3008–3000, 1418–1417, 1385–1370, and 1126–1085 cm−1 were employed. Semiquantitative determination by measurement of the absorbance at 3005.6 cm−1 is proposed, using the equation y=0.0071x+0.1301 (R 2=0.983, SE=0.012). The percentage of lard in lard/GF blends was also correlated to the absorbance at 1417.85 cm−1 (y=0.0053x+0.0821, with R 2=0.9233, SE=0.019) and at 1377.58 cm−1 (y=0.0069x+0.1327, with R 2=0.9426, SE=0.022). For blends of lard with cow body fat (CBF) bands in the range 3008–3006 cm−1 and at 1417.8 and 966 cm−1 were used for qualitative detection. The equation y=−0.005x+0.3188 with R 2=0.9831 and SE=0.0086 was obtained for semiquantitative determination at 966.22 cm−1.
Article
Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
Article
Fourier transform infrared (FTIR) spectroscopy combined with chemometrics of partial least square (PLS) and discriminant analysis (DA) has been developed for simple analysis of lard in the mixtures with body fats of lamb (LBF), cow (Cow-BF), and chicken (Ch-BF). The spectral bands correlated with lard, LBF, Cow-BF and Ch-BF as well as their lard blends were scanned, interpreted, and identified. Qualitative differences among FTIR spectra are proposed as a basis for differentiating between the lard and its blends. DA with Mahalanobis distance principle in entire range of mid infrared (3300 - 650 cm-1) was successfully provide an alternate method to differentiate lard and that in the mixtures with LBF, Cow-BF, and Ch-BF. Quantitative analysis using PLS calibration model is proposed to measure the percentages of lard in LBF, Cow-BF, and C-BF at selected fingerprint region (1500 - 900 cm-1). The equation obtained between actual lard concentration in the mixture with LBF and FTIR predicted concentration in calibration model is y = 0.995 × + 0.098 with coefficient of determination (R2) was 0.995 and root mean standard error of calibration (RMSEC) of 0.98. The actual percentages of lard mixed with Cow-BF and Ch-BF were also correlated to FTIR predicted percentages at 1500 - 900 cm-1 using equations of y = 0.999× + 0.016 (R2 = 0.999, RMSEC = 0.61); and y = 1.002× + 0.034 (R2 = 0.998, RMSEC = 0.73), respectively.
Article
Proper product description is of crucial importance in ensuring fair trading practices and enabling consumers to make informed choices and is therefore addressed in some detail in UK food legislation. This paper will briefly examine the historical development of UK food laws and the meaning of "authenticity" within the context of current legislation, particularly with respect to meat and fish products. The food authenticity programme of the UK Ministry of Agriculture, Fisheries and Food (MAFF) is discussed, outlining its R and D programme and detailing the types of topics under consideration, and how selection of surveillance projects is determined.
Principal Component Analysis -a powerful tool in computing marketing information
  • C Constantin
C. Constantin, "Principal Component Analysis -a powerful tool in computing marketing information," Bulletin of the Transilvania University of Braşov Series V: Economic Sciences, vol.7, no.56, 2014.
Halal Food Issues from Islamic and Modern Science Perspectives
  • N A Fadzlillah
  • Y B Che Man
  • M A Jamaludin
  • A Rahman
  • H A Al-Kahtani
N.A. Fadzlillah, Y.B. Che Man, M.A. Jamaludin, A.S Rahman, H.A. Al-Kahtani, "Halal Food Issues from Islamic and Modern Science Perspectives," 2nd International Conference on Humanities. Historical and Social Sciences IPEDR, vol. 17, 2011.
Perkembangan dalam ingredien makanan: cabaran Malaysia dalam menangani isu Halal
  • S Yusof
  • A Rahim
S. Yusof, A. Abd Rahim, J. Jalil, Perkembangan dalam ingredien makanan: cabaran Malaysia dalam menangani isu Halal, in Penjenamaan Halal Satu Paradigma Baru. ed. Mohd Noorizzuddin Nooh. Bangi: USIM. 99-120. 2007