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Meat fraud has emerged as a growing concern globally. The adulteration, substitution, or mislabeling of meat products has resulted in negative economic effects, such as unfair competition in the global marketplace, as well as ethical concerns, because consumers want to know what is in the meat they are consuming. Ismail H. Boyaci of Hacettepe University and his team have explored using laser-induced breakdown spectroscopy (LIBS) for protein-based analysis and the identification of meat species. Spectroscopy recently spoke to Boyaci about this work.
The major conclusion that was drawn from your research is that LIBS is an effective technique to use for the protein-based analysis and identification of meat species. How did you and your team arrive at this conclusion?
Meat and meat products are the main animal protein sources in the human diet and are consumed all over the world. The high price and nutritional value are the major factors that lead to adulteration, substitution, or mislabeling of meat and meat products. Today, adulteration or partial substitution of expensive species with cheaper species is an important risk factor when it comes to human health, allergies, and religion. To prevent meat fraud, identification of species origin has significant importance for both the consumers who want to be informed about the kind of meat they consume and the producers who are suffering from the unfair competition in the meat market. The meat industry can benefit from the features of the LIBS technique, which is a multi-elemental analysis method and allows the analysis of many samples, high sensitivity, and rapid analysis.
In this study, species identification of processed meat products, namely salami and fermented sausage, and the determination of the adulteration ratios, were investigated using LIBS. The averaged LIBS spectra obtained for the bulk protein structure of pure beef, pork, and chicken salami and sausage samples revealed a complex structure and contained more than one emission line for each element. LIBS spectra were identified using the National Institute of Standard and Technology (NIST) Atomic Spectral Database. To identify and classify the meat species, principal component analysis (PCA) was used. The results obtained from the PCA analysis showed that LIBS can be used to identify meat species in processed meat products using bulk protein structures and fractions rich in sarcoplasmic and myofibrillar proteins. Furthermore, partial least square (PLS) analysis was applied to determine adulteration ratios. The PLS results showed performance indicators, such as root mean square error of calculation (RMSEC), root mean square error of prediction (RMSEP), relative standard deviation (RSD), relative error of prediction (REP), limit of detection (LOD), and limit of quantitation (LOQ) values, all of which revealed that LIBS is a satisfactory method to determine adulteration. Overall, the developed method allowed the identification of meat species in salami and fermented sausage products, which are highly processed products. All declared species were successfully detected by using LIBS in all the examined samples. According to the results, it can be suggested that LIBS is a promotive tool for protein-based analysis to identify meat species in processed meat products.
In the past, the detection of meat fraud was performed on the species-specific proteins and through DNA analysis. Why was LIBS-based protein analysis the best technique to use for this study compared to other field or laboratory methods?
Even though the detection of meat fraud has been performed based on species-specific proteins or DNA analysis, identification of meat species in processed products is generally much more difficult than in raw meat. Processed meat products have a complex nature in terms of composition, which poses significant challenges for meat species identification. In addition, both their processing conditions, including heat or pressure and product formulations such as additives, can obstruct the analysis of species-specific proteins because that analysis is highly dependent on the stability of the proteins. Because of the low tolerance of proteins to heat and pressure during processing, analytical method development efforts were shifted to search for heat-stable biomarkers to be used in the identification of species in processed meat products. All of these methods developed to date are molecular analysis methods. The main difficulty in methods based on molecular analysis is evaluating protein biomarkers that are significantly affected by changes in the three-dimensional (3D) structure, molecular form, weight, and compound. To overcome this problem and create a more stable analysis method, we evaluated the strategy of researching with a non-molecular method and using more stable components as an indicator, which would be less affected by the process conditions, inputs, and processing method. Therefore, one answer to this question is to investigate the stable components of protein biomarkers, namely their elemental compositions, that do not change during the process.
Even though there have been successful applications of other methods, the presented work differs from the previously applied methods in that it handles the same problem with a different perspective from the molecular approach. LIBS-based protein analysis is a novel approach that can overcome the problems caused by molecular instability, by analyzing only the elemental content of samples. In addition, our work is a pioneer study to investigate the utility of the LIBS technique in protein-based or proteomic analysis in complex food matrices. Studying protein molecules by molecular methods is a conventional research technique and methodology. However, looking at this problem from an elemental perspective has produced very successful results in terms of the detection and determination limits, accuracy, and reproducibility. In addition, compared to other methods, the fact that LIBS can be used for the analysis of many samples and is not affected by molecular instability of the proteins, provides a great advantage.
In many studies, sample preparation steps require high purity protein. However, in our methodology, the bulk protein structures were prepared using the basic protein precipitation method; they were not in high purity form, which provided a relatively simple preparation method for analysis, minimized possible errors, and shortened the analysis preparation process.
Furthermore, because LIBS examines the elemental composition in a sample that is highly resistant to environmental factors, such as temperature and pressure, it is remarkably successful in revealing variations in the content of the sample set subjected to the same sample preparation steps.
You mentioned that molecular analysis methods face several challenges that limits their effectiveness in evaluating protein biomarkers (1). Why are molecular analysis methods not ideal for food analysis studies, such as the one your team conducted?
The success of molecular methods in food analysis and the improvements that have taken place are indisputable. In particular, the determination of species-specific proteins and protein-based methods, which include electrophoretic and immunological techniques, have been used to identify meat species. However, their usefulness is limited for processed products because of the stability limitations of the protein structures. In addition, using molecular methods for determining proteins has turned out to be ineffective because they depend on several parameters, including the 3D structure, conformation, molecular form, and weight of the protein. As a result, species analysis in processed meat products was shifted to DNA-based or peptide biomarker methods, which indicates that the scientific community is still searching for more stable markers.
At this point, our approach is to create a new concept by basing the identification on a non-molecular structure instead of finding a new molecular biomarker. If we are successful, we think that this approach will create a new field of use in terms of LIBS and proteomics analysis.
What challenges did you face in this study?
The lack of a previous study in the process of monitoring the process conditions, formulation, and interspecies differentiation of processed meat products brought up the necessity of conducting a study like the main matrix but with a relatively less complex structure. Because of this issue, the protein-based LIBS analysis study that was used was the one performed in our previous work, where we used bulk proteins, actins, and myosin fractions of raw meat samples (3). That study was carried out to reveal the protein-based LIBS approach and its system requirements. Making up for the existing knowledge deficit in this respect and studying the system requirements and analysis systematics in raw samples in terms of the analysis success have prevented possible spectral problems in the complex structure of processed products. Thus, a basis was established for protein-based LIBS analysis in processed products, which was the main target.
As a team working on this subject for the first time, the whole process, from the parameters of the system to the preparation of the samples and the stability of the analysis, increased our creativity by enabling us to carry out a study completely independent of the literature, but the lack of a guideline study caused us to have unforeseen problems and the study process to be longer than planned. However, at the end of the day, the problems we faced gave us the opportunity to create a more robust and reliable method.
You and your team utilized several chemometric methods to classify meat products and principal component analysis (PCA) and partial least squares (PLS) analysis to determine the adulteration ratio. Why were these techniques important for your study?
Chemometrics is a key tool in LIBS analysis because of its stability and reliability in processing the large data set collected. Also, foods have complex and nonhomogeneous matrices (carbohydrates, lipids, proteins, and enzymes) and may also contain various additives. Even when the foodstuffs are in a solid state, they contain certain amounts of liquids such as water and oil. Because of their complex structure, every food item gives a different response to laser interaction.
The LIBS spectra of processed meat samples showed the following emission lines: Mg, Ca, K, Fe, Zn, Na, Hα, C, O, N, and molecular species, such as the Swan carbon band. It is very difficult to make a visual assessment of the spectral variation belonging to meat species by including all elements in the spectrum. However, similar spectral patterns were observed with different intensity levels for the elemental content of meat species. PCA was used for bulk protein and protein fractions rich in sarcoplasmic and myofibrillar proteins of salami and sausage samples. PLS analysis was applied to determine adulteration ratios. The use of one or more elements or emission lines for this type of analysis can lead to errors because of not using the entire spectral pattern. The contribution of minor peaks rather than major, a classification that should be made for species close to each other, is observed at a critical level. For this reason, simultaneous monitoring of interspecies differentiation with multiple components, as was done in this study, is of great importance in terms of evaluating the contribution of each component and increasing the sensitivity of the analysis. Thus, chemometrics methods were important in our study.
You reference in your paper that the high price and scarcity of meat are major factors that lead to mislabeling meat products (2). Are there any specific regions of the world that are more susceptible to meat fraud than others? What brought about the exploration of this topic?
Authenticity and traceability of meat, and the potential for mixing with species not declared on the label, are issues of concern in modern society. The access worldwide consumers have to clear and reliable information about the food they consume is constantly increasing with technological innovation, which becomes especially critical in the case of processed meat products, where a simple visual inspection does not easily distinguish between different ingredients.
Furthermore, there is an increasing demand for traditional and regional meat products, which are perceived by consumers as high-quality foods with added value. Products with a complex supply chain are susceptible adulteration. This situation is not specific to particular regions. It is carried out in many countries around the world for different purposes and different applications. The incidence of such adulteration in food products is high, mostly because of low economic power, high population, high product prices, and high demand for food products. The situations where this situation poses a great threat are in underdeveloped or developing countries. However, adulteration can be carried out by all kinds of producers who want to achieve economic gain, and reports in recent years have revealed that it is an issue that is encountered all over the world, including in developed countries, and that precautions need to be taken.
What are your next steps in this work?
Primarily, our research goals involve testing this new approach in different species and revealing how it performs according to the degree of closeness between species. In addition, it is critical for food analysis and monitoring to reveal the usage performance not only for meat products, but also for other protein-based species determination analyses. As a team, we think that we have revealed a new and exciting potential use of the LIBS technique.
(1) B. Sezer, A. Bjelak, H.M Velioglu, and I.H. Boyaci, Food. Chem. 372, 131245 (2022).
(2) M. Saderi, A. Saderi, and G. Rahimi, Iranian Journal of Veterinary Research 14(1), 29–34 (2013).
(3) B. Sezer, A. Bjelak, H.M. Velioglu, and I.H. Boyaci, Meat Sci. 172, 108361 (2021).
Ismail Hakki Boyaci is a professor of food engineering at the Department of Food Engineering of Hacettepe University (HU), Turkey. He holds a PhD (2001, HU, Turkey) in food engineering. Boyaci received his PhD by working on the development of biosensors for multiple detection systems and worked as research fellow at the Faculty of Mathematics and Physics at Charels University in the Czech Republic in 1997. He also worked as a postdoctoral research fellow on microbial immunosensors in the group of Dr. William R. Heineman at the Chemistry Department, at the University of Cincinnati, in Cincinnati, Ohio. His research interests include biosensors based on optical and electrochemical sensors and development of spectroscopic techniques, including Raman, SERS, NIR, and LIBS. Direct correspondence to: email@example.com.