A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method

Detection and quantification of pork adulteration in beef meatballs with Raman spectroscopy and near infrared spectroscopy

One of the main halal concepts requires that food is free from pork substances. Muslim-majority countries establish halal regulations that require the screening of processed meat products, such as meatballs, are screened for adulteration with pork meat to guarantee appropriate halal certification for consumers. Currently, halal authorities rely on the analysis of DNA, protein, or fat with RT-PCR, LC-MS, or GC-FID, which are reliable but are not suitable for rapid screening of large numbers of samples. Hence, high throughout screening tools are demanded to identify suspected samples. Vibrational spectroscopy methods such as Raman spectroscopy (RS) and Near Infrared spectroscopy (NIRS) are widely studied as fast and non-destructive methods for compositional analysis of agrifood products. Therefore, the aim of this study was to evaluate their potential for screening of suspected meatball samples. To this end, different batches of pure beef meatballs and meatballs with different levels of adulteration (3, 5, 10, 50, and 100 % w/w) were prepared and scanned in backscattering (RS) and reflectance (NIRS) mode in intact and cut form. The acquired Raman spectra had dominant peaks at 1657 cm-1, 1443 cm-1 and 1299 cm-1, which were attributed to saturated and unsaturated fat, while the dominant peaks in the NIR spectra corresponded to O-H bonds of water (1457 nm and 1934 nm). The cross-sectioned configuration was found to provide more stable classification performance compared to measurements on intact meatballs for both RS and NIRS. The accuracy of the partial least squares-discriminant analysis (PLS-DA) models for cross-sectioned samples using four latent variables ranged from 52.50 % to 85.00 % for RS and from 58.97 % to 75.00 % for NIRS. The performance of RS and NIRS shows little difference, but RS provides better insights on primary component of meat. For further research, improving the quality of Raman signal with a higher excitation wavelength laser or RS techniques that minimize fluorescence interference may improve model performance.

The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection

Artificial intelligence (AI) tends to be extensively used to develop reliable, fast, and inexpensive tools for authenticity control. Initially applied for food differentiation as an alternative to statistical methods, AI tools opened a new dimension in adulteration identification based on images. This comprehensive review aims to emphasize the main pillars for applying AI for food authentication: (i) food classification; (ii) detection of subtle adulteration through extraneous ingredient addition/substitution; and (iii) fast recognition tools development based on image processing. As opposed to statistical methods, AI proves to be a valuable tool for quality and authenticity assessment, especially for input data represented by digital images. This review highlights the successful application of AI on data obtained through laborious, highly sensitive analytical methods up to very easy-to-record data by non-experimented personnel (i.e., image acquisition). The enhanced capability of AI can substitute the need for expensive and time-consuming analysis to generate the same conclusion.

Novel analysis based on Raman spectroscopy in nutrition science

Modern research in nutrition science is transitioning from classical methodologies to advanced analytical strategies, in which Raman spectroscopy plays a crucial role. Raman spectroscopy and its derived techniques are gaining recognition in nutrition science for their features, such as high-speed, non-destructive analysis, label-free multiple detection and high sensitivity. Raman-enhancing techniques have further improved the sensitivity of Raman spectroscopy and widely extended its detection and imaging applications in nutrient analysis, as well as in ancillary tasks for nutrition research, such as nutrient status evaluation, nutrient interaction and metabolism studies. Further development of Raman-based analytical approaches lies in the improvement of instruments with higher precision, as well as the incorporation of other analytical techniques and advanced data analysis tools. This paper provides a comprehensive review of the application of nanoscience and nanotechnology, with a specific focus on Raman technology, in the field of food and nutrition science research. Instead of delving into the quantitative or qualitative detection capabilities of Raman technology, we highlight the remarkable food analysis and nutrition research methods established by this technology. Generally, this review introduces the characteristics and applications of Raman technology in nutrition analysis and discusses the limitations and future prospects of Raman spectroscopy for nutrition monitoring.

Simple and rapid identification of beef within 30 min using a new food nucleic acid release agent combined with direct-fast qPCR

Simple and rapid molecular detection technologies for authenticating animal species are urgently needed for food safety and authenticity. This study established a new direct-fast quantitative polymerase chain reaction (qPCR) detection technology for beef to achieve rapid and on-site nucleic acid detection in food. This technology can complete nucleic acid extraction in 4 min using a new type of food nucleic acid-releasing agent, followed by direct amplification of the DNA sample by fast qPCR in 25 min. The results indicated that direct-fast qPCR can specifically identify beef and can also identify 0.00001% of beef components in artificially simulated meat mixtures, with a detection precision variation coefficient of <4%. This method can be used to effectively identify beef in different food samples. As a simple, fast, and accurate molecular detection technology for beef, this method may provide a new tool for the on-site detection of beef components in food.

Insight into Pelargonium odoratissimum Essential Oil Preservative Properties Effect on Ground Beef

Pelargonium plants are very popular and well-known for their essential oils (EOs), which are used for medicinal purposes and in food. This study focused on the EO of Pelargonium odoratissimum. First, its composition and antioxidant and antimicrobial activity were evaluated, and finally, its efficacy as a natural preservative in ground beef was tested. The main EO constituents were citronellol (40.0%), nerol (15.3%), and citronellyl formate (12.6%). The antibacterial activity of POEO showed that Enterococcus faecalis ATCC 29212 was the most susceptible strain compared to the other eight strains tested. The antioxidant activity, as measured by the DPPH assay, showed a dose-dependent effect with an IC50 comparable to the standard used, gallic acid. Aerobic plate count, psychotropic bacteria, and Enterobacteriaceae, including Salmonella, were reduced by the addition of Pelargonium odoratissimum essential oils. The oxidative stability was significantly improved compared to the untreated sample. Additionally, the results for metmyoglobin demonstrated a notable preservative effect on sensory properties, including appearance, odor, color, and overall acceptability. The ability to discriminate between all samples and correlate protein and lipid oxidation processes, microbiological characteristics, and sensory measurements was made possible by principal component analysis and heat maps. This research shows the potential benefits of using POEO in the preservation of ground beef by effectively extending shelf life and improving product safety.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Adulteration detection technologies used for halal/kosher food products: an overview

In the Islamic and Jewish religions, there are various restrictions that should be followed in order for food products to be acceptable. Some food items like pork or dog meat are banned to be consumed by the followers of the mentioned religions. However, illegally, some food producers in various countries use either the meat or the fat of the banned animals during food production without being mentioned in the label on the final products, and this considers as food adulteration. Nowadays, halal or kosher labeled food products have a high economic value, therefore deceiving the consumers by producing adulterated food is an illegal business that could make large gains. On the other hand, there is an insistent need from the consumers for getting reliable products that comply with their conditions. One of the main challenges is that the detection of food adulteration and the presence of any of the banned ingredients is usually unnoticeable and cannot be determined by the naked eye. As a result, scientists strove to develop very sensitive and precise analytical techniques. The most widely utilized techniques for the detection and determination of halal/kosher food adulterations can be listed as High-Pressure Liquid Chromatography (HPLC), Capillary Electrophoresis (CE), Gas Chromatography (GC), Electronic Nose (EN), Polymerase Chain Reaction (PCR), Enzyme-linked Immuno Sorbent Assay (ELISA), Differential Scanning Calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Near-infrared (NIR) Spectroscopy, Laser-induced Breakdown Spectroscopy (LIBS), Fluorescent Light Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy and Raman Spectroscopy (RS). All of the above-mentioned techniques were evaluated in terms of their detection capabilities, equipment and analysis costs, accuracy, mobility, and needed sample volume. As a result, the main purposes of the present review are to identify the most often used detection approaches and to get a better knowledge of the existing halal/kosher detection methods from a literature perspective.

Detection of Pork in Beef Meatballs Using LC-HRMS Based Untargeted Metabolomics and Chemometrics for Halal Authentication

Adulteration of high-quality meat products using lower-priced meats, such as pork, is a crucial issue that could harm consumers. The consumption of pork is strictly forbidden in certain religions, such as Islam and Judaism. Therefore, the objective of this research was to develop untargeted metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) combined with chemometrics for analysis of pork in beef meatballs for halal authentication. We investigated the use of non-targeted LC-HRMS as a method to detect such food adulteration. As a proof of concept using six technical replicates of pooled samples from beef and pork meat, we could show that metabolomics using LC-HRMS could be used for high-throughput screening of metabolites in meatballs made from beef and pork. Chemometrics of principal component analysis (PCA) was successfully used to differentiate beef meatballs and pork meatball samples. Partial least square-discriminant analysis (PLS-DA) clearly discriminated between halal and non-halal beef meatball samples with 100% accuracy. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) perfectly discriminated and classified meatballs made from beef, pork, and a mixture of beef-pork with a good level of fitness (R²X = 0.88, R²Y = 0.71) and good predictivity (Q² = 0.55). Partial least square (PLS) and orthogonal PLS (OPLS) were successfully applied to predict the concentration of pork present in beef meatballs with high accuracy (R² = 0.99) and high precision. Thirty-five potential metabolite markers were identified through VIP (variable important for projections) analysis. Metabolites of 1-(1Z-hexadecenyl)-sn-glycero-3-phosphocholine, acetyl-l-carnitine, dl-carnitine, anserine, hypoxanthine, linoleic acid, and prolylleucine had important roles for predicting pork in beef meatballs through S-line plot analysis. It can be concluded that a combination of untargeted metabolomics using LC-HRMS and chemometrics is promising to be developed as a standard analytical method for halal authentication of highly processed meat products.

Identification of meat species in processed meat products by using protein based laser induced breakdown spectroscopy assay

The detection of meat fraud and mislabeling in processed meat products is a raising concern for consumers. The aim of this study was to develop and demonstrate the potential of protein-based laser induced breakdown spectroscopy (LIBS) method to be used for the identification of beef, chicken, and pork in fermented sausage and salami products. In this respect, bulk protein and protein fractions rich in sarcoplasmic and myofibrillar protein of sausage and salami products were obtained and subjected to LIBS analysis. LIBS spectrum was evaluated with chemometric methods to classify meat species and determine adulteration ratio by using principal component analysis and partial least square analysis, respectively. Limit of detection values for chicken and pork adulteration in beef sausage were found as 3.68 and 3.83% for myofibrillar fraction, while those values in beef salami were found as 3.80 and 3.47% for sarcoplasmic fraction, respectively.

A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers’ urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

Portable Raman Spectrometer as a Screening Tool for Characterization of Iberian Dry-Cured Ham

Dry-cured Iberian ham is officially classified into different commercial categories according to the pig’s breed and feeding regime. These reach very different prices, thus promoting labelling fraud and causing great damage to the food sector. In this work, a method based on Raman spectroscopy was explored as a rapid in situ screening tool for Iberian ham samples. A total of 110 samples were analyzed to assess the potential of this technique to differentiate purebred, crossbred, acorn-fed and feed-fed dry-cured Iberian ham. A continuous signal probably due to sample fluorescence was obtained, which hid the Raman scattering signal. Therefore, chemometric treatment was applied in order to extract non-apparent information. High validated classification rates were obtained for feeding regime (83.3%) and breed (86.7%). In addition, an interlaboratory study was carried out to confirm the applicability of the method with 52 samples, obtaining a validated rate above 80%.

Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy

With increasing demand for fast and reliable techniques for intact meat discrimination, we explore the potential of Raman spectroscopy in combination with three chemometric techniques to discriminate beef, lamb and venison meat samples. Ninety (90) intact red meat samples were measured using Raman spectroscopy, with the acquired spectral data preprocessed using a combination of rubber-band baseline correction, Savitzky-Golay smoothing and standard normal variate transformation. PLSDA and SVM classification were utilized in building classification models for the meat discrimination, whereas PCA was used for exploratory studies. Results obtained using linear and non-linear kernel SVM models yielded sensitivities of over 87 and 90 % respectively, with the corresponding specificities above 88 % on validation against a test set. The PLSDA model yielded over 80 % accuracy in classifying each of the meat specie. PLSDA and SVM classification models in combination with Raman spectroscopy posit an effective technique for red meat discrimination.

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.

The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review

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.

Current analytical methods for porcine identification in meat and meat products

Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.

Identification of camel species in food products by a polymerase chain reaction-lateral flow immunoassay

With an increased demand for camel meat, camel meat-related food products are susceptible to food fraud. To effectively authenticate camel-containing foods, a novel analytical technique based on polymerase chain reaction (PCR)-lateral flow immunoassay (LFI) was developed. The camel-specific PCR primers were designed to target at the mitochondrial COI gene. Both of the in silico and in vitro tests confirmed that the PCR-LFI was specific. A limit of detection of 0.1% w/w of camel meat in beef was achieved for both the raw and cooked (i.e. boiling and deep frying) meat samples. This novel method was used to authenticate 20 processed camel-meat products purchased from local grocery stores in China and online. Two products purchased online were identified as containing no camel meat. Overall, this novel PCR-LFI method is ideal for governmental laboratories to rapidly authenticate camel-meat containing food products.

Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef

Australian grass and grain-fed beef products attract premium prices at sale and several beef processors market beef underwritten by production system claims. This preliminary investigation assessed the feasibility of using Raman spectroscopy to detect differences in the chemical composition of subcutaneous fat from cattle raised in extensive and intensive production systems. Raman spectra, fatty acid composition, β-carotene composition and objective colour measurements were measured on 150 grass and 150 grain-fed cattle. Spectral differences at peaks including 1069 cm^-1, 1127 cm^-1, 1301 cm^-1 and 1445 cm^-1 suggest that Raman spectra is able to detect differences in saturated fatty acids, which were significantly higher in carcases from grain-fed cattle. Differences in spectra at 1658 cm^-1 were observed, however further research is required to investigate the cause of this spectral feature. Overall, this study indicated that Raman spectroscopy is a potential tool for the authentication of beef carcases from grass and grain-fed production systems.

Advances in Sheep and Goat Meat Products Research

The main goal of this chapter was to review the state of the art in the recent advances in sheep and goat meat products research. Research and innovation have been playing an important role in sheep and goat meat production and meat processing as well as food safety. Special emphasis will be placed on the imaging and spectroscopic methods for predicting body composition, carcass and meat quality. The physicochemical and sensory quality as well as food safety will be referenced to the new sheep and goat meat products. Finally, the future trends in sheep and goat meat products research will be pointed out.

Quantitative analysis of lard in animal fat mixture using visible Raman spectroscopy

Food adulteration is a serious issue that requires verification and strict management due to healthcare, morality, and social value problems. In the context of fat, food manufacturers blend lard with vegetable oils or animal fats for convenience and gaining economic benefits. Thus, we herein report the classification of 4 animal fats, e.g., beef tallow, pork lard, chicken fat, duck oil, using Raman spectroscopy combined with simple calculation of intensity ratios of Raman signal at vibrational modes corresponding to unsaturated fatty acids and total fatty acids. Various calculated values of the species were compared to find a feature that is able to classify each fats using Raman peak ratio. As a result, we suggested "Oil gauge (OG)" as a standard feature for classification of the fats in Raman analysis field. Furthermore, a quantification of the lard in other fat was accomplished with good linear correlation using the OG values.

Study on the Characteristic Spectral Properties for Species Identification of Animal-Derived Feedstuff Using Fourier Transform Infrared Spectroscopy

The objective of the present study was to explore the effective spectral bands related to lipid characteristics in spectra of raw animal-derived feedstuff and figure out which marked spectral regions (single or combined) contributed more to species discrimination. A total of 82 meat and bone meals, including porcine, poultry, bovine, ovine, and fish, were studied. Raw materials, extracted lipid, and defatted samples were simultaneously analyzed and calculated using Fourier transform infrared (FT-IR) spectroscopy in combination with chemometric methods. Taking the spectra of lipid as references, five marked spectral regions considered the main lipid characteristic regions were found in the raw animal-derived feedstuff spectra. In the study, single and combined marked spectral bands were investigated and proved to have better performance than the whole spectra of raw terrestrial animal-derived feedstuff and fishmeal. For the discrimination of five animal species, the regions of 1800-1650 cm^-1, 1500-1330 cm^-1, 1260-1060 cm^-1, and 790-640 cm^-1 presented better results; for the classification of three categories, the regions of 3100-2800 cm^-1, 1800-1650 cm^-1, and 1500-1330 cm^-1 showed the best results.

Implementation of chemometrics in quality evaluation of food and beverages

Conventional methods for food quality evaluation based on chemical or microbiological analysis followed by traditional univariate statistics such as ANOVA are considered insufficient for some purposes. More sophisticated instrumental methods including spectroscopy and chromatography, in combination with multivariate analysis-chemometrics, can be used to determine food authenticity, identify adulterations or mislabeling and determine food safety. The purpose of this review is to present the current state of knowledge on the use of chemometric tools for evaluating quality of food products of animal and plant origin and beverages. The article describes applications of several multivariate techniques in food and beverages research, showing their role in adulteration detection, authentication, quality control, differentiation of samples and comparing their classification and prediction ability.

Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods

Conventional methods for determining fat content and fatty acids (FAs) composition are generally based on the solvent extraction and gas chromatography techniques, respectively, which are time consuming, laborious, destructive to samples and require use of hazard solvents. These disadvantages make them impossible for large-scale detection or being applied to the production line of meat factories. In this context, the great necessity of developing rapid and nondestructive techniques for fat and FAs analyses has been highlighted. Measurement techniques based on near-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance and hyperspectral imaging have provided interesting and promising results for fat and FAs prediction in varieties of foods. Thus, the goal of this article is to give an overview of the current research progress in application of the four important techniques for fat and FAs analyses of muscle foods, which consist of pork, beef, lamb, chicken meat, fish and fish oil. The measurement techniques are described in terms of their working principles, features, and application advantages. Research advances for these techniques for specific food are summarized in detail and the factors influencing their modeling results are discussed. Perspectives on the current situation, future trends and challenges associated with the measurement techniques are also discussed.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.