The employment of FTIR spectroscopy in combination with chemometrics for analysis of rat meat in meatball formulation

Research on CO2/CH4/N2 competitive adsorption characteristics of anthracite coal from Shanxi Sihe coal mine

This study aims to solve the problem of unsatisfactory development and utilization of coalbed methane and CO2 storage efficiency. It is focused on the adsorption behavior of CO2, CH4, and N2 in the macromolecular structure model of Shanxi Sihe coal mine anthracite, as well as the competitive adsorption behavior of CO2/CH4 and CH4/N2 binary gas mixtures with different ratios. Experimental analysis such as elemental analysis, solid 13C nuclear magnetic resonance (13C NMR), Fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis were used to construct the Shanxi Sihe coal mine model of the macromolecular structure of anthracite coal. The Grand Canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulation methods were used to study the adsorption capacity and heat characteristics of CO2, CH4, and N2 at different temperatures using a molecular model of anthracite coal from Shanxi Sihe coal mine, as well as the competitive adsorption characteristics of CO2/CH4 and CH4/N2 binary mixtures. The mechanism of the influence of temperature and gas properties on the adsorption capacity and heat of adsorption was revealed from a microscopic perspective. The results indicated that the aromatic carbon content of anthracite in the Sihe coal mine, Shanxi is 81.19%, and the ratio Xbp of aromatic bridgehead carbon to surrounding carbon is 0.489. The aromatic structure is mainly composed of pyrene and anthracene. The molecular formula of the macromolecular structure model of anthracite in Shanxi Sihe coal mine is C233H157O13N2. The adsorption capacity and equivalent adsorption heat of the macromolecular model for adsorbing single-component gas CO2/CH4/N2 decrease with the increase in temperature. The temperature has the greatest impact on CO2 adsorption capacity and adsorption heat, followed by CH4 and N2. Under the competitive adsorption conditions of CO2/CH4 and CH4/N2 binary mixtures, the higher the partial pressure of a single-component gas in the mixture, the greater the adsorption capacity of the gas. The difference in the adsorption heat of CH4 and N2 is smaller than that of CH4 and CO2. The conclusions obtained from the study can provide technical and theoretical support for formulating reasonable drainage methods for coalbed methane wells.

Construction and optimization of macromolecular structure model of Tiebei lignite

Mastering the molecular structure of coal is important for the effective utilization of coal. For a detailed study of the microstructural characteristics of Tiebei lignite, its molecular structure was characterized by elemental analysis, solid 13C nuclear magnetic resonance (13C NMR), Fourier-transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). The results showed that the aromatic carbon content of Tiebei lignite was 51.98%, the aromatic carbon structure was mainly composed of benzene and naphthalene, and the ratio of aromatic bridgehead carbon to surrounding carbon Xbp was 0.14. Oxygen existed in phenol, ether, carbonyl, and carboxyl; nitrogen-containing structures mainly existed in the form of pyrrole and pyridine; sulfur mainly existed in thiophene sulfur; and aromatic substitution was mainly in the form of trisubstitution. The molecular formula of the macromolecular structure model of Tiebei lignite was C190H161O57N2, and the 13C NMR spectrum of the model was in good agreement with the experimental results, which fully verified the accuracy of the macromolecular structure model of Tiebei lignite. The construction of a macromolecular structure model of Tiebei lignite is essential to intuitively understand the molecular structure characteristics of Tiebei lignite and to provide theoretical support and guidance for the micromechanism research and prevention of lignite spontaneous combustion and other disasters.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

Volatile compounds, texture, and color characterization of meatballs made from beef, rat, wild boar, and their mixtures

The purpose of this research was to characterize the volatile compounds, texture, and color profile of meatballs made from beef, rat, wild boar, and their combinations. Volatile compounds were analyzed using SPME/GC-MS and multivariate data analysis (PCA, PLS-DA). Additionally, several textural features such as hardness, gumminess, chewiness, cohesiveness, and colour (L, a∗, b∗, C, and h) were also analyzed. The findings revealed that texture and color characteristics can only be used to differentiate meatballs based on their raw meat materials when meat adulterants are used in high concentrations (≥50%). PLS-DA analysis of volatile data revealed distinct groupings among various types of meatballs, including meatballs adulterated with rat or wild boar meat at the lowest percentage used in this study (20%). By using VIP and correlation coefficient, the strongest markers in beef, rat, and wild boar meatballs were identified as (Z)-2-amino-5-methyl-benzoic acid, 2-heptenal, and cyclobutanol, respectively. Nonanal was consistently found as a significant marker in the meatballs made from a mixture of beef-rat and beef-wild boar at different ratios. This study demonstrated that the volatile profile of meat is more reliable than physicochemical profiles for developing an analytical tool for quickly identifying undesired meat in meat-derived products.

Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as "Pure (80 samples)" and "Adulterated (90 samples)". Lard was mixed with the ratio of 10-50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700-1800 cm^-1 and 2800-3000 cm^-1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

The famous Turkish rose essential oil: Characterization and authenticity monitoring by FTIR, Raman and GC-MS techniques combined with chemometrics

There is a necessity for rapid, robust, easy, accurate and cost-effective methodologies for the quality control of essential oils from medicinal and aromatic plants. Rosa damascena essential oil is a high-value natural product with its unique quality properties and economic importance. This research evaluated the capability of Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy and gas chromatography-mass spectrometry (GC-MS) techniques combined with chemometrics for determination of the authenticity of R. damascena essential oil. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully employed with 100% accuracy for discrimination of authentic R. damascena essential oil samples from fraudulent commercial samples. Consistent results were obtained by FTIR, Raman and GC-MS techniques. Two of twenty commercial samples were determined as authentic R. damascena essential oil samples using the three analytical techniques. Findings showed that FTIR and Raman spectroscopy combined with chemometrics could be used as reliable, robust, rapid, accurate and low-cost analytical techniques for quality evaluation of R. damascena essential oil.

Analysis of pork adulteration in the corned products using FTIR associated with chemometrics analysis

Meat-based foods such as beef corned became one of the targets of counterfeiting with pork because relatively cheaper. This becomes a serious problem for Muslims, especially in Indonesia. One method that can be used to detect fat was Fourier transform infrared (FTIR) spectrophotometry. The purpose of this study was to quantitatively analyze and a group of corned beef and corned pork using FTIR spectrophotometry combined with chemometrics. Reference samples corned pork-beef made of 7 various concentration (0%, 25%, 35%, 50%, 65%, 75%, 100%) and 6 product samples purchased in the Umbulharjo, Yogyakarta. Extraction was carried out by the soxhlet apparatus using n-hexane technical solvent for 4 – 5 hours at 69 – 70 °C. Fat analyzed using FTIR spectrophotometry for generating infrared spectral data then processed with Partial least square (PLS) chemometrics for quantitative analysis and Principal component analysis (PCA) for grouping. Results of quantitative analysis chemometrics PLS, selected areas fingerprints for analysis corned pork-beef was 1180 – 730 cm-1 with R2 0.9833; RMSEC 2.06%; RMSEP 1.65% and RMSECV 2.22%. The results of PCA showed groupings in different quadrants between corned pork 100% and corned beef 100%. Results showed that FTIR spectrophotometry combined with chemometrics can be used for quantitative analysis and grouping of pork corned and beef corned on the market but it can not identify pork in corned after choking process.

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Comparative review and the recent progress in detection technologies of meat product adulteration

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

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.

Analysis of Lard in Sausage Using Fourier Transform Infrared Spectrophotometer Combined with Chemometrics

Sausage is one of foods which must be confirmed halal to consumers. Meat is commonly used in producing sausages, especially beef. However, due to high cost of meat producer usually mixes the ingredients with other cheaper meats, such as pork. This study aimed to analyze the differences in the spectral profile of lard and beef in the sausages using Fourier transform infrared (FTIR). Lard and beef tallow was extracted using Soxhlet apparatus at ±70°C for 6 h with n-hexane. After extraction, lard and beef tallow was evaporated. Then obtained fats were stored in eppendorf and analyzed using FTIR spectrophotometer. The results were then combined with chemometrics such as Partial least squares (PLS) for the quantitative analysis and principal component analysis (PCA) for classification. PLS and PCA analysis was performed on 1200-1000 cm^-1. The results of the analyzed PLS provided the linear regression equation y = 0.921x + 4.623 with R ² = 0.985 and root-mean-square error of calibration (RMSEC) = 2.094%. External validation root-mean-square error of prediction (RMSEP) was 4.77% and internal validation root-mean-square-error cross-validation (RMSECV) was 5.12%. The results of the PCA analysis showed the classification of different quadrants between 100% pork sausage and 100% beef sausage. Thus, it can be concluded that FTIR spectroscopy method combined with chemometrics can be applied to identify the presence of pork in the sausage.

Comparison of transmission FTIR and ATR spectra for discrimination between beef and chicken meat and quantification of chicken in beef meat mixture using ATR-FTIR combined with chemometrics

Detecting meat adulteration for quality control and accurate labeling is important and needs convenient analytical methods. This study aimed to investigate and compare the application of the transmission and ATR approaches of FTIR followed by principal component analysis (PCA) to not only discriminate between chicken and beef meat but also quantizing chicken portion of mixtures. Two different approaches are presented; spectra preprocessing with focus on wavenumber region of 1700-1071 cm^-1, and no preprocessed where PCA was applied on the whole spectra range of mid-FTIR. The results suggest that applying PCA on specified preprocessed spectra could detect hidden relationships between variables in chicken and beef in both approaches. PCA successfully clustered these kinds of meats when applied on transmission mode spectra without any preprocessing treatment, while applying it on ATR mode's raw spectra failed to cluster them. Additionally, the preprocessed ATR-FTIR spectrum was used to prepare regression models by Partial Least Square Regression (PLS-R) and artificial neural networks (ANN) for predicting presence and percentage of chicken meat in the beef meat mixture. The results demonstrated the superiority of ANN over PLS-R in this assessment with an R2 of 0.999.

Identification of lard on grilled beef sausage product and steamed beef sausage product using fourier transform infrared (FTIR) spectroscopy with chemometric combination

Many issues are spreading about the use of lard in food, one of it is sausage. Sausage is one of the processed foods which are prone of containing pork. In Indonesia, grilled and steamed sausages are popular for children and adults. One of the method which is developed to analyze fat in grilled and steamed sausage products was FTIR spectrophotometry combined with chemometrics. This research aims to develop an analysis method using FTIR spectrophotometry combined with chemometrics to analyze lard content in grilled and steamed beef sausage. Reference sausage made from a mixture of pork and beef. This research was designed by making each 7 concentrations variants of pork and steamed sausage samples (100%, 75%, 65%, 50%, 35%, 25% and 0%). Five samples from market were taken from various beef sausage traders. The fat extraction used n-hexane solvent at the temperature of 70 °C for 5 hours. The extracted fat was analyzed by FTIR spectrophotometry combined with chemometrics. The results of spectrum were analyzed using Horizon MBTM to obtain optimum wave number of steamed sausages in the range of 1000 – 791 cm-1 and grilled sausages in the range of1070 – 796 cm-1. The analysis of steamed sausage with Partial Least Square (PLS) is obtained the equation y = 0.9977x + 0.1166; and the value of R2 0.9977; RMSEC 1.22%; RMSEP 0.22%; and RMSECV 1.26%. The PLS analysis of grilled sausage is obtained the equation y = 0.9972x + 0.1379; and the value of R2 0.9972; RMSEC 1.27%; RMSEP 0.42%; and RMSECV 0.18%. The conclusion of this research is that the FTIR method combined with chemometrics is the proper method for analyzing fat in sausages. The analysis result using Principle Component Analysis (PCA) was obtained from 3 of 5 samples in the market which had the similar physicochemical properties of lard sausage.

The employment of Fourier transform infrared spectroscopy coupled with chemometrics techniques for traceability and authentication of meat and meat products

Meat-based food such as meatball and sausages are important sources of protein needed for the human body. Due to different prices, some unethical producers try to adulterate high-price meat such as beef with lower priced meat like pork and rat meat to gain economical profits, therefore, reliable and fast analytical techniques should be developed, validated, and applied for meat traceability and authenticity. Some instrumental techniques have been applied for the detection of meat adulteration, mainly relied on DNA and protein using polymerase chain reaction and chromatographic methods, respectively. But, this method is time-consuming, needs a sophisticated instrument, involves complex sample preparation which make the method is not suitable for routine analysis. As a consequence, a simpler method based on spectroscopic principles should be continuously developed. Food samples are sometimes complex which resulted in complex chemical responses. Fortunately, a statistical method called with chemometrics could solve the problems related to complex chemical data. This mini-review highlights the application of Fourier-transform infrared spectroscopy coupled with numerous chemometrics techniques for authenticity and traceability of meat and meat-based products.