Analysis of lard in sausage using Fourier transform infrared spectrophotometer combined with chemometrics

INTRODUCTION The halalness of a food product is critically taken into consideration in consuming food products. As Allah SWT says in Surah Al-Baqarah verse 173, “only forbidden to you are carrion, blood, pork, and animals slaughtered not by the name of Allah but by the name of idols. But whoever is forced to eat it, while he is no persecution, and he is not also exceeding the limits, then there is no sin for him. Again indeed, Allah is Forgiving, Most Merciful.” The sausage is one of the processed meat products, which is much appreciated by many people in Indonesia. These food products are made from meat that is mashed, seasoned, and wrapped in a casing so it has a distinctive taste and symmetrical size. So far, most of the sausage raw material is beef, although some are derived from pork or chicken.[1] Halal food products, are closely related to raw materials, and processing.[2] Sausage is a food product made from meat. Usually chicken or beef. Beef or chicken can be replaced by pork.[3] The existence of pork in the sausage can be detected by Fourier transform infrared (FTIR) spectrophotometer. FTIR spectroscopy is the latest method of infrared (IR), which is already widely used for the analysis of food oil.[4] The analysis can be done by looking at the spectral pattern of a fat sample using an FTIR spectrophotometer.[5] Furthermore, contaminants of pigs in “abon” products also can be detected using real time PCR, with primers that are specific in mitochondria.[6] The oil analysis method using FTIR has been developed because it is easier, faster, cheaper, and eco-friendly.[7] The development of analytical methods using FTIR has now been combined with chemometrics techniques. Chemometrics is a chemical discipline that uses mathematics, statistics, and formal logic to design or select optimal experimental procedures and provide maximum chemical information relevant to analyzing chemical data.[8] This combination makes analysis better, especially for testing oils and fats or a mixture of both.[9] This study aimed to analyze pork in processed food products in the form of sausages using FTIR spectroscopy method combined with chemometrics. The novelty of this research was to use different fat samples from different animals. Although the samples were sauces, the result will be different because different fat samples also had different wave numbers. MATERIALS AND METHODS Materials Beef, pork, and sausages were all purchased from the Godean market, the Kranggan market, and Sleman area, respectively. n-Hexane and Na2SO4 anhydrous were purchased from the City of Yogyakarta. Tools The instruments used in the experiment included the following: Blenders, analytic scales, Eppendorf tubes, Soxhlet, FTIR spectrophotometer (Clairet Scientific, Northampton, UK) with deuterated triglycine sulfate detectors (deuterated triglycine sulfate) and The FTIR spectra were processed using FTIR software of Horizon MB version 3.013.1 (ABB, Canada). Methods Making reference sausages Sausages were made by mixing beef, pork, and spices as per weight [Table 1].Table 1: Sausage formula mix beef and pork refinementFat extraction Fat was extracted with n-hexane solvents by Soxhlet apparatus at a temperature of ± 70oC for 6h. Then, Na2SO4 anhydrous was added and shaken vigorously. The extracted fat then kept in an appendix at low temperatures.[10] Fat analysis Fats derived from sausages of various concentrations and market samples were analyzed using an FTIR spectrophotometer. Fat/oil were placed on ATR crystals at controlled temperatures (20oC).[11] Data analysis The data from FTIR analysis were processed using the chemometrics analysis program. The multivariate calibration model made with Horizon MB software uses principal component analysis (PCA) and partial least squares (PLS) techniques. An evaluation with parameters such as R2 (coefficient of determination) and RMSEC (root-mean-square error of calibration) was carried out. The RMSEP and RMSECV can be calculated following equation:[12] where N is the number of data sets, , pre is the prediction value of the sample, and yi, ref is the reference value or the actual value.[12] Thus, we have where N is the number of data sets, is the prediction value of the sample, and , ref is the reference value or the actual value. RESULT Extraction results Extraction was done at a temperature of ±70oC for 6h. The extraction process used n-hexane solvent because it has a non-polar solubility, low boiling point (easy to separate using evaporation), economical. After the final extraction, the oil solution was added with Na2SO4 anhydrate to remove water content. The water content in oil will affect the spectral reading. The oil color obtained was yellowish. Fourier transform infrared spectrophotometer spectra analysis Fat extracted with Soxhlet from 100% pork fat and 100% cow fat were analyzed by FTIR spectrophotometer at wave numbers 3000-600 cm-1. Response Spectra is a functional group of fats [Figure 1].Figure 1: FTIR spectrum from pure beef sausage (100% beef sausage) and pure pork sausage (100% pork sausage)Table 2 shows wave numbers and functional groups that explain the peaks in the spectra. Figure 2 presents the FTIR spectra of various sausages in various concentrations.Table 2: Function clusters and vibration models on beef fat and lardFigure 2: Spectrum of FTIR sausage various concentrations reference (0%–100%)Quantitative analysis using partial least squares Quantitative analysis of fat extracted using Partial Least Square (PLS) calibration.[2] Accuracy of PLS model was evaluated by coefficient of determination (R2), while the precision of analytical method was assessed using root Mean Square Error of Calibration (RMSEC) and Root Mean Square Error of Prediction (RMSEP).[13] The classification among meatball samples was carried out using chemometrics of Principal Component Analysis (PCA) [Figure 3].Figure 3: The results of PLS analysis, curve of the relationship between actual value (x-axis) and prediction value (y-axis) in reference sausageFat grouping using principal component analysis Sausage formula contains 100% pork and 100% beef classified using chemometrics from Principal Component Analysis (PCA). The wavenumber used is optimized and the wave number used for quantitative analysis, which is 1200-1000 cm-1.[14] The discriminant of beef and lard are presented on [Figure 4]. Samples were obtained from the Sleman area and the City of Yogyakarta. The results of grouping with samples on the market are presented in Figure 5.Figure 4: The results of PCA analysis of (A) 100% beef fat and (B) 100% lardFigure 5: The results of PCA analysis (A) 100% beef sausage and (B) 100% pork sausage, and S (1, 2, 3, 4, and 5) market sausage samplesDISCUSSION Fourier transform infrared spectrophotometer spectra analysis Figure 1 shows the function clusters and vibrational models found in beef fat and lard. Table 2 shows the peak area of the wave numbers 1747 and 1744 cm–1. There is a significant difference in absorbance between the two types of fat. In the region of the wave number 1750-1717 cm-1 is the carbonyl group (C=O) ester of triacylglycerol.[7] The wave number peak 1161 cm–1 is the stretching vibration of the C–O cluster in the ester, and shows a significant difference between lard and beef fat so that in this area analysis of differences in the profile of lard with cow fat is done. The FTIR spectra showed that the differences both pork and beef are 1200–650 cm–1. Figure 2 presents the FTIR spectra of various sausages in various concentrations. Quantitative analysis using partial least squares The linear regression between the actual concentration and the results of the FTIR-PLS prediction shows quite good results, namely the linear regression equation y = 0.921x + 4.623 with R2 = 0.985. The R2 value indicates the ability of a method to produce a rate proportional analysis to the concentration sample. The value of R2 approaches 1 showed that the linear relationship between the actual value and prediction Value is good and RMSEC, RMSEP, and RMSECV values [ZERO WIDTH SPACE][ZERO WIDTH SPACE]are low, indicate an error that occurred in the analysis is low.[13] The RMSEC value is 2.094%. The RMSEC value is used to evaluate errors in the calibration model.[15] RMSEP value of 4.77% RMSECV value 5.12%. The smaller RMSEP and RMSECV values indicate a smaller error so that the model built has an ability that is getting better in the analysis.[16] Fat grouping using principal component analysis The results of the analysis on both types of fats, namely 100% beef (A) and 100% lard (B), suggest that the two fats are in different quadrants and are separated by great distances. At wave numbers 1200-1000 cm-1 can be used for the analysis of a mixture of lard and beef fat. The distance between pork fat and cow fat is very far.[17] This shows that there is a difference between pork fat and beef fat [Figure 4]. Then an analysis of the sausage samples sold in the community was carried out. Samples were obtained from the Sleman area and the City of Yogyakarta. The sample is S (1, 2, 3, 4, and 5) [Figure 5]. It is known that all samples are in the 100% cow quadrant. This means that there is no counterfeiting of the market sausage sample by using non-halal meat or fat (pork). CONCLUSION The coefficient of determination (R2) of 0.985 and the RMSEC value of 2.094% can be determined by using the FTIR spectrophotometry combined with multivariate PLS calibration at 1200–1000 cm–1 wave number. FTIR spectrophotometry combined with PCA multivariate calibration can serve as an accurate and reliable method for the classification of lard and beef fat in the market. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

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