Identification of pork in beef meatballs using Fourier transform infrared spectrophotometry and real-time polymerase chain reaction

A study on development of Fourier-transform infrared spectrophotometric method combined with principle component analysis as well as real-time polymerase chain reaction for determination of pork–beef mixture in meatballs has been performed. A lipid component extracted from pork and beef in meatballs is analyzed using Fourier-transform infrared spectroscopy, while DNA extracted from meatball was analyzed using real-time polymerase chain reaction. The correlation between actual and predicted concentration of lard using Fourier-transform infrared spectroscopy was performed by aid of partial least squares, while grouping of lard and beef fat components in meatball was carried out by Fourier-transform infrared spectra coupled with principle component analysis. The results showed that Fourier-transform infrared spectra at wavenumbers of 1000–1200 cm^?1 coupled with partial least square and principle component analysis are successfully used for quantification and classification of pork in beef meatballs. The relationship between actual value and predicted value of lard (lipid fraction obtained from meatballs containing pork) with Fourier-transform infrared spectrophotometric method revealed good correlation, with coefficient determination (R²) value of 0.997 and standard error of calibration of 0.04%. Principle component analysis is able to classify samples containing pork and beef meatballs. Fourier-transform infrared spectroscopy using normal spectra is fast technique for identification and quantification of lard extracted from pork in meatball. In addition, real-time polymerase chain reaction using Leptin Primer–AJ 865080 can be used for amplification of pork DNA specifically in meatballs containing pork.     

Dairy products discrimination according to the milk type using an electrochemical multisensor device coupled with chemometric tools

This study shows the potential application of a potentiometric electronic tongue coupled with a lab-made DataLogger device for the classification of dairy products according to the type of milk used in their production, i.e., natural, fermented and UHT milk. The electronic tongue device merged a commercial pH electrode and 15 lipid/polymeric membranes, which were obtained by a drop-by-drop technique. The potentiometric signal profiles gathered from the 16 sensors, during the analysis of the 11 dairy products (with ten replicate samples), together with principal component analysis showed that dairy samples could be naturally grouped according to the three types of milk evaluated. To further investigate and verify this capability, a linear discriminant analysis together with a simulated annealing variable selection algorithm was also applied to the electrochemical data, which were randomly split into two datasets, one used for model training and internal-validation using a repeated K-fold cross-validation procedure (with 64% of the data); and the other for external validation purposes (containing the remaining 36% of the data). The multivariate supervised strategy used allowed establishing a classification model, based on the potentiometric information of four sensor lipid membranes, which enabled achieving a successful discrimination rate of 100% for both internal- and external-validation processes. The demonstrated versatility of the built electronic tongue for discriminating dairy products according to the type of milk used in their production combined with its simplicity, low-cost and fast time analysis may envisage a possible future application in dairy industry.     

Analysis of lard in meatball broth using Fourier transform infrared spectroscopy and chemometrics

Meatball is one of the favorite foods in Indonesia. For the economic reason (due to the price difference), the substitution of beef meat with pork can occur. In this study, FTIR spectroscopy in combination with chemometrics of partial least square (PLS) and principal component analysis (PCA) was used for analysis of pork fat (lard) in meatball broth. Lard in meatball broth was quantitatively determined at wavenumber region of 1018–1284 cm^− 1. The coefficient of determination (R²) and root mean square error of calibration (RMSEC) values obtained were 0.9975 and 1.34% (v/v), respectively. Furthermore, the classification of lard and beef fat in meatball broth as well as in commercial samples was performed at wavenumber region of 1200–1000 cm^− 1. The results showed that FTIR spectroscopy coupled with chemometrics can be used for quantitative analysis and classification of lard in meatball broth for Halal verification studies. The developed method is simple in operation, rapid and not involving extensive sample preparation.