Classification of pig fat samples from different subcutaneous layers by means of fast and non-destructive analytical techniques

Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review

Fat is one of the most important traits determining the quality of pork. The composition of the fat greatly influences the quality of pork and its processed products, and contribute to defining the overall carcass value. However, establishing an efficient method for assessing fat quality parameters such as fatty acid composition, solid fat content, oxidative stability, iodine value, and fat color, remains a challenge that must be addressed. Conventional methods such as visual inspection, mechanical methods, and chemical methods are used off the production line, which often results in an inaccurate representation of the process because the dynamics are lost due to the time required to perform the analysis. Consequently, rapid, and non-destructive alternative methods are needed. In this paper, the traditional fat quality assessment techniques are discussed with emphasis on spectroscopic techniques as an alternative. Potential spectroscopic techniques include infrared spectroscopy, nuclear magnetic resonance and Raman spectroscopy. Hyperspectral imaging as an emerging advanced spectroscopy-based technology is introduced and discussed for the recent development of assessment for fat quality attributes. All techniques are described in terms of their operating principles and the research advances involving their application for pork fat quality parameters. Future trends for the non-destructive spectroscopic techniques are also discussed.

Calculating the iodine value for Italian heavy pig subcutaneous adipose tissue from fatty acid methyl ester profiles

In this work, different equations were compared as for their effectiveness in predicting the iodine value (IV), based on fatty acid (FA) composition of subcutaneous adipose tissue of Italian heavy pigs. In particular, six equations were tested: AOCS (1); modified AOCS (2), including all unsaturated FA (UFA); regression models obtained using the stepwise regression procedure as variable selection method, calculated considering only UFA (3) or all the FA (4); regression models obtained using the backward elimination procedure, calculated considering only UFA (5) or all the FA (6). The comparison of the equations performance, estimated using an external test set, showed that the use of regression models led to significant enhancements of prediction accuracy with respect to the AOCS equations. Using both equations 4 and 6, the average paired differences between experimental and predicted IV values were not statistically significant. Therefore, it is possible to use these equations for IV estimation of the subcutaneous adipose tissue of Italian heavy pigs.

Classification of frankfurters by FT-Raman spectroscopy and chemometric methods

Frankfurters are widely consumed all over the world, and the production requires a wide range of meat and non-meat ingredients. Due to these characteristics, frankfurters are products that can be easily adulterated with lower value meats, and the presence of undeclared species. Adulterations are often still difficult to detect, due the fact that the adulterant components are usually very similar to the authentic product. In this work, FT-Raman spectroscopy was employed as a rapid technique for assessing the quality of frankfurters. Based on information provided by the Raman spectra, a multivariate classification model was developed to identify the frankfurter type. The aim was to study three types of frankfurters (chicken, turkey and mixed meat) according to their Raman spectra, based on the fatty vibrational bands. Classification model was built using partial least square discriminant analysis (PLS-DA) and the performance model was evaluated in terms of sensitivity, specificity, accuracy, efficiency and Matthews’s correlation coefficient. The PLS-DA models give sensitivity and specificity values on the test set in the ranges of 88%–100%, showing good performance of the classification models. The work shows the Raman spectroscopy with chemometric tools can be used as an analytical tool in quality control of frankfurters.