Abstract
Traditional methods for the determination of meat quality-relevant parameters are rather time-consuming and destructive, whereas spectroscopic methods offer fast and non-invasive measurements. This review critically deals with the application of handheld and portable Raman devices in the meat sector. Some published articles on this topic tend to convey the impression of unrestricted applicability of mentioned devices in this field of research. Furthermore, results are often subjected to over-optimistic interpretations without being underpinned by adequate test set validation. On the other hand, deviations in reference methods for meat quality assessment and the inhomogeneity of the meat matrix pose a challange to Raman spectroscopy and multivariate models. Nonetheless, handheld and portable Raman devices show considerable potential for some applications in the meat sector.
Highlights
Meat industry as well as meat science have put forth numerous analytical methods for meat quality assessment in order to ensure edibility, to be in compliance with control authority regulations and to offer consumers high quality products and satisfy their needs
Since the pH is of particular importance to meat quality evaluation [63], serveral studies have tried to correlate pH measurements with Raman spectra obtained from handheld devices ready for mobile use [Scheier & Schmidt (2013) [55], Scheier et al (2014, 2015) [56,57], Fowler et al (2015b, 2018) [51,52], Nache et al (2016) [58]]
Different meats and experimental conditions were used in this study compared to the study of Fowler et al (2015b) [51], the failure of this work in predicting L* and drip loss from Raman spectra demonstrates that any results concerning these two parameters should be viewed with caution—especially if they are not test set validated
Summary
Meat industry as well as meat science have put forth numerous analytical methods for meat quality assessment in order to ensure edibility, to be in compliance with control authority regulations and to offer consumers high quality products and satisfy their needs. Such a set of samples is called test set or independent validation set and the resulting error is referred to as root mean square error of prediction (RMSEP) This validation step is essential in order to estimate the future model performance in predicting completely new and unknown samples and to avoid both overfitting and unterfitting. In our point of few, the ideal procedure for the development of spectroscopic applications in meat science would be to procure samples for the establishment of a calibration model at a time and after this, again procure new samples at another time for the purpose of testing the prediction ability (performance) of the developed calibration model It is to be assumed, that other validation approaches than the one just mentioned above—especially cross validation—will lead to biased, less trustworthy or even useless and impractical multivariate models due to the already described reasons of pronounced inhomogeneity and biological variability of meat samples. For a comprehensive review on quality assessment of meat and fish with benchtop Raman devices, the interested reader is referred to Ref. [44]
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