Application of NIR to meat

The texture of meat is important ultimately as an indicator of acceptability to the consumer when eaten (Morgan et al., 1991). However, the appearance and texture of meat post mortem is influenced by the slaughter process as well as by the inherent characteristics of the meat. This becomes a stage, therefore, at which measurement and ultimately control are desirable. Two conditions may arise as a result of increased pre-slaughter stress endured by the animal. Low pH caused by a rapid development of stress in pigs prior to slaughter results in PSE (pale, soft, exudative) meat which has an open texture. Where the stress is of a longer duration, DFD (dark, firm, dry) meat results in both pork and beef. In this case the meat has a higher than average pH and the muscle fibres are more closely packed.

The texture of meat following slaughter has been investigated using spectroscopic means, and a number of instruments have been commercialised on this basis. Generally, such systems rely on changes in texture causing differences in the light scattering nature of the muscle fibres. Typically this has been achieved for the interior of joints using visible spectroscopy with wavelengths in the range 600-690 nm (Fortin, 1989) and at 700 nm (Murray et al., 1989) having been suggested. In addition, devices based on colorimetric assessment rather than information about a specific wavelength have also been developed (Irie and Swatland, 1992). More recently, however, NIR has also been applied to the assessment of meat texture.

Given the published use of visible spectroscopy, it is not surprising that a number of studies have also been directed at the development of NIR calibrations for the assessment of meat tenderness (Lanza, 1983; Hildrum et al., 1994; Byrne et al., 1998). For the most part, these studies have resulted in calibrations relating meat texture to NIR properties for samples assessed at the same point in time. The last of these, however (Byrne et al., 1998), investigated the use of NIR spectra collected at earlier times post mortem to predict the tenderness of beef aged for 14 days. This approach was extended by R0dbotten et al. (2000) who developed NIR calibrations based on an assessment of meat tenderness using a Warner Bratzler shear-press device for beef M. longissimus dorsi stored for two and seven days. In this case, however, NIR spectra were collected both pre and post mortem. Although further work was felt to be required, the results indicated that the method may have potential.

In addition to the use of instrumented methods of assessing tenderness, Hildrum and Nilsen (2000), developed calibrations against sensory measures. Both reference method approaches gave results having squared correlation coefficients in the range ~0.5-0.7. While again this would be seen as being at the lower limit of acceptability for use of such calibrations, the potential of the technique for such assessment was further demonstrated.

In addition to work using the higher wavelengths, the near visible region (750-1100 nm) has also been applied to the analysis of meat tenderness (Venel et al., 2001). When a range of M. longissimus dorsi samples was assessed, calibrations for tenderness (Warner-Bratzler shear force) gave the best performance, although this indicated limited potential for a global calibration solution (R = 0.51). However, when specific calibrations were developed for segregated sample sets (e.g. separated by animal grade or pHu), slightly better performance was observed.

The storage history of meat is also an important consideration for processors in terms of both microbiological safety and the texture of the meat. The use of 'fresh' or 'frozen' labels has, historically, been challenging due to difficulty in defining general temperature limits on which to base a definition (Windham et al., 1996). Nevertheless, these workers investigated NIR as a means of assessing the storage conditions under which chicken breast meat had been kept. The meat was classified as falling into one of five classes representing samples stored at 4, 0, -3, -12 and -18 °C. Discriminant analysis based on principal components analysis (PCA) functions was shown to allow correct classification of 85 and 75 % of the unfrozen and frozen chicken breasts respectively.

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