Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe
Introduction
The development of screening methods for fast classification of meat quality is demanded by the industry. Near infrared spectroscopy (NIRS) has shown to be a rapid and effective tool for meat quality assessment (Monin, 1998). It is an easy to use, non-destructive, accurate and robust technique which allows determination of several parameters simultaneously. NIRS has been successfully used for determining food components much as fat, moisture and protein content in the last two decades (Brøndum et al., 2000, Hildrum et al., 1995a, Wählby and Skjöldebrand, 2001, Williams and Norris, 1987). Its use has until recently been restricted to off-line measurements, but on-line NIR instruments have frequently been used in the last few years (Isaksson et al., 1996, Schwarze, 1996, Tøgersen et al., 1999). Moreover, NIRS is used in other and more complex applications much as the evaluation of sensory characteristics of meat. This is because instrumental techniques like Warner Bratzler shear force, Hunter colour measurement and sensory evaluation – the usual techniques for meat quality assessment – are destructive, time consuming and unsuitable for on-line application. Thus, the prediction of beef sensory attributes such as tenderness using NIRS has been extensively reviewed (Byrne et al., 1998, Hildrum et al., 1994, Liu et al., 2003, Liu, Venel et al., 2001, Park et al., 1998, Rødbotten et al., 2001). Prediction of pork quality using models based on NIRS has been reported less extensively. However, there are some contributions on the usefulness of NIR to predict pork quality attributes (Geesink et al., 2003); drip-loss, and water-holding capacity in fresh pork (Brøndum et al., 2000, Forrest et al., 2000) and to determine the RN− phenotype in pigs, based on the fact that this character is associated with production of inadequate meat for cured and cooked ham (Josell et al., 2001, Josell, Martinsson et al., 2000).
Considering the lack of information on the use of NIRS to predict sensory quality of cured product from pigs, the objective of this study was to investigate the feasibility of visible and NIR spectroscopy for the classification of dry-cured hams as a function of their texture and colour evaluation. Furthermore, in order to facilitate the use of NIRS for process quality control in the meat industry, an NIR instrument fitted with a fiber optic probe was used. This assembly is supposed to be appropriate to obtain spectra directly from the muscle without any sample pre-treatment.
Section snippets
Samples
Hams were obtained from a total of 117 pigs that were slaughtered in five pig killings distributed along one year. They were provided by five suppliers that used different crosses including Duroc, Landrace and Large White. The duration of the post-mortem period was 3–5 days and then hams were subjected to the traditional process for producing Spanish dry-cured ham that involves treatment of the raw ham with curing salts (NaNO2 and KNO3), followed by standing, salt-coated, at 2–3 °C for about 0.8
Spectral data
Mean spectra of near infrared radiation from the target samples are presented in Fig. 2. Differences in absorbance related to pastiness and anomalous colour appearance were found. Pasty and defective colour samples seem to have higher NIR absorbance than normal samples. There are two broad bands at approximately 420 and 560 nm in the visible region, and five broad bands around 980, 1200, 1450, 1790 and 1940 nm, in the NIR. Bands at 980, 1450 and 1940 nm are most likely due to water (Osborne et
Conclusions
The results obtained lead to the conclusion that NIRS can be used as a helpful tool for classifying dry-cured ham as a function of their texture and colour quality with a precision similar to that obtained by sensory evaluation by experts. It is worth to note that the measurements were made using a fiber optic probe and a whole slice instead of a minced sample.
These results were obtained using a laboratory made device. Refinement of this may allow for non-destructive and rapid quality
Acknowledgements
Financial support from Spanish Comisión Interministerial de Ciencia y Tecnología (CICyT) is gratefully acknowledged (Proyect BQU2002-01333). R&D and Quality Department of Campofrío Alimentación S.A., in especial Juan Ángel García, Julio Tapiador, Roberto Rodríguez and Jesús Rodríguez, are thanked for supplying the samples. NIR spectral data were obtained using NIR instrument located at the NIR/MIR Spectroscopy Unit of the Central Service for Research Support (SCAI) of the University of Córdoba.
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2019, Journal of Food Engineering