Article pp.159-162 du Vol.23 n°1 (2003)

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SCIENCES DES ALIMENTS, 23(2003) 159-162

FOCUS : JSMTV

Prediction of organoleptic and technological characteristics of pork meat by near infrared

spectroscopy

A. Meulemans¹, O. Dotreppe², B. Leroy¹, L. Istasse² and A. Clinquart¹

INTRODUCTION

The consumer demand in terms of meat quality and the need for industry to control quality parameters justify the increasing interest for alternative analytical methods to assess quality. Near Infrared Spectroscopy (NIR) is a fast and non destructive technique and appears to be one of the most promising potential methods. For instance, Dotreppe et al. (2000) and Leroy et al. (2000), using NIR, predicted the technological and organoleptic properties and fatty acids content of several beef cuts. In pork meat fat content, water holding capacity and che- mical composition have also been evaluated by NIR (Irie 1999, Brondum et al., 2000).

OBJECTIVES

The aim of the present study was to use NIR as a prediction tool for the evaluation of technological and organoleptic characteristics in pork meat. Several mathematical models were calculated on the basis of the samples used for calibration; then the quality of the prediction models was evaluated on independent samples.

1. Dept. Food Science (Technology).

2. Dept. Animal Production (Nutrition).

University of Liège, Faculty of Veterinary Medicine, Sart Tilman, Bât. B43, 4000 Liège, Belgium 4-FOCUS (159-162) Page 159 Jeudi, 15. mai 2003 8:36 20

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160 Sci. Aliments 23(1), 2003 A. Meulemans et al.

MATERIAL AND METHODS

Muscle longissimus thoracis corresponding to the 12^th rib was taken 24 hours post mortem on 181 pork carcasses. The samples were cut in two sli- ces in the lab. The first slice was used to measure technological and organoleptic characteristics using reference methods (Meulemans et al., 2002). The second slice was ground and homogenized on day 1 in a mincer, the NIR measurements being taken on day 2 using the Fourier Transform spectrometer Bomen MB160D in the 4000 to 12000 cm^–1 spectral range. The spectra were recorded in reflection mode using the axiom FDR–320 probe. There were 32 records for each spectrum and three spectra were taken on each homogenized sample. An average spectrum was calculated to build the data base.

The first 80 samples were used to build calibration model for the different parameters. The mathematical treatment was carried on using Grams/32 (Galactic) software. Principal component analysis (PCA) was previously perfor- med to detect aberrant or abnormal spectra. Different mathematical pretreate- ments (correction of the base line, normalization, derivations,…) were tested.

Finally, the models were calculated with the PLS (Partial Least Square) algo- rithm. The number of terms was determined by cross-validation. For each parameter, the best model was chosen on the basis of the lowest Standard Error of Cross-Validation (SECV). Each parameter was then predicted on the 101 remaining samples using the selected model. The difference between the predicted and the reference values allowed the calculation of a Standard Error of Predic- tion (SEP).

RESULTS AND DISCUSSION

The performances of the model obtained for the different technological and organoleptic parameters are reported in table 1 (SECV and Determination Coef- ficient – R²cv) along with some statistics for the description of the samples used for calibration of the NIR instrument. The quality of the mathematical models varied according to the parameters. The best models were obtained for CIE L*

and drip loss with a R²cv of 0,62 and 0,54 respectively. Pedersen and Engelsen (2001) reported similar findings from 66 samples with a correlation coefficient (r) of 0,77 (R² = 0,59) for drip losses ranging from 0,63% to 7,83%. In the present experiment, the two other parameters related to colour (CIE a* and CIE b*) were less well modelized since the R²cv was equal to 0,40 and 0,38 respectively. The model related to pH (day 1) was of low quality, partially explained by the low variation of the measured pH values on day 1, the pH varying between 5,2 and 5,8. There were no models for cooking loss and tenderness (WBPSF: Warner- Bratzler peak shear force).

The spectra obtained from the 101 remaining samples which were independent of the samples used for calibration purposes were used to predict the dif-

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Prediction of organoleptic and technological characteristics of pork meat by near infrared spectroscopy 161

ferent studied parameters. The comparison between the predicted values by NIR and the reference values was used to calculate the performances of the prediction of models (SEP and R²p). The results are given in table 2. Overall, the performances of the models were quite weak since the highest R²p was obtained for CIE b* (R² = 0,32). The prediction performances (R²p) for CIE L*, CIE a*

and drip loss were far worse than the performances of the mathematical models in table 1. Such a loss of quality in the model could be explained by a too small spectral variability within the samples used to calibrate the NIR instrument.

Sources of variation such as temperature during the spectrum acquisition or high noise level within calibration and prediction sets of data could also induce a reduction of performances.

Table 1

Statistical results of models obtained in reflection mode on homogenized pork meat (N: number of samples, Min: minimum, Max: maximum, Mean,

SD: standard deviation, SECV and R²cv)

Table 2

Validation of prediction models (N: number of samples, Min: minimum, Max: maximum, Mean, SD: standard deviation, SEP and R²p)

Calibration statistics

Items N Min Max Mean SD SECV R²cv

CIE L* (%) 75 47.3 65.8 54.2 4.2 2.53 0.62

CIE a* 77 2.9 9.7 6.3 1.3 1.22 0.40

CIE b* 77 11.5 19.3 15.5 1.6 1.18 0.38

pH (day 1) 80 5.2 5.8 5.4 0.1 0.09 0.12

Drip loss (%) 74 1.6 9.7 5.6 2.1 1.41 0.54

Cooking loss (%) *

WBPSF (N) *

Technological and organoleptic parameters : CIE L*, a*, b* ; pH ; Drip loss ; Cooking loss ; tenderness (WBPSF). * no model.

Validation statistics

Items N Min Max Mean SD SEP R²p

CIE L* (%) 101 43.9 66.4 55.0 5.1 4.74 0.18

CIE a* 73 2.4 10.7 6.7 2.0 1.87 0.15

CIE b* 73 12.8 19.8 15.8 1.5 1.34 0.32

pH (day 1) 89 5.1 5.6 5.4 0.1 0.08

Drip loss (%) 78 2.4 10.8 6.6 1.9 2.35

Cooking loss (%) ˚

WBPSF (N) ˚

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162 Sci. Aliments 23(1), 2003 A. Meulemans et al.

CONCLUSIONS

The present study illustrated the good potentiality of the NIR technology to predict different parameters associated with pork meat, particularly the drip loss. It should be noted that the validation of the technique with a set of independent samples is necessary in order to control the real performance of the models and their ability to predict the different parameters. The models could be improved by using a larger number of samples in order to increase the variability in the data base and by a better control of all the factors supposingly influencing the spectral data.

ACKNOWLEDGEMENTS

The present study was founded by the Direction Générale des Technologies de la Recherche et de l’Energie Région Wallonne – Belgium (convention NIR n˚ 981/3723).

PERTINENT LITERATURE

BRONDUM J., MUNK L., HENCKEL P., KARLSSON A., TORNBERG E. and ENGELSEN S.B., 2000. Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat Science, 55, 177-185.

DOTREPPE O., LAMBOTTE S., LEROY B., CLINQUART A., LECOCQ H. and ISTASSE L., 2000. The use of near infrared spectroscopy to determine fat content and fatty acid composition in beef meat. Procee- dings of the 46^th International Congress of Meat Science and Technology, Buenos Aires, Vol. I : 376-377.

IRIE M., 1999. Evaluation of Porcine Fat with Fiber-Optic Spectroscopy. Journal of Ani- mal Science, 77, 2680-2683.

LEROY B., LAMBOTTE S., DOTREPPE O., LECOCQ H., ISTASSE L. and CLIN- QUART A., 2000. Prediction of techno-

logical and organoleptic properties of beef cuts by near infrared spectroscopy. Proceedings of the 46^th Interna- tional Congress of Meat Science and Technology, Buenos Aires, Vol. II : 610- 611.

MEULEMANS A., DOTREPPE O., LEROY B., LECOCQ H., ISTASSE L. and CLIN- QUART A., 2002. Prediction of technological and organoleptic properties of porcine meat by near infrared spectroscopy. Pro- ceedings of the 48^th International Con- gress of Meat Science and Technology, Roma, Vol. II : 822-823.

PEDERSEN D.K. and ENGELSEN B., 2001.

Early prediction of quality of porcine meat by FT-IR spectroscopy. Proceedings of the 47^th International Congress of Meat Science and Technology, Krakow, Vol. I : 204-205.

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