Article pp.128-131 du Vol.23 n°1 (2003)

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128-131 Sci. Aliments 23(1), 2003 I. Allais et al.

FOCUS : JSMTV

Monitoring the texture of meat emulsions by front-face fluorescence spectroscopy

I. Allais

¹

, E. Dufour

²

, A. Pierre

²

, C. Viaud

²

, V. Chevallereau

¹

, C. Curt

¹

, N. Perrot

¹

1 – INTRODUCTION

The management of the quality of a food product requires the ability to anti- cipate its final properties. To reach this goal, on-line or at-line methods allowing real time measurements during the manufacturing process and prediction of the characteristics of the final products will be useful tools that can provide feed- back/forward control of the process. Considering meat emulsions, the characteristics of the final product are partly related to the characteristics of the batter at the end of chopping. In this study, the meat emulsions were characterized by the determination of the texture profile and by fluorescence spectroscopy. The aim of this work was to investigate the ability of the fluorescence spectra to dis- criminate products made from different formulations and the relations between fluorescence spectra and texture measurements.

2 – MATERIAL & METHODS

Different meat emulsions were prepared by varying three factors: the formulation (lean/fat ratio of 0.7, 1.47 and 3.63), the chopping speed (1000, 2000, 3000 rpm) and the chopping time (1, 3, 7 min) following a complete factorial design. The levels of each factor were chosen in a wide experimental domain compared with industrial parameters in order to obtain a large scale of products.

1. Cemagref – UMR Genial – Équipe Automatique et Qualité Alimentaire, 24, av. des Landais, 63 172 Aubière Cedex, France.

2. Département Qualité et Economie Alimentaires et UR Typicité des Produits Alimentaires – ENITA Clermont Ferrand – site de Marmilhat, 63370 Lempdes, France.

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Monitoring the texture of meat emulsions by front-face fluorescence spectroscopy 129

Meat emulsions were prepared in a fixed tank chopper (CDH 30) of 30 l capacity, the formulation contained a variable lean/fat ratio and a fixed quantity for the others ingredients (ice, gelling binding agent, nitrite salt, seasoning, liquid carmine). The protocol is summarised figure 1.

Figure 1 Experimental protocol

The measures were performed in triplicate on each sample. Principal Com- ponent Analysis and Canonical Correlation Analysis were performed on the spectral and texture data.

3 – RESULTS

3.1 Discrimination of samples using fluorescence spectra

Principal Component Analysis was used to investigate the discriminant ability of the fluorescence spectra recorded on the meat emulsions varying by formula and the manufacturing parameters (chopping velocity and time).

Both batter and sausages spectra were analyzed. They allowed to discrimi- nate batters and sausages from their formula (figures 2 et 3) and batters according to the velocity of chopping. But the spectra recorded on the batters and the sausages did not allow a discrimination according to the chopping time.

Formulation

Chopping in controlled conditions

Mixing : 1 min – 500 rpm then 1 min – 1000 rpm Chopping : 1 to 7 min – 1000 to 3000 rpm

Measurements on meat emulsions

Measurements on the frankfurters Stuffing and cooking (70°C-30min)

A : fat 44% – lean 30%

B: fat 30% – lean 44%

C : fat 16 % – lean 58%

Texture measurement by texture profile analysis (Lloyds Instruments TA 500, 2 bites test, speed :

100 mm/s, 20 mm cylindric probe, 8 mm depth) Fluorescence spectroscopy measurement (Spex-Jobin Yvon – Fluoromax, front-face device, excitation radiation set at 56°, excit. 290 nm, em. : 305-400 nm, resolution : 0.5 nm)

Texture measurement by texture profile analysis (speed : 100 mm/s, 10 mm cylindric probe, 4 mm depth)

Fluorescence spectroscopy measurement (same conditions)

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130 Sci. Aliments 23(1), 2003 I. Allais et al.

Figure 2

PCA similarity map 1-2 for the spectra recorded on the batters

Figure 3

PCA similarity map 1-2 for the spectra recorded on the sausages

3.2 Relationship between front-face fluorescence spectra and texture measurements

Canonical correlation analysis was applied on the two data sets containing the fluorescence spectra and 9 texture attributes calculated from profile texture analysis of the batters. Results showed that the first two pairs of canonical variates were highly correlated with squared canonical correlation coefficients

A

B

C

–20 –10 10 20

20 15

10

5

–5

–10 –15

–20

30 40

0 0

Component 1 – Inertia : 74.1%

Componant 2 – Inertia 16.3%

A B C

–25 –20 –20

–15 –10 –10

10

10 15

20

20 30

–5 5 00

A

A B C

B

C Component 1 – Inertia : 73.4%

Componant 2 – Inertia 20.2%

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Monitoring the texture of meat emulsions by front-face fluorescence spectroscopy 131

equal to 0.89 and 0.85, respectively (figure 4) . They indicated, at a level of confidence of 95%, that the fluorescence spectra of tryptophan are related to texture measurements performed on the batters.

In a first step, canonical correlation analysis was applied on the two data sets containing the fluorescence spectra and texture of sausages. It appeared that texture attributes didn’t show any significant correlation with the spectra.

Then, products chopped at a low velocity (1000 rpm) which showed a large heterogeneity were excluded from the data base. CCA performed on this new data base showed that the first two pairs of canonical variates were highly correlated with squared canonical correlation coefficients equal to 0.97 et 0.93 (level of confidence of 95%), respectively. It is concluded that the fluorescence spectra of tryptophan recorded on the sausages manufactured at velocity higher than 1000 rpm are related to texture measurements.

Figure 4

Similarity map showing the two first canonical variates calculated from the fluorescence spectra of tryptophan and the texture measurements

for batters (canonical correlation coefficient = 0.89)

4 – CONCLUSION

The presented results are a first step in order to develop a rapid method for the prediction of texture characteristics of meat emulsions from frontal fluorescence spectra.

–2.3 –1.3 –0.3 –0.7 –1.7 –2.7

–2.9 –1.9 –0.9 –0.1 –1.1 –2.1 –3.1

First canonical variate calculated on fluorescence spectra First canonical variate calculated on texture