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Application of Design of Experiment for Detection of Meat Fraud with a Portable Near-Infrared Spectrometer

[ Vol. 14 , Issue. 1 ]

Author(s):

V. Wiedemair, M. De Biasio, R. Leitner, D. Balthasar and C.W. Huck*   Pages 58 - 67 ( 10 )

Abstract:


Background: Meat fraud generated a huge outrage amongst customers in 2013 in Europe due to the horsemeat scandal. Portable and hand-held optical near-infrared (NIR, 4,000 12,500 cm-1/800-2,500 nm) spectroscopy sensors are traded as promising fast, non-invasive and easy analytical tools that might be applicable at any independent place of inspection. In order to embrace the on-going trend towards instrumental miniaturization, it was the aim of the present feasibility study to evaluate the application of Design of Experiment for frequently applied portable micro-electro-mechanical system (MEMS) based spectrometer by comparing its performance to a bench-top Fourier-Transform polarization near-infrared (FT-NIR) instrument.

Methods: 63 samples of different meat types (beef: 9, chicken: 10, mutton: 10, turkey: 10, pork: 10, horse meat: 14) were measured in order to classify the meat-type using a portable micro-electromechanical system (MEMS) based spectrometer and a bench-top Fourier-Transform polarization nearinfrared (FT-NIR) instrument, in order to compare the performance of both systems. In a second step different meat types were minced together in order to investigate the level of adulteration which can be detected using MEMS and FT-NIR. Design of Experiment (DoE) was applied to enhance results.

Results: The accuracy of MEMS versus FT-NIR for identifying whole / minced pieces of chicken, pork, turkey, beef and mutton meat (63 samples) against horse meat appeared to be 75.0-100.0% (MEMS) vs. 62.5%-100.0% (FT-NIR) for whole pieces and 75.0-100.0% (MEMS and FT-NIR) for minced meat. When mincing different types of meat together, a maximum of 4 and 1 factors were required for establishing a PLS-R model using again the spectra recorded with MEMS and FT-NIR, respectively. The resulting quality parameters for the MEMS device were: R2=0.06-0.62, Standard Error of Cross Valdiation (SECV)= 17.33-32.91, Ratio of Performance to Deviation (RPD) =0,54-1,70 and for the FTNIR system: R2=0.85-0.94, SECV=7.52-13.83%, RPD=2.2-5.7 (FT-NIR). The limit of detection was found at 10% for the MEMS and at 1% for the FT-NIR device.

Conclusion: Meat classification can be performed using the bench-top FT-NIR as well as the hand-held MEMS-NIR. Mincing the meat samples does not necessarily improve classification accuracy as information about the surface structure is lost. NIRS prediction models for adulterations were established for the bench-top system. Prediction models for the hand-held device are inconclusive and have to be improved by a larger sample set and/or further progress in miniaturization technique. Low level adulteration (<10%) may also be predictable with NIRS, but continuative research is necessary.

Keywords:

Design of experiment, meat fraud, near-infrared, hand-held spectrometer, MEMS, polarization.

Affiliation:

Institute of Analytical Chemistry and Radiochemistry, CCB – Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80/82, 6020 Innsbruck, CTR Carinthian Tech Research AG, High Tech Campus Villach Europastrasse 12, St. Magdalen, 9524 Villach, CTR Carinthian Tech Research AG, High Tech Campus Villach Europastrasse 12, St. Magdalen, 9524 Villach, TOMRA Sorting Systems, Otto-Hahn Straße 6, 56218 Mulheim- Karlich, Institute of Analytical Chemistry and Radiochemistry, CCB – Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80/82, 6020 Innsbruck

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