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

Author(s): V. Wiedemair, M. De Biasio, R. Leitner, D. Balthasar, C.W. Huck*.

Journal Name: Current Analytical Chemistry

Volume 14 , Issue 1 , 2018

Graphical 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, 4,00012,500 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.

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Article Details

Year: 2018
Page: [58 - 67]
Pages: 10
DOI: 10.2174/1573411013666170207121113

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