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The demand of halal meat products is growing globally.Therefore, it is important to detect adulterations and food fraud attempts in a fast, non-invasive manner for example by using hand-held near-infrared (NIR) devices. In this study, samples of pork, lamb, beef and chicken were measured pure and in mixtures of 2, 5, 10, 25 and 50% pork in the non-pork meat samples,respectively. Five sensors were tested with varying wavelength range: Scio (740-1070 nm), Linksquare (400-1000 nm), Tellspec(900-1700 nm), MicroNIR (900-1650 nm), ASD Labspec 4 High-Res (350-1700 nm). A one-class-classification approach was used for data analysis, applying pork as the target group. For comparison,thresholds of the models were chosen to correctly identify100% of the pork samples and 75% of all mixtures. Comparing the sensors upon the correct detection of all halal meat samples,i.e., no-pork containing ones, the Scio and the ASD Labspec performed best with an outcome of 34% and 32%, respectively. The Linksquare, MicroNIR and Tellspec were able to correctly identify27%, 27%, and 10%, respectively, of the halal products. Concluding,the application of these five NIR devices are challenging when it comes to the detection of meat products from different species. Nonetheless, the usage of this application in combination with suitable chemometric approaches may contribute to the detection of food fraud in halal products.
|Title of host publication||OCM 2021 - Optical Characterization of Materials: Conference Proceedings|
|Editors||J. Beyerer, T. Längle|
|Publication status||Published - 26 Feb 2021|
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