What is it about?

We wondered if it is possible to identify ground meat species using a simple and reliable technique, preferably not requiring extensive sample preparation ... Near-infrared spectroscopy combined with chemometric data modeling is an interesting approach to achieve this challenging goal. Bearing in mind access to small portable NIR instruments, the analysis can be made outside the laboratory. The proposed approach is cost-effective, fast, and reliable. Constructed classification models using the soft independent modeling of class analogies method (SIMCA) and one-class partial least-squares regression model (OC-PLS) are characterized by a high specificity with respect to beef, lamb, and pork.

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Why is it important?

Efficient identification of meat species is important mostly from the perspective of food control. Moreover, it is vital to take into account consumer safety, preferences as well as religious reasons. To decrease the price of a product, ground meat can be adulterated with a less expensive substitute. The use of certain meat species, for religious reasons, is not allowed. The possibility to detect certain meat species is also essential from the perspective of consumer safety. For instance, the consumption of meat from some wide animals increases the risk of transmitting certain diseases, including COVID-19.


Very rapid progress in the miniaturization of the NIR instruments opens a unique possibility to advance and increase food control. Probably, in the near future, regular consumers will be able to assess food safety themselves, during shopping, using simple-to-use portable NIR devices. Maybe this possibility will be available soon via personal smartphones.

Professor Michal Daszykowski
University of Silesia in Katowice, Poland

Read the Original

This page is a summary of: Identification of ground meat species using near-infrared spectroscopy and class modeling techniques – Aspects of optimization and validation using a one-class classification model, Meat Science, May 2018, Elsevier,
DOI: 10.1016/j.meatsci.2018.01.009.
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