Food fingerprinting

Label-free fingerprinting system using Laser-induced Breakdown Spectroscopy (LIBS) to improve food defense and combat adulteration, contamination, and fraud

Food production, processing, and supply are becoming globally integrated, providing unprecedented access to a continuously broadening array of products. The inevitable expansion of a service network from supplier to customer and the inclusion of global actors inherently increases the risks associated with that supply chain. Monitoring food products also become more difficult in the presence of growing complexity and the growing variety of the offered goods. Verifying the integrity of the products by monitoring and fingerprinting the ingredients is a well-understood concept. Still, its practical implementation requires a sophisticated combination of biophysical and chemical analysis tools, statistical methods, data processing, and machine learning techniques. Numerous biophysical approaches have been proposed in the context of label-free testing, authentication, and fingerprinting of food products. These include chromatography, mass spectroscopy, ELISA, conventional Raman, surface-enhanced Raman scattering (SERS), Fourier transform infrared (FT–IR) spectroscopy, and nuclear magnetic resonance. The use of laser-induced breakdown spectroscopy (LIBS) in biological or agricultural applications is relatively rare, as the technique is typically employed in material science and basic physics research. However, recently there has been a growing interest in LIBS owing to the rich information content of the data, negligible cost of individual measurement, and affordable instrumentation. LIBS is based on atomic optical emission spectroscopy, using a high-power pulse laser that ablates, atomizes, and ionizes a tiny amount of the analyte to produce a plasma plume. The generated plasma contains a mixture of atoms, ions, and free electrons from the examined material. Upon cooling of plasma, some energy is emitted, and the optical spectroscopy in the LIBS device acquires the spectral signal conveying information about the elemental composition of the sample. The researched technology integrates LIBS and statistical machine learning to fingerprint and classifies alpine-style cheeses for the purpose of authentication. The system includes a bench-top or portable LIBS system, data normalization, pre-processing and reduction algorithms, and machine-learning unsupervised and supervised classification methods.

Page updated on 2022-12-07 17:29:54 -0500