Food adulteration detection and measurement with NIR hyperspectral imaging
22 February 2016 • Author(s): Marena Manley, Professor, Stellenbosch University / Federico Marini, University Researcher, University of Rome ‘La Sapienza’
Food adulteration, e.g. adulteration of spices, can be incidental or intentional. Incidental adulteration occurs when foreign substances are added to a food due to ignorance, negligence or using inadequate facilities. Intentional adulteration, also referred to as economic adulteration, entails the deliberate addition of inferior materials to a food to improve appearance qualities and value for economical gain. Nevertheless, adulteration of food products may lead to legal, medical or religious disputes.
Over the years several analytical methods have been considered to reliably detect adulterants in food products. Vibrational spectroscopy has received particular attention due to it being environmentally-friendly, not requiring sample preparation and providing immediate results. One of the advantages of performing spectroscopy measurements in the near-infrared (NIR) compared to the mid-infrared (MIR) region is its ability to penetrate deeper into the sample and to measure larger areas or volumes1 . Although the sample area analysed with NIR spectroscopy is much larger than in the case of MIR spectroscopy, the results are still reported as an average measurement. NIR hyperspectral imaging, which represents the new frontier of spectroscopic techniques, advances on conventional (or bulk) NIR spectroscopy by providing an additional spatial dimension1 .
NIR hyperspectral imaging is similar to conventional colour photography, where for each basic unit of the image (i.e. a pixel) the levels of green, red and blue are recorded; the only difference being that, instead of only three spectral intensities, an entire profile is collected for each pixel or spatial point. NIR hyperspectral imaging is different to RGB photography or multi-spectral in that the bands involved are narrow and numerous, thus producing a continuous profile. The main characteristic of this technique is the main outcome, i.e. the hyperspectral image representing hundreds of thousands of NIR spectra. A hyperspectral image thus consists of a three dimensional array (cube) of data resulting from collecting the spectral intensity as a function of three different sources of variation (two spatial coordinates, x and y as well as one spectral wavelength coordinate, z) (Figure 1, page 71).
As shown in Figure 1 (page 71) an NIR hyperspectral image carries a large amount of information, as one may extract the full spectrum at each pixel, which can be used for qualitative or quantitative analysis at microscopic level (typical pixel size of 30micron). Conversely, one could analyse the two dimensional (2D) image at a specific wavelength to look for the distribution, homogeneity or heterogeneity of a particular constituent, e.g. oil (absorbing at ca. 2310nm)1 . This is of value when dealing with food quality control issues, especially for the detection and/or identification of contaminants/adulterants or potentially hazardous substances or when the characterisation of the texture or microstructure of products are considered. The possibility of operating at pixel or object level, make this approach the analysis technique of choice when detection or quantification of adulterants is at stake. Analysis of single spectra at pixel level also enables assessment of sample homogeneity or heterogeneity. NIR hyperspectral imaging is thus the ideal technique to use when measuring heterogeneous samples.
ABF Ingredients ANDEROL EUROPE BV Avantes Berndorf Band GmbH BIOTECON Diagnostics GmbH Bruker BioSpin Cargo Oil AB Elea GmbH Engilico FUCHS LUBRITECH GmbH GLOBALG.A.P. Foodplus GmbH InS Services (UK) Ltd IONICON Analytik GmbH JAX INC. JBT Corporation LUBRIPLATE Lubricants Company NETZSCH Pumpen & Systeme GmbH NSF International Ocean Optics PCE Instruments UK Ltd R-Biopharm Rhone Ltd Sandvik Process Systems Stancold SteriBeam The Tintometer® Group Thermo Fisher Scientific TOMRA Sorting Food Uhde High Pressure Technologies GmbH Verner Wheelock Vikan UK Ltd