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Infrared (IR) and near-infrared (NIR) spectroscopy are fast, easy-to-use techniques with a history of being used for food applications such as those for measuring protein, moisture, and fat content. Food fraud and adulteration has become of particular concern to the industry over the past few years following reports of incidents in the media, with herbs and spices identified as one of the key problem areas.
This paper explores the numerous challenges materials scientists and engineers face, from the time it takes to bring new materials to market to the difficulties delivering suitable formulations and testing against specified criteria, and how those can be resolved.
Near-infrared (NIR) spectroscopy is an important technique for materials checking at various stages of the manufacturing process, but is particularly useful at the raw materials inspection stage. Raw materials come in a variety of physical forms including liquids, gels, and solids, requiring a versatile instrument with convenient, interchangeable sampling modules to cater to the entire range of raw materials encountered.
Herbal lifestyle products are widely used as alternatives to medicines around the world, with as many as 80% of people using them as a primary source of healthcare in developing countries. These treatments are commonly regarded by scientific papers, and on some occasions the media, as being inferior to orthodox treatment. This is due to the variation between herbal formulations which will not be present in so called ‘single-chemical’ drugs. The reasons for the aforementioned variation involve several factors including storage, environmental conditions, handling and unintentional or intentional contamination (adulteration).
NIR Spectroscopy is a useful technique for raw materials identification and verification, but the sophistication of the technique might differ based on the sample. If the materials to be identified are spectroscopically dissimilar, it is often only necessary to use a simple distance measure such as a spectral difference for identification. If the spectra are similar, on the other hand, it may be necessary to use more sophisticated techniques which take into consideration both the intra- and inter-material spectral variation for identification and classification. The SIMCA (Soft Independent Modelling of Class Analogy) algorithm, a Principal Component Analysis (PCA) method, provides such an example.