Adulteration and allergenic food materials are a common problem all around the world. European legislation (EU) 1169/2011/EC requires labelling of food products with respect to the presence of allergenic components including nuts, cereals or any other food products. Therefore, rapid methods for analysis of food ingredients are required to enforce this legislation. Near Infrared Spectroscopy (NIRS) being fast and non-destructive could be a candidate technique. Present study investigates the potential of NIRS (896–1686 nm) and chemometrics to classify thirty different cereals and nineteen different nuts based on their spectral signatures. The aim was to perform the specificity analysis for peanuts to detect its presence in various food materials. As a first step, Principal Components (PCs) modelling was used to perform a primary classification. PCs provided a classification of the samples into five major groups as gluten, non-gluten, high fatty acid, high fibre and omega-3 fatty acid. To perform segregation within the class identified as nuts intermixed with oilseeds (high fatty acid class), Partial Least Square Discriminant Analysis (PLSDA) was performed. First two discriminant vectors obtained from PLSDA were successfully able to segregate a group identified as peanuts and pine nuts, from other nuts (almond) and cereals (sesame and flaxseed). However, to segregate peanuts from pine nuts, first and third discriminant vectors were used. Results concluded that NIRS combined with chemometrics is a robust method for specificity analysis of peanuts from different cereals and nuts.
|Number of pages||9|
|Publication status||Published - 1 Nov 2016|
- Food allergy