Differentiation of organic and non-organic apples using near infrared reflectance spectroscopy — A pattern recognition approach

With the organic food market on the rise, organic food fraud has become an issue to consumers, producers and the market. Traditional methods of food quality determination are time consuming and require expert laboratory analysis. Recent studies based on spectroscopic analysis have shown its potential effectiveness in non-destructive food analysis. This paper explores the use of low cost Near Infrared Spectroscopy (NIRS) combined with a pattern recognition approach for the differentiation of organic and non-organic apples. The spectra of organic and non-organic Gala apples are measured using a low cost and portable NIR Spectrometer. A pattern recognition pipeline is proposed, where spectra data are pre-processed and then classified into organic and non-organic. Baseline correction and normalization are used in pre-processing, and Partial Least Squares Discriminant Analysis (PLS-DA) is used for classification. The experimental results show that the apple samples can be classified into organic and non-organic ones with accuracies of over 96%. The results and the fact the NIR spectrometer used was low cost and portable suggest this is potentially a cost effective solution to the detection of organic food fraud.