Food fraud, the sale of goods that have in some way been mislabelled or tampered with, is an increasing concern, with a number of high profile documented incidents in recent years. These recent incidents and their scope show that there are gaps in the food chain where food authentication methods are not applied or otherwise not sufficient and more accessible detection methods would be beneficial. This paper investigates the utility of affordable and portable visible range spectroscopy hardware with partial least squares discriminant analysis (PLS-DA) when applied to the differentiation of apple types and organic status. This method has the advantage that it is accessible throughout the supply chain, including at the consumer level. Scans were acquired of 132 apples of three types, half of which are organic and the remaining non-organic. The scans were preprocessed with zero correction, normalisation and smoothing. Two tests were used to determine accuracy, the first using 10-fold cross-validation and the second using a test set collected in different ambient conditions. Overall, the system achieved an accuracy of 94% when predicting the type of apple and 66% when predicting the organic status. Additionally, the resulting models were analysed to find the regions of the spectrum that had the most significance. Then, the accuracy when using three-channel information (RGB) is presented and shows the improvement provided by spectroscopic data.
- pattern recognition