Evaluation of olive oil quality using a miniature spectrometer: a machine learning approach

Food authentication and quality checks can be carried out by applying machine learning algorithms on spectral data acquired from miniature spectrometers. This is a very appealing solution as the cost-effectiveness of miniature spectrometers extends the range of consumer electronics available for ordinary citizens in the fight against food fraud, widens the range of their applications and shortens the processing time for any in-situ scenario. In this paper, a study of olive oil purity and quality check feasibility carried out on spectral data acquired from a miniature spectrometer is presented. The aim is to gauge the ability of such a device to differentiate between pure olive oil from ones adulterated with vegetable oils on a relatively large dataset. The paper presents a pipeline encompassing various steps including data pre-processing, dimension reduction, classification, and regression analysis. That is, data collected from miniature spectrometers can be of low quality and exhibit distortions, high dimensionality, and collinearity. Hence, various filtering techniques including wavelets analysis, and normalisation algorithms including multiplicative scatter correction are used for pre-processing. Once the dimensionality is reduced using PCA, classical machine learning classification and regression analysis algorithms are deployed as part of a quality evaluation pipeline. This includes SVM, LDA, KNN, Random Forest and PLS. The obtained results show that very high rates of up to 98% can be achieved. An important consequence is that cost-effective miniature spectrometers augmented with a suitable machine learning component can attain comparable results obtained using non-portable and more expensive spectrometers.