Machine learning feature extraction for predicting the ageing of olive oil

Monitoring the quality of extra virgin olive oil (EVOO) during its life cycle is of particular importance due to its influence on health-related characteristics and its significance for the oil industry. For this reason it is critical to find an easy-to-perform, non-destructive and affordable method to monitor the quality of EVOO and detect its degradation due to ageing. The following study explores a machine learning approach based on fluorescence measurements for predicting oil changes arising from the ageing process. The proposed method specifically predicts the quality parameters that are required for an olive oil to qualify as extra virgin. In particular, the two properties considered in this analysis are the UV absorbance at 232 and 268 nm (K-232 and K-268), both critical markers of the quality of extra virgin oil. To achieve this goal, a large dataset of fluorescence measurements was analysed, comprising 720 excitation-emission matrices of twenty-four different oils initially labeled as extra virgin. The samples were aged under accelerated conditions at 60 degrees C in the dark for nine weeks and their properties were measured at ten different time steps during the process. Two different machine learning pipelines were implemented for the prediction of K-232 and K-268. In a first approach, the model was trained on all the ten ageing steps of each oil and learned to predict all the ten steps of an unseen oil. In a second approach, the model was trained on one single ageing on multiple oils and step for all the oils and learned to predicta single ageing step. The results demonstrate the potential of the proposed approach.