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 (K232 and K268), 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 °C in the dark for nine weeks and their properties were measured at ten different time steps during the process.
The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K232.
Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (CondCNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.
The chemical analysis of food is essential to monitor and guarantee its quality. The determination of the chemical parameters, like the concentration of particular substances, is performed by specialized laboratories and is a time-consuming and costly process. Therefore, alternative methods with easier handling are of great interest. Among these fluorescence spectroscopy offers great opportunities. Fluorescence spectra are one-dimensional arrays of values already successfully employed together with artificial neural networks for classification problems in chemistry, physics, and other fields. However, the extraction of specific quantities from the spectra poses a much harder challenge. This work analyzes and compares the ability of feed-forward neural networks (FFNN) and one-dimensional convolutional neural networks (1D-CNN) to extract relevant features from fluorescence spectra of olive oils. The results indicate that 1D-CNN, contrary to FFNN, successfully predicts the chemical parameters with high accuracy. The great advantages of the proposed method are: 1) the possibility of using optical methods instead of time-consuming chemical ones, like chromatography, 2) the lack of any special sample handling, like dilution and 3) the lack of any pre-processing of the data. The problem of small datasets, which may arise for novel techniques like the proposed one, is also addressed statistically by using the leave-one-out resampling technique.
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