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A novel optical fibre sensor has been developed for on-line monitoring of oil quality within industrial equipment. The monitoring is based on chromatic modulation because of the inherent self-referencing demonstrated by this technique. The sensor is shown to exhibit long-term stability and is capable of operating up to a temperature of 120 degrees C. Several industrial oils have been examined using the sensor, and typical modulation depths of 20% were obtained with resolution in the range of 0.1%. This is sufficient to record some interesting physical and chemical changes related to the reaction kinetics of oil degradation. The sensor is also capable of detecting oil leaks within a system since it can distinguish between the chromaticities of oil and air.

Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

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