The increasing industrial demand for lipases has lead to the identification of new sources of enzymes with unique properties and generated a great interest in applying rapid, reliable, specific, selective, and sensitive analytical methods for evaluating their catalytic activity. This work offers a professional look on the achievements and the emerging strategies for lipases activity determination, involving volumetry, spectrometry, radioactive assays, immunoassays, conductimetry, chromatography, and biosensors. The principle, the advantages, and the deficiencies of each of these methods are addressed in detail.

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.