Condition-Based Diagnosis of Mechatronic Systems Using a Fractional Calculus Approach

While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model’s complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

Detection of Arah1 (a major peanut allergen) in food using an electrochemical gold nanoparticle-coated screen-printed immunosensor

A gold nanoparticle-coated screen-printed carbon electrode was used as the transducer in the development of an electrochemical immunosensor for Ara h 1 (a major peanut allergen) detection in food samples. Gold nanoparticles (average diameter=32 nm) were electrochemically generated on the surface of screen-printed carbon electrodes. Two monoclonal antibodies were used in a sandwich-type immunoassay and the antibody–antigen interaction was electrochemically detected through stripping analysis of enzymatically (using alkaline phosphatase) deposited silver. The total time of the optimized immunoassay was 3 h 50 min. The developed immunosensor allowed the quantification of Ara h 1 between 12.6 and 2000 ng/ml, with a limit of detection of 3.8 ng/ml, and provided precise (RSD <8.7%) and accurate (recovery >96.6%) results. The immunosensor was successfully applied to the analysis of complex food matrices (cookies and chocolate), being able to detect Ara h 1 in samples containing 0.1% of peanut.

Detection of the peanut allergen Ara h 6 in foodstuffs using a voltammetric biosensing approach

A voltammetric biosensor for Ara h 6 (a peanut allergen) detection in food samples was developed. Gold nanoparticle-modified screen-printed carbon electrodes were used to develop a sandwich-type immunoassay using two-monoclonal antibodies. The antibody-antigen interaction was detected through the electrochemical detection of enzymatically deposited silver. The immunosensor presented a linear range between 1 and 100 ng/ml, as well as high precision (inter-day RSD ≤9.8 %) and accuracy (recoveries ≥96.7 %). The detection and quantification limits were 0.27 and 0.88 ng/ml, respectively. It was possible to detect small levels of Ara h 6 in complex food matrices.