Data processing from electrical signals acquired by an E-nose system used for quality control of cocoa

Abstract

Abstract This research consists of the implementation of different pattern recognition methods applied to discriminate and classify the physical signals (electrical) acquired by an artificial electronic nose system composed of a gas sensor array of 10 units coupled with a data acquisition board that were used to perform a sensor chamber that receives the electrical information from volatile organic compounds generated by cacao beans. A concentration chamber for samples conditioning of fermented beans was used and the samples were fermented around 72 and 144 hours while the cacao samples infected with monilia were over-fermented. For obtaining the temperature inside of the sensor chamber, a digital temperature sensor was implemented by using a Peltier Cell mechanism which was controlled through a classical algorithm. At the design stage, a data acquisition system composed of an Arduino card and a graphical interface made in LabView was developed for data storing, controlling, and signals monitoring. For the electrical signals treatment and data analysis, two pattern recognition models were applied by using Python software where two signals pre-processing methods such as Euclidean normalization and Roboust Scaling were used afterward with data processing techniques as principal component analysis and clusters analysis, obtaining a 96.51% of variance in the two first components.