Fast and efficient food quality control using electronic noses: adulteration detection achieved by Unfolded Cluster Analysis coupled with Time-Windows selection

The objective of this work is to report the improvements obtained in the discrimination of complex aroma samples with subtle differences in odor pattern, by the use of a fast procedure suitable for the cases of measurements in the field demanding decisionmaking in real time using a portable electronic nose. This device consists of a sensor array which records changes in conductivity as a function of time when aroma molecules reach the sensors. The core of the method consists of applying Unfolded Cluster Analysis to selected Time Windows (abbreviated by the acronym UCATW) within the temporal evolution of the aroma profile recorded by the gas sensors, yielding an efficient, fast and reliable data analysis tool that is easy to perform for electronic nose users. The performance of this data handling was tested in two case studies of food adulteration. The results demonstrated that this methodology enables to discriminate highly similar samples, herewith reducing the probability of achieving a wrong grouping due to the use of flawed data. The automation of this type of analysis is simple and improves the efficiency of the device significantly, herewith reducing the time of sensor´s signals recording that is necessary for a reliable assessment of the studied system. The results were validated by clustering the sample component scores that are obtained by applying PARAFAC to the original three-dimensional data array. An additional validation was obtained by means of a Leave-One-Out-Resampling procedure.