Dairy products discrimination according to the milk type using an electrochemical multisensor device coupled with chemometric tools
This study shows the potential application of a potentiometric electronic tongue coupled with a lab-made DataLogger device for the classification of dairy products according to the type of milk used in their production, i.e., natural, fermented and UHT milk. The electronic tongue device merged a commercial pH electrode and 15 lipid/polymeric membranes, which were obtained by a drop-by-drop technique. The potentiometric signal profiles gathered from the 16 sensors, during the analysis of the 11 dairy products (with ten replicate samples), together with principal component analysis showed that dairy samples could be naturally grouped according to the three types of milk evaluated. To further investigate and verify this capability, a linear discriminant analysis together with a simulated annealing variable selection algorithm was also applied to the electrochemical data, which were randomly split into two datasets, one used for model training and internal-validation using a repeated K-fold cross-validation procedure (with 64% of the data); and the other for external validation purposes (containing the remaining 36% of the data). The multivariate supervised strategy used allowed establishing a classification model, based on the potentiometric information of four sensor lipid membranes, which enabled achieving a successful discrimination rate of 100% for both internal- and external-validation processes. The demonstrated versatility of the built electronic tongue for discriminating dairy products according to the type of milk used in their production combined with its simplicity, low-cost and fast time analysis may envisage a possible future application in dairy industry.
KeywordsDairy products Electronic tongue Principal component analysis Linear discriminant analysis Simulated annealing algorithm
This study was funded in part by the Ministry of Research, Technology and Higher Education, the Republic of Indonesia, Project 001/SP2H/LT/DRPM/IV/2017. The authors also thank the Directorate General of Islamic Education and Instituto Politécnico de Bragança, Portugal, for their support throughout the completion of this work. This work was financially supported by Project POCI-01–0145-FEDER-006984 – Associate Laboratory LSRE-LCM, Project UID/BIO/04469/2013 – CEB and strategic project PEst-OE/AGR/UI0690/2014 – CIMO all funded by FEDER - Fundo Europeu de Desenvolvimento Regional through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia, Portugal.
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