Chemometrics in Sensor Technology

Dear Colleagues,

Chemometrics is the chemical discipline that uses mathematical and statistical approaches to design experiments and provide relevant chemical information by analyzing complex experimental datasets. This discipline provides a useful tool for selecting operating conditions for sensor devices, optimizing synthesis process of sensing materials, computing mathematical models for multivariate calibration of sensor devices, and performing pattern recognition methods for sensor array data analysis.

Quality by design is a tool used for optimizing multifactor processes via the design of experiments and the multivariate analysis of collected data. Since the characteristics of sensing materials depends on synthesis factors and the performance of sensor devices depends on operating conditions, quality by design ensures the quality of sensor devices because it selects synthesis parameters for materials with suitable properties and sensor operating parameters to perform analyses under useful experimental settings.

Multivariate analysis is a method used for performing a multifactor calibration for more accurate analysis considering many factors affecting the experimental signal output from the sensor.

Chemometrics plays a key role in the electronic and nose. These devices are composed of three main components: a pattern recognition analyzer, a sensors array, and a signal processor. The former is software for chemometric analysis of sensor array data. Numerous chemometric methods for sensor array data analysis are available to discriminate between different samples, such as principal component analysis, discriminant function analysis, hierarchical cluster analysis, soft independent modelling by class analogy, partial least squares, artificial neural networks, and support vector machines.

Prof. Dr. Fabio Leccese
Dr. Fabio Zaza
Guest Editors

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• quality by design
• electronic tongue
• electronic nose
• multivariate calibration
• pattern recognition
• differential sensing
• principal component analysis
• artificial neural networks
• support vector machines