A comparison of different chemometrics approaches for the robust classification of electronic nose data

• Published in: Analytical and bioanalytical chemistry Vol. 406; no. 29; pp. 7581 - 7590
• Main Authors: Gromski, Piotr S., Correa, Elon, Vaughan, Andrew A., Wedge, David C., Turner, Michael L., Goodacre, Royston
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2014; Springer; Springer Nature B.V
• Subjects: Accuracy; Actuators; Algorithms; Analysis; Analytical Chemistry; Artificial Intelligence; Biochemistry; Biomimetics - methods; Characterization and Evaluation of Materials; Chemical sensors; Chemical weapons; Chemistry; Chemistry and Materials Science; Classification; Comparative analysis; Conductometry - methods; Data analysis; Data Interpretation, Statistical; Discriminant analysis; Electronic noses; Food Science; Gases - analysis; Humans; Kernels; Laboratory Medicine; Mathematical models; Model accuracy; Monitoring/Environmental Analysis; Nose; Odorants - analysis; Pattern recognition; Pattern Recognition, Automated - methods; Polynomials; Remote sensors; Reproducibility of Results; Research Paper; Sensitivity and Specificity; Sensors; Statistical analysis; Statistical models; Support vector machines; Support vector machines; Bootstrapping; Linear discriminant analysis; Partial least squares-discriminant analysis; Cross-validation; Random forests

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to...