Supervised dimension reduction for optical vapor sensing

• Main Authors: Kittle, J.D (Author), Meier, M. (Author), Yee, X.C (Author)
• Format: Article
• Language: English
• Published: Royal Society of Chemistry 2022
• Subjects: Air quality; Air quality assessment; Detection and identifications; Discriminant analysis; Food quality; Least squares approximations; Low concentrations; Optical-; Optical sensing; Photonic crystals; Principal component analysis; Principal-component analysis; Quality assurance; Quality control; Supervised dimension reductions; Vapor detection; Vapor sensing
• Online Access: View Fulltext in Publisher

 Detecting and identifying vapors at low concentrations is important for air quality assessment, food quality assurance, and homeland security. Optical vapor sensing using photonic crystals has shown promise for rapid vapor detection and identification. Despite the recent advances of optical sensing using photonic crystals, the data analysis method commonly used in this field has been limited to an unsupervised method called principal component analysis (PCA). In this study, we applied four different supervised dimension reduction methods on differential reflectance spectra data from optical vapor sensing experiments. We found that two of the supervised methods, linear discriminant analysis and least-squares regression PCA, yielded better interclass separation, vapor identification and improved classification accuracy compared to PCA. © 2022 The Royal Society of Chemistry