title: Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine.
abstract: Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable.
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA lan.sun@viavisolutions.com.
authors.forename: Lan
authors.initials: L
authors.lastname: Sun
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA.
authors.forename: Chang
authors.initials: C
authors.lastname: Hsiung
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Pederson
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Zou
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA.
authors.forename: Valton
authors.initials: V
authors.lastname: Smith
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: von Gunten
authors.affiliation: Viavi Solutions Inc. (formerly JDSU), Santa Rosa, CA, USA.
authors.forename: Nada A
authors.initials: NA
authors.lastname: O'Brien
mesh_terms: D016002:Discriminant Analysis; D004867:Equipment Design; D016018:Least-Squares Analysis; D008904:Miniaturization / Q000295:instrumentation; D004364:Pharmaceutical Preparations / Q000032:analysis*; D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared / Q000295:instrumentation*; D060388:Support Vector Machine
chemical_list: D004364:Pharmaceutical Preparations
keywords: MicroNIR; NIR; Near-infrared spectroscopy; RMID; SVM; large-scale classification; model transferability; raw material identification; support vector machine