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. 2016 May;70(5):816-25.
doi: 10.1177/0003702816638281. Epub 2016 Mar 30.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

Lan Sun  1 , Chang Hsiung  2 , Christopher G Pederson  2 , Peng Zou  2 , Valton Smith  2 , Marc von Gunten  2 , Nada A O'Brien  2
Affiliations

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

Lan Sun et al. Appl Spectrosc. 2016 May.

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.

Keywords: MicroNIR; NIR; Near-infrared spectroscopy; RMID; SVM; large-scale classification; model transferability; raw material identification; support vector machine.

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Figures

Figure 1.
Figure 1.
MicroNIR spectrometer equipped with a vial holder and tethered to a rugged 7” Windows 8.1 tablet for pharmaceutical raw material identification.
Figure 2.
Figure 2.
PCA-SVM plot for 19 pharmaceutical compounds based on the SVM model.
Figure 3.
Figure 3.
Model validation using different lots of material and different MicroNIR units. (a) Same-unit-same-lot (SUSL) validation; (b) same-unit-cross-lot (SUXL) validation; (c) cross-unit-cross-lot (XUXL) validation. Unit# denotes the spectrometer number. T#P# denotes the spectrometer number for training (T#) and the spectrometer number for testing or prediction (P#).
Figure 4.
Figure 4.
NIR spectra of the training set from 253 pharmaceutical compounds. (a) Raw spectra; (b) pretreated spectra.
Figure 5.
Figure 5.
Estimation probabilities of class membership.
See this image and copyright information in PMC

References

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