Analysis of NIR spectroscopy data for blend uniformity using semi-nonnegative Matrix Factorization

Size: px

Start display at page:

Download "Analysis of NIR spectroscopy data for blend uniformity using semi-nonnegative Matrix Factorization"

Pierce Baldwin
5 years ago
Views:

1 Analysis of NIR spectroscopy data for blend uniformity using seminonnegative Matrix Factorization Nicolas Sauwen 1, Adriaan Blommaert 1, Tatsiana Khamiakova 2, Michel Thiel 2, Jan Dijkmans 2, Ana Tavares da Silva 2 1 OpenAnalytics, 2 Janssen October 4, 2018 NCS Paris

2 Outline Blending NIR spectroscopy Nonnegative Matrix Factorization (NMF) SemiNMF Blend uniformity 2/17

3 Blending Powder blending pharmaceuticals production of solid Goal = blend uniformity: consistent and correct API proportion per dose Assessment blend uniformity: Traditional approach: repetitive sampling PAT approach: NIR spectroscopy 3/17

NIR spectroscopy Sample illumination by NIR electromagnetic waves (780 2500nm) Molecules absorb photon energy corresponding to difference in discrete

4 NIR spectroscopy Sample illumination by NIR electromagnetic waves ( nm) Molecules absorb photon energy corresponding to difference in discrete vibrational energy states peaks in absorption spectrum NIR is fast, online, requires minimal sample preparation and has higher sample penetration than (mid)ir 4/17

5 Blend uniformity based on NIR: methods Qualitative methods: directly consider variability of spectra Moving Block Standard Deviation (MBSD) Principal Component Analysis (PCA) Quantitative methods: express concentration variations Partial Least Squares (PLS) 5/17

6 Blend uniformity based on NIR: methods Qualitative methods: directly consider variability of spectra Moving Block Standard Deviation (MBSD) Principal Component Analysis (PCA) Quantitative methods: express concentration variations Partial Least Squares (PLS) Nonnegative Matrix Factorization: semiquantitative method 6/17

7 Nonnegative Matrix Factorization (NMF) User input: rank r initial factor matrices W 0 and/or H 0 Semiquantitative because NMF solution is not unique: nonconvex problem scaling scores and loadings 7/17

8 Nonnegative Matrix Factorization (NMF) Partsbased representation interpretable factors NIR spectral decomposition: Columns of W: pure compound spectra Rows of H: relative proportion of pure compounds 8/17

9 NIR preprocessing Baseline shifts and nonlinearities introduced by light scatter Scatter correction (MSC, SNV) Spectral derivatives (SavitzkyGolay) Input data are no longer nonnegative 9/17

10 SemiNonnegative Matrix Factorization Nonnegativity constraint is only imposed on scores matrix H: X WH such that i, j : 0 H i,j NMF W k+1 f (X, W k, H k ) H k+1 f (X, W k+1, H k ) Updating function f (X, W, H) incorporates nonnegativity constraint SemiNMF 1 (X,, ) (X,, ) W k+1 f 1 W k H k H k+1 f 2 W k+1 H k Only updating function f 2 (X, W, H) incorporates nonnegativity constraint 1 Multiplicative update rules, see Ding et al., IEEE Trans Pattern Anal Mach Intell, /17

11 SemiNMF: example Blending process 4 compounds: API + 3 excipients NMF rank = 4 Initialization: measured pure compound spectra in W 0 H 0 through nonnegative least squares 11/17

12 Blend uniformity: methodology Traditional methodology: global variability statistic such as the Hotellings T 2 monitored over a moving time window. We use: SemiNMF trends associated with compounds focus on API Location and scale modeling in function of blending rotation (GAMLSS) Endpoint detection: relative difference with predicted endvariation 12/17

13 Model for location and scale Normal locationscale model fitted with the gamlsspackage Mean structure Penalized spline or parametric regression model Variance structure: parametric model Exponential decay with offset: σ = A e kt + b Mixing and demixing model (Bauman and Futac, 1996): e k t 1 A 2 e k t 2 σ = A1 + (1 ) 13/17

14 GAMLSS model prediction Model prediction seminmftrend Model prediction standard deviation 14/17

15 Blend uniformity: endpoint prediction Endpoint = difference from end standard deviation (e.g. 3%) Confidence intervals for uncertainty estimate Based on covariance matrix of the parameter estimates Repeated simulation 15/17

16 R packages on CRAN spectralanalysis: preprocessing, visualization and analysis of spectral data (NIR, IR, Raman) hnmf: NMF algorithms, postprocessing and visualization of NMF results gamlss: functions for fitting Generalized Additive Models for Location, Scale and Shape 16/17

17 17/17

Putting Near-Infrared Spectroscopy (NIR) in the spotlight. 13. May 2006

Putting Near-Infrared Spectroscopy (NIR) in the spotlight 13. May 2006 0 Outline What is NIR good for? A bit of history and basic theory Applications in Pharmaceutical industry Development Quantitative