Multivariate data-modelling to separate light scattering from light absorbance: The Optimized Extended Multiplicative Signal Correction OEMSC
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1 Multivariate data-modelling to separate light scattering from light absorbance: The Optimized Extended Multiplicative Signal Correction OEMSC The importance of spectral preprocessing in data analysis for aquaphotomics Harald Martens Dept. Engineering Cybernetics, Norwegian U. of Science and Technology, Trondheim, Norway.
2 When studying complex systems, vis/nir spectoscopy is informative. Many causes of variation: Light scattering, path length, nonlinear responses, organic absorbers, temperature effects, water concentration, water changes, Example: Belusov-Zhabotinsky reaction. Anna Zhyrova, Dalibor Stys, U. South Bohemia:
3 When studying complex systems, vis/nir spectoscopy is informative. Many causes of variation: Light scattering, path length, nonlinear responses, organic absorbers, temperature effects, water concentration, water changes, Example: Belusov-Zhabotinsky reaction. Anna Zhyrova, Dalibor Stys, U. South Bohemia:
4 When studying complex systems, vis/nir spectoscopy is informative. Many causes of variation: Light scattering, path length, nonlinear responses, organic absorbers, temperature effects, water concentration, water changes, Example: Belusov-Zhabotinsky reaction. Anna Zhyrova, Dalibor Stys, U. South Bohemia:
5 NIT of «difficult samples» : Mixtures of wheat protein and wheat starch powders (5 different ratios: 0,25,50,75,100% protein) Different sample thickness, different sample packing 2 technical replicates MSC: Martens, H., Jensen, S.Å. and Geladi, P. (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proc. Nordic Symp. on Applied Statistics, (O.H.J. Christie, ed.) June Stokkand Forlag Publ., Skagenkaien 12, N-4000 Stavanger, Norway, ISBN , Geladi, P., MacDougall, D. and Martens, H. (1985): Linearization and scatter-correction for near-infrared reflectance of meat. Applied Spectroscopy, 39, 3, EMSC: H.Martens and E.Stark (1991) Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. J.Pharmaceutical & Biomedical Analysis 9(8), Martens, H., Pram Nielsen, J. and Balling Engelsen, S (2003) Light Scattering and Light Absorbance Separated by Extended Multiplicative Signal Correction. Application to Near-Infrared Transmission Analysis of Powder Mixtures. Anal. Chem. 75 (3) pp Go to Multivariate calibration? Bad news
6 NIT of «difficult samples» : Mixtures of wheat protein and wheat starch powders (5 different ratios: 0,25,50,75,100% protein) Different sample thickness, different sample packing 2 technical replicates MSC: Martens, H., Jensen, S.Å. and Geladi, P. (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proc. Nordic Symp. on Applied Statistics, (O.H.J. Christie, ed.) June Stokkand Forlag Publ., Skagenkaien 12, N-4000 Stavanger, Norway, ISBN , Geladi, P., MacDougall, D. and Martens, H. (1985): Linearization and scatter-correction for near-infrared reflectance of meat. Applied Spectroscopy, 39, 3, PLS regression: Imperfect. Needs 5 PCs. Should need only 1 PC EMSC: H.Martens and E.Stark (1991) Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. J.Pharmaceutical & Biomedical Analysis 9(8), Martens, H., Pram Nielsen, J. and Balling Engelsen, S (2003) Light Scattering and Light Absorbance Separated by Extended Multiplicative Signal Correction. Application to Near-Infrared Transmission Analysis of Powder Mixtures. Anal. Chem. 75 (3) pp Bad news
7 NIT of «difficult samples» : Mixtures of wheat protein and wheat starch powders (5 different ratios: 0,25,50,75,100% protein) Different sample thickness, different sample packing 2 technical replicates MSC: Martens, H., Jensen, S.Å. and Geladi, P. (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proc. Nordic Symp. on Applied Statistics, (O.H.J. Christie, ed.) June Stokkand Forlag Publ., Skagenkaien 12, N-4000 Stavanger, Norway, ISBN , Geladi, P., MacDougall, D. and Martens, H. (1985): Linearization and scatter-correction for near-infrared reflectance of meat. Applied Spectroscopy, 39, 3, The solution, of course: MSC or SNV! EMSC: H.Martens and E.Stark (1991) Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. J.Pharmaceutical & Biomedical Analysis 9(8), Martens, H., Pram Nielsen, J. and Balling Engelsen, S (2003) Light Scattering and Light Absorbance Separated by Extended Multiplicative Signal Correction. Application to Near-Infrared Transmission Analysis of Powder Mixtures. Anal. Chem. 75 (3) pp
8 NIT of «difficult samples» : Mixtures of wheat protein and wheat starch powders (5 different ratios: 0,25,50,75,100% protein) Different sample thickness, different sample packing 2 technical replicates MSC: Martens, H., Jensen, S.Å. and Geladi, P. (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proc. Nordic Symp. on Applied Statistics, (O.H.J. Christie, ed.) June Stokkand Forlag Publ., Skagenkaien 12, N-4000 Stavanger, Norway, ISBN , Geladi, P., MacDougall, D. and Martens, H. (1985): Linearization and scatter-correction for near-infrared reflectance of meat. Applied Spectroscopy, 39, 3, EMSC: H.Martens and E.Stark (1991) Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. J.Pharmaceutical & Biomedical Analysis 9(8), Martens, H., Pram Nielsen, J. and Balling Engelsen, S (2003) Light Scattering and Light Absorbance Separated by Extended Multiplicative Signal Correction. Application to Near-Infrared Transmission Analysis of Powder Mixtures. Anal. Chem. 75 (3) pp Problem: MSC regression model could not estimate the offset and slope properly. Confused by large chemical variations. Really bad news
9 NIT of «difficult samples» : Mixtures of wheat protein and wheat starch powders (5 different ratios: 0,25,50,75,100% protein) Different sample thickness, different sample packing 2 technical replicates MSC: Martens, H., Jensen, S.Å. and Geladi, P. (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proc. Nordic Symp. on Applied Statistics, (O.H.J. Christie, ed.) June Stokkand Forlag Publ., Skagenkaien 12, N-4000 Stavanger, Norway, ISBN , Geladi, P., MacDougall, D. and Martens, H. (1985): Linearization and scatter-correction for near-infrared reflectance of meat. Applied Spectroscopy, 39, 3, EMSC: H.Martens and E.Stark (1991) Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. J.Pharmaceutical & Biomedical Analysis 9(8), Martens, H., Pram Nielsen, J. and Balling Engelsen, S (2003) Light Scattering and Light Absorbance Separated by Extended Multiplicative Signal Correction. Application to Near-Infrared Transmission Analysis of Powder Mixtures. Anal. Chem. 75 (3) pp The solution, of course: EMSC with known analyte spectra!
10 NIT of «difficult samples» : Mixtures of wheat protein and wheat starch powders (5 different ratios: 0,25,50,75,100% protein) Different sample thickness, different sample packing 2 technical replicates MSC: Martens, H., Jensen, S.Å. and Geladi, P. (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proc. Nordic Symp. on Applied Statistics, (O.H.J. Christie, ed.) June Stokkand Forlag Publ., Skagenkaien 12, N-4000 Stavanger, Norway, ISBN , Geladi, P., MacDougall, D. and Martens, H. (1985): Linearization and scatter-correction for near-infrared reflectance of meat. Applied Spectroscopy, 39, 3, EMSC: H.Martens and E.Stark (1991) Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. J.Pharmaceutical & Biomedical Analysis 9(8), Martens, H., Pram Nielsen, J. and Balling Engelsen, S (2003) Light Scattering and Light Absorbance Separated by Extended Multiplicative Signal Correction. Application to Near-Infrared Transmission Analysis of Powder Mixtures. Anal. Chem. 75 (3) pp Using known constituent spectra in the EMSC model: Difference spectrum between pure protein and pure starch Good news, but needs extra info
11 Stabilize the preprocessing without any prior knowledge raw data mean centred O.D. O.D O.D Wavelength,nm Wavelength,nm Mean EMSC without known analyte spectra (Morten Beck Rye): No known constituent spectra used in the EMSC model. Instead: Included known wavelength info: After EMSC with wavelength and its square (, 2 ) Good news, but nonlinear Wavelength,nm O.D mean centred Wavelength,nm O.D Mean
12 RMSEP(protein) Optimized EMSC (OEMSC ) Z Input, to be optimized for GoodC Init. AOpt=7,RMSEP= GoodC spectrum, optimized Cal. subset Initial EMSC estimated correction spectrum AOpt= RMSEP(Y)= nmsepwgts= Optimized EMSC OptMethod=1 for Gluten Optimized EMSC, centred OptMethod=1 for Gluten RMSEPY # of PCR PCs, k:=start.emsc ro=opt.emsc Estimate a correction spectrum in the EMSC model (in addition to, 2 ) Simplex optimization of rmsep(protein) Good news: linear
13 Optimize the EMSC model automatically The mean spectrum, m 2 Baseline 1,1,1,1,1,1 Correction spectrum (optimized)
14 Optimize the EMSC model automatically The mean spectrum, m EMSC: a) Estimation: Project input each sample spectrum on these model spectra. b) Correction: Subtract baseline level and - effects. Divide by m-effects. OEMSC: 2 Baseline 1,1,1,1,1,1 Correction spectrum (optimized) 1) Define V = First e.g. 5 PCA PCs of the untreated sample spectra. The correction spectrum will be a linear combination tv by optimizing t(1 x 5). 2) Find optimal correction spectrum. Here: Guess initial t. Then Simplex optimization of t for a chosen criterion (e.g. RMSEP(y) of leverage-corrected PCR). Many alternative opt.criteria possible!
15 OEMSC model overfitted? 3.1 Cal: input Cal: After EMSC Cal. subset OD OD wavelength wavelength Pred: input Pred: After EMSC Test subset OD OD wavelength wavelength Not overfitted
16 Optimized EMSC parameter: proportional to analyte variable optimized for (protein) Multivariate calibration unnecessary? Cal. Conventional EMSC: Test
17 Does OEMSC also work for other data? Limited experience One example: protein in ground wheat from log(1/r) Water structure: which criterion to optimize?
18 Software EMSC: The Unscrambler ( PLS Toolbox ( OEMSC: Matlab (preliminary) from
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