Demonstrating the Value of Data Fusion

Transcription

1 Demonstrating the Value of Data Fusion Thomas I. Dearing 1 Wesley Thompson and Brian Marquardt 1 Carl Rechsteiner Jr Applied Physics Labs, University of Washington 2. Chevron Energy Technology, Richmond

2 Introduction Data Fusion Concept Take multiple sources of information and fuse them together the unit is greater than a sum of its parts. Think: Vodka Tequila White Rum Triple Sec Gin Lemon Juice Syrup Cola Long Island Iced Tea

3 Introduction Data Fusion Concept Take multiple sources of information and fuse them together the unit is greater than a sum of its parts. Think: Raman IR GC HPLC NMR Mass Spec LIBS UV-Vis Chemometric Model PROCESS OPTIMIZATION

4 Distillation Fraction Samples

5 Measurements Process Raman and IR Require no sample preparation. Rapid measurements. Fiber Optics. Complimentary methods. Proton NMR Unique proton environments. Molecular structure. Non destructive. Completely orthogonal method to vibrational methods.

6 Experimental Setup MT React-IR ic1 Kaiser RXN1 Raman Experimental Setup Kaiser RXN2 785nm Raman

7 Experimental Instrumental Setup RAMAN Total exposure time of 1min. 1nm excitation wavelength. Raman ball probe. Raman spectra collected from a shift of 1cm -1 to 189cm -1. Three fractions analyzed IR Final spectra composites of 124 FT spectra. Collected over about 1min. FT-IR fiber optic probe. MCT Detector cooled with liquid nitrogen. Spectra collected from 65cm -1 to 2cm -1. Three fractions analyzed

8 Raman Spectra 15 x 14 Fraction 1 Fraction 2 Fraction 3 x x nm Signal Intensity /Au 1 5 Signal Intensity /Au Signal Intensity /Au nm Signal Intensity /Au Raman Shift /cm Raman Shift /cm -1 Signal Intensity /Au Raman Shift /cm Raman Shift /cm -1 Signal Intensity /Au Raman Shift /cm -1 x Raman Shift /cm -1

9 Raman Spectra 1nm - Baseline Corrected 5 Fraction 1 Fraction Signal Intensity /Au Signal Intensity /Au Raman Shift /cm Fraction Raman Shift /cm -1 Signal Intensity /Au Raman Shift /cm -1

10 NMR Setup 1 H NMR Spectra were recorded on Bruker AVANCE 5 MHz NMR Spectrometer. Analysis time was 7 min which could be brought reduced to less than1 min. The resonance frequency was 5MHz Frequency mathematically reduced to 6MHz Comparable to online instruments. Three fractions analyzed.

11 Down Sampling of NMR NMR down sampled using co-addition of points. Discrete window of points selected and added together. Size of window determines final frequency. Different frequencies examined. 6MHz closest to process frequency

12 Before and After x Before x After 4 3 Signal Intensity /Au Signal Intensity /Au Chemical Shift /ppm Chemical Shift /ppm

13 Effect of Down Sampling API Calibrations and Predictions Using Co-Addition.4.35 Relative Error in Prediction /% RMSEC RMSEP Sampling Frequency /Hz

14 Data Analysis Data Analysis IR, Raman and NMR data used to build optimized PLS models to predict three parameters. Parameters predicted: API, Yield from Crude by % Weight, and H 2 content. Model quality to be determine by the Relative Standard Error in Prediction. Fused Data Use chemometric methods to join together Raman, IR and NMR data. Extract correlated data sources to yield improved predictions. Capture variance between models.

15 Raman Spectra Signal Intensity /Au HEAVY FRACTION Raman Shift /cm -1

16 IR Spectra.3.25 HEAVY FRACTION Signal Intensity /Au Wavenumbers /cm

17 NMR Spectra x x 1 7 Signal Intensity /Au Signal Intensity /Au HEAVY FRACTION Chemical Shift /ppm Chemical Shift /ppm

18 Data Fusion Procedure IR NMR RAMAN SCALED IR SCALE SCALED NMR SCALE POLYFIT + SCALE RAMAN POLYFIT + SCALE IR NMR RAMAN FUSED DATA Dearing, T.; Thompson, W.; Rechsteiner, C.; Marquardt, B.; J. Applied Spectroscopy, February 211, Volume 65, Issue 2, pp

19 Fused Spectra 1 IR NMR RAMAN Normalized Signal Intensity Variables

20 Model Building PLS models built for the individual spectroscopic methods. Models optimized using the Relative Standard Error in Prediction. PLS models used to predict the three quality assurance parameters. Raman, IR and NMR data fused together. Optimized PLS models again used to predict the three quality assurance parameters.

21 Model Building Main Goal: Reduce Model Errors Improve Prediction of QC Parameters Improve Process Control Achieve Process Optimization

22 RSEP /% Fused Models Comparison of Individual Models to Fused Model API Weight % H2 Raman IR NMR Fused

23 INITIAL FERMENTATION MEDIA ANALYSIS USING DATA FUSION

24 Fermentation Data Fusion IR LIBS RAMAN SCALED IR SCALE SCALED LIBS SCALE POLYFIT + SCALE RAMAN POLYFIT + SCALE IR LIBS RAMAN FUSED DATA

25 Fused Raw Spectra Fused IR, Raman and LIBS Spectra.9.8 IR LIBS Raman.7.6 Signal Intensity Variables

26 Fused Data Classification of Media Samples/Scores Plot of Fused Data Matrix of IR, LIBS and Raman LiuMedia1xSupp Legend Innoculated Noninnoculated Scores on PC 2 (9.63%) LeightDoiSupp and NutrientBroth2x LeightDoiSupp XYT2 ModG1xMediaSupp NutrientBroth1x and XYT Scores on PC 1 (77.77%)

27 Fused Data Classification of Media Scores on PC 3 (4.1%).5 Liu1xSupp LeightDoiSupp and NutrientBroth2x NutrientBroth1x XYT2 ModG1xMediaSupp Scores on PC 2 (9.63%) Scores on PC 1 (77.77%)

28 Fused Data Loadings Plots Loadings on PC 1 (77.77%), Loadings on PC 2 (9.63%), Loadings on PC 3 (4.1%) Variables/Loadings Plot for Fused Data Matrix of IR, LIBS and Raman Loadings on PC 1 (77.77%) Loadings on PC 2 (9.63%) Loadings on PC 3 (4.1%) Variable

29 Summary Fused models are better predictors (smaller errors) compared to the individual models. Better predictors means greater process control. Fused models are greater than a sum of their parts. Concept of data fusion can be rolled out to many different applications including pharmaceuticals, foods, fine chemical and instrument validation.

30 Thank You!! Questions?????