Measurement and Calibration of near- and mid- infrared Spectroscopy to predict the Biomass Composition for optimal Pretreatment

Transcription

1 Measurement and Calibration of near- and mid- infrared Spectroscopy to predict the Biomass Composition for optimal Pretreatment H. Wulfhorst, M. Flüggen, S. Maurer, S. Möhring, J. Roth, N.Tippkötter Bioprocess Engineering, University of Kaiserslautern, Germany

2 Table of contents Introduction: BioSats project Motivation: Why using infrared (IR) spectroscopy for biomass analysis? Definition of analytic concept Reference analytics with Sugar Standards Multivariate Data Analysis Application to Biomass Summary & Outlook 2

3 Central biorefinery Local pretreatment Introduction: BioSats project plant residue materials Conditioning of raw materials Biomass pretreatment and fractionation hemicellulose Cellulose lignin Depolymerization of biomass fractions Transformation to value added products Downstream processing end products Analytic and monitoring of substrate und product Process modeling 3

4 Central biorefinery Local pretreatment Introduction: BioSats project plant residue materials Conditioning of raw materials Biomass pretreatment and fractionation hemicellulose Cellulose lignin Depolymerization of biomass fractions Transformation to value added products Downstream processing end products Analytic and monitoring of substrate und product Process modeling 4

5 Central biorefinery Local pretreatment Motivation Need of biomass monitoring Composition of biomass varies depending on feedstock Feedstock forward control hemicellulose Cellulose end products Fast reaction on biomass changes Adaptation of pretreatment Standardization of raw materials lignin Keeping process fluctuations at a minimum Intermediates backward control Optimizing raw material conversion Consistent product quality 5

6 Motivation Why using infrared spectroscopy for biomass analysis? Widely used to characterize agricultural products Fast analysis of composition Non-destructive Application to solids, liquids and slurries Off-line and On-line measurement with near (IR) 6

7 Definition of analytic Concept Recovery studies spiking Reference analytic Mixed Sugar Standards Spectra Biomass Calibration data set Validation data set Spectra Chemometric Calibration Validation Quantitative Analysis Stable Calibration Model 7

8 Measurement of infrared Spectra FT-mid-IR-spectrometer Spectrum 1, Perkin Elmer UV/ViS/NIR spectrometer Lambda 750, Perkin Elmer mid infrared region, 40 cm cm -1 near- IR spectra, 10 nm - 18 nm Glucose, xylose and carboxymethyl cellulose (CMC) standards in water Concentration of glucose varied over from 0 to 150 g/l Xylose and CMC from 0 to 50 g/l Random combinations of sugar concentrations in individual samples 8

9 Reference Analytic with Sugar Standards Mid-IR spectra of mixed sugar standards Random concentrations of glucose in a range g/l, xylose and CMC 0-50 g/l; FT-mid-IR-spectrometer, Spectrum 1 (Perkin Elmer). Ratio glucose/xylose/cmc 12 cm cm -1 Fingerprint region of glucose Overlapping in the wide wavelength areas 9

10 Reference Analytic with Sugar Standards Near-IR spectra of mixed sugar standards Random concentrations of glucose in a range g/l, xylose and CMC 0-50 g/l; UV/ViS spectrometer Lambda 750 of Perkin Elmer 16 18nm 1. overtone region, the first C-H stretching nm 2. overtone region, C-H stretching nm combination C H stretching Overlapping of the spectra of three carbohydrates Need of statistical analysis 10

11 Multivariate Data Analysis (MVA) Principle Component Analysis (PCA) Principal component transformation Matrices of Principal Component Analysis X 2 X 1 X2 X1 Decomposition data matrix into scores, loadings and residue matrix Variables with same information are combined to principle components (PC) New variables scores describe similarities between samples Loadings: look like a spectrum, show impact of variable for a principle component Reduction of complexity W. Kessler, 27. Multivariate Datenanalyse für die Pharma-, Bio- und Prozessanalytik, WILEY-VCH Verlag, Weinheim, s

12 Multivariate Data Analysis (MVA) Partial least squares (PLS) regression To correlate the absorption and concentration X data: factor matrix T and score matrix P Y data: factor matrix Q and score matrix U Y data influence the PCA of X data Simultaneously reduces the amount of data and does the regression X-variables may be highly correlated and inter-correlated Validation External Validation set W. Kessler, 27. Multivariate Datenanalyse für die Pharma-, Bio- und Prozessanalytik, WILEY-VCH Verlag, Weinheim, s

13 Multivariate Data Analysis Calibration result of mid infrared spectra cm -1,mixed standards, PLS-1 of Glucose, Unscrambler (9.1, CAMO software AS, Norway) R 2 = 0.98 RSMEC*=3.14 RSMEP*= first principal components describe 99,6 % of the initial variance High difference between RSMEC and RSMEP Need to improve predictive ability of the calibration models High RMSEC and RMSEP for the calibration of near-ir-spectrum Possible solution wavelength selection *RMSEC and RMSEP (Root-Mean-Square Error of Calibration and Prediction) Lehrgebiet Institute of Bioverfahrenstechnik Bioprocess Engineering 13

14 Wavelength selection Ant Colony Optimization algorithm (ACO) Specific wavelength for glucose, xylose and CMC Proposed by Dorigo et al. in Food source Nest Ants find the shortest path from the nest to a food source Exchange information in the form of pheromone 14

15 Wavelength selection Ant Colony Optimization algorithm (ACO) Selection of specific wavelengths by best fitting values Principle Component Analysis X 2 X 1 X2 Artificial ants Y i X1 Multiple linear regression (MLR) x x... x Iterative loop : Ants construct/modify random solutions Update the pheromone trails to find an optimum Calibration only with information at relevant wavelengths 0 1 i1 2 i2 p ip i Shamsipur et al. 26. Ant colony optimisation: a powerful tool for wavelength selection, J. Chemometrics, 20:

16 Multivariate Data Analysis Calibration results after wavelength selection mid infrared near infrared Glucose R 2 = 0.98 RSMEC=3.96 RSMEP=5.80 Xylose R 2 = 0.93 RSMEC=4.28 RSMEP=4.36 Glucose R 2 = 0.95 RSMEC=12.81 RSMEP=13.12 Xylose R 2 = 0.89 RSMEC=6.07 RSMEP=

17 Multivariate Data Analysis Calibration Calibration results of near- and mid-ir-spectra Validation Spectra Analyt Maths PC wavenumber [cm -1 ] R 2 RSMEC RSMEP mid-ir Glucose PLS mid-ir Xylose PLS mid-ir CMC PLS mid-ir Glucose ACO /PCA/MLR , 1032, mid-ir Xylose ACO /PCA/MLR , 1309, mid-ir CMC ACO /PCA/MLR , wavelength [nm] near-ir Glucose ACO /PCA/MLR , 1744, 1592, , 12 near-ir Xylose ACO /PCA/MLR , 1720, 1696, 1692, , 1664, 1388 near-ir CMC ACO /PCA/MLR , 1704, 1696, 1628, , 1512 Improved by wavelength selection using ACO Number of wavelength significantly reduced 17

18 Application to Biomass Recovery studies spiking Reference analytic Mixed Sugar Standards Spectra Biomass Calibration data set Validation data set Spectra Chemometric Calibration Validation Quantitative Analysis Stable Calibration Model 18

19 Application to Biomass Glucose prediction in presence of biomass Random concentrations of glucose in a range g/l, xylose 0-50 g/l; grass particles; Mid infrared, PLS1-Calibration model y = x R 2 = 0.96 Biomass does not affect the calculation of the sugar 19

20 Summary & Outlook Summary Calibration model of near- and mid infrared spectra using standard solutions Quantification the of sugar concentrations from the absorption of their mixtures ACO algorithm improves the quality of the calibration models Calibration in presence of biomass Outlook Analytic tools involving real biomass and spiking with defined standards Online monitoring of conversion process using light-fiber optics Near- and mid infrared correlation spectroscopy 20

21 Acknowledgement Thank you for your attention! Chair of Bioprocess Engineering, University of Kaiserslautern