Amrhein, M.

Dabros, M.

Marison, I.W.

von Stockar, U.

Infoscience - École polytechnique fédérale de Lausanne

Poster BE-19 presented at the 12th European Congress on Biotechnology (ECB12), August 21-24 2005, Copenhagen, DenmarkOn-line Recalibration of FTIR Measurements UsingMetabolite Spiking and Dynamic Orthogonal ProjectionM. Dabros, I.W. Marison and U. von StockarLaboratory of Chemical and Biochemical Engineering (LGCB)Swiss Federal Institute of Technology (EPFL)Station 6, CH-1015 Lausanne, Switzerlandhttp://lgcb.epfl.chM. AmrheinAutomatic Control Laboratory (LA)Swiss Federal Institute of Technology (EPFL)Station 9, CH-1015 Lausanne, Switzerlandhttp://lawww.epfl.chIntroductionFourier-transform infrared (FTIR) spectroscopyincreasingly used in bioprocess monitoring andcontrol applicationscharacteristic absorbance peaks in the fingerprintregion (1800-800 cm-1)non-invasiveness, in-situ sterilizabilityability to provide real-time concentration profiles forseveral metabolites simultaneouslyExperimental Setup and CalibrationApplication: anaerobic batch cultureof Saccharomyces cerevisiae CBS 426on glucose in a 2L laboratory RC1calorimeterMonitoring: single-beam FTIR(ReactIR 4000 from Mettler-Toledo)equipped with a flow-through cell.Single water background spectrumcollected at beginning of experimentConclusions and Future WorkOn-line reduction of SEP has been achieved by spiking the culture with known amounts of analytes and using DOP to correct the calibration model.The main advantage of this novel approach is that the reference samples did not need to be analyzed by off-line methods; thus, the model adaptation is performed in real-time.Future work will include applying the DOP approach to a fed-batch culture where continuous feeding is replaced by substrate pulses.A calibration model for five analytes was developed using mean-centereddata, Partial Least Squares (PLS) and a 7-level multivariate design [2]The choice of the number of PLS factors was based on Predictive ResidualError Sum of Squares (PRESS) plots of leave-one-out cross-validationExperimental ResultsBioreactorMixture of KnownConcentrationof Key Analytes[1] Zeaiter, M., J. M. Roger, et al. (2005). "Dynamic Orthogonal Projection. A new method to maintain the on-line robustness of multivariate calibrations. Application to NIR based monitoring of wine fermentations." Preprintsubmitted to Elsevier Preprint.[2] Brereton, R. (1997). "Multilevel multifactor designs for multivariate calibration." The Analyst, 122, p. 1521–1529.[3] Kornmann, H. (2003). Monitoring and control of exopolysaccharides production by Gluconacetobacter Xylinus I-2281 using in-situ mid-infrared spectroscopy. PhD thesis, Swiss Federal Institute of Technology, Lausanne.DOP Algorithm [1] using Metabolite Spiking1. Scale each pulse composition to fall into thecalibration design (linearity between analyteconcentration and absorbance to be checked)2. Estimate the delta spectrum that should havebeen obtained in the absence of signal drift fromthe calibration set using a Gaussian kernelfunction of the scaled pulse composition3. Compute the orthogonal correction from thedifference between the augmenting set ofestimated and on-line delta spectra (additive driftassumed)4. Recalibrate the model based on theorthogonally corrected calibration data setsThe application of DOP lead to a significant reduction in thestandard error of prediction (SEP) for each analyteThe results were compared to the commonly used In-ProcessStandard (IPS) method [3], where the calibration matrix isaugmented by on-line reference standardsMid-IR Spectra Before and After a Pulse00.010.020.030.040.050.060.070.0895010001050110011501200125013001350140014501500Wavenumber [cm-1]AbsorbanceBefore AfterFour mixture pulses were injected with acomposition corresponding to a multiple(1/5) of the calibration design centre:2.5 g/L of Glucose,2.5 g/L of Ethanol,0.2 g/L of Ammonium,0.4 g/L of Phosphate,0.4 g/L of GlycerolDelta SpectrumSpectrum After Pulse – Spectrum Before Pulse00.0040.0080.0120.01695010001050110011501200125013001350140014501500Wavenumber [cm-1]AbsorbanceSignal drift was observed in the resulting delta spectra (see figure to the left)Linearity between mixed analyte concentration and absorbance was verifiedby Principal Component Analysis (PCA) of 8 mixtures with linearly dependentcompositions (1 significant factor observed)Each pulse composition and delta spectrum were multiplied by a factor of 5 toscale it to the mean of the calibration data setFollowing each pulse, the calibration model was corrected by DOP in real-timeto reduce the effect of signal driftImproved prediction performance was achieved for glucose, ethanol andglycerol (see figures and table below)A novel on-line modeladaptation approachDelta Spectra00.0020.0040.0060.0080.010.0120.0140.01695010001050110011501200125013001350140014501500Wavenumber [cm-1]AbsorbanceDelta Spectrum - Pulse 1Delta Spectrum - Pulse 2Delta Spectrum - Pulse 3Delta Spectrum - Pulse 4Methodology for single-beam instrumentsTo maintain the online robustness of the calibrationmodel and to reduce the effect of signal drift,planned pulse additions of known amounts of monitoredanalytes are injected into the culture mediuma novel model adaptation method known as DynamicOrthogonal Projection (DOP) [1] is used to fine-tune thecalibration model on-lineStochastic drift of the measured spectra in timeThe FTIR signal is sensitive to short-term fluctuationsand long-term driftUsing dual-beam instruments, the drift effect can beeliminated by taking a background spectrum beforeeach measurementUsing single-beam instruments, mathematical modelcorrection methods can be appliedAnalyteConcentrationRange [g/L]Spectral Range[cm-1]PLSFactorsStandard Errorof Calibration(SEC) [g/L]Glucose 0 - 25 1500 - 950 12 0.347Ethanol 0 - 25 1300 - 950 8 0.327Ammonium 0 - 2 1500 - 950 4 0.037Phosphate 0 - 4 1500 - 950 13 0.042Glycerol 0 - 4 1150 - 950 7 0.222Delta spectrumcorresponds to the pulse-injected mixtureserves as an on-line reference measurement051015202510:00 13:00 16:00 19:00 22:00 01:00Time of dayGlucose Concentration [g/L]FTIR PredictionFTIR Prediction using on-line DOPHPLC ResultsPulse #1Pulse #2Pulse #3Pulse #4051015202510:00 13:00 16:00 19:00 22:00 01:00Time of dayEthanol Concentration [g/L]FTIR PredictionFTIR Prediction using on-line DOPHPLC ResultsPulse #1Pulse #2Pulse #3Pulse #4024681010:00 13:00 16:00 19:00 22:00 01:00Time of dayGlycerol Concentration [g/L]FTIR PredictionFTIR Prediction using on-line DOPHPLC ResultsPulse #1Pulse #2Pulse #3Pulse #4AnalyteSEP withoutDOP [g/L]SEP withDOP [g/L]Improvementwith DOPImprovementwith IPSGlucose 2.600 1.662 36.1% 17.1%Ethanol 1.149 0.833 27.5% 9.1%Ammonium 0.187 0.144 23.1% 1.3%Phosphate 0.646 0.456 29.4% 35.9%Glycerol 2.717 0.783 71.2% 33.4%

On-line Recalibration of FTIR Measurements Using Metabolite Spiking and Dynamic Orthogonal Projection

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