1. Comparison of PLS regression and Artificial Neural Network for the processing of the Electronic Tongue data from fermentation growth media monitoring Alisa Rudnitskaya 1, Andrey Legin 1, Dmitri Kirsanov 1, Boris Seleznev 1, Kim Esbensen 2, John Mortensen 3, Lars Houmøller 2, Yuri Vlasov 1 1 Laboratory of Chemical Sensors, Chemistry Department, St. Petersburg University, Russia; www.electronictongue.com 2, Aalborg University Esbjerg, Denmark; 3 Department of Life Science and Chemistry, Roskilde University Centre, Denmark.

2. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 2 Industrial use of filamentous fungi batch fermentation Fungi: Aspergillus, Penicillium etc Citric acid Food stuffs Enzymes Pharmaceuticals Food additives

3. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 3 Purpose of the study Development of rapid analytical methodology to follow-up batch fermentation processes and for quantitative analysis of broths –Evaluation of Electronic Tongue (ET) for following-up of the batch fermentation processes and quantitative analysis of broths on the example of Aspergillus Niger batch culture medium –Application and comparison of different chemometric techniques for multivariate calibration of ET

4. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 4 Experimental set-up Samples Background: 0.5 gL -1 KCl, 1.5 gL -1 KH 2 PO 4, 0.5 gL -1 MgSO 4, 1 mlL -1 of Vishniac trace element solution, pH 6 SampleTime, hCitratePyruvateOxalateGlucoseGlycerol,MannitolErythritolNH 4 Cl 1000045.30.140.05014.0 21.800045.30.170.07014.0 35.300044.00.240.05013.6 47.90.50042.70.350.050.0213.2 510.51.402.641.40.410.050.0312.8 611.61.707.838.90.520.030.0512.0 712.62.2010.436.30.590.030.0711.2 813.72.6013.033.70.690.030.1010.4 914.73.3018.129.80.760.050.149.2 1015.33.6020.727.20.860.050.168.4 1115.83.8023.325.90.930.070.198.0 1216.34.0025.923.31.040.090.247.2 1316.83.8028.520.71.100.100.266.4 1417.13.8028.519.41.170.100.286.0 1517.43.8031.118.11.280.120.295.6 1617.93.8033.715.51.380.130.314.8 1718.44.0038.913.01.480.170.404.0 1818.94.3044.09.11.590.210.452.8 1919.54.70.346.66.51.660.220.472.0 20 5.01.649.25.21.730.280.521.6 2120.55.32.659.61.31.900.400.600.4 2221.15.52.462.201.900.470.620 1. Solutions simulating growth media of real fermentation processes involving Aspergillus niger 2. Same solutions with 10mM of sodium azide added.

5. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 5 Measurements ET comprising 10 potentiometric chemical sensors with polymeric membranes Direct and fast (few minutes) measurements No sample preparation Experimental set-up Data processing Data splitting into calibration, monitoring and test sets (D-optimal design) Multivariate calibration PLS-regression Feed-forward neural network Software used:Unscrambler v. 7.8 by CAMO AS, Norway; NeuroSolutions by NeuroDimensions Inc, USA

6. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 6 Determination of ammonium, oxalate, citrate content and time elapsed from the media inoculation in the model growth media using ET Average relative error of prediction, % AmmoniumOxalateCitrateTime 126117without sodium azide 106 7with sodium azide Calibration of ET by PLS regression for each component separately Results for the test set

7. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 7 Non-linearity of the sensors’ responses Calibration of ET w.r.t. ammonium concentration using PLS-regression

8. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 8 Response of the NH 4 -sensitive electrode to NH 4 + on the growth medium Detection limits to NH 4 + : Discrete electrode - 3.07 pNH 4 Sensor array - 3.7 pNH 4 Nikolski equation:

9. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 9 Non-linear calibration methods Non-linear regression Artificial neural networks Advantages -Flexibility -Noise tolerance Drawbacks -Prone to overfitting

10. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 10 Feed-forward neural network Learning Local error function: e j = -  E/  I j for output layer: e o = f’(I o ) (y-ŷ) for hidden layers: e s j = f’(I s j )  (e s+1 s w s+1 kj ) Weight update:  w s ij = -  (  E/  w s ij ) =  e s j x s-1 i x1 x3 x2 I, f(I) w s ij Input layer Hidden layer Output layer ŷ Weight - w s ij Input function: I s j =  x s-1 i *w s ij Transfer function: f(I) Forward pass E =ly-ŷl Error back-propagation Hyperbolic tangent :

11. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 11 Neural network validation Evolution of training and monitoring errors during ANN training. Calibration of ET w.r.t. oxalate concentration

12. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 12 Data splitting into calibration, monitoring and test sets using D-optimal design Basic idea of D-optimal design: finding a design matrix that maximizes the determinant D of the initial data matrix, i.e. finding a set of samples that are maximally independent of each other. Ideal distribution: if calibration set contains n samples, monitoring and test sets should contain between n/2 and n samples each. In this case: calibration set – 22 samples, monitoring set – 11 samples, test set – 21 samples.

13. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 13 Optimization of the neural network architecture Aim: minimization of prediction error AND number of network parameters (weights), i.e. hidden and input neurons. Optimized ANN for calibration w.r.t. content of : Ammonium: 5  2  1 Oxalate: 5  3  1 Citrate: 7  2  1

14. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 14 Determination of ammonium, oxalate and citrate content and time elapsed from the media inoculation in the model growth media using ET Average relative error of prediction, % Calibration methodAmmoniumOxalateCitrateTimeSamples ANN 6682without sodium azide 7772with sodium azide 116122all data PLS 126117without sodium azide 106 7with sodium azide

15. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 15 PCA score plot of ET measurements in growth media with and without sodium azide added

16. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 16 Non-linearity of the sensors’ responses Calibration of ET w.r.t. to ammonium concentration using ANN

17. WCS-4, February 15—18, 2005, Moscow (Chernogolovka), Russia A. Rudnitskaya et al St. Petersburg University 17 Conclusions An ET system comprising a sensor array based on ten PVC- plasticized cross-sensitive potentiometric chemical sensors was successfully applied to simultaneous determination of ammonium, oxalate and citrate content in simulated fermentation media closely resembling real-world samples typical of a process involving Aspergillus niger. Feed-forward neural network was found to be superior to PLS regression for the ET data fitting due to better consideration of non- linearity of the sensor potentials/concentration relationship particularly at low concentration levels. The average prediction errors for key metabolites’ concentrations in the given ranges was about 6-8% when using a feed-forward artificial neural network for ET calibration. Content of three key components of the growth media can be measured by ET in the presence of 10 mM sodium azide, which is commonly used to suppress microbial activity after sampling. ET was demonstrated to be promising for monitoring fermentation processes.