Industrial use of filamentous fungi batch fermentation

1. Comparison of PLS regression and Artificial Neural Network for the processing of the Electronic Tongue data from fermentation growth media monitoring Alisa Rudnitskaya1, Andrey Legin1, Dmitri Kirsanov1, Boris Seleznev1, Kim Esbensen2, John Mortensen3, Lars Houmøller2, Yuri Vlasov1 1 Laboratory of Chemical Sensors, Chemistry Department, St. Petersburg University, Russia; www.electronictongue.com 2, Aalborg UniversityEsbjerg, Denmark; 3 Department of Life Science and Chemistry, Roskilde University Centre, Denmark.

2. Industrial use of filamentous fungibatch fermentation Fungi: Aspergillus, Penicillium etc Citric acid Enzymes Pharmaceuticals Food stuffs Food additives 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 A. Rudnitskaya et al St. Petersburg University

4. Experimental set-upSamples Background: 0.5 gL-1 KCl, 1.5 gL-1 KH2PO4, 0.5 gL-1 MgSO4, 1 mlL-1 of Vishniac trace element solution, pH 6 1. Solutions simulating growth media of real fermentation processes involving Aspergillus niger 2. Same solutions with 10mM of sodium azide added. A. Rudnitskaya et al St. Petersburg University

5. Experimental set-up • Measurements • ET comprising 10 potentiometric chemical sensors with polymeric membranes • Direct and fast (few minutes) measurements • No sample preparation • 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 A. Rudnitskaya et al St. Petersburg University

6. Determination of ammonium, oxalate, citrate contentand time elapsed from the media inoculation in the model growth media using ET Calibration of ET by PLS regression for each component separately Results for the test set A. Rudnitskaya et al St. Petersburg University

7. Non-linearity of the sensors’ responsesCalibration of ET w.r.t. ammonium concentration using PLS-regression A. Rudnitskaya et al St. Petersburg University

8. Nikolski equation: Response of the NH4-sensitive electrode to NH4+ on the growth medium Detection limits to NH4+: Discrete electrode - 3.07 pNH4 Sensor array - 3.7 pNH4 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 A. Rudnitskaya et al St. Petersburg University

10. Forward pass wsij x1 I, f(I) wsij x2 ŷ I, f(I) I, f(I) x3 Output layer I, f(I) x3 Hidden layer Input layer Error back-propagation Feed-forward neural network Weight - wsij Input function: Isj = xs-1i*wsij Transfer function: f(I) Learning Local error function: ej = -E/ Ij for output layer: eo = f’(Io) •(y-ŷ) for hidden layers: esj = f’(Isj) •(es+1s• ws+1kj) Weight update: wsij = - • ( E/  wsij) = • esj• xs-1i Hyperbolic tangent: E =ly-ŷl 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 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. 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 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 A. Rudnitskaya et al St. Petersburg University

15. PCA score plot of ET measurements in growth media with and without sodium azide added 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 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. A. Rudnitskaya et al St. Petersburg University