Industrial use of filamentous fungi batch fermentation
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Transcript of Industrial use of filamentous fungi batch fermentation
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 University Esbjerg, Denmark;3 Department of Life Science and Chemistry, Roskilde University Centre, Denmark.
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 2
Industrial use of filamentous fungi batch fermentation
Fungi: Aspergillus, Penicillium etc
Citric acid
Food stuffs
Enzymes
Pharmaceuticals
Food additives
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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
Sample Time, h Citrate Pyruvate Oxalate Glucose Glycerol, Mannitol Erythritol NH4Cl
1 0 0 0 0 45.3 0.14 0.05 0 14.0
2 1.8 0 0 0 45.3 0.17 0.07 0 14.0
3 5.3 0 0 0 44.0 0.24 0.05 0 13.6
4 7.9 0.5 0 0 42.7 0.35 0.05 0.02 13.2
5 10.5 1.4 0 2.6 41.4 0.41 0.05 0.03 12.8
6 11.6 1.7 0 7.8 38.9 0.52 0.03 0.05 12.0
7 12.6 2.2 0 10.4 36.3 0.59 0.03 0.07 11.2
8 13.7 2.6 0 13.0 33.7 0.69 0.03 0.10 10.4
9 14.7 3.3 0 18.1 29.8 0.76 0.05 0.14 9.2
10 15.3 3.6 0 20.7 27.2 0.86 0.05 0.16 8.4
11 15.8 3.8 0 23.3 25.9 0.93 0.07 0.19 8.0
12 16.3 4.0 0 25.9 23.3 1.04 0.09 0.24 7.2
13 16.8 3.8 0 28.5 20.7 1.10 0.10 0.26 6.4
14 17.1 3.8 0 28.5 19.4 1.17 0.10 0.28 6.0
15 17.4 3.8 0 31.1 18.1 1.28 0.12 0.29 5.6
16 17.9 3.8 0 33.7 15.5 1.38 0.13 0.31 4.8
17 18.4 4.0 0 38.9 13.0 1.48 0.17 0.40 4.0
18 18.9 4.3 0 44.0 9.1 1.59 0.21 0.45 2.8
19 19.5 4.7 0.3 46.6 6.5 1.66 0.22 0.47 2.0
20 20 5.0 1.6 49.2 5.2 1.73 0.28 0.52 1.6
21 20.5 5.3 2.6 59.6 1.3 1.90 0.40 0.60 0.4
22 21.1 5.5 2.4 62.2 0 1.90 0.47 0.62 0
1. Solutions simulating growth media of real fermentation processes involving Aspergillus niger
2. Same solutions with 10mM of sodium azide added.
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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, %
Ammonium Oxalate Citrate Time
12 6 11 7 without sodium azide
10 6 10 7 with sodium azide
Calibration of ET by PLS regression for each component separately
Results for the test set
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 7
Non-linearity of the sensors’ responsesCalibration of ET w.r.t. ammonium concentration using PLS-regression
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am (
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U s
core
s (8
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T scores (75%)
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 8
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 pNH4-5 -4 -3 -2 -1
40
60
80
100
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140 C(K+) = 0.018M
E,
mV
logC(NH4
+)
jiji akanF
RTEE ln0
Nikolski equation:
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 9
Non-linear calibration methods
•Non-linear regression•Artificial neural networks
•Advantages
-Flexibility
-Noise tolerance
•Drawbacks
-Prone to overfitting
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 10
Feed-forward neural network
Learning
Local error function: ej = -E/ Ij
for output layer: eo = f’(Io) •(y-ŷ)
for hidden layers: esj = f’(Is
j) • (es+1s• ws+1
kj)
Weight update: wsij = - • ( E/ ws
ij) = • esj • xs-1
i
x1
x3
x3
x2
I, f(I)
I, f(I)
I, f(I) I, f(I)
wsij
wsij
Input layerHidden layer
Output layer
ŷ
Weight - wsij
Input function: Isj = xs-1
i*wsij
Transfer function: f(I)
Forward pass
E =ly-ŷl
Error back-propagation
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Hyperbolic
tangent: xx
xx
ee
eexf
)(
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 11
Neural network validation
Evolution of training and monitoring errors during ANN training. Calibration of ET w.r.t. oxalate concentration
0 200 400 600 800 10000,00
0,02
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Modeling Overfitting
Stopping point
Err
or
Iterations
Training Monitoring
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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.
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PC
2
PC1
Calibration Monitoring Test
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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.
1 2 3 40,000
0,001
0,002
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0,006
MS
E o
f C(o
xala
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pre
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in te
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et
Number of hidden neurons
Number of inputs 9 7 5
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Number of inputs 9 7 5
Optimized ANN for calibration w.r.t. content of :
Ammonium: 5 2 1
Oxalate: 5 3 1
Citrate: 7 2 1
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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 method Ammonium Oxalate Citrate Time Samples
ANN
6 6 8 2 without sodium azide
7 7 7 2 with sodium azide
11 6 12 2 all data
PLS
12 6 11 7 without sodium azide
10 6 10 7 with sodium azide
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 15
PCA score plot of ET measurements in growth media with and without sodium azide added
-40 -20 0 20 40 60 80-20
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PC
2 (1
1%)
PC1 (78%)
without azide with azide
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. Petersburg University 16
Non-linearity of the sensors’ responses
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Input
Hidden neuron 1 Hidden neuron 2
Calibration of ET w.r.t. to ammonium concentration using ANN
WCS-4, February 15—18 , 2005, Moscow (Chernogolovka), Russia
A. Rudnitskaya et alSt. 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.