The presentation provides an overview of two-layer machine learning model that can classify the type of biomolecules present in the medium (in the first layer) and predict the concentration of the material (in the second layer). Bacteria have been used as the known biological material using Electrical Impedance Spectroscopy (EIS Data).

1. Biosensors
Design Project | Rishabh Garg | BITS - Pilani | Goa

2. RISHABH GARG
BITS - PILANI | GOA
Biosensors
 A Biosensor is an analytical device, used for the detection of a chemical substance,
that combines a biological component with a physicochemical detector.
 Compared with conventional or larger analytical instruments, Biosensors have the
advantages of speed, low cost, nondestructive property, and on-site detection.
 They have been extensively used in fundamental bioresearch, food safety,
environmental monitoring, disease diagnosis, and drug screening.
 All Biosensors inevitably have some irregular signal noise.

3. RISHABH GARG
BITS - PILANI | GOA
ML in Biosensors
 Researchers are looking for breakthroughs in other aspects to improve the
performance of Biosensors. Herein, the analysis of sensing data based on machine
learning (ML) is in focus.
 ML can effectively process big sensing data for complex matrices or samples. The
other benefit of ML in Biosensors includes the possibility of obtaining reasonable
analytical results from noisy and low-resolution sensing data that may be heavily
overlapped with each other.
 Proper deployment of ML methods can discover hidden relations between sample
parameters and sensing signals through data visualization, and mine interrelations
between signals and bioevents.
 Biosensors are inevitably affected by sample matrix and operating conditions. When
Biosensors are used on-site, they can significantly interfere with contamination. ML
can check the signal and answer the question “does the signal look right?” It can
also “correct” sensor performance variations due to biofouling and interferences in
real samples.

4. RISHABH GARG
BITS - PILANI | GOA
ML in Biosensors
 Noise is always included in the sensing signals. The signal from Biosensors
changes over seconds or minutes, while signal interference such as electrical noise
can occur on the sub second timeline. Therefore, it is possible to train ML models to
distinguish the signal from the noise.
 By discovering latent objects and patterns using ML algorithms, sensing data can be
interpreted easily and effectively.
 ML can assist biosensor readout directly, automatically, accurately, and rapidly,
which is very important for on-site detection or diagnosis. A CNN algorithm-assisted
optical imaging method was developed to predict the diagnostic results.
 ML has been used to design more desirable Biosensors. Metamaterials with
negative permeability and permittivity have been employed to amplify the detection
signal of surface plasmon resonance (SPR)-based Biosensors.

5. RISHABH GARG
BITS - PILANI | GOA
ML in Biosensors

6. RISHABH GARG
BITS - PILANI | GOA
 Electrochemical impedance spectroscopy (EIS) are popular among EC Biosensors.
The equivalent circuit models are always applied to extract key parameters of EIS
data. EIS is performed by applying a sinusoidal electric potential to a test sample
and recording the impedance over a range of frequencies.
 Surface enhanced Raman spectroscopy (SERS) can acquire intrinsic fingerprint
information on an analyte in a complex matrix. However, many analytes and the
substance in the matrix have similar or overlapping spectra. It is tedious or
impossible to manually distinguish them.
 Fluorescence imaging-based dPCR is a promising technology for gene diagnostics.
However, it is needed to tune the parameters of the threshold segmentation in each
analysis. It is also limited to analysis of images with uneven brightness induced by
poor camera imaging or nonuniform illumination.
Detection Techniques

7. RISHABH GARG
BITS - PILANI | GOA
Detection Techniques
 Cyclic Voltammetry (CV) - Voltammetry sensors apply electric potential to a
“working” electrode and measure the current response, which is affected by analyte
oxidation or reduction. CV curves (cyclic voltammograms) can serve as a fingerprint
of the sensor response.
 Enose and Etongue - Both sensor types rely on an array of semi-specific sensors,
each of which interacts to a different degree with a wide range of analytes. Several
challenges involve changes in the sensor data, which affect the performance of the
trained model. A common phenomenon is when the sensor array response changes
over time or upon prolonged expose under identical conditions.

8. RISHABH GARG
BITS - PILANI | GOA
Detection Techniques
 Imaging sensors utilize an array of optical sensors such as a CMOS array. Images
of the specimen can be used to identify the target presence and concentration as
the molecules exhibit different coloration, fluorescence, or light scattering, with
varying morphology and spatial distribution.
 Spectroscopy using X-rays: CNN algorithms are used for image analysis in such
cases. Since CNN requires extensive computation, it is currently difficult to analyze
the data in real time
As a general observation, principal component analysis (PCA) combined with
support vector machine (SVM) and various artificial neural network (ANN)
algorithms have shown outstanding performance in a variety of tasks.

9. RISHABH GARG
BITS - PILANI | GOA
EIS Data
 Electrochemical impedance spectra can be collected periodically at various cycle
numbers and various state of charges, producing vast amounts of data. Fitting each
spectrum to an equivalent circuit can lead to physical insights about the evolution of
the bacterial concentration.
 This type of spectroscopy is radiation free and non-invasive, meaning that it does
not interfere with the results of the sample during the detection process. Multiple dry
electrodes can also be placed to check the accuracy of the results. The cost of
setup is also lower than most methods.
 Unlike the imaging data, it is comparatively computationally inexpensive and hence
can be analysed in real time. Both machine learning and deep learning techniques
can be used depending on the size of the dataset.

10. RISHABH GARG
BITS - PILANI | GOA
EIS Data
 The sensitive operating conditions for EIS experiments can also be easily (as
compared to other approaches) tuned by appropriate signal processing methods
and machine learning models designed to check the signal parameters and errors in
conditions.
Consider dPCR for example. A high-quality mask using R-CNN needs to be made to adjust for
uneven illumination. In most spectroscopy methods, min-max scaling and normalization seems
to play a heavy role in determining the accuracy of the model. EIS data is relatively less
influenced by scaling and normalization.
 The impedance can be measured in the presence or absence of a redox couple,
which is referred to faradic and non-faradic impedance measurement, respectively.

11. RISHABH GARG
BITS - PILANI | GOA
Comparison For ML Algorithms
Study 1
Raw EIS data and their equivalent circuit models are collected from the literature, and the
support vector machine (SVM) is used to analyze these data. Comparing with other
machine learning algorithms, SVM achieves the best comprehensive performance in this
database. As a result, the optimized SVM model can efficiently figure out the most suitable
equivalent circuit model of the given EIS spectrum.
Study 2
XGBoost and a support vector regression (SVR) machine learning models were compared
to establish a quantitative relationship between multiple impedance parameters and the
bacterial concentration under the effect of inhibitors. The results showed that XGBoost
improved the quantitative analysis accuracy toward inhibitors (0.175–0.375 μL/mL), based
on the bacteria growth in the solution. The prediction error decreased as the incubation
time of the E. coli culture was extended.

12. RISHABH GARG
BITS - PILANI | GOA
Study 3
Different cell growth features were measured with the impedance instrument and analyzed
using an equivalent model for data fitting and support vector regression (SVR) for data
processing.
Study 4
We use of a simple, open-source support vector machine learning algorithm for analyzing
impedimetric data in lieu of using equivalent circuit analysis. In all conditions tested, the
open-source classifier performed as well, or better, than equivalent circuit analysis.
Study 5
EIS data were exported and transformed into samples with 152 features that represent both
real and imaginary impedance at frequencies from 100kHz to 1Hz. The number of features
was selected to satisfy expected confidence levels for principal components analysis. A total
of 54 EIS scans were randomly split into two groups, with 80% of the data used as the
training set and 20% used as the testing set.
Comparison For ML Algorithms

13. RISHABH GARG
BITS - PILANI | GOA
Available Options
Option 1
Use SVM model and train it to find the equivalent circuit using online dataset.
Option 2
Use impedance.py python library for fitting the circuit directly to the lab data.
Option 3
Use XGBoost / SVM Model to directly train on the EIS Data (more feasible in case of
complex circuit geometries).

14. RISHABH GARG
BITS - PILANI | GOA
Option - 1
Use SVM model and train it to find the equivalent circuit using online dataset.
 Current limitation: Not much labelled online data is available for training the
algorithm. Generally researchers have manually labelled the equivalent circuit for
training their algorithms.
 Hence, this approach might require manual effort even before applying the
algorithms. Moreover, most libraries provided by the researchers give better
performance than such manual effort.
 Therefore Option 2 seems better than Option 1.

15. RISHABH GARG
BITS - PILANI | GOA
Option - 2
Use impedance.py python library or GUI software such as Z-view for fitting
the circuit directly to the lab data.
 Then we find the relevant concentration dependent parameters using the type of
equivalent circuit which determine the bacterial concentration.
 Then we can plot the relevant concentration dependent parameter vs bacterial
concentration.
 Z-View is NOT free of charge. Free version: results are poor, especially in what
concerns the fittings.

16. RISHABH GARG
BITS - PILANI | GOA
Option - 3
Use XGBoost / SVM Model to directly train on the EIS Data.
 One can focus on the desired algorithmic metrics rather than the intrinsic knowledge
of Nyquist Plots and Randle circuits.
 No need to find the concentration dependent parameter and equivalent circuits.

17. RISHABH GARG
BITS - PILANI | GOA
Best Fit
 Option 3 seems the most intuitive and easy.
 We can focus on directly improving the metrics of the required algorithms rather
than focussing on development of intermediate plots and processes.
 Online data available for option 1 is very less and checking the results of individual
algorithms for circuit parameters seems a tedious task.
 Using impedance.py or other GUI software is easier than Option 1 but is less
intuitive than Option 3.
 Model metrics are yet to be compared between Option 2 and Option 3.

18. RISHABH GARG
BITS - PILANI | GOA
Sample Experiment - 1
Option 2
 Demonstration of Option 2 (when we know the circuit model in advance) using
impedance.py library and online data:
https://github.com/rishabhgargdps/ml_Biosensors/blob/master/option_2.ipynb

19. RISHABH GARG
BITS - PILANI | GOA
Alternative Approach - 1
 ML models can be applied after extracting the circuit parameters. SVR model can be
used where the features are the circuit parameters and the target is the bacterial
concentration.
 Might not be very useful, still can simplify the process of finding the relation between
concentration dependent parameter and bacterial concentration.
impedance spectroscopy (EIS) is a commonly used biosensor technique for detecting foodborne bacteria. In this paper, we
show a machine learning-based EIS biosensor method that can be used to detect the effect of a low-dose inhibitor (e.g.,
hydrogen peroxide) on Escherichia coli (E. coli). After obtaining the minimum inhibitory concentration (MIC), the inhibitor
concentration and below was applied to E. coli solution. EIS data were obtained by binding the target bacteria to the
electrode surface through antibodies, and then, the impedance parameters were fitted by the Randles model. XGBoost and
a support vector regression (SVR) machine learning models were compared to establish a quantitative relationship
between multiple impedance parameters and the bacterial concentration under the effect of inhibitors. The results showed
that XGBoost improved the quantitative analysis accuracy toward inhibitors (0.175–0.375 μL/mL), based on the bacteria
growth in the solution. After different low concentrations of the inhibitor were added into a standard bacterial solution for
1 h, 2 h and 3 h, the maximum prediction error of the inhibitor concentration was 4.95%, 1.03% and 0.46%, respectively.
The prediction error decreased as the incubation time of the E. coli culture was extended. These results pave the way for
the automation of an accurate EIS biosensor for analyzing foodborne microorganisms under various low doses of
inhibitors or drugs.
Journal of Electroanalytical Chemistry Volume 877, 15 November 2020, 114534

20. RISHABH GARG
BITS - PILANI | GOA
Alternative Approach - 2
 In order to entirely prevent the use of equivalent circuits for circuit parameters and
not directly apply ML models on impedance data for better results, we can make use
of PCA (Principal Component Analysis) for extracting the parameter responsible for
changes in concentration as written in the below mentioned extract:
Another approach

21. RISHABH GARG
BITS - PILANI | GOA
Proposed Plan
Option 3
 Demonstration of SVR model directly on impedance data
 First, PCA is applied to extract the most important features responsible for variance
in concentration.
 Then SVR is applied using the extracted features as input and bacterial
concentration as the output.
 Will require data from the lab. No online data available in any similar experiment
which contains the target variable (concentration in this case).

22. RISHABH GARG
BITS - PILANI | GOA
Biosensors
Thank you !