Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation

Emine Sezer; Sinan Akgöl

doi:10.17776/csj.1861928

Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation

Abstract

Developing robust calibration models for electrochemical biosensors is frequently constrained by the high cost of data collection and the non-linear nature of biological signals. This small data limitation leads to overfitting in regression models and restricts the sensor's sensitivity. This study introduces an Artificial Intelligence (AI)-driven framework to enhance prediction accuracy in low-concentration ranges and stabilize sensor response without hardware modifications. For this purpose, three independent sensor datasets (C-Reactive Protein, Vitamin C, and Vitamin E) using Machine Learning (ML) algorithms benchmarked against Linear Regression were analysed. To mitigate data scarcity, four augmentation strategies were evaluated: Gaussian noise to emulate instrumental variability, statistical SMOGN, Deep Learning-based CTGAN, and interpolation based-Mixup to reinforce signal continuity. Results indicated that linear models failed (R² < 0.10) to capture complex kinetics. While baseline ML models showed instability due to overfitting, data augmentation substantially improved generalization. Notably, physically motivated methods like Mixup and Gaussian Noise outperformed complex Deep Learning approaches, increasing Test R² values from 0.40 to 0.65. These findings demonstrate that AI-based augmentation can effectively stabilize non-linear models and extend the quantifiable range of biosensors.

Keywords

Artificial Intelligence, Data Augmentation, Electrochemical Biosensors, Machine Learning Regression, Small Data

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Details

Primary Language

English

Subjects

Electroanalytical Chemistry, Sensor Technology, Electrochemistry

Journal Section

Research Article

Authors

Emine Sezer ^*
0000-0003-4776-6436
Türkiye

Sinan Akgöl
0000-0002-8528-1854
Türkiye

Publication Date

June 29, 2026

Submission Date

January 12, 2026

Acceptance Date

May 7, 2026

Published in Issue

Year 2026 Volume: 47 Number: 3

DOI

https://doi.org/10.17776/csj.1861928

IZ

https://izlik.org/JA55YX26GW

APA

Sezer, E., & Akgöl, S. (2026). Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation. Cumhuriyet Science Journal, 47(3), 483-495. https://doi.org/10.17776/csj.1861928

AMA

1.Sezer E, Akgöl S. Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation. CSJ. 2026;47(3):483-495. doi:10.17776/csj.1861928

Chicago

Sezer, Emine, and Sinan Akgöl. 2026. “Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation”. Cumhuriyet Science Journal 47 (3): 483-95. https://doi.org/10.17776/csj.1861928.

EndNote

Sezer E, Akgöl S (June 1, 2026) Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation. Cumhuriyet Science Journal 47 3 483–495.

IEEE

[1]E. Sezer and S. Akgöl, “Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation”, CSJ, vol. 47, no. 3, pp. 483–495, June 2026, doi: 10.17776/csj.1861928.

ISNAD

Sezer, Emine - Akgöl, Sinan. “Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation”. Cumhuriyet Science Journal 47/3 (June 1, 2026): 483-495. https://doi.org/10.17776/csj.1861928.

JAMA

1.Sezer E, Akgöl S. Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation. CSJ. 2026;47:483–495.

MLA

Sezer, Emine, and Sinan Akgöl. “Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation”. Cumhuriyet Science Journal, vol. 47, no. 3, June 2026, pp. 483-95, doi:10.17776/csj.1861928.

Vancouver

1.Emine Sezer, Sinan Akgöl. Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation. CSJ. 2026 Jun. 1;47(3):483-95. doi:10.17776/csj.1861928

As of 2026, Cumhuriyet Science Journal will be published in six issues per year, released in February, April, June, August, October, and December

Unlocking the Non-Linear Frontier: A New Era of Robust Biosensing by Data Augmentation

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