Use of the Feature Scaling and Machine Learning Techniques on Optical Fiber Biosensors for the Detection of Neuroprotector IL-10 in Serum of a Murine Model with Cerebral Ischemia

TL;DR

This paper explores how scaling techniques and machine learning can improve optical fiber biosensors for detecting IL-10 in mouse serum after cerebral ischemia.

Contribution

The novel use of robust scaling with SVM classifiers to enhance biosensor performance for IL-10 detection in a murine model.

Findings

Robust scaling with CMZI biosensors achieved an F1-score of 1 for IL-10 detection.

PCA and SVM with different scaling techniques improved biosensor reliability.

Using full spectral data with scaling outperformed traditional amplitude-based analysis.

Abstract

Typically, response analysis of optical fiber biosensors focuses on changes in amplitude and wavelength shifts in the biosensor spectrum; therefore, not all of the spectral range is used for this analysis. On the other hand, if the entire spectrum is used, it is possible to leverage the current data in the spectrum and thus improve the performance of the biosensor. To do this, it is necessary to analyze a large amount of data present in each measured spectrum. This task can be made easier by using dimensionality reduction techniques. In addition, it is necessary to establish which spectral regions provide relevant information. Scaling techniques are mathematical data preprocessing tools used in machine learning to adjust the numerical scale of variables so that they have comparable weight and even highlight those characteristics that provide more information. To our knowledge, the use of these techniques in the development of optical fiber biosensors is not very common, which is why we believe they represent an attractive topic of study in this area. With the help of scaling techniques, we can modify the scale of the data so that all the information contained in the spectrum is used, regardless of its magnitude. In this work, two biosensors based on a chirped long period fiber grating (CLPFG) and a chirped Mach–Zehnder interferometer (CMZI) were developed for the detection of interleukin-10 (IL-10). Principal component analysis (PCA) was used as a dimensionality reduction technique together with a support vector machine (SVM) classifier with four different scaling techniques, standardization, minimum–maximum scaling, robust scaling, and a custom transformer, to compare the IL-10 detection performance of the biosensors. The results showed that robust scaling in CMZI performed best in detecting IL-10, with an F1-score equal to 1, as well as better reliability in detecting the protein.

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