Machine learning for optical chemical multi-analyte imaging | Why we should dare and why it’s not without risks

TY - JOUR

T1 - Machine learning for optical chemical multi-analyte imaging | Why we should dare and why it’s not without risks

AU - Zieger, Silvia Elisabeth

AU - Koren, Klaus

PY - 2023/6

Y1 - 2023/6

N2 - Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O2 and pH with a mean absolute error of < 4.50·10−2 and < 1.96·10−1, respectively, and a root mean square error of < 2.12·10−1 and < 4.42·10−1, respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.

AB - Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O2 and pH with a mean absolute error of < 4.50·10−2 and < 1.96·10−1, respectively, and a root mean square error of < 2.12·10−1 and < 4.42·10−1, respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.

KW - Supervised pattern recognition

KW - XGBoost

KW - Decision tree algorithm

KW - Intensity-based sensing

KW - pH

KW - Dissolved oxygen

U2 - 10.1007/s00216-023-04678-8

DO - 10.1007/s00216-023-04678-8

M3 - Journal article

C2 - 37071140

SN - 1618-2642

VL - 415

SP - 2749

EP - 2761

JO - Analytical and Bioanalytical Chemistry

JF - Analytical and Bioanalytical Chemistry

IS - 14

ER -