Advancing Luminescent Biosensors and Nanostructure Synthesis with Machine Learning

By Dr. Sampath LonkaAug 5 2023

Artificial intelligence (AI) and machine learning (ML) have revolutionized scientific research, including analytical chemistry. ML techniques have become valuable tools for designing nanomaterials, developing sensing platforms, and analyzing data for diverse analyte detection.

In a paper published in the journal TrAC Trends in Analytical Chemistry, researchers explored ML's practical applications in luminescent sensing, focusing on recent publications covering luminescent nanomaterial synthesis and ML-assisted sensors such as electrochemiluminescence, fluorescence, and chemi- and bio-luminescent sensors.

Background

Since the creation of the "perceptron" in 1957, artificial neural networks (ANN) and machine learning (ML) have garnered global attention. As a subset of artificial intelligence, ML focuses on developing algorithms that analyze and interpret data, enabling computers to learn and make decisions akin to the human brain. This technology serves as the foundation for AI-driven applications in various fields, including engineering, chemistry, medicine, and bioanalytical chemistry.

In the present study, researchers explored the implementation of ML in bioanalytical research and sensing, specifically in luminescent biosensors and nanostructure synthesis. The aim is to showcase how ML accelerates data analysis, facilitates the prediction of material properties, and enhances sensor performance in diverse applications.

ML in nanostructure synthesis

The synthesis of nanomaterials, such as nanostructures, traditionally involves time-consuming and expensive laboratory experiments. To expedite this process, researchers are turning to ML to develop more effective chemical synthesis routes and optimize existing materials. ML techniques can learn from past experiments, guiding future synthesis procedures and reducing time, cost, and chemical waste.

For luminescent materials, ML has been successfully applied to optimize the synthesis of carbon dots, colloidal quantum dots, noble metal nanoclusters, metal halides, and up-conversion nanoparticles. This approach allows researchers to explore a broader parameter space efficiently, leading to faster advancements in the field and reducing the need for extensive laboratory experimentation.

ML-assisted luminescence sensing

Luminescence sensing, including fluorescence, chemiluminescence, and electrochemiluminescence, has gained importance in various fields due to its simplicity, sensitivity, and potential for portable sensors. However, these strategies generate noisy and complex data that can benefit from ML analysis.

The rise of smartphone cameras has enabled image-based sensing for target measurements, where luminescent signals can be translated into numeric data using different color spaces. Traditional image processing techniques can be unreliable due to camera optics and illumination conditions. By incorporating ML algorithms, these limitations can be overcome, allowing for more accurate and reliable results.

In the healthcare industry, ML-assisted electrochemiluminescence (ECL) has been utilized for ultra-sensitive and specific detections, aiding in the early diagnosis of diseases. Similarly, ML algorithms have been employed in fluorescence-based sensing platforms for environmental monitoring, food safety control, and medical diagnosis. ML helps in the precise detection of various analytes by extracting patterns from non-linear data, making it suitable for point-of-care testing.

Advantages of chemi- and bio-luminescence with ML

Chemi- and bio-luminescence offer advantages over traditional fluorescence techniques, but their potential has not been fully exploited due to limitations. However, ML-assisted assays have shown promise in harnessing the capabilities of these luminescence strategies.

In one study, a chemiluminescence-based machine learning-assisted assay was developed to profile phenolic compounds in wines, enabling the determination of antioxidant properties. Similarly, bioluminescence strategies, which emit light from luciferin reactions, have been employed in whole-cell sensing arrays for detecting antibiotics with the aid of ML.

ML-assisted sensor arrays

Sensor arrays, which rely on selective elements to interact with analytes, have gained popularity due to their accuracy and multiplexing capabilities. ML algorithms have shown greater reliability than traditional chemometrics methods in distinguishing meaningful patterns in array-based fluorescence sensors. ML algorithms were applied to distinguish different antibiotics, proteins, and pathogens successfully.

ML-assisted ECL sensor arrays have also emerged, focusing on medical applications such as diagnosing acute myocardial infarction (AMI). Using support vector machine (SVM) models, highly sensitive ECL arrays can simultaneously detect multiple biomarkers for precise disease diagnosis.

Challenges and perspectives

While ML in analytical chemistry presents challenges in data collection and integration, it offers significant benefits such as improved accuracy and faster results. To maximize its advantages, user-friendly software and tools must be developed to facilitate integration into research practices.

Looking ahead, ML holds great potential for growth in analytical chemistry, particularly in point-of-care testing and wearable sensors for health monitoring. Integrating ML with smartphone applications can enhance the accuracy and reliability of point-of-care devices. ML-assisted sensing platforms can also revolutionize onsite monitoring of environmental and food pollutants in remote areas.

Conclusion

In summary, ML has proven advantageous in luminescent biosensors, particularly in nanomaterial synthesis and sensor development. As smart technologies become more prevalent, analytical and bioanalytical chemistry will likely shift towards developing smart, continuous, and precise point-of-care sensors for health and environmental monitoring. ML's role in these developments will be crucial in advancing these fields and transforming how we approach biosensing and nanostructure synthesis.