Machine learning–based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe

Noreldeen, Hamada A. A.; He, Shao‐Bin; Huang, Kai-Yuan; Zhu, Chen-Ting; Zhou, Qing-Lin; Peng, Hua-Ping; Deng, Hao‐Hua; Chen, Wei

doi:10.1007/s00216-022-04372-1

Cited by 7 publications

(2 citation statements)

References 51 publications

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“…The same authors also reported a similar method for the detection of vitamin B6 using cysteamine/N-acetyl-l-cysteine/AuNCs and convolutional neural networks [185] and the detection of metal ions using random forest and SVM for classification and LDA and PCA to reduce dimensionality [186].…”

Section: Luminescent Sensorsmentioning

confidence: 98%

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.

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Section: Luminescent Sensorsmentioning

confidence: 98%

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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“…28 Moreover, the use of a single probe as a sensor array to differentiate between several similar analytes also has unique advantages, such as reducing workload and costs. Therefore, the use of AuNCs, depending on their unique photoluminescent properties, 18,30 to identify many similar analytes in complex media is strongly required.…”

mentioning

confidence: 99%

Feature Selection Assists BLSTM for the Ultrasensitive Detection of Bioflavonoids in Different Biological Matrices Based on the 3D Fluorescence Spectra of Gold Nanoclusters

Noreldeen

Huang

et al. 2022

Anal. Chem.

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Rapid and on-site qualitative and quantitative analysis of small molecules (including bioflavonoids) in biofluids are of great importance in biomedical applications. Herein, we have developed two deep learning models based on the 3D fluorescence spectra of gold nanoclusters as a single probe for rapid qualitative and quantitative analysis of eight bioflavonoids in serum. The results proved the efficiency and stability of the random forest-bidirectional long short-term memory (RF-BLSTM) model, which was used only with the most important features after deleting the unimportant features that might hinder the performance of the model in identifying the selected bioflavonoids in serum at very low concentrations. The optimized model achieves excellent overall accuracy (98–100%) in the qualitative analysis of the selected bioflavonoids. Next, the optimized model was transferred to quantify the selected bioflavonoids in serum at nanoscale concentrations. The transferred model achieved excellent accuracy, and the overall determination coefficient (R2) value range was 99–100%. Furthermore, the optimized model achieved excellent accuracies in other applications, including multiplex detection in serum and model applicability in urine. Also, LOD in serum at nanoscale concentration was considered. Therefore, this approach opens the window for qualitative and quantitative analysis of small molecules in biofluids at nanoscale concentrations, which may help in the rapid inclusion of sensor arrays in biomedical and other applications.

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Heavy metal pollutant detection using nanozymes

Noreldeen,

Deng,

Chen

2024

Nanozymes

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Machine learning–based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe

Cited by 7 publications

References 51 publications

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Feature Selection Assists BLSTM for the Ultrasensitive Detection of Bioflavonoids in Different Biological Matrices Based on the 3D Fluorescence Spectra of Gold Nanoclusters

Heavy metal pollutant detection using nanozymes

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