Augmentations for selective multi-species quantification from infrared spectroscopic data

Ibrahim, Emad Al; Farooq, Aamir

doi:10.1016/j.chemolab.2023.104913

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…These models can be trained to detect molecules in a mixture 295,296 or quantify their concentrations. 297,298 For example, special augmentations can be used to filter out unwanted complexity (unknown interfering species, as shown in Figure 5) and focus only on the target species. 297 Spectroscopic information can be used to build ML-based chemometric models that map spectral measurements to mixture properties.…”

Section: Continuum-scale Chemical Mixturesmentioning

confidence: 99%

“…In many cases, large synthetic data sets can be simulated based on the Beer–Lambert law to train ANN models, where laboratory data are employed to account for noise and real-world biases. These models can be trained to detect molecules in a mixture , or quantify their concentrations. , For example, special augmentations can be used to filter out unwanted complexity (unknown interfering species, as shown in Figure ) and focus only on the target species …”

Section: Continuum-scale Chemical Mixturesmentioning

confidence: 99%

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Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

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Section: Continuum-scale Chemical Mixturesmentioning

confidence: 99%

Section: Continuum-scale Chemical Mixturesmentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

Optical sensor for BTEX detection: Integrating machine learning for enhanced sensing

Hashemitaheri,

Ebrahimi,

de Silva

et al. 2024

Advanced Sensor and Energy Materials

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VOC-Certifire: Certifiably Robust One-Shot Spectroscopic Classification via Randomized Smoothing

Sy,

Ibrahim,

Farooq

2024

ACS Omega

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Spectroscopic methods are advantageous for gas detection with applications ranging from safety to operational efficiency. Despite the potential of laser-based sensors, real-world challenges, such as noise, interference and unseen conditions, hinder the accurate identification of species. The use of conventional machine learning (ML) models is constrained by extensive data requirements and their limited adaptability to new conditions. Although augmentation-based strategies have proven to improve the robustness of machine learning models, they still do not offer a complete defense. To address these challenges, this study focuses on three primary goals: first, to detect pressure-induced spectral broadening using simple yet effective augmentations; second, to bypass the need for extensive data sets by deploying a one-shot learning approach that can identify up to 12 volatile organic compounds (VOCs); and third, to provide a provable certification for the one-shot learning model predictions via randomized smoothing. To assess the effectiveness of our proposed augmentations and randomized smoothing, we perform a comparative study with four distinct models: VOC-net, VOC-lite, VOC-plus, and VOC-certifire. Remarkably, the one-shot learning model proposed herein, VOC-certifire, delivers predictions that match the baseline model VOC-net. The VOC-certifire predictions not only exhibit robustness and reliability but are also certified within a predefined norm radius. Such a certification is particularly useful for gas detection, where the robustness, precision and consistency are key to well-informed decision-making.

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Augmentations for selective multi-species quantification from infrared spectroscopic data

Cited by 5 publications

References 37 publications

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Optical sensor for BTEX detection: Integrating machine learning for enhanced sensing

VOC-Certifire: Certifiably Robust One-Shot Spectroscopic Classification via Randomized Smoothing

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