Deep learning in analytical chemistry

Debus, B.; Parastar, Hadi; Harrington, Peter de B.; Kirsanov, Dmitry

doi:10.1016/j.trac.2021.116459

Cited by 101 publications

(58 citation statements)

References 63 publications

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“…It has unique advantages in image processing, target detection, target tracking, and so on. In recent years, CNN learning algorithms have been successfully applied in many fields, especially in the field of analytical chemistry [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Wang

Deng

Jiang

2022

Foods

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This work provides a novel approach to monitor the aflatoxin B1 (AFB1) content in maize by near-infrared (NIR) spectra-based deep learning models that integrates Markov transition field (MTF) image coding and a convolutional neural network (CNN) strategy. According to the data structure characteristics of near-infrared spectra, new structures of one-dimensional CNN (1D-CNN) and two-dimensional MTF-CNN (2D-MTF-CNN) were designed to construct a deep learning model for the monitoring of AFB1 in maize. The results obtained showed that compared with the 1D-CNN model, the performance of the 2D-MTF-CNN model had been significantly improved, and its root mean square error of prediction, coefficient of predictive determination, and relative percent deviation were 1.3591 μg·kg−1, 0.9955, and 14.9386, respectively. The results indicate that the MTF is an effective data encoding technique for converting one-dimensional spectra into two-dimensional images. It more intuitively reflects the intrinsic characteristics of the NIR spectra from a new perspective and provides richer spectral information for the construction of deep learning models, which can ensure the detection accuracy and generalization performance of deep learning quantitative detection models. This study provides a new analytical perspective for the chemometrics analysis of the NIR spectroscopy.

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Section: Methodsmentioning

confidence: 99%

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Wang

Deng

Jiang

2022

Foods

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“…CNN is a popular category of deep learning methods in computer vision. Recently, it has also been widely used in chemistry [76], especially analytical chemistry [77]. It basically consists of an input layer, convolutional layers, pooling layers, dense layers and an output layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

et al. 2022

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Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

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“…In recent years, machine learning (ML) technology has developed rapidly in analytical chemistry [14] . It has shown excellent performance in solving various complex issues, including disease detection [15] , drug designing [16] , toxicity analysis [17] and screening of materials [18] , etc.…”

Section: Introductionmentioning

confidence: 99%

Chemical Reaction Spectrum: A Holographic Image for Characterizing Multi-component Chemical Mixtures

Duan¹,

Chen

Lee*

et al. 2022

Preprint

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Multi-component chemical mixtures (MCMs) and their various effects always concerned in analytical chemistry, but current analytical techniques based on test-tube experiments often involves many high-cost and laborious operations. Today’s pop machine-learning (ML) technology has exhibited their successes in dealing with the analysis task of various complex systems. Predictably, the introduction of ML will radically accelerate the exploration of many fields involving mixture analysis. But the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this study, we proposed a chemical imaging strategy to visualize various mixtures as some feature images by using ink-jet printing technology based on combinatorial chemistry. Here, these feature images were as a novel data form of chemical reaction spectrum (CRS), which can comprehensively describe and record the reaction characteristics of the complex sample. Compared with common imaging methods, the CRS with high-throughput chemical reaction dots is an efficient and economic information visualization way for the MCM sample. It is expected to be an important data acquisition approach for the application of ML in the field of chemistry in future.

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Deep learning in analytical chemistry

Cited by 101 publications

References 63 publications

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Chemical Reaction Spectrum: A Holographic Image for Characterizing Multi-component Chemical Mixtures

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