A practical convolutional neural network model for discriminating Raman spectra of human and animal blood

Dong, Jichen; Hong, Mingjian; Xu, Yuheng; Zheng, Xiangquan

doi:10.1002/cem.3184

Cited by 44 publications

(35 citation statements)

References 28 publications

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“…32 ANNs are another machine learning approach that has successfully been applied on spectral data for classification purposes. 33,34 ANN often proved superior compared to SVM and PLS-DA approaches. 29,33 An example of ANNs applied on NIR data includes cellulose pulp dryness determination in industrial processing.…”

Section: Introductionmentioning

confidence: 99%

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Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Kranenburg

Verduin

Weesepoel

et al. 2020

Drug Testing and Analysis

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On-scene drug detection is an increasingly significant challenge due to the fastchanging drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.

show abstract

Section: Introductionmentioning

confidence: 99%

“…33,34 ANN often proved superior compared to SVM and PLS-DA approaches. 29,33 An example of ANNs applied on NIR data includes cellulose pulp dryness determination in industrial processing. 29…”

Section: Introductionmentioning

confidence: 99%

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Kranenburg

Verduin

Weesepoel

et al. 2020

Drug Testing and Analysis

View full text Add to dashboard Cite

show abstract

“…Deep learning can be a time saver assuming that the deep neural network is powerful enough to tolerate the corrupting effects and can be trained on raw data without any preprocessing to reach a satisfying performance. This has been shown in references utilizing convolutional neural networks or stacked contractive auto‐encoders . The kernels of the trained network were shown to work as smoothing, derivative/slope recognizers, thresholding and spectral region selection, which are basically preprocessing steps .…”

Section: Deep Learning For Vibrational Spectroscopymentioning

confidence: 87%

Deep learning a boon for biophotonics?

Pradhan

Guo

Ryabchykov

et al. 2020

Journal of Biophotonics

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This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

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“…Conventional identification methods based on human inspection can be time‐consuming and require a high level of expertise 7,8 . Alternatively, chemical analyses of bacterial components have increasingly been applied for the classification of microorganisms because the methods are faster, cheaper, and have been shown to be reliable 9–11 .…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for preprocessing‐free bacterial Spectra identification

Kim

Lee

Ahn

et al. 2020

Journal of Chemometrics

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Identifying bacterial species is essential to epidemiological surveillance. However, the determination of bacterial species is a tedious and labor‐intensive process. Various machine learning methods have been used for identifying bacterial species with mass spectral fingerprints. Although machine learning methods achieve real‐time identification without human experts, it still requires data preprocessing. To address this issue, we proposed a unified solution for the identification of bacterial species with a convolutional neural network. The neural network automatically determined species according to their mass spectra without the preprocessing steps. The convolutional and pooling layers in the neural network could replace the binning, baseline correction, and scaling procedures. Moreover, because of the explainable structure, the model could identify important regions of spectra to discriminate each bacterial species. We used spectral samples obtained from the fatty acid methyl esters of 10 samples from 16 bacterial species (a total of 16) to demonstrate the usefulness of the proposed method by comparing it with existing classification methods preceded by preprocessing. The comparison results confirmed that the proposed method outperformed the alternatives in terms of classification accuracy and robustness. Moreover, the classification results of the proposed method are interpretable.

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A practical convolutional neural network model for discriminating Raman spectra of human and animal blood

Cited by 44 publications

References 28 publications

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Deep learning a boon for biophotonics?

Convolutional neural network for preprocessing‐free bacterial Spectra identification

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