TeaNet augments scarce spectroscopic data via masked spectrum reconstruction to train DNNs that outperform CNNs and better identify key wavenumbers.
Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics
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abstract
Deep learning methods are used on spectroscopic data to predict drug content in tablets from near infrared (NIR) spectra. Using convolutional neural networks (CNNs), features are ex- tracted from the spectroscopic data. Extended multiplicative scatter correction (EMSC) and a novel spectral data augmentation method are benchmarked as preprocessing steps. The learned models perform better or on par with hypothetical optimal partial least squares (PLS) models for all combinations of preprocessing. Data augmentation with subsequent EMSC in combination gave the best results. The deep learning model CNNs also outperform the PLS models in an extrapolation chal- lenge created using data from a second instrument and from an analyte concentration not covered by the training data. Qualitative investigations of the CNNs kernel activations show their resemblance to wellknown data processing methods such as smoothing, slope/derivative, thresholds and spectral region selection.
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UNVERDICTED 2representative citing papers
Stacking ensembles of PLS, SVR and Ridge regression on preprocessed NIR spectra achieve RPD greater than 2.0 for C and N prediction in Inceptisol and Oxisol soils using cross-validation and Kennard-Stone holdout.
citing papers explorer
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Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers
TeaNet augments scarce spectroscopic data via masked spectrum reconstruction to train DNNs that outperform CNNs and better identify key wavenumbers.
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Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance
Stacking ensembles of PLS, SVR and Ridge regression on preprocessed NIR spectra achieve RPD greater than 2.0 for C and N prediction in Inceptisol and Oxisol soils using cross-validation and Kennard-Stone holdout.