# Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics

arXiv: https://arxiv.org/abs/1710.01927 · HTML: https://mlsanity.com/paper/1710.01927/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders](https://mlsanity.com/paper/2504.16130/) (2025)
- [Convolutional Neural Networks in Vis-NIR Chemometrics: From Contradiction to Conditional Design](https://mlsanity.com/paper/2605.02636/) (2026)
- [Raman spectral analysis of mixtures with one-dimensional convolutional neural network](https://mlsanity.com/paper/2106.05316/) (2021)
- [Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis](https://mlsanity.com/paper/2409.19688/) (2024)
- [Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers](https://mlsanity.com/paper/2606.21289/) (2026)
- [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](https://mlsanity.com/paper/2607.00834/) (2026)
- [Attributing Preprocessing Invariance in Spectral Foundation Models](https://mlsanity.com/paper/2608.14227/) (2026)

## Abstract (arXiv)

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.