# Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis

arXiv: https://arxiv.org/abs/1906.10242 · HTML: https://mlsanity.com/paper/1906.10242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis](https://mlsanity.com/paper/2212.14623/) (2022)

## Abstract (arXiv)

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance - partial least squares discriminant analysis when signal-to-noise ratio and training sample size are sufficient.