**Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One**

**Artificial Neural Networks (ANNs) for Spectral** 

DOI: 10.5772/intechopen.71039

#### Z. Li, X. Zhang, G. A. Mohua and Vassili Karanassios Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71039

Z. Li, X. Zhang, G. A. Mohua and

#### **Abstract**

Vassili Karanassios

Artificial neural networks (ANNs) are evaluated for spectral interference correction using simulated and experimentally obtained spectral scans. Using the same data set (where possible), the predictive ability of shallow depth ANNs was validated against partial least squares (PLS, a traditional chemometrics method). Spectral interference (in the form of overlaps between spectral lines) is a key problem in large-size, long focal length inductively coupled plasma-optical emission spectrometry (ICP-OES). Unless corrected, spectral interference can be sufficiently severe to the point of preventing precise and accurate analytical determinations. In miniaturized, microplasma-based optical emission spectrometry with a portable, short focal length spectrometer (having poorer resolution than its large-size counterpart), spectral interference becomes even more severe. To correct it, we are evaluating use of deep learning ANNs. Details are provided in this chapter.

**Keywords:** artificial neural networks (ANNs), artificial intelligence, machine learning, deep learning, spectral interference, PLS, ICP, microplasma, portable optical emission spectrometry
