Identification of spectral lines of elements using artificial neural networks

Abstract

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

Semi-Automatic Elemental Identification of Laser-Induced Breakdown Spectra Using Wavelength Similarity Coefficient

“…Several approaches addressing line emission identification have been reported. Neural networks 1 and fuzzy logic 2 are examples of artificial intelligence based methods. Other similarity tests, i.e., procedures to quantify the likeness of two entities, are those based on statistics, such as fast Fourier transform (FFT), 3 dot–product, 4 and correlation 5 among others.…”