Related Experiment Video
Updated: Oct 29, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
Published on: August 19, 2021
Prediction of a single Gaussian shape of spectral line measured with low-dispersion spectrometer by using machine
1National Institutes for Quantum and Radiological Science and Technology, 801-1 Mukoyama, Naka, Ibaraki 311-0193, Japan.
Abstract:
We have developed a denoising autoencoder based neural network (NN) method to determine a spectral line intensity with an uncertainty lower than the uncertainty determined by fitting the spectral line. The NN method processes the measured raw spectral line shape, providing a single Gaussian shape based on the training dataset, which consists of synthetically prepared Doppler shift and broadening free spectral lines in the present work. It is found that the uncertainty reduction level significantly depends on the training dataset. Limitations originating from the training dataset are also discussed.
More Related Videos
10:25Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
08:12Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
Published on: March 13, 2013
Related Concept Videos
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration
According to Hooke's law, the vibrational frequency is directly proportional to...
¹H NMR: Interpreting Distorted and Overlapping Signals
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
Emission Spectra
UV–Vis Spectroscopy: Beer–Lambert Law
Atomic Emission Spectroscopy: Lab