Infrared (IR) Spectroscopy

Latest News


A Researcher from Lomonosov Moscow State University has developed a convolutional neural network (CNN) model for Fourier transform infrared (FT-IR) spectra recognition. This AI-based system is capable of classifying 17 functional groups and 72 coupling oscillations with remarkable accuracy, providing a significant boost to material analysis in fields like organic chemistry, materials science, and biology.

Refrigerator room. Factory style cold room ice. Generative AI. | Image Credit: © Aiakos - stock.adobe.com.

In this article, tunable diode laser absorption spectroscopy (TDLAS) is used to measure ammonia leakage, where a new denoising method combining empirical mode decomposition with the Savitzky-Golay smoothing algorithm (EMD-SG) is proposed to improve the signal-to-noise ratio (SNR) of absorbance signals.

Bright background with contemporary effect. Vivid grunge gradient. Acid colors. Trendy vector illustration with noise dust texture | Image Credit: © annetdebar - stock.adobe.com.

We wrap up our discussion of the mid-infrared spectra of inorganic compounds by looking at the spectra of silicates, nitrates, and phosphates. We will see that silicates have complex surface chemistry, and that infrared spectroscopy can tell us something about this. We will note that, of the five families of inorganics examined, the wavenumber ranges for the polyatomic anion stretching peaks in several of these functional groups overlap. However, polyatomic anion bending vibration peaks can be used to distinguish the five types of inorganics studied.