Best of the Week: Infrared Spectral Recognition, Google Interview, Dual-Comb Absorption Spectroscopy

This week, Spectroscopy published a variety of articles on the hottest topics in analytical spectroscopy and beyond. These articles highlight a wide range of spectroscopic techniques and application areas. Below, we’ve highlighted some of the most popular articles, according to our readers and subscribers. Happy reading!

AI-Based Neural Networks Revolutionize Infrared Spectra Analysis

A researcher from Lomonosov Moscow State University has developed a convolutional neural network (CNN) model for Fourier transform infrared (FT-IR) spectra recognition (1). This AI-based system can classify 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 (1).

How Google is Using Optical Sensors in its Wearable Technology

In this interview, associate editorial director Caroline Hroncich and senior technical editor Jerome Workman, Jr. spoke with Pete Richards, senior staff research scientist at Google, to discuss how the company is using light technology in its Fitbit and other wearable devices (2).

Deep Learning Advances Gas Quantification Analysis in Near-Infrared Dual-Comb Spectroscopy

Researchers from Tsinghua University and Beihang University in Beijing have developed a deep-learning-based data processing framework that significantly improves the accuracy of dual-comb absorption spectroscopy (DCAS) in gas quantification analysis (3). By using a U-net model for etalon removal and a modified U-net combined with traditional methods for baseline extraction, their framework achieves high-fidelity absorbance spectra, even in challenging conditions with complex baselines and etalon effects (3).

Optical Constants of Mixed Crude Oil in Visible Waveband Based on the Double-Thickness Transmittance Method

The study investigated the optical properties of mixed crude oil by measuring the optical constants of samples composed of two crude oils mixed in various proportions. Using the double-thickness transmittance method based on transmittance spectra in the 420–900 nm wavelength range, the researchers measured the transmittance spectra of mixed crude oil and quartz samples (4). They calculated the refractive indexes and absorption coefficients for these samples, finding that in the 610–850 nm range, the refractive indexes of the optical cell were between 1.4345 and 1.4729, with absorption coefficients from 7.09E-8 to 2.43E-7 (4). For mixed crude oil, refractive indexes ranged from 1.5132 to 2.2233, and absorption coefficients from 3.66E-07 to 1.27E-06 (4). Errors in the calculations were minimal, between 0.0243 and 0.1, attributed to measurement inaccuracies and sample absorption. The study also concluded that there is no linear relationship between the optical constants and the mixing ratio of the samples (4).

Unveiling the Mysteries of Scorpion Fluorescence: Insights from Ultraviolet Excitation

A recent study from researchers at East China Normal University examined scorpion fluorescence across different UV bands, discovering complex fluorescence dynamics within various body segments of adult scorpions. Because scorpion exoskeletons offer unique benefits to this creature, studying the arachnid’s biological processes is of interest to researchers (5). Their findings challenge previous understanding of scorpion fluorescence and offer insights into the biological significance of this phenomenon (5).

References

(1) Workman, Jr., J.; AI-Based Neural Networks Revolutionize Infrared Spectra Analysis. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/ai-based-neural-networks-revolutionize-infrared-spectra-analysis (accessed 2024-05-15).

(2) Hroncich, C.; Workman, Jr., J. How Google is Using Optical Sensors in its Wearable Technology. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/how-google-is-using-optical-sensors-in-its-wearable-technology (accessed 2024-05-15).

(3) Workman, Jr., J. Deep Learning Advances Gas Quantification Analysis in Near-Infrared Dual-Comb Spectroscopy. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/deep-learning-advances-gas-quantification-analysis-in-near-infrared-dual-comb-spectroscopy (accessed 2024-05-15).

(4) Qi, H.; Li, H.; Wang, Q.; Li, H.; Zhang, X. Optical Constants of Mixed Crude Oil in Visible Waveband Based on the Double-Thickness Transmittance Method. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/optical-constants-of-mixed-crude-oil-in-visible-waveband-based-on-the-double-thickness-transmittance-method (accessed 2024-05-16).

(5) Wetzel, W. Unveiling the Mysteries of Scorpion Fluorescence: Insights from Ultraviolet Excitation. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/unveiling-the-mysteries-of-scorpion-fluorescence-insights-from-ultraviolet-excitation (accessed 2024-05-16).