October 23rd 2024
Five invited speakers joined Joseph Smith, the 2024 Emerging Leader in Molecular Spectroscopy, on stage to speak about trends in hyperspectral imaging, FT-IR, surface enhanced Raman spectroscopy (SERS), and more during the conference in Raleigh.
Best of the Week: The Future of Forensic Analysis, Next-Gen Mineral Identification
September 20th 2024Top articles published this week include a preview of our upcoming “The Future of Forensic Analysis” e-book, a few select offerings from “The Future of Forensic Analysis,” and a news story about next-generation mineral identification.
Next-Gen Mineral Identification: Fusing LIBS and Raman Spectroscopy with Machine Learning
September 17th 2024A pioneering study integrates laser-induced breakdown spectroscopy (LIBS) with Raman spectroscopy (RS) and applies machine learning (ML) to achieve exceptional accuracy in mineral identification. The combined approach not only leverages the strengths of both techniques but also enhances classification precision, achieving up to 98.4% accuracy.
AI-Powered Spectroscopy Faces Hurdles in Rapid Food Analysis
September 4th 2024A recent study reveals on the challenges and limitations of AI-driven spectroscopy methods for rapid food analysis. Despite the promise of these technologies, issues like small sample sizes, misuse of advanced modeling techniques, and validation problems hinder their effectiveness. The authors suggest guidelines for improving accuracy and reliability in both research and industrial settings.
Non-Linear Memory-Based Learning Advances Soil Property Prediction Using vis-NIR Spectral Data
September 3rd 2024Researchers from Zhejiang University have developed a new non-linear memory-based learning (N-MBL) model that enhances the prediction accuracy of soil properties using visible near-infrared (vis-NIR) spectroscopy. By comparing N-MBL with traditional machine learning and local modeling methods, the study reveals its superior performance, particularly in predicting soil organic matter and total nitrogen.
Revolutionizing Analytical Chemistry: The AI Breakthrough
July 10th 2024Artificial intelligence (AI) is reshaping analytical chemistry by enhancing data analysis and optimizing experimental methods. This study explores AI's advancements, challenges, and future directions in the field, emphasizing its transformative potential and the need for ethical considerations.
LEGO Bricks: A New Standard for Evaluating Fluorescence in Raman Spectroscopy
July 1st 2024Researchers have proposed an innovative approach to tackling fluorescence interference in Raman spectroscopy by using LEGO blocks as standard samples. This new method offers a low-cost, rugged, and reproducible alternative to the complex liquid mixtures traditionally used in such studies, marking a significant advancement in the field of spectroscopic analysis.
Light and AI Unite: Raman Breakthrough in Noninvasive Lung Cancer Detection
June 26th 2024Harun Hano, Charles H. Lawrie, and Beatriz Suarez, et al. from the Department of Physics at the University of the Basque Country (UPV/EHU), in Spain; and the IKERBASQUE─Basque Foundation for Science in Spain have published a research paper in the journal ACS Omega describing the use of Raman spectroscopy with specialized data treatment for the diagnosis of lung cancer.
Innovative New Method Speeds Up Correction of ATR Infrared Spectra
May 20th 2024Researchers at the Leibniz Institute of Photonic Technology have developed a rapid method to correct infrared attenuated total reflection (ATR) infrared spectra, essential for accurate analysis in various scientific fields. By bypassing iterative processes, this approach enhances efficiency and precision.
Deep Learning Advances Gas Quantification Analysis in Near-Infrared Dual-Comb Spectroscopy
May 15th 2024Researchers 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. 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.
AI-Based Neural Networks Revolutionize Infrared Spectra Analysis
May 13th 2024A 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.