Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

A 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.

Researchers 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.

Food authentication is becoming increasingly important. In this article, the editors of Spectroscopy outline the methodology used in a recent study used to analyze paprika.

By providing automated tools and guidance, an ECS would aim to streamline the calibration process, improve calibration transfer, enhance operator efficiency, and improve the overall consistency and reliability of analytical results produced using advanced chemometrics and machine earning techniques.

A recent study from Sichuan, China, leveraged a few spectroscopic techniques with chemometrics to analyze key components of the beer brewing process.

Here, we spotlight a few recent studies that explored the integration of artificial intelligence (AI) with spectroscopic techniques.

Artificial 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.

Researchers 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.

Harun 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.

A recent study from Central South University in China examined how to assess cobalt content in soil.

Korean scientists recently tested a new light detection and ranging (LiDAR)-based system for improving autonomous recognition systems.

The emergence of artificial intelligence (AI) has revolutionized spectroscopic techniques, including surface-enhanced Raman spectroscopy (SERS).

A recent study examined two chemometric methods for generating prediction rules.

Henan University scientists recently developed a new deep learning-based prediction model for classifying nonclassical secreted proteins.

Scientists at Saint Petersburg Electrotechnical University introduced a new method for leveraging Fourier Transform Infrared Reflection Anisotropy Spectroscopy for the analysis of semiconductors.

Researchers 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.

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. 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.

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.

Two researchers hailing from Wageningen University and the Norwegian University of Life Sciences collaborated on a new method that improves on existing multilinear predictive modeling methods.

Here are the top five articles that the editors of Spectroscopy published this week.

Scientists are using Raman spectroscopy and AI technology to streamline the pathological classification process.

A recent study offers an inside look as to how chemometric modeling is directly impacting greener analytical solutions.

Experts from BASF, the French National Institute for Agriculture, Food, and Environment, and other institutions discussed how their teams are using chemometrics to improve their work.

In a recent journal article, scientists wrote about on the impact machine learning algorithms have in measuring water quality, and how this technology can be evolved further.

Here are the top five articles that the editors of Spectroscopy published this week.