Best of the Week: NMR Approaches, Tutorial on Biosensors, Grassland Monitoring

This week, Spectroscopy published a variety of articles highlighting recent studies in several application areas. Key techniques highlighted in these articles include nuclear magnetic resonance (NMR), Raman spectroscopy, mass spectrometry, and synchrotron-based X-ray absorption spectroscopy. Happy reading!

NMR Approaches to Understanding Ionic Liquid Behavior

In this exclusive interview from the Eastern Analytical Symposium (EAS) conference, Damodaran Krishnan Achary, director of the NMR Facility at the University of Pittsburgh, sits down with Spectroscopy to explain how advanced NMR spectroscopy uniquely reveals both the structure and dynamic behavior of ionic liquids, which are capabilities unmatched by infrared spectroscopy or X-ray crystallography (1). Achary highlights how NMR enables detailed analysis of cation–anion interactions, ion mobility, and ion pairing through diffusion and NOE experiments, even allowing theoretical conductivity predictions. Achary emphasizes the relevance of these insights for applied research areas such as carbon capture (1). His work underscores NMR’s power not only for structural elucidation but also for guiding the design of next-generation functional materials (1).

A Closer Look at Biosensors: A Practical Tutorial for Spectroscopists

This tutorial article explains the fundamentals and practical applications of biosensors, highlighting their growing importance in spectroscopy for detecting biomolecules, pathogens, metabolites, and cellular responses. By integrating a biological recognition element with an electrical, optical, or mechanical transducer, biosensors convert biochemical events into measurable signals (2). This tutorial covers the major biosensor types, including electrochemical, optical, and mechanical systems. It also illustrates how they are applied in medical diagnostics, environmental monitoring, food safety, biotechnology, and security. The article also outlines common challenges, including immobilization, calibration drift, and matrix interference, while emphasizing that mastering biosensor principles can significantly expand spectroscopic analytical capabilities (2).

What Are the Latest Advancements in Nondestructive Spectral Analysis for Cultural Heritage Conservation?

This article highlights researcher Huizhi Han’s review article, which explored how non-destructive spectroscopic and imaging technologies are transforming the study and preservation of historical paintings. Because these artworks are fragile, modern techniques, such as X-ray radiography, multispectral imaging, Raman spectroscopy, mass spectrometry, and synchrotron-based X-ray absorption spectroscopy, enable detailed chemical and structural analysis without damaging samples (3). Han emphasizes the rise of multi-analytical strategies that combine complementary tools to reveal hidden layers, restorations, pigment degradation, and artistic techniques (3). The review article also highlights some of the remaining challenges in cultural heritage analysis, including achieving micro-scale resolution without physical contact and integrating diverse spectral data sets through artificial intelligence (AI) to support predictive conservation (3).

Using Hyperspectral Remote Sensing for Smarter Grassland Monitoring

A recent study published in the journal Ecological Informatics evaluated whether hyperspectral remote sensing combined with machine learning (ML) can reliably predict grassland forage quality and biomass across different climate zones using a universal model (4). Using one of the most comprehensive global hyperspectral data sets, the researchers found that random forest regression provided the highest overall accuracy, and that forage quality was far easier to predict than biomass because of consistent plant chemical traits across ecosystems. However, no model achieved full global transferability, highlighting the need for expanded spectral databases and approaches that account for local variation to improve future grassland monitoring and land-management strategies (4).

Deep-Learning Approaches for Soil Diagnostics in Precision Agriculture

A recent team from Japan developed a deep-learning approach that uses inductively coupled plasma (ICP) spectral data to rapidly and accurately predict key soil physicochemical properties, offering a more sustainable alternative to traditional soil testing. By analyzing 1941 samples from seven countries, their model achieved strong agreement with laboratory measurements (R² > 0.9 for most parameters), demonstrating its potential to improve fertilizer management, reduce environmental impact, and lower costs for farmers (5). Although larger data sets and refined architectures are needed before large-scale adoption, this method could transform soil diagnostics, especially in developing regions where testing accessibility remains limited (5).

References

(1) Hroncich, C. NMR Approaches to Understanding Ionic Liquid Behavior. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/nmr-ionic-liquids-damodaran-krishnan-achary (accessed 2025-11-26).

(2) Workman, Jr., J. A Closer Look at Biosensors: A Practical Tutorial for Spectroscopists. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/a-closer-look-at-biosensors-a-practical-tutorial-for-spectroscopists (accessed 2025-11-26).

(3) Wetzel, W. What Are the Latest Advancements in Nondestructive Spectral Analysis for Cultural Heritage Conservation? Spectroscopy. Available at: https://www.spectroscopyonline.com/view/what-are-the-latest-advancements-in-nondestructive-spectral-analysis-for-cultural-heritage-conservation-(accessed 2025-11-26).

(4) Wetzel, W. Using Hyperspectral Remote Sensing for Smarter Grassland Monitoring. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/using-hyperspectral-remote-sensing-for-smarter-grassland-monitoring (accessed 2025-11-26).

(5) Wetzel, W. Deep-Learning Approaches for Soil Diagnostics in Precision Agriculture. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/deep-learning-approaches-for-soil-diagnostics-in-precision-agriculture (accessed 2025-11-26).