Best of the Week: Chemometrics in the Age of AI, Vibrational Spectroscopy Trends

This week, Spectroscopy published a variety of articles highlighting recent studies in several application areas. Key techniques highlighted in these articles include Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), near-infrared (NIR) spectroscopy, and tip-enhanced Raman spectroscopy (TERS). Happy reading!

Chemometrics in the AI Age: Bridging Tradition and Machine Intelligence

Chemometrics has long used tools now labeled as artificial intelligence (AI) and machine learning (ML), emphasizing the importance of interpretability in models. In a recent interview with Spectroscopy, Paolo Oliveri of the University of Genoa discusses how balancing AI enthusiasm with chemometric traditions is crucial for understanding experimental systems and data quality (1). He suggests that artificial data augmentation is a potential strategy to address the need for large data sets in deep learning applications (1). To accomplish this objective, it will take collaboration between chemometricians and data scientists can lead to optimized algorithms for specific chemical problems (1).

The Most Important Vibrational Spectroscopy Trends of 2025

Vibrational spectroscopy is undergoing rapid transformation driven by advances in artificial intelligence (AI), miniaturization, nanofabrication, and imaging. AI and machine learning (ML) now serve as core infrastructure for preprocessing, classification, calibration, anomaly detection, and even molecular structure prediction from spectra. Portable near-infrared (NIR) and Raman instruments are expanding spectroscopy’s reach beyond laboratories, though they require strong chemometric support. Surface-enhanced methods such as surface-enhanced Raman spectroscopy (SERS), surface-enhanced infrared absorption (SEIRA), and tip-enhanced Raman spectroscopy (TERS) are becoming more powerful and reproducible, especially when paired with ML. Hyperspectral imaging and multimodal data fusion enable detailed chemical mapping of complex materials. As applications broaden, reliability demands—calibration transfer, uncertainty quantification, and model lifecycle management—are redefining best practices.

Understanding the Microstructural and Mechanical Evolution of Semi-Crystalline Polyimide Films

A recent study published in the journal Polymer Degradation and Stability examined how high-temperature oxidative aging alters the structure and mechanics of semi-crystalline polyimide films (3). Lead author Maryam Shakiba and her graduate student Santiago Marin used X-ray diffraction (XRD), Raman spectroscopy, and tensile testing to show that aging increases crystallinity, drives molecular changes, and causes stiffening and embrittlement. They developed a viscoelastic–viscoplastic constitutive model that predicts stress–strain behavior using degradation indicators from XRD and Raman data, eliminating the need for recalibration at each aging stage (3). Their framework enables physics-based, experimentally informed predictions of long-term polymer performance in extreme aerospace and electronics environments (3).

Identifying Tree Species in the Amazon with Spectroscopy

A new study in Global Ecology and Conservation shows that full-range spectroscopy can accurately identify Amazonian tree species despite the region’s extreme biodiversity and limited taxonomic expertise (4). Researchers from the National Institute for Amazonian Research (INPA, Brazil) analyzed spectral signatures from fresh leaves, inner bark, and outer bark across 26 species in three major ecosystems, achieving up to 98% accuracy with leaves (4). The spectral models proved robust across habitats and even in external validations over 300 km away (4). Although the high-performance ASD FieldSpec 4 demonstrates strong potential, its size and cost limit field use, underscoring the need for portable, affordable spectrometers to scale biodiversity monitoring and conservation efforts.

Deep Learning Model Sharpens Maize Nitrogen and Chlorophyll Monitoring

A recently published study from Heilongjiang Bayi Agricultural University introduces a high-accuracy, explainable deep learning model that significantly improves nondestructive nitrogen and chlorophyll estimation in maize canopies using hyperspectral data. In the study, the researchers tested a new model, Convolutional Neural Networks-Gated Recurrent Units-Convolutional Block Attention Module (CNN-GRU-CBAM), demonstrating how it can outperform traditional methods and achieve high R² values and low root mean squared error (RMSE) for nitrogen and chlorophyll (5). The findings of this study unveil how explainable AI and data-driven strategies can help improve agricultural monitoring processes (5).

References

1. Chasse, J. Chemometrics in the AI Age: Bridging Tradition and Machine Intelligence. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/chemometrics-in-the-ai-age-bridging-tradition-and-machine-intelligence (accessed 2025-12-11).
2. Workman, Jr., J. The Most Important Vibrational Spectroscopy Trends of 2025. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/the-most-important-vibrational-spectroscopy-trends-of-2025 (accessed 2025-12-11).
3. Wetzel, W. Understanding the Microstructural and Mechanical Evolution of Semi-Crystalline Polyimide Films. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/understanding-the-microstructural-and-mechanical-evolution-of-semi-crystalline-polyimide-films (accessed 2025-12-11).
4. Wetzel, W. Identifying Tree Species in the Amazon with Spectroscopy. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/identifying-tree-species-in-the-amazon-with-spectroscopy (accessed 2025-12-11).
5. Wetzel, W. Deep Learning Model Sharpens Maize Nitrogen and Chlorophyll Monitoring. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/deep-learning-model-sharpens-maize-nitrogen-and-chlorophyll-monitoring (accessed 2025-12-11).