Growing consumer demand for value-added foods has accelerated the emergence of fortified products enriched with biologically active compounds. Fruit juices, commonly fortified with vitamins, minerals or plant-based extracts, represent a major segment of this market. However, the assurance of authenticity and compositional integrity of such products remains challenging, particularly in the absence of rapid, non-destructive analytical methods. The joint Austria–Hungary project aims to establish spectroscopy-assisted quality control tools for fortified fruit juices, combining near-infrared (NIR), infrared (IR) and Raman spectroscopy with advanced chemometrics. This multidisciplinary collaboration seeks to deliver reliable, non-invasive analytical solutions for industry, promoting consumer protection, reducing food fraud and supporting sustainable waste recycling through the valorisation of plant-based by-products.
This interview is conducted on the occasion of Professor Ana Garrido-Varo’s receipt of the Karl Norris Award 2025, in recognition of her distinguished career and outstanding contributions to the field of Near Infrared Spectroscopy.
Lactate levels in blood serve as a key physiological indicator of stress and welfare in animals during slaughterhouse processes. Elevated lactate levels lead to an accumulation of lactic acid in the muscle tissue. This rapid lactic acid production lowers the muscle pH, potentially resulting in undesirable meat quality traits such as pale, soft and exudative (PSE) meat. Optical spectroscopic techniques offer a potential solution for measuring blood lactate levels in real-time as a process analytical technology (PAT) tool in slaughterhouses. This article demonstrates the potential of near-infrared (NIR) and Raman spectroscopy for measuring lactate levels in animal blood after exsanguination. All experiments were carried in real-world slaughterhouse conditions. Furthermore, regression modelling was performed to relate spectra with reference lactate values. The results demonstrated that both and NIR and Raman spectroscopy can predict lactate content in blood in slaughterhouse conditions. Furthermore, the relation of lactate values and final meat quality was also evaluated. The findings from the study can be used to develop PAT systems for continuous blood measurement in slaughterhouse conditions.
The utilization of infrared (IR) spectroscopy, particularly near-infrared (NIR) spectroscopy, has grown in South America in recent years, as demonstrated by the wide range of recent applications. While NIR spectroscopy is gaining traction in many fields, it is not yet a widely adopted technology in South America as in other regions of the world due to the cost of instrumentation as well as the lack of critical mass and training (e.g. university level) in both spectroscopy and chemometrics. Efforts have been made by different research groups and universities in Argentina, Brazil, and Chile. However, small countries like Uruguay did not have the opportunity to access to formal training due to many reasons. Consequently, the first seminar and workshop in Spanish on the application of NIR spectroscopy and chemometrics was organised by the Faculty of Agronomy (Montevideo, Uruguay) in May 2025.
This report summarizes the near-infrared (NIR) spectroscopy session IR04, “Near-Infrared Spectroscopy: Innovations and Applications,” held at the SciX 2025 conference (52nd Annual Conference of FACSS) in Covington, Kentucky, USA. Within the broader focus of SciX on analytical chemistry and spectroscopy, the session underlined the established role of NIR as a versatile tool for quantitative and diagnostic measurements. Presentations covered a spectrum of topics including new instrument architectures, advances in chemometric modeling and data handling, and application-driven case studies from environmental, industrial, and pharmaceutical contexts. Particular emphasis was placed on calibration strategies, model performance, and interpretability, as well as on the integration of NIR data with other spectroscopic information to build more comprehensive analytical frameworks. Together, the contributions provided an overview of current trends in NIR spectroscopy, illustrating how methodological developments and best practices in data analytics are extending the scope of NIR across diverse molecular systems and sample types.
Portable spectroscopic technologies are increasingly transforming analytical science by enabling reliable, on-site data acquisition in fields ranging from agriculture and food quality control to medicine, forensics, energy, and even astrophysics. This article presents an overview of the opportunities and challenges arising from the miniaturization of near-infrared (NIR) and Raman devices, highlights selected applications, and outlines the global vision of “spectroscopy without borders.” By bridging the gap between benchtop accuracy and field accessibility, portable instruments empower researchers and practitioners to address urgent societal challenges, particularly in the context of global population growth, food security, and sustainability. This contribution is based on an invited plenary lecture presented at the XLIV—Colloquium Spectroscopicum Internationale—CSI conference in Ulm, Germany, July 27–31, 2025.
For more than four decades, Yukihiro Ozaki has advanced molecular spectroscopy across an unusually wide spectral span, from far-ultraviolet (FUV) to terahertz/far-infrared (FIR), with deep contributions in Raman (including SERS/TERS), NIR spectroscopy, and ATR-FUV spectroscopy. His work couples fundamental theory and instrumentation with impactful applications, notably pioneering medical Raman studies of disease processes, developing ATR-FUV methods that opened access to σ-electron chemistry, and expanding anharmonic quantum-chemical modeling for IR/NIR interpretation. These efforts, together with innovations in NIR imaging and enhanced NIR via surface plasmon resonance, have influenced physical and analytical chemistry, nanomaterials, and bioscience. In recognition of these achievements, Ozaki received the 2025 Ellis R. Lippincott Award for lifetime accomplishments spanning Raman, NIR, surface-enhanced Raman scattering (SERS and TERS), ATR-FUV, two-dimensional correlation spectroscopy (2D-COS), and chemometrics.
This study demonstrates the application of open-source deep learning models from the computer vision domain to streamline tasks in near-infrared (NIR) hyperspectral imaging (HSI) data processing. Specifically, it demonstrates a challenging case of dry matter prediction in mango fruits under conditions typical for fruit importers and exporters, who often need to assess the quality parameters of fruit packed in boxes. NIR HSI offers a non-destructive alternative to traditional hot air oven drying for dry matter determination. However, processing HSI images of fruit boxes presents challenges due to objects touching, overlapping, or being partially hidden, which complicates traditional HSI analysis. Modern artificial intelligence (AI) approaches can simplify such HSI data analysis, and integrating AI with chemometric modeling represents a promising future direction for NIR HSI data processing.
