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Updated: Sep 10, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
Handheld NIR spectroscopy for real-time on-site food quality and safety monitoring
Krzysztof B Beć1, Justyna Grabska1, Christian W Huck1
1University of Innsbruck, Institute of Analytical Chemistry and Radiochemistry, Innsbruck, Austria.
Abstract:
This chapter reviews the applications and future directions of portable near-infrared (NIR) spectroscopy in food analytics, with a focus on quality control, safety monitoring, and fraud detection. Portable NIR spectrometers are essential for real-time, non-destructive analysis of food composition, and their use is rapidly expanding across various stages of the food production chain-from agriculture and processing to retail and consumer applications. The functional design of miniaturized NIR spectrometers is examined, linking the technological diversity of these sensors to their application potential in specific roles within the food sector, while discussing challenges related to thermal stability, energy efficiency, and spectral accuracy. Current trends in data analysis, including chemometrics and artificial intelligence, are also highlighted, as the successful application of portable spectroscopy heavily depends on this key aspect of the analytical process. This discussion is based on recent literature, with a focus on the last five years, and addresses the application of portable NIR spectroscopy in food quality assessment and composition analysis, food safety and contaminant detection, and food authentication and fraud prevention. The chapter concludes that portable NIR spectroscopy has significantly enhanced food analytics over the past decade, with ongoing trends likely to lead to even wider adoption in the near future. Future challenges related to ultra-miniaturization and emerging consumer-oriented spectrometers emphasize the need for robust pre-calibrated models and the development of global models for key applications. The integration of NIR spectrometers with cloud computing, IoT, and machine learning is expected to drive advancements in real-time monitoring, predictive modeling, and data processing, fitting the growing demand for improved safety, quality, and fraud detection from the farm to the fork.
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