Hyperspectral Imaging as an Intelligent Nondestructive Food Quality Assessment Tool

Authors: Komalpreet Kaur; Shubhra Shekhar; Dhritiman Saha; Kamlesh Prasad

Book: Transforming Food Analysis and Control: Non-Destructive Technologies and Innovations

Editors: K. Prasad, S. Shekhar, M. K. Paswan, and R. Ahuja

Publisher: SLIET Longowal

Publication year: 2026

Publication date: 2026-07-20

ISBN: 978-81-990304-1-1 (e-book); 978-81-990304-0-4 (hardback)

Chapter pages: 213–250

DOI: To be added after DOI registration.

Abstract

Ensuring food safety and quality is a worldwide issue that demands swift, dependable, and non-invasive assessment techniques. Traditional analytical methods like chromatography and mass spectrometry, while precise, are destructive, labour intensive, and not ideal for real-time industrial use. Hyperspectral Imaging (HSI), which combines imaging and spectroscopy, has emerged as a promising optical sensing method that captures both spatial and spectral data across numerous adjacent wavelength bands. This dual capability enables in-depth analysis of the chemical composition, physical structure, and contaminant identification in food matrices. This paper presents the core principles, components, and data-acquisition techniques of HSI, highlighting its benefits over conventional RGB, NIR, and multispectral imaging systems. Additionally, it delves into recent uses of HSI in assessing food quality, such as detecting adulteration, estimating moisture and protein levels, and analysing microbial contamination. HSI, combined with machine learning and deep learning, has amplified its potential for automated, real-time quality inspection and classification. Despite its benefits, industrial adoption is hindered by high system costs, data redundancy, and limited algorithmic generalisation. Future developments should aim at compact hardware design, effective spectral data reduction, and interpretable AI models for scalable implementation. In summary, HSI is a revolutionary, non-destructive approach to intelligent monitoring systems, promoting improved safety, authenticity, and sustainability in contemporary food production.

Keywords: Hyperspectral Imaging; Non-Destructive Testing; Food Quality; Machine Vision System; Deep Learning; Artificial Intelligence

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