Abstract Near-infrared spectroscopy (NIR) has emerged as a powerful non-destructive analytical technique for quality assessment, authentication, and process monitoring in the sugar industry. Given sugar’s critical role in determining the physicochemical and sensory properties of confectionery products, rapid and reliable methods for monitoring sugar composition and quality are essential. Despite the widespread use of NIR, existing literature lacks a comprehensive synthesis that addresses the specific challenges of both the sugar beet and sugarcane industries simultaneously. This review provides a comprehensive and critical overview of recent advances in the application of NIR spectroscopy for sugar analysis. Fundamental principles of NIR spectroscopy, spectral preprocessing strategies, and chemometric approaches including partial least squares regression, classification methods, and machine learning-based models are discussed in relation to their performance in predicting key quality parameters. In addition, applications of NIR for sugar authentication, including differentiation between organic and conventional sugars and detection of adulteration, are reviewed. Emerging trends such as portable and handheld NIR devices, integration with artificial intelligence and machine learning, hyperspectral and multispectral imaging, and real-time process monitoring in industrial environments are highlighted. By bridging the gap between laboratory-scale research and industrial-scale implementation of AI-driven portable technologies, this review offers a unique perspective on the digital transformation of sugar quality control. Despite significant progress, challenges related to spectral interferences, sample variability, calibration robustness, and model transferability remain major barriers to large-scale industrial implementation. By synthesizing recent findings and identifying current limitations and future research needs, this review aims to support the broader adoption of NIR spectroscopy as a rapid, cost-effective, and reliable tool for sugar quality assessment and process optimization.
Eriklioglu et al. (Sun,) studied this question.