Applications of Near-Infrared Spectroscopy in Pear Quality Assessment: A Comprehensive Review

Abstract

Near-Infrared (NIR) Spectroscopy has become a pivotal non-destructive technology in agricultural production and fruit quality assessment, particularly in the pear industry. This review comprehensively examines recent advancements in NIR Spectroscopy for evaluating critical quality parameters such as Soluble Solids Content (SSC), firmness, and moisture content, which are essential for determining optimal harvest timing, improving storage practices, and extending shelf life. The evolution of portable NIR spectrometers is explored, emphasizing their increasing suitability for real-time, in-field applications while addressing challenges such as calibration variability and environmental factors. Additionally, the integration of NIR spectroscopy with imaging technologies like Hyperspectral Imaging (HSI) and Multispectral Imaging (MSI) is highlighted for their potential to enhance the precision and scope of fruit quality assessment. The review further considers incorporating low-cost technologies, such as electronic noses and machine vision, with NIR spectroscopy to create cost-effective and robust multi-sensor platforms. By synthesizing these developments, the review identifies gaps in current methodologies, such as limited model generalization across diverse pear varieties, and proposes solutions like data fusion and advanced machine learning approaches. These strategies are poised to improve model adaptability and accuracy, ensuring NIR spectroscopy remains at the forefront of pear quality assessment. Future research directions emphasize the need for integrating cutting-edge technologies with NIR spectroscopy to achieve more sustainable and efficient agricultural practices.