Data Analysis Algorithms in Hyperspectral Imaging for Nondestructive Quality Assessment of Citrus Fruits: A Review

Hyperspectral imaging (HSI), which integrates spatial and continuous spectral information, has shown considerable potential for nondestructive citrus quality assessment. However, HSI data inherently suffer from high dimensionality, strong inter-band correlation, and substantial redundancy. Consequently, extracting reliable quality evaluation results from such complex spectral information relies heavily on effective algorithm design. This review summarizes recent advances in data-analysis algorithms for HSI-based nondestructive citrus quality assessment, focusing on three major tasks: maturity assessment; disease, pest damage, and bruising detection; and internal physicochemical and nutritional attribute prediction. Representative approaches, including chemometrics, machine learning, deep learning, transfer learning, and multimodal fusion, are reviewed and compared from the perspective of task-specific challenges. Existing studies indicate that maturity assessment has developed relatively mature algorithmic pathways, whereas disease and bruise detection require effective enhancement of weak abnormal signals and robust suppression of environmental and structural interference. Internal physicochemical and nutritional attribute prediction, especially for titratable acidity (TA) and vitamin C (VC), remains challenging because of weak spectral responses, complex nonlinear relationships, and limited cross-scenario stability. Future research should emphasize standardized datasets, informative wavelength selection, lightweight model design, interpretable learning, and multi-task collaborative modeling. This review provides a systematic reference for algorithm design and system optimization in HSI-based citrus quality assessment.