Effects of Harvest Timing on Navel Orange Quality During Storage Using Hand-Held NIRS

This study investigated the influence of harvest timing on the quality of navel oranges by the use of hand-held near-infrared spectroscopy (NIRS) device. ‘Newhall’ navel oranges were harvested in seven batches from 200 to 249 days after full bloom (DAFB). The samples were stored under cold storage (5 °C) and at room temperature (25 °C), respectively. Harvest-stage SSC and weight loss during storage were analyzed, and SSC-related trends in stored fruit were further evaluated at the 49-day endpoint. The results indicated that cold storage helped reduce weight loss in navel oranges. SSC exhibited a trend of increasing first and then decreasing with harvest date, reaching a peak at DAFB of 228. The NIRS model corrected by external parameter orthogonalization (EPO) achieved the best prediction performance for SSC, with R2p = 0.60, RMSEP = 1.38%, and RPD = 1.58 under room temperature, and R2p = 0.49, RMSEP = 1.46%, and RPD = 1.40 under cold storage. The EPO method reduced temperature-related interference and improved the robustness of SSC prediction under different storage conditions. Separately, based on measured postharvest weight loss and SSC changes, navel oranges harvested at 214–228 DAFB showed relatively better storage behavior under 5 °C conditions, with an estimated storage duration of approximately 62 days. Early-harvested fruit exhibited stronger storability but lower flavor quality, whereas late-harvested fruit had better eating quality but shorter storage life. It should be noted that these results correspond to different optimization objectives, including storability-oriented and quality-balanced criteria. This study provides preliminary evidence for the potential application of handheld NIRS in navel orange quality monitoring during storage, and offers an exploratory framework rather than a decision-making system for investigating the relationship between harvest timing and storage conditions. However, its predictive performance is moderate and further validation is required before practical application in production or decision-making systems.