Evaluation of NIRS portable sensors for the determination of the physicochemical quality and maturity of intact mangoes

• NIRS technology provides a reliable tool to support harvest decisions. • Portable NIRS sensors evaluated for in situ assessment of mango quality and maturity. • SSC was accurately predicted to determine the optimal mango harvest time. • Pre-calibrated NIRS quality models may not be universally applicable. Determining the on-tree maturity stage of mango fruits is essential for optimising harvest timing and maximising postharvest quality. This study investigates the feasibility of Near Infrared Spectroscopy (NIRS) as a reliable technology for establishing the optimal harvest time of mangoes. To achieve this, two portable near infrared (NIR) spectrophotometers —the MicroNIR™ Pro 1700 and the pre-calibrated F-751 Mango Quality Meter— with distinct technical specifications were systematically compared for in situ , on-tree analysis, a novel approach under Spanish production conditions. A total of 210 mangoes from the ‘Osteen’ and ‘Keitt’ cultivars were analysed. Predictive NIRS models were developed using the MicroNIR™ Pro 1700 to estimate skin colour, flesh colour, pulp firmness, dry matter (DM), and soluble solids content (SSC), employing modified partial least squares (MPLS) regression and various spectral pre-treatments. The F-751, with commercial calibrations based on other mango cultivars, was used solely to predict DM and SSC. The residual predictive deviation for cross-validation (RPD cv ) values for the combined ‘Osteen’ and ‘Keitt’ sample set confirmed the efficacy of the MicroNIR™ in assessing flesh colour (L*) (1.83), pulp firmness (1.80), DM (1.55), and SSC (3.42). These findings confirm the viability of using NIRS technology as a reliable decision-support tool for monitoring the on-tree evolution of mango internal quality and determining the optimal harvest time. However, the routine implementation of the F-751 pre-calibrated models proved unsuitable for accurate predictions when applied to Spanish-grown mangoes. Therefore, this work addresses the limitations of generic, pre-calibrated models and provides practical recommendations for enhancing the reliability of commercial devices.