Cross‐regional and temporal generalizability of portable visible spectroscopy for olive maturity classification
David Mojaravscki, Paulo S Graziano MagalhãesAbstract
BACKGROUND
Olive maturity governs lot destination and the yield–quality trade‐off in olive oil production, yet it is still monitored by subjective visual scoring of the Jaén index. Spectroscopic alternatives have relied mainly on near‐infrared instruments, with scarce evidence from Brazilian orchards and almost no evaluation of spatial or temporal transfer. The preserny study investigated whether a portable visible spectrophotometer (400–700 nm, 31 wavelengths) combined with CIE L * a * b * colorimetry and machine learning can recover per‐fruit ripening stage (six classes) with encouraging robustness across the evaluated Brazilian orchards.
RESULTS
A dataset of 7058 Arbequina olives from three orchards in Rio Grande do Sul and São Paulo across two harvest seasons (2023–2024) was classified by 10 algorithms under 15 preprocessing hypotheses and five validation protocols of increasing rigour. Within‐dataset accuracy reached 99.06% (Extra Trees, = 0.986), whereas leave‐one‐orchard‐out validation reached 95.74% (SVM‐RBF, = 0.935), geographic transfer between states 96.60% and the conservative inter‐annual direction 93.98%. Ordinal metrics (quadratic weighted kappa ≥ 0.982; accuracy within ± 1 class 99.7%) showed that residual errors were almost exclusively single‐step. The 530–680 nm region carried most of the discriminative information, consistent with chlorophyll‐to‐anthocyanin pigment dynamics.
CONCLUSION
Visible‐only portable spectroscopy classified per‐fruit olive maturity with high accuracy under the evaluated conditions, and quantified a deployment gap of 3–5 percentage points between within‐dataset cross‐validation and cross‐region or inter‐annual hold‐outs, comprising a gap that random cross‐validation alone conceals. Because errors are confined to adjacent maturity stages, the approach suggests potential for future automated lot‐level maturity index estimation in post‐harvest quality control of extra virgin olive oil. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.