A novel approach to upgrade infrared spectroscopy calibrations for nutritional contents in fresh grapevine organs

Infrared spectroscopy is widely used in viticulture. Spectroscopy correlates spectral properties with reference data to obtain calibrations later used to predict the analyte content in new samples with a single spectral measurement. However, the main limitation lies in generating the reference data required to build robust prediction calibrations. This study proposes a data generation strategy to obtain reference data for larger spectral datasets. A reduced sample set was used to develop initial calibrations. These initial calibrations were subsequently applied to predict the reference data in larger spectral datasets. Calibrations for nitrogen, carbon, and hydrogen content were then attempted using the larger generated datasets. The initial nitrogen calibrations per organ showed coefficients of determination in validation (R2val) between 80.08 and 89.93%. The root mean square errors of prediction (RMSEP) ranged from 0.10 to 0.18% dry matter, and the residual predictive deviations in validation (RPD) were between 2.27 and 3.19. The larger predicted datasets showed improved prediction accuracy with coefficients of determination in validation values above 91.79%, root mean square errors of prediction below 0.14% dry matter, and residual predictive deviations in validation above 3.49. The carbon calibrations showed, on average, a 20% increase in the coefficient of determination in validation decreased root mean square errors of prediction and increased residual predictive deviations in validation. The hydrogen calibrations showed a similar increase in prediction accuracy. The results showed the suitability of using reduced sample sets to generate the reference data of larger datasets capable of yielding more accurate prediction calibrations.