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- Spatiotemporal modelling of the quality and ripening of two cultivars of "Algarve Citrus" orchards at different edaphoclimatic conditionsPublication . Cavaco, Ana M.; Cruz, Sandra P.; Antunes, M. Dulce; Guerra, Rui; Pires, Rosa; Afonso, Andreia M.; Brazio, António; Silva, Leonardo; Lucas, Marcia Rosendo; Daniel, Mariana; Panagopoulos, ThomasAlgarve Citrus are non-climacteric Protected Geographical Indication (PGI) commodities. They are harvested with minimal levels of juice content (>35 %), soluble solids content (SSC) (>10 %) and maturation index (MI) (>8), as required by the respective PGI normative reference. These internal quality attributes (IQA) are usually determined in small samples of fruit collected from the orchards close to harvest. This study aimed to use geostatistics to help predict the optimal harvest date (OHD) of two sweet orange (Citrus sinensis (L.) Osbeck) cultivars, namely, 'Newhall', and 'Valencia Late', at two different edaphoclimatic conditions observed in the locations of Quarteira, at the coast, and Paderne, near a mountainous area. Two orchards of 0.5-0.7 ha per cultivar were chosen and a total of 25 trees were georeferenced within each orchard, comprising 100 sampling points/trees. Firmness, juice content, SSC and MI of fruit were determined through time. In general, the fruit grown in Quarteira showed higher SSC and MI and lower firmness values, ripening two months earlier than those grown in Paderne, although the full effect of the various edaphoclimatic factors on these results are not fully understood. However, geospatial modelling of ripening has shown a large variability within the orchards, with some IQA evolution patterns observed in some orchards and/or cultivars but not in the others. Specifically, 1) a negative correlation between the firmness and MI spatial patterns; 2) a variable decay rate of firmness, much faster in Paderne for 'Valencia Late'; 3) local minima in juice content, below 35 %, observed in restricted spatial areas and in specific time periods, and which were clearer in 'Newhall'. These local variations highlight the need for an optimized management based on geospatial modelling. For example, the variable decay rate of firmness must be taken into account during fruit harvest and postharvest handling. On the other side, the observation of localized plots with juice content below 35 % must be contextualized in the broader picture of the entire orchard which, in the present study, always had consistent temporal average level above 35 %. This study has provided evidence that fruit ripening variability should be considered in the site-specific orchard management of citrus to optimize their harvest date.
- Ripening assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) on tree by SW-NIR reflectance spectroscopy-based calibration modelsPublication . Pires, Rosa; Guerra, Rui Manuel Farinha das Neves; Cruz, Sandra; Antunes, MDC; Brazio, António; Afonso, Andreia M.; Daniel, Mariana; Panagopoulos, Thomas; Gonçalves, Isabel; Cavaco, Ana M.The aim of this study was the non-destructive assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) ripening, based on the prediction of internal quality attributes (IQA) by short-wave near-infrared reflectance spectroscopy (SW-NIRS) calibration models. Spectra from fruit of 50 trees located in two different orchards, were acquired on tree using a customized portable visible near-infrared (vis-NIR) system. Partial least squares (PLS) was used to build the various IQA calibration models. The models were tested through internal validation (IV) and external validation (EV). Generally, the IV results were always superior to those of EV: regarding IV, a high regression coefficient (R2) and low root mean square error of prediction (RMSEP) were achieved, revealing a good predictive performance for juice pH (R2 = 0.80; RMSEP = 0.10; SDR = 2.23), soluble solids content (SSC) (R2 = 0.79; RMSEP = 0.75 %; SDR = 2.27), titratable acidity (TA) (R2 = 0.73; RMSEP = 0.24 % citric acid; SDR = 1.94) and the maturation index (MI) (R2 = 0.80; RMSEP = 1.38; SDR = 2.2). The best EV predictions were obtained for TA (R2 = 0.69; RMSEP = 0.38 % citric acid; SDR = 1.24), and MI (R2 = 0.69; RMSEP = 2.07; SDR = 1.49). Calibration models for glucose, fructose and sucrose showed medium-coarse predictions for both validation strategies. A detailed investigation of MI models was performed, to understand the causes of their poor EV results. In the context of EV, model updating strategies were explored by using some validation samples to improve the calibration model. The methods of bias correction and spiking were tested, showing a clear improvement in the predictions.