Light Regulation of Carotenoid Biosynthesis in the Peel of Mandarin and Sweet Orange Fruits.

Carotenoids are the pigments responsible for the coloration of the peel and pulp of Citrus fruits. Light is one of the major environmental factors influencing coloration and carotenoid content and composition of fleshy fruits and therefore their commercial and nutritional quality. Agronomical observations indicate that citrus fruits exposed to sunlight develop a brighter peel coloration than shaded fruit inside the tree canopy. In the present study, the effect of light deprivation on carotenoid profile, and in the expression of genes of carotenoid metabolism and their precursors have been analyzed in fruits of Clemenules mandarin (Citrus clementine) and Navelina orange (Citrus sinensis). Fruit shading accelerated peel degreening, chlorophyll degradation, and reduced chloroplastic-type carotenoids. Time-course shading experiments revealed that the stage of fruit ripening appears to be determinant for the effect of darkness in carotenoid biosynthesis. Fruit shading produced a down-regulation of the expression of key carotenoids biosynthetic genes (PSY, PDS, ZDS1, LCY2a, LCY2b, and CHX). However, expression of MEP pathway genes (DXS, HDR1, and GGPPS1) and the carotenoid cleavage dioxygenase, CCD4b1, responsible of the formation of the apocarotenoid ß-citraurin, were not substantially affected by dark-grown conditions. The content of abscisic acid (ABA), an end product of the carotenoid pathway, was not affected by the light regime, suggesting that effect of shading on the precursor's pool is not sufficient to impair ABA synthesis. A moderate increase in total carotenoid and in the expression of biosynthetic genes was observed in mature dark-grown mandarin and orange fruits. Collectively, results suggest that light stimulates carotenoid biosynthesis in the peel of citrus fruits but a light-independent regulation may also operate.

Rapid methods for extracting and quantifying phenolic compounds in citrus rinds.

Conventional methods for extracting and quantifying phenolic compounds in citrus rinds are time consuming. Rapid methods for extracting and quantifying phenolic compounds were developed by comparing three extraction solvent combinations (80:20 v/v ethanol:H2O; 70:29.5:0.5 v/v/v methanol:H2O:HCl; and 50:50 v/v dimethyl sulfoxide (DMSO):methanol) for effectiveness. Freeze-dried, rind powder was extracted in an ultrasonic water bath at 35°C for 10, 20, and 30 min. Phenolic compound quantification was done with a high-performance liquid chromatography (HPLC) equipped with diode array detector. Extracting with methanol:H2O:HCl for 30 min resulted in the optimum yield of targeted phenolic acids. Seven phenolic acids and three flavanone glycosides (FGs) were quantified. The dominant phenolic compound was hesperidin, with concentrations ranging from 7500 to 32,000 µg/g DW. The highest yield of FGs was observed in samples extracted, using DMSO:methanol for 10 min. Compared to other extraction methods, methanol:H2O:HCl was efficient in optimum extraction of phenolic acids. The limit of detection and quantification for all analytes were small, ranging from 1.35 to 5.02 and 4.51 to 16.72 µg/g DW, respectively, demonstrating HPLC quantification method sensitivity. The extraction and quantification methods developed in this study are faster and more efficient. Where speed and effectiveness are required, these methods are recommended.

The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest behaviour of 'Nules Clementine' mandarin fruit.

The use of chemometrics to analyse Vis/NIRS signal collected from intact 'Nules Clementine' mandarin fruit at harvest, to predict the rind physico-chemical profile after eight weeks postharvest was explored. Vis/NIRS signals of 150 fruit were obtained immediately after harvest. Reference data on the rind were obtained after eight-week storage, including colour index (CI), rind dry matter (DM), and concentration of sugars. Partial least squares (PLS) regression was applied to develop models. Principal component analysis (PCA) followed by PLS-discriminant analysis (PLS-DA) were used to classify fruit according to canopy position. Optimal PLS model performances for DM, sucrose, glucose and fructose were obtained using multiple scatter correction pre-processing, showing respective residual predictive deviation (RPD) of 3.39, 1.75, 2.19 and 3.08. Clusters of sample distribution in the PCA and PLS-DA models based on canopy position were obtained. The results demonstrated the potential applications of Vis/NIRS to predict postharvest behaviour of mandarin fruit.