Assessment of rind quality of ‘Nules Clementine’ mandarin fruit during postharvest storage: 2. Robust Vis/NIRS PLS models for prediction of physico-chemical attributes

• Published in: Scientia horticulturae Vol. 165; pp. 421 - 432
• Main Authors: Magwaza, Lembe Samukelo, Opara, Umezuruike Linus, Cronje, Paul J.R., Landahl, Sandra, Nieuwoudt, Hélène H., Mouazen, Abdul M., Nicolaï, Bart M., Terry, Leon A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 22.01.2014
• Subjects: Canopy position; Carbohydrates; Non-destructive; Physiological disorder; Rind breakdown; Spiking; Vis/NIRS; Vis/NIRS; Carbohydrates; Rind breakdown; Non-destructive; Spiking; Physiological disorder; Canopy position
• ISSN: 0304-4238; 1879-1018
• DOI: 10.1016/j.scienta.2013.09.050

•Vis/NIRS models for prediction of mandarin postharvest rind quality were validated.•Rind carbohydrates associated with rind breakdown disorder were predicted.•Spiking of models with few samples from prediction set improved model performance.•The robustness of PLS models across two seasons and four orchards was demonstrated.•Models developed using collective data of all orchards and seasons performed better. The robustness of visible to near infrared spectroscopy (Vis/NIRS) models is a crucial requirement for assessment of fruit quality parameters. This study was conducted to investigate the performance of partial least squares (PLS) models developed with data from individual orchard locations with those developed from combined orchard locations and two seasons in predicting postharvest rind physico-chemical properties related to susceptibility of ‘Nules Clementine’ mandarins to progressive rind breakdown disorder (RBD). Vis/NIRS signals were acquired on freshly harvested fruit and reference physico-chemical properties were measured after 8 weeks of storage at 8±0.5°C, including incidence of RBD, rind hue angle (h°), rind dry matter, and non-structural carbohydrates (sucrose, glucose, fructose, total carbohydrates) concentration. PLS regression with leave-one-out full cross validation was used to develop calibration models of studied parameters. The models were externally validated using data from a different location or season not included in the calibration. Prediction performance of PLS models of a single orchard location validated with data of an independent location was low but encouraging, with residual predictive deviation (RPD) values ranging from 0.95 to 1.58 for fructose models. The fructose calibration models developed using two combined orchard locations had higher prediction accuracy (RPD ranging between 1.32 and 1.97) than models of one orchard location. The performance of models developed from three orchard locations in 2012 to predict parameters of 2011 was better (RPD for fructose model=2.50) than models developed from individual orchards and two combined orchards. Results from this study demonstrated that Vis/NIRS models offer considerable robustness for non-invasive prediction of rind quality attributes which might predispose ‘Nules Clementine’ mandarin fruit to RBD.