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

Abstract 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.

[2]  Eberhard Hartung,et al.  Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process , 2012, Journal of Dairy Research.

[3]  Abdul Mounem Mouazen,et al.  Effect of spiking strategy and ratio on calibration of on-line visible and near infrared soil sensor for measurement in European farms , 2013 .

[4]  Sun Xudong,et al.  Nondestructive assessment of quality of Nanfeng mandarin fruit by a portable near infrared spectroscopy , 2009 .

[5]  Ralph P. Tatam,et al.  Application of optical coherence tomography to non-destructively characterise rind breakdown disorder of ‘Nules Clementine’ mandarins , 2013 .

[6]  B. Kowalski,et al.  Multivariate instrument standardization , 1991 .

[7]  Paul J.R. Cronje,et al.  Prediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy , 2012 .

[8]  Saeid Minaei,et al.  Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges , 2012 .

[9]  J. Guthrie,et al.  Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness , 2005 .

[10]  K. Walsh,et al.  Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content , 2006 .

[11]  Hai-yan Yu,et al.  Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits , 2006, Journal of Zhejiang University SCIENCE B.

[12]  Wouter Saeys,et al.  Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy , 2005 .

[13]  B. B. Wedding,et al.  Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit , 2013 .

[14]  B. Nicolai,et al.  Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance , 2010 .

[15]  Abdul Mounem Mouazen,et al.  In situ Determination of Growing Stages and Harvest Time of Tomato (Lycopersicon Esculentum) Fruits Using Fiber-Optic Visible—Near-Infrared (Vis-NIR) Spectroscopy , 2011, Applied spectroscopy.

[16]  P. Cronjé,et al.  Fruiting position during development of 'Nules Clementine' mandarin affects the concentration of K, Mg and Ca in the flavedo , 2011 .

[17]  P. Cronjé,et al.  Postharvest rind breakdown of ‘Nules Clementine’ mandarin is influenced by ethylene application, storage temperature and storage duration , 2011 .

[18]  J. Guthrie,et al.  Robustness of NIR Calibrations for Soluble Solids in Intact Melon and Pineapple , 1998 .

[19]  K. Peiris,et al.  Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers : Implications for the development and use of NIR spectrometric techniques , 1999 .

[20]  Daniel Cozzolino,et al.  Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality , 2011 .

[21]  Josse De Baerdemaeker,et al.  Non-Destructive Evaluation: Detection of External and Internal Attributes Frequently Associated with Quality and Damage , 2014 .

[22]  A. Mouazen,et al.  Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms , 2011 .

[23]  Wouter Saeys,et al.  Application of visible and near-infrared reflectance spectroscopy (Vis/NIRS) to determine carotenoid contents in banana (Musa spp.) fruit pulp. , 2009, Journal of agricultural and food chemistry.

[24]  W. E. Morf.  Time-dependent selectivity behavior and dynamic response of silver halide membrane electrodes to interfering ions , 1983 .

[25]  K. Peiris,et al.  Near-infrared Spectrometric Method for Nondestructive Determination of Soluble Solids Content of Peaches , 1998 .

[26]  A. Peirs,et al.  Effect of biological variability on the robustness of NIR models for soluble solids content of apples , 2003 .

[27]  Annia García Pereira,et al.  Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques , 2006 .

[28]  Nuria Aleixos,et al.  Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks , 2013, Food and Bioprocess Technology.

[29]  D. L. Wetzel.  Near-Infrared Reflectance Analysis , 1983 .

[30]  T Puchert,et al.  Near-infrared chemical imaging (NIR-CI) for counterfeit drug identification--a four-stage concept with a novel approach of data processing (Linear Image Signature). , 2010, Journal of pharmaceutical and biomedical analysis.

[31]  A. Khan,et al.  Tree age and canopy position affect rind quality, fruit quality and rind nutrient content of ‘Kinnow’ mandarin (Citrus nobilis Lour × Citrus deliciosa Tenora) , 2012 .

[32]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[33]  Wouter Saeys,et al.  NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit—A Review , 2012, Food and Bioprocess Technology.

[34]  Dolores Pérez-Marín,et al.  Application of NIRS for Nondestructive Measurement of Quality Parameters in Intact Oranges During On-Tree Ripening and at Harvest , 2013, Food Analytical Methods.

[35]  L. Rodriguez-Saona,et al.  Application of NIR and MIR spectroscopy in quality control of potato chips. , 2009 .

[36]  K. Walsh,et al.  Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature , 2003, Applied spectroscopy.

[37]  Lembe S. Magwaza,et al.  Canopy position affects rind biochemical profile of ?Nules Clementine? mandarin fruit during postharvest storage , 2013 .

[38]  Karen I. Theron,et al.  Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. , 2010 .

[39]  K. Miyamoto,et al.  Non-Destructive Determination of Sugar Content in Satsuma Mandarin Fruit by near Infrared Transmittance Spectroscopy , 1995 .

[40]  A. McBratney,et al.  Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy , 2010 .

[41]  R. Littell,et al.  Variability in Juice Quality of 'Valencia∑ Sweet Orange and Sample Size Estimation for Juice Quality Experiments , 2003 .

[42]  Martine Dorais,et al.  Nondestructive measurement of fresh tomato lycopene content and other physicochemical characteristics using visible-NIR spectroscopy. , 2008, Journal of agricultural and food chemistry.

[43]  Jitendra Paliwal,et al.  Near-infrared spectroscopy and imaging in food quality and safety , 2007 .

[44]  Bart De Ketelaere,et al.  Chapter 15 – Non-destructive Evaluation: Detection of External and Internal Attributes Frequently Associated with Quality and Damage , 2009 .

[45]  José Antonio Cayuela,et al.  Intact orange quality prediction with two portable NIR spectrometers , 2010 .

[46]  B. B. Wedding,et al.  Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR spectroscopy. , 2011, Journal of the science of food and agriculture.

[47]  J. E. Guerrero,et al.  Implementation of LOCAL Algorithm with Near-Infrared Spectroscopy for Compliance Assurance in Compound Feedingstuffs , 2005, Applied spectroscopy.

[48]  Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy , 2011 .

[49]  Paul J.R. Cronje,et al.  Evaluation of Fourier transform-NIR spectroscopy for integrated external and internal quality assessment of Valencia oranges , 2013 .

[50]  N. P. Khumalo.  FACTORS AFFECTING POST-STORAGE QUALITY OF 'NULES CLEMENTINE' MANDARIN FRUIT WITH SPECIAL REFERENCE TO RIND BREAKDOWN , 2006 .

[51]  César Guerrero,et al.  Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy. , 2010 .

[52]  V. Mcglone,et al.  Vis/NIR estimation at harvest of pre- and post-storage quality indices for 'Royal Gala' apple , 2002 .

[53]  Lembe S. Magwaza,et al.  Assessment of rind quality of ‘Nules Clementine’ mandarin during postharvest storage: 1. Vis/NIRS PCA models and relationship with canopy position , 2014 .

[54]  Quantifying the effects of fruit position in the canopy on physical and biochemical properties and predicting susceptibility to rind breakdown disorder of ?Nules Clementine? mandarin ( Citrus reticulate Blanco) using Vis/NIR spectroscopy , 2013 .