Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango

Abstract The effect of harvest year on near-infrared spectroscopy (NIRS) prediction models to determine postharvest quality of mango was evaluated. Diffuse reflectance spectra in region of 700–1100 nm were used to develop calibration models for firmness, total soluble solids (TSS), titratable acidity (TA) and ripening index (RPI) using partial least squares (PLS) regression analysis. The results showed that model robustness was influenced by harvest year. High prediction error was found when models from single harvest year were used to predict the data of other years, whereas using combined data from two or three years for calibration greatly enhanced the prediction accuracy. The prediction models established from three-year data performed the most suitably for prediction of TSS (R2 = 0.9; SEP = 1.2%), firmness (R2 = 0.82; SEP = 4.22 N), TA (R2 = 0.74; SEP = 0.38 %) and RPI (R2 = 0.8; SEP = 0.8). Classification of mango ripeness was successfully achieved using second derivative pretreated spectra with an accuracy of more than 80%. The results indicated that NIRS can be used as a reliable non-destructive technique for mango quality assessment and a robust model could be developed when effect of harvest year was taken into account.

[1]  S. Kanlayanarat,et al.  Near infrared spectroscopic evaluation of fruit maturity and quality of export Thai mango (Mangifera indica L. var. Namdokmai). , 2014 .

[2]  Jorge Chanona-Pérez,et al.  Computer Vision System Applied to Classification of “Manila” Mangoes During Ripening Process , 2014, Food and Bioprocess Technology.

[3]  C. Camps,et al.  Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy , 2009 .

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

[5]  Baohua Zhang,et al.  A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy , 2013 .

[6]  Mohd Zubir MatJafri,et al.  Peak Response Identification through Near-Infrared Spectroscopy Analysis on Aqueous Sucrose, Glucose, and Fructose Solution , 2012 .

[7]  Mathieu Lechaudel,et al.  An overview of preharvest factors influencing mango fruit growth, quality and postharvest behaviour , 2007 .

[8]  Busarakorn Mahayothee,et al.  Effects of variety, ripening condition and ripening stage on the quality of sulphite-free dried mango slices , 2007 .

[9]  M. Baloch,et al.  Effect of harvesting and storage conditions on the post harvest quality and shelf life of mango (Mangifera indica L.) fruit , 2012 .

[10]  G. Self,et al.  Quality Control of Mango Fruit during Postharvest by Near Infrared Spectroscopy , 2014 .

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

[12]  Tom Fearn,et al.  Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .

[13]  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 .

[14]  R. Carle,et al.  Harvest maturity specification for mango fruit (Mangifera indica L. ‘Chok Anan’) in regard to long supply chains , 2011 .

[15]  P. Wanitchang,et al.  Non-destructive maturity classification of mango based on physical, mechanical and optical properties , 2011 .

[16]  W. Boonsupthip,et al.  Utilization of partially ripe mangoes for freezing preservation by impregnation of mango juice and sugars , 2011 .

[17]  Joachim Müller,et al.  Effect of irrigation on near-infrared (NIR) based prediction of mango maturity , 2010 .

[18]  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 .

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

[20]  Kerry B. Walsh,et al.  Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy , 2011 .

[21]  Mohd Zubir MatJafri,et al.  NIR Spectroscopic Properties of Aqueous Acids Solution , 2012, Molecules.

[22]  Joachim Müller,et al.  Non-destructive determination of β-carotene content in mango by near-infrared spectroscopy compared with colorimetric measurements , 2015 .

[23]  S. Kawano,et al.  Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy , 1998 .

[24]  N. Utsunomiya,et al.  Changes in Physical and Chemical Properties during Maturation of Mango Fruit (Mangifera indica L. 'Irwin') Cultured in a Plastic Greenhouse , 2000 .

[25]  Z. Schmilovitch,et al.  Determination of mango physiological indices by near-infrared spectrometry , 2000 .

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

[27]  Y. Ying,et al.  Use of FT-NIR spectrometry in non-invasive measurements of internal quality of ‘Fuji’ apples , 2005 .

[28]  M. Léchaudel,et al.  Physiological age at harvest regulates the variability in postharvest ripening, sensory and nutritional characteristics of mango (Mangifera indica L.) cv. Coghshall due to growing conditions. , 2012, Journal of the science of food and agriculture.

[29]  Wolfram Spreer,et al.  Harvest maturity detection for 'Nam Dokmai #4' mango fruit (Mangifera indica L.) in consideration of long supply chains , 2012 .

[30]  Reinhold Carle,et al.  Accumulation of all-trans-beta-carotene and its 9-cis and 13-cis stereoisomers during postharvest ripening of nine Thai mango cultivars. , 2005, Journal of agricultural and food chemistry.

[31]  Sumio Kawano,et al.  Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy , 2004 .

[32]  Steven A. Sargent,et al.  Climate changes and potential impacts on postharvest quality of fruit and vegetable crops: A review , 2010 .

[33]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[34]  Elhadi M. Yahia,et al.  Maintaining mango (Mangifera indica L.) fruit quality during the export chain , 2011 .

[35]  Riccardo Leardi,et al.  Multivariate calibration of mango firmness using vis/NIR spectroscopy and acoustic impulse method. , 2009 .

[36]  D. Slaughter,et al.  Methods to analyze physico-chemical changes during mango ripening: A multivariate approach , 2011 .

[37]  Kerry B. Walsh,et al.  Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy , 1997 .

[38]  Stephen R. Delwiche,et al.  Soluble Solids and Simple Sugars Measurement in Intact Mango Using Near Infrared Spectroscopy , 2008 .

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

[40]  Kerry B. Walsh,et al.  Prediction of mango eating quality at harvest using short-wave near infrared spectrometry , 2007 .

[41]  K. Narsaiah,et al.  Quality parameters of mango and potential of non-destructive techniques for their measurement — a review , 2010, Journal of food science and technology.

[42]  Manuela Zude,et al.  WAVELENGTH SELECTION FOR PREDICTING PHYSICOCHEMICAL PROPERTIES OF APPLE FRUIT BASED ON NEAR‐INFRARED SPECTROSCOPY , 2007 .