Novel Nondestructive Technique to Determine Optimum Harvesting Stage of ‘Ataúlfo’ Mango Fruit
Keywords:Mangifera indica, maturity stage, skin color, dry matter, spectrometer F-750
A portable spectrometer was validated to determine optimum harvesting stage of ‘Ataúlfo’ using dry matter and skin color as fruit indicators. To build the model, samples were collected as follows: a. Unripe; b. Green Mature 1; c. Green Mature 2; d. Green Mature 3; and e. Fully mature. Fruit were scanned with a near infrared spectrometer at three temperatures (15, 25, and 35 °C). Skin color (‘a’ value) was measured with a Minolta 400 colorimeter. DM was attained in a conventional oven by drying samples for 72 h at 60 °C. Model was built and validated three times. The best model linearity was obtained on skin color ‘a’ (R2 = 0.98), whereas for DM the R2 was only 0.70. For the first validation, the best predicted value was skin color ‘a’ with an R2 = 0.9144, followed by DM with an R2 = 0.7056. On the second validation, the adjusted predicted value for skin color ‘a’ had an R2 = 0.8798, while DM had an R2 = 0.4445. When comparing NIR versus Heat Units Accumulation, in Nayarit, ‘Ataúlfo’ skin color average difference between the spectrometer vs the colorimeter was only -0.04. For ‘Ataúlfo’ from Sinaloa, skin color average difference was only -0.06, but the correlation was higher (R2 = 0.90). In conclusion, measuring skin color with the NIR spectrometer has potential as a nondestructive technique to determine the optimum harvesting stage of ‘Ataúlfo’ mango.
Foreign Agricultural Service. 2018. Agency Shutdown Plan Contingency Plans. 4-28. www.ams.usda.gov/rules-regulations/research-promotion/mango.
Yashoda, H. M., Prabha, T. N., and Tharanathan, R.N. (2006). Mango ripening: changes in cell wall constituents in relation to textural softening. J. Sci. Food Agric. 86:713-721. DOI: 10.1002/jsfa.2404.
Saranwong, S., Sornsrivichai, J., and Kawano, S. (2004). Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biol. Technol. 31:137-145. DOI: 10.1016/j.postharvbio.2003.08.007.
Brecht, J. K., Sargent, S. A., Kader, A. A., Mitcham, E. J., Maul, F., Brecht, P. E. y Menocal, O. (2011). Manual de prácticas para el mejor Manejo postcosecha del mango. HS 1190. 78 p.
Resende Nassur, R. C. M., González-Moscoso, S., Crisosto, G., De Freitas, S. T., and Crisosto, C. (2014). Establishing a minimum quality index for the ‘ready to eat’ mango. V Postharvest Unlimited, ISHS International Conference, 10-13 June 2014. Greece. P. 104.
De Luca, C. (2016). Dry Matter matters. Australian Mangoes. 7 p. The Fresh Centre, Brisbane Markets, Rocklea, QLD, Australia (07) 3278 email@example.com.
Walsh, K. and Anderson, N. (2017). Factors that influence dry matter. Mango Matters. 26:23-25.
IMPI. Instituto Mexicano de Protección Intelectual. (2016). Denominaciones de Origen. Orgullo de México. Mango Ataúlfo del Soconusco Chiapas. P. 123-137. Editorial Pax México, Librería Carlos Cesarman, S.A.
EMEX, A. C. (2020). Exportación de mango mexicano. https:// www.mangoemex.com/exportacion-importacion-de-mango-de-mexico/. Consulted in May 2021.
Bao Chau, K. L., Dyer, E. B., Feig, J. L., Chien, A. L., and Del Bino, S. (2019). Research Techniques Made Simple: Cutaneous colorimetry: A Reliable Technique for Objective Skin Color Measurement. J. Inv. Derm. 140:3-12. DOI:10.1016/j.jid.2019.11.003.
Lee, S. K., and Coggins C. W. (1982). Dry weight method for determination of avocado fruit maturity. Calif. Avocado Soc. Yearb. 66:67–70.
Sjostrom, M., and Wold, S. (1983). A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables. Anal. Chimica Acta. 150: 61-70. DOI: 10.1016/S0003-2670(00)85460-4.
Osuna G., J. A., Ortega, D. A., Cabrera, H. y Vázquez, V. (2007). El Uso de Unidades Calor como una Tecnología Viable para Determinar Momento Óptimo de Cosecha en el Mango Ataulfo. Ecotech 3:12-13.
Zou, X., Li, Y., and Zhao, J. (2007). Using genetic algorithm interval partial least squares selection of the optimal near infrared wavelength regions for determination of the soluble solids content of ‘Fuji’ apple. J. Near Infrared Spectroscopy. 15:153-159. DOI: 10.1255/jnirs.732.
Guthrie, J., Walsh, K., and Reid, D. (2005). Assessment of internal quality attribute of mandarin fruit. NIR calibration model development. Austral. J. Agr. Res. 56:405-416. DOI:10.1071/AR04299.
Ying, Y. B., Liu, Y. D., Wang, J. P., Fu, X. P., and Li, Y. B. (2005). Fourier transform near-infrared determination of soluble solids and available acid in intact peaches. Trans. Amer. Soc. Agr. Eng. 48:229-234. DOI: 10.13031/2013.17922.
Manley, M., Joubert, E., Myburgh, L., Lotz, E., and Kidd, M. (2007). Prediction of soluble solids content and post-storage internal quality of Bulida apricots using near infrared spectroscopy. J. Near Infrared Spectroscopy. 15:179-188.
Mahayothee, B., Muhlbauer, W., Neihart, S., Leitenberger, M., and Carle, R. (2004). Nondestructive determination of maturity of Thai mangoes by near-infrared spectroscopy. Acta Hort. 645:581-588. DOI: 10.17660/ActaHortic.2004.645.76.
Anderson, N. T., Subedi, P. P., and Walsh, K. B. (2017). Manipulation of mango fruit dry matter content to improve eating quality. Scientia Horticulturae. 226:316-321.
Taira, E., Nakamura, S., Hiyane, R., Honda, H., and Ueno, M. (2017). Development of a nondestructive measurement system for mango fruit using near infrared spectroscopy. Engineering and Applied Science Research. 44(3):189-192. DOI:10.14456/easr.2017.28.
Al-Sanabani, D. G. A., Solihin, M. I., Pui, L. P., Astuti, W., Ang, C. K., and Hong, L. W. (2019). Development of non-destructive mango assessment using Handheld Spectroscopy and Machine Learning Regression. J. Phys.: Conf. Ser. 1367 012030. DOI:10.1088/1742-6596/1367/1/012030.
Polinar, Y. Q., Yaptenco, K. F., Peralta, E. K., Agravante, J. U. (2019). Near-infrared spectroscopy for non-destructive prediction of maturity and eating quality of ‘Carabao’ mango (Mangifera indica L.) fruit. Agricultural Engineering International: CIGR Journal, 21(1): 209–219.
Cocetta, G., Beghi, R., Mignani, I., and Spinardi, A. (2017). Nondestructive Apple Ripening Stage Determination Using the Delta Absorbance Meter at Harvest and after Storage. Hort-technology. 27(1):54-64. DOI: 10.21273/HORTTECH03495-16.
Delwiche, S. R., Mekwatanakarn, W., and Wang, C. Y. (2008). Soluble solids and simple sugars measurement in intact mango using near infrared spectroscopy. Hort-Technology. 18:410-416. DOI: 10.21273/HORTTECH.18.3.410.
Walsh, K. B., Blasco, J., Zude-Sasse, M., and Sun, X. (2020). Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biol. Technol. 168:1-17. DOI: 10.1016/j.postharvbio.2020.111246.
Pathare, P. B., Opara, U. L., and Al-Said, F. A. (2013). Colour measurement and analysis in fresh and processed foods: a review. Food Bioprocess Technol. 6:36-60.
Walsh, K.B., & Subedi, P.P. (2016). In-field monitoring of mango fruit dry matter for 562 maturity estimation. Acta Horticulturae, 1119, 273-278. 563.
How to Cite
Copyright (c) 2021 Jorge A. Osuna-Garcia, Jesús Daniel Olivares-Figueroa, Peter Toivonen, Hilda Pérez, Ricardo Goenaga, Mary Graciano
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.