Postharvest respiration of fruits and environmental factors interaction: An approach by dynamic regression models

Artemio Pérez-López; Martha Ramírez-Guzmán; Teodoro Espinosa-Solares; Eleazar Aguirre-Mandujano; Carlos Villaseñor-Perea

doi:10.17268/sci.agropecu.2020.01.03

Autores/as

Artemio Pérez-López Departamento de Ingeniería Agroindustrial, Universidad Autónoma Chapingo, 56230, Texcoco. http://orcid.org/0000-0002-9844-697X
Martha Ramírez-Guzmán Colegio de Postgraduados, Campus Montecillo. 56230, Montecillo, Texcoco. http://orcid.org/0000-0002-8840-3706
Teodoro Espinosa-Solares Departamento de Ingeniería Agroindustrial, Universidad Autónoma Chapingo, 56230, Texcoco. http://orcid.org/0000-0002-7581-0249
Eleazar Aguirre-Mandujano Departamento de Preparatoria Agrícola, Universidad Autónoma Chapingo, 56230, Texcoco. http://orcid.org/0000-0002-4403-358X
Carlos Villaseñor-Perea Departamento de Ingeniería Mecánica Agrícola, Universidad Autónoma Chapingo, 56230, Texcoco. http://orcid.org/0000-0002-7404-022X

DOI:

https://doi.org/10.17268/sci.agropecu.2020.01.03

Palabras clave:

respiration rate, time series, dynamic regression model, exogenous variables, transfer function.

Resumen

The respiratory metabolism of fruits is affected by multiple internal (product) and external (environmental) factors that often interact with each other. Among the external factors that have the greatest influence on respiration are temperature, air composition, moisture content and illumination. The aim of this paper is to elucidate the influence of environmental factors on the respiration rate of peach fruits based on transfer models obtained by dynamic regression modelling (ARIMAX). The fitted ARIMA models met the criteria of parsimony and white noise in residuals. The estimated coefficients of each model were statistically significant under the Durbin-Watson (DW), Akaike (AIC) and Schwarz (SBC) criteria. Transfer functions revealed 0.15% and 1.9% increase, and 0.001% decrease in the respiration rate of the peach fruit for each unit of change in temperature, relative humidity and illumination of the storage environment, respectively. The respiration rate response took place 1-8 minutes after the change in environmental variables had occurred. It was concluded that the dynamic regression modelling is reliable for predicting the physiological response of fruits the effect of external factors imposed continuously during postharvest handling.

Citas

Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G. M. 2015. Time Series Analysis: Forecasting and Control. John Wiley and Sons Inc., Hoboken, New Jersey. USA. 712 pp.

Bowles, D.J. 1993. Local and systemic signals in the wound response. Seminar in Cell Biology 4(2): 103-111.

Gong, D.D.; Cao, S.F.; Sheng, T.; Shao, J.R.; Song, C.B.; Wo, F.C.; Chen, W.; Yang, Z.F.. 2015. Eﬀect of blue light on ethylene biosynthesis, signalling and fruit ripening in postharvest peaches. Scientia Horticulturae 197: 657–664.

Hipel, K.W.; McLeod, A.I. 1994. Developments in Water Science. Time series modelling of water resources and environmental systems. Vol. 45. Elsevier Science B.V. Amsterdam, The Netherlands. 1013 pp.

Ho, Q.T.; Rogge, S.; Verboven, P.; Verlinden, B. E.; Nicolaï, B. 2016. Stochastic modelling for virtual engineering of controlled atmosphere storage of fruit. Journal of Food Engineering 176: 77-87.

Jalalkamali, A.; Moradi, M.; Moradi, N. 2015. Application of several artificial intelligence models and ARMAX model for forecasting drought using the Standardized Precipitation Index. International journal of environmental science and technology 12:1201-1210.

Jere, S; Moyo, E. 2016. Modelling Epidemiological Data Using Box-Jenkins Procedure. Open Journal of Statistics 6(2):295-302.

Li, L.; Miao, S.; Tu, Q.; Duan, S.; Li, Y.; Han, J. 2020. Dynamic dependence modelling of wind power uncertainty considering heteroscedastic effect. International journal of electrical power & energy systems 116: 1-13.

Lin-Ya, C.; Dan Jeric A.R.; Chen-Yi, L.; Ta-Te, L. 2019. Modelling and Forecasting of Greenhouse Whitefly Incidence Using Time-Series and ARIMAX Analysis. IFAC-PapersOnLine 52(30): 196-201.

Liu, L.H.; Zarabas, D.; Bennett, L.; Aguas P.; Wooton, B.W. 2009. Effects of UV-C, red light and sun light on the carotenoid content and physical qualities of tomatoes during post-harvest storage. Food Chemistry 115(2): 495-500.

Maçaira, P.M.; Tavares-Thomé, A.M.; Cyrino-Oliveira, F.L.; Carvalho-Ferrer, A.L. 2018. Time series analysis with explanatory variables: A systematic literature review. Environmental Modelling and Software 107: 199–209.

Özdemir, Ì.Z. 2016. Effect of light treatment on the ripening of banana fruit during postharvest handling. Fruits 71(2): 115-122.

Pankratz, A. 1991. Forecasting with Dynamic Regression Models, John Wiley & Sons, Inc. USA. 402 pp.

Pérez-López, A.; Chávez-Franco, S.H.; Villaseñor-Perea, C.A.; Espinosa-Solares, T.; Hernández-Gómez, L.H.; Lobato-Calleros, C. 2014. Respiration rate and mechanical properties of peach fruit during storage at three maturity stages. Journal of Food Engineering 142: 111-117.

Qureshi, M.N.; Bilal, M.; Ayyub, R.M.; Ayyub, S. 2014. Modeling of mango production in Pakistan. Science International 26(3): 1227-1231.

Ramírez-Guzmán, M.E. 1993. SAS macro for analyzing step and impulse response functions derived from transfer function models. Proceedings of the Eighteenth Annual SAS® Users Group International Conference New York, pp. 990–995. New York.

Ruby-Figueroa, R.; Saavedra, J.; Bahamonde, N.; Cassano, A. 2017. Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models. Journal of Membrane Science 524: 108-116.

Saltveit, M.E. 2016a. Respiratory metabolism. In: S. Pareek (Ed.). Postharvest Ripening Physiology of Crops. Pp. 139–156. CRC Press, Boca Raton. USA.

Saltveit, M.E. 2016b. Water Loss from harvested horticultural commodities. In: S. Pareek (Ed.). Postharvest Ripening Physiology of Crops. Pp. 139–156. CRC Press, Boca Raton. USA.

SAS Institute Inc. 2018. SAS/ETS 15.1 User’s Guide. Cary, NC. USA.

Whitelock, D.P.; Brusewits, G. H.; Ghajar, A. J. 1999. Thermal/Physical properties affect predicted weight loss of fresh peaches. Transaction of the ASAE 42(4): 1047-1054.

Xiao, Z.; Lester, G.E.; Luo Y.; Xie, Z.K.; Yu, L.L.; Wang, Q. 2014. Effect of light exposure on sensorial quality, concentrations of bioactive compounds and antioxidant capacity of radish microgreens during low temperature storage. Food Chemistry 151: 472-479.