Determination of a multiple linear regression model for the prediction of Pol in sugarcane (Saccharum officinarum)

Authors

DOI:

https://doi.org/10.35622/

Keywords:

agroindustry, efficiency, optimization, production, quality

Abstract

In the sugar manufacturing process, Pol in cane is a quality parameter. However, there is a problem in its determination, since it is conditioned by the complexity of the calculation, which forced this research to be carried out to provide a feasible, dynamic and economical solution. The objective was to develop a predictive model that allows predicting Pol in cane more quickly and easily from six independent variables: brix in juice, Pol in juice, non-Pol in juice, purity of juice, fiber in cane and cane juice. The research was quantitative with an explanatory and transversal design, where data collected during the 2023-2024 harvest at a sugar mill in the southern region of Guatemala was available. 23,470 records were analyzed for each variable. The results of the multiple linear regression analysis demonstrated that the variables Pol in juice and fiber in cane directly affect the prediction of Pol in cane by evidencing standardized coefficients with statistical significance. A solid mathematical model was generated that attributes high explanatory capacity to the variables Pol in juice and fiber in cane. The formula equation is: Pol in cane = 3.642 + (0.80 x Pol in juice) – (0.242 x fiber in cane). It is concluded that the new proposal for calculating Pol in cane establishes a balance between its simplicity and precision, will facilitate its application and will positively impact decision-making in sugar production.

Author Biographies

  • Flavio Reyes, Universidad Hipócrates

    Ingeniero Agroindustrial por la Universidad de San Carlos de Guatemala, con grado de Doctor en Investigación Social por la Universidad Panamericana, Doctor en Ingeniería Industrial, y un Posdoctorado en Metodología de la Investigación y Producción Científica por el Instituto Universitario de Innovación Ciencia y Tecnología (Perú). Además, posee una Maestría en Ciencias en Administración Agroindustrial por la Universidad del Valle de Guatemala. Se desempeña como Docente en el Centro Universitario del Sur de la Universidad de San Carlos, donde también imparte clases en programas de postgrado, y en la Universidad Rural de Guatemala. Es evaluador externo de la Secretaría de Ciencia y Tecnología de Guatemala, miembro conferencista de la Cámara de Conferencistas, Expositores y Oradores, miembro honorífico del Comité Editorial de la Revista Multidisciplinaria Voces de América y el Caribe (REMUVAC), y miembro activo del Colegio de Ingenieros Químicos de Guatemala y de la Asociación de Técnicos Azucareros de Guatemala.

  • Estuardo Monroy, Universidad Hipócrates

    Ingeniero Químico por la Universidad de San Carlos de Guatemala, con grado de Doctor Honoris Causa por la Universidad Autónoma Andragógica de Miami (Estados Unidos), Posdoctorado en Metodología de la Investigación y Producción Científica por el Instituto Universitario de Innovación Ciencia y Tecnología (Perú) y una Maestría en Biotecnología Industrial y Agroalimentaria por la Universidad de Almería (España). Se ha desempeñado como docente en la Universidad de San Carlos de Guatemala, donde alcanzó la jubilación, y como asesor técnico de ingenios azucareros en Centroamérica. Posee amplias especializaciones en áreas de tecnología y control de calidad en procesos azucareros, así como en estándares internacionales de evaluación de la conformidad, incluyendo ISO 17025, ISO 9001, ISO 22000, ISO 14001, ISO 45001, BPM, HACCP, FSSC 22000, FSPCA, OHSAS 18001, GLOBAL GAP, entre otros, destacando su experiencia en métodos analíticos, calibración y capacitaciones.

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Published

2024-12-20

Issue

Vol. 3 No. 2 (2024)

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Artículos

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Copyright (c) 2024 Flavio Reyes, Estuardo Monroy (Autor/a)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Reyes, F., & Monroy, E. (2024). Determination of a multiple linear regression model for the prediction of Pol in sugarcane (Saccharum officinarum). Revista Ciencia Agraria, 3(2), 38-51. https://doi.org/10.35622/