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Nonlinear Regression and Michaelis‐Menten Approaches for Modeling Respiration Dynamics of Tomato Under Hermetic Storage Condition.

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Title: Nonlinear Regression and Michaelis‐Menten Approaches for Modeling Respiration Dynamics of Tomato Under Hermetic Storage Condition.
Authors: Nishad, Praween1 (AUTHOR), Mangaraj, Shukadev2 (AUTHOR) sukhdev0108@gmail.com, Thakur, Rajeev Ranjan3 (AUTHOR) rajeevranjan1435@gmail.com, Kumar, Ranjeet4 (AUTHOR), Sami, Rokayya5 (AUTHOR), Kate, Adinath Eknath2 (AUTHOR)
Source: Journal of Food Process Engineering. Apr2026, Vol. 49 Issue 4, p1-11. 11p.
Subjects: Nonlinear regression, Michaelis-Menten equation, Farm produce quality, Oxygen consumption, Temperature effect, Storage, Mathematical models
Abstract: The respiration rate (RR) of fresh produce is a critical factor influencing its postharvest quality and shelf life. For the effective design of any storage system, it is essential to understand the impact of storage temperature and duration on respiration dynamics. This study investigates the respiratory behavior of fresh tomato (cv. Avinash‐2) at five different temperatures (10°C, 20°C, 25°C, 30°C, and 35°C) using the hermetic storage system. The experimental data were utilized to develop predictive mathematical models, including nonlinear regression function (RF) and enzyme kinetics based Michaelis–Menten (MM) model. Model validation was conducted at 17°C storage temperature, demonstrating a strong correlation between predicted and observed RR. Among the two models, the MM model exhibited superior predictive accuracy, making it a reliable tool for forecasting RR in tomatoes under different storage conditions. The findings of this study provide valuable insights for optimizing storage strategies, reducing postharvest losses, and improving fresh produce supply chain management. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Food Process Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193255943
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  Data: Nonlinear Regression and Michaelis‐Menten Approaches for Modeling Respiration Dynamics of Tomato Under Hermetic Storage Condition.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Food+Process+Engineering%22">Journal of Food Process Engineering</searchLink>. Apr2026, Vol. 49 Issue 4, p1-11. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Nonlinear+regression%22">Nonlinear regression</searchLink><br /><searchLink fieldCode="DE" term="%22Michaelis-Menten+equation%22">Michaelis-Menten equation</searchLink><br /><searchLink fieldCode="DE" term="%22Farm+produce+quality%22">Farm produce quality</searchLink><br /><searchLink fieldCode="DE" term="%22Oxygen+consumption%22">Oxygen consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Temperature+effect%22">Temperature effect</searchLink><br /><searchLink fieldCode="DE" term="%22Storage%22">Storage</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink>
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  Data: The respiration rate (RR) of fresh produce is a critical factor influencing its postharvest quality and shelf life. For the effective design of any storage system, it is essential to understand the impact of storage temperature and duration on respiration dynamics. This study investigates the respiratory behavior of fresh tomato (cv. Avinash‐2) at five different temperatures (10°C, 20°C, 25°C, 30°C, and 35°C) using the hermetic storage system. The experimental data were utilized to develop predictive mathematical models, including nonlinear regression function (RF) and enzyme kinetics based Michaelis–Menten (MM) model. Model validation was conducted at 17°C storage temperature, demonstrating a strong correlation between predicted and observed RR. Among the two models, the MM model exhibited superior predictive accuracy, making it a reliable tool for forecasting RR in tomatoes under different storage conditions. The findings of this study provide valuable insights for optimizing storage strategies, reducing postharvest losses, and improving fresh produce supply chain management. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Food Process Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1111/jfpe.70460
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1
    Subjects:
      – SubjectFull: Nonlinear regression
        Type: general
      – SubjectFull: Michaelis-Menten equation
        Type: general
      – SubjectFull: Farm produce quality
        Type: general
      – SubjectFull: Oxygen consumption
        Type: general
      – SubjectFull: Temperature effect
        Type: general
      – SubjectFull: Storage
        Type: general
      – SubjectFull: Mathematical models
        Type: general
    Titles:
      – TitleFull: Nonlinear Regression and Michaelis‐Menten Approaches for Modeling Respiration Dynamics of Tomato Under Hermetic Storage Condition.
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            NameFull: Nishad, Praween
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            NameFull: Mangaraj, Shukadev
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            NameFull: Thakur, Rajeev Ranjan
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            NameFull: Kumar, Ranjeet
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            NameFull: Sami, Rokayya
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 49
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