Statistical Analysis of Temperature Sensors Applied to a Biological Material Transport System: Challenges, Discrepancies, and a Proposed Monitoring Methodology

Conventional methods for transporting biological materials typically use dry ice or ice for preservation but often overlook important aspects of temperature monitoring and metrological control. These methods generally do not include temperature sensors to track the thermal conditions of the materials during transport, nor do they apply essential metrological practices such as regular sensor calibration and stability checks. This lack of precise monitoring poses significant risks to the integrity of temperature-sensitive biological materials. This study presents a statistical analysis of DS18B20 digital temperature sensors used in an experimental refrigeration system based on thermoelectric modules. The aim was to verify sensor consistency and investigate sources of measurement error. The research was motivated by a prior phase of study, which revealed significant discrepancies of approximately 3 °C between experimental temperature data and numerical simulations. To investigate a potential cause, we conducted a case study analyzing measurements from three identical temperature sensors (same model, brand, and manufacturer). Statistical analyses included ANOVA (analysis of variance) and Tukey’s test with a 95% confidence interval. Since the data did not follow a normal distribution (p-value < 0.05), non-parametric methods such as the Kruskal–Wallis and Levene’s procedures were also applied. The results showed that all sensors recorded statistically significant different temperature values (p-value < 0.05). Although experimental conditions were kept consistent, temperature differences of up to 0.37 °C were observed between sensors. This finding demonstrates an inherent inter-sensor variability that, while within manufacturer specifications, represents a source of systematic error that can contribute to larger discrepancies in complex systems, highlighting the need for individual calibration.

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Processes
Published
2025-06-16
DOI
https://doi.org/10.3390/pr13061904
Primary Topic
Advanced Chemical Sensor Technologies
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article
Field-Weighted Citation Impact
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article

Statistical Analysis of Temperature Sensors Applied to a Biological Material Transport System: Challenges, Discrepancies, and a Proposed Monitoring Methodology

Alisson Cocci de Souza, Rodrigo Gustavo Dourado da Silva, Andrezza Carolina Carneiro Tomás, José Eduardo Ferreira de Oliveira et al.
Processes
Advanced Chemical Sensor Technologies
article

Statistical Analysis of Temperature Sensors Applied to a Biological Material Transport System: Challenges, Discrepancies, and a Proposed Monitoring Methodology

Alisson Cocci de Souza, Rodrigo Gustavo Dourado da Silva, Andrezza Carolina Carneiro Tomás, José Eduardo Ferreira de Oliveira, Álvaro Antônio Villa Ochoa, Paula Suemy Arruda Michima, José Ângelo Peixoto da Costa, Felipe Roque de Albuquerque Neto
article en

Abstract

Conventional methods for transporting biological materials typically use dry ice or ice for preservation but often overlook important aspects of temperature monitoring and metrological control. These methods generally do not include temperature sensors to track the thermal conditions of the materials during transport, nor do they apply essential metrological practices such as regular sensor calibration and stability checks. This lack of precise monitoring poses significant risks to the integrity of temperature-sensitive biological materials. This study presents a statistical analysis of DS18B20 digital temperature sensors used in an experimental refrigeration system based on thermoelectric modules. The aim was to verify sensor consistency and investigate sources of measurement error. The research was motivated by a prior phase of study, which revealed significant discrepancies of approximately 3 °C between experimental temperature data and numerical simulations. To investigate a potential cause, we conducted a case study analyzing measurements from three identical temperature sensors (same model, brand, and manufacturer). Statistical analyses included ANOVA (analysis of variance) and Tukey’s test with a 95% confidence interval. Since the data did not follow a normal distribution (p-value < 0.05), non-parametric methods such as the Kruskal–Wallis and Levene’s procedures were also applied. The results showed that all sensors recorded statistically significant different temperature values (p-value < 0.05). Although experimental conditions were kept consistent, temperature differences of up to 0.37 °C were observed between sensors. This finding demonstrates an inherent inter-sensor variability that, while within manufacturer specifications, represents a source of systematic error that can contribute to larger discrepancies in complex systems, highlighting the need for individual calibration.

ProcessesVol. 13(6)
Universidade Federal de Pernambuco (BR), Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco (BR), Instituto de Aeronáutica e Espaço (BR), Universidade Federal Rural de Pernambuco (BR)
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Universidade Federal de Pernambuco
Openalex Percentile: Top 87%
Advanced Chemical Sensor Technologies
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