<p>Functional Capability of Nigerian Stored Products Research Institute Smoking Kiln in Drying Herring Fish</p>

This study evaluated the functional capability of the Nigerian Stored Products Research Institute Smoking Kiln (NSPRI SK) in drying herring fish (Clupeidae), which are widely consumed in southwestern Nigeria. Due to the highly perishable nature of herring fish and its rich nutritional profile, high in omega-3 fatty acids, proteins, and essential vitamins, efficient preservation techniques are essential. Traditional drying methods often result in quality deterioration and prolonged drying times; therefore, there is a need to optimize modern drying technologies. A total of 24.3 kg of herring fish was sectioned into head, middle, tail, and whole parts and subjected to drying using the NSPRI SK. The study employed mathematical modeling (Page, Logarithmic, Two-Term Exponential, and Approximation of Diffusion models) and machine learning techniques to analyze drying kinetics, with time and temperature as predictors. Moisture ratios were determined, and multiple linear regression analysis was used to assess the significance of variables on the drying rate. Data analysis was performed using Microsoft Power BI and R version 4.2.2. The results revealed that time had a significantly greater influence rate than temperature across all fish parts (p < 0.05). Among the models tested, the logarithmic model best described the drying behavior of the head and whole fish sections, while the approximation of diffusion model provided the best fit for the middle and tail sections, each exhibiting the lowest RMSE values. Machine learning models explained up to 97% of drying rate variations, indicating high predictive accuracy. Proximate analysis showed that smoke drying with the NSPRI SK significantly reduced moisture content from 77.35% in fresh fish to 13.87%, crude fat was reduced from 7.00 to 2.30%, while crude protein increased from 9.00 to 15.00%, thus enhancing protein content. Microbial analysis also confirmed a reduced bacterial and mold load in NSPRI SK-treated samples compared to conventional methods. Overall, the NSPRI SK demonstrated strong efficacy in drying herring fish while retaining nutritional quality and minimizing microbial growth. This highlights its potential, thereby positioning it as a suitable and sustainable technology for fish preservation in Nigeria. The study recommends the logarithmic and approximation of diffusion models for accurate prediction of drying kinetics in similar contexts.

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Publication Details

Journal
Cureus Journal of Engineering.
Published
2026-04-21
DOI
https://doi.org/10.7759/s44388-025-00049-4
Primary Topic
Food Drying and Modeling
Type
article
Field-Weighted Citation Impact
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article

<p>Functional Capability of Nigerian Stored Products Research Institute Smoking Kiln in Drying Herring Fish</p>

Dauda Alani Dikko, Oluwatosin Ojo, Taiwo Oluwatoyin Ajao, Adedolapo Shotonwa et al.
Cureus Journal of Engineering.
Food Drying and Modeling
article

<p>Functional Capability of Nigerian Stored Products Research Institute Smoking Kiln in Drying Herring Fish</p>

Dauda Alani Dikko, Oluwatosin Ojo, Taiwo Oluwatoyin Ajao, Adedolapo Shotonwa, JO Famakinwa, Akeju BM, Jimoh AA, Olayiwola A, Solomon I, Popoola ST
article en

Abstract

This study evaluated the functional capability of the Nigerian Stored Products Research Institute Smoking Kiln (NSPRI SK) in drying herring fish (Clupeidae), which are widely consumed in southwestern Nigeria. Due to the highly perishable nature of herring fish and its rich nutritional profile, high in omega-3 fatty acids, proteins, and essential vitamins, efficient preservation techniques are essential. Traditional drying methods often result in quality deterioration and prolonged drying times; therefore, there is a need to optimize modern drying technologies. A total of 24.3 kg of herring fish was sectioned into head, middle, tail, and whole parts and subjected to drying using the NSPRI SK. The study employed mathematical modeling (Page, Logarithmic, Two-Term Exponential, and Approximation of Diffusion models) and machine learning techniques to analyze drying kinetics, with time and temperature as predictors. Moisture ratios were determined, and multiple linear regression analysis was used to assess the significance of variables on the drying rate. Data analysis was performed using Microsoft Power BI and R version 4.2.2. The results revealed that time had a significantly greater influence rate than temperature across all fish parts (p &lt; 0.05). Among the models tested, the logarithmic model best described the drying behavior of the head and whole fish sections, while the approximation of diffusion model provided the best fit for the middle and tail sections, each exhibiting the lowest RMSE values. Machine learning models explained up to 97% of drying rate variations, indicating high predictive accuracy. Proximate analysis showed that smoke drying with the NSPRI SK significantly reduced moisture content from 77.35% in fresh fish to 13.87%, crude fat was reduced from 7.00 to 2.30%, while crude protein increased from 9.00 to 15.00%, thus enhancing protein content. Microbial analysis also confirmed a reduced bacterial and mold load in NSPRI SK-treated samples compared to conventional methods. Overall, the NSPRI SK demonstrated strong efficacy in drying herring fish while retaining nutritional quality and minimizing microbial growth. This highlights its potential, thereby positioning it as a suitable and sustainable technology for fish preservation in Nigeria. The study recommends the logarithmic and approximation of diffusion models for accurate prediction of drying kinetics in similar contexts.

Cureus Journal of Engineering.
Openalex Percentile: Top 7%
Food Drying and Modeling
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