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Predictive Assessment of Long-Term Radiological Exposure from Consumption of Common Spices in Southern Nigeria

by Adonuja Joy Amuofu, Akpolile Franklin Anita, Onojame Prince Omamoke, Ovowa Oghenefejiro Faith, Okeyode Itunu Comfort
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 86
Year of Publication: 2026
Authors: Adonuja Joy Amuofu, Akpolile Franklin Anita, Onojame Prince Omamoke, Ovowa Oghenefejiro Faith, Okeyode Itunu Comfort
10.5120/ijca2026926488

Adonuja Joy Amuofu, Akpolile Franklin Anita, Onojame Prince Omamoke, Ovowa Oghenefejiro Faith, Okeyode Itunu Comfort . Predictive Assessment of Long-Term Radiological Exposure from Consumption of Common Spices in Southern Nigeria. International Journal of Computer Applications. 187, 86 ( Mar 2026), 27-45. DOI=10.5120/ijca2026926488

@article{ 10.5120/ijca2026926488,
author = { Adonuja Joy Amuofu, Akpolile Franklin Anita, Onojame Prince Omamoke, Ovowa Oghenefejiro Faith, Okeyode Itunu Comfort },
title = { Predictive Assessment of Long-Term Radiological Exposure from Consumption of Common Spices in Southern Nigeria },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2026 },
volume = { 187 },
number = { 86 },
month = { Mar },
year = { 2026 },
issn = { 0975-8887 },
pages = { 27-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number86/predictive-assessment-of-long-term-radiological-exposure-from-consumption-of-common-spices-in-southern-nigeria/ },
doi = { 10.5120/ijca2026926488 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-03-20T22:55:06.650795+05:30
%A Adonuja Joy Amuofu
%A Akpolile Franklin Anita
%A Onojame Prince Omamoke
%A Ovowa Oghenefejiro Faith
%A Okeyode Itunu Comfort
%T Predictive Assessment of Long-Term Radiological Exposure from Consumption of Common Spices in Southern Nigeria
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 86
%P 27-45
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study performs predictive modeling for long-term radiological exposure associated with food spice consumption in Southern Nigeria, employing empirical radionuclide data for risk classification. Activity concentration data for naturally occurring radionuclides (40K, 226Ra, and 232Th) measured in 94 locally processed and imported spice samples were used for the predictive modeling. Multiple linear regression indicated a strong dependence of cumulative dose on radionuclide concentration, with 232Th being identified as the most prominent contributor, accounting for the highest variability in cumulative dose. Logistic regression indicated 100% classification accuracy for differentiation between low- and moderate-risk spice samples. Principal component analysis indicated that more than 70% of total variance is explained in the first two components, with 40K and 232Th being prominent in principal loading, while 226Ra contributed very little to principal components due to its low activity concentration. Empirically, mean activity concentration data for 40K (86.81 Bq kg⁻¹), 232Th (33.78 Bq kg⁻¹), and 226Ra (3.03 Bq kg⁻¹) were associated with mean annual committed effective doses of 10.56 µSv y⁻¹ for Delta State, 9.82 µSv y⁻¹ for Ogun State, respectively, which are well below the public dose limit of 1 mSv y⁻¹. Excess lifetime risk estimates were within the acceptable limits of 10⁻⁶-10⁻⁴. In conclusion, results from predictive modeling clearly establish that health risk associated with long-term radiological exposure from spice consumption is insignificant, while forming a strong basis for early risk detection. It is recommended that there is a need for incorporation of predictive-probabilistic models into routine dietary radiological risk assessments for proactive food safety regulation for health protection.

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Index Terms

Computer Science
Information Sciences

Keywords

Predictive modelling radiological risk food spices logistic regression principal component analysis

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