,

USE OF EMERGING TECHNOLOGIES FOR DETECTION OF AFLATOXIN IN MAIZE

INTRODUCTION

Aflatoxins are highly toxic secondary metabolites produced by Aspergillus flavus and A. parasiticus, fungi that frequently contaminate staple crops like maize and groundnuts. In Kenya, aflatoxin outbreaks have had severe health and economic repercussions, especially in rural areas where maize is a dietary staple and post-harvest handling practices are often inadequate (Wanjiru et al., 2023; Mahato et al., 2019). Detecting and managing aflatoxin contamination is therefore critical for food safety, public health, and trade integrity (Sirma et al., 2018).

BACKGROUND OF STUDY

Emerging technologies (ET) for aflatoxin detection in maize are revolutionizing food safety by providing rapid, non-destructive, and cost-effective alternatives to traditional methods. These technologies leverage advanced optical and imaging techniques to identify aflatoxin contamination, which is crucial for preventing health risks associated with these potent carcinogens. The integration of these technologies into food safety protocols can significantly enhance the detection and management of aflatoxins in maize, a staple food in many regions. Below are some key emerging technologies and their applications in aflatoxin detection.

Emerging Technologies for detection of aflatoxin in maize
Source: Authors’ construct, 2025

Hyperspectral Imaging

  • Hyperspectral imaging techniques, including reflectance, fluorescence, and Raman imaging, have shown high accuracy in detecting aflatoxins in maize. For instance, fluorescence and short-wave infrared (SWIR) imaging achieved classification accuracies of 95.7% with no false negatives at a 10 μg/kg cutoff, making them promising tools for rapid screening (Rashid et al., 2022).
  • SWIR hyperspectral imaging has been used to detect aflatoxin B1 in individual maize kernels, employing principal component analysis and support vector machine classification to effectively identify contamination levels (Long et al., 2022).

Fluorescence Spectroscopy

  • Fluorescence spectroscopy, both one- and two-photon induced, offers a non-destructive method for detecting aflatoxins. This technique exploits the intrinsic fluorescence differences between contaminated and uncontaminated maize, providing a basis for real-time industrial scanning (Kim et al., 2010).
  • A handheld fluorescence spectrometer has been developed, demonstrating high sensitivity in detecting aflatoxins in maize, with potential for integration along the food chain to improve food safety (Smeesters et al., 2023).

Portable Detection Devices

  • A low-cost, portable device using a normalized difference fluorescence index (NDFI) method has been developed for detecting and sorting aflatoxin-contaminated maize kernels. This device significantly reduces aflatoxin levels, making it suitable for use in developing countries (Yao et al., 2023).

Electronic Nose Technology

  • The use of metal oxide sensor-based electronic noses has been explored for detecting aflatoxin contamination. These devices offer a cost-effective screening method with classification accuracies ranging from 58% to 88%, depending on the contamination level and validation method (Machungo et al., 2023).

While these technologies offer promising advancements in aflatoxin detection, challenges remain in their widespread adoption. Factors such as low awareness among farmers, especially in regions like Tanzania, hinder the use of these smart technologies. Increasing awareness and access to these technologies are crucial for their effective implementation (Marijani et al., 2025). Additionally, integrating these technologies with traditional methods and ensuring their adaptability to various environmental conditions will be essential for comprehensive aflatoxin management strategies.

Enzyme-linked immunosorbent assay (ELISA) is a widely used method for detecting aflatoxins, which are toxic compounds produced by certain molds. ELISA offers a rapid, sensitive, and cost-effective approach for aflatoxin detection in various matrices, including food, feed, and herbal medicines. The method’s adaptability and efficiency make it a preferred choice for routine screening and quality control. Below are key aspects of ELISA’s application in aflatoxin detection.

Standard Substance and Detection Method

  • A universal standard substance for aflatoxin detection using ELISA has been developed, which involves a rabbit anti-mouse antibody that can recognize various mouse anti-aflatoxin monoclonal antibodies. This approach allows for the quantitative detection of different aflatoxin concentrations in samples without the need for multiple standard substances, enhancing the method’s versatility and safety(Li et al., 2012).
Simplified Detection Process
  • A streamlined ELISA method for aflatoxin B1 detection omits complex pretreatment steps like nitrogen blowing. This method uses a methanol aqueous solution for sample extraction and a straightforward detection process involving antigen-coated plates and enzyme amplification, resulting in a stable and rapid detection of aflatoxin B1(Jiang et al., 2017).

Application in Herbal Medicines

  • ELISA has been adapted for detecting aflatoxins in Chinese herbal medicines, with optimized antibody concentrations and reaction systems. This method achieves high recovery rates and low relative standard deviations, making it effective for quality control in traditional medicine production(Tiegui et al., 2020).
Enhanced Sensitivity with Magnetic Nanoparticles
  • A variant of ELISA using magnetic nanoparticles significantly lowers the detection limit and reduces test duration. This method is particularly effective for detecting aflatoxin B1 at very low concentrations, demonstrating the potential for enhanced sensitivity and efficiency in aflatoxin detection(Petrakova et al., 2015).

Comparative Evaluation with Other Methods

  • ELISA kits have been compared with high-performance liquid chromatography (HPLC) for detecting aflatoxins in feedstuffs. ELISA showed good performance with high recovery rates and detection limits below maximum residue levels, proving to be a reliable alternative to more complex analytical methods like HPLC(Maggira et al., 2022).

While ELISA is a powerful tool for aflatoxin detection, it is important to consider the specific requirements of the testing environment, such as the availability of equipment and the number of samples. In some cases, more sophisticated methods like HPLC may be preferred for their precision and accuracy, especially in research settings or when regulatory compliance is critical.

Problem statement

Despite Kenya having set national aflatoxin standards, contamination levels in cereals and feeds often exceed safe limits (Wanjiru et al., 2023). Manual and laboratory-based testing methods are costly, slow, and inaccessible for most smallholder farmers. This leads to unchecked exposure and significant health burdens, including hepatocellular carcinoma and stunted growth in children (Kumar et al., 2021; Mahato et al., 2019).

Research Questions

  1. How can emerging digital technologies improve the early detection of aflatoxins in Kenyan crops?
  2. What role can weather data and remote sensing play in predicting aflatoxin risk zones?
  3. Which digital tools offer the most cost-effective, scalable solutions for aflatoxin monitoring in rural Kenya?

Objectives

  1. To identify suitable digital technologies for aflatoxin detection.
  2. To assess the feasibility of integrating weather data for aflatoxin risk prediction.
  3. To engage stakeholders in evaluating digital tools for adoption and to analyze the cost-benefit ratio of digital versus conventional methods.

Justification

Kenya faces a persistent challenge of aflatoxin contamination due to its tropical climate, poor post-harvest practices, and limited access to effective detection tools (Sirma et al., 2018). Emerging digital technologies such as hyperspectral imaging, machine learning, and mobile diagnostics provide promising, scalable alternatives to traditional methods. These innovations can enable early warning systems, improve response times, and reduce health risks (Mahato et al., 2019; Kumar et al., 2021).

Summary of Research & Literature Review

Traditional aflatoxin detection methods like ELISA, HPLC, and TLC are precise but require lab infrastructure, technical skills, and time (Mahato et al., 2019; Kumar et al., 2021). Recent advancements offer rapid, field-deployable techniques:

Stakeholders

  • Smallholder Farmers: Primary producers and direct beneficiaries of real-time aflatoxin detection.
  • Government Agencies: Including the Ministry of Agriculture and Livestock Development and KEPHIS for policy and enforcement.
  • Research Institutions: Such as KALRO and ILRI, who drive R&D.
  • NGOs & International Bodies: Like FAO, which supports training and tool deployment.
  • Tech Companies & Startups: Key in developing and deploying digital detection platforms.
  • Local Markets and Traders: Impacted by aflatoxin standards and enforcement mechanisms.

Solution Approach: Integrating Weather Data

Weather conditions significantly influence aflatoxin outbreaks. Temperature, humidity, and rainfall during pre- and post-harvest stages determine fungal growth and toxin production (Mahato et al., 2019; Kumar et al., 2021). Integrating weather data allows:

  • Satellite Remote Sensing: To monitor real-time climatic changes.
  • Agro-meteorological Forecasts: To predict high-risk aflatoxin seasons.
  • Mobile-based Alert Systems: To inform farmers when to harvest, dry, or store crops.
  • AI-powered Decision Support Systems: To advise on mitigation strategies based on weather-driven risk maps.

This predictive framework can minimize contamination before it occurs, improving food safety and reducing economic losses (Mahato et al., 2019).

References

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ADDiCT

Aflatoxin Digital Detection in Crops Technology 

YA-GH-1 (Kenya)

a woman hanging a vase on a shelf

Ideas on Designing the solution based on the questions

How might we use emerging digital technologies to improve the early detection of aflatoxins in Kenyan crops?

A. Problems – Major User Pains Being Addressed
B. What is the Idea? (What is happening?)

The idea is to develop a digital, real-time aflatoxin detection and risk prediction system that uses sensor data, machine learning, and weather analytics to detect and warn farmers, traders, and regulators of aflatoxin contamination risks early in the production cycle—from pre-harvest to storage.

C. How Does the Idea Work? (Tools, Technologies Used)
  1. Portable Biosensor Devices or Smartphone-based Detection Kits
    Used for field-level, rapid aflatoxin testing (e.g., lateral flow devices or mobile-camera scanning).
  2. Weather Data Integration + Predictive AI Models
    Uses rainfall, humidity, and temperature data to predict aflatoxin risk zones via GIS and machine learning.
  3. Digital Dashboard / Mobile App
    A central platform for alerts, data logging, farmer advisories, and marketplace traceability.
D. Where and When? (Context, Channels, Touchpoints)
  • Where: Rural farming areas, aggregation points, markets, and storage sites.
  • When: Throughout the crop lifecycle—especially during post-harvest drying and storage stages.
  • Touchpoints: Mobile phones (USSD or apps), extension officers, agro-dealers, cooperatives, SMS alert systems.
E.  Strengths of the Idea
  1. Scalability: Digital tools can be easily scaled to other counties or crops.
  2. Real-time Action: Enables early interventions before contaminated food enters the value chain.
  3. Data-Driven Insights: Generates valuable data for regulators, buyers, and policymakers.
F. Weaknesses of the Idea
  1. Digital Divide: Limited smartphone/internet access among rural farmers may reduce adoption.
  2. Initial Cost of Devices: Biosensor kits or IoT devices may still be unaffordable without subsidies.
  3. Data Reliability: Inconsistent weather data or sensor calibration can affect prediction accuracy.
G. How Will Stakeholders Benefit?
  1. Farmers: Gain access to low-cost, real-time testing tools and weather-based alerts to improve post-harvest practices and crop value.
  2. Traders and Aggregators: Can verify grain safety before distribution, reducing risk of rejected shipments and building buyer trust.
  3. Government and Regulators: Receive geo-tagged contamination reports and risk maps for better surveillance and policy response.
  4. Consumers: Benefit from safer, toxin-free food products in the market.
  5. Agri-Tech Startups and Researchers: Gain opportunities to pilot and refine AI and biosensor tools in real-world settings.
I. Target Groups
  1. Smallholder Maize Farmers
  2. Agro-processors and Grain Aggregators
  3. Food Safety and Agricultural Extension Agencies

What role can weather data and remote sensing play in predicting aflatoxin risk zones?

A. Problems – Major User Pains Being Addressed
B. What is the Idea? (What is happening?)

The idea is to use weather data and remote sensing technologies to create predictive maps of aflatoxin risk zones. By analyzing real-time environmental data—especially rainfall, temperature, humidity, and vegetation stress—this system will alert stakeholders in advance so they can take preventative action (e.g., drying crops early, improving storage, applying bio-control).

C. How Does the Idea Work? (Tools, Technologies Used)
  1. Satellite Remote Sensing & GIS Mapping
     Identifies land surface conditions, crop stress, and microclimates linked to fungal growth.
  2. Weather Station & Forecast Data (e.g., NASA POWER, TAHMO)
     Provides high-resolution, historical and predictive data on humidity, rainfall, and temperature trends.
  3. AI/ML Risk Prediction Models
     Trains on weather + historical aflatoxin data to generate real-time aflatoxin risk heatmaps and forecasts.
D. Where and When? (Context, Channels, Touchpoints)
  • Where: High-risk maize-growing counties in Kenya (e.g., Makueni, Kitui, Machakos, Kakamega).
  • When: Key crop growth stages (flowering, pre-harvest, post-harvest) and during high moisture seasons.
  • Touchpoints: Mobile apps for farmers and extension officers, SMS alerts, dashboards for government, agro-dealers, and cooperatives.
E. Strengths of the Idea
  1. Predictive Power: Prevents losses before contamination happens by identifying hotspots.
  2. Scalable and Wide Coverage: Satellite data covers large areas at low cost.
  3. Supports Policy and Planning: Enables data-driven decisions at national and county levels.
F. Weaknesses of the Idea
  1. Accuracy Depends on Data Quality: Poor calibration or resolution can reduce model precision.
  2. Limited Access in Remote Areas: Farmers without smartphones or network coverage may miss alerts.
  3. Requires Initial Technical Setup: Integration of sensors, GIS, and AI systems demands upfront expertise and funding.
G. How Will Stakeholders Benefit?
  1. Farmers: Receive localized alerts and seasonal forecasts to time harvests and drying, reducing crop losses.
  2. Policy Makers & Government Agencies: Use risk maps to focus inspections, distribute safe storage, and deploy interventions efficiently.
  3. Researchers & Data Scientists: Gain new datasets to refine aflatoxin forecasting and build context-specific models.
  4. Food Processors & Exporters: Can avoid high-risk sourcing areas, ensuring compliance with food safety standards.
  5. Insurance Providers & Financiers: Use risk profiles to develop weather-indexed insurance and offer credit tied to safer zones.
I. Target Groups
  1. Smallholder Farmers in High-Risk Agroecological Zones
  2. County-Level Agricultural Extension Services
  3. National Food Safety Regulators (e.g., KEPHIS, Ministry of Health)

Which digital tools offer the most cost-effective, scalable solutions for aflatoxin monitoring in rural Kenya?

A. Problems – Major User Pains Being Addressed
  1. High cost of aflatoxin testing equipment
    Lab-based methods like HPLC or ELISA are expensive and unavailable to most rural farmers and aggregators.
  2. Low digital access and technical literacy
    Most rural farmers lack the digital infrastructure or training to use complex tools.
  3. Fragmented and manual data collection
    Food safety monitoring relies on slow, paper-based or reactive systems that lack traceability or scalability.
B. What is the Idea? (What is happening?)

The idea is to deploy low-cost, mobile-enabled aflatoxin detection and monitoring tools that are user-friendly, scalable, and designed for rural contexts. These tools combine portable diagnostics, mobile apps, and cloud-based dashboards to make aflatoxin testing and reporting faster, cheaper, and more accessible across Kenya’s grain value chains.

C. How Does the Idea Work? (Tools, Technologies Used)
  1. Mobile Phone-Based Test Kits (e.g., smartphone-integrated lateral flow devices)
    Farmers use rapid test strips and scan them with a phone app that detects aflatoxin levels instantly.
  2. USSD/SMS-based Alert and Reporting Systems
    Allows farmers and extension agents without smartphones to receive warnings and report contamination events.
  3. Cloud Dashboards & Data Platforms (e.g., KoboToolbox, Open Data Kit)
    Aggregates field-level testing results to inform supply chains, policy makers, and researchers.
D. Where and When? (Context, Channels, Touchpoints)
  • Where: On-farm, at grain collection points, and within informal markets.
  • When: During post-harvest handling, drying, and storage stages.
  • Touchpoints: Mobile apps, SMS codes, WhatsApp bots, and digital dashboards linked to county extension officers.
E. Strengths of the Idea
  1. Affordable and farmer-friendly: Built for low-tech users with minimal training.
  2. Scalable: Can be rolled out across thousands of farms without the need for lab infrastructure.
  3. Real-time traceability: Digitally logs contamination data and locations for faster interventions.
F. Weaknesses of the Idea
  1. Accuracy Limitations: Rapid tests may lack the precision of laboratory methods.
  2. Data Connectivity Issues: In remote areas, syncing data to cloud platforms may be delayed.
  3. User Training Needs: Even simple tools may require basic sensitization and support.
G. How Will Stakeholders Benefit? 
  1. Farmers: Can test crops affordably before sale, increasing market access and reducing losses.
  2. Grain Buyers & Traders: Receive verified aflatoxin data to ensure product safety and build consumer trust.
  3. Government Agencies (e.g., KEBS, Ministry of Agriculture): Gain access to real-time national contamination maps to inform interventions.
  4. NGOs and Extension Services: Use data to target training, storage support, and early warnings more effectively.
  5. Tech Startups: Get a platform to expand innovation in food safety and build public-private partnerships.
I. Target Groups
  1. Smallholder Maize and Groundnut Farmers
  2. Grain Traders and Aggregators in Informal Markets
  3. Extension Officers and Community-Based Agri-Agents