Special Issue

Special Issue on Mycotoxin Detection in Food and Water Using Emerging Harris Hawks Algorithm

Mycotoxin pollution of food and water is a serious risk to public health with consequences from acute poisoning to chronic disease. The detection of these toxic substances is still problematic due to the complexity of testing conditions and the inadequacy of conventional analytical techniques which are frequently time-consuming, costly and need expert operation. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) is changing the paradigm of food safety by enabling data-based models to detect automatically. The models are used in conjunction with biosensors, spectroscopy and imaging methods to facilitate rapid identification of contaminants. State-of-the-art optimization methods the Harris Hawks Optimization (HHO) algorithm has been under limelight for its high global search capability, flexibility and convergence rate. When used for biosensor signal processing or tuning AI models, HHO has the potential to enhance detection sensitivity, eliminate false positives and enhance feature selection in high-dimensional datasets. This makes it as a viable potential for increasing accuracy, speed and reliability of mycotoxin detection systems in a wide range of food and water matrices.

Aside from its inception in solving intricate path planning issues, the HHO algorithm is currently being investigated in biochemical sensing and data modeling tasks. For mycotoxin detection, HHO can be employed to optimize several steps of the detection workflow such as estimating biosensor parameters, adjusting classification boundaries in DL models and identifying optimal input features for spectral analysis. Relative to traditional optimization techniques HHO provides a good compromise between exploration and exploitation that guarantees strong performance even for nonlinear or noisy data sets common in food safety surveillance. Its capacity to avoid convergence to local optima makes it especially useful in intricate detection applications where signal obscuration through variability in food matrices may occur. Also, when integrated with nanomaterial-fortified biosensors HHO can facilitate the creation of portable real-time monitoring devices that are cost-efficient as well as scalable. This integration of intelligent optimization and sensing technologies is a major breakthrough in food and water safety.

The merging of AI-based models with optimization methodologies including the HHO algorithm provides a viable platform to develop mycotoxin detection in food and water. The combined power improves accuracy, sensitivity and real-time monitoring ability. Therefore, it is appropriate to bring this research forward to identify advance developments in smart detection technologies and their increasing contribution to enhancing public health and food safety surveillance systems.

Topics of Interest:

  • Spectroscopy-Assisted Detection of Mycotoxins in Agricultural Products
  • Machine Learning-Based Feature Selection for Enhanced Mycotoxin Identification
  • Deep Learning Models for Mycotoxin Detection in Water Samples
  • Nanomaterial-Enhanced Biosensors for Rapid Detection
  • Electrochemical Sensor Development for On-Site Mycotoxin Monitoring
  • Surface Plasmon Resonance Techniques in Mycotoxin Detection
  • Fluorescence-Based Imaging in Mycotoxin Detection
  • Automated Food Safety Monitoring Using Enzyme-Based Biosensors
  • Aptamer-Based Sensors Integrated with AI for Mycotoxin Screening
  • Optimization of Spectral Feature Extraction with Swarm Intelligence
  • Calibration of Biosensors Using Genetic Algorithms
  • Low-Cost Smart Sensors for Rural Mycotoxin Detection
  • Designing High-Performance Mycotoxin Detectors with Artificial Antibody Systems

Important Dates:

  • Manuscript Submission Deadline: 7th March 2026
  • Authors Notification: 7th June 2026
  • Revised Papers Due: 8th August 2026
  • Final Notification: 8th October 2026

Submission Guidelines:

The journal’s guidelines for manuscript preparation are available at:
https://itjfs.com/index.php/ijfs/about/submissions

Manuscripts must be submitted electronically via:
https://www.itjfs.com/index.php/ijfs

 

Inquiries regarding the content of the papers should be directed to:

Guest Editor:

Ahmad Abubakar Suleiman

Department of Fundamental and Applied Sciences,

Universiti Teknologi PETRONAS, Seri Iskandar,

Malaysia,

Email id: [email protected],  [email protected],

    [email protected]

G Scholar: https://scholar.google.com/citations?hl=en&user=y5QE7uMAAAAJ&