World food businesses are encountering issues due to a demand-supply gap in the era of Industry 4.0. Bioprocessing companies are using artificial intelligence and machine learning tools to meet modern consumer demands. The food and bioprocessing sectors are adjusting computational intelligence according to their needs, as it is a wide and complicated field. In this line, food process development using Artificial Intelligence and Machine Learning focuses on the limitations of traditional practices in the development of engineered byproducts in terms of nutritional and olfactory characteristics, as well as how artificial intelligence can be used to overcome these constraints. Pre-plant process design is normally done by statistical measurement in the fruit, bakery, and bio-constituent recovery industries; however, a novel paradigm of modelling and validation will be provided here with the use of machine earning.

Application of swarm intelligence to tackle optimization problems, as well as a comparison of nature-inspired algorithms with statistical processes such as response surface methodology. Sensory attributes, compositional analyses, and quality attributes that result from complex organic reactions in byproducts are categorized and clustered based on feature selection. The use of fuzzy logic to specify organoleptic qualities and difficult-to-quantify packing circumstances using numerical data is reported. Microbial growth curves, dehydration kinetics, and pigmentation prediction can all be predicted using neural networks. In terms of bioactive potential and nutrient forecasting in an emulated bio composition, nutritionally fortified food material manufacturing is reported.

Another area of focus for the agricultural and bioprocess industries is industrial optimization. Researchers are using a variety of metaheuristic and hybrid strategies to improve model optimization and prediction, although these techniques have yet to be fully explored.

Data is used in both post-harvest processing and bioprocess engineering. Prior to analysis, the complex data matrix retrieved from experimental runs must be processed. In the realm of quality control analysis and food safety, regression, classification, and clustering applications are quite important.

A gold mine of possibility is being overlooked due to a lack of research on the proposed subject. Dairy science, fish-meat-poultry technology, cereal processing technology, traditional food products, and phytochemical science are among the fields where there is little study on the use of data science.


Potential topics include, but are not limited, to the following:

  • Predictive modeling and Optimisation techniques used in food science
  • Dataset development in food Artificial vision system development: Modelling techniques used to improve food quality
  • Machine learning techniques for enhancing the productivity and the quality of food products
  • Metaphase and Big Data in the Food Industry
  • Blockchain technology and Food security through smart technologies



  • Neural net
  • Fuzzy logic application
  • Data Science
  • Bio-inspired optimisation
  • Process design
  • Image analysis
  • Artificial sensory systems


Guest Editors

Lead Guest Editor

Dr. Slim Smaoui

Laboratory of Microbial Biotechnology and Engineering Enzymes (LMBEE), Center of Biotechnology of Sfax (CBS), University of Sfax, Road of Sidi Mansour Km 6, P.O. Box 1177, Sfax 3018, Tunisia

[email protected]


Dr. Tanmay Sarkar

Department of Food Processing Technology, Malda Polytechnic, West Bengal Council of Technical Education, Govt. of West Bengal, Malda-732102, India

[email protected]


Pr. Anka Trajkovska Petkoska

Faculty of Technology and Technical Sciences, St. Clement of Ohrid University of Bitola, Dimitar Vlahov, 1400 Veles, Republic of North Macedonia

[email protected]


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Last Submission due date: October 30, 2023

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