New publication: Smetana et al. (2025) J Insects Food Feed, in press

This article presents a comprehensive guide to data analysis methods for insect research in food and feed production. As the insect farming sector rapidly expands, researchers face a growing need for robust, standardized analytical tools to optimize breeding, processing, genetics, and sustainability. We bridge traditional statistical techniques like ANOVA and regression, with advanced approaches such as machine learning, bioinformatics, and life cycle assessment. The guide offers practical advice for experimental design, data handling, and model selection, catering to both newcomers and experts. By consolidating methodologies across disciplines, this BugBook chapter aims to improve research transparency, foster innovation, and ensure the replicability of findings—ultimately supporting the sustainable growth of insect-based food and feed systems.

Link to full study: doi.org/10.1163/23524588-bja10209

*Example of general structure of the neural network construction for the production of protein yields based on the parameters of the input feeds.*

References

2025

BugBook: Data analysis methods in studies of insects for food and feed

Sergiy Smetana, Carl Coudron, David Deruytter, Aditya Francis, Juan Jose Pascual, Thomas Klammsteiner, Noah Lemke, Christoph Sandrock, and Raffaele Zanoli

Journal of Insects as Food and Feed, May 2025

Abstract doi Bib PDF

Abstract In recent decades, research on insect production for food and feed has expanded significantly, driven by advances in farming, processing, genetics, and sustainability. Various data analysis methods, from traditional statistics to advanced machine learning, are used to optimise aspects of insect-based systems. In production, methods like analysis of variance (ANOVA) and regression analysis help improve breeding conditions and growth rates, while multivariate analyses support processing studies by evaluating nutritional and microbial safety. Genetic research leverages bioinformatics, genome-wide association studies (GWAS), and quantitative genetics to enhance traits like yield and disease resistance. Sustainability assessments use life cycle analysis (LCA) with Monte Carlo simulations to measure environmental impacts. Emerging tools, such as neural networks and support vector machines, are gaining traction for predicting feed conversion ratios and disease detection. Despite progress, a comprehensive guide that bridges classic and novel data analysis methods in insect research is still lacking. This study aims to address this gap by offering an accessible manual for researchers and professionals. It will consolidate methodologies across disciplines, highlighting foundational tools for beginners while showcasing advanced techniques for experts. Topics include the application of tailored methods like chitin and protein accounting, sensory analysis, consumer preference modelling, and data visualisation to improve stakeholder communication. By enhancing methodological rigor and fostering transparency, this guide will promote innovation, facilitate data interchange, and ensure the replicability of findings. Ultimately, it aims to drive sustainable advancements in the mass production of insects for food and feed.
@article{smetana_bugbook_2025, title = {{BugBook}: {Data} analysis methods in studies of insects for food and feed}, volume = {(in press)}, shorttitle = {{BugBook: Data analysis}}, url = {https://brill.com/view/journals/jiff/aop/article-10.1163-23524588-bja10209/article-10.1163-23524588-bja10209.xml}, doi = {10.1163/23524588-bja10209}, language = {eng}, number = {aop}, urldate = {2025-05-23}, journal = {Journal of Insects as Food and Feed}, author = {Smetana, Sergiy and Coudron, Carl and Deruytter, David and Francis, Aditya and Pascual, Juan Jose and Klammsteiner, Thomas and Lemke, Noah and Sandrock, Christoph and Zanoli, Raffaele}, month = may, year = {2025}, publisher = {Wageningen Academic}, keywords = {data analysis methods, genetic research, insect production, machine learning, Published Articles, sustainability assessment}, pages = {1--27}, dimensions = true, }