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

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.