Development of chemometric models based on spectroscopic data to predict food quality parameters

• Grant number: 24/16277-6
• Support Opportunities: Regular Research Grants
• Start date: March 01, 2026
• End date: February 28, 2029
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Analytical Chemistry
• Principal Investigator: Wanessa Melchert Mattos
• Grantee: Wanessa Melchert Mattos
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• City of the host institution: Piracicaba
• Associated researchers: Douglas Fernandes Barbin ; Gisele Gonçalves Bortoleto

Abstract

With technological advancements, the food industry has shown increasing interest in improving quality parameters, as the composition of inputs directly impacts the final product. Integrating analytical methodologies and chemometrics provides an effective approach to managing the large volume of data generated by automation, computerization, and modernization of analytical instrumentation. Mathematical models further facilitate rapid and accurate evaluations. In the food industry, chemometrics is essential for exploring and interpreting relevant data, verifying markers, authenticating foods, developing mathematical models for process evaluation, and analyzing interactions between different quality parameters. Combining chemometrics and analytical techniques enables the development of analytical procedures that require little or no pretreatment and are rapid, consistent, objective, and cost-effective. This project aims to apply chemometric tools in the food industry, specifically focusing on coffee beans and hops. The goal is to develop emerging technologies and analytical methods, exploring innovative ways to assess product quality following green analytical chemistry principles. The project will utilize chemical composition assessments, reflectance and fluorescence data, and hyperspectral imaging analysis in the Vis-SWIR range to achieve this. (AU)