# Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis–NIR–SWIR Spectroscopy: A Machine Learning Approach

## TL;DR

This study uses machine learning and spectroscopy to assess how heat affects the phenolic compounds in functional flours, offering a non-destructive method for quality control.

## Contribution

A novel non-destructive method combining Vis–NIR–SWIR spectroscopy and machine learning to evaluate thermal effects on phenolic compounds in functional flours.

## Key findings

- Random Forest models achieved high accuracy in classifying flours by type, baking temperature, and phenolic concentration.
- Legume and wheat flours retained higher phenolic content under mild heat, while grape seed and olive stone flours showed stability at higher temperatures.
- Spectral ranges beyond the visible region were sufficient for accurate classification, reducing reliance on color as a confounding factor.

## Abstract

Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000–2500 nm and 1400–2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control.