FTIR Spectroscopic Analysis of Plant Proteins and Correlation with Functional Properties.

Abstract

Background: The development of plant-based products faces challenges like raw material standardization and time-consuming functionality measurements. FTIR spectroscopy provides a quick, non-destructive way to analyze protein molecular characteristics.

Objective: This study explored the classification capability of FTIR in analyzing five plant protein isolates-soy, mung bean, pea, fava bean, and lentil-and assessed its predictive ability for functional property measurement such as water absorption capacity (WAC), oil absorption capacity (OAC), solubility (SOL), foaming, and emulsification.

Methods: Functional properties were calculated using traditional methods of measurements. Principal component analysis (PCA) and partial least-squares (PLS) regression analysis were used to study FTIR spectra and their correlation with functional properties.

Results: PCA revealed distinct clusters for each protein source based on their FTIR spectra, indicating molecular differences. WAC and OAC prediction models showed strong correlations, with prediction correlation coefficients (Rp) of more than 0.99 and cross-validation correlation coefficients (Rcv) ranging from 0.85 to 0.92. Models for SOL and emulsifying activity index (EAI) display promising potential. Moreover, WAC and OAC predictions exhibited robust results with protein blends of various ratios. The expanded WAC model predicted with an Rp of 0.99 and an Rcv of 0.95, while the expanded OAC model had an Rp of 0.99 and an Rcv of 0.84.

Conclusion: The results underscore FTIR has the potential to identify plant proteins, aiding in raw material verification and QC as well as being an alternative to analyzing functional properties of plant proteins.

Highlights: This study demonstrates the potential of FTIR spectroscopy as a rapid, non-destructive tool for plant protein characterization and functional property prediction. FTIR successfully distinguished five plant protein isolates-soy, mung bean, pea, fava bean, and lentil-through PCA-based spectral clustering. Strong predictive models for water and oil absorption capacities (WAC and OAC) were developed, with prediction correlation coefficients (Rp) values exceeding 0.99 and cross-validation correlation coefficients (Rcv) ranging from 0.84 to 0.95. Functional property predictions for solubility (SOL) and emulsifying activity index (EAI) showed promising potential. These findings highlight FTIR's capability for protein classification, raw material verification, and rapid functional property assessment in quality control applications.