Patel Janvi D, Gao Zili, He Lili
University of Massachusetts Amherst, Department of Food Science, 100 Holdsworth Way, Amherst, MA 01003, USA.
J AOAC Int. 2025 May 1;108(3):348-356. doi: 10.1093/jaoacint/qsaf005.
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.
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.
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.
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.
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.
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.
植物基产品的开发面临着诸如原材料标准化和功能特性测量耗时等挑战。傅里叶变换红外光谱（FTIR）提供了一种快速、无损的方法来分析蛋白质分子特征。
本研究探讨了FTIR对五种植物分离蛋白（大豆、绿豆、豌豆、蚕豆和小扁豆）的分类能力，并评估了其对诸如吸水性（WAC）、吸油性（OAC）、溶解度（SOL）、起泡性和乳化性等功能特性的预测能力。
使用传统测量方法计算功能特性。主成分分析（PCA）和偏最小二乘（PLS）回归分析用于研究FTIR光谱及其与功能特性的相关性。
PCA显示，基于FTIR光谱，每种蛋白质来源都有明显的聚类，表明分子差异。WAC和OAC预测模型显示出很强的相关性，预测相关系数（Rp）超过0.99，交叉验证相关系数（Rcv）在0.85至0.92之间。SOL和乳化活性指数（EAI）模型显示出有前景的潜力。此外，不同比例蛋白质混合物的WAC和OAC预测结果稳健。扩展的WAC模型预测的Rp为0.99，Rcv为0.95，而扩展的OAC模型的Rp为0.99，Rcv为0.84。
结果强调FTIR有潜力识别植物蛋白，有助于原材料验证和质量控制，并且是分析植物蛋白功能特性的一种替代方法。
本研究证明了FTIR光谱作为一种快速、无损工具用于植物蛋白表征和功能特性预测的潜力。FTIR通过基于PCA的光谱聚类成功区分了五种植物分离蛋白——大豆、绿豆、豌豆、蚕豆和小扁豆。开发了水和油吸收能力（WAC和OAC）的强预测模型，预测相关系数（Rp）值超过0.99，交叉验证相关系数（Rcv）在0.84至0.95之间。溶解度（SOL）和乳化活性指数（EAI）的功能特性预测显示出有前景的潜力。这些发现突出了FTIR在质量控制应用中对蛋白质分类、原材料验证和快速功能特性评估的能力。
