Özdoğan Gözde, Gowen Aoife
School of Biosystems and Food Engineering, University College Dublin (UCD), Belfield, Dublin, D04 V1W8, Ireland.
Curr Res Food Sci. 2025 Apr 12;10:101054. doi: 10.1016/j.crfs.2025.101054. eCollection 2025.
Protein and gluten content is one of the most crucial quality characteristics in the wheat industry. However, these properties are measured after grinding wheat kernels into the flour. In this study, grain samples from 38 different wheat cultivars were collected, and their protein, wet and dry gluten content were measured traditionally. Spectral information was obtained using three non-destructive instruments, including benchtop visible-near infrared hyperspectral imaging (HSI), portable short wavelength infrared HSI and Fourier-Transform near-infrared spectroscopy from both whole grains and their flour samples. Partial least squares regression (PLSR) and Gaussian process regression (GPR) with three spectral pre-treatments were used to compare performances and Neighborhood Component Analysis was applied for wavelength selection. Through HSI, wheat kernels revealed their protein and gluten content with remarkable precision, achieving R values exceeding 0.97 using GPR based on whole kernel data utilising four wavelengths in the Visible range. The key novelty of this work is that it demonstrates the suitability of visible range hyperspectral imaging for direct prediction of protein and gluten with high accuracy, without the need for sample grinding, thus underscoring the significance of visible spectral information in determining protein and gluten-related parameters.
蛋白质和麸质含量是小麦产业中最关键的品质特征之一。然而，这些特性是在将小麦籽粒磨成面粉后进行测量的。在本研究中，收集了38个不同小麦品种的籽粒样本，并传统地测量了它们的蛋白质、湿面筋和干面筋含量。使用三种无损仪器获取光谱信息，包括台式可见-近红外高光谱成像（HSI）、便携式短波红外HSI以及来自全谷物及其面粉样本的傅里叶变换近红外光谱。使用具有三种光谱预处理的偏最小二乘回归（PLSR）和高斯过程回归（GPR）来比较性能，并应用邻域成分分析进行波长选择。通过HSI，小麦籽粒能够以极高的精度显示其蛋白质和麸质含量，基于全籽粒数据利用可见范围内的四个波长通过GPR实现的R值超过0.97。这项工作的关键新颖之处在于，它证明了可见范围高光谱成像适用于直接高精度预测蛋白质和麸质，无需样本研磨，从而突出了可见光谱信息在确定蛋白质和麸质相关参数方面的重要性。
