Bentonite, mainly composed of montmorillonite, has unique physicochemical properties, such as a high swelling capacity, low hydraulic conductivity, and high cation exchange capacity. The properties of bentonite significantly depend on its montmorillonite content, making quantifying montmorillonite essential for evaluating bentonite. Traditional methods such as X-ray diffraction analysis often encounter difficulties due to the structural and elemental variability of clay minerals. In contrast, spectroscopy can provide a fast and cost-effective alternative with the benefits of straightforward preprocessing and measurement. This study aimed to develop calibration models for predicting montmorillonite content in bentonite using visible and near-infrared (Vis–NIR) spectral features combined with partial least squares regression (PLSR) analysis. Quartz, feldspar, Ca-bentonite (KCa-B), and Na-bentonite (GNa-B) were used in this study. Montmorillonites (KCa-M and GNa-M) were extracted from their respective bentonites. Binary and ternary mixtures of these minerals were then prepared and analyzed spectrally in the 350–2500 nm range. Correlations between montmorillonite content and spectral features were derived using PLSR, with evaluation via the leave-one-out cross-validation method. The resulting model demonstrated high accuracy with R and RMSE values of 0.917 and 8.6 wt% for Ca-montmorillonite and 0.936 and 7.5 wt% for Na-montmorillonites, respectively. Independent validation confirmed the effectiveness of the model. Furthermore, adjustments for humidity based on Vis–NIR spectral variations can potentially enhance the precision of the prediction. The study highlights the potential of Vis-NIR spectroscopy as a reliable tool for predicting montmorillonite content in bentonite.

中文翻译：

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使用可见光近红外光谱和 PLSR 分析预测膨润土中的蒙脱石含量：湿度和矿物学变异性的影响


膨润土主要成分为蒙脱石，具有独特的理化性质，如高膨胀能力、低导水率、高阳离子交换能力等。膨润土的性能很大程度上取决于其蒙脱石含量，因此定量蒙脱石对于评估膨润土至关重要。由于粘土矿物的结构和元素变化，X 射线衍射分析等传统方法经常遇到困难。相比之下，光谱学可以提供一种快速且经济高效的替代方案，具有直接预处理和测量的优点。本研究旨在开发校准模型，利用可见光和近红外（Vis-NIR）光谱特征并结合偏最小二乘回归（PLSR）分析来预测膨润土中的蒙脱石含量。本研究中使用了石英、长石、钙膨润土 (KCa-B) 和钠膨润土 (GNa-B)。蒙脱土（KCa-M 和 GNa-M）是从各自的膨润土中提取的。然后制备这些矿物的二元和三元混合物，并在 350-2500 nm 范围内进行光谱分析。使用 PLSR 得出蒙脱石含量和光谱特征之间的相关性，并通过留一交叉验证方法进行评估。所得模型显示出较高的准确性，钙蒙脱石的 R 和 RMSE 值分别为 0.917 和 8.6 wt%，钠蒙脱石的 R 和 RMSE 值分别为 0.936 和 7.5 wt%。独立验证证实了该模型的有效性。此外，根据可见光-近红外光谱变化调整湿度可以潜在地提高预测的精度。该研究强调了可见光-近红外光谱作为预测膨润土中蒙脱石含量的可靠工具的潜力。