Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ( R p 2 ) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

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用于快速测定氨基树脂固含量的近红外光谱

近红外 (NIR) 光谱是一种快速可靠的评估氨基树脂性能的技术。定义这些树脂的成本和性能的一项重要特性是固体含量 (SC)。这项工作通过将 NIR 光谱与偏最小二乘 (PLS) 回归相结合，研究了氨基树脂 SC 的预测。共获得了 990 个氨基树脂的工业 NIR 光谱，并按照 2/3 的比例随机拆分用于校准，1/3 用于验证。最佳模型实现了 0.32% (m/m) 的预测均方根误差 (RMSEP) 和 81% 的预测确定系数 (R p 2 )。发现标准正态变量 (SNV) 是 NIR 预处理，可为模型构建提供最佳结果。向两种氨基树脂中加入水表明 NIR 模型对水的加入没有反应，尽管水对 SC 值有很大贡献。由此可以得出的推论是，氨基树脂的 NIR 模型使用与 SC 相关的氨基树脂的 NIR 特性，并从中预测最可能的 SC，而不是查看影响氨基树脂 SC 的所有成分.