The use of Raman spectroscopy for reaction monitoring has been successfully applied over the past few decades. One complication in such usage is the applicability for quantitative reaction studies. This analytical problem is intrinsic to any reaction system, and is part of the larger transinformation necessary to bridge qualitative Raman spectroscopic information through multivariate calibration approaches. Another compounding issue is the presence of unknown component spectra that are encountered when investigating novel reactions, which is often fraught with either a lack of understanding of reaction stoichiometries/mechanisms, or inability to isolate reaction intermediates or products for obtaining their Raman spectra. To overcome these analytical challenges, three numerical approaches are tested using the model styrene–butyl acrylate co-polymerization reaction. They are partial least squares regression (PLSR), a novel minimisation of mixture spectrum residuals (MMSR) algorithm, and the first ever attempt at combining band-target entropy minimisation curve resolution with multilinear regression (BTEM-MLR). Multivariate calibrations are performed using inline Raman spectra from co-polymerization monitored with offline NMR to estimate the concentration of monomers without the need for additional information on reaction intermediates or products. All three multivariate calibration approaches produce comparable success. The specific calibration dataset utilized and relative Raman molar intensities of chemical species directly impact the quality of calibration. Furthermore, both MMSR and BTEM yield additional spectral reconstruction of the unknown co-polymer Raman spectrum.

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规避未知组分光谱的校准方法，用于定量原位拉曼监测共聚反应

在过去的几十年中，已经成功地将拉曼光谱法用于反应监测。这种用法的一个复杂之处是定量反应研究的适用性。这个分析问题对于任何反应系统都是固有的，并且是通过多变量校准方法桥接定性拉曼光谱信息所需的较大转换信息的一部分。另一个复杂的问题是，在研究新型反应时会遇到未知的组分光谱，这常常是由于对反应化学计量/机理缺乏了解，或者无法分离反应中间体或产物以获得其拉曼光谱。为了克服这些分析挑战，使用模型苯乙烯-丙烯酸丁酯共聚反应测试了三种数值方法。它们是偏最小二乘回归（PLSR），一种新颖的混合频谱残差最小化（MMSR）算法，也是将带目标熵最小化曲线分辨率与多线性回归（BTEM-MLR）相结合的首次尝试。使用离线拉曼光谱监测共聚所得的在线拉曼光谱进行多变量校准，以估计单体浓度，而无需其他有关反应中间体或产物的信息。三种多元校准方法均取得了相当的成功。使用的特定校准数据集和化学物种的相对拉曼摩尔强度直接影响校准的质量。此外，