Queiroz Ana Luiza P, Kerins Brian M, Yadav Jayprakash, Farag Fatma, Faisal Waleed, Crowley Mary Ellen, Lawrence Simon E, Moynihan Humphrey A, Healy Anne-Marie, Vucen Sonja, Crean Abina M
SSPC Pharmaceutical Research Centre, School of Pharmacy, University College Cork, Cork, Ireland.
SSPC Pharmaceutical Research Centre, School of Pharmacy, Trinity College Dublin, Dublin, Ireland.
Cellulose (Lond). 2021;28(14):8971-8985. doi: 10.1007/s10570-021-04093-1. Epub 2021 Jul 27.
Microcrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine MCC crystallinity index (%CI) from Raman spectra of 30 commercial batches using Raman probes with spot sizes of 100 µm (MR probe) and 6 mm (PhAT probe). A principal component analysis model separated Raman spectra of the same samples captured using the different probes. The %CI was determined using a previously reported univariate model based on the ratio of the peaks at 380 and 1096 cm. The univariate model was adjusted for each probe. The %CI was also predicted from spectral data from each probe using partial least squares regression models (where Raman spectra and univariate %CI were the dependent and independent variables, respectively). Both models showed adequate predictive power. For these models a general reference amorphous spectrum was proposed for each instrument. The development of the PLS model substantially reduced the analysis time as it eliminates the need for spectral deconvolution. A web application containing all the models was developed.
The online version contains supplementary material available at 10.1007/s10570-021-04093-1.
微晶纤维素（MCC）是一种半结晶材料，由于原材料来源和制造条件的变化，其结晶度具有固有变异性。MCC结晶度的变异性会导致下游工艺的变异性。本研究的目的是建立模型，使用光斑尺寸为100μm的拉曼探针（MR探针）和6mm的拉曼探针（PhAT探针），从30个商业批次的拉曼光谱中确定MCC结晶度指数（%CI）。主成分分析模型分离了使用不同探针采集的相同样品的拉曼光谱。使用先前报道的基于380和1096cm处峰的比率的单变量模型确定%CI。对每个探针的单变量模型进行了调整。还使用偏最小二乘回归模型（其中拉曼光谱和单变量%CI分别为因变量和自变量）从每个探针的光谱数据预测%CI。两个模型均显示出足够的预测能力。对于这些模型，为每种仪器提出了一个通用的参考非晶光谱。PLS模型的开发大大减少了分析时间，因为它无需进行光谱去卷积。开发了一个包含所有模型的网络应用程序。
在线版本包含可在10.1007/s10570-021-04093-1获取的补充材料。
