Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer.

Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models.

An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R² and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses.

It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach.

校准转移或标准化的目的是在不同的光谱仪器上或在变化的条件下创建统一的光谱响应，而无需针对每种情况进行完整的重新校准。在当前的研究中，该策略被用于构建在线多元校准模型，并随后使用同一光谱仪在连续的工业生产线中在线使用它们。

首先，在实验室规模下在线建立定量多元模型，以预测硬表面清洁剂中两种主要成分的浓度。通过使一组小规模校准样品的拉曼光谱相对于其参考浓度值回归，开发了偏最小二乘（PLS）模型以量化所研究的液体洗涤剂组合物中的表面活性剂含量。在用一组独立的验证样本评估模型性能之后，考虑到将这些在线校准模型传输到在线制造装置中，应用单变量斜率/偏差校正。通过简单地校正在线设置中的模型预测，该标准化技术可以快速轻松地转移PLS回归模型，

为了评估转移的回归模型的预测质量，进行了广泛的统计分析。在转移之前和之后，将两个模型的R ²和RMSEP进行比较，以评估它们的大小是否相似。然后执行T检验以调查转移的回归线的斜率和截距在统计上是否分别与1和0没有区别。此外，检查是否没有明显的偏差。还执行F检验，以评估转移回归线的线性并调查转移和验证回归线的统计一致性。最后，一对t-使用间隔假设进行测试以将原始的在线模型与经斜率/偏置校正的在线模型进行比较。

结果表明，在校正斜率/偏差后，表面活性剂1和表面活性剂2的校准模型可产生令人满意的在线预测。尽管表面活性剂1通过了八项统计测试中的七项，但推荐的验证参数对表面活性剂2的成功率为100％。因此，可以得出结论，建议的策略是通过单变量斜率/预测值的偏差校正提供了成功的标准化方法。