A novel multiple linear multivariate NIR calibration model-based strategy for in-line monitoring of continuous mixing

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Abstract

The capability of near infra-red (NIR) spectroscopy to predict many different variables, such as concentration and humidity, has been demonstrated in many published works. Several of those articles have been in the subject of real time prediction of continuous operations. However, those demonstrations have been for narrow ranges of the variables, especially for powder concentration, which could present a nonlinear behavior of the NIR absorbance as a function of the entire range of concentration. This work developed a novel strategy to predict the entire range of powder concentration using multiple linear NIR calibration models. The root mean standard error of prediction and relative standard deviation (RSD) parameters were used to establish the number of the multiple linear calibration models; other statistical features were used to establish the correct prediction. It was found that a minimum number of linear partial least squares (PLS) calibration models were necessary to accurately predict the range from 0 to 100% w/w. This technique could also be used with other nonlinear behaviors. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3123–3132, 2014

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