Autonomous Calibration and the Impact on Process Analysis and Control – The use of multivariate analysis in processing spectroscopic data is far from new; the processing of NIR data in petroleum refineries dates to the early 1980s and in the food industry well before that. Although the computers have improved in performance leading to speed ups in the calibration process, the procedures being followed have not changed significantly since the 1980s. Intriguingly, we have made decisions on the corporate level that work against each other. We are installing more spectrometers and at the same time we are reducing staffing for spectrometer calibration and maintenance. A change in approach is mandated. In the spirit of automation, there are tools from the general statistics realm that can be applied to simplify the work involved in optimizing a calibration through selection of samples. Robust techniques require some set-up of parameters, but once the parameters are established for an application, they are often usable in every other instance of that application. The result is a one-pass means of selecting optimal samples for a calibration problem and in turn simplifies the assignment of model rank. This approach is completely independent of hardware configuration and can be used with any software – plus it solves two problems: It is a selection process that can be completely automated; and It is objective and does not rely on the relative skill of a specific analyst. The ultimate goal is to integrate spectroscopic measurements in a process setting with the same simplicity-of-effort with which we install temperature sensors. Presented by Brian Rohrback, Infometrix, Inc.