Autonomous Calibration
Brian Rohrback – Infometrix
Randy Pell – Infometrix
Scott Ramos – Infometrix

The use of chemometrics 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 both the chemometrics and the general statistics realms that can be applied to simplify the work involved in optimizing a calibration. Robust statistical techniques require some set-up of parameters, but once established for an application, they are often useable 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 solves two problems:

Optimizing Chromatographic Interpretation
Brian Rohrback – Infometrix, Inc.

The heartbeat of the process environment is in the data we collect, but we are not always efficient in translating our data streams into actionable information. The richest source of process information comes from spectrometers and chromatographs and, for many applications, these prove to be the cheapest, most adaptable, and most reliable technologies available. In chromatography, there is a rich history and the chemometrics role is well defined but rarely placed into routine practice. This paper will provide a retrospective of routine processing solutions that have solved problems in pharmaceutical, clinical, food, environmental, chemical, and petroleum applications. It also discusses how to use tech borrowed from other fields to provide more consistent and objective GC results, automate translation of the raw traces into real-time information streams, and create databases that can be used across plant sites or even across industries.