On‐line near infrared (NIR) spectroscopy enables detailed monitoring of many important types of industrial fermentation processes to document progress and to ensure comparable end‐product quality. In this work a newly developed modification of multivariate statistical process control (MSPC) charts based on on‐line NIR allows easy and efficient identification of abnormal fermentation runs, even at an early stage of the fermentation, which is critical for industrial production monitoring. This study especially focuses on alignment of cultivations through usage of three different time concepts: absolute time, relative time and ‘biological process time’. For the investigated batch fermentation the absolute time concept for alignment is not acceptable. Using the relative time concept or the ‘biological process time’ concept gave similar possibilities for early warning of abnormal batch runs for completed runs. For on‐line use, however, the relative time concept cannot be used, which is the reason for our development and introduction of a new ‘biological process time’ concept. Copyright © 2004 John Wiley & Sons, Ltd.

NIR instruments in combination with multivariate data calibration models are useful tools in process monitoring. The calibration models often need to overcome matrix effects and must be re-calibrated over time to compensate for, e.g., changes in process parameters or raw materials, a task often involving manual expert assistance, which is a bottleneck for more widespread application of the technique. To ease the re-calibration, the BIOPRO consortium launched the “Hands free” project to research and develop automated methods for handling data sets and calibration of partial least squares (PLS) models. The intention was to find methods and algorithms which could be operated either by non-experts or fully automatically. Part of this effort was to develop a graphical user interface (GUI) application in MATLAB to enable rapid dissemination of methods to partners in the consortium and early testing by non-programmers. The resulting application, called Fulgur, can assist in handling near infrared (NIR) instrument calibration datasets, identifying outliers and developing and assessing PLS model performance with standard plots. Plots are focused on performance over time as it is usual to collect NIR data over longer time spans when monitoring industry processes. In this article, we present an overview of the application for end-users and explain the outlier detection and model performance plotting in more depth. Source code for the application together with a more in-depth user guide is available for download from models.life.ku.dk. In the next article, we will introduce the source code for potential developers and the principles behind the programming architecture used to develop the application.

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Near infrared (NIR) instruments in combination with multivariate data calibration models are useful tools in process monitoring. However, the need for expert assistance with calibration and maintenance of the models is a bottleneck limiting their more widespread use. In the previous article [NIR news 26(8), 13-16 (2015)], the MATLAB graphical application called Fulgur was introduced. Fulgur was developed as part of the BIOPRO "Hands Free" project with the goal of developing automated methods for handling data sets and calibrations of partial least squares models for NIR instruments. In this article, the Model-View-Controller programming design principles needed for developing the application are described in more depth as an introduction for potential developers who may want to use and expand the Fulgur framework. The programing design principles may also be of interest to chemometricians who want to program and develop their own GUI applications in MATLAB. The source code for the application together with more in-depth user and developer guides and tutorials for programming plugins is available for download from models.life.ku.dk.