Supervision of Food Manufacturing Processes Using Optical Process Analyzers – An Overview { Viktoria Zettel [1] , Muhammad Haseeb Ahmad [1] , Tetyana Beltramo [1] , Bernhard Hermannseder [1] , Annika Hitzemann [1] , Marius Nache [1] , Olivier Paquet-Durand [1] , Thomas Scho ¨ ck [1] , Florian Hecker [1] , Bernd Hitzmann [1], * www.ChemBioEngRev.de ª 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ChemBioEng Rev 2016, 3, No. 5, 219–228 219 Abstract The food industry is the fourth largest industrial sector in Germany. The eagerness for innovation is classified as low. The food industry faces signifi- cantly larger challenges compared to the chemical industry since the demands of raw materials on pro- cessing are higher and more complex. In this contri- bution, the characteristics of food manufacturing are presented. The potential of optical process ana- lyzers based on NIR, fluorescence, and Raman spec- troscopy as well as on digital image analysis is demonstrated. These process analyzers can provide important information on raw materials, intermedi- ate and end products, and improve the automation grade of production processes. Keywords: Food monitoring, Optical sensors, Process analytics, Process control Received: July 28, 2016; accepted: July 29, 2016 DOI: 10.1002/cben.201600013 1 Introduction The food industry in Germany has an annual turnover of 172.18 billion € and is therefore the fourth biggest industrial branch (2014, Federal Office of Statistics, VCI, Germany). The three biggest industrial sectors herein are, with respect to the turnover in 2014, slaughter and meat processing (35.2 billion €), dairy processing (27.2 billion €), and baking goods and pasta (15.6 billion €). However, the cost structures in these sectors are different. The labor cost in percent of gross production value for baked goods and pasta is 31.3 %, which is roughly four times higher compared to slaughter and meat processing with 7.7 %, and dairy processing with 6.8 % (values of 2013, Federal Office of Statistics, BMEL, Germany). In Europe, the food and drink industry is the biggest sector with a turnover of 14.9 % [1]. From the point of innovation of this sector, it is estimated to be in the lower range of a ranking [2]. Many steps of the overall processing in the food industry will be performed in a time- or recipe-controlled manner. Most control actions are carried out manually. Closed loop controls are only rarely found, e.g., for temperature and filling levels. Most processes are driven by experience. The automation level is low and only a few process analyzers can be found. ‘‘If you do not measure, you cannot manage!‘‘ This state- ment, adjudged to Peter Drucker, points out the fact, that pro- cess analyzers are very important to run a process efficiently. Especially in process analytics many new techniques have been developed during the last decade. However, most of the new developments from the universities did not reach industrial applications yet. In this contribution, the characteristics of food production processes as well as modern developments of process analyzers based on optical sensors will be presented. Examples of image analysis and spectroscopic techniques based on NIR, Raman and fluorescence spectroscopy together with the applied evalu- ation methods will be discussed. Optics-based analyzers can be applied without direct contact to the sample and are therefore non-invasive. No reagent is required and no sample prepara- tion is necessary. Due to the fact that the raw measurement is available without a time delay, the evaluation is very fast. Many substances can frequently be determined simultaneously. Con- sequently, these analyzers can be used for online, in situ or real-time applications. Using practical examples, the potential of the process analyzers for the increase of the automation grade for the food industry will be discussed. ————— [1] Viktoria Zettel, Muhammad Haseeb Ahmad, Tetyana Beltramo, Bernhard Hermannseder, Annika Hitzemann, Marius Nache, Olivier Paquet-Durand, Dr. Thomas Scho ¨ck, Dr. Florian Hecker, Prof. Dr. Bernd Hitzmann (corresponding author) University of Hohenheim, Institute of Food Science and Biotech- nology, Process Analytics and Cereal Science (150i), Garbenstr. 23, 70599 Stuttgart, Germany. E-Mail: Bernd.Hitzmann@uni-hohenheim.de { updated English version of DOI: 10.1002/cite.201500097