@inproceedings{6158, author = {{Müller, Ulrich and Blome, André and Luttmann, Mario}}, location = {{Bonn}}, title = {{{Ressourceneinsparung und Individualisierung bei Lebensmittelherstellungsprozessen mit Hilfe von Industrie-4.0-Methoden - Digitalisierung startet mit der Datenbeschaffung}}}, year = {{2021}}, } @inproceedings{6171, author = {{Schneider, Jan and Dammann_, Anna and Schwarzer, Knut and Müller, Ulrich}}, location = {{Siegen}}, title = {{{Pasteurisation von Getränken: Verweilzeitverteilungen in der KZE und Chemischer Temperatur-Zeit-Indikator zur Prüfung von KZE-Anlagen}}}, year = {{2021}}, } @article{6689, abstract = {{Free amino nitrogen (FAN) concentrations in beer mash can be determined with machine learning algorithms from near-infrared (NIR) spectra. NIR spectroscopy is an alternative to a classical chemical analysis and allows for the application of inline process quality control. This study investigates the capabilities of different machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision Tree Regressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR), K-nearest neighbours (KNN) regression as well as Support Vector Regression (SVR) to predict the FAN content in beer mash from NIR spectra. Various pre-processing strategies such as principal component analysis (PCA) and data standardization were used to process NIR data that were used to train the machine learning algorithms. Algorithm training was conducted with NIR data obtained from 16 beer mashes with varying FAN concentrations. The trained models were then validated with 4 beer mashes that were not used for model training. Machine learning algorithms based on linear regression showed the highest prediction accuracy on unpre-processed data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L (R2 = 0.96) and a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The FAN concentration range of the investigated samples was between approx. 180 and 220 mg/L. Machine learning based NIR spectra analysis is an alternative to classical chemical FAN level determination methods and can also be used as inline sensor system.}}, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, issn = {{0723-1520}}, journal = {{Brewing science }}, keywords = {{mashing, NIR, machine learning, FAN}}, number = {{9/10}}, pages = {{107 -- 121}}, publisher = {{Carl}}, title = {{{Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms}}}, doi = {{https://doi.org/10.23763/BrSc21-10wefing}}, volume = {{74}}, year = {{2021}}, } @inproceedings{6734, author = {{Pauli, Daniel and Neumaier, Michael}}, location = {{Magdeburg}}, title = {{{Einsatz von technischer Sensorik im Projekt DproFood }}}, year = {{2021}}, } @inproceedings{6824, author = {{Conradi, Florian}}, location = {{Köln}}, title = {{{Recyclate Transparency – Einsatz datenintensiver und inlinefähiger Sensoren zur echtzeitfähigen, stufenübergreifenden Untersuchung von recyceltem PET}}}, year = {{2021}}, } @inproceedings{6825, author = {{Conradi, Florian}}, location = {{Hard}}, title = {{{Rezyklaterkennung in PET-­Preforms}}}, year = {{2021}}, } @inproceedings{6840, author = {{Pauli, Daniel and Wisser, Stephanie}}, location = {{Online}}, title = {{{Datenanalyse und autonome Prognostik zur Verbesserung der Transparenz und Sicherheit von Lebensmitteln}}}, year = {{2021}}, } @inproceedings{6842, author = {{Pauli, Daniel and Neumaier, Michael and Scharf, Matthias and Funke, Carsten}}, location = {{Online}}, title = {{{Von der offline zur online Qualitätskontrolle mittels Echtzeit- und Fingerprint-Analytik}}}, year = {{2021}}, } @misc{5423, abstract = {{Preservation of juices is essential to obtain microbial safe products. There are various established methods as pasteurization. Heretofore, only the kinetic figures of microbial inactivation were considered but not those of reaction impairing the chemical quality. For a gentler processing, knowledge of the kinetics of relevant chemical conversion reactions is necessary. 5-(Hydroxymethyl)-furfural (HMF) formation and the color change of juices are important attributes. The non-isothermal Rhim method was used to determine the activation energy and pre-exponential factor for HMF formation in different juices and an isothermal method for the reaction order. Values for the activation energy from 133 to 303 kJ/mol were obtained with a zeroth reaction order. A correlation between HMF and the color change could be found. Based on the kinetic figures, lines with equal effects for the chemical changes and for the lethal effect on microorganisms were calculated. Time-temperature settings for the gentlest treatment could be found.}}, author = {{Katsch, Linda and Methner, Frank-Jürgen and Schneider, Jan}}, booktitle = {{International Journal of Food Engineering }}, issn = {{1556-3758}}, keywords = {{absorption at 420 nm, HMF, kinetic figures, line of equal effect, pasteurization.}}, number = {{9}}, pages = {{703--713}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{Kinetic studies of 5-(Hydroxymethyl)-furfural formation and change of the absorption at 420 nm in fruit juices for the improvement of pasteurization plants }}}, doi = {{https://doi.org/10.1515/ijfe-2020-0324}}, volume = {{17}}, year = {{2021}}, } @misc{5504, author = {{Katsch, Linda and Schneider, Jan}}, booktitle = {{Brauwelt}}, issn = {{1439-5177}}, number = {{14}}, pages = {{340--343}}, publisher = {{Fachverlag Hans Carl GmbH}}, title = {{{Potential für eine schonendere Pasteurisation}}}, year = {{2021}}, } @misc{5418, abstract = {{Pasteurization especially high-temperature short time (HTST) heating is a widely used preservation method which inactivates microorganisms and enzymes, but also degrades compounds as L-ascorbic acid. For a gentle dimensioning of a pasteurization plant the knowledge of the kinetic figures is important. Activation energy, reaction order and pre-exponential factor of the L-ascorbic acid degradation in a model solution, apple, orange and black currant juice were determined. Lines of equal effects, which indicate different time-temperature combinations for the degradation, could be derived and compared with the lethal effect on microorganisms. The activation energies were located in the area of 25 to 44 kJ/mol for all samples except of orange juice (74 kJ/mol) in the range of 40-90 ?C with a zeroth reaction order. Based on these values, the lines of equal effects showed a lesser degradation at higher temperatures and shorter holding times even in the typical setting range of pasteurization plants.}}, author = {{Katsch, Linda and Methner, F.-J. and Schneider, Jan}}, booktitle = {{BrewingScience}}, issn = {{1613-2041}}, number = {{7/8}}, pages = {{85 -- 94}}, publisher = {{Fachverlag Hans Carl GmbH}}, title = {{{Kinetic studies of L-ascorbic acid degradation in fruit juices for the improvement of pasteurization plants}}}, doi = {{https://doi.org/10.23763/BrSc20-13katsch}}, volume = {{73}}, year = {{2020}}, } @article{5419, abstract = {{Continuous mashing provides advantages compared to conventional batch-wise mashing in terms of space time yield. The majority of fermentable sugars are generated during the so-called “β-amylase rest” (62–64 ◦C). These low molecular sugars are fermented later in the brewing process by yeasts and therefore determine the beer attenuation degree. Biological malt variations complicate the application of a continuous system in industrial scale particularly concerning targeted quality parameters. The aim is the prediction of sugar formation from process parameters for a real time control system. Therefore, a semi-empirical model for sugar formation in a continuous stirred tank reactor (CSTR) system was developed under incorporation of the residence time distri- bution (RTD). The here presented model, which focuses on the “β-amylase rest”, is able to predict fermentable sugar concentrations in the continuous “β-amylase rest” with sufficient accuracy, in contrast to models that only use the flow rate and the reactor volume to determine the reaction time. However, the precision and trueness depend on the quality of the empirical data acquired previously in laboratory experiments for the selected temperature and raw material quality.}}, author = {{Wefing, Patrick and Conradi, Florian and Trilling-Haasler, Marc and Neubauer, Peter and Schneider, Jan}}, journal = {{Biochemical Engineering Journal }}, keywords = {{Continuous mashing, Residence time distribution, Beer, Enzyme bioreactor, Maltose rest}}, title = {{{Approach for modelling the extract formation in continuous conducted "beta-amylase rest" as part of the production of beer mash with targeted sugar content}}}, doi = {{10.1016/j.bej.2020.107765}}, volume = {{164}}, year = {{2020}}, } @article{5424, abstract = {{Near infrared spectroscopy in combination with a transflection probe was investigated as inline measurement in a continuous flash pasteurizer system with a sugar-water model solution. Robustness and reproducibility of fluctuations of recorded spectra as well as trueness of the chemometric analysis were compared under different process parameter settings. Variable parameters were the flow rate (from laminar flow at 30 L/h to turbulent flow at 90 L/h), temperature (20 to 100 degrees C) and the path length of the transflection probe (2 and 4 mm) while the pressure was kept constant at 2.5 bar. Temperature and path length were identified as the most affecting parameters, in case of homogenous test medium. In case of particle containing systems, the flow rate could have an impact as well. However, the application of a PLS model, which includes a broad temperature range, and the correction of prediction results by applying a polynomial regression function for prediction errors, was able to compensate these effects. Also, a path length of 2 mm leads to a higher accuracy. The applied strategy shows that by the identification of relevant process parameters and settings as well as the establishment of a compensation strategy, near infrared spectroscopy is a powerful process analytical tool for continuous flash pasteurization systems.}}, author = {{Weishaupt, Imke and Zimmer, Manuel and Neubauer, Peter and Schneider, Jan}}, isbn = {{0022-1147}}, issn = {{1750-3841}}, journal = {{Journal of Food Science}}, keywords = {{flash pasteurization, inline near infrared spectroscopy, multivariate data analysis, process condition influences, sugar-water-solution model beverage}}, number = {{7}}, pages = {{2020 -- 2031}}, title = {{{Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer}}}, doi = {{10.1111/1750-3841.15307}}, volume = {{85}}, year = {{2020}}, } @inproceedings{5426, author = {{Zimmer, Manuel and Schneider, Jan}}, location = {{Lemgo}}, title = {{{Thermal pasteurisationof food in glass containers –Near Infrared studies as groundwork for cyber-physical systems}}}, year = {{2020}}, } @inproceedings{5427, author = {{Wefing, Patrick and Conradi, C. and Beckhoff, St.-Bumke}}, title = {{{Auf dem Weg zur Smart FOODFACTORY, Prototyp einer kontinuierlichen Maisch Anlage}}}, year = {{2020}}, } @article{5428, author = {{Schneider, Jan and Beckhoff-Bumke, S. and Ali, W.}}, journal = {{elektroniknet.de}}, title = {{{Digitalisierung - Bierbrauen 4.0}}}, year = {{2020}}, } @article{5429, author = {{Wefing, Patrick and Conradi, Florian and Trilling-Haasler, Marc and Schuster, Rudolf and Gossen, Arthur and Schneider, Jan}}, journal = {{Brauwelt}}, number = {{15 - 16}}, pages = {{413 -- 416}}, publisher = {{Carl Hanser Verlag}}, title = {{{Maischen 4.0 – kontinuierliche Maischanlage}}}, year = {{2020}}, } @misc{8405, author = {{Wefing, Patrick and Conradi, Florian and Beckhoff-Bumbke, Steffen}}, location = {{Bielefeld}}, title = {{{Closed Loop Systems Engineering}}}, year = {{2020}}, } @inproceedings{1994, abstract = {{In the filling and packaging industry, the trend is towards self-diagnosis, optimization, and quality monitoring of processes. The aim is to increase production volumes and the quality. These concepts require continuous monitoring and anomaly detection of the filling process. In addition, a root cause analysis of the failure is required because not every failure can be simulated or measured previously. Standard anomaly detection methods have no integrated root cause analysis. In this paper a fusion system is utilises for the detection of different unknown anomalies and also the failure source of them. The performance of this method is benchmarked with a real-word filling process.}}, author = {{Bator, Martyna and Dicks, Alexander and Deppe, Sahar and Lohweg, Volker}}, booktitle = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2019) }}, isbn = {{978-1-7281-0304-4}}, issn = {{1946-0759}}, location = {{ Zaragoza, Spain }}, publisher = {{IEEE}}, title = {{{Anomaly Detection with Root Cause Analysis for Bottling Process}}}, doi = {{10.1109/ETFA.2019.8869514}}, year = {{2019}}, } @inproceedings{2001, author = {{Dicks, Alexander and Wissel, Christian and Bator, Martyna and Lohweg, Volker}}, booktitle = {{Kommunikation und Bildverarbeitung in der Automation - Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2018}}, location = {{Lemgo}}, pages = {{331--345}}, publisher = {{Springer}}, title = {{{Bildverarbeitung im industriellen Umfeld von Abfüllanlagen}}}, doi = {{10.1007/978-3-662-59895-5_24}}, year = {{2019}}, } @inproceedings{6159, author = {{Müller, Ulrich and Blome, André and Meyer, A.}}, location = {{Lemgo}}, title = {{{Von I4.0 zu LM4.0 – Vielfalt, Sinnhaftigkeit und Mitwirkung von LM-Technologen}}}, year = {{2019}}, } @inproceedings{5437, author = {{Schwarzer, Knut and Katsch, Linda and Weishaupt, Imke and Schneider, Jan}}, location = {{München}}, title = {{{Cyberphysisches System zur thermischen Entkeimung von Getränken}}}, year = {{2019}}, } @article{5440, author = {{Meyer, Alexander and Blome, André and Müller, Ulrich}}, journal = {{Getreide Mehl und Brot }}, number = {{01}}, publisher = {{Verlag Moritz Schäfer}}, title = {{{Von Industrie 4.0 zu Lebensmittel 4.0 – Vielfalt, Sinnhaftigkeit und die Mitwirkung von Lebensmitteltechnologen}}}, year = {{2019}}, } @inproceedings{5443, author = {{Zhang, Fan and Pinkal, K. and Conradi, Florian and Wefing, Patrick and Schneider, Jan and Niggemann, Oliver}}, location = {{Melbourne, Australia}}, title = {{{Quality Control of Continuous Wort Production through Production Data Analysis in Latent Space}}}, year = {{2019}}, } @inproceedings{5444, author = {{Conradi, Florian and Wefing, Patrick and Schneider, Jan}}, location = {{Antwerpen}}, title = {{{Near infrared spectroscopy and mashing – a promising approach for real time inline quality control?}}}, year = {{2019}}, } @inproceedings{5445, author = {{Wefing, Patrick and Conradi, Florian and Schneider, Jan}}, location = {{Antwerpen}}, title = {{{Laboratory plant for a Continuous Closed Loop controlled Mashing aided by digital technologies}}}, year = {{2019}}, } @inproceedings{5457, author = {{Schneider, Jan and Wefing, Patrick and Conradi, Florian}}, location = {{Rust}}, title = {{{ Industry 4.0 and Continuous Mashing. - Design of a „closed loop controlled mashing“ pilot plant }}}, year = {{2019}}, } @inproceedings{5459, author = {{Wefing, Patrick and Conradi, Florian and Schneider, Jan}}, location = {{Braunschweig}}, title = {{{Industrie 4.0 in der Lebensmittelindustrie – Entwicklung einer „Closed Loop Controlled“ Maischeanlage}}}, year = {{2019}}, } @article{5464, author = {{Zimmer, Manuel and Schneider, Jan}}, journal = {{Croatian Journal of Food Science and Technology}}, publisher = {{Faculty of Food Technology Osijek}}, title = {{{Near-infrared diffuse reflectance spectroscopy for discriminating fruit and vegetable products preserved in glass containers}}}, year = {{2019}}, } @inproceedings{5465, author = {{Schneider, Jan and Weishaupt, Imke and Schwarzer, Knut and Katsch, Linda}}, location = {{München}}, title = {{{Cyberphysisches System zur thermischen Entkeimung von Getränken}}}, year = {{2019}}, } @inproceedings{5466, author = {{Zimmer, Manuel and Conradi, Florian and Wefing, Patrick and Schneider, Jan}}, location = {{Antwerpen}}, title = {{{Non-invasive on-line monitoring of the secondary bottle fermentation process using near infrared spectroscopy}}}, year = {{2019}}, } @inproceedings{5468, author = {{Zimmer, Manuel and Schneider, Jan}}, location = {{Sevilla}}, title = {{{Non-invasive diffuse reflectance near-infrared spectroscopy for on-line monitoring and characterisation of food and beverages in sealed glass containers}}}, year = {{2019}}, } @inproceedings{5469, author = {{Weishaupt, Imke and Zimmer, Manuel and Schneider, Jan}}, location = {{Sevilla}}, title = {{{Gentle Flash Pasteurization of Fruit Juices through Product Identification and Characterization using Near-Infrared Spectroscopy as Inline Analytical Method}}}, year = {{2019}}, } @misc{5470, author = {{Katsch, Linda and Schneider, Jan}}, location = {{Dresden}}, number = {{S1}}, pages = {{127}}, title = {{{Schonende Pasteurisation von Fruchtsäften: Nicht-Isotherme kinetische Untersuchung qualitätsbestimmender Inhaltsstoffe}}}, doi = {{ https://doi.org/10.1002/lemi.201951127}}, volume = {{73}}, year = {{2019}}, } @inproceedings{5471, author = {{Conradi, Florian and Wefing, Patrick and Zhang, Fan and Schneider, Jan}}, location = {{Dresden}}, number = {{S1}}, pages = {{S103--S103}}, publisher = {{WILEY‐VCH}}, title = {{{Echtzeitqualitätssicherung enzymkatalysierter technologischer Prozesse am Beispiel des Maischens im Rahmen der Bierherstellung}}}, doi = {{10.1002/lemi.201951103}}, volume = {{73}}, year = {{2019}}, } @inproceedings{5473, author = {{Sürmeli, Baris Gün and Weishaupt, Imke and Schwarzer, Knut and Schneider, Jan}}, title = {{{Beverage Classification Using Linear Discriminant Analysis with Covariance Matrix Shrinkage}}}, year = {{2019}}, } @inproceedings{5474, author = {{Schneider, Jan and Regtmeier, J. and Conradi, Florian and Wefing, Patrick}}, location = {{Bielefeld}}, title = {{{Das Labor in der Leitung – Smarte Qualitätskontrolle von Lebensmitteln durch innovative Sensortechnik}}}, year = {{2019}}, } @inproceedings{5475, author = {{Schneider, Jan and Conradi, Florian and Wefing, Patrick and Weishaupt, Imke and Zimmer, Manuel and Schwarzer, Knut}}, location = {{Lemgo}}, title = {{{Muss man die Aufheizzonen einer KZE in die PE-Berechnung einbeziehen?}}}, year = {{2019}}, } @inproceedings{5476, author = {{Schneider, Jan and Zimmer, Manuel and Weishaupt, Imke and Schattenberg, Britta and Conradi, Florian and Schwarzer, Knut}}, location = {{Bielefeld}}, title = {{{Lebensmittelverschwendung – Welchen Beitrag kann die Digitalisierung leisten? }}}, year = {{2019}}, } @inproceedings{2006, abstract = {{We present an approach for feature extraction in the context of condition monitoring of a bottling process. A special focus lies on the characterisation and evaluation of liquid textures. The approach will feed into a sensor and information fusion system to monitor a bottling process. Requirements like real-time capabilities, data reduction and resource limitations necessitate a fusion approach which capture physical effects of different sensors, extract appropriate features and combine them into one state for the complete filling process. Special attention is paid to the feature extraction of the visual sensor signals to monitor the filling level, the amount of foam and the degree of turbulence in the liquid.}}, author = {{Bator, Martyna and Wissel, Christian and Dicks, Alexander and Lohweg, Volker}}, booktitle = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, location = {{Torino, Italy}}, title = {{{Feature Extraction for a Conditioning Monitoring System in a Bottling Process.}}}, doi = {{ 10.1109/ETFA.2018.8502472}}, year = {{2018}}, } @inproceedings{6160, author = {{Blome, André and Meyer, A. and Müller, Ulrich}}, location = {{Lemgo}}, title = {{{Von I4.0 zu LM4.0 – Vielfalt, Sinnhaftigkeit und Mitwirkung von LM-Technologen}}}, year = {{2018}}, } @inproceedings{5630, author = {{Conradi, F. P. and Wefing, Patrick and Pinkal, K. and Zhang, Fan and Niggemann, Oliver and Schneider, Jan}}, location = {{Gent}}, title = {{{Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe}}}, year = {{2018}}, } @inproceedings{5631, author = {{Wefing, Patrick and Conradi, F. P. and Fuchs, Lara and Schoppmeier, J. W. and Pinkal, K. and Niggemann, Oliver and Schneider, Jan}}, location = {{Gent}}, title = {{{Laboratory plant for a closed loop-controlled continuous (CLCC) mashing}}}, year = {{2018}}, } @inproceedings{5633, author = {{Conradi, F. P. and Wefing, Patrick and Pinkal, K. and Zhang, Fan and Niggemann, Oliver and Schneider, Jan}}, location = {{Berlin}}, title = {{{Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe}}}, year = {{2018}}, } @inproceedings{5634, author = {{Wefing, Patrick and Conradi, Florian}}, location = {{Berlin}}, title = {{{Industrie 4.0 und Maischen – Aufbau einer Pilotanlage zum closed loop controlled mashing}}}, year = {{2018}}, } @inproceedings{5635, author = {{Zimmer, Manuel and Schneider, Jan}}, title = {{{Near-infrared spectroscopy on food in glass containers - discrimination studies in the context of Industry 4.0}}}, year = {{2018}}, } @article{5637, author = {{Zimmer, Manuel and Schneider, Jan}}, journal = {{FoodLab}}, number = {{4}}, title = {{{Nahinfrarot-Spektroskopie - Schlüsseltechnologie für die Pasteurisation im Zeitalter von Industrie 4.0}}}, volume = {{2018}}, year = {{2018}}, } @inproceedings{5642, author = {{Wefing, Patrick and Conradi, F. P.}}, location = {{Berlin}}, title = {{{Industrie 4.0 und Maischen – Aufbau einer Pilotanlage zum closed loop controlled mashing}}}, year = {{2018}}, } @inproceedings{5643, author = {{Zimmer, Manuel and Schneider, Jan}}, title = {{{Near-infrared spectroscopy o n food in glass containers - discrimination studies in the context of Industry 4.0}}}, year = {{2018}}, } @inproceedings{5666, author = {{Meyer, Alexander and Blome, André and Müller, Ulrich}}, editor = {{Meyer, Alexander}}, location = {{Bremerhaven}}, title = {{{Von Industrie 4.0 zu Lebensmittel 4.0 und der Einfluss der Lebensmitteltechnologie}}}, year = {{2018}}, } @inproceedings{5670, author = {{Blome, André and Meyer, Alexander and Müller, Ulrich}}, location = {{Lemgo}}, title = {{{Von I4.0 zu LM4.0 – Vielfalt, Sinnhaftigkeit und Mitwirkung von LM-Technologen) }}}, year = {{2018}}, } @inproceedings{5581, author = {{Conradi, Florian and Wefing, Patrick}}, location = {{Lemgo}}, title = {{{Innovation in der Brauerei - Konzept einer kontinuierlichen Maischeanalge durch intelligente Vernetzung mit moderner Sensortechnik}}}, year = {{2017}}, } @inproceedings{5544, author = {{Schneider, Jan}}, booktitle = {{TWA der VLB Berlin e. V., Berlin, 24.10.2016}}, location = {{Berlin}}, title = {{{Smart Food Technology – eine neue Partnerschaftinitiative von Hochschule und Industrie: welches Potenzial hat die Digitalisierung?}}}, year = {{2016}}, } @inproceedings{5553, author = {{Schneider, Jan}}, booktitle = {{Lebensmittel 4.0, Vortrag, Lemgo, 12.12.2016}}, location = {{Lemgo}}, title = {{{Potenziale von Industrie 4.0 in der Qualitätssteuerung}}}, year = {{2016}}, }