@misc{7444, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, booktitle = {{38th Congress of the European Brewery Convention (EBC 2022) : held 30 May - 1 June 2022, Madrid, Spain}}, isbn = {{978-1-7138-7038-8}}, location = {{Madrid}}, publisher = {{Curran Associates, Inc.}}, title = {{{Machine learning aided free amino nitrogen determination in beer mash with an inline NIR transflectance }}}, year = {{2022}}, } @misc{8404, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, location = {{Berlin }}, title = {{{Machine learning aided free amino nitrogen determination in beer mash with an inline NIR transflectance }}}, year = {{2022}}, } @misc{8424, author = {{Katsch, Linda and Conradi, Florian and Wefing, Patrick and Schneider, Jan}}, booktitle = {{38th Congress of the European Brewery Convention (EBC 2022) : held 30 May - 1 June 2022, Madrid, Spain }}, isbn = {{978-1-7138-7038-8 }}, location = {{Madrid}}, publisher = {{Curran Associates, Inc.}}, title = {{{Determination and prediction of the final attenuation and quality parameters in beer with near-infrared spectroscopy}}}, year = {{2022}}, } @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{6732, author = {{Conradi, Florian}}, location = {{Leuven, Belgien}}, title = {{{Real time quality controlled mashing – a successful approach using inline near infrared spectroscopy and advanced data analysis}}}, 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}}, } @misc{8405, author = {{Wefing, Patrick and Conradi, Florian and Beckhoff-Bumbke, Steffen}}, location = {{Bielefeld}}, title = {{{Closed Loop Systems Engineering}}}, 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{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}}, } @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}}, } @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{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{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{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{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}}, }