@article{6839,
  abstract     = {{Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r<jats:sup>2</jats:sup> 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r<jats:sup>2</jats:sup> 0.98 ± 0.01. Thus, the model accuracy ±10–30 % is well within the standard safety margins. }},
  author       = {{Sürmeli, Baris Gün and Weishaupt, Imke and Schwarzer, Knut and Moriz, Natalia and Schneider, Jan}},
  issn         = {{1751-6552}},
  journal      = {{Journal of Near Infrared Spectroscopy}},
  keywords     = {{Beverage pasteurization, heat impact control, prediction outlier elimination}},
  number       = {{6}},
  pages        = {{339--351}},
  publisher    = {{Sage Publishing}},
  title        = {{{Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy}}},
  doi          = {{10.1177/09670335211057233}},
  volume       = {{29}},
  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}},
}

@inbook{6934,
  author       = {{Schneider, Jan}},
  booktitle    = {{50 Jahre Technische Hochschule Ostwestfalen-Lippe}},
  editor       = {{Hofmann, Martin Ludwig and Lemme, Kathrin and Löffl, Josef and Nautz, Jürgen}},
  isbn         = {{978-3-88778-622-9}},
  keywords     = {{Lebensmitteltechnologie, Lebensmittel-Ethik, Nachhaltigkeit, Institut für Lebenmitteltechnologie}},
  pages        = {{101--115}},
  publisher    = {{Spurbuchverlag}},
  title        = {{{Lebensmitteltechnologie in ihrer gesellschaftlichen Verflechtung}}},
  year         = {{2021}},
}

@inbook{7095,
  author       = {{Schattenberg, Britta and Stake, Kirsten and Schneider, Jan}},
  booktitle    = {{Behr´s Jahrbuch für die Lebensmittelwirtschaft 2022}},
  isbn         = {{978-3-95468-810-4}},
  publisher    = {{Behr´s Verlag}},
  title        = {{{Einsatzmöglichkeiten von Allulose zur Kalorienreduktion in Biermischgetränken}}},
  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}},
}

@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{12805,
  abstract     = {{n recent decades, the demand for palm oil has constantly increased and with it the cultivation of oil palms. After a period of 25 years, the oil yield of the palm trees decreases and they are felled. The trees are cut into pieces and remain on the plantations. However, due to their high moisture and sugar content, fungi and molds cause problems for replanting. The use of the wood for the timber industry is difficult due to its structural characteristics. Biotechnological processes use microorganisms to produce relevant industrial products. The basis for each process is a culture medium that contains all necessary nutrients, especially carbohydrates. The culture medium makes up a high percentage of the costs, so alternative, cheaper substrates are preferred. In this review, we show and compare different analyses of the sap mechanically pressed from the oil palm trunk regarding its sugar and nutrient content. The total sugar concentration in the palm sap varies between 16.97–140 g L<jats:sup>−1</jats:sup> and it is mainly composed of glucose, fructose, and sucrose. The comparison with common nutrient media and the results of fermentation processes already carried out on a laboratory scale show that palm sap offers great potential as a fermentation medium for biotechnological conversion into industrially relevant products. }},
  author       = {{Dirkes, Rabea and Neubauer, Pia Rebecca and Rabenhorst, Jürgen}},
  booktitle    = {{Biofuels, Bioproducts and Biorefining}},
  issn         = {{1932-1031}},
  keywords     = {{oil palm, trunks, pressed sap, Elaeis guineensis, sugar, nutrients, review}},
  number       = {{3}},
  pages        = {{931--944}},
  publisher    = {{Wiley}},
  title        = {{{Pressed sap from oil palm (<scp><i>Elaeis guineensis</i></scp>) trunks: a revolutionary growth medium for the biotechnological industry?}}},
  doi          = {{10.1002/bbb.2201}},
  volume       = {{15}},
  year         = {{2021}},
}

@misc{12835,
  abstract     = {{Delayed-release dosage forms are mainly manufactured as batch processes and include coated tablets, pellets, or particles with gastric resistant polymers. Authors propose a novel approach using the hot-melt extrusion technique to prepare delayed release dosage forms via a continuous manufacturing process, a new trend in the pharmaceutical industry. A full factorial design was employed to correlate input variables, including stearic acid (SA) content, drug content, and pellet size with drug release properties of the pellets. PLS fit method suitably elaborated the relationship between input and output variables with reasonably good fit and goodness of prediction. All three input factors influenced drug release in enzyme-free simulated gastric fluid (SGF) after 120 min; however, SA content did not significantly affect drug dissolution in the enzyme-free simulated intestinal fluid (SIF). An optimized formulation and design space were determined by overlaying multiple contours established from regression equations. The continuous manufacturing process was successfully monitored using inline near-infrared (NIR) and inline particle size analysis, with drug load and pellet size being well-controlled within the design space. The obtained pellets released less than 5% after 120 min in SGF and more than 85% and 95% after 30 min and 45 min, respectively, after switching to SIF. (C) 2020 American Pharmacists Association (R). Published by Elsevier Inc. All rights reserved.}},
  author       = {{Vo, Anh Q. and Kutz, Gerd and He, Herman and Narala, Sagar and Bandari, Suresh and Repka, Michael A.}},
  booktitle    = {{Journal of Pharmaceutical Sciences}},
  issn         = {{1520-6017}},
  keywords     = {{Continuous manufacturing, Delayed-release, FT-NIR, Inline particle size analysis, Hot melt extrusion}},
  number       = {{12}},
  pages        = {{3598--3607}},
  publisher    = {{Elsevier BV}},
  title        = {{{Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis}}},
  doi          = {{10.1016/j.xphs.2020.09.007}},
  volume       = {{109}},
  year         = {{2020}},
}

@misc{13817,
  abstract     = {{Features are extracted from electric current drive signals. The drive has intact and defective components. This results in 11 different classes with different conditions. Each condition has been measured several times by 12 different operating conditions, this means by different speeds, load moments and load forces. The current signals are measured with a current probe and an oscilloscope on two phases.}},
  author       = {{Bator, Martyna}},
  publisher    = {{UCI Machine Learning Repository}},
  title        = {{{Dataset for Sensorless Drive Diagnosis}}},
  doi          = {{10.24432/C5VP5F}},
  year         = {{2013}},
}