@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{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}},
}