References
Badr, S., Okamura, K., Takahashi, N., Ubbenjans, V., Shirahata, H., &
Sugiyama, H. (2021). Integrated design of biopharmaceutical
manufacturing processes: Operation modes and process configurations for
monoclonal antibody production. Computers and Chemical
Engineering, 153 . doi:10.1016/j.compchemeng.2021.107422
Borras, E., Ferre, J., Boque, R., Mestres, M., Acena, L., & Busto, O.
(2015). Data fusion methodologies for food and beverage authentication
and quality assessment - A review. Analytica Chimica Acta, 891 ,
1-14. doi:10.1016/j.aca.2015.04.042
Breiman, L. (2001). Random forests. Machine Learning, 45 (1),
5-32. doi:Doi 10.1023/A:1010933404324
Brestich, N., Rüdt, M., Büchler, D., & Hubbuch, J. (2018). Selective
protein quantification for preparative chromatography using variable
pathlength UV/Vis spectroscopy and partial least squares regression.Chemical Engineering Science, 176 , 157-164.
doi:10.1016/j.ces.2017.10.030
Burgstaller, D., Jungbauer, A., & Satzer, P. (2019). Continuous
integrated antibody precipitation with two-stage tangential flow
microfiltration enables constant mass flow. Biotechnology and
Bioengineering, 116 (5), 1053-1065. doi:10.1002/bit.26922
Capito, F., Skudas, R., Kolmar, H., & Stanislawski, B. (2013). Host
cell protein quantification by fourier transform mid infrared
spectroscopy (FT-MIR). Biotechnology and Bioengineering, 110 (1),
252-259. doi:10.1002/bit.24611
Carta, G., & Jungbauer, A. (2010). Protein Chromatography:
Process Development and Scale-Up .
Cataldo, A. L., Burgstaller, D., Hribar, G., Jungbauer, A., & Satzer,
P. (2020). Economics and ecology: Modelling of continuous primary
recovery and capture scenarios for recombinant antibody production.Journal of Biotechnology, 308 , 87-95.
doi:10.1016/j.jbiotec.2019.12.001
Chen, C. S., Ando, K., Yoshimoto, N., & Yamamoto, S. (2021). Linear
flow-velocity gradient chromatography—An efficient method for
increasing the process efficiency of batch and continuous capture
chromatography of proteins. Biotechnology and Bioengineering,
118 (3), 1262-1272. doi:10.1002/bit.27649
Chmielowski, R. A., Mathiasson, L., Blom, H., Go, D., Ehring, H., Khan,
H., . . . Roush, D. (2017). Definition and dynamic control of a
continuous chromatography process independent of cell culture titer and
impurities. Journal of Chromatography A, 1526 , 58-69.
doi:10.1016/j.chroma.2017.10.030
Christler, A., Scharl, T., Sauer, D. G., Köppl, J., Tscheließnig, A.,
Toy, C., . . . Dürauer, A. (2021). Technology transfer of a monitoring
system to predict product concentration and purity of biopharmaceuticals
in real-time during chromatographic separation. Biotechnology and
Bioengineering, 118 (10), 3941-3952. doi:10.1002/bit.27870
Davis, R. R., Suber, F., Heller, I., Yang, B., & Martinez, J. (2021).
Improving mAb capture productivity on batch and continuous downstream
processing using nanofiber PrismA adsorbents. Journal of
Biotechnology, 336 , 50-55. doi:10.1016/j.jbiotec.2021.06.004
di Sciascio, F., & Amicarelli, A. N. (2008). Biomass estimation in
batch biotechnological processes by Bayesian Gaussian process
regression. Computers & Chemical Engineering, 32 (12), 3264-3273.
doi:https://doi.org/10.1016/j.compchemeng.2008.05.015
Ding, C., Ardeshna, H., Gillespie, C., & Ierapetritou, M. (2022).
Process design of a fully integrated continuous biopharmaceutical
process using economic and ecological impact assessment.Biotechnology and Bioengineering, 119 (12), 3567-3583.
doi:10.1002/bit.28234
Ender, L., & Maciel Filho, R. (2003) Neural networks applied to a
multivariable nonlinear control strategies. In: Vol. 15. Computer
Aided Chemical Engineering (pp. 190-195).
Erickson, C. B., Ankenman, B. E., & Sanchez, S. M. (2018). Comparison
of Gaussian process modeling software. European Journal of
Operational Research, 266 (1), 179-192.
doi:https://doi.org/10.1016/j.ejor.2017.10.002
Eslami, T., Jakob, L. A., Satzer, P., Ebner, G., Jungbauer, A., &
Lingg, N. (2022a). Productivity for free: Residence time gradients
during loading increase dynamic binding capacity and productivity.Separation and Purification Technology, 281 .
doi:10.1016/j.seppur.2021.119985
Eslami, T., Steinberger, M., Csizmazia, C., Jungbauer, A., & Lingg, N.
(2022b). Online optimization of dynamic binding capacity and
productivity by model predictive control. Journal of
Chromatography A, 1680 . doi:10.1016/j.chroma.2022.463420
Fahrmeir, L., Kneib, T., & Lang, S. (2004). Penalized structured
additive regression for space-time data: A Bayesian perspective.Statistica Sinica, 14 (3), 731-761.
Farid, S. S. (2019). Integrated Continuous Biomanufacturing:
Industrialization on the Horizon. Biotechnology Journal, 14 (2).
doi:10.1002/biot.201800722
Farid, S. S., Pollock, J., & Ho, S. V. (2015). Evaluating the Economic
and Operational Feasibility of Continuous Processes for Monoclonal
Antibodies. In Continuous Processing in Pharmaceutical
Manufacturing (pp. 433-456).
Feidl, F., Garbellini, S., Vogg, S., Sokolov, M., Souquet, J., Broly,
H., . . . Morbidelli, M. (2019). A new flow cell and chemometric
protocol for implementing in-line Raman spectroscopy in chromatography.Biotechnology Progress, 35 (5). doi:10.1002/btpr.2847
Feidl, F., Vogg, S., Wolf, M., Podobnik, M., Ruggeri, C., Ulmer, N., . .
. Morbidelli, M. (2020). Process-wide control and automation of an
integrated continuous manufacturing platform for antibodies.Biotechnology and Bioengineering, 117 (5), 1367-1380.
doi:10.1002/bit.27296
Felfödi, E., Scharl, T., Melcher, M., Dürauer, A., Wright, K., &
Jungbauer, A. (2020). Osmolality is a predictor for model-based real
time monitoring of concentration in protein chromatography.Journal of Chemical Technology and Biotechnology, 95 (4),
1146-1152. doi:10.1002/jctb.6299
Gareth, J., Witten, D., Hastie, T., & Tibishirani, R. (2021).Elements of Statistical Learning .
Gerstweiler, L., Bi, J., & Middelberg, A. P. J. (2021). Continuous
downstream bioprocessing for intensified manufacture of
biopharmaceuticals and antibodies. Chemical Engineering Science,
231 . doi:10.1016/j.ces.2020.116272
Gerstweiler, L., Billakanti, J., Bi, J., & Middelberg, A. P. J. (2022).
Control strategy for multi-column continuous periodic counter current
chromatography subject to fluctuating inlet stream concentration.Journal of Chromatography A, 1667 .
doi:10.1016/j.chroma.2022.462884
Ghisaidoobe, A. B. T., & Chung, S. J. (2014). Intrinsic Tryptophan
Fluorescence in the Detection and Analysis of Proteins: A Focus on
Forster Resonance Energy Transfer Techniques. International
Journal of Molecular Sciences, 15 (12), 22518-22538.
doi:10.3390/ijms151222518
Ghose, S., Nagarath, D., Hubbard, B., Brooks, C., & Cramer, S. M.
(2004a). Erratum: Use and optimization of a dual-flowrate loading
strategy to maximize throughput in protein-A affinity chromatography
(Biotechnology Progress (2004) 20 (830-840)). Biotechnology
Progress, 20 (5), 1614. doi:10.1021/bp040029x
Ghose, S., Nagrath, D., Hubbard, B., Brooks, C., & Cramer, S. M.
(2004b). Use and optimization of a dual-flowrate loading strategy to
maximize throughput in protein-A affinity chromatography.Biotechnology Progress, 20 (3), 830-840. doi:10.1021/bp0342654
Glassey, J., Ignova, M., Ward, A. C., Montague, G. A., & Morris, A. J.
(1997). Bioprocess supervision: Neural networks and knowledge based
systems. Journal of Biotechnology, 52 (3), 201-205. doi:Doi
10.1016/S0168-1656(96)01645-8
Godawat, R., Brower, K., Jain, S., Konstantinov, K., Riske, F., &
Warikoo, V. (2012). Periodic counter-current chromatography - design and
operational considerations for integrated and continuous purification of
proteins. Biotechnology Journal, 7 (12), 1496-1508.
doi:10.1002/biot.201200068
Gomis-Fons, J., Andersson, N., & Nilsson, B. (2020). Optimization study
on periodic counter-current chromatography integrated in a monoclonal
antibody downstream process. Journal of Chromatography A, 1621 .
doi:10.1016/j.chroma.2020.461055
Gomis-Fons, J., Yamanee-Nolin, M., Andersson, N., & Nilsson, B. (2021).
Optimal loading flow rate trajectory in monoclonal antibody capture
chromatography. Journal of Chromatography A, 1635 , 461760.
doi:https://doi.org/10.1016/j.chroma.2020.461760
Guidance for Industry: PAT-A Framework for Innovative Pharmaceutical
Development, Manufacturing, and Quality Assurance. (2004). US
Department of Health and Human Services Food and Drug Administration .
Heeter, G. A., & Liapis, A. I. (1996). Multi-component perfusion
chromatography in fixed bed and periodic counter current column
operation. Journal of Chromatography A, 734 (1), 105-123.
doi:10.1016/0021-9673(95)01147-1
Helgers, H., Schmidt, A., & Strube, J. (2022). Towards Autonomous
Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing
Using a Dynamic Metabolic Model. Processes, 10 (2).
doi:10.3390/pr10020316
Hofner, B., Boccuto, L., & Göker, M. (2015). Controlling false
discoveries in high-dimensional situations: Boosting with stability
selection. BMC Bioinformatics, 16 (1).
doi:10.1186/s12859-015-0575-3
Hofner, B., Hothorn, T., Kneib, T., & Schmid, M. (2011). A framework
for unbiased model selection based on boosting. Journal of
Computational and Graphical Statistics, 20 (4), 956-971.
doi:10.1198/jcgs.2011.09220
Hong, M. S., Severson, K. A., Jiang, M., Lu, A. E., Love, J. C., &
Braatz, R. D. (2018). Challenges and opportunities in biopharmaceutical
manufacturing control. Computers and Chemical Engineering, 110 ,
106-114. doi:10.1016/j.compchemeng.2017.12.007
Hutter, C., von Stosch, M., Bournazou, M. N. C., & Butté, A. (2020).
Knowledge Transfer Across Cell Lines Using Hybrid Gaussian Process
Models With Entity Embedding Vectors. Knowledge transfer across
cell lines using Hybrid Gaussian Process models with entity embedding
vectors .
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An
introduction to statistical learning. 112 .
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021a).
Classification. An Introduction to Statistical Learning: With
Applications in R , 129-195.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021b). An
Introduction to Statistical Learning: with Applications in R.
Konstantinov, K. B., & Cooney, C. L. (2015). White paper on continuous
bioprocessing May 20-21, 2014 continuous manufacturing symposium.Journal of Pharmaceutical Sciences, 104 (3), 813-820.
doi:10.1002/jps.24268
Kornecki, M., & Strube, J. (2018). Process analytical technology for
advanced process control in biologics manufacturing with the aid of
macroscopic kinetic modeling. Bioengineering, 5 (1).
doi:10.3390/bioengineering5010025
Krippl, M., Dürauer, A., & Duerkop, M. (2020). Hybrid modeling of
cross-flow filtration: Predicting the flux evolution and duration of
ultrafiltration processes. Separation and Purification Technology,
248 . doi:10.1016/j.seppur.2020.117064
Krippl, M., Kargl, T., Duerkop, M., & Dürauer, A. (2021). Hybrid
modeling reduces experimental effort to predict performance of serial
and parallel single-pass tangential flow filtration. Separation
and Purification Technology, 276 . doi:10.1016/j.seppur.2021.119277
Kumar, A., Udugama, I. A., Gargalo, C. L., & Gernaey, K. V. (2020). Why
is batch processing still dominating the biologics landscape? Towards an
integrated continuous bioprocessing alternative. Processes,
8 (12), 1-19. doi:10.3390/pr8121641
Lali, N., Jungbauer, A., & Satzer, P. (2022). Traceability of products
and guide for batch definition in integrated continuous
biomanufacturing. Journal of Chemical Technology and
Biotechnology, 97 (9), 2386-2392. doi:10.1002/jctb.6953
Lecun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon,
I., . . . Hubbard, W. (1989). Handwritten Digit Recognition -
Applications of Neural Network Chips and Automatic Learning. Ieee
Communications Magazine, 27 (11), 41-46. doi:Doi 10.1109/35.41400
Lee, C., Lee, J., & Lee, A. (2000). Statistics for Business and
Financial Economics, Vol. 1. . World Scientific 2000 .
Lee, J., Shin, J., & Realff, M. J. (2018). Machine learning: Overview
of the recent progresses and implications for the process systems
engineering field. Computers & Chemical Engineering, 114 ,
111-121. doi:10.1016/j.compchemeng.2017.10.008
Li, X. Y., Luan, F., Si, H. Z., Hu, Z., & Liu, M. C. (2007). Prediction
of retention times for a large set of pesticides or toxicants based on
support vector machine and the heuristic method. Toxicology
Letters, 175 (1-3), 136-144. doi:10.1016/j.toxlet.2007.10.005
Li, Z., Gu, Q., Coffman, J. L., Przybycien, T., & Zydney, A. L. (2019).
Continuous precipitation for monoclonal antibody capture using
countercurrent washing by microfiltration. Biotechnology Progress,
35 (6). doi:10.1002/btpr.2886
Liggins, M., Hall, D., & Llinas, J. (2017). Handbook of
Multisensor Data Fusion: Theory and Practice .
Lopes, J. A., Costa, P. F., Alves, T. P., & Menezes, J. C. (2004).
Chemometrics in bioprocess engineering: Process analytical technology
(PAT) applications. Chemometrics and Intelligent Laboratory
Systems, 74 (2 SPEC.ISS.), 269-275. doi:10.1016/j.chemolab.2004.07.006
Luttmann, R., Bracewell, D. G., Cornelissen, G., Gernaey, K. V.,
Glassey, J., Hass, V. C., . . . Mandenius, C. F. (2012). Soft sensors in
bioprocessing: A status report and recommendations. Biotechnology
Journal, 7 (8), 1040-1048. doi:10.1002/biot.201100506
Mandenius, C. F., & Gustavsson, R. (2015). Mini-review: Soft sensors as
means for PAT in the manufacture of bio-therapeutics. Journal of
Chemical Technology and Biotechnology, 90 (2), 215-227.
doi:10.1002/jctb.4477
Melcher, M., Scharl, T., Spangl, B., Luchner, M., Cserjan, M., Bayer,
K., . . . Striedner, G. (2015). The potential of random forest and
neural networks for biomass and recombinant protein modeling in
Escherichia coli fed-batch fermentations. Biotechnology Journal,
10 (11), 1770-1782. doi:10.1002/biot.201400790
Minton, A. P. (2016). Recent applications of light scattering
measurement in the biological and biopharmaceutical sciences.Analytical Biochemistry, 501 , 4-22. doi:10.1016/j.ab.2016.02.007
Müller, D., Klein, L., Lemke, J., Schulze, M., Kruse, T., Saballus, M.,
. . . Zijlstra, G. (2022). Process intensification in the biopharma
industry: Improving efficiency of protein manufacturing processes from
development to production scale using synergistic approaches.Chemical Engineering and Processing - Process Intensification,
171 . doi:10.1016/j.cep.2021.108727
Narayanan, H., Seidler, T., Luna, M. F., Sokolov, M., Morbidelli, M., &
Butté, A. (2021). Hybrid Models for the simulation and prediction of
chromatographic processes for protein capture. Journal of
Chromatography A, 1650 . doi:10.1016/j.chroma.2021.462248
Nikita, S., Thakur, G., Jesubalan, N. G., Kulkarni, A., Yezhuvath, V.
B., & Rathore, A. S. (2022). AI-ML applications in bioprocessing: ML as
an enabler of real time quality prediction in continuous manufacturing
of mAbs. Computers and Chemical Engineering, 164 .
doi:10.1016/j.compchemeng.2022.107896
Oliveira, A. L. (2019). Biotechnology, Big Data and Artificial
Intelligence. Biotechnology Journal, 14 (8).
doi:10.1002/biot.201800613
Pappenreiter, M., Döbele, S., Striedner, G., Jungbauer, A., & Sissolak,
B. (2022). Model predictive control for steady-state performance in
integrated continuous bioprocesses. Bioprocess and Biosystems
Engineering, 45 (9), 1499-1513. doi:10.1007/s00449-022-02759-z
Patel, B. A., Gospodarek, A., Larkin, M., Kenrick, S. A., Haverick, M.
A., Tugcu, N., . . . Richardson, D. D. (2018). Multi-angle light
scattering as a process analytical technology measuring real-time
molecular weight for downstream process control. Mabs, 10 (7),
945-950. doi:10.1080/19420862.2018.1505178
Patil, R., & Walther, J. (2018) Continuous manufacturing of recombinant
therapeutic proteins: Upstream and downstream technologies. In:
Vol. 165. Advances in Biochemical Engineering/Biotechnology (pp.
277-322).
Ramakrishna, A., Maranholkar, V., Hadpe, S., Iyer, J., & Rathore, A.
(2022). Optimization of multi flow rate loading strategy for process
intensification of Protein A chromatography. Journal of
Chromatography Open, 2 , 100049.
doi:https://doi.org/10.1016/j.jcoa.2022.100049
Rasmussen, C. E., & Gaussian, W. C. (2021). Processes for Machine
Learning .
Rathore, A., Li, X. H., Bartkowski, W., Sharma, A., & Lu, Y. F. (2009).
Case Study and Application of Process Analytical Technology (PAT)
towards Bioprocessing: Use of Tryptophan Fluorescence as At-line Tool
for Making Pooling Decisions for Process Chromatography.Biotechnology Progress, 25 (5), 1433-1439. doi:10.1002/btpr.212
Rathore, A., Nikita, S., & Jesubalan, N. G. (2022a). Digitization in
bioprocessing: The role of soft sensors in monitoring and control of
downstream processing for production of biotherapeutic products.Biosensors and Bioelectronics: X, 12 .
doi:10.1016/j.biosx.2022.100263
Rathore, A., & Shereef, F. (2022b). Innovating manufacturing technology
in emerging economies. Nature Biotechnology, 40 (12), 1714-1716.
doi:10.1038/s41587-022-01499-5
Roch, P., & Mandenius, C. F. (2016). On-line monitoring of downstream
bioprocesses. Current Opinion in Chemical Engineering, 14 ,
112-120. doi:10.1016/j.coche.2016.09.007
Rolinger, L., Rüdt, M., & Hubbuch, J. (2021). Comparison of UV- and
Raman-based monitoring of the Protein A load phase and evaluation of
data fusion by PLS models and CNNs. Biotechnology and
Bioengineering, 118 (11), 4255-4268. doi:10.1002/bit.27894
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020). Explainable
Machine Learning for Scientific Insights and Discoveries. IEEE
Access, 8 , 42200-42216. doi:10.1109/ACCESS.2020.2976199
Rüdt, M., Briskot, T., & Hubbuch, J. (2017). Advances in downstream
processing of biologics – Spectroscopy: An emerging process analytical
technology. Journal of Chromatography A, 1490 , 2-9.
doi:10.1016/j.chroma.2016.11.010
Rudt, M., Vormittag, P., Hillebrandt, N., & Hubbuch, J. (2019). Process
monitoring of virus-like particle reassembly by diafiltration with
UV/Vis spectroscopy and light scattering. Biotechnology and
Bioengineering, 116 (6), 1366-1379. doi:10.1002/bit.26935
Sauer, D. G., Melcher, M., Mosor, M., Walch, N., Berkemeyer, M.,
Scharl-Hirsch, T., . . . Dürauer, A. (2019). Real-time monitoring and
model-based prediction of purity and quantity during a chromatographic
capture of fibroblast growth factor 2. Biotechnology and
Bioengineering, 116 (8), 1999-2009. doi:10.1002/bit.26984
Scheffel, J., Isaksson, M., Gomis-Fons, J., Schwarz, H., Andersson, N.,
Norén, B., . . . Nilsson, B. (2022). Design of an integrated continuous
downstream process for acid-sensitive monoclonal antibodies based on a
calcium-dependent Protein A ligand. Journal of Chromatography A,
1664 . doi:10.1016/j.chroma.2022.462806
Sellberg, A., Holmqvist, A., Magnusson, F., Andersson, C., & Nilsson,
B. (2017). Discretized multi-level elution trajectory: A
proof-of-concept demonstration. Journal of Chromatography A,
1481 , 73-81. doi:10.1016/j.chroma.2016.12.038
Sellberg, A., Nolin, M., Löfgren, A., Andersson, N., & Nilsson, B.
(2018) Multi-flowrate Optimization of the Loading Phase of a Preparative
Chromatographic Separation. In: Vol. 43. Computer Aided Chemical
Engineering (pp. 1619-1624).
Simon, L. L., Pataki, H., Marosi, G., Meemken, F., Hungerbühler, K.,
Baiker, A., . . . Chiu, M. S. (2015a). Assessment of recent process
analytical technology (PAT) trends: A multiauthor review. Organic
Process Research and Development, 19 (1), 3-62. doi:10.1021/op500261y
Simon, L. L., Pataki, H., Marosi, G., Meemken, F., Hungerbuhler, K.,
Baiker, A., . . . Chiu, M. S. (2015b). Assessment of Recent Process
Analytical Technology (PAT) Trends: A Multiauthor Review. Organic
Process Research & Development, 19 (1), 3-62. doi:10.1021/op500261y
Steinwandter, V., Borchert, D., & Herwig, C. (2019). Data science tools
and applications on the way to Pharma 4.0. Drug Discovery Today,
24 (9), 1795-1805. doi:10.1016/j.drudis.2019.06.005
Sun, Y. N., Shi, C., Zhong, X. Z., Chen, X. J., Chen, R., Zhang, Q. L.,
. . . Lin, D. Q. (2022). Model-based evaluation and model-free strategy
for process development of three-column periodic counter-current
chromatography. Journal of Chromatography A, 1677 .
doi:10.1016/j.chroma.2022.463311
Thakur, G., Hebbi, V., & Rathore, A. S. (2020). An NIR-based PAT
approach for real-time control of loading in Protein A chromatography in
continuous manufacturing of monoclonal antibodies. Biotechnology
and Bioengineering, 117 (3), 673-686. doi:10.1002/bit.27236
Tharmalingam, T., Wu, C. H., Callahan, S., & Goudar, C. T. (2015). A
framework for real-time glycosylation monitoring (RT-GM) in mammalian
cell culture. Biotechnology and Bioengineering, 112 (6),
1146-1154. doi:10.1002/bit.25520
Varmuza, K., & Filzmoser, P. (2009). Introduction to multivariate
analysis in chemometrics. CRC Press .
Vetter, F. L., Zobel-Roos, S., & Strube, J. (2021). Pat for continuous
chromatography integrated into continuous manufacturing of biologics
towards autonomous operation. Processes, 9 (3).
doi:10.3390/pr9030472
Vogg, S., Wolf, M. K. F., & Morbidelli, M. (2018) Continuous and
integrated expression and purification of recombinant antibodies.
In: Vol. 1850. Methods in Molecular Biology (pp. 147-178).
Walch, N., Scharl, T., Felföldi, E., Sauer, D. G., Melcher, M., Leisch,
F., . . . Jungbauer, A. (2019). Prediction of the Quantity and Purity of
an Antibody Capture Process in Real Time. Biotechnology Journal,
14 (7). doi:10.1002/biot.201800521
Wasalathanthri, D. P., Rehmann, M. S., Song, Y., Gu, Y., Mi, L., Shao,
C., . . . Li, Z. J. (2020). Technology outlook for real-time quality
attribute and process parameter monitoring in biopharmaceutical
development—A review. Biotechnology and Bioengineering,
117 (10), 3182-3198. doi:10.1002/bit.27461
Westad, F., & Marini, F. (2015). Validation of chemometric models - A
tutorial. Analytica Chimica Acta, 893 , 14-24.
doi:10.1016/j.aca.2015.06.056
Winter, P., Weissenböck, s., Schwald, C., Doms, T., Vogt, T.,
Hochreiter, S., & Nessler, B. (2021). Trusted Artificial Intelligence:
Towards Certification of Machine Learning Applications. Vienna,
March 17th, 2021, TÜV AUSTRIA Group, Johannes Kepler University Linz –
Institute for Machine Learning .
Wold, S., Trygg, J., Berglund, A., & Antti, H. (2001). Some recent
developments in PLS modeling. Chemometrics and Intelligent
Laboratory Systems, 58 (2), 131-150. doi:10.1016/S0169-7439(01)00156-3
Yu, Z., Reid, J. C., & Yang, Y. P. (2013). Utilizing Dynamic Light
Scattering as a Process Analytical Technology for Protein Folding and
Aggregation Monitoring in Vaccine Manufacturing. Journal of
Pharmaceutical Sciences, 102 (12), 4284-4290. doi:10.1002/jps.23746
Zhao, H. Y., Brown, P. H., & Schuckt, P. (2011). On the Distribution of
Protein Refractive Index Increments. Biophysical Journal, 100 (9),
2309-2317. doi:10.1016/j.bpj.2011.03.004
Zhao, L., Fu, H. Y., Zhou, W., & Hu, W. S. (2015). Advances in process
monitoring tools for cell culture bioprocesses. Engineering in
Life Sciences, 15 (5), 459-468. doi:10.1002/elsc.201500006
Zhong, X., Gallagher, B., Liu, S., Kailkhura, B., Hiszpanski, A., &
Han, T. Y. J. (2022). Explainable machine learning in materials science.npj Computational Materials, 8 (1). doi:10.1038/s41524-022-00884-7