{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T22:46:30Z","timestamp":1770417990477,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["605086"],"award-info":[{"award-number":["605086"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established \u2019soft sensing\u2019 method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing.<\/jats:p>","DOI":"10.3390\/s22082835","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"2835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5632-8780","authenticated-orcid":false,"given":"Konrad","family":"Mulrennan","sequence":"first","affiliation":[{"name":"Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"},{"name":"Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9520-8976","authenticated-orcid":false,"given":"Nimra","family":"Munir","sequence":"additional","affiliation":[{"name":"Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"},{"name":"Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8798-7455","authenticated-orcid":false,"given":"Leo","family":"Creedon","sequence":"additional","affiliation":[{"name":"Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"},{"name":"Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3531-1928","authenticated-orcid":false,"given":"John","family":"Donovan","sequence":"additional","affiliation":[{"name":"Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"},{"name":"Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1998-070X","authenticated-orcid":false,"given":"John G.","family":"Lyons","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Informatics, Technological University of the Shannon, Dublin Road, N37 HD68 Athlone, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1434-1215","authenticated-orcid":false,"given":"Marion","family":"McAfee","sequence":"additional","affiliation":[{"name":"Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"},{"name":"Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.polymdegradstab.2017.01.009","article-title":"Abiotic and biotic environmental degradation of the bioplastic polymer poly(lactic acid): A review","volume":"137","author":"Karamanlioglu","year":"2017","journal-title":"Polym. Degrad. Stab."},{"key":"ref_2","first-page":"2996","article-title":"Process-induced monomer on a medical-grade polymer and its effect on short-term hydrolytic degradation","volume":"119","author":"Nikkola","year":"2010","journal-title":"J. Appl. Polym. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Auras, R., Lim, L.T., Selke, S., and Tsuji, H. (2010). Crystallization and thermal properties. Poly(lactic acid)-Synthesis, Structures, Properties, Processing and Application, Wiley-Blackwell. Chapter 9.","DOI":"10.1002\/9780470649848"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e3287","DOI":"10.1002\/cem.3287","article-title":"Envelopes: A new chapter in partial least squares regression","volume":"34","author":"Cook","year":"2020","journal-title":"J. Chemom."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Almeida, J., Bezerra, M., Markl, D., Berghaus, A., Borman, P., and Schlindwein, W. (2020). Development and Validation of an in-line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process. Pharmaceutics, 12.","DOI":"10.3390\/pharmaceutics12020150"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.nbt.2013.10.005","article-title":"In-line monitoring of thermal degradation of PHA during melt-processing by Near-Infrared spectroscopy","volume":"31","author":"Pratt","year":"2014","journal-title":"New Biotechnol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guo, X., Lin, Z., Wang, Y., He, Z., Wang, M., and Jin, G. (2019). In-line monitoring the degradation of polypropylene under multiple extrusions based on Raman spectroscopy. Polymers, 11.","DOI":"10.3390\/polym11101698"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Munir, N., Nugent, M., Whitaker, D., and McAfee, M. (2021). Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions. Pharmaceutics, 13.","DOI":"10.3390\/pharmaceutics13091432"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1109\/JSEN.2018.2885609","article-title":"Design and Applications of Soft Sensors in Polymer Processing: A Review","volume":"19","author":"Abeykoon","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aca.2013.12.002","article-title":"Non-linear calibration models for near infrared spectroscopy","volume":"813","author":"Ni","year":"2014","journal-title":"Anal. Chim. Acta"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.aca.2011.03.006","article-title":"Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data","volume":"692","author":"Balabin","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jprocont.2019.03.016","article-title":"A feature-based soft sensor for spectroscopic data analysis","volume":"78","author":"Shah","year":"2019","journal-title":"J. Process Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20590","DOI":"10.1109\/ACCESS.2017.2756872","article-title":"Data Mining and Analytics in the Process Industry: The Role of Machine Learning","volume":"5","author":"Ge","year":"2017","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12868","DOI":"10.1109\/JSEN.2020.3033153","article-title":"A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes","volume":"21","author":"Jiang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.chemolab.2015.12.011","article-title":"Review of soft sensor methods for regression applications","volume":"152","author":"Souza","year":"2016","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.chemolab.2017.12.007","article-title":"Support vector regression coupled with wavelength selection as a robust analytical method","volume":"172","author":"Soares","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1039\/C4JA00217B","article-title":"A novel approach for the quantitative analysis of multiple elements in steel based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR)","volume":"29","author":"Zhang","year":"2014","journal-title":"J. Anal. At. Spectrom."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.chemolab.2018.10.007","article-title":"Small moving window calibration models for soft sensing processes with limited history","volume":"183","author":"Kneale","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118715","DOI":"10.1016\/j.ijpharm.2019.118715","article-title":"Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm","volume":"571","author":"McKinley","year":"2019","journal-title":"Int. J. Pharm."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/app.45898","article-title":"Visualization of hydrolysis in polylactide using near-infrared hyperspectral imaging and chemometrics","volume":"135","author":"Muroga","year":"2018","journal-title":"J. Appl. Polym. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.polymertesting.2018.06.002","article-title":"A soft sensor for prediction of mechanical properties of extruded PLA sheet using an instrumented slit die and machine learning algorithms","volume":"69","author":"Mulrennan","year":"2018","journal-title":"Polym. Test."},{"key":"ref_22","unstructured":"FDA (2021). Development and Submission of Near Infrared Analytical Procedures Guidance for Industry."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1208\/s12249-018-1091-3","article-title":"Application of FT-NIR Analysis for In-line and Real-Time Monitoring of Pharmaceutical Hot Melt Extrusion: A Technical Note","volume":"19","author":"Vo","year":"2018","journal-title":"AAPS PharmSciTech"},{"key":"ref_24","unstructured":"Massart, D., Vandeginste, B., Deming, S., Michotte, Y., and Kaufman, L. (2003). Chemometrics and the Analytical Process, Elsevier."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jbiosc.2021.04.002","article-title":"Soft-sensor development for monitoring the lysine fermentation process","volume":"132","author":"Tokuyama","year":"2021","journal-title":"J. Biosci. Bioeng."},{"key":"ref_26","unstructured":"Jolliffe, I.T. (2002). Principal components analysis. Springer Ser. Stat., 374\u2013377."},{"key":"ref_27","first-page":"173","article-title":"Principal Component Regression by Principal Component Selection","volume":"22","author":"Lee","year":"2015","journal-title":"Commun. Stat. Appl. Methods"},{"key":"ref_28","unstructured":"Vogt, W. (2015). Ridge Regression. Dictionary of Statistics & Methodology, SAGE Publications, Inc."},{"key":"ref_29","first-page":"447","article-title":"Alternative method for choosing ridge parameter for regression","volume":"4","author":"Dorugade","year":"2010","journal-title":"Appl. Math. Sci."},{"key":"ref_30","first-page":"1","article-title":"Feature Selection using LASSO","volume":"30","author":"Fonti","year":"2017","journal-title":"VU Amst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1111\/j.1467-9868.2005.00527.x","article-title":"Erratum: Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_32","first-page":"155","article-title":"Support Vector Regression Machines","volume":"Volume 9","author":"Mozer","year":"1997","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1255\/jnirs.412","article-title":"Least-squares support vector machines for chemometrics: An introduction and evaluation","volume":"12","author":"Cogdill","year":"2004","journal-title":"J. Infrared Spectrosc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"120024","DOI":"10.1016\/j.ijpharm.2020.120024","article-title":"The optimization of process analytical technology for the inline quantification of multiple drugs in fixed dose combinations during continuous processing","volume":"592","author":"Dadou","year":"2021","journal-title":"Int. J. Pharm."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/2835\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:47Z","timestamp":1760136587000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/2835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":36,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22082835"],"URL":"https:\/\/doi.org\/10.3390\/s22082835","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]}}}