Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine LearningEnabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data Subject The topic of the resource artificial neural network-multilayer perceptron (ANN-MLP); data quality; machine learning; NIR spectroscopy Description An account of the resource Abstract: An increasingly large dataset of pharmaceuticsdisciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robustdata on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset (http://www.models.life.ku.dk/Tablets) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. Highlights: We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation Model extension from lab-scale to pilot-scale successfully demonstrated Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). Creator An entity primarily responsible for making the resource Ali H.; Muthudoss P.; Ramalingam M.; Kanakaraj L.; Paudel A.; Ramasamy G. Source A related resource from which the described resource is derived AAPS PharmSciTech, Vol-24, No. 1 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1208/s12249-022-02493-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1208/s12249-022-02493-5</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146104488&amp;doi=10.1208%2Fs12249-022-02493-5&amp;partnerID=40&amp;md5=c6810fee3b90926dcfbe92fa893e2a65" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146104488&amp;doi=10.1208%2fs12249-022-02493-5&amp;partnerID=40&amp;md5=c6810fee3b90926dcfbe92fa893e2a65</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15309932; PubMed ID: 36627410 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ali H., Christ (Deemed to Be University), Karnataka, Bangalore, 560029, India; Muthudoss P., A2Z4.0 Research and Analytics Private Limited, Tamilnadu, Chennai, 600062, India; Ramalingam M., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Tamilnadu, Chennai, 603103, India; Kanakaraj L., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Tamilnadu, Chennai, 603103, India; Paudel A., Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, Graz, 8010, Austria, Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, Graz, 8010, Austria; Ramasamy G., Christ (Deemed to Be University), Karnataka, Bangalore, 560029, India