[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127593-en":3,"doc-seo-127593-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127593,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Use of Machine Learning Tools and NIR Spectra to Estimate Residual Moisture in Freeze-Dried Products","Residual Moisture (RM) in freeze-dried products is a key Critical Quality Attribute (CQA) because it directly impacts the stability of the active pharmaceutical ingredient (API). Karl-Fischer (KF) titration is the common reference method but is destructive and time-consuming. The paper develops a novel approach combining near-infrared (NIR) spectroscopy with machine learning to predict RM using linear regression and neural network models. Model robustness is evaluated through parity and absolute error plots, considering wavelength range, spectral shape, and dataset size, and demonstrating improved accuracy over linear models.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nUse of machine learning tools and NIR spectra to estimate residual moisture in freeze-dried products  \nOriginal  \nUse of machine learning tools and NIR spectra to estimate residual moisture in freeze-dried products / Massei, Ambra; Falco, Nunzia; Fissore, Davide. -In: SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY. -ISSN 1386-1425. -STAMPA. -293:(2023), p. 122485. [10 . 1016/j.saa.2023. 122485]  \nAvailability:  \nThis version is available at: 11583/2976207 since: 2023-02-20T09:21:58Z  \nPublisher:  \nelsevier  \nPublished  \nDOI:10.1016/j.saa.2023.122485  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n07 November 2024  \nSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [ISSN: 0022-3549], 293,  \nArticle 122485.  \nDOI: 10.1016/j.saa.2023.122485  \n[https://www.sciencedirect.com/science/article/pii/S1386142523001701](https://www.sciencedirect.com/science/article/pii/S1386142523001701)  \nUse of Machine Learning Tools and NIR Spectra to Estimate Residual  \nMoisture in Freeze-Dried Products  \nAmbra Massei1,2, Nunzia Falco2, Davide Fissore1  \n1. Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, corso Duca degli Abruzzi  \n25, 10129 Torino  \n2. Global Pharmaceutical Development Department, Merck Serono SpA, via Luigi Einaudi 11, 00012 Guidonia Montecelio (Roma)  \nAbstract  \nResidual Moisture (RM) in freeze-dried products is one of the most important critical quality attributes (CQAs) to monitor, since it affects the stability of the active pharmaceutical ingredient (API) . The standard experimental method adopted for the measurements of RM is the Karl-Fischer (KF) titration, that is a destructive and time-consuming technique. Therefore, Near-Infrared (NIR) spectroscopy was widely investigated in the last decades as an alternative tool to quantify the RM. In the present paper, a novel method was developed based on NIR spectroscopy combined with machine learning tools for the prediction of RM in freeze-dried products. Two different types of models were used: a linear regression model and a neural network based one. The architecture of the neural network was chosen so as to optimize the prediction of the residual moisture, by minimizing the root mean square error with the dataset used in the learning step. Moreover, the parity plots and the absolute error plots were reported, allowing a visual evaluation of the results. Different factors were considered when developing the model, namely the range of wavelengths considered, the shape of the spectra and the type of model. The possibility of developing the model using a smaller dataset, obtained with just one product, that could be then applied to a wider range of products was investigated, as well as the performance of a model developed for a dataset encompassing several products. Different formulations were analyzed: the main part of the dataset was characterized by a different percentage of sucrose in solution (3%, 6% and 9% specifically); a smaller part was made up of sucrose-arginine mixtures at different percentages and only one formulation was characterized by another excipient, the trehalose. The product-specific model for the 6% sucrose mixture was found consistent for the prediction of RM in other sucrose containing mixtures and in the one containing trehalose, while failed for the dataset with higher percentage of arginine. Therefore, a global model was developed by including a certain percentage of all the available dataset in the calibration phase. Results presented and discussed in this paper demonstrate the higher accuracy and robustness of the machine learning based model with respect to the linear models.  \nKeywords  \nFreeze-drying, Residual moisture, Near-Infrared Spectroscopy, Karl Fischer titration, Machine L","cbCailx1fyXEWRV1","https://ap.wps.com/l/cbCailx1fyXEWRV1","pdf",2825004,1,43,"English","en",105,"# Abstract\n# Introduction\n## Freeze-drying and residual moisture as a CQA\n## Reference measurement via Karl-Fischer titration\n# Method Overview\n## NIR spectroscopy combined with machine learning\n## Linear regression and neural network models\n## Model development factors\n# Experimental Design\n## Dataset scope and formulation types\n## Product-specific vs global modeling\n# Results and Discussion\n## Parity and absolute error evaluation\n## Accuracy and robustness compared to linear models","[{\"question\":\"Why is residual moisture (RM) a critical quality attribute in freeze-dried products?\",\"answer\":\"RM affects the stability of the active pharmaceutical ingredient (API), making it essential to monitor as part of Critical Quality Attributes (CQAs).\"},{\"question\":\"What is the reference method for measuring RM, and what are its limitations?\",\"answer\":\"Karl-Fischer (KF) titration is commonly used, but it is destructive and time-consuming. This motivates the search for faster alternatives.\"},{\"question\":\"How do machine learning models using NIR spectra improve RM prediction?\",\"answer\":\"The study compares a linear regression model with a neural-network model trained to minimize RMSE, and evaluates results using parity and absolute error plots. The machine learning based model shows higher accuracy and robustness than linear models, including cases with smaller or global datasets.\"}]","Use of Machine Learning Tools and NIR Spectra to Estimate Residual Moisture in Freeze-Dried Products | PDF",1785940161,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"use-of-machine-learning-tools-and-nir-spectra-to-estimate-residual-moisture-in-freeze-dried-products","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/use-of-machine-learning-tools-and-nir-spectra-to-estimate-residual-moisture-in-freeze-dried-products/127593/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is residual moisture (RM) a critical quality attribute in freeze-dried products?","Question",{"text":76,"@type":77},"RM affects the stability of the active pharmaceutical ingredient (API), making it essential to monitor as part of Critical Quality Attributes (CQAs).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the reference method for measuring RM, and what are its limitations?",{"text":81,"@type":77},"Karl-Fischer (KF) titration is commonly used, but it is destructive and time-consuming. This motivates the search for faster alternatives.",{"name":83,"@type":74,"acceptedAnswer":84},"How do machine learning models using NIR spectra improve RM prediction?",{"text":85,"@type":77},"The study compares a linear regression model with a neural-network model trained to minimize RMSE, and evaluates results using parity and absolute error plots. 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