AIMC Topic: Powders
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Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.
Pharmaceutical research
Apr 17, 2025
PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, id...
Real-time component-based particle size measurement and dissolution prediction during continuous powder feeding using machine vision and artificial intelligence-based object detection.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Mar 20, 2025
This work presents a system, in which machine vision combined with artificial intelligence-based image analysis was used to determine the component-based particle size distribution of pharmaceutical powder blends. The blends consisted of acetylsalicy...
Near-infrared spectroscopy assisted by random forest for predicting the physicochemical indicators of yak milk powder.
Food chemistry
Mar 1, 2025
High-efficiency and cost-effective detection of physicochemical indicators is essential for the quality control of yak milk powder. Herein, a rapid and simultaneous detection method based on miniaturized near-infrared (NIR) spectroscopy and chemometr...
Scale-independent solid fraction prediction in dry granulation process using a gray-box model integrating machine learning model and Johanson model.
International journal of pharmaceutics
Feb 15, 2025
We propose a novel approach for predicting the solid fraction after roller compaction processes. It is crucial to predict and control the solid fraction, as it has a significant impact on the product quality. The solid fraction can be theoretically p...
Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging.
Journal of pharmaceutical and biomedical analysis
Jan 26, 2025
Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control method...
Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution.
Scientific reports
Jan 6, 2025
This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to preven...
Methodology for quality risk prediction for milk powder production plants with domain-knowledge-involved serial neural networks.
Food chemistry
Nov 17, 2024
In dairy enterprises, predicting product quality attributes that are influenced by operating parameters is a major task. To reduce quality loss in production, a prediction-based quality control method is proposed in this study. In particular, a seria...
Data-driven insights into the properties of liquisolid systems based on machine learning algorithms.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Oct 6, 2024
Liquisolid systems (LS) represent a formulation approach where liquid drug or its dispersion is transformed into a powder with good flowability and compactibility, leading to enhanced drug dissolution and bioavailability. Many research groups have fo...
Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking.
International journal of pharmaceutics
Sep 16, 2024
The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compacti...
Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine.
International journal of pharmaceutics
Jul 6, 2024
Medicines remain ineffective for over 50% of patients due to conventional mass production methods with fixed drug dosages. Three-dimensional (3D) printing, specifically selective laser sintering (SLS), offers a potential solution to this challenge, a...
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