广泛的数据集蠕动泵精度提高在制药环境。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Davide Privitera, Alessandro Mecocci, Sandro Bartolini
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引用次数: 0

摘要

我们发布了制药生产中蠕动泵剂量输出的综合数据集,其中准确性对药品质量和患者安全至关重要,包括149,847次测量,体积从0.1到2.0 ml。使用工业灌装系统在受控条件下获取数据,使用校准的称重设备。据我们所知,这是第一个记录如此大范围内泵行为的数据集。该数据集旨在构建一个可靠的工具,能够从短期精度到长期稳定性的角度进行调查,提供各种操作条件下蠕动泵行为的详细见解。此外,该数据集整合了多个卷的补偿结果,记录了统计和基于人工智能的补偿策略,从而举例说明了为了提高剂量准确性而采取的一些补偿策略如何改变剂量的统计行为。该资源通过支持质量控制系统的优化和新补偿策略的验证,直接解决制药行业的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.

Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.

Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.

Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.

We publish a comprehensive dataset of peristaltic pump dosing outputs in pharmaceutical manufacturing, where accuracy is crucial for drug quality and patient safety, consisting of 149,847 measurements spanning volumes from 0.1 to 2.0 ml. An industrial filling system was used to acquire data under controlled conditions, using calibrated weighing equipment. To the best of our knowledge, this is the first dataset documenting pump behavior across such a wide range of volumes. The dataset aims to constitute a solid tool that enables investigation from short-term precision to long-term stability standpoints, providing detailed insights into peristaltic pump behavior under various operating conditions. Additionally, the dataset incorporates compensation outcomes across multiple volumes, documenting both statistical and AI-based compensation strategies, thus exemplifying how the statistical behavior of the dosing can change in response to some compensation strategies aimed to improve dosing accuracy. This resource directly addresses pharmaceutical industry needs by supporting optimization of quality control systems and validation of novel compensation strategies.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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