已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales

造粒 主成分分析 偏最小二乘回归 回归分析 人工智能 极限抗拉强度 相似性(几何) 无监督学习 计算机科学 数学 模式识别(心理学) 数据库 材料科学 统计 复合材料 图像(数学)
作者
Yoshihiro Hayashi,Miho Noguchi,Takuya Oishi,Takashi Ono,Kotaro Okada,Yoshinori Onuki
出处
期刊:International Journal of Pharmaceutics [Elsevier BV]
卷期号:641: 123066-123066 被引量:1
标识
DOI:10.1016/j.ijpharm.2023.123066
摘要

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R2= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小白完成签到,获得积分10
刚刚
Glowing发布了新的文献求助10
1秒前
tanjing0912发布了新的文献求助10
1秒前
善学以致用应助乐观囧采纳,获得10
3秒前
3秒前
一二宝发布了新的文献求助10
4秒前
4秒前
5秒前
6秒前
ovoclive完成签到,获得积分10
7秒前
lxgz发布了新的文献求助10
7秒前
leon111发布了新的文献求助10
9秒前
贺贺完成签到,获得积分10
9秒前
9秒前
空林饮溪完成签到 ,获得积分10
10秒前
浅音应助程依婷采纳,获得10
10秒前
烟花应助十分十分佳采纳,获得10
10秒前
Jing完成签到,获得积分10
12秒前
沦落而完成签到,获得积分10
13秒前
浮游应助橘涂采纳,获得10
13秒前
科研通AI6应助铮铮铁骨采纳,获得10
13秒前
Lydia发布了新的文献求助10
14秒前
15秒前
17秒前
义气的安白完成签到,获得积分10
17秒前
17秒前
爆米花应助王艺霖采纳,获得10
17秒前
禾苗发布了新的文献求助10
18秒前
19秒前
19秒前
luwenbin发布了新的文献求助10
20秒前
汉堡包应助务实的犀牛采纳,获得10
20秒前
张轩完成签到,获得积分10
20秒前
温存完成签到,获得积分10
21秒前
牛蛙丶丶发布了新的文献求助10
21秒前
22秒前
22秒前
nn发布了新的文献求助10
22秒前
22秒前
xnzhl发布了新的文献求助10
23秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Zeolites: From Fundamentals to Emerging Applications 1500
Encyclopedia of Materials: Plastics and Polymers 1000
Architectural Corrosion and Critical Infrastructure 1000
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
Hidden Generalizations Phonological Opacity in Optimality Theory 1000
Handbook of Social and Emotional Learning, Second Edition 900
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4924963
求助须知:如何正确求助?哪些是违规求助? 4195117
关于积分的说明 13030291
捐赠科研通 3966853
什么是DOI,文献DOI怎么找? 2174302
邀请新用户注册赠送积分活动 1191684
关于科研通互助平台的介绍 1101172