Multi-layer perceptron for detection of different class antibiotics from visual fluorescence response of a carbon nanoparticle-based multichannel array sensor

人工智能 计算机科学 荧光 碳纳米颗粒 班级(哲学) 感知器 图层(电子) 纳米颗粒 碳纤维 模式识别(心理学) 人工神经网络 纳米技术 材料科学 光学 算法 物理 复合数
作者
Saptarshi Mandal,Dipanjyoti Paul,Sriparna Saha,Prolay Das
出处
期刊:Sensors and Actuators B-chemical [Elsevier BV]
卷期号:360: 131660-131660 被引量:18
标识
DOI:10.1016/j.snb.2022.131660
摘要

Lack of automated accurate decision-making along with an on-site detection system impedes the identification of substances of environmental concern. In pursuit of making this feasible, we interconnected our optical fluorescence array sensing strategy with the predictive analytics of artificial intelligence. Herein, we developed a Carbon Nanoparticle-based nine-channel fluorescence array sensing method for the detection of six antibiotics of different classes that are precariously dumped in the environment from various industrial and animal husbandry sources. The fluorescence responses of the arrays in the presence or absence of six antibiotics were captured digitally and these were utilized as feature values for the identification of classes using machine learning and deep learning algorithms. Among the seven tested multi-class classification algorithms, Multi-layer Perceptron (MLP) with Generative Adversarial Nets stimulated augmented data set (Aug-MLP) outdid the others in recognizing the antibiotics. Most importantly, the performance of Aug-MLP is comparable to fluorescence spectroscopic discrimination that outclasses human visual judgment. The whole methodology was found to adapt well in real samples like extracts of poultry feeds. In a nutshell, a nanotechnology-deep learning interfaced semi-automated on-site multi-class antibiotic detection strategy has been developed that could be extended for inexpensive and expedited detection of other chemical entities. • Generative Adversarial Nets (GANs) have been used for the first time in visual fluorescence-based sensing. • Carbon nanoparticle-based visual fluorescence array sensor assimilated to predictive analytics of artificial intelligence. • Seven multi-class supervised algorithms employed to recognise various antibiotics through CMYK extraction of digital images. • Generative Adversarial Nets (GANs) aided Multi-Layer Perceptron (MLP) surpassed visual judgement to detect antibiotics. • Nanotechnology-deep learning interfaced semi-automated, inexpensive, point-of-care, antibiotic detection strategy developed.
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
zhouxuefeng发布了新的文献求助10
刚刚
1秒前
kanwenxian完成签到,获得积分20
1秒前
期刊发布了新的文献求助50
1秒前
共享精神应助小夫同学采纳,获得10
2秒前
2秒前
小蘑菇应助树袋采纳,获得10
2秒前
科研通AI5应助xn201120采纳,获得10
3秒前
3秒前
hh完成签到,获得积分20
4秒前
王彬完成签到,获得积分10
4秒前
晚来天欲雪完成签到,获得积分20
6秒前
Lc应助蓝桉采纳,获得20
6秒前
11秒前
XXXXL完成签到,获得积分10
13秒前
麦苗果果发布了新的文献求助10
15秒前
小夫同学发布了新的文献求助10
15秒前
16秒前
英姑应助谦让小松鼠采纳,获得10
16秒前
BKEL完成签到,获得积分10
19秒前
19秒前
lalala驳回了SciGPT应助
21秒前
kanwenxian发布了新的文献求助10
22秒前
今后应助解语花采纳,获得10
23秒前
七慕凉应助解语花采纳,获得10
23秒前
FashionBoy应助pineapple yang采纳,获得20
23秒前
麦苗果果完成签到,获得积分10
23秒前
Irene完成签到,获得积分10
24秒前
小二郎应助蓁66采纳,获得10
25秒前
25秒前
Hello应助陈曦采纳,获得10
25秒前
领导范儿应助hh采纳,获得10
26秒前
27秒前
艺涵发布了新的文献求助10
29秒前
孙燕应助闪闪泥猴桃采纳,获得30
30秒前
32秒前
32秒前
33秒前
34秒前
ss发布了新的文献求助30
34秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989550
求助须知:如何正确求助?哪些是违规求助? 3531774
关于积分的说明 11254747
捐赠科研通 3270278
什么是DOI,文献DOI怎么找? 1804966
邀请新用户注册赠送积分活动 882125
科研通“疑难数据库(出版商)”最低求助积分说明 809176