Development of one-class classification method for identifying healthy T. granosa from those contaminated with uncertain heavy metals by LIBS

马氏距离 激光诱导击穿光谱 主成分分析 污染 数学 人工智能 模式识别(心理学) 分类器(UML) 计算机科学 光谱学 物理 生态学 量子力学 生物
作者
Zongwu Xie,Xiaoyi Feng,Xiao Chen,Guangzao Huang,Xiaojing Chen,Limin Li,Wen Shi,Chengxi Jiang,Shuwen Yu
出处
期刊:International Journal of Agricultural and Biological Engineering 卷期号:16 (4): 200-205
标识
DOI:10.25165/j.ijabe.20231604.7666
摘要

Laser-induced breakdown spectroscopy (LIBS) can be used for the rapid detection of heavy metal contamination of Tegillarca granosa (T. granosa), but an appropriate classification model needs to be constructed. In the one-class classification method, only target samples are needed in training process to achieve the recognition of abnormal samples, which is suitable for rapid identification of healthy T. granosa from those contaminated with uncertain heavy metals. The construction of a one-class classification model for heavy metal detection in T. granosa by LIBS has faced the problem of high-dimension and small samples. To solve this problem, a novel one-class classification method was proposed in this study. Here, the principal component scores and the intensity of the residual spectrum were combined as extracted features. Then, a one-class classifier based on Mahalanobis distance using the extracted features was constructed and its threshold was set by leave-one-out cross-validation. The sensitivity, specificity and accuracy of the proposed method were reached to 1, 0.9333 and 0.9667 respectively, which are superior to the previously reported methods. Keywords: laser-induced breakdown spectroscopy, Heavy metal contamination, Tegillarca granosa, one-class classification DOI: 10.25165/j.ijabe.20231604.7666 Citation: Xie Z H, Feng X A, Chen X, Huang G Z, Chen X J, Li L M, et al. Development of one-class classification method for identifying healthy T. granosa from those contaminated with uncertain heavy metals by LIBS. Int J Agric & Biol Eng, 2023; 16(4): 201-206.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
刚刚
刚刚
1秒前
精明寒蕾发布了新的文献求助200
1秒前
FashionBoy应助wpj采纳,获得10
2秒前
鹄望完成签到,获得积分10
3秒前
3秒前
背后的书文完成签到,获得积分10
3秒前
3秒前
yyauthor发布了新的文献求助10
4秒前
5秒前
5秒前
5秒前
传奇3应助耍酷鼠标采纳,获得10
6秒前
ephore应助最重中之重采纳,获得30
7秒前
刘英俊应助干爆瓶颈采纳,获得10
9秒前
9秒前
9秒前
chenhunhun完成签到,获得积分10
10秒前
10秒前
summer发布了新的文献求助10
12秒前
FashionBoy应助学术混子采纳,获得10
12秒前
胡关完成签到,获得积分10
12秒前
ghq发布了新的文献求助10
13秒前
小Y发布了新的文献求助10
13秒前
13秒前
14秒前
14秒前
积极墨镜发布了新的文献求助30
15秒前
Leung发布了新的文献求助30
15秒前
16秒前
枳酒完成签到,获得积分10
16秒前
16秒前
zzz4743应助忧郁凌波采纳,获得30
17秒前
17秒前
18秒前
科研小菜应助gd1997采纳,获得30
18秒前
依古比古发布了新的文献求助10
19秒前
xia123完成签到,获得积分10
19秒前
小玲仔发布了新的文献求助10
19秒前
高分求助中
【本贴是提醒信息,请勿应助】请在求助之前详细阅读求助说明!!!! 20000
One Man Talking: Selected Essays of Shao Xunmei, 1929–1939 1000
The Three Stars Each: The Astrolabes and Related Texts 900
Yuwu Song, Biographical Dictionary of the People's Republic of China 800
Multifunctional Agriculture, A New Paradigm for European Agriculture and Rural Development 600
Challenges, Strategies, and Resiliency in Disaster and Risk Management 500
Bernd Ziesemer - Maos deutscher Topagent: Wie China die Bundesrepublik eroberte 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 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 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2481074
求助须知:如何正确求助?哪些是违规求助? 2143677
关于积分的说明 5467101
捐赠科研通 1866260
什么是DOI,文献DOI怎么找? 927580
版权声明 563007
科研通“疑难数据库(出版商)”最低求助积分说明 496245