Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.
Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.
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DOI：
10.1021/acs.analchem.2c02666
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发表时间：
2022-11-08
影响因子：
7.4
通讯作者：
McNear, Kelly
中科院分区：
文献类型：
--
作者：
Balytskyi, Yaroslav;Bendesky, Justin;Paul, Tristan;Hagen, Guy M.;McNear, Kelly
拉曼光谱与机器学习技术相结合，作为一种快速，灵敏和无标记的识别方法，在许多应用中具有很大的前景。这种方法在对训练阶段遇到的化学物质的光谱进行分类时表现良好。也就是说，神经网络已知的物种。然而，在现实世界的环境中，例如在临床应用中，总会有物质的光谱尚未被获取。当神经网络在测试阶段遇到这些新物种时，误报的数量变得无法控制，限制了这些技术的实用性，特别是在公共安全应用中。为了克服这些障碍，我们实现了最近引入的熵开集和目标层损失函数。为了证明这种方法的有效性和效率，我们编制了一个数据库的高光谱拉曼图像的40个化学物种分离成三类分类。已知类由20种生物相关的氨基酸组成，被忽略的类是10种生物相关的化学物质，而从未见过的类是10种神经网络从未见过的化学物质。我们证明，这种方法不仅使网络能够有效地分离未知物种，同时保持已知物种的高准确性并减少误报，而且比机器学习技术中的当前黄金标准表现得更好。这为在各种实际应用中使用拉曼光谱与我们新颖的机器学习算法相结合打开了大门。可用性和实施情况：可在https://github.com/BalytskyiJaroslaw/RamanOpenSet.git网站上免费查阅。
Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network. However, in real-world settings, such as in clinical applications, there will always be substances whose spectra have not yet been taken. When the neural network encounters these new species during the testing phase, the number of false positives becomes uncontrollable, limiting the usefulness of these techniques, especially in public safety applications. To overcome these barriers, we implemented the recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficacy and efficiency of this approach, we compiled a database of hyperspectral Raman images of 40 chemical species separating them into three class categorizations. The known class consisted of 20 biologically relevant species comprising amino acids, the ignored class was 10 “irrelevant” species comprising bio-related chemicals, and the never seen before class was 10 various chemical species that the neural network had not seen before. We show that this approach not only enables the network to effectively separate the unknown species while preserving high accuracy on the known ones and reducing false positives but also performs better than the current gold standards in machine learning techniques. This opens the door to using Raman spectroscopy, combined with our novel machine learning algorithm, in a variety of practical applications. Availability and implementation: freely available on the web at https://github.com/BalytskyiJaroslaw/RamanOpenSet.git.
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人工智能方法与激光镊拉曼光谱相结合进行微生物鉴定
影响因子：
7.4
作者：
Lu, Weilai;Chen, Xiuqiang;Fu, Yu Vincent
通讯作者：
Fu, Yu Vincent
影响因子：
4.5
作者：
Beyssac, Olivier
通讯作者：
Beyssac, Olivier
影响因子：
3.4
作者：
Maruthamuthu MK;Raffiee AH;De Oliveira DM;Ardekani AM;Verma MS
通讯作者：
Verma MS
DOI：
10.1109/5326.827457
发表时间：
2000-02-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
影响因子：
--
作者：
De Stefano, C;Sansone, C;Vento, M
通讯作者：
Vento, M
影响因子：
7.4
作者：
Barman, Ishan;Dingari, Narahara Chari;Kang, Jeon Woong;Horowitz, Gary L.;Dasari, Ramachandra R.;Feld, Michael S.
通讯作者：
Feld, Michael S.