Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte
Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte
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引导主成分分析 (GPCA)：一种改进已知分析物检测的简单方法
DOI：
10.1039/d3an00820g
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Gardner B
中科院分区：
文献类型：
--
作者：
Gardner B
在医疗环境中应用拉曼光谱的兴趣越来越大，范围从支持实时临床决策，例如手术切缘到协助病理学家进行疾病分类。然而，由于探测高度异质的动态生物材料的复杂性增加，在医疗环境中采用仍然存在许多障碍。这种固有的挑战也可能限制更高级别的分析方法的部署，例如人工智能（AI），包括卷积神经网络（CNN），因为缺乏训练目的所需的基础事实，即在复杂的临床样本中。主成分分析（PCA）是一种无监督的数据简化方法（正交线性变换），由于其简化复杂光谱数据分析的能力，已在光谱学中广泛使用了30多年。然而，由于PCA是无监督的，因此特征本身会出现混合，并且它们的排名可能会在实验之间变化。在这里，我们提出了引导PCA（GPCA），一种简单的方法，允许PCA与光谱数据进行指导，以确保一个一致的排名的关键目标部分的参考（指导）光谱的数据集。这简化了分析，提高了PCA分析的鲁棒性，并提高了定量和检测限，降低了RMSE。
There is increasing interest in the application of Raman spectroscopy in a medical setting, ranging from supporting real-time clinical decisions e.g. surgical margins to assisting pathologists with disease classification. However, there remain a number of barriers for adoption in the medical setting due to the increased complexity of probing highly heterogeneous, dynamic biological materials. This inherent challenge can also limit the deployment of higher level analytical approaches such as Artificial Intelligence (AI) including convolutional neural networks (CNN), as there is a lack of a ground truth required for training purposes i.e. in complex clinical samples. Principal component analysis (PCA) is an unsupervised data reduction approach (orthogonal linear transformation) that has been used extensively in spectroscopy for 30+ years, due to its capability to simplify analysis of complex spectroscopic data. However, due to PCA being unsupervised features will inherently appear mixed and their rank may vary between experiments. Here we propose Guided PCA (GPCA), a simple approach that allows PCA to be guided with spectral data to ensure a consistent rank of a key target moiety by the inclusion of a reference (guiding) spectrum to the data set. This simplifies analysis, increases robustness of PCA analysis and improves quantification and the limits of detection and decreases RMSE.