基于三维荧光光谱法的蛋白质分类鉴定特征提取方法的性能,Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

三维激发发射矩阵 (EEM) 荧光光谱法用于区分包含牛血清白蛋白、神经降压素、卵清蛋白、蓖麻毒素、牛胰腺胰蛋白酶和猪胰腺胰蛋白酶的蛋白质样品。将具有和不具有参数化的两种特征提取方法应用于光谱数据，以评估它们在蛋白质样品之间的区分性能。通过k均值聚类算法和基于主成分分析(PCA)的特征值提取程序进行蛋白质样品的鉴别。结果发现，没有参数化的特征提取方法表现最好，在捕获两个主成分 (PC) 的情况下正确归因于 100% 的光谱数据。使用光谱参数化提取的特征未能在相同条件下从牛胰腺中分离蓖麻毒素和胰蛋白酶。在没有光谱参数化的情况下，PCA 捕获的较少维数和独特的主成分表明相应蛋白质样品的光谱分辨特征。通过在固定激发波长下使用每个光谱进行聚类，发现与常见固有荧光团匹配的激发波长对分类精度更敏感。讨论了从 EEM 提取的光谱特征对主要成分的贡献，并证明了它们在六个蛋白质样本中的特征区分能力。这些结果表明，适当的特征提取方法与 PCA 分析相结合，可作为光谱诊断工具用于在物种水平上区分蛋白质样品。当 EEM 用于探索周围环境中的蛋白质时，我们的研究提供了有关计算策略的基本参考。

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Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.