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Towards normalization selection of Raman data in the context of protein glycation: application of validity indices to PCA processed spectra
被引:17
作者:
Fatima, Alsamad
[1
]
Cyril, Gobinet
[1
]
Vincent, Vuiblet
[1
,4
]
Stephane, Jaisson
[2
]
Olivier, Piot
[1
,3
]
机构:
[1] Univ Reims, BioSpecT EA 7506, Lab Translat Biospect, UFR Pharm, Reims, France
[2] Univ Reims, MEDyC UMR CNRS URCA 7369, Lab Biochem & Mol Biol, Fac Med, Reims, France
[3] Univ Reims, Platform Cellular & Tissular Imaging PICT, Reims, France
[4] CHU Reims, Pathol Lab, Dept Nephropathol, Reims, France
来源:
关键词:
END-PRODUCTS;
RECONSTRUCTED SKIN;
SPECTROSCOPY;
COLLAGEN;
CLASSIFICATION;
MICROSCOPY;
PRE;
D O I:
10.1039/c9an02155h
中图分类号:
O65 [分析化学];
学科分类号:
070302 ;
081704 ;
摘要:
Raman spectroscopy is a candidate technique for diagnosis applications in medicine due to its high molecular specificity. Optimizing the pre-treatment applied for Raman data is important for exploiting Raman signals and ensuring their relevance in medical diagnosis. One of the crucial steps in data pre-processing, normalization, can affect significantly the result interpretation. To select the appropriate normalization method, a strategy based on validity indices (VI) is proposed in this study. VI are based on measuring the quality of data partitioning without involving a full sequence of supervised classification. The approach was tested on Raman data acquired from control and in vitro glycated proteins (albumin and collagen). Protein glycation is a process involved in the molecular ageing of tissues that leads to the formation of products altering the functional and structural properties of proteins. Different methods of normalization were applied on the data sets: integrated intensity of the phenylalanine band, integrated intensity of the amide I band, standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) that performs simultaneously baseline correction and normalization. Following normalization, principal component analysis (PCA) was applied and VI were calculated from PCA scores resulting from each of the normalization methods mentioned. Based on VI quantitative values, our experiments permit to illustrate the effect of normalization on the data separability of control and glycated samples, and to determine the most appropriate normalization and simultaneously the most discriminant principal components to exploit vibrational information associated with glycation-induced modifications. In parallel, principal component analysis - linear discriminant analysis (PCA-LDA) was carried out for positioning the interest of VI in regard to a common chain of data processing.
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页码:2945 / 2957
页数:13
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