Projection to latent correlative structures, a dimension reduction strategy for spectral-based classification

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Guillaume Erny (Autor) FEUP; Elsa Brito (Autor) Outra; Ana Bárbara Pereira (Autor) Outra; Andreia Bento-Silva (Autor) Outra; Maria Carlota Vaz Patto (Autor) Outra; Maria Rosario Bronze (Autor) Outra

Revista

Título: RSC Advances

Vol. 2014

ISSN: 2046-2069

Editora: Royal Society of Chemistry

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Classificação Científica

CORDIS: Ciências Físicas > Química > Química analítica

Projetos Associados

LEPABE - Financiamento Plurianual 2020-2023 da Unidade de I&D LEPABE - Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia

Finnee - Finnee - De espectros a fórmulas

PortASAP - European network for the promotion of portable, affordable and simple analytical platforms

Outras Informações

ID Authenticus: P-00V-H1D

DOI: 10.1039/d1ra03359j

Abstract (EN): Latent variables are used in chemometrics to reduce the dimension of the data. It is a crucial step with spectroscopic data where the number of explanatory variables can be very high. Principal component analysis (PCA) and partial least squares (PLS) are the most common. However, the resulting latent variables are mathematical constructs that do not always have a physicochemical interpretation. A new data reduction strategy, named projection to latent correlative structures (PLCS), is introduced in this manuscript. This approach requires a set of model spectra that will be used as references. Each latent variable is the relative similarity of a given spectrum to a pair of reference spectra. The latent structure is obtained using every possible combination of reference pairing. The approach has been validated using more than 500 FTIR-ATR spectra from cool-season culinary grain legumes assembled from germplasm banks and breeders' working collections. PLCS has been combined with soft discriminant analysis to detect outliers that could be particularly suitable for a deeper analysis.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6