Classification of olive oils according to their cultivars based on second-order data using LC-DAD
| Autor: | Alejandro C. Olivieri, Ana M. Jiménez-Carvelo, Antonio González-Casado, Carlos Cruz, Luis Cuadros-Rodríguez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Multivariate statistics
02 engineering and technology 01 natural sciences Least squares Analytical Chemistry Chromatography detector Olea THREE-WAY DATA CLASSIFICATION METHOD Cultivar Least-Squares Analysis Olive Oil Principal Component Analysis Chemistry Soft independent modelling of class analogies business.industry RANDOM FOREST 010401 analytical chemistry Ciencias Químicas Discriminant Analysis Pattern recognition 021001 nanoscience & nanotechnology MULTIVARIATE CURVE RESOLUTION LIQUID CHROMATOGRAPHY 0104 chemical sciences Random forest OLIVE OIL AUTHENTICATION Principal component analysis Química Analítica Artificial intelligence 0210 nano-technology business CIENCIAS NATURALES Y EXACTAS Chromatography Liquid Multivariate classification |
| Popis: | Second-order data acquired using liquid chromatography coupled to a diode array detector were used to classify extra virgin olive oils samples according to their cultivars. The chromatographic fingerprints from the epoxidised fraction were obtained using normal-phase liquid chromatography. To reduce the data matrices two strategies were employed: (1) multivariate curve resolution-alternating least squares (MCR-ALS) and (2) a new strategy proposed in this work based on the fusion of the mean data profiles in both spectral and time domains. Several conventional chemometric tools were then applied to both raw and reduced data: principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), soft independent modelling of class analogies (SIMCA) and n-way partial least-squares-discriminant analysis (NPLS-DA). Furthermore, an emergent multivariate classification method known as random forest (RF) has been first applied to second-order data. It was shown that RF is more efficient than conventional tools. Indeed, the obtained sensibility, specificity and accuracy are 1.00, 0.92 and 0.95 respectively; these performance metrics are significantly better than the values found for the other methods. Fil: Jiménez Carvelo, Ana María. Universidad de Granada; España Fil: Cruz, Carlos M.. Universidad de Granada; España Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina Fil: González Casado, Antonio. Universidad de Granada; España Fil: Cuadros Rodríguez, Luis. Universidad de Granada; España |
| Databáze: | OpenAIRE |
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