On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks
| Autor: | N. González-Viveros, Hector H. Cerecedo-Nunez, J. Castro-Ramos, Pilar Gomez-Gil |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Support Vector Machine
Mean squared error Spectrum Analysis Raman 01 natural sciences Analytical Chemistry 0404 agricultural biotechnology Linear regression Partial least squares regression Classifier (linguistics) Least-Squares Analysis Mathematics Artificial neural network 010401 analytical chemistry Discriminant Analysis Water 04 agricultural and veterinary sciences General Medicine Linear discriminant analysis 040401 food science 0104 chemical sciences Support vector machine Linear Models Neural Networks Computer Biological system Sugars Nonlinear regression Food Science |
| Zdroj: | Food chemistry. 352 |
| ISSN: | 1873-7072 |
| Popis: | In this paper, we present an analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions. Indeed, we analysed our method for the estimation of sucrose in three solid industrialized food products: donuts, cereal, and cookies. Concentrations were estimated in two ways: using a non-linear fitting system, and using a classifier. Our experiments showed that both the classifier and the fitting systems performed better than a Support Vector Machine (SVM), a Linear Discriminant Analysis (LDA), a Linear Regression (LR), and interval Partial Least Squares (iPLS). The best-case obtained by an FFNN for water solutions was 93.33% of classification and 3.51% of Root Mean Square Error in Prediction (RMSEP), compared with 82.22% obtained by a LDA. Our proposed method got an RMSEP of 1% for the best-case obtained with the food products. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect