Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7590028 | Food Chemistry | 2016 | 6 Pages |
Abstract
In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Kamila de Sá Oliveira, Layce de Souza Callegaro, Rodrigo Stephani, Mariana Ramos Almeida, Luiz Fernando Cappa de Oliveira,