Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

Alternative fat substitutes for beef burger: technological and sensory characteristics

“…The acceptance test was performed using a 9-point hedonic scale (9 = I like extremely, 1 = I dislike extremely) and the purchase probability using a 5-point scale (5 = I definitely would buy the product; 1 = I certainly would not buy the product). The tests were conducted in an enclosed cab with white illumination, and the samples were placed on a white workbench and coded with three random digits (Cruz et al 2013a). Sensory data were statistically analyzed by a three-way internal preference mapping (Nunes et al 2011).…”

Quality evaluation of parmesan-type cheese: a chemometric approach

“…Chemometrics is a science of multidisciplinary nature which involves multivariate statistics, mathematical modeling, and information technology, specifically applied to chemical data (Cruz et al, 2013). Chemometric methods have been a useful tool in evaluating the quality and identity control of processing parameters for dairy products (Souza et al, 2011).…”

“…Souza et al (2011) applied PCA and HCA tools in a study on the presence of adulterants in Brazilian UHT milk. Cruz et al (2013) used chemometrics methods to discriminate low-fat and full-fat yogurt characteristics. Karoui & Baerdemaeker (2007), in a review paper, mentioned several studies using advanced analytical methods coupled with chemometric tools in dairy products, mainly with regard to cheese identification of authenticity of PDO, ripening period, and effects of different…”

The Possibility of Applying the EM-PCA Procedure to Lake Water

“…The percentage explained by the fi rst two dimensions is 71%, and according to [19] it is very high. In literature it is assumed that if the percentage explained by the fi rst two dimensions is 75%, then it is statistically signifi cant [34]. In [51] the authors prefer 70% and 64% in [52] the percentage explained by the fi rst two dimensions, as it is very signifi cant statistically.…”

“…PCs are actually orthogonal and linear combinations of the data for their maximum diversity. Simultaneously, each of the PCs carries different information about data variability [3,7,34]. PCA allows us to group the experimental data and present them in the form of several PCs, and PCs contain nearly identical information to the experimental data.…”