Analyzing sensory data using non-linear preference learning with feature subset selection

15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004The quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers’ ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postulate to learn from consumers’ preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data base

Solid-phase extraction and high-performance liquid chromatographic determination of polyphenols in apple musts and ciders

An improved analytical method was developed for the determination of polyphenols in the apple products must and cider. Phenolic compounds were fractionated into neutral and acidic groups by means of a solid-phase extraction method. The analytical method proposed was effective for the quantitation of phenolic compounds; recoveries between 84% and 111% were obtained, and the relative standard deviation was usually less than 5%

Phenolic Profile of Asturian (Spain) Natural Cider

The polyphenolic composition of natural ciders from the Asturian community (Spain), during 2 consecutive years, was analyzed by RP-HPLC and the photodiode-array detection system, without previous extraction (direct injection). A total of 16 phenolic compounds (catechol, tyrosol, protocatechuic acid, hydrocaffeic acid, chlorogenic acid, hydrocoumaric acid, ferulic acid, (-)-epicatechin, (+)-catechin, procyanidins B2 and B5, phloretin-2¢-xyloglucoside, phloridzin, hyperin, avicularin, and quercitrin) were identified and quantified. A fourth quercetin derivative, one dihydrochalcone-related compound, two unknown procyanidins, three hydroxycinnamic derivatives, and two unknown compounds were also found. Among the low-molecular-mass polyphenols analyzed, hydrocaffeic acid was the most abundant compound, representing more than 80% of the total polyphenolic acids. Procyanidins were the most important family among the flavonoid compounds. Discriminant analysis was allowed to correctly classify more than 93% of the ciders, according to the harvest year; the most discriminant variables were an unknown procyanidin and quercitrin