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

Characterization of Cider Apple Fruits According to Their Degree of Ripening. A Chemometric Approach

A chemometric study was carried out in order to typify cider apples according to their degree of ripening. Several chemical variables (sugars, organic acids, amino acids, polyphenols, and pectins) were analyzed using HPLC and FIA methods. Univariate data treatment was not sufficient to allow the apple varieties to be differentiated according to their stage of ripening. Two linear combinations of original variables, ascertained by principal component analysis (PCA), provided an adequate data structurization. To classify apples by their degree of ripening, a mathematical decision rule was established with a prediction capacity of 85% using a LDA method; the most relevant variables in the canonical function ascertained by LDA were sugars, pectins, malic acid, glycine, serine, valine, and glutamic acid. The use of the PLS-2 algorithm demonstrated the influence of the ripening process on the chemical composition of the fruits (R2: 91.7%) and furthermore allowed authors to differentiate apple varieties according to their degree of ripening