Biomedical subjects

Flavio Belliardo

Publications and source records attributed to Flavio Belliardo.

3 recordsLinked to original sources

PubMed2006

Comparison of volatile concentrations in hand-squeezed juices of four different lemon varieties.

Fresh juices of four Italian cultivars of lemons (Citrus limon Burm) have been analyzed by headspace solid phase microextraction coupled to gas chromatography and gas chromatography-mass spectrometry (GC-MS). The best results have been obtained with the 2 cm 50/30 microm divinylbenzene/carboxen on poly(dimethylsiloxane) fiber, using a homogenization time of 1 h at 40 degrees C and a sampling period of 30 min. A total of 35 volatile compounds have been identified by GC-MS, and their relative amounts have been calculated by adding internal standard to the samples. Differences in composition of lemon juices volatile components have been observed. Verdello Siracusano lemon juice has the highest amount of volatile compounds (50.28 mg/L), followed by Interdonato (8.39 mg/L), Primo Fiore Capo d'Orlando (5.75 mg/L), and Femminello Siracusano (2.62 mg/L) juices. Volatile compounds mainly consist of mono- and sesquiterpene hydrocarbons and oxygenated molecules (aldheydes, monoterpene alcohols, and monoterpene esters). Headspace solid phase microextraction coupled to a gas chromatograph equipped with a specific sulfur detector, a sulfur chemiluminescence detector, let us detect and quantify dimethyl sulfide compound at the microgram/liter level in lemon juices.

Alcohols↗

PubMed2006

Headspace-solid-phase microextraction in the analysis of the volatile fraction of aromatic and medicinal plants.

Headspace (HS)-solid-phase microextraction (SPME) has assumed an ever increasing importance as a technique for HS sampling to study the composition of the HS of medicinal and aromatic plants. HS-SPME has mainly been applied for (a) studying the composition of the volatile fraction, including in addition to or as an alternative to other sampling techniques; (b) monitoring the biological phenomena involved with the volatile fraction of a plant; (c) discriminating between species, subspecies, varieties, cultivars, or chemotypes; and (d) quality control of plant samples. A review of 108 articles published during 2000-2005 is presented covering the use of HS-SPME in the field of aromatic and medicinal plants, selection of the most effective fiber and sampling conditions, comparison of HS-SPME and other volatile fraction sample preparation techniques, and the advantages and limits of HS-SPME when applied to medicinal and aromatic plants.

Chromatography, Liquid↗

PubMedDate not supplied

Headspace-solid-phase microextraction fast GC in combination with principal component analysis as a tool to classify different chemotypes of chamomile flower-heads (Matricaria recutita l.).

Headspace-solid-phase microextraction gas chromatography-principal component analysis (HS-SPME GC-PCA) is proposed as a complementary or alternative method to essential oil (EO) GC-PCA in order to discriminate between flower-heads of chamomile of different chemotypes. Ninety-two EOs and the headspaces sampled by HS-SPME of the corresponding chamomile flower-heads were examined by conventional GC and fast GC (F-GC) and the results submitted to statistical analysis by PCA. HS-SPME F-GC-PCA showed itself to be a rapid technique by which to distinguish chamomile flower-head chemotypes a produced results in agreement with the accepted EO classification. Using this method, the analysis time was reduced from at least 4.5 h with EO conventional GC to less than 1 h with HS-SPME F-GC. This approach can thus successfully be used as an analytical decision maker in order to reduce the number of time-consuming EO conventional GC analyses by limiting them to those samples that cannot unequivocally be classified. The EO conventional GC and HS-SPME F-GC results of PCA were very uniform, but they did not provide quantitative correlations between the components as determined by the two methods. A different statistical approach and a larger number of samples will be needed in order to correlate components in the headspace sampled by SPME and those in the corresponding EO quantitatively through a function.

Chromatography, Gas↗