Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 6 de 6
Filter
Add more filters










Database
Language
Publication year range
1.
Molecules ; 23(9)2018 Sep 16.
Article in English | MEDLINE | ID: mdl-30223605

ABSTRACT

This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.


Subject(s)
Mangifera/chemistry , Plant Extracts/analysis , Volatile Organic Compounds/analysis , Cluster Analysis , Discriminant Analysis , Food Quality , Gas Chromatography-Mass Spectrometry , Least-Squares Analysis , Principal Component Analysis , Solid Phase Microextraction
2.
J Chromatogr Sci ; 56(2): 166-176, 2018 Feb 01.
Article in English | MEDLINE | ID: mdl-29069322

ABSTRACT

Two-phase micro-electrodriven membrane extraction (EME) procedure for the pre-concentration of selected non-steroidal anti-inflammatory drugs (NSAIDs) in aquatic matrices was investigated. Agarose film was used as interface between donor and acceptor phase in EME which allowed for selective extraction of the analytes prior to high performance liquid chromatography-ultraviolet detection. Charged analytes were transported from basic aqueous sample solution through agarose film into 1-octanol as an acceptor phase at 9 V potential. Response surface methodology in conjunction with the central composite design showed good correlations between extraction time and applied voltage (R2 > 0.9358). Under optimized extraction conditions, the method showed good linearity in the concentration range of 0.5-500 µg L-1 with coefficients of determination, r2≥ 0.9942 and good limits of detection (0.14-0.42 µg L-1) and limits of quantification (0.52-1.21 µg L-1). The results also showed high enrichment factors (62-86) and good relative recoveries (72-114%) with acceptable reproducibilities (RSDs ≤ 7.5% n = 3). The method was successfully applied to the determination of NSAIDs from tap water and river water samples. The proposed method proved to be rapid, simple and requires low voltage and minute amounts of organic solvent, thus environmentally friendly.


Subject(s)
Anti-Inflammatory Agents, Non-Steroidal , Chromatography, High Pressure Liquid/methods , Water Pollutants, Chemical , 1-Octanol , Anti-Inflammatory Agents, Non-Steroidal/analysis , Anti-Inflammatory Agents, Non-Steroidal/isolation & purification , Electrochemical Techniques/instrumentation , Electrochemical Techniques/methods , Equipment Design , Limit of Detection , Linear Models , Reproducibility of Results , Water Pollutants, Chemical/analysis , Water Pollutants, Chemical/isolation & purification
3.
Article in English | MEDLINE | ID: mdl-28142101

ABSTRACT

The aim of this study was to investigate and apply supported ionic liquid membrane (SILM) in two-phase micro-electrodriven membrane extraction combined with high performance liquid chromatography-ultraviolet detection (HPLC-UV) for pre-concentration and determination of three selected antidepressant drugs in water samples. A thin agarose film impregnated with 1-hexyl-3-methylimidazolium hexafluorophosphate, [C6MIM] [PF6], was prepared and used as supported ionic liquid membrane between aqueous sample solution and acceptor phase for extraction of imipramine, amitriptyline and chlorpromazine. Under the optimized extraction conditions, the method provided good linearity in the range of 1.0-1000µgL-1, good coefficients of determination (r2=0.9974-0.9992) and low limits of detection (0.1-0.4µgL-1). The method showed high enrichment factors in the range of 110-150 and high relative recoveries in the range of 88.2-111.4% and 90.9-107.0%, for river water and tap water samples, respectively with RSDs of ≤7.6 (n=3). This method was successfully applied to the determination of the drugs in river and tap water samples. It is envisaged that the SILM improved the perm-selectivity by providing a pathway for targeted analytes which resulted in rapid extraction with high degree of selectivity and high enrichment factor.


Subject(s)
Antidepressive Agents/analysis , Ionic Liquids/chemistry , Liquid Phase Microextraction/methods , Water Pollutants, Chemical/analysis , Borates/chemistry , Chromatography, High Pressure Liquid/methods , Drinking Water/chemistry , Imidazoles/chemistry , Limit of Detection , Linear Models , Reproducibility of Results , Rivers/chemistry
4.
Molecules ; 21(5)2016 Apr 30.
Article in English | MEDLINE | ID: mdl-27144555

ABSTRACT

E. longifolia is attracting interest due to its pharmacological properties and pro-vitality effects. In this study, an online SPE-LC approach using polystyrene divinyl benzene (PSDVB) and C18 columns was developed in obtaining chromatographic fingerprints of E. longifolia. E. longifolia root samples were extracted using pressurized liquid extraction (PLE) technique prior to online SPE-LC. The effects of mobile phase compositions and column switching time on the chromatographic fingerprint were optimized. Validation of the developed method was studied based on eurycomanone. Linearity was in the range of 5 to 50 µg∙mL(-1) (r² = 0.997) with 3.2% relative standard deviation of peak area. The developed method was used to analyze 14 E. longifolia root samples and 10 products (capsules). Selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were applied to the fingerprint datasets of 37 selected peaks to evaluate the ability of the chromatographic fingerprint in classifying quality of E. longifolia. Three groups were obtained using CA. DA yielded 100% correlation coefficient with 19 discriminant compounds. Using PCA, E. longifolia root samples were clearly discriminated from the products. This study showed that the developed online SPE-LC method was able to provide comprehensive evaluation of E. longifolia samples for quality control purposes.


Subject(s)
Chromatography, Liquid/methods , Eurycoma/chemistry , Plant Extracts/chemistry , Plant Roots/chemistry , Quality Control , Quassins/chemistry
5.
Environ Monit Assess ; 184(2): 1001-14, 2012 Jan.
Article in English | MEDLINE | ID: mdl-21494831

ABSTRACT

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, ß-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.


Subject(s)
Environmental Monitoring/methods , Rivers/chemistry , Water Pollutants, Chemical/analysis , Water Pollution, Chemical/statistics & numerical data , Cholestanol/analysis , Cluster Analysis , Discriminant Analysis , Pesticides/analysis , Polycyclic Aromatic Hydrocarbons/analysis , Principal Component Analysis , Pyrenes/analysis , Sitosterols
6.
Water Res ; 43(20): 5023-30, 2009 Dec.
Article in English | MEDLINE | ID: mdl-19896157

ABSTRACT

Faecal sterols detection is a promising method for identifying sources of faecal pollution. In this study, faecal contamination in water samples from point source (sewage treatment plants, chicken farms, quail farms and horse stables) was extracted using the solid phase extraction (SPE) technique. Faecal sterols (coprostanol, cholesterol, stigmasterol, beta-sitosterol and stigmastanol) were selected as parameters to differentiate the source of faecal pollution. The results indicated that coprostanol, cholesterol and beta-sitosterol were the most significant parameters that can be used as source tracers for faecal contamination. Chemometric techniques, such as cluster analysis, principal component analysis and discriminant analysis were applied to the data set on faecal contamination in water from various pollution sources in order to validate the faecal sterols' profiles. Cluster analysis generated three clusters: coprostanol was in cluster 1, cholesterol and beta-sitosterol formed cluster 2, while cluster 3 contained stigmasterol and stigmastanol. Discriminant analysis suggested that coprostanol, cholesterol and beta-sitosterol were the most significant parameters to discriminate between the faecal pollution source. The use of chemometric techniques provides useful and promising indicators in tracing the source of faecal contamination.


Subject(s)
Feces/chemistry , Solid Phase Extraction/methods , Sterols/analysis , Water Pollutants/analysis , Cholestanol/analysis , Cholestanol/chemistry , Cholesterol/analysis , Cholesterol/chemistry , Environmental Monitoring/methods , Sitosterols/analysis , Sitosterols/chemistry , Sterols/classification , Sterols/isolation & purification , Stigmasterol/analysis , Stigmasterol/chemistry , Water Pollutants/classification , Water Pollutants/isolation & purification
SELECTION OF CITATIONS
SEARCH DETAIL
...