The combination of two sensitive, selective and reproducible reversed phase liquid chromatographic (RP-HPLC) methods was developed for the determination of artesunate (AS), its active metabolite dihydroartemisinin (DHA) and mefloquine (MQ) in human plasma. Solid phase extraction (SPE) of the plasma samples was carried out on Supelclean LC-18 extraction cartridges. Chromatographic separation of AS, DHA and the internal standard, artemisinin (QHS) was obtained on a Hypersil C4 column with mobile phase consisting of acetonitrile-0.05 M acetic acid adjusted to pH 5.2 with 1.0M NaOH (42:58, v/v) at the flow rate of 1.50 ml/min. The analytes were detected using an electrochemical detector operating in the reductive mode. Chromatography of MQ and the internal standard, chlorpromazine hydrochloride (CPM) was carried out on an Inertsil C8-3 column using methanol-acetonitrile-0.05 M potassium dihydrogen phosphate adjusted to pH 3.9 with 0.5% orthophosphoric acid (50:8:42, v/v/v) at a flow rate of 1.00 ml/min with ultraviolet detection at 284 nm. The mean recoveries of AS and DHA over a concentration range of 30-750 ng/0.5 ml plasma and MQ over a concentration of 75-1500 ng/0.5 ml plasma were above 80% and the accuracy ranged from 91.1 to 103.5%. The within-day coefficients of variation were 1.0-1.4% for AS, 0.4-3.4% for DHA and 0.7-1.5% for MQ. The day-to-day coefficients of variation were 1.3-7.6%, 1.8-7.8% and 2.0-3.4%, respectively. Both the lower limit of quantifications for AS and DHA were at 10 ng/0.5 ml and the lower limit of quantification for MQ was at 25 ng/0.5 ml. The limit of detections were 4 ng/0.5 ml for AS and DHA and 15 ng/0.5 ml for MQ. The method was found to be suitable for use in clinical pharmacological studies.
The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.