METHODS: Five fresh-pooled blood samples were sent to participating laboratories twice each year. The results were evaluated against target values assigned by the National Glycohemoglobin Standardization Program network laboratories; a passing criterion of +/-7% of the target value was used. Measurement uncertainty at Hb A(1c) concentrations of 7.0% and 8.0% were determined.
RESULTS: A total of 276 laboratories from 11 countries took part in the Hb A(1c) survey. At the Hb A(1c) concentrations tested method-specific interlaboratory imprecision (CVs) were 1.1%-13.9% in 2005, 1.3%-10.1% in 2006, 1.2%-8.2% in 2007, and 1.1%-6.1% in 2008. Differences between target values and median values from the commonly used methods ranged from -0.24% to 0.22% Hb A(1c) in 2008. In 2005 83% of laboratories passed the survey, and in 2008 93% passed. At 7.0% Hb A(1c), measurement uncertainty was on average 0.49% Hb A(1c).
CONCLUSIONS: The use of accuracy-based proficiency testing with stringent quality criteria has improved the performance of Hb A(1c) testing in the Asian and Pacific laboratories during the 4 years of assessment.
METHODS: In this study, prior to synthesis, quality control analysis method for 18F-Fluorocholine was developed and validated, by adapting the equipment set-up used in 18F-Fluorodeoxyglucose (18FFDG) routine production. Quality control on the 18F-Fluorocholine was performed by means of pH, radionuclidic identity, radio-high performance liquid chromatography equipped with ultraviolet, radio- thin layer chromatography, gas chromatography and filter integrity test.
RESULTS: Post-synthesis; the pH of 18F-Fluorocholine was 6.42 ± 0.04, with half-life of 109.5 minutes (n = 12). The radiochemical purity was consistently higher than 99%, both in radio-high performance liquid chromatography equipped with ultraviolet (r-HPLC; SCX column, 0.25 M NaH2PO4: acetonitrile) and radio-thin layer chromatography method (r-TLC). The calculated relative retention time (RRT) in r-HPLC was 1.02, whereas the retention factor (Rf) in r-TLC was 0.64. Potential impurities from 18F-Fluorocholine synthesis such as ethanol, acetonitrile, dimethylethanolamine and dibromomethane were determined in gas chromatography. Using our parameters, (capillary column: DB-200, 30 m x 0.53 mm x 1 um) and oven temperature of 35°C (isothermal), all compounds were well resolved and eluted within 3 minutes. Level of ethanol and acetonitrile in 18F-Fluorocholine were detected below threshold limit; less than 5 mg/ml and 0.41 mg/ml respectively. Meanwhile, dimethylethanolamine and dibromomethane were undetectable.
CONCLUSION: A convenient, efficient and reliable quality control analysis work-up procedure for 18FFluorocholine has been established and validated to comply all the release criteria. The convenient method of quality control analysis may provide a guideline to local GMP radiopharmaceutical laboratories to start producing 18F-Fluorocholine as a tracer for prostate cancer imaging.
OBJECTIVE: In this study, we report a rapid method for the residue analysis of IND and its metabolites, viz., IND-carboxylic acid, diaminotriazine, and triazine indanone in a wide range of palm oil matrices using liquid chromatography-tandem mass spectrometry (LC-MS/MS).
METHOD: The optimized sample preparation workflows included two options: (1) acetonitrile extraction (QuEChERS workflow), followed by freezing at -80°C and (2) acetonitrile extraction, followed by cleanup through a C18 solid phase extraction (SPE) cartridge. The optimized LC runtime was 7 min. All these analytes were estimated by LC-MS/MS multiple reaction monitoring.
RESULTS: Both sample preparation methods provided similar method performance and acceptable results. The limit of quantification (LOQ) of IND, IND-carboxylic acid, and triazine indanone was 0.001 mg/kg. For diaminotriazine, the LOQ was 0.005 mg/kg. The method accuracy and precision complied with the SANTE/12682/2019 guidelines of analytical quality control.
CONCLUSIONS: The potentiality of the method lies in a high throughput analysis of IND and its metabolites in a single chromatographic run with high selectivity and sensitivity. Considering its fit-for-purpose performance, the method can be implemented in regulatory testing of IND residues in a wide range of palm oil matrices that are consumed and traded worldwide.
HIGHLIGHTS: This work has provided a validated method for simultaneous residue analysis of indaziflam and its metabolites in crude palm oil and its derived matrices with high sensitivity, selectivity, and throughput.
RESULTS: The key volatile compounds and aroma profile of six pineapple varieties grown in Malaysia were investigated by gas chromatography-olfactometry (GC-O), gas-chromatography-mass spectrometry and qualitative descriptive sensory analysis. A total of 59 compounds were determined by GC-O and aroma extract dilution analysis. Among these compounds, methyl-2-methylbutanoate, methyl hexanoate, methyl-3-(methylthiol)-propanoate, methyl octanoate, 2,5-dimethyl-4-methoxy-3(2H)-furanone, δ-octalactone, 2-methoxy-4-vinyl phenol, and δ-undecalactone contributed greatly to the aroma quality of the pineapple varieties, due to their high flavour dilution factor. The aroma of the pineapples was described by seven sensory terms as sweet, floral, fruity, fresh, green, woody and apple-like.
CONCLUSION: Inter-relationship between the aroma-active compounds and the pineapples revealed that 'Moris' and 'MD2' covaried majorly with the fruity esters, and the other varieties correlated with lesser numbers of the fruity esters. Hierarchical cluster analysis (HCA) was used to establish similarities among the pineapples and the results revealed three main groups of pineapples.
METHODS: 3317 raw digital mammograms were processed with Volpara(®) (Matakina Technology Ltd, Wellington, New Zealand) to obtain fibroglandular tissue volume (FGV), breast volume (BV) and VBD. Errors in parameters including CBT, kVp, filter thickness and mAs were simulated by varying them in the Digital Imaging and Communications in Medicine (DICOM) tags of the images up to ±10% of the original values. Errors in detector gain and offset were simulated by varying them in the Volpara configuration file up to ±10% from their default values. For image noise, Gaussian noise was generated and introduced into the original images.
RESULTS: Errors in filter thickness, mAs, detector gain and offset had limited effects on FGV, BV and VBD. Significant effects in VBD were observed when CBT, kVp, detector offset and image noise were varied (p control is essential to keep the parameter errors within reasonable bounds. Volpara appears robust within those bounds, albeit for more advanced applications such as tracking density change over time, it remains to be seen how accurate the measures need to be.