Literature DB >> 27863003

Fractional factorial design based microwave-assisted extraction for the determination of organophosphorus and organochlorine residues in tobacco by using gas chromatography-mass spectrometry.

Ling Hao1,2, Haifang Li1, Jin-Ming Lin1.   

Abstract

Sample preparation is often the main bottleneck in analyzing biological samples. Particularly, effective evaluation of sample preparation conditions usually involves multiple factors and tedious and time-consuming experiments. In this study, fractional factorial design, specifically orthogonal array testing, was employed to screen and optimize multiple extraction parameters in concise but representative experiments. An efficient and sensitive method was developed to determine organophosphorus and organochlorine pesticide residues in tobacco, via microwave-assisted extraction and gas chromatography coupled with mass spectrometry detection. With orthogonal array design, screening, and optimization tests were subsequently conducted to determine the range, impact rank, and possible interactions of extraction temperature, time, microwave power, additive salt, and additive water. Orthogonal array testing selectively reduces the size and cost of experiments and meanwhile provides more information compared to the traditional experimental design that optimizes one factor at a time. A good linear range (0.02-2.00 μg/mL), limits of detection (0.001-0.098 μg/mL), and recovery rates (70.4-107.1%) were demonstrated by spiking known concentrations of multiple pesticide standards in tobacco samples. The established method was then successfully applied to the determination of multipesticide residues in raw tobacco leaves and commercial cigarettes.
© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  fractional factorial design; gas chromatography coupled with mass spectrometry; microwave-assisted extraction; orthogonal array testing; pesticide residue analysis

Mesh:

Substances:

Year:  2016        PMID: 27863003     DOI: 10.1002/jssc.201600706

Source DB:  PubMed          Journal:  J Sep Sci        ISSN: 1615-9306            Impact factor:   3.645


  2 in total

1.  Comprehensive urinary metabolomic characterization of a genetically induced mouse model of prostatic inflammation.

Authors:  Ling Hao; Yatao Shi; Samuel Thomas; Chad M Vezina; Sagar Bajpai; Arya Ashok; Charles J Bieberich; William A Ricke; Lingjun Li
Journal:  Int J Mass Spectrom       Date:  2018-09-22       Impact factor: 1.986

2.  Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease.

Authors:  Ling Hao; Jingxin Wang; David Page; Sanjay Asthana; Henrik Zetterberg; Cynthia Carlsson; Ozioma C Okonkwo; Lingjun Li
Journal:  Sci Rep       Date:  2018-06-18       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.