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MSPD for <strong>Pesticide</strong>s in Fish and Foods 289 Fig. 1. (opposite page) (A) Preparation of the sample by MSPD; (B) preparation and elution of the MSPD column.
290 Barker The mobile phase composition is a 7:3 ratio of 0.01 M aqueous NaH 2PO 4 (pH 6.0) (phosphate buffer) and ACN, respectively, delivered at a flow rate of 0.4 mL/min. Mobile phase solvents are filtered over a 0.45-µm membrane filter. All solvents are thoroughly purged with helium before use. A 10-µL aliquot of muscle extract is injected for LC analysis (see Note 9). 3.4. Identification and Quantification Individual triazines are identified by their retention time relative (±1%) to reference standards and their ultraviolet diode array spectrum. Peak purity measurements are also conducted to ensure the absence of coeluting interferences. Quantification of the individual triazines is conducted using the relative peak height (or area) of the detected peak to those observed from fortified samples plotted as a standard curve, fitted to the linear regression line and equation for the line so derived (see Notes 9–14). 3.5. Data Analysis The statistical parameters used to validate this method for triazine analysis include linearity of response, precision, and accuracy. Specificity and sensitivity are also evaluated. 1. Linearity of response: A standard curve covering the range of concentrations from 0.35 to 8 ppm for standards and extracts is made using the mean of the area of the replicate samples (see Note 10). 2. Precision is evaluated by examining intraassay variability and interassay variability. The values for the intraassay variation are derived from the coefficient of variation (CV) of means plus or minus standard deviation (SD) of peak areas of three repetitions of analysis of the same sample for each concentration. Overall intraassay variability of the method is determined by averaging the CVs for the five concentrations (0.35–8 ppm) examined. The values for the interassay variability are derived from the CV of peak areas of three distinct samples extracted and assayed on three different days. Overall interassay variation (±SD) of the method is obtained by averaging the CV of each individual concentration (0.35–8 ppm; see Note 11). 3. Recovery is evaluated by dividing the average peak area of each concentration obtained after extraction by the average peak area of the respective standard concentration obtained without extraction. The average percentage recovery P is calculated by summing the recovery percentage for each sample concentration on each separate day and dividing by three. The relative standard deviation of P is calculated by determining the standard deviation of P and dividing by P (see Note 12). 4. Specificity is calculated as the percentage of true negative samples of the number of known negatives tested (see Note 13). 5. Sensitivity is calculated as the percentage of correctly identified positive samples (true positives) of the total number of known positives tested (see Note 14) within the limits of the assay. 4. Notes 1. To date, silica-based support materials have been the most frequently used for MSPD. The use and effect of synthetic polymer-based solid supports is a subject for further study, particularly supports that possess unique surface and pore chemistries, such as hydrophobic interaction supports. For silica-based materials, however, studies have shown that the pore size is of minor importance in MSPD. This effect could vary with the sample and
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TM METHODS IN BIOTECHNOLOGY � 19
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M E T H O D S I N B I O T E C H N O
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© 2006 Humana Press Inc. 999 River
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vi Preface candidates for a relevan
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viii Contents 9 Analysis of Pentach
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x Contents 30 On-Line Admicelle-Bas
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xii Contributors JANE C. CHUANG •
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xiv Contributors JOHN PATSIAS • A
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4 Martínez Vidal et al. excellent
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6 Martínez Vidal et al. Table 1 MS
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8 Martínez Vidal et al. 7. Check t
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F2 10 Martínez Vidal et al. where
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12 Martínez Vidal et al. 11. Inter
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14 Martínez Vidal et al. 11. Kohlm
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Pyrethroids in Blood Plasma and Uri
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19 Fig. 1. Metabolism of the pyreth
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36 Barr et al. Because the metaboli
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38 Table 1 Pesticides Included in t
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40 Barr et al. 4. Spike the first p
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42 Table 3 High-Resolution Mass Spe
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44 Table 3 High-Resolution Mass Spe
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46 Barr et al. 4. Griffin, P., Maso
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OCPs in Human Fluids by SPDE and GC
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62 Barr et al. exposures (2). For c
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64 Table 1 The Target Analytes, Abb
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66 Fig. 1. Structures of target ana
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68 Barr et al. Table 3 Pesticides a
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70 Barr et al. 3.2.3.2. NATIVE STAN
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72 Barr et al. 8. Inject each sampl
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74 Barr et al. Table 5 Optimized Pr
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76 Barr et al. 3.7.4. Intra- and In
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78 Barr et al. 11. Hill, R. H., Jr.
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80 Fustinoni et al. Fig. 1. Molecul
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82 Fustinoni et al. Develop the che
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84 Fustinoni et al. the ETU and ETU
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86 Fustinoni et al. 3.3. Biological
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88 Fustinoni et al. of pesticides s
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2,4-D and MCPA in Human Urine 91 7
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2,4-D and MCPA in Human Urine 93 4.
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2,4-D and MCPA in Human Urine 95 Ta
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2,4-D and MCPA in Human Urine 101 F
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2,4-D and MCPA in Human Urine 103 6
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106 Jardim, Pozzebon, and Queiroz S
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108 Jardim, Pozzebon, and Queiroz 3
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110 Jardim, Pozzebon, and Queiroz A
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112 Zhang been widely used to detec
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114 Zhang 3. Maintain the ion sourc
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116 Zhang Fig. 1. Mass ion chromato
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118 Zhang 15. Crespin, M. A., Galle
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120 Knopp The search for alternativ
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122 Knopp 4. Dissolve 40 mg BSA in
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124 Knopp Fig. 2. Scheme of tracer
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126 Knopp Fig. 3. Flow chart of the
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128 Knopp Fig. 4. Phenoxyalcanoic a
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130 Knopp 18. No or only minimal sa
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HTS-IS-ELISA for Biomonitoring Chlo
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150 van Hemmen, van der Jagt, and B
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166 Glass the collector and the mom
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168 Glass 3.2.1. Selection of Appli
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170 Glass Fig. 2. A field layout fo
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172 Glass questions that may arise
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174 Glass 3.5.3. Labeling of Media
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176 Glass Again, this can be relate
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Determination of Insecticides in In
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185 Fig. 3. Total ion current (TIC)
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187 Fig. 5. TIC chromatogram (y-axi
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Exposure to Malathion and Metabolit
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208 Araque et al. tive xenoestrogen
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210 Araque et al. 3. Methods The me
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212 Araque et al. values of 0.1 to
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214 Araque et al. estradiol and tra
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Internal Quality Criteria 219 17 Qu
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Internal Quality Criteria 221 instr
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223 Fig. 1. Basic components of the
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Internal Quality Criteria 225 Fig.
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Internal Quality Criteria 227 Resul
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Internal Quality Criteria 229 5. Th
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Pesticide Analysis by ELISA 231 18
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Pesticide Analysis by ELISA 233 4.
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Page 248 and 249: QuEChERS Approach for Pesticides 23
Page 262 and 263: 253 Table 2 Conditions for the GC-M
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Page 273 and 274: 264 Di Muccio et al. 1. Introductio
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Page 279 and 280: 270 Di Muccio et al. evaporator. In
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Page 295 and 296: 286 Barker shorten turnaround time,
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Page 305 and 306: 296 Barker 43. Pico, Y., Molto, J.
Page 307 and 308: 298 Picó ultraviolet absorbance, i
Page 309 and 310: 300 Picó 3. Attach the conditioned
Page 311 and 312: 302 Picó Fig. 2. EDA-only vs conve
Page 313 and 314: 304 Picó Fig. 3. CE-MS/ESI electro
Page 315 and 316: 306 Picó Fig 4. CE-MS/ESI electrop
Page 317 and 318: 308 Picó tration. The optimum samp
Page 320 and 321: Residues in Grain and Dried Foodstu
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Page 329 and 330: 320 Papadopoulou-Mourkidou, Papadak
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α-Hexachlorocyclohexane in Food 33
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Screening of Pesticides by HS-SPME-
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361 Table 3 (Continued) Recoveries
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In-Tube SPME-LC-MS of Herbicides in
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379 Fig. 7. Total ion and SIM chrom
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LC/LC for Pesticide Analysis 383 29
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LC/LC for Pesticide Analysis 385 Fi
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LC/LC for Pesticide Analysis 389 re
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LC/LC for Pesticide Analysis 391 4.
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LC/LC for Pesticide Analysis 399 sa
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LC/LC for Pesticide Analysis 403 19
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406 Pérez-Bendito, Rubio, and Meri
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412 Table 2 Mean Recovery (%) and (
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Molecular Imprinted Solid-Phase Ext
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436 Papadopoulou-Mourkidou, Patsias
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438 Fig. 1. Simplified flow diagram
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Analysis of Organotins by GC-HRMS 4
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457 Table 1 Calibration Curves for
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459 Table 4 Summary of Positive Ion
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Triazine in Water 467 34 Determinat
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Triazine in Water 469 with various
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Triazine in Water 471 Fig. 1. A chr
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Triazine in Water 473 Fig. 2. LC/AP
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475 Table 3 General Conditions Used
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Triazine in Water 477 (>60%) except
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Triazine in Water 479 13. Koskinen,
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482 Rodríguez-Mozaz, López de Ald
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Index 491 Index Adipose tissue anal
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Index 493 high-throughput screening
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Index 495 Food analysis, see Atrazi
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Index 497 HPLC, see High-performanc
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Index 499 quality control, 98 recov
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Index 501 Pyrethroids, air sampling
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Index 503 Uncertainty, estimation,
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