Dispersive Liquid–Liquid Microextraction (DLLME) and LC-MS/MS Analysis for Multi-Mycotoxin in Rice Bran: Method Development, Optimization and Validation
Abstract
:1. Introduction
2. Results and Discussion
2.1. Liquid Chromatography Mass Spectrometry (LC-MS/MS) Optimization
2.2. Dispersive Liquid–Liquid Microextraction (DLLME)
Selection of Dispersive Solvent, Extraction Solvent and Addition of Salt for Extraction of Mycotoxin in Rice Bran
2.3. Optimising of DLLME by Box-Behnken Approach and Response Optimization Using Composite Desirability
2.4. Validation
2.4.1. Specificity and Matrix-Effects (ME)
2.4.2. Limit of Detection (LOD), Limit of Quantitation (LOQ) and Linearity
2.4.3. Accuracy, Precision and Recovery
2.4.4. Occurrence of Multi-Mycotoxin in Industrial and Commercial Samples
3. Conclusions
4. Materials and Methods
4.1. Chemicals and Reagents
4.2. Instrumental and Analytical Conditions
4.3. Sample Preparation
4.3.1. Extraction Procedure
4.3.2. Dispersive Liquid–Liquid Microextraction Procedure
4.3.3. Selection of Dispersive Solvent, Extraction Solvent and Addition of Salt
4.3.4. Response Surface Methodology Using Box–Behnken Design
4.3.5. Response Optimization
4.4. Validation
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Analyte | Polarity | Precursor Ion (m/z) | Product Ion (m/z) | Collision Energy (CE) | Tube Lens | Retention Time (min) |
---|---|---|---|---|---|---|
AFB1 | + | 313 | 285 | 21 | 94 | 9.7 |
269 | 25 | |||||
AFG1 | + | 329 | 243 | 22 | 86 | 9.0 |
311 | 25 | |||||
AFB2 | + | 315 | 287 | 25 | 95 | 9.4 |
259 | 28 | |||||
AFG2 | + | 331 | 313 | 23 | 86 | 8.7 |
189 | 35 | |||||
STG | + | 325 | 310 | 25 | 76 | 12.3 |
281 | 34 | |||||
HT-2 | + | 442 | 263 | 20 | 70 | 10.7 |
215 | 20 | |||||
T-2 | + | 484 | 305 | 20 | 70 | 11.4 |
245 | 20 | |||||
ZEA | + | 319 | 283 | 8 | 86 | 11.9 |
301 | 10 | |||||
DAS | + | 384 | 247 | 20 | 70 | 10.4 |
307 | 20 | |||||
FB1 | + | 722 | 352 | 34 | 126 | 11.2 |
334 | 37 | |||||
FB2 | + | 706 | 336 | 34 | 120 | 12.2 |
318 | 35 | |||||
DON | + | 297 | 249 | 9 | 90 | 6.3 |
175 | 23 | |||||
OTA | + | 404 | 239 | 25 | 90 | 12.4 |
358 | 20 |
Target Analyte | Final Equation | R2 | ANOVA p-Value | |
---|---|---|---|---|
Model | Lack of Fit | |||
AFB1 | −718,670 + 7156 A − 15,649 B + 24,688 C − 11.86 A2+ 697 B2 − 1369 C2 + 20.6 AB − 85.2 AC + 191 BC | 0.934 | 0.01 | 0.57 |
AFG1 | 44,372 + 1536 A − 1881 B − 17,849 C − 2.41 A2 −188 B2 + 402 C2 + 12.5 AB + 53.7 AC − 221 BC | 0.946 | 0.01 | 0.43 |
AFB2 | −59,869 + 1822 A + 120 B − 15,664 C − 3.79 A2 − 39 B2 + 126 C2 + 9.1 AB + 51.7 AC − 423 BC | 0.938 | 0.01 | 0.21 |
AFG2 | −4299 + 513 A + 2432 B − 11,286 C − 1.016 A2 − 215 B2 + 359 C2 − 1.0 AB + 24.7 AC− 61 BC | 0.866 | 0.03 | 0.41 |
STG | −995,532 + 22,381 A − 144,405 B − 141,900 C − 44.8 A2 + 5697 B2 + 2733 C2 + 232 AB + 26 AC + 6119 BC | 0.775 | 0.06 | 0.76 |
T-2 | −112,185 + 3512 A − 16,011 B − 15,247 C − 6.71 A2 − 722 B2 − 274 C2 + 52.5 AB + 30.8 AC + 1289 BC | 0.954 | 0.00 | 0.30 |
HT-2 | 58,093 + 768 A + 6157 B − 25,799 C − 2.66 A2 − 455 B2 + 728 C2 + 5.6 AB + 61.3 AC − 354 BC | 0.865 | 0.04 | 0.91 |
ZEA | −28,806,469 + 407,356 A − 882,897 B − 475,031 C − 829 A2 + 43,282 B2 − 24,239 C2 + 2063 AB + 782 AC −27,500 BC | 0.854 | 0.02 | 0.31 |
DAS | −43,534 + 6319 A + 32,240 B − 72,094 C − 10.44 A2 − 2154 B2 + 3002 C2 + 24 AB + 111 AC − 2973 BC | 0.883 | 0.01 | 0.82 |
FB1 | −54,518 + 408 A + 12,533 B + 9554 C − 1.046 A2 + 266.0 B2 − 801 C2 − 25.53 AB + 9.50 AC − 1002 BC | 0.957 | 0.01 | 0.23 |
FB2 | −724,670 + 9510 A − 151,680 B + 67,179 C − 25.6 A2 + 1067 B2 12,128 C2 + 439 AB + 209 AC + 11,944 BC + 209 AC + 11,944 BC | 0.829 | 0.04 | 0.75 |
OTA | −700,860 + 12,471 A − 140,637 B − 22,763 C − 24.5 A2 + 2232 B2 − 4309 C2 + 213 AB + 7 AC + 7 AC + 11,293 BC | 0.807 | 0.05 | 0.81 |
Target Analyte | Peak Height | |
---|---|---|
Before Optimization | After Optimization | |
AFB1 | 11,282 ± 1484 | 23,011 ± 1411 |
AFG1 | 12,186 ± 477 | 21,256 ± 1636 |
AFB2 | 5833 ± 928 | 10,256 ± 289 |
AFG2 | 3245 ± 725 | 10,460 ± 122 |
STG | 12,847 ± 588 | 87,240 ± 1939 |
T-2 | 16,959 ± 3429 | 54,895 ± 1414 |
HT-2 | 19,540 ± 896 | 11,042 ± 1416 |
ZEA | 47,244 ± 2849 | 136,034 ± 13,576 |
DAS | 6958 ± 7393 | 43,421 ± 3427 |
FB1 | 3245 ± 382 | 76,253 ± 7302 |
FB2 | 12,847 ± 790 | 37,199 ± 7099 |
OTA | 16,959 ± 101 | 10,154 ± 1529 |
Mycotoxin | LOD (ng g−1) | LOQ (ng g−1) | Linearity Range (ng g−1) | R2 |
---|---|---|---|---|
AFB1 | 0.5 | 1.5 | 0.5–1.5 | 0.992 |
AFG1 | 0.5 | 1.5 | 0.5–1.5 | 0.992 |
AFB2 | 1.0 | 3.0 | 1.5–4.5 | 0.992 |
AFG2 | 1.0 | 3.0 | 1.5–4.5 | 0.994 |
STG | 2.5 | 5.0 | 2.5–7.5 | 0.990 |
T-2 | 2.5 | 5.0 | 2.5–7.5 | 0.990 |
HT-2 | 2.5 | 5.0 | 2.5–7.5 | 0.992 |
ZEA | 2.5 | 5.0 | 2.5–7.5 | 0.992 |
DAS | 2.5 | 5.0 | 2.5–7.5 | 0.993 |
FB1 | 50 | 150 | 50–150 | 0.990 |
FB2 | 25 | 75 | 25–75 | 0.991 |
OTA | 1.25 | 3.75 | 2.5–7.5 | 0.993 |
Mycotoxin | Concentration (ng g−1) | Within Assay Precision (%) (n = 7) | Between Assay Precision (%) (n = 21) | Accuracy (%) (n = 21) | Recovery (%) ±SD (n = 7) |
---|---|---|---|---|---|
AFB1 | 1.5 | 11.6 | 11.3 | 113.7 (11.7) | 74.6 ± 0.1 |
AFG1 | 1.5 | 11.1 | 9.1 | 115.1 (8.8) | 80.5 ± 0.0 |
AFB2 | 3.0 | 15.3 | 15.1 | 112.5 (15.1) | 70.2 ± 0.1 |
AFG2 | 3.0 | 16.2 | 16.1 | 112.8 (16.5) | 74.8 ± 0.2 |
STG | 5.0 | 17.9 | 18.5 | 74.6 (13.5) | 99.4 ± 0.1 |
T-2 | 5.0 | 13.1 | 7.9 | 119.1 (9.8) | 76.6 ± 0.2 |
HT-2 | 5.0 | 14.3 | 14.5 | 108.6 (13.8) | 72.8 ± 0.9 |
ZEA | 5.0 | 13.9 | 12.8 | 102.1 (1.1) | 95.1 ± 0.9 |
DAS | 5.0 | 14.2 | 7.9 | 62.3 (4.9) | 90.1 ± 0.3 |
FB1 | 150 | 5.6 | 12.0 | 101.0 (8.1) | 71.9 ± 0.8 |
FB2 | 75 | 6.5 | 14.6 | 73.1 (7.1) | 82.5 ± 1.3 |
OTA | 3.75 | 14.6 | 14.5 | 60.2 (14.6) | 87.0 ± 0.2 |
Mean Concentration (ng g−1) ± SD (n = 2) | ||||||
---|---|---|---|---|---|---|
AFB1 | AFG1 | AFB2 | AFG2 | FB1 | FB2 | |
S2 | 1.69 ± 0.17 | 0.11 ± 0.01 | 0.33 ± 0.07 | 6.49 ± 1.38 | 157.44 ± 0.52 | 28.3 ± 0.91 |
S3 | 2.13 ± 0.50 | 0.10 ± 0.02 | 0.33 ± 0.01 | 2.70 ± 0.27 | n.d. | n.d. |
S5 | 1.08 ± 0.22 | 0.08 ± 0.01 | 0.37 ± 0.17 | 6.19 ± 0.91 | n.d. | n.d. |
S6 | 1.09 ± 0.29 | 0.12 ± 0.03 | 0.36 ± 0.04 | 8.07 ± 0.06 | n.d. | n.d. |
S7 | 1.67 ± 0.02 | 0.08 ± 0.07 | 0.36 ± 0.07 | 2.34 ± 0.59 | n.d. | n.d. |
S9 | 2.19 ± 0.21 | 0.07 ± 0.02 | 0.20 ± 0.05 | 1.45 ± 0.41 | n.d. | n.d. |
S17 | 0.34 ± 0.02 | 0.10 ± 0.01 | 2.72 ± 0.71 | 2.85 ± 0.27 | 77.70 ± 0.62 | 75.56 ± 0.73 |
S20 | 0.27 ± 0.04 | 0.12 ± 0.01 | 3.88 ± 1.60 | 5.41 ± 1.31 | n.d. | n.d. |
S21 | 0.34 ± 0.04 | 0.12 ± 0.05 | 1.74 ± 0.22 | 2.76 ± 0.30 | n.d. | n.d. |
S22 | 0.31 ± 0.07 | 0.14 ± 0.07 | 1.55 ± 0.96 | 6.39 ± 1.50 | n.d. | n.d. |
Variables | Symbol | Range and Level | ||
---|---|---|---|---|
Low | Central | High | ||
Volume of extraction solvent (µL) | A | 150 | 225 | 300 |
Concentration of salt (%) | B | 0 | 5 | 10 |
Volume of water (mL) | C | 3 | 6.5 | 10 |
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Salim, S.A.; Sukor, R.; Ismail, M.N.; Selamat, J. Dispersive Liquid–Liquid Microextraction (DLLME) and LC-MS/MS Analysis for Multi-Mycotoxin in Rice Bran: Method Development, Optimization and Validation. Toxins 2021, 13, 280. https://doi.org/10.3390/toxins13040280
Salim SA, Sukor R, Ismail MN, Selamat J. Dispersive Liquid–Liquid Microextraction (DLLME) and LC-MS/MS Analysis for Multi-Mycotoxin in Rice Bran: Method Development, Optimization and Validation. Toxins. 2021; 13(4):280. https://doi.org/10.3390/toxins13040280
Chicago/Turabian StyleSalim, Sofiyatul Akmal, Rashidah Sukor, Mohd Nazri Ismail, and Jinap Selamat. 2021. "Dispersive Liquid–Liquid Microextraction (DLLME) and LC-MS/MS Analysis for Multi-Mycotoxin in Rice Bran: Method Development, Optimization and Validation" Toxins 13, no. 4: 280. https://doi.org/10.3390/toxins13040280