Mycotoxin Analysis in Peanut Butter Using Captiva EMR—Lipid Cleanup and LC/MS/MS

Applications | 2019 | Agilent TechnologiesInstrumentation
Sample Preparation, Consumables, LC/MS, LC/MS/MS, LC/QQQ
Industries
Food & Agriculture
Manufacturer
Agilent Technologies

Importance of the Topic


Mycotoxins are toxic secondary metabolites produced by fungi that can contaminate peanuts during cultivation or storage. Regulatory agencies worldwide set strict limits for these compounds in food products due to their carcinogenic and immunosuppressive effects. The complex peanut butter matrix, rich in fats and proteins, poses a challenge for accurate quantitation of trace mycotoxins, making robust cleanup and sensitive detection methods essential for reliable food safety testing.

Objectives and Study Overview


This study aimed to develop and validate a streamlined workflow for simultaneous analysis of 13 multiclass mycotoxins in peanut butter. The approach combined a QuEChERS extraction with Agilent Captiva EMR—Lipid cartridge cleanup, followed by LC/MS/MS quantitation. Key performance metrics such as recovery, precision, linearity, and matrix removal were evaluated at multiple spiking levels to demonstrate method suitability for routine food testing laboratories.

Methodology and Used Instrumentation


Sample Preparation and Cleanup:
  • Weighed 5 g of peanut butter and hydrated with water, followed by extraction with acetonitrile containing 2 % formic acid and QuEChERS salts.
  • After centrifugation, the acetonitrile layer was passed through a 3 mL Captiva EMR—Lipid cartridge to selectively remove lipids by size exclusion and hydrophobic interaction.
  • Cleaned extracts were diluted with aqueous ammonium formate/formic acid before analysis.

LC/MS/MS Configuration:
  • Agilent 1290 Infinity II UHPLC with Poroshell 120 EC-C18 guard and analytical columns at 40 °C, 0.5 mL/min gradient elution (5 mM ammonium formate/0.1 % formic acid and acetonitrile:methanol + 0.1 % formic acid).
  • Agilent 6490 triple quadrupole with Jet Stream ionization in dynamic MRM mode, operating in both positive and negative ion modes with optimized gas temperatures, flows, and voltages.

Main Results and Discussion


Linearity and Quantitation:
  • Calibration curves for all 13 mycotoxins displayed R² values between 0.993 and 0.998 using a 1/x² weighting scheme, with accuracies within ±10 % of expected concentrations.
Recovery and Precision:
  • Overall recoveries ranged from 78.7 % to 119.1 % across low, medium, and high spiking levels, with relative standard deviations below 17 %.
  • Fumonisin classes exhibited lower extraction efficiency in acetonitrile but showed improved solubility upon acidification.
Matrix Removal Assessment:
  • GC/MS full-scan comparison of peanut butter extracts revealed approximately 60 % reduction of total lipid-related peaks after Captiva EMR—Lipid cleanup, with substantial removal of later-eluting lipids and partial reduction of early-eluting co-extractives.

Benefits and Practical Applications


This methodology offers a cost-effective, easy-to-implement solution for multiclass mycotoxin analysis in high-fat matrices. The selective lipid cleanup reduces matrix effects, extends instrument uptime, and lowers maintenance requirements. Minimal specialized training is required, making it ideal for quality control and regulatory compliance laboratories.

Future Trends and Applications


Advancements may include adaptation of EMR-Lipid cleanup to automated platforms and high-throughput formats. The workflow could be extended to other fatty food matrices such as dairy products, oils, and meat. Integration with broader multiresidue assays and application in non-targeted screening approaches will further enhance food safety monitoring.

Conclusion


The combination of QuEChERS extraction, Captiva EMR—Lipid cleanup, and UHPLC-MS/MS detection provides a robust, selective, and sensitive method for quantifying multiple mycotoxins in peanut butter. The validated workflow meets stringent regulatory requirements and supports reliable food safety testing in complex lipid-rich samples.

References


  • Offiah N.; Adesiyun A. Occurrence of Aflatoxins in Peanuts, Milk, and Animal Feed in Trinidad. J. Food Prot. 2007, 70(3), 771–775.
  • FDA Guidance for Industry: Action Levels for Poisonous or Deleterious Substances in Human Food and Animal Feed. FDA, 2018.
  • Commission Regulation (EC) No 1881/2006 setting maximum levels for certain contaminants in foodstuffs. Official Journal of the European Union, 2006.
  • Zhang K.; et al. Determining Mycotoxins in Baby Foods and Animal Feeds Using Stable Isotope Dilution and LC-MS/MS. J. Agric. Food Chem. 2014, 62, 8935–8943.
  • Zhao L.; Lucas D. Multiclass Multiresidue Veterinary Drug Analysis in Beef Using Agilent Captiva EMR—Lipid Cleanup and LC/MS/MS. Agilent Technologies, Application Note 5991-8598EN.
  • Lucas D.; Zhao L. Multiclass Mycotoxin Analysis in Cheese Using Agilent Captiva EMR—Lipid Cleanup and LC/MS/MS. Agilent Technologies, Application Note 5991-8694EN.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

Downloadable PDF for viewing
 

Similar PDF

Mycotoxin Analysis in Infant Formula Using Captiva EMR—Lipid Cleanup and LC/MS/MS
Multiclass Mycotoxin Analysis in Cheese Using Agilent Captiva EMR—Lipid Cleanup and LC/MS/MS
Sample Clean-Up in the Fast Lane
Sample Clean-Up in the Fast Lane
2018|Agilent Technologies|Presentations
Determination of Mycotoxins in Peanuts With Enhanced Matrix Removal—Lipid by LC/MS/MS