The Application Notebook
In the present study, a novel GC–MS TQ method was developed and validated for the simultaneous analysis of 2-MCPD, 3-MCPD, and glycidyl fatty acid esters in edible oil. This method was subsequently applied to quantitation of these contaminants in commercial edible oil samples.
Esterified and free forms of 2-MCPD, 3-MCPD, and glycidol are heat-induced contaminants found in various types of processed food (1). Numerous studies have investigated the levels of these contaminants in oils and fats from different sources, and mitigation strategies are currently under development. However, to support future research where higher sensitivity and selectivity are necessary, analytical solutions based on triple-quadrupole mass spectrometers will be required. In the present study, a novel GC–MS TQ method was developed and validated for the simultaneous analysis of 2-MCPD, 3-MCPD, and glycidyl fatty acid esters in edible oil. This method was subsequently applied to quantitation of these contaminants in commercial edible oil samples.
Methods and Materials
Fatty acid esters of 2-MCPD, 3-MCPD, and glycidol in oil were extracted and derivatized using phenylboronic acid according to the AOCS Official Method Cd 29a-13 (2). The analyte standards used were 1,3-dipalmitoyl-2-chloropropanediol (2-MCPD), 1,2-dipalmitoyl-3-chloropropanediol (3-MCPD), and glycidyl palmitate (GlyP). The internal standards were 1,2-dipalmitoyl-3-chloropropanediol-d5 (3-MCPD-d5) and glycidyl palmitate-d5 (GlyP-d5). The extracts were analyzed on a GCMS-TQ8040 NX system (Shimadzu Corporation). Separation was performed using a 30 m × 0.25 mm, 1.0-μm SH-Rxiâ1MS capillary column (Shimadzu Corporation). Detailed instrumental conditions are presented in Table 1, and MRM parameters for the different analytes are shown in Table 2.
Results
MRM chromatograms of the analytes (using target MRM transitions) showed good peak shapes (Figure 1). The retention times of 3-MCPD-d5, 3-MCPD, 2-MCPD, glycidol-d5, and glycidol were 18.4 min, 18.6 min, 19.6 min, 21.6 min, and 21.7 min, respectively.
Method validation was performed to assess parameters such as sensitivity, accuracy, precision, linearity, and repeatability using calibration standards or quality control (QC) samples. Subsequently, this method was applied to quantitation of the different analytes in commercially available edible oil samples.
Sensitivity
The sensitivity of the assay was examined by measuring the signal to noise (S/N) ratio of the analyte peaks. The LOD (limit of detection) was defined as S/N ratio of at least 5. The LOD for this method was 0.003 μg of 2-MCPD and 3-MCPD and 0.006 μg of glycidol. The limit of quantitation (LOQ) was defined as S/N ratio of at least 10. The LOQ of this method was 0.01 μg of 2-MCPD and 3-MCPD and 0.024 μg of glycidol.
Accuracy and Precision
QC samples at three concentrations (QC1, QC2, and QC3) were used to investigate the accuracy and precision of the method. High accuracy and precision were demonstrated for this method as the accuracies of the QC samples were all within 100 ± 7% and the %RSD were all < 10% (n = 6).
Linearity and Repeatability
Excellent repeatability of the peak areas of consecutive injections were achieved for all analytes (< 3%) (n = 6). Eight calibration standards for 2-MCPD and 3-MCPD ranging 0.010–0.930 μg and glycidol 0.024–2.130 μg were used to construct the calibration curves. The calibration curves (Figure 2) for all analytes showed excellent linearity (R2 > 0.998) (n = 6).
Application of Method for Quantitation of Analytes in Commercially Available Oil Samples
The validated method was applied to quantitation of esters of 2-MCPD, 3-MCPD, and glycidol in edible oil samples from different sources (Table 3, Figure 3). Generally, samples containing palm oil were found to contain the highest levels of the contaminants.
Conclusions
In summary, a novel GC–MS method was developed and validated for the simultaneous analysis of 2- and 3-MCPD and glycidol fatty acid esters in edible oil. This method showed more than threefold improvement in sensitivity compared to the official method currently available and demonstrated excellent linearity, repeatability, accuracy, and precision. Application of this method to the analyses of commercially available edible oil samples confirmed that samples containing palm oil show higher levels of contaminants.
References
Shimadzu Europa GmbH
Albert-Hahn-Str. 6–10,
D-47269 Duisburg, Germany
Tel.: +49 203 76 87 0
Fax: +49 203 76 66 25
E-mail: shimadzu@shimadzu.eu
Website: www.shimadzu.eu
GC–TOF-MS Finds 250 Volatile Compounds in E-Cigarette Liquids
November 1st 2024A study has used gas chromatography coupled to a time-of-flight mass spectrometer to build an electron ionization mass spectra database of more than 250 chemicals classified as either volatile or semi-volatile compounds. An additional, confirmatory layer of liquid chromatography–mass spectrometry analysis was subsequently performed.
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.
Multivariate Design of Experiments for Gas Chromatographic Analysis
November 1st 2024Recent advances in green chemistry have made multivariate experimental design popular in sample preparation development. This approach helps reduce the number of measurements and data for evaluation and can be useful for method development in gas chromatography.