Methodology development based on "dilute and shoot" and QuEChERS for determination of multiple mycotoxins in cocoa by LC-MS/MS

Study of co-occurrence of mycotoxins in cocoa beans in Brazil by liquid chromatography-tandem mass spectrometry (LC-MS/MS)

In this study, 135 samples of cocoa beans collected in the Amazon and Atlantic Forest regions of Brazil were analysed to evaluate the possible co-occurrence of 34 mycotoxins. The results indicate that 42% of the cocoa samples exhibited quantifiable levels for 11 mycotoxins: aflatoxins (AFs) B1, B2 and G1; ochratoxin A; citrinin; cyclopiazonic acid; tenuazonic acid; paxilline; sterigmatocystin; zearalenone and fumonisin B2. Of the samples, 18% exhibited the co-occurrence of up to six mycotoxins. No toxins belonging to the groups of trichothecenes or ergot alkaloids were detected. Contingency analysis of the incidence of mycotoxins did not show significant differences between the two regions evaluated. Seven samples were contaminated with AFs, while only one contained ochratoxin A above 10 μg kg-1. The accuracy of the method was evaluated by proficiency testing for ochratoxin A, where satisfactory Z-scores were obtained.

Ultra-sensitive determination of Ochratoxin A in coffee and tea samples using a novel semi-automated in-syringe based coagulant-assisted fast mycotoxin extraction (FaMEx) technique coupled with UHPLC-MS/MS

In this study, we demonstrated a novel semi-automated in-syringe-based coagulant-assisted liquid-liquid microextraction (IS-CGA-LLME) as fast mycotoxin extraction (FaMEx) technique coupled with ultra-high-performance liquid chromatography connected with a tandem-mass spectrometer (UHPLC-MS/MS) for the quantification of mycotoxin (Ochratoxin A, OT-A) in coffee and tea samples. IS-CGA-LLME is a three-step extraction process that includes extraction of OT-A from sample matrix using low-volume solvent extraction, then the extractant was cleaned-up using a coagulation process, and finally, the decolorized/matrix removed sample solution was processed for LLME for target analyte's pre-concentration. The final extractant was analyzed using UHPLC-MS/MS for OT-A quantification. Under the optimized experimental conditions, highly sensitive detection and quantification limits were obtained at 0.001 and 0.003 ng g^-1 for OT-A with excellent extraction recovery (93-111%) and precision <10%. These results proved that the developed method is a simple, highly sensitive, semi-automated, low-matrix effect and efficient procedure for the determination of mycotoxins in food samples.

Recent advances in simultaneous detection strategies for multi-mycotoxins in foods

Mycotoxin contamination has become a challenge in the field of food safety testing, given the increasing emphasis on food safety in recent years. Mycotoxins are widely distributed, in heavily polluted areas. Food contamination with these toxins is difficult to prevent and control. Mycotoxins, as are small-molecule toxic metabolites produced by several species belonging to the genera Aspergillus, Fusarium, and Penicillium growing in food. They are considered teratogenic, carcinogenic, and mutagenic to humans and animals. Food systems are often simultaneously contaminated with multiple mycotoxins. Due to the additive or synergistic toxicological effects caused by the co-existence of multiple mycotoxins, their individual detection requires reliable, accurate, and high-throughput techniques. Currently available, methods for the detection of multiple mycotoxins are mainly based on chromatography, spectroscopy (colorimetry, fluorescence, and surface-enhanced Raman scattering), and electrochemistry. This review provides a comprehensive overview of advances in the multiple detection methods of mycotoxins during the recent 5 years. The principles and features of these techniques are described. The practical applications and challenges associated with assays for multiple detection methods of mycotoxins are summarized. The potential for future development and application is discussed in an effort, to provide standards of references for further research.

Development and Validation of QuEChERS Followed by UHPLC-ToF-MS Method for Determination of Multi-Mycotoxins in Pistachio Nuts

Pistachios are one of the types of tree nut fruits with the highest mycotoxin contamination, especially of aflatoxins, worldwide. This study developed a Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method that was followed by Ultra-High Performance Liquid Chromatography combined with Time-of-Flight Mass Spectrometry (UHPLC-ToF-MS) for the determination of mycotoxins in pistachios. Different approaches to dispersive solid phase extraction as a clean-up method for high lipid matrices were evaluated. For this, classic sorbents such as C18 (octadecyl-modified silica) and PSA (primary secondary amine), and new classes of sorbents, namely EMR-Lipid (enhanced matrix removal-lipid) and Z-Sep (modified silica gel with zirconium oxide), were used. The QuEChERS method, followed by Z-Sep d-SPE clean-up, provided the best analytical performance for aflatoxins (AFB1, AFB2, AFG1 and AFG2), ochratoxin A (OTA), zearalenone (ZEA), toxin T2 (T2) and toxin HT-2 (HT2) in pistachios. The method was validated in terms of linearity, sensitivity, repeatability, interday precision and recovery; it achieved good results according to criteria imposed by Commission Regulation (EC) no. 401/2006. The method was applied to real samples and the results show that pistachios that are available in Portuguese markets are safe from mycotoxins that are of concern to human health.

Validation of an analytical method based on QuEChERS and LC-MS/MS to quantify nine mycotoxins in plant-based milk

Plant-based beverages (popularly known as vegetable milk) have become increasingly important in recent years. However, the nonexistence of information on mycotoxin contamination is noticeable. We herein describe the development and validation of an analytical methodology that employs QuEChERS and LC-MS/MS for the simultaneous determination of nine mycotoxins (aflatoxins B1, B2, G1, and G2, fumonisins B1 and B2, ochratoxin A, zearalenone, and citreoviridin) in seven types of vegetable milk (peanut, oat, rice, cashew, maize, soybean, and coconut). The method provided the following quantification limits, recoveries at the lowest validated concentration and relative standard deviations under repeatability conditions at the lowest validated concentration, respectively: aflatoxin B1 (0.023 μg/l, 84.98 and 9.23%); aflatoxin B2 (0.024 μg/, 93.00 and 4.85%); aflatoxin G1 (0.057 μg/l, 98.85 and 5.53%); aflatoxin G2 (0.031 μg/l, 96.64 and 4.08%); fumonisin B1 (2.166 μg/l, 75.55 and 16.78%); fumonisin B2 (1.105 μg/l, 70.47 and 11.89%); ochratoxin A (0.104 μg/l, 72.05 and 5.12%); zearalenone (8.093 μg/l, 107.10 and 6.37%); citreoviridin (1.305 μg/l, 97.25 and 7.28%). The method uses small amounts of samples, solvents, and other inexpensive reagents with no need for laborious clean-up and pre-concentration steps. Its attractive characteristics (simplicity, low cost compared to procedures that use immunoaffinity columns, and full compatibility with routine analyses) make it potentially valuable. As a proof-of-principle, the validated methodology was applied to seven commercial samples of different compositions showing that some were contaminated with aflatoxins and ochratoxin A.

Rapid identification model based on decision tree algorithm coupling with 1H NMR and feature analysis by UHPLC-QTOFMS spectrometry for sandalwood

Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus (Dalbergia and Pterocarpus) and confused species (Gluta sp.) of sandalwood were quickly and efficiently identified. Rapid identification model based on ¹H NMR and decision tree (DT) algorithm was firstly developed for the identification of sandalwood, and the accuracy was improved by introducing the AdaBoost algorithm. The accuracy of the final model was above 95%. And the feature components between different species of sandalwood were further explored using UHPLC-QTOFMS and NMR spectrometry. The results showed that 183 compounds were identified, among which 99 were known components, 84 were unknown components. The ¹H NMR and ¹³C NMR signals of 505 samples were assigned, among them, 14 compounds were attributed, characteristic chemical shift intervals with great differences in the model were analysed. Furthermore, the fragmentation pattern of different compounds from sandalwood, in both positive and negative ion ESI modes, was summarized. The results showed a potential and rapid tool based on DT, NMR spectroscopy and UHPLC-QTOFMS, which had performed great potential for rapid identification and feature analysis of sandalwood.

Development and validation of an analytical method for the extraction, identification, and quantification of multi-mycotoxins in beer using a modified QuEChERS procedure and UHPLC-MS/MS

Although beer is one of the most popular alcoholic beverages in the world, there is no specific legislation regarding contaminants, especially mycotoxins, for this product. The present manuscript reports the development and validation of an analytical methodology based on the QuEChERS approach, followed by quantification via UHPLC-MS/MS for the simultaneous determination of seventeen mycotoxins in beer. During the validation, amatrix effect was observed for 82% of the analytes. Linearity and recovery were evaluated using spiked blank samples, and the chosen methodology proved to be efficient for all analytes, with recoveries ranging from 71 to 118%, excepting ergonovine, for which recovery of 57% was achieved. Precision was estimated in terms of repeatability and reproducibility, with variations from 2.6 to 28.2% and 9.7 to 28.7%, respectively. The detection (LOD) and quantification (LOQ) limits, determined from the values of CCα and CCβ, ranged from 0.26 to 117 µgkg^-1 and from 0.30 to 135 µgkg^-1, respectively. Measurement uncertainties were based on the bottom-up methodology, with uncertainties ranging from 0.03 to 17 µgkg^-1. Finally, thirty-eight beer samples, collected at the local market, were analysed, and 16 of them showed contamination by deoxynivalenol in concentrations ranging from 159 ± 26 µgkg^-1 to 648 ± 106 µgkg^-1.