Figures
2.1 Application of Bayes’ theorem and conditional probability for a screening test on the analysis of mycotoxins in maize samples. PSUSP/NC : Probability of suspect result from the validation study. This represents the probability that a screening test yields a suspect result when analysing a known non-compliant sample. PNC/SUSP: Probability of non-compliance with a suspect result under real-world conditions. This indicates the probability that an unknown sample is non-compliant when the screening test produces a suspect result. 11
2.2 Comparison of the results from the application of Bayesian statistics to the hypothetical analysis of 1000 samples. The results demonstrate that for both methods the vast majority (green area) are correctly identified as compliant. Moreover, from the low number of suspect samples, the majority of samples (red area) is correctly non-compliant. 15
3.1 No description. 24
5.1 Radar graph comparing the four main technologies for mycotoxins analysis considered in a laboratory network environment. Parameters of time-to-result, consumable costs, equipment cost, acceptance, performance quality, ease of use, throughput and sustainability are rate from 0 (bad) to 10 (excellent). 99
6.1 BaySpec Portability™ mass spectrometer. (A) Instrument with ESI source installed (foreground), and (B) sample loop used with TD-ESI or TD-APCI ionization sources. 128
6.2 Spectra collected using the solid sample loop with positive mode APCI. A. air blank, B. Wheat with non-detectable DON, C. Wheat with 1.6 mg/kg DON. Arrow indicates signals in the range of m/z 297 to 301. [DON + H]+ has m/z 297. 129
6.3 Schematic of the ESI source depicted in Figure 1. 130
6.4 Fumonisins in a reference sample of maize. FB1 2.2 mg/kg, FB2 0.6 mg/kg, FB3 0.3 mg/kg. FB4 was added as an internal standard. Reproduced from Maragos et al. 2022. 131
6.5 Schematic of the API source. A. Unmodified (original) source, B. Modified to spray a mixture of liquid from a syringe pump and air from an aquarium pump. Reproduced from Maragos (2023). 132
6.6 T-2 toxin in a highly contaminated sample of wheat. 11.4 mg/kg T-2 and 0.5 mg/kg HT-2. Reproduced from Maragos (2023). 133
7.1 Comparison of FB1 and FB6 (a) elution profiles with retention time, (b) negative MS/MS showing identical spectra, and (c) positive MS/MS showing clear differences for FB1, which can lose all three backbone hydroxyl groups prior to losing a tricarballylic ester vs FB6 which loses a single water molecule. This figure is reproduced from https://doi.org/10.1002/rcm.7374. 156
7.2 Comparison of the isobaric mycotoxins 15ADON and 3ADON. The elution profiles (a) of 15ADON and 3ADON with near identicial retention time (b) positive MS/MS showing that the compounds have similar product ions. The isomers can be distinguished by the relative abundance of product ions, specifically m/z 137 being more prominent for 15ADON. 157
8.1 Illustration of constitutional and distributional heterogeneity in a theoretical bag of pistachios. 165
8.2 Variation of aflatoxin test results from analysis of replicate samples taken from five randomly selected lots of peanuts (Whitaker et al., 1972). 167
8.3 Relationship between variability (as measured by %RSD) of test data with analyte concentration for aflatoxins (mg/kg) in peanuts (Whitaker et al., 1972), OTA (mg/kg) in green coffee (Vargas et al., 2004), and DON (mg/kg) in wheat (Tittlemier et al., 2019a). 175
9.1 Cumulative costs of LC-MS vs. TLC and LC-UV/FLD for 100 samples/year. At such a low throughput, the total cost and the cost per sample of LC-MS are higher than those of TLC or LC-UV/FLD. 212
9.2 Cumulative costs of LC-MS vs. TLC and LC-UV/FLD at 1000 samples/year. The total cost and cost per sample of LC-MS are lower than those of TLC or LC-UV/FLD due to better economies of scale in sample analysis, leading to significant savings. 213
9.3 Cumulative costs of LC-MS vs. TLC and LC-UV/FLD at 10,000 samples/year. With such a high throughput, the total cost and cost per sample of LC-MS are lower than those of TLC or LC-UV/FLD due to the economies of scale of sample analysis, leading to significant savings. 213
10.1 Effect of water availability on the relative contamination with mycotoxins in wheat grains. Safe zone: zone with no fungal growth/toxin production; Zone of uncertainty: zone with potential fungal growth/toxin production. Unsafe zone: Zone with fungal growth and mycotoxin contamination. OTA- Ochratoxin A. Source: Magan et al. (2020). 217
11.1 Mid-infrared spectra of standard solutions of deoxynivalenol and its derivatives after evaporation of the solvent. Recorded using an Invenio X FT-IR spectrometer (Bruker Optics, Ettlingen, Germany) equipped with a single-bounce diamond attenuated total reflection accessory. Spectral resolution 4 cm-1, 128 scans averaged per spectra. DON = deoxynivalenol. 251
11.2 Greenness of 3 mycotoxin screening techniques. Left: lateral flow assay (LFA), middle: mid-infrared spectroscopic (MIRS) method, and right: near-infrared (NIRS) method. Details of the methods can be found in (Bosman et al., 2023; Femenias et al., 2023; Tyska et al., 2021). Evaluation was performed using the greenness metric AGREE, which is based on the 12 principles of green analytical chemistry. 1: Directness of analysis; 2: Sample size; 3: Measurement location (e.g. on-site); 4: Sample handling steps; 5: Automation of sample handling; 6: Derivatization; 7: Amount of waste generated; 8: Sample throughput and multi-analyte capability; 9: Use of energy; 10: Reagents from renewable sources; 11: Toxicity of reagents; 12: Operator safety. The metric yields a score from 0-1, red indicates non-environmental friendliness while dark green is ideal. 253
11.3 Popular spectral acquisition modes used for mycotoxin screening with infrared spectroscopy. A: Attenuated total reflection mid-infrared spectroscopy (ATR MIRS). B: Diffuse reflection near-infrared spectroscopy (DR NIRS). 254
12.1 Structures of aflatoxin G1 and G2. 286
12.2 Structures of aflatoxin B1 and B2. 286
12.3 Chemical structures of the most common Fusarium toxins. 286
12.4 Chemical structures of the most common Alternaria mycotoxins. 287
12.5 Chromatographic separation of 12 Alternaria toxins (Scheibenzuber et al., 2022). 287
12.6 Structures of some ergot alkaloids. 288
12.7 Epimerisation of ergometrine. 288
Tables
2.1 Required method performance characteristics for semi-quantitative and quantitative screening methods of pharmacologically active substances according to Commission Implementation Regulation (EU) 2021/808 (European Commission, 2021). The precision and trueness of quantitative methods need to comply with the criteria set in this Regulation, while for semi-quantitative methods the precision has to be assessed, but compliance with these criteria is not necessary and indicated by the brackets (x). 9
2.2 Performance profile of a screening method expressed in terms of rate of false positive results measured on blanks, 25 % and 50 % of the mass fraction level of interest (Lattanzio et al. 2013). The level of interest corresponds to the STC. 11
2.3 Application of the Bayes’ theorem to the determination of DON in wheat (Lattanzio et al. 2013) as shown in Table 2.2. The values for the Probability of suspect result (%) are obtained from the validation study and the corresponding values for the Probability of sample falling in the mass fraction range (%) represent the prior knowledge on the expected contamination level of the material. STC = Screening target concentration. The posterior probability PNC/SUSP is calculated applying equation (3). 14
4.1 Occurrence of aflatoxins in food and feed commodities and biological samples originating from African and Asian countries, published since 2023. 50
4.2 Occurrence of ochratoxin A in food and feed commodities and biological samples originating from African and Asian countries, published since 2023. 55
4.3 Occurrence of fumonisins in food and feed commodities and biological samples originating from African and Asian countries, published since 2023. 57
4.4 Occurrence of deoxynivalenol in food and feed commodities and biological samples originating from African and Asian countries, published since 2023. 58
4.5 Occurrence of zearalenone in food and feed commodities and biological samples originating from African and Asian countries, published since 2023. 60
5.1 Short explanation on features considered and rated for each individual analytical technique. 106
6.1 Sources and properties of some commercially available portable mass spectrometers. 123
8.1 Published research demonstrating heterogeneous distributions of mycotoxins. 166
8.2 Contribution of sampling, sample processing, and analysis stages to the total mycotoxin test result variance at the established maximum level in selected scenarios. Sampling plan parameters are taken from sampling plans provided by the Codex Alimentarius Commission (Codex, 2019). Variances were obtained using the specified sampling plan parameters in the Mycotoxin Sampling Plan Tool (http://tools.fstools.org/mycotoxins/). 171
9.1 Mycotoxin screening and confirmatory techniques. 210
9.2 Annual costs and benefits (savings) of LC-MS vs. TLC and LC-UV/FLD used for multi-mycotoxin analysis (100 samples/year, 6 mycotoxins). 211
9.3 Annual costs and benefits (savings) of LC-MS vs. TLC and LC-UV/FLD used for multi-mycotoxin analysis (1000 samples/year, 6 mycotoxins). 212
9.4 Annual costs and benefits (savings) of LC-MS vs. TLC and LC-UV/FLD used for multi-mycotoxin analysis (10,000 samples/year, 6 mycotoxins). 212
11.1 Cost estimation for mycotoxin screening using IR spectroscopy in €. 252
12.1 Maximum levels for aflatoxins in cereals (European Commission, 2023). 288
12.2 Summary of analytical methods used for the analysis of aflatoxins in cereal samples (n.d. = not determined). 290
12.3 Maximum levels for DON, ZEN, and fumonisins in cereals and cereal-based foods (European Commission, 2023). 289
12.4 Maximum levels for the sum of T2 and HT2 toxin in cereals and cereal products (European Commission, 2023). 289
12.5 Summary methods used for the analysis of Fusarium toxins in cereal samples 294
12.6 Summary of LC-MS/MS methods used for the analysis of Alternaria toxins in cereal samples. n.d. = not determined. 298
12.7 Maximum levels of ergot alkaloids in diverse foodstuffs (European Commission, 2023). 304
12.8 Summary of methods used for the analysis of ergot alkaloids in cereal samples (ergometrine=Em, ergotaminem=Et, ergocristine=Ecr, ergokryptine=Ekr, ergosine=Es, ergocornine=Eco). 305
12.9 Summary of analytical multi-mycotoxin methods used in cereal samples. 309
12.10 Overview of analytical methods to determine various mycotoxins in cereal products. 331
12.11 LC-MS/MS multi-methods for the analysis of mycotoxins in beer samples. 336