Report Title: Method Validation for the Analysis of Glucosinolates in Swedes

Report Title: Method Validation for the Analysis of Glucosinolates in Swedes Prepared for DairyNZ Inc by Hill Laboratories Date submitted: March, 2016 Disclaimer This report was prepared solely for DairyNZ Incorporated. The information contained within this report should not be taken to represent the views of DairyNZ. While all reasonable endeavours have been made to ensure the accuracy of the investigations and the information contained in the report, Hill Laboratories expressly disclaims any and all liabilities contingent or otherwise to any party other than DairyNZ Incorporated or DairyNZ Limited that may arise from the use of the information. Copyright Copyright in this publication (including text, graphics, logos and icons) is owned by or licensed to DairyNZ Incorporated. No person may in any form or by any means use, adapt, reproduce, store, distribute, print, display, perform, publish or create derivative works from any part of this publication or commercialise any information, products or services obtained from any part of this publication without the written consent of DairyNZ Incorporated. This report has been funded by New Zealand dairy farmers through DairyNZ.

Method Validation Report Page: 1 of 18 Method Validation (redacted for commercial reasons) Determination of Glucosinolates in Swedes by Orbitrap LC-MS 1. Introduction This report summarises the method performance and resulting fitness for purpose for the method under consideration. Wherever possible, the validation work was carried out according to the EURACHEM Guide and the requirements outlined in the QC Manual, Chapter 5, Validation (KB Item: 2124). This validation covers the validation of this new method. This method is an in-house method based on methods in the literature and NZ academic organizations. 2. Context of the Method Validation 2.1 Background In the winter of 2014, many Southland cows became ill, with significant stock losses. Symptoms such as photosensitivity and weight loss were seen along with veterinary reports of liver toxicity. One common factor associated with the sick animals was having foraged on swede crops. Factors such as the variety of swede crop grown, the mild winter weather, and the growing patterns of the plants were considered possible factors at play. It was theorized that the glucosinolate levels in the plants could be higher than normal and causing the toxicity. The use of brassica crops such as swedes as a winter forage feed is well-established in New Zealand. They are a productive, high quality feed that is used as an alternative in times of unproductive grass-based pastures, as a supplement for stock health, or as a part of pasture management. Field of Swedes (source: agrihq.co.nz) Glucosinolates are naturally occurring components of brassica plants that give them a bitter taste and form the basis for anti-cancer and antioxidant health benefits of brassica vegetables in humans. Well over one hundred different glucosinolates have been identified so far. They are a protective defence by plants to ward off pests, being released from the plant cells with cutting, chewing or leaf damage. The enzyme

Method Validation Report Page: 2 of 18 myrosinase that is present inside the plant cells metabolises glucosinolates into a variety of compounds, some beneficial, some harmful. While the benefits or toxicity of the metabolites largely depends on the dose, isothiocyanates are considered healthpromoting whereas nitriles are generally toxic. Glucosinolate structure (source: Wikipedia) Industry body DairyNZ contracted Hill Labs to develop a test for measuring glucosinolate levels in swedes. Results from analysis of swedes on affected and nonaffected farms were to be combined with veterinary analyses and survey information to form an investigative report by DairyNZ on the reasons for the cow deaths. 2.2 Method Purpose and Scope This validated method applies to 29 total analytes (28 Glucosinolates and sulphur methyl cysteine oxide (SMCO), another plant component). A complete list can be found in Section 3.1.1. The method was validated using swede bulb and leaf as well as rape seed, and it is therefore assumed that validated matrices include the following parts of a swede plant: flower, leaf, bulb and related sub-parts (crown, new leaf, etc) as well as seed material that is similar to rape seed. Both freeze-dried and fresh-frozen material were used in development and validation, with negligible differences between the two, therefore both forms can be used in the method. Typical levels range from undetectable (<1 ppb in vial) to very high (~700 ppb), which equates to 40 mg/kg and 28,000 mg/kg (0.02 2.7%) in freeze-dried material. Freeze-dried material is roughly ten times more concentrated per kilogram than fresh swede material due to its 85-90% water content, however all results are reported on a dry matter basis. 2.3 Principles of the Method Swede tissue is extracted with 70% methanol. The extract is analysed using the Thermo QExactive Orbitrap mass spectrometer (a High Resolution Accurate Mass instrument) with chromatographic separation by UPLC and detection in full-scan mode with positive/negative switching and concurrent detection using the collision cell. Full scan data in negative mode is used for the quantification of the analytes, but the additional settings are in place for retrospective untargeted investigative analysis in the future if required. 2.4 Analytical Performance Requirements The method was expected to have good precision and a broad calibration range with minimal matrix effects and similar quantifiable glucosinolate levels as both the published and unpublished reference methods. 2.5 Test Method Procedure Sampling Samples for this project were collected in the field by DairyNZ, with the aim of collecting the plant parts that were most likely to be affected by higher GSL concentrations. Upon sampling, the swede material was dissected into components

Method Validation Report Page: 3 of 18 (bulb, crown, lower stem, upper stem, old leaf, new leaf and seed head). As no agreed sampling protocol was available, the samples were either transported on ice and stored frozen at -20 C or snap frozen in liquid nitrogen, transported on dry ice and stored at - 80 C. A quantity of the -20 C-frozen bulb and leaf material were provided to Hill Laboratories as development materials, and are now routinely used as quality control samples, described as Bulb QC and Leaf QC. These materials were blended frozen at Hills, and either used as fresh-frozen material or freeze-dried at DairyNZ and returned to Hills for analysis. Other project samples for analysis were provided freeze-dried by DairyNZ. It is hoped that future investigations will determine which field collection method is most suitable for the purposes of an ongoing commercial method. Extraction Swede tissue can be used freeze-dried or fresh-frozen. It must be homogenized very well and kept frozen if used as fresh-frozen material. Freeze-dried material is milled, with mill cleaning between each sample. Either 100 mg (freeze-dried) or 1 g (frozen) material is extracted with 20 ml of 70% methanol on a shaker, then centrifuged, syringe filtered and diluted 200x in water for analysis. In contrast, literature methods report extractions using either boiling water or hot/boiling 70% methanol. In side-by-side tests in our laboratory, however, these heated extractions did not extract higher levels of glucosinolates or provide any other advantages. Based on these comparisons, and for ease of use, room temperature 70% methanol extractions were chosen. Table 1.Glucosinolate Levels in Rapeseed CRM, Room Temperature versus Hot Methanol Extraction Conditions µmol/g DW Compound Analyte RT MeOH Hot MeOH Epiprogoitrin Quantitative 2.71 2.82 Glucoalyssin Semi 1.87 1.65 Glucoberteroin Semi 0.88 0.93 Glucobrassicanapin Semi 17.29 18.25 Glucobrassicin Semi 0.27 0.29 Glucoerucin Quantitative 0.44 0.41 Gluconapin Quantitative 28.25 29.30 Gluconapoleiferin Semi 5.14 5.16 Gluconasturtiin Quantitative 1.96 1.70 Glucoraphanin Quantitative 1.07 1.02 Hydroxyglucobrassicin Semi 6.83 6.91 Progoitrin Quantitative 71.26 69.92 Sinigrin Quantitative 0.18 0.15 Total 138 139 Analysis Chromatographic Conditions (Redacted as commercially sensitive) Mass Spectrometry Conditions (Redacted as commercially sensitive)

Method Validation Report Page: 4 of 18 Result Calculations Results are converted from a ppm concentration to a final result of µmol/g Dry Weight using the dry matter test result. The molecular mass used for each compound are in the following table. Semiquantitative compounds are calculated as sinigrin equivalents. See Section 3.1.1 for additional information on this. Table 3. Molecular Weights of Glucosinolates Used in Calculations Common name Average Mass Epiprogoitrin 389.3993 Glucoalyssin Glucobarbarin or Glucosibarin Glucoberteroin Glucobrassicanapin Glucobrassicin Glucocheirolin Glucodehydroerucin Glucoerucin 421.5073 Glucohesperin Glucohirsutin Glucoibarin Glucoiberin 423.4801 Glucoiberverin Glucolesquerellin Gluconapin 373.3999 Gluconapoleiferin Gluconasturtiin 423.4585 Glucoraphanin 437.5067 Glucoraphenin 435.4908 Glucotropaeolin 409.4320 Hydroxyglucobrassicin Methoxyglucobrassicin Neoglucobrassicin Progoitrin 389.3993 Sinalbin 425.4314 Sinigrin 359.3733 SMCO Dry Matter Dry matter was determined at 103 C using an accredited method (KB Item 6064). 3. Method Performance Characteristics The following data gives a representation of method performance as it occurred over both development and validation of the method. With the exception of Figure 6 in Section 3.5 on ruggedness of fresh frozen samples, all data contained in this report was obtained using freeze-dried Bulb and Leaf QC materials. The rapeseed CRM has not been freeze-dried but already has a low moisture content of 4.1%.

Method Validation Report Page: 5 of 18 Additionally, since the introduction of duplicate instrument injections was introduced relatively late in development, most of the data contained here is only from single injections. Exceptions to this are in Sections 3.2 and 3.3.1 where calibration, method precision and uncertainty of measurement are described. 3.1 Identification of the Measurand 3.1.1 Confirmation of Identity, Selectivity and Specificity Of the 29 analytes in the method, 11 of them have standards and are quantitative (see table below for details). The other 18 are considered semi-quantitative and have been identified solely based on their mass to charge ratio (m/z) and fragments they produce. Ten of the semi-quant compounds were found within the three matrices used in development (swede bulb, swede leaf, or rapeseed CRM), can be confirmed with confidence, and have specific Retention Times (RT) applied to them. Two compounds (Glucobarbarin or Glucosibarin 1 and 2) have been seen intermittently in development which has resulted in their having tentative retention times assigned to them, but since neither compound appears regularly with good signal, confidence of their identification is limited. Also, given that these two compounds are isobaric, they cannot be distinguished from each other with a sufficient degree of confidence. Six semi-quantitative compounds (glucocheirolin, glucohesperin, glucohirsutin, glucoibarin, glucoiberverin and glucolesquerellin) were not seen in any of the development matrices or tested samples. The method, in particular the mass spectrometer, is able to identify the presence/absence of these compounds on the basis of their exact mass, however any tentative identification will require further analysis/verification if found. Table 4. Identifying Parameters of Glucosinolates Compound RT Chemical Formula Exact Mass (m/z) Quantitative or Semi-Quantitative Epiprogoitrin 3.55 C11H19NO10S2 388.0378 1 Quant Glucoalyssin 4.7 C13H25NO10S3 450.0568 Semi-Quant Glucobarbarin or Glucosibarin 1 5.6 C15H21NO10S2 438.0534 2 Semi-Quant Glucobarbarin or Glucosibarin 2 6.6 C15H21NO10S2 438.0534 2 Semi-Quant Glucoberteroin 7.4 C13H25NO9S3 434.0619 Semi-Quant Glucobrassicanapin 6.3 C12H21NO9S2 386.0585 Semi-Quant Glucobrassicin 6.57 C16H20N2O9S2 447.0537 Semi-Quant Glucocheirolin 4 C11H21NO11S3 438.0204 Semi-Quant Glucodehydroerucin 6.4 C12H21NO9S3 418.0306 Semi-Quant Glucoerucin 6.4 C12H23NO9S3 420.0462 Quant Glucohesperin 4 C14H26NO10S3 463.0646 Semi-Quant Glucohirsutin 4 C16H31NO10S3 492.1037 Semi-Quant Glucoibarin 4 C15H29NO10S3 478.0881 Semi-Quant Glucoiberin 2.28 C11H21NO10S3 422.0255 Quant Glucoiberverin 4 C11H21NO9S3 406.0306 Semi-Quant Glucolesquerellin 4 C14H27NO9S3 448.0775 Semi-Quant Gluconapin 5.05 C11H19NO9S2 372.0429 Quant Gluconapoleiferin 4.8 C12H21NO10S2 402.0534 Semi-Quant Gluconasturtiin 7.3 C15H21NO9S2 422.0585 Quant

Method Validation Report Page: 6 of 18 Glucoraphanin 3.5 C12H23NO10S3 436.0411 Quant Glucoraphenin 3.8 C12H21NO10S3 434.0255 Quant Glucotropaeolin 6.3 C14H19NO9S2 408.0429 Quant Hydroxyglucobrassicin 5.4 C16H20N2O10S2 463.0487 Semi-Quant Methoxyglucobrassicin 7.45 C17H22N2O10S2 477.0643 3 Semi-Quant Neoglucobrassicin 8.1 C17H22N2O10S2 477.0643 3 Semi-Quant Progoitrin 2.97 C11H19NO10S2 388.0378 1 Quant Sinalbin 4.68 C14H19NO10S2 424.0378 Quant Sinigrin 3.35 C10H17NO9S2 358.0272 Quant SMCO 1.1 C4H9NO3S 150.0230 Semi-Quant Isobaric compounds: 1 Epiprogoitrin and Progoitrin (388.0378 m/z). The method uses standards for each of these compounds so it was simple to distinguish them based on retention time. 2 Glucobarbarin and Glucosibarin (stereoisomers, 438.0534 m/z). It is impossible to distinguish between these two compounds with this method because we were unable to source analytical standards of either compound to use for identification. 3 Methoxyglucobrassicin and Neoglucobrassicin (477.0643 m/z). Identification was based on order of RT in the literature as well as observation of a 446 m/z fragment for neoglucobrassicin in development tests. Sinigrin Equivalence In order to approximate the relative analyte concentrations, the semi-quantitative compounds are analysed as sinigrin equivalents. This is the protocol typically used in the literature for quantifying the vast majority of glucosinolates for which no commercial standards are available. It is done by reading back the peak area of the semiquantitative compound into the sinigrin calibration curve. The sinigrin molecular mass is used for these compounds when converting results from grams to moles. It's worth noting that this equivalence is an approximation of quantification so the caveats must be understood. In the literature where UV methods are used, Relative Response Factors are applied to the various compound results that attempt to correct for the differences in UV response that each compound would have compared to sinigrin. These Relative Response Factors can vary by as much as ten-fold, and there will be inherent uncertainty in these factors. With mass spectrometry, each compound will have a different ionization efficiency, and LC-MS detection is vulnerable to matrix effects as well. Differing ionization efficiency means some compounds will ionize better than others, resulting in a higher or lower signal. Glucosinolate standard curves by LC- MS can vary in slope by as much as three-fold, hence accurate quantification in LC-MS is completely reliant on having a pure standard of each target analyte *. Therefore it's important to understand the caveats of each method of detection when comparing the various analytes to sinigrin. Fortunately, in the samples analysed as part of the DairyNZ study, the contribution of the semiquantitative analytes to the total glucosinolate results (which were the most important results for DairyNZ) was typically <5%, so the uncertainty introduced by the semi-quantitative analytes to the total glucosinolate result was fairly modest. *Glucosinolates, Structures and Analysis in Foods, Don Brian Clarke, Analytical Methods 2010.

Method Validation Report Page: 7 of 18 3.1.2 Interferences While high-resolution accurate mass spectrometry is a specific and highly accurate technology, LC-MS data in general is prone to matrix interferences, either as enhancement or suppression of ionization in the ion source. In order to evaluate this influence, a series of diluted swede extracts were spiked with a standard solution at the level of 100 ppb. If matrix effects were present, we would expect to see a matrix concentration-dependent effect on the response of the analytes. All recoveries were above 100%, so while this could indicate a small level of signal enhancement, it is unlikely to be so because the apparent bias does not vary with the concentration of matrix. Since the data showed no signs of significant suppression or enhancement with a dependence on dilution level, the method can be considered essentially free of matrix effects. The three sets of isobaric compounds do have the potential to interfere with each other, if they were to coelute. Under the conditions used in this validated method, peak boundaries do not have this issue. No other interferences were observed during the development or validation of this method. Figure 2. The relative response (normalized to blank standard) for each compound in the matrix standards. Note: Progoitrin levels are much higher than other analytes in the matrices, such that the error associated with dilution and analysis will be amplified by the relatively high levels of incurred residue. This is presumed to account for the particularly high observed relative bias in the sample spikes vs the blank standard for progoitrin. 3.2 Working and Calibration Range An eight point calibration curve was used, spanning three orders of magnitude, ranging from 1 ppm down to 1 ppb and including a blank. The area counts of the standard responses were fit using a quadratic curve fit with 1/X weighting, ignoring the origin. The R 2 of the fit was always greater than 0.990. The differences between actual and theoretical values ( Residuals ) of the standards were less than 20%. An exception to this is the lowest standard (1 ppb), which could vary by as much as 35%, which is typical variation seen at or near a detection limit. Two sets of standards were injected for every data set and both calibration points were used in the curve fit.

Method Validation Report Page: 8 of 18 (Note that for sample analysis, duplicate injections are performed and the average taken. It is not possible to take this approach when constructing calibration curves due to limitations of the software, however constructing calibration curves using two injections per standard achieves the same outcome. If it were possible to plot only the averages of the duplicate injections and fit a curve to those, then the residuals would be appreciably lower. For this reason, the residuals given here understate the precision of the method, with the precision data given in Section 3.3.1 being more meaningful in this regard.) The working range of the method is the range encompassed by the calibration standards, with the exception of epiprogoitrin. All of the standards except epiprogoitrin have acceptable peak response at the 1ppb standard level, where the residuals are not higher than 35%. This is achievable for epiprogoitrin only at a minimum 3 ppb level. The upper limit for all compounds is the top standard, 1000 ppb. There is good fit and acceptable residuals for all compounds using this range. See Appendix A for examples of typical calibration curves. 3.3 Accuracy Figure 3. Graphical representation of the residuals for each calibration point shows the curve fit is very good for all points, but with higher variation at the lower points. 3.3.1 Precision (Standard Deviation) Precision was determined by testing the rapeseed CRM, the Bulb QC and the Leaf QC repeatedly with multiple replicates over many days to quantify the variability. Intra-batch precision The following graph shows data for three (for the CRM) or four (for the Bulb and Leaf QCs) sets of triplicate extractions, with each set of triplicate extractions performed on the same day, but different days for each set. Each data point is the average of the three results for one analyte. With the exception of three points, all of the data shows variation less than 15%. The vast majority of data over 100 mg/kg is under 10%.

Method Validation Report Page: 9 of 18 Figure 4. Intra-Batch Precision Inter-batch precision The following graph shows data for eleven (for the CRM) or thirteen (for the Bulb and Leaf QCs) sets of data points analysed across five days, with one to three tests per day. Each data point is the average of all of the results for one analyte. With the exception of one point, all of the data shows variation less than 25%, with most less than 15%. With the exception of three points, all of the data over 100 mg/kg is at or under 15%. Figure 5. Inter-Batch Precision Section 3.3.3 contains further information on uncertainty from a data set of 168 pairs of data from 11 duplicate analyses performed for DairyNZ. This is probably a more useful data set to consider when estimating uncertainty.

Method Validation Report Page: 10 of 18 3.3.2 Trueness (Bias and Recovery) Recovery Recovery is quite difficult to assess since many of the analytes to be measured have significant endogenous levels in the available matrices. Spiking standards on top of already present levels can push them outside the method limits and introduce additional levels of variation that make this calculation complicated. However, during method development, an analysis was done by spiking the standards into a blank and carrying through the extraction. Blank spike recoveries that were spiked in the ~80-100 ppm range had recoveries between 94% and 102%. Compound Table 5. Recovery Data per Analyte BlkSpk Conc (ppm) Spike Conc (ppm) Expected Conc Recovery Epiprogoitrin 0.779 77.91 0.771 101% Glucoerucin 0.992 101.77 1.008 98% Glucoiberin 0.767 78.78 0.780 98% Gluconapin 1.011 107.70 1.066 95% Gluconasturtiin 0.973 95.97 0.950 102% Glucoraphanin 0.880 92.20 0.913 96% Glucoraphenin 0.884 89.86 0.890 99% Glucotropaeolin 0.887 94.91 0.940 94% Progoitrin 0.719 77.30 0.765 94% Sinalbin 0.953 98.96 0.980 97% Sinigrin 0.728 77.76 0.770 95% Bias A glucosinolate Certified Reference Material (CRM) was obtained from European Reference Materials (ERM) as whole rapeseed. The rapeseed CRM is the best way bias can be measured in this method, although it is still quite a relative comparison to make. The Certificate of Analysis for the CRM provides only a total glucosinolate level (not specific levels for the individual glucosinolates), however, ERM does also provide a list of the predominant analytes found. These results are derived from a method which uses a desulfation process on the glucosinolates, followed by analysis with UV-HPLC. This is significantly different from this method and so is only to be used as a guideline for comparison. There are also two cases in the literature where values for individual glucosinolates were listed. The literature sources also use the desulfo-uv-hplc method. Table 6. Comparison of Rapeseed CRM Values to Literature Values Compound Quant or Semi-Quant Average (µmol/g DW) ERMCRM Report Predominant Lit. Level A Lit. Level B Epiprogoitrin Quant 2.91 yes 1.92 Glucoalyssin Semi-Quant 0.73 yes Glucoberteroin Semi-Quant 0.29 Glucobrassicanapin Semi-Quant 7.78 yes 4.94 5.17 Glucobrassicin Semi-Quant 0.11 yes 0.10 0.16

Method Validation Report Page: 11 of 18 Glucoerucin Quant 0.46 Gluconapin Quant 30.81 yes 24.09 25.54 Gluconapoleiferin Semi-Quant 2.53 yes 1.83 2.08 Gluconasturtiin Quant 2.02 yes 0.91 Glucoraphanin Quant 1.09 Hydroxyglucobrassicin Semi-Quant 3.74 yes 3.91 3.94 Neoglucobrassicin Semi-Quant 0.18 yes 0.31 Progoitrin Quant 70.87 yes 61.15 62.66 Sinigrin Quant 0.17 Total 124 99 99 100 Based on the total glucosinolate level, the Hills method has a +25% relative bias compared with the existing reported methodologies. It is considered, however, that the Hills data is likely to be more accurate of the data sets for the following reasons: As previously discussed in the Sinigrin Equivalence portion of Section 3.1.1, it is not known what (if any) UV response factors were used in determining the sinigrin equivalence concentrations in the ERM and two literature data sets. This is a potential source of significant error. The +25% relative bias in the Hills method is driven principally by gluconapin (contributing +6% to the final result) and progoitrin (contributing +8% to the total result), which are both fully quantitative analytes via the method described here. One caveat is that analytical standards of erroneously low purity will give erroneously high results for samples analysed against those calibration curves; this possibility was not tested with independently sourced standards given the difficulty/impossibility of sourcing these. 3.3.3 Uncertainty of Measurement Uncertainty of measurement (UoM) is a difficult parameter to measure with this limited set of data, as it will appear artificially low due to the relatively controlled conditions present in this validation and the limited level of varying circumstances that typically occur over time for a method. Bearing this limitation in mind, UoM was estimated by statistically examining the duplicate variation seen in the analyses performed for DairyNZ: Eleven samples were analysed in duplicate, most on separate days, resulting in 168 individual pairs of results suitable for statistical analysis (see references section for source of data). It was assumed that the UoM for all analytes would be comparable, and so all analyte data was collapsed into a single data set. The data was examined is such a way so as to allow construction of a function that describes the change in estimated UoM with change in analyte concentration (adapted from Eurachem): =2 + This methodology gives S0 = 0.03 µmol/g dry wt and S1 = 0.04, indicating that at higher levels the RSDs will be ±4% and the UoM ±8%.

Method Validation Report Page: 12 of 18 As is typical for analyses of this type, the contribution of bias towards UoM has been disregarded, given the difficulty in measuring bias. 3.4 Detection and Reporting Limits The reporting limits (described as detection limits in the customer reports) for this method are given as the lowest calibration standard, which is 1 ppb for all analytes except for epiprogoitrin which is 3 ppb. Peaks are visible below this point, but variability gets higher and confidence of peak identification declines, making quantification more unreliable. At the 1 ppb standard level, the signal/noise level given by the instrument software is infinite, or occasionally at a level of 10 17 or higher. The signal to noise in a typical result for the 3 ppb epiprogoitrin standard is approximately 4 x 10 17. Efforts were not made to formally determine the actual LODs of this method, as lowlevel data below the nominated reporting limits will be of little value to our customers. However, given that S0 was eventually calculated to be 0.008 µmol/g dry wt using the data generated for DairyNZ, our reporting limit for analytes will be amended to be 0.03 µmol/g dry wt. 3.5 Ruggedness and Robustness As observed during development and validation, the method is very rugged and robust. The only parameter that needs control is the integrity of the fresh-frozen samples, if used. To investigate this further, the Leaf and Bulb QCs were used fresh-frozen, where they were extracted straight away, then left on the bench to thaw for ten or thirty minutes before re-subsampling and extracting. Some analytes were affected by the extended thawing time in the Bulb QC, as seen in Figure 6 below. No effects were seen in the Leaf QC. Therefore it is important to keep the samples frozen, without leaving them on the bench for more than ten minutes before the 70% methanol is added. Figure 6. T0, T10 and T30 minute time points for the bulb QC sample, which was left to thaw at room temperature.

Method Validation Report Page: 13 of 18 Stability of the prepared standards in vial was measured over two months and the area counts were found to be stable. Further extensions of this over longer time periods may be tested in the future. Effect of Blend Fineness During method development there seemed to be an undesirable level of variation. The freeze-dried samples were subsequently blended more finely to see if blend fineness would improve precision. For the results that were above the detection limits, the %CV for the analyses of 3 replicate samples dropped from being an average of 10.9 % for the original samples to 8.5 % for the well-blended samples. Photo 1. Photo 2. Leaf Super and Orig Blend Bulb Super and Orig Blend Table 7. Comparison of Blend Fineness on Variation of Results for Each Matrix Note that these analyses were performed using only single injections, as opposed to the duplicate injections that subsequently became routine. Therefore, given that the data in Table 7 is being impacted by relatively high variability associated with only single injections, the improvement in precision by blending finely is more significant than suggested by the data, and blending to greater fineness is therefore considered an important element of the methodology.

Method Validation Report Page: 14 of 18 Effect of Sub-Sample Size Super-milled freeze-dried material was subsampled in triplicate at 100 mg, 200 mg and 500 mg, with each subsample being taken through the extraction and analysis. Neither the observed concentration nor amount of variation (%CV) for the three replicate samples varied between each sample size, indicating that a subsample size of 100 mg was sufficient for the well-homogenised material. Figure 7. Glucosinolate levels did not differ based on sub-sample size. Figure 8. Variation between replicates did not differ based on sub-sample size. Effect of Single Versus Duplicate Instrument Injections Another attempt at reducing variation between replicates was moving from a single instrument injection to doing two injections and averaging the result. For one validation experiment where the CRM, Bulb QC and Leaf QC were each extracted in triplicate, with each extract injected twice, the %CV between the replicates was lowered from 7.5% to 5.7% by doing two injections, for results above the detection limit.

Method Validation Report Page: 15 of 18 Table 7. Variation per Analyte Compared for an Averaged Two Injections Versus a Single Injection Instrument robustness for semi-quant analytes A situation occurred half way through the project sample analysis where the instrument was discovered to have changed its physical configuration. See internal QOWQ 58645 regarding the issue, which to the best of our ability, can be possibly attributed to the source position being shifted and/or a blockage in the nitrogen source flow. Upon correction, it was discovered that the semi-quantitative analyte levels were off by a factor of ~2. This level change is likely caused by changed ionization conditions. The quantitative analyte levels were unchanged, because the use of standards was selfcorrecting. The first half of the project samples were therefore re-analysed and amended reports were issued. This issue highlights the risk involved with semi-quantitative analysis, but can at least be discovered and monitored by the use of QCs and CRMs in the method. 4. Conclusions and Fitness for Purpose 5. References This method is selective, robust, sensitive to a reporting limit of generally 1 ppb in vial (or 40 mg/kg in freeze-dried material) and 0.03 µmol/g dry wt, with a UoM of ±6% for medium- to high-level results. The observed +25% relative bias on total glucosinolate level is acceptable, especially given the increased specificity, sensitivity and analytical range associated with Orbitrap technology. Therefore, the validation of this method has proved that it performs well, meets expectations and is fit for purpose. It's also important to remember that the semi-quantitative nature of glucosinolate quantification without specific standards adds a level of uncertainty to those data points, both in this method and in the literature, and that different sample preparation and instrument detection platforms will also create innate differences when comparisons are made. Reference The Fitness for Purpose for Analytical Methods; A Laboratory Guide to Method Validation and Related Topics (EURACHEM Guide)

Method Validation Report Page: 16 of 18 Glucosinolates, structures and analysis in foods, Don Brian Clarke, Analytical Methods 2010 Rapid Profiling of Intact Glucosinolates in Arabidopsis Leaves by UHPLC QTOFMS Using a Charged Surface Hybrid Column. Gaetan Glauser et al, Phytochemical Analysis 2012 Screening of plant toxins in food, feed and botanicals using full-scan high-resolution (Orbitrap) mass spectrometry. HGJ Mol et al, Food Additives and Contaminants 2011 Test Method Dry Matter at 103 C Residues Test Method SOP: Plant Tissue Grinding TM: Determination of Glucosinolates in Swedes by Orbitrap LC-MS UoM data to support GSL validation report

Method Validation Report Page: 17 of 18 Appendix A: Calibration Curves KB Item: 38206 Version: 1 RESTRICTED DOCUMENT - Do Not Print Or Copy

Method Validation Report Page: 18 of 18 KB Item: 38206 Version: 1 RESTRICTED DOCUMENT - Do Not Print Or Copy