The scope applies to bioanalytical methods based on immuno-recognition or receptor binding (such as ELISA, dip-sticks, lateral flow devices, immuno-sensors) and physicochemical methods based on chromatography or direct detection by mass spectrometry (e.g. ambient MS). Other methods (e.g. thin layer chromatography) are not excluded provided the signals generated relate directly to the mycotoxins of interest and allow that the principle described hereunder is applicable.
The specific requirements apply to methods of which the result of the measurement is a numerical value, for example a (relative) response from a dip-stick reader, a signal from LC-MS, etc., and that normal statistics apply.
The requirements do not apply to methods that do not give numerical values (e.g. only a line that is present or absent), which require different validation approaches. Specific requirements for these methods are provided in point 4.3.3.
This document describes procedures for the validation of screening methods by means of an inter-laboratory validation, the verification of the performance of a method validated by means of an inter-laboratory exercise and the single-laboratory validation of a screening method.
Screening target concentration (STC): the concentration of interest for detection of the mycotoxin in a sample. When the aim is to test compliance with regulatory limits, the STC is equal to the applicable maximum level. For other purposes or in case no maximum level has been established, the STC is predefined by the laboratory.
Screening method: means method used for selection of those samples with levels of mycotoxins that exceed the screening target concentration (STC), with a given certainty. For the purpose of mycotoxin screening, a certainty of 95 % is considered fit-for-purpose. The result of the screening analysis is either ‘ negative ’ or ‘ suspect ’ . Screening methods shall allow a cost-effective high sample-throughput, thus increasing the chance to discover new incidents with high exposure and health risks to consumers. These methods shall be based on bio-analytical, LC-MS or HPLC methods. Results from samples exceeding the cut-off value shall be verified by a full re-analysis from the original sample by a confirmatory method.
‘ Negative sample ’ means the mycotoxin content in the sample is < STC with a certainty of 95 % (i.e. there is a 5 % chance that samples will be incorrectly reported as negative).
‘ False negative sample ’ means the mycotoxin content in the sample is > STC but it has been identified as negative.
‘ Suspect sample ’ (screen positive) means the sample exceeds the cut-off level (see below) and may contain the mycotoxin at a level higher than the STC. Any suspect result triggers a confirmatory analysis for unambiguous identification and quantification of the mycotoxin.
‘ False suspect sample ’ is a negative sample that has been identified as suspect.
‘ Confirmatory methods ’ means methods that provide full or complementary information enabling the mycotoxin to be identified and quantified unequivocally at the level of interest.
Cut-off level: the response, signal, or concentration, obtained with the screening method, above which the sample is classified as ‘ suspect ’ . The cut-off is determined during the validation and takes the variability of the measurement into account.
Negative control (blank matrix) sample: a sample known to be free (1) of the mycotoxin to be screened for, e.g. by previous determination using a confirmatory method of sufficient sensitivity. If no blank samples can be obtained, then material with the lowest obtainable level might be used as long as the level allows the conclusion that the screening method is fit for purpose.
Positive control sample: sample containing the mycotoxin at the screening target concentration, e.g. a certified reference material, a material of known content (e.g. test material of proficiency tests) or otherwise sufficiently characterised by a confirmatory method. In the absence of any of the above, a blend of samples with different levels of contamination or a spiked sample prepared within laboratory and sufficiently characterised can be used, provided it can be proven that the contamination level has been verified.
The aim of the validation is to demonstrate the fitness of purpose of the screening method. This is done by determination of the cut-off value and determination of the false negative and false suspect rate. In these two parameters performance characteristics such as sensitivity, selectivity, and precision are embedded.
Screening methods can be validated by inter-laboratory or by single laboratory validation. If inter-laboratory validation data is already available for a certain mycotoxin/matrix/STC combination, a verification of method performance is sufficient in a laboratory implementing the method.
Mycotoxins:
The validation shall be performed for every individual mycotoxin in the scope. In case of bio-analytical methods that give a combined response for a certain mycotoxin group (e.g. aflatoxins B 1 , B 2 , G 1 & G 2 ; fumonisins B 1 & B 2 ), applicability must be demonstrated and limitations of the test mentioned in the scope of the method. Undesired cross-reactivity (e.g. DON-3-glycoside, 3- or 15-acetyl-DON for immuno-based methods for DON) is not considered to increase the false negative rate of the target mycotoxins, but may increase the false suspect rate. This unwanted increasing will be diminished by confirmatory analysis for unambiguous identification and quantification of the mycotoxins.
Matrices:
An initial validation should be performed for each commodity, or, when the method is known to be applicable to multiple commodities, for each commodity group. In the latter case, one representative and relevant commodity is selected from that group (see table A).
Sample set:
The minimum number of different samples required for validation is 20 homogeneous negative control samples and 20 homogeneous positive control samples that contain the mycotoxin at the STC, analysed under intermediate precision (RSD Ri ) conditions spread over 5 different days. Optionally, additional sets of 20 samples containing the mycotoxin at other levels can be added to the validation set to gain insight to what extent the method can distinguish between different mycotoxin concentrations.
Concentration:
For each STC to be used in routine application, a validation has to be performed.
Validation through collaborative trials shall be done in accordance with an internationally recognised protocol on collaborative trials (e.g. ISO 5725:1994 or the IUPAC International Harmonised Protocol) which requires inclusion of valid data from at least eight different laboratories. Other than that, the only difference compared to single laboratory validations is that the ≥ 20 samples per commodity/level can be evenly divided over the participating laboratories, with a minimum of two samples per laboratory.
The (relative) responses for the negative control and positive control samples are taken as basis for the calculation of the required parameters.
Screening methods with a response proportional with the mycotoxin concentration
For screening methods with a response proportional with the mycotoxin concentration the following applies:
Cut-off = R STC – t-value 0,05 * SD STC
mean response of the positive control samples (at STC)
one tailed t-value for a rate of false negative results of 5 % (see table B)
standard deviation
Screening methods with a response inversely proportional with the mycotoxin concentration
Similarly, for screening methods with a response inversely proportional with the mycotoxin concentration, the cut-off is determined as:
Cut-off = R STC + t-value 0,05 * SD STC
By using this specific t-value for establishing the cut-off value, the rate of false negative results is by default set at 5 %.
Fitness for purpose assessment
Results from the negative control samples are used to estimate the corresponding rate of false suspect results. The t-value is calculated corresponding to the event that a result of a negative control sample is above the cut off value, thus erroneously classified as suspect.
=
(cut off – mean blank )/SD blank
for screening methods with a response proportional with the mycotoxin concentration
or
=
( mean blank – cut off)/SD blank
for screening methods with a response inversely proportional with the mycotoxin concentration
From the obtained t-value, based on the degrees of freedom calculated from the number of experiments, the probability of false suspect samples for a one tailed distribution can either be calculated (e.g.. spread sheet function ‘ TDIST ’ ) or taken from a table for t-distribution.
The corresponding value of the one tailed t-distribution specifies the rate of false suspect results.
This concept is described in detail with an example in Analytical and Bioanalytical Chemistry DOI 10.1007/s00216 -013-6922-1.
When new mycotoxins are added to the scope of an existing screening method, a full validation is required to demonstrate the suitability of the method.
If the screening method is known or expected to be applicable to other commodities, the validity to these other commodities shall be verified. As long as the new commodity belongs to a commodity group (see Table A) for which an initial validation has already been performed, a limited additional validation is sufficient. For this, a minimum of 10 homogeneous negative control and 10 homogeneous positive control (at STC) samples shall be analysed under intermediate precision conditions. The positive control samples shall all be above the cut-off value. In case this criterion is not met, a full validation is required.
For screening methods that have already been successfully validated through a collaborative laboratory trial, the method performance shall be verified. For this a minimum of 6 negative control and 6 positive control (at STC) samples shall be analysed. The positive control samples shall all be above the cut-off value. In case this criterion is not met, the laboratory has to perform a root-cause analysis to identify why it cannot meet the specification as obtained in the collaborative trial. Only after taking corrective action it shall re-verify the method performance in its laboratory. In case the laboratory is not capable to verify the results from the collaborative trial, it will need to establish its own cut-off in a complete single laboratory validation.
After initial validation, additional validation data are acquired by including at least two positive control samples in each batch of samples screened. One positive control sample is a known sample (e.g. one used during initial validation), the other is a different commodity from the same commodity group (in case only one commodity is analysed, a different sample of that commodity is used instead). Inclusion of a negative control sample is optional. The results obtained for the two positive control samples are added to the existing validation set.
At least once a year the cut-off value is re-established and the validity of the method is re-assessed. The continuous method verification serves several purposes:
quality control for the batch of samples screened
providing information on robustness of the method at conditions in the laboratory that applies the method
justification of applicability of the method to different commodities
allowing to adjust cut-off values in case of gradual drifts over time.
The validation report shall contain:
A statement on the STC
A statement on the obtained cut-off.
Note: The cut-off must have the same number of significant figures as the STC. Numerical values used to calculate the cut-off need at least one more significant figure than the STC. U.K.
A statement on calculated false suspected rate
A statement on how the false suspected rate was generated.
Note: The statement on the calculated false suspected rate indicates if the method is fit-for-purpose as it indicates the number of blank (or low level contamination) samples that will be subject to verification. U.K.
Commodity groups for the validation of screening methods
| a If a buffer is used to stabilise the pH changes in the extraction step, then this commodity group can be merged into one commodity group ‘ High water content ’ . | ||
| b ‘ Difficult or unique commodities ’ should only be fully validated if they are frequently analysed. If they are only analysed occasionally, validation may be reduced to just checking the reporting levels using spiked blank extracts. | ||
| Commodity groups | Commodity categories | Typical representative commodities included in the category |
|---|---|---|
| High water content | Fruit Juices | Apple juice, grape juice |
| Alcoholic beverages | Wine, beer, cider | |
| Root and tuber vegetables | Fresh ginger | |
| Cereal or fruit based purees | Purees intended for infants and small children | |
| High oil content | Tree nuts | Walnut, hazelnut, chestnut |
| Oil seeds and products thereof | Oilseed rape, sunflower, cotton-seed, soybeans, peanuts, sesame etc. | |
| Oily fruits and products thereof | Oils and pastes (e.g. peanut butter, tahina) | |
| High starch and/or protein content and low water and fat content | Cereal grain and products thereof | Wheat, rye, barley, maize, rice, oats Wholemeal bread, white bread, crackers, breakfast cereals, pasta |
| Dietary products | Dried powders for the preparation of food for infants and small children | |
| High acid content and high water content a | Citrus products | |
| ‘Difficult or unique commodities’b | Cocoa beans and products thereof, copra and products thereof, coffee, tea Spices, liquorice | |
| High sugar low water content | Dried fruits | Figs, raisins, currants, sultanas |
| Milk and milk products | Milk | Cow, goat and buffalo milk |
| Cheese | Cow, goat cheese | |
| Dairy products (e.g. milk powder) | Yogurt, cream | |
One tailed t-value for a false negative rate of 5 %
| Degrees of Freedom | Number of replicates | t-value (5 %) |
|---|---|---|
| 10 | 11 | 1,812 |
| 11 | 12 | 1,796 |
| 12 | 13 | 1,782 |
| 13 | 14 | 1,771 |
| 14 | 15 | 1,761 |
| 15 | 16 | 1,753 |
| 16 | 17 | 1,746 |
| 17 | 18 | 1,74 |
| 18 | 19 | 1,734 |
| 19 | 20 | 1,729 |
| 20 | 21 | 1,725 |
| 21 | 22 | 1,721 |
| 22 | 23 | 1,717 |
| 23 | 24 | 1,714 |
| 24 | 25 | 1,711 |
| 25 | 26 | 1,708 |
| 26 | 27 | 1,706 |
| 27 | 28 | 1,703 |
| 28 | 29 | 1,701 |
| 29 | 30 | 1,699 |
| 30 | 31 | 1,697 |
| 40 | 41 | 1,684 |
| 60 | 61 | 1,671 |
| 120 | 121 | 1,658 |
| ∞ | ∞ | 1,645] |
Textual Amendments
[F1Samples are considered free of analyte if the amount present in the sample does not exceed more than 1/5 th of the STC. If the level can be quantified with a confirmatory method, the level must be taken into consideration for the validation assessment.]
Textual Amendments