T. B. Whitaker

Developments in mycotoxin analysis: an update for 2015-2016

This review summarises developments in the determination of mycotoxins over a period between mid-2015 and mid-2016. Analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone are covered in individual sections. Advances in proper sampling strategies are discussed in a dedicated section, as are methods used to analyse botanicals and spices and newly developed liquid chromatography mass spectrometry based multi-mycotoxin methods. This critical review aims to briefly discuss the most important recent developments and trends in mycotoxin determination as well as to address limitations of presented methodologies.

Sampling Foods for Mycotoxins

It is difficult to obtain precise and accurate estimates of the true mycotoxin concentration of a bulk lot when using a mycotoxin-sampling plan that measures the concentration in only a small portion of the bulk lot. A mycotoxin-sampling plan is defined by a mycotoxin test procedure and a defined accept/reject limit. A mycotoxin test procedure is a complicated process and generally consists of several steps: (1) a sample of a given size is taken from the lot, (2) the sample is ground (comminuted) in a mill to reduce its particle size, (3) a subsample is removed from the comminuted sample, and (4) the mycotoxin is extracted from the comminuted subsample and quantified. Even when using accepted test procedures, there is uncertainty associated with each step of the mycotoxin test procedure. Because of this variability, the true mycotoxin concentration in the lot cannot be determined with 100% certainty by measuring the mycotoxin concentration in a sample taken from the lot. The variability for each step of the mycotoxin test procedure, as measured by the variance statistic, is shown to increase with mycotoxin concentration. Sampling is usually the largest source of variability associated with the mycotoxin test procedure. Sampling variability is large because a small percentage of kernels are contaminated and the level of contamination on a single seed can be very large. Methods to reduce sampling, sample preparation and analytical variability are discussed.

Variability of aflatoxin test results

Using 12 lb samples, 280 g subsamples, the Waltking method of analysis, and densitometric procedures, the sampling, subsampling, and analytical variances associated with aflatoxin test procedures were estimated. Regression analysis indicated that each of the above variance components is a function of the concentration of aflatoxin in the population being tested. Results, for the test procedures given above, showed that sampling constitutes the greatest single source of error, followed by subsampling and analysis. Functional relationships are presented to determine the sampling, subsampling, and analytical variance for any size sample, subsample, and number of analyses.

Standardisation of mycotoxin sampling procedures: an urgent necessity

Abstract A mycotoxin sampling plan is defined by the mycotoxin test procedure (sample size, sample preparation method, and analytical method) and the accept/reject limit. Because of the variability associated with each step of the mycotoxin test procedure, the true mycotoxin concentration of a bulk lot cannot be determined with 100% certainty. As a result, some lots will be misclassified by the sampling program. Some good lots will be rejected by the sampling plan (seller’s risk or false positives) and some bad lots will be accepted by the sampling plan (buyer’s risk or false negatives). The magnitude of these risks is directly related to the magnitude of the variability associated with the mycotoxin test procedure. It is difficult for an exporter to have an effective control program when regulatory limits and sample designs differ greatly among trading countries. In order to facilitate trade and provide protection for the consumer, it would be desirable for all trading countries to have the same mycotoxin limits and sample plan. While standardization of sampling plans among trading nations is important, any standardised sampling plan must be designed to minimize both the seller’s and buyer’s risks to the lowest possible levels that resources will allow. Reducing the variability of the mycotoxin test procedure will reduce both the buyer’s and seller’s risks. It is important to understand the sources of error in the mycotoxin test procedure so the errors can be effectively reduced. The sampling step usually is the largest source of error due to the extreme mycotoxin distribution among kernels in the lot. As an example, sampling (5 kg), sample preparation (USDA subsampling mill and 250 g subsample), and analysis (TLC) accounted for 83%, 9%, and 8% of the total aflatoxin testing error, respectively, when testing raw shelled peanuts for aflatoxin. Examples are given to show how increasing sample size reduces sampling error; increasing the fineness of grind and using larger subsamples reduces sample preparation error, and increasing the number of aliquots analyzed and using improved technology (HPLC versus TLC) decreases analytical error. International organizations such as FAO/WHO have used scientific techniques to evaluate and design aflatoxin sampling plans for raw shelled peanuts traded in the export market.

Comparison of the observed distribution of aflatoxin in shelled peanuts to the negative binomial distribution

Suitability of the negative binomial distribution for use in estimating the probabilities associated with sampling lots of shelled peanuts for aflatoxin analysis has been studied. Large samples, called “minilots,” were drawn from 29 lots of shelled peanuts contaminated with aflatoxin. These minilots were subdivided into ca. 12 lb samples which were analyzed for aflatoxin. The mean and variance of these aflatoxin determinations for each minilot were determined. The shape parameterk and the mean aflatoxin concentrationm were estimated for each minilot. A regression analysis indicated the functional relationship betweenk andm to be:k=(2.0866+2.3898m) × 10−6. The observed distribution of sample concentrations from each of the 29 minilots was compared to the negative binomial distribution by means of the Kolmogorov-Smirnov test. The null hypothesis that each of the true unknown distribution functions was negative binomial was not rejected at the 5% significance level for all 29 comparisons.

Theoretical investigations into the accuracy of sampling shelled peanuts for aflatoxin

Within a population of shelled peanuts, aflatoxin may be concentrated in less than 0.5% of the peanuts. Those peanuts containing aflatoxin might have concentrations up to 1,000,000 µg of aflatoxin per kilogram of peanuts. Because of the distribution pattern, sample means vary widely, and the true average level of aflatoxin in the population is difficult to estimate. The objective of this study was to determine the effect of sample size, N, on sampling accuracy. The negative binomial distribution of aflatoxin since it allowed for a high probability of zero counts along with small probabilities of large counts. Using both the Monte Carlo technique and a direct computation method, the effect of sample size on sampling accuracy was quantitatively described.

Effect of processing on recovery and variability associated with immunochemical analytical methods for multiple allergens in a single matrix: dark chocolate.

Among the major food allergies, peanut, egg, and milk are the most common. The immunochemical detection of food allergens depends on various factors, such as the food matrix and processing method, which can affect allergen conformation and extractability. This study aimed to (1) develop matrix-specific incurred reference materials for allergen testing, (2) determine whether multiple allergens in the same model food can be simultaneously detected, and (3) establish the effect of processing on reference material stability and allergen detection. Defatted peanut flour, whole egg powder, and spray-dried milk were added to cookie dough at seven incurred levels before baking. Allergens were measured using five commercial enzyme-linked immunosorbent assay (ELISA) kits. All kits showed decreased recovery of all allergens after baking. Analytical coefficients of variation for most kits increased with baking time, but decreased with incurred allergen level. Thus, food processing negatively affects the recovery and variability of peanut, egg, and milk detection in a sugar cookie matrix when using immunochemical methods.

Variability associated with testing corn for aflatoxin

The sampling, subsampling (both coarse and fine ground meal), and analytical variances associated with testing shelled corn for aflatoxin were estimated by the use of 500 g samples, 50 g subsamples, and the CB method of analysis. The magnitudes of the variance components increased with an increase in the aflatoxin concentration. Functional relationships were developed to predict the variance for a given aflatoxin concentration and any size sample, subsample, and number of analyses. At 20 ppb total aflatoxin, the coefficient of variantion associated with a 4.54 kg sample, 1 kg subsample of coarsely ground meal (passes a #14 screen), a 50 g subsample of finely ground meal (passes a #20 screen) and one analysis were 21, 8, 11, and 26%, respectively.

Detecting mycotoxins in agricultural commodities.

It is difficult to obtain precise and accurate estimates of the true mycotoxin concentration of a bulk lot when using a mycotoxin-sampling plan that measures the concentration in a small portion of the bulk lot. A mycotoxin-sampling plan is defined by a mycotoxin test procedure and a defined accept/reject limit. A mycotoxin test procedure is a complicated process and generally consists of several steps: (a) a sample is taken from the lot, (b) the sample is ground (comminuted) in a mill to reduce particle size, (c) a subsample is removed from the comminuted sample, and (d) the mycotoxin is extracted from the comminuted subsample and quantified. Even when using accepted test procedures, there is variability associated with each step of the mycotoxin test procedure. Because of this variability, the true mycotoxin concentration in the lot cannot be determined with 100% certainty by measuring the mycotoxin concentration in a sample taken from the lot. The variability for each step of the mycotoxin test procedure, as measured by the variance statistic, is shown to increase with mycotoxin concentration. Sampling is usually the largest source of variability associated with the mycotoxin test procedure. Sampling variability is large because a small percentage of kernels are contaminated and the level of contamination on a single seed can be very large. Methods to reduce sampling, sample preparation, and analytical variability are discussed.

Sampling granular foodstuffs for aflatoxin

Methodology is described for the design and evaluation of testing programs to estimate aflatoxin concentrations in lots of granular foodstuffs. Use of operating characteristic curves and of the prior distribution of lot concentrations for comparing and evaluating processor and consumer risks related to testing programs are demonstrated. Operating characteristic curves, computed from a system of equations that accounts for errors in sampling, subsampling, and analysis are developed for the 1976 peanut aflatoxin testing program in the United States. Estimates are given of aflatoxin concentration in lots accepted and rejected by the testing program.