Quantitative assessment of the Salmonella distribution on fresh-cut leafy vegetables due to cross-contamination occurred in an industrial process simulated at laboratory scale.

Fecal pathogen distributions in fresh-cut leafy vegetables are essential to develop suitable sampling plans so as to detect pathogen contaminations. In this study, a typical fresh-cut lettuce process was reproduced at pilot scale with different initial inoculum levels of Salmonella on lettuce (6-7, 4 and 1 log CFU/g). The pathogen was determined in all processed lettuce samples (n ≥ 50) and obtained count data were used to fit different probability distributions. The study showed that Salmonella is homogenously distributed on fresh-cut leafy vegetables as a result of processing (mainly washing) at all contamination levels. Negative binomial and Poisson-lognormal distributions were suitable to describe pathogen distribution at the high and medium levels. coefficient of variation modified (CV) indicated no overdispersion (i.e. clustering). Nevertheless, further research will be needed to assess the effect of using disinfectants in washing water on the final distribution pattern of pathogens in processed fresh-cut leafy vegetables.

Modelling survival kinetics of Staphylococcus aureus and Escherichia coli O157:H7 on stainless steel surfaces soiled with different substrates under static conditions of temperature and relative humidity.

The survival of Escherichia coli O157:H7 and Staphylococcus aureus on stainless steel surfaces with Saline Solution (SS), Tryptone Soy Broth (TSB) and meat purge was studied, and based on results, mathematical models describing survival of pathogens as a function of time were proposed. Results indicated that S. aureus was able to survive longer than E. coli O157:H7 in all substrates. The type of substrate had a greater impact on the survival of E. coli O157:H7. This microorganism only remained viable for 8 and 50 h (hours) on surfaces with SS and TSB, respectively while on meat purge, the microorganism could be recovered after 200 h. For S. aureus, SS and TSB led to similar survival times (250 h) whereas on meat purge, survival capacity increased to 800 h. Survival data for S. aureus could be well described by a log-linear model or a Weibull model depending on the type of substrate (R(2) > 0.85). E. coli O157:H7 displayed an evident tail zone which made the Weibull model more appropriate (R(2) > 0.94). These survival models may be used in quantitative risk assessment to produce more accurate risk estimates. Finally, the results highlight the importance of performing effective cleaning procedures to prevent cross contamination.

Development of a risk-based methodology for estimating survival and growth of enteropathogenic Escherichia coli on iceberg-lettuce exposed at short-term storage in foodservice centers.

Ready-to-eat lettuce is a food commodity prone to contamination by pathogenic microorganisms if processing and distribution conditions as well as handling practices are not effective. A challenge testing protocol was applied to ready-to-eat iceberg-lettuce samples by inoculating different initial contamination levels (4.5, 3.5 and 2.5 log cfu/g) of Escherichia coli strain (serotype O158:H23) subsequently stored at 8, 12, 16, 20 and 24°C for 6h. A polynomial regression model for log difference (log(diff)) was developed at each inoculum level studied through the calculation of the effective static temperature (T(eff)). Furthermore, the developed model was integrated within a risk-based approach with real time/Temperature (t/T) data collected in three Spanish foodservice centers: school canteens, long-term care facilities (LTCF) and hospitals. Statistical distributions were fitted to t/T data and estimated log(diff) values were obtained as model outputs through a Monte Carlo simulation (10,000 iterations). The results obtained at static conditions indicated that the maintenance of the lettuce at 8°C slightly reduced the E. coli population from -0.4 to -0.5 log cfu/g. However, if chill chain is not maintained, E. coli can grow up to 1.1 log cfu/g at temperatures above 16°C, even at low contamination levels. Regarding log(diff) estimated in foodservice centers, very low risk was obtained (log(diff)<1.0 log cfu in all cases). Mean T(eff) values obtained in hospitals were the lowest ones (11.1°C) and no growth of E. coli was predicted in >92% of simulated cases. The results presented in this study could serve food operators to set time/Temperature requirements for ready-to-eat foods in foodservice centers, providing a scientific basis through the use of predictive modeling. These findings may also serve to food safety managers to better define the control measures to be adopted in foodservice centers in order to prevent food-borne infections.

Studying the growth boundary and subsequent time to growth of pathogenic Escherichia coli serotypes by turbidity measurements.

The presence of Escherichia coli in contaminated food products is commonly attributed to faecal contamination when they are improperly handled and/or when inactivation treatments fail. Adaptation of E. coli at low pH and a(w) levels can vary at different temperatures depending on the serotype, thus more detailed studies are needed. In this work, a screening to assess the growth of four pathogenic serotypes of E. coli (O55:H6; O59:H21; O158:H23 and O157:H7) was performed. Subsequently, boundary models were elaborated with the fastest serotype selected at different temperatures (8, 12 and 16 degrees C), and inoculum levels (2, 3 and 4log cfu/mL) as function of pH (7.00-5.00) and a(w) (0.999-0.960). Finally, the growth kinetics of E. coli was described in the conditions that allowed growth. Results obtained showed that the serotypes O157:H7 and O59:H21 did not grow at more stringent conditions (8 degrees C; pH 5.50), while the E. coli O158:H23 was the best adapted, resulting in faster growth. The logistic regression models presented a good adjustment to data observed since more than 96.7% of cases were correctly classified. The growth interface was shifted to more limited conditions as the inoculum size was higher. Detection times (t(d), h) and their variability were higher at low levels of the environmental factors studied. This work provides insight on the growth kinetics of E. coli at various environmental conditions.

Evaluation of hygiene practices and microbiological quality of cooked meat products during slicing and handling at retail.

Cooked meat ready-to-eat products are recognized to be contaminated during slicing which, in the last years, has been associated with several outbreaks. This work aimed to find out possible relation between the hygiene practice taking place at retail point during slicing of cooked meat products in small and medium-sized establishments (SMEs) and large-sized establishments (LEs) and the microbiological quality of sliced cooked meat products. For that, a checklist was drawn up and filled in based on scoring handling practice during slicing in different establishments in Cordoba (Southern Spain). In addition, sliced cooked meats were analyzed for different microbiological indicators and investigated for the presence of Listeria spp. and Listeria monocytogenes. Results indicated that SMEs showed a more deficient handling practices compared to LEs. In spite of these differences, microbiological counts indicated similar microbiological quality in cooked meat samples for both types of establishments. On the other hand, Listeria monocytogenes and Listeria inocua were isolated from 7.35% (5/68) and 8.82% (6/68) of analyzed samples, respectively. Positive samples for Listeria spp. were found in establishments which showed acceptable hygiene levels, though contamination could be associated to the lack of exclusiveness of slicers at retail points. Moreover, Listeria spp presence could not be statistically linked to any microbiological parameters; however, it was observed that seasonality influenced significantly (P<0.05) L. monocytogenes presence, being all samples found during warm season (5/5). As a conclusion, results suggested that more effort should be made to adequately educate handlers in food hygiene practices, focused specially on SMEs.

Modelling the growth boundaries of Staphylococcus aureus: Effect of temperature, pH and water activity.

The microbial behaviour of five enterotoxigenic strains of Staphylococcus aureus was studied in the growth/no growth domain. A polynomial logistic regression equation was fitted using a stepwise method to study the interaction of temperature (8, 10, 13, 16 and 19 degrees C), pH (4.5; 5.0; 5.5; 6.0; 6.5 7.0 and 7.5) and water activity (A(w)) (19 levels ranging from 0.867 to 0.999) on the probability of growth. Out of the 284 conditions tested, 146 were chosen for model data and 138 intermediate conditions for validation data. A growth/no growth transition was obtained by increasing the number of replicates per condition (n=30) in comparison to other published studies. The logistic regression model showed a good performance since 96.6% (141 out of 146 conditions) of the conditions for model data and 92.0% (127 out of 138 conditions) for validation data were correctly classified. The predictions indicated an abrupt growth/no growth interfaces occurred at low levels of temperature, pH and A(w). At 8 degrees C, S. aureus grew only at optimum levels of pH and A(w) while at temperatures above 13 degrees C, growth of S. aureus was observed at pH=4.5 and A(w)=0.96 (13 degrees C), 0.941 (16 degrees C) and 0.915 (19 degrees C). The optimal pH at which growth of S. aureus was detected earlier was 6.5. However, a slight decrease of the probability of growth was noticed in the pH interval of 7.0-7.5 at more stringent conditions. The ability of S. aureus to grow at low A(w) was shown since growth was detected at A(w)=0.867 (T=19 degrees C; pH=7.0). Finally, a comparison of model predictions with literature data on growth/no growth responses of S. aureus in culture media and cooked meat was made. Model predictions agreed with published data in 94% of growth cases and in 62% of no growth cases. The latter discordance is highly associated to other environmental factors (such as other preservatives, strains etc.) included in published models that did not match the ones included in our study. This study can help manufacturers in making decision on the most appropriate formulations for food products in order to prevent S. aureus growth and enterotoxin production along their shelf-life.

Searching for new mathematical growth model approaches for Listeria monocytogenes.

Different secondary modeling approaches for the estimation of Listeria monocytogenes growth rate as a function of temperature (4 to 30 degrees C), citric acid (0% to 0.4% w/v), and ascorbic acid (0% to 0.4% w/v) are presented. Response surface (RS) and square-root (SR) models are proposed together with different artificial neural networks (ANN) based on product functions units (PU), sigmoidal functions units (SU), and a novel approach based on the use of hybrid functions units (PSU), which results from a combination of PU and SU. In this study, a significantly better goodness-of-fit was obtained in the case of the ANN models presented, reflected by the lower SEP values obtained (< 24.23 for both training and generalization datasets). Among these models, the SU model provided the best generalization capacity, displaying lower RMSE and SEP values, with fewer parameters compared to the PU and PSU models. The bias factor (B(f)) and accuracy factor (A(f)) of the mathematical validation dataset were above 1 in all cases, providing fail-safe predictions. The balance between generalization properties and the ease of use is the main consideration when applying secondary modeling approaches to achieve accurate predictions about the behavior of microorganisms.

Survey of temperature and consumption patterns of fresh-cut leafy green salads: risk factors for listeriosis.

Increasing demand for fresh-cut or ready-to-eat fruits and vegetables, developed to meet the consumer need for quick and convenient products, has prompted extensive research into their microbiological quality, safety, processing, and packaging. The microbial ecology of Listeria monocytogenes is recognized as a major safety concern for fresh-cut produce. A survey was performed to collect information on consumption patterns of fresh-cut leafy green salads and the temperature of domestic refrigerators. Salad consumption was low-moderate: 24.3% of respondents never purchased fresh-cut leafy green salads; of those who reported buying these products, 7.41% did so more than twice a week, 17.28% once or twice a week, 29.63% once or twice a month, and 45.68% occasionally. Saving time and convenience were the advantages most widely reported by consumers. A total of 9.9% of respondents did not always respect the "use-by" date of fresh-cut salads, a negative practice that could contribute to the risk of listeriosis. Temperatures reported in domestic refrigerators were compatible with the growth of L. monocytogenes on ready-to-eat salads. Variations in average temperature followed a normal distribution, N(6.62, 2.56), while the variability of temperature variance was described by a gamma distribution, G(2.00, 1.00). As expected, when a time of day-temperature profile was plotted over a 24-h period, changes corresponding to the transition between day and night were observed. Knowledge of consumption patterns and consumer hygiene practices is essential, first in assessing the risk of listeriosis (risk assessment) and second in taking measures to manage that risk (risk management).

Extracting additional risk managers information from a risk assessment of Listeria monocytogenes in deli meats.

The risk assessment study of Listeria monocytogenes in ready-to-eat foods conducted by the U.S. Food and Drug Administration is an example of an extensive quantitative microbiological risk assessment that could be used by risk analysts and other scientists to obtain information and by managers and stakeholders to make decisions on food safety management. The present study was conducted to investigate how detailed sensitivity analysis can be used by assessors to extract more information on risk factors and how results can be communicated to managers and stakeholders in an understandable way. The extended sensitivity analysis revealed that the extremes at the right side of the dose distribution (at consumption, 9 to 11.5 log CFU per serving) were responsible for most of the cases of listeriosis simulated. For concentration at retail, values below the detection limit of 0.04 CFU/g and the often used limit for L. monocytogenes of 100 CFU/g (also at retail) were associated with a high number of annual cases of listeriosis (about 29 and 82%, respectively). This association can be explained by growth of L. monocytogenes at both average and extreme values of temperature and time, indicating that a wide distribution can lead to high risk levels. Another finding is the importance of the maximal population density (i.e., the maximum concentration of L. monocytogenes assumed at a certain temperature) for accurately estimating the risk of infection by opportunistic pathogens such as L. monocytogenes. According to the obtained results, mainly concentrations corresponding to the highest maximal population densities caused risk in the simulation. However, sensitivity analysis applied to the uncertainty parameters revealed that prevalence at retail was the most important source of uncertainty in the model.

Modeling the growth of Listeria monocytogenes in pasteurized white asparagus.

Growth of Listeria monocytogenes in pasteurized white asparagus was monitored at different storage temperatures (4, 10, 20, and 30 degrees C). Among the main microbial kinetic parameters, growth rate (mu) per hour was calculated at each temperature using the Baranyi-Roberts model. L. monocytogenes was able to grow at all temperatures, although at 4 degrees C only a slight increment of the microbial population was observed (approximately 1 log CFU/g) after 300 h of storage. Subsequently, two different secondary modeling approaches were proposed to study the relationship between mu and storage temperature: the Arrhenius and Ratkowsky models. Although both models properly described the data observed, smaller values of root mean square error (RMSE) and standard error of prediction (SEP) were obtained with the Ratkowsky model, providing a better goodness of fit (Ratkowsky model: RMSE = 0.010, SEP = 21.23%; Arrhenius model: RMSE = 0.026, SEP = 54.37%). The maximum population density (MPD) was calculated at each temperature studied. A clear dependence between MPD and temperature was found; lower temperatures produced lower values of MPD. This finding confirmed the Jameson effect, indicating that multiple hurdles in the food-processing chain plus lower temperatures reduced L. monocytogenes growth. Predicting the growth of L. monocytogenes along the food chain will help to reduce microbial risks associated with consumption of pasteurized white asparagus.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

Effect of preincubation temperature and pH on the individual cell lag phase of Listeria monocytogenes, cultured at refrigeration temperatures.

The impact of precultural temperature and pH on the distribution of the lag phase of individual Listeria monocytogenes cells was assessed during preincubation at 7 degrees C, using a dilution protocol to obtain single cells, and optical density measurements to estimate the individual lag phase. Firstly, the pure temperature effect (37, 15, 10, 7, 4 and 2 degrees C) was investigated on a subsequent growth at 7 degrees C and pH 7.4. Secondly, low precultural temperatures (10, 7 and 4 degrees C) were combined with a controlled pH at 7.4 and 5.7 with a subsequent growth at 7 degrees C and at different pH values (7.4, 6.0 and 5.5). For all temperature-pH combinations, the individual cell lag phase was determined using a three-phase linear growth model. It was observed that at low precultural temperatures (2, 4 and 7 degrees C), a high proportion of L. monocytogenes cells were able to grow at 7 degrees C with almost no lag phase, consequently, the resulting distributions were positively skewed. Beside this, the variability observed was lower than at higher precultural temperatures. Regarding the precultural pH effect, at pH 7.4 the mean values of the lag phases were shorter at lower preincubation temperatures; while at pH 5.7 small pH transitions produced shorter individual lag phases at all precultural temperatures. The quantification of the effect of precultural conditions on the individual cell lag phase duration would improve the accuracy of the existing growth models, especially when a series of processing and storage steps are linked together in a process model or exposure assessment. Distributions will be fitted to the data for every set of conditions, generating useful tools for further risk assessment purposes.

Management of microbiological safety of ready-to-eat meat products by mathematical modelling: Listeria monocytogenes as an example.

The recent Commission Regulation (EC) No 2073/2005 establishes microbiological criteria in foods. For the pathogen Listeria monocytogenes in the category ready-to-eat foods able to support its growth, other than those intended for infants and for special medical purposes, two different microbiological criteria are proposed: (i) L. monocytogenes levels should be <100 cfu/g throughout the shelf-life of the product, (ii) absence in 25 g of the product at the stage before the food has left the immediate control of the food business operator, who has produced it. The application of either the first or the second of these criteria depends on whether or not the manufacturer is able to demonstrate that the level of L. monocytogenes in the food product will not exceed 100 cfu/g throughout its shelf-life. This demonstration should be based on physico-chemical characteristics of the target product and consultation of scientific literature, and, when necessary, on quantitative models and/or challenge tests. Once the characteristics of the product as well as scientific literature show that the pathogen has potential to grow on a specific food commodity, it seems adequate to use quantitative models and/or perform challenge tests to study the extent to which L. monocytogenes could grow. In this study, we aim to illustrate with an example in cooked ham the application of quantitative models as a tool to manage the compliance with these criteria. Two approaches were considered: deterministic and probabilistic, in three different commercial brands (A, B, and C). The deterministic approach showed that the limit 100 cfu/g was exceeded largely at the end of the shelf-life of all three; however, when reducing the storage time, the level of L. monocytogenes remained below 100 cfu/g in B. The probabilistic approach demonstrated very low percentiles corresponding to 100 cfu/g; when reducing the storage time, percentiles for three products increased, especially in products B and C (from 4.92% to 75.90%, and from 0.90% to 73.90%, respectively). This study shows how different storage times influence the level of L. monocytogenes at the end of the shelf-life of cooked ham, and, depending on the level reached, the microbiological criterion applied should be different, as stated above. Beside this, the choice of either point-estimate or probabilistic approach should be determined by the competent sanitary authority, and, in case of selecting the second approach, a certain percentile for the level 100 cfu/g should be established.

Modeling transfer of Escherichia coli O157:H7 and Staphylococcus aureus during slicing of a cooked meat product.

Cross contamination is one of the most important contributing factors in foodborne illnesses originating in household environments. The objective of this research was to determine the transfer coefficients between a contaminated domestic slicing machine and a cooked meat product, during slicing. The microorganisms tested were Staphylococcus aureus (Gram positive) and Escherichia coli O157:H7 (Gram negative). The results showed that both microorganisms were able to transfer to all slices examined (20 successively sliced) and at different inoculum levels on the blade (10(8), 10(6) and 10(4)cfu/blade). The results also showed that the number of log cfu transferred per slice, during slicing, decreased logarithmically for both microorganisms at inoculum levels of 8 and 6log cfu/blade. The type of microorganism significantly influenced transfer coefficients (p<0.05) and there was an interaction between inoculum level and transfer coefficient for S. aureus (p<0.05), but not E. coli O157:H7. Finally, to describe bacterial transfer during slicing, two models (log-linear and Weibull) were fitted to concentration on slice data for both microorganisms (at 6 and 8 log cfu/blade), obtaining a good fit to data (R(2)⩾0.73).

Modelling the growth of Leuconostoc mesenteroides by Artificial Neural Networks.

The combined effect of temperature (10.5 to 24.5 degrees C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the predicted specific growth rate (Gr), lag-time (Lag) and maximum population density (yEnd) of Leuconostoc mesenteroides under aerobic and anaerobic conditions, was studied using an Artificial Neural Network-based model (ANN) in comparison with Response Surface Methodology (RS). For both aerobic and anaerobic conditions, two types of ANN model were elaborated, unidimensional for each of the growth parameters, and multidimensional in which the three parameters Gr, Lag, and yEnd are combined. Although in general no significant statistical differences were observed between both types of model, we opted for the unidimensional model, because it obtained the lowest mean value for the standard error of prediction for generalisation. The ANN models developed provided reliable estimates for the three kinetic parameters studied; the SEP values in aerobic conditions ranged from between 2.82% for Gr, 6.05% for Lag and 10% for yEnd, a higher degree accuracy than those of the RS model (Gr: 9.54%; Lag: 8.89%; yEnd: 10.27%). Similar results were observed for anaerobic conditions. During external validation, a higher degree of accuracy (Af) and bias (Bf) were observed for the ANN model compared with the RS model. ANN predictive growth models are a valuable tool, enabling swift determination of L. mesenteroides growth parameters.

Performance of response surface and Davey model for prediction of Staphylococcus aureus growth parameters under different experimental conditions.

The combined effect of different temperatures (7 to 19 degrees C), pH levels (4.5 to 8.5), sodium chloride levels (0 to 8%), and sodium nitrite levels (0 to 200 ppm) on the predicted growth rate and lag time of Staphylococcus aureus under aerobic and anaerobic conditions was studied. The two predictive models developed, response surface (RS) and the Davey model, provided reliable estimates of the two kinetic parameters studied. The RS provided better predictions of maximum specific growth rate, with bias factors of 1.06 and 1.31 and accuracy factors of 1.17 and 1.37, respectively, in aerobic and anaerobic conditions. The Davey model performed more accurately for lag time, with a bias factor of 1.12 and an accuracy factor of 1.49, for both aerobic and anaerobic conditions. Predictive growth models are a valuable tool, enabling swift determination of Staphylococcus aureus growth rate and lag time. These data are essential for ensuring staphylococcus-related quality and safety of food products.

Determination of ready-to-eat vegetable salad shelf-life.

The shelf-life of ready-to-eat vegetable salads established by manufacturers is usually 7-14 days depending on the type of vegetable, and is determined by loss in organoleptic qualities. A more objective method to predict shelf-life and spoilage would be desirable. The present study monitored the evolution of spoilage organisms in a mixed salad of red cabbage, lettuce and carrot stored at 4 degrees C, 10 degrees C and 15 degrees C. Changes in carbon dioxide and oxygen concentrations and pH were also monitored. Predictive modelling was used to established a theoretical shelf-life time as a function of temperature. Lactic acid bacteria at levels of 10(6) cfu/g appeared to be related to both spoilage and theoretically-predicted shelf-life values.

Mineral content modifications during ripening of asparagus (Asparagus officinalis, L.).

The essential elements: calcium (Ca), magnesium (Mg), sodium (Na) potassium (K) and phosphorus (P) were analyzed in fresh asparagus to determine the effect of the ripening of the asparagus on the mineral content. Asparagus samples were classified in two groups by diameter (< 11 mm and > 14 mm). Asparagus from a sample group with the same diameter were divided into two portions (apical and basal) according to distance from the tip. The concentrations of calcium, magnesium and phosphorus increased with the ripening process of the asparagus while the content of sodium decreased when the white asparagus turned into a green ripening state. No significant differences were established for potassium. The green ripening state was the group with the greater concentration of calcium, magnesium and phosphorus. Statistically significant differences (p < 0.001) were observed between portions of asparagus (tip and rest of stem) in the contents of the five mineral elements analyzed. The levels of mineral elements investigated increased notably in the tip of the asparagus with the exception of sodium and potassium of which the levels in the apical portion decreased or hardly modified. The variance analyses determined statistically significant differences (p < 0.001) in the concentration of magnesium, sodium and phosphorus between asparagus diameters (< 11 and > 14 mm) and no significant differences (p > 0.05) were found for calcium and potassium. The mean element levels were (mg/kg dry weight): Ca = 324 +/- 1186; Mg = 1818 +/- 490; Na = 368 +/- 86; K = 37297 +/- 4167 and P = 6809 +/- 2481.

Influence of vegetative cycle of asparagus (Asparagus officinalis L.) on copper, iron, zinc and manganese content.

The essential elements copper (Cu), iron (Fe), zinc (Zn) and manganese (Mn) were analyzed in fresh asparagus to determine the effects of the vegetative cycle of the plant on the micronutrient content. Asparagus samples were classified in two groups by diameter (< 11 mm and > 14 mm). Asparagus from a sample group with the same diameter were divided into two portions (apical and basal) according to distance from the tip. The concentrations of copper, iron, zinc and manganese increased during the vegetative cycle of the asparagus, mainly in the apical portion which showed significantly greater concentrations with respect to the basal portion. The > 14 mm diameter asparagus presented higher levels of copper, zinc and manganese, whereas the concentration of iron was greater in the < 11 mm diameter asparagus. The mean element levels were (mg/kg dry weight): Cu, 18.9 +/- 3.9; Fe, 91.7 +/- 33.7; Zn, 69.5 +/- 24.6 and Mn, 20.9 +/- 5.0).