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Application of data mining techniques to predict the production of aflatoxin B1 in dry-cured ham
Food Control 108 (2020) 106884
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Application of data mining techniques to predict the production of aflatoxin B1 in dry-cured ham
T
Belén Peromingoa,1, Daniel Caballerob,c,1, Alicia Rodrígueza, Andrés Carob, Mar Rodrígueza,∗ a
Food Hygiene and Safety, Meat and Meat Products Research Institute. Faculty of Veterinary Science, University of Extremadura, Avda. de las Ciencias, s/n, ES-10003, Cáceres, Spain b Department of Computer Science, Meat and Meat Products Research Institute, University of Extremadura, Avda. de las Ciencias, s/n, ES-10003, Cáceres, Spain c Chemometrics and Analytical Technology, Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, DK-1958, Frederiksberg C, Denmark
ARTICLE INFO
ABSTRACT
Keywords: Dry-cured ham Aflatoxins Aspergillus spp. Data mining Prediction
Dry-cured ham may be contaminated with aflatoxin B1 (AFB1) produced by Aspergillus spp. Temperature and water activity (aw) are two key parameters that affect both ham ripening and AFB1 production. The objective of this study was to predict AFB1 production by Aspergillus parasiticus and Aspergillus flavus strains in conditions related to dry-cured ham ripening using data mining techniques. J48 decision tree, isotonic regression (IR), and multiple linear regression (MLR) were tested to (a) classify and predict AFB1 concentration as a function of different days, temperatures and aw values and (b) predict the beginning of AFB1 production as a function of different temperatures and aw values. For this, a model system based on a dry-cured ham-based medium was used. The percentage of correct classification was higher than 75%. R values to predict the concentration of AFB1 when applying MLR were 0.81, being higher than those obtained after using IR. The models developed were validated with experimental data obtained after inoculating samples of dry-cured ham with two aflatoxigenic strains. The predicted AFB1 concentration showed correlation coefficients ≥0.74 and prediction errors ≤0.38, confirming the feasibility of the prediction equations obtained. This information may help to make informed decisions to minimise the hazard posed by AFB1 in dry-cured ham.
1. Introduction Dry-cured ham is a traditional meat product ripened for about 12–48 months. During this period, moulds grow on the surface of the hams (Núñez, Rodríguez, Bermúdez, Córdoba, & Asensio, 1996). Although most of these moulds positively contribute to the sensorial properties of the final product (Martín, Córdoba, Aranda, Córdoba, & Asensio, 2006); some moulds may produce mycotoxins that persist in this meat product (Peromingo, Rodríguez, Núñez, Silva, & Rodríguez, 2018; Rodríguez, Rodríguez, Martín, Núñez, & Córdoba, 2012). Aflatoxins (AFs) are frequently detected in dry-cured ham. These mycotoxins, which are mainly produced by Aspergillus flavus and Aspergillus parasiticus (Schmidt–Heydt, Abdel–Hadi, Magan, & Geisen, 2009) possess mutagenic, immunotoxic, carcinogenic, and teratogenic effects on consumers (Kotsonis, Burdock, & Flamm, 2001). Among them, aflatoxin B1 (AFB1) was classified as group 1A carcinogen (IARC, 2002) due to its harmful effects on human being health.
Both mould growth and mycotoxin production in foods are influenced by various factors such as nutrient availability, solute concentration, competition with other microorganisms, pH, time, water activity (aw) and temperature (Lozano-Ojalvo, Rodríguez, Bernáldez, Córdoba, & Rodríguez, 2013), being the two late factors the most important ones (Dantigny, Guilmart, & Bensoussan, 2005). In addition, Peromingo, Rodríguez, Bernáldez, Delgado, and Rodríguez (2016), have shown that growth and AFs production by Aspergillus spp. are closely related to the temperature and aw values during the long ripening of hams. Therefore, the evaluation of the relationship between ripening conditions and AFs production should be well-established. Besides, assessing the time when toxigenic moulds will be able to produce AFs in hams is of paramount importance to discern if the initial mould growth could suppose a hazard due to AFs accumulation. The use of mathematical models for quantifying and predicting microbial behaviour may be useful to ensure food safety (Lahlali,
Corresponding author. E-mail address: [email protected] (M. Rodríguez). URL: http://higiene.unex.es/ (M. Rodríguez). 1 These authors contributed equally to this work. ∗
https://doi.org/10.1016/j.foodcont.2019.106884 Received 17 May 2019; Received in revised form 5 September 2019; Accepted 7 September 2019 Available online 07 September 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
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Serrhini, & Jijakli, 2005). In predictive microbiology, mathematical models are used to predict the growth of different microorganisms and subsequent toxin synthesis as well as to study their response against environmental factors. An increasing number of studies is now available dealing with the modelling approach to predict mould colonization and AFs accumulation in various food products and model systems (Marín, Ramos, & Sanchis, 2012; Mousa, Ghazali, Jinap, Ghazali, & Radu, 2013; Yogendrarajah et al., 2016). But so far no predictive models were developed to assess the probability that these mycotoxins, especially AFB1, will be produced in a cured meat model system simulating the ripening of dry-cured ham. Knowledge Discovery in Databases (KDD) techniques were proposed to identify hidden information from large datasets (Fayyad, PiatetskyShapiro, & Smyth, 1996). Certainly, KDD consist of several tasks, being data mining the main step of the whole process of knowledge discovery. Use of intensive computational methods for data analysis is nowadays possible because of the reduction in the costs of storage devices, the improvement in procedures for data processing, and the increase in computing power (Mitchell, 1999). To date, data mining techniques have been applied to food mycology to modelling the influence of temperature and aw on the growth and AFs production by various Aspergillus spp. (Aldars-García, Berman, Ortiz, Ramos, & Marín, 2018; Marín et al., 2012). However, no predictive data mining model has been developed yet to determine the probability of such mycotoxins being produced by two AFs-producing mould species in a ham model system. For this study, AFB1 production data from four Aspergillus spp. strains grown in conditions similar to those achieved during the ripening process of dry-cured ham obtained by Peromingo et al. (2016) were used. The objective of this study was to predict the AFB1 production by two strains each of A. parasiticus and A. flavus in conditions that match those for dry-cured ham ripening using data mining techniques. The validity of the developed methods was tested on real samples of dry-cured ham.
the different stages of dry-cured ham ripening. All experiments were performed in three replicates. 2.3. Aflatoxin B1 analysis The methodology proposed by Peromingo et al. (2016) for extraction and quantification of AFB1 from MBA and DHA was used in this study. Isolation of AFB1 from samples started from mixing them with 5 mL of chloroform (Panreac Quimica S.L.U., Barcelona, Spain) before shaking them at 150 rpm at room temperature overnight in an orbital shaker. Next, the extracts were completely dried in the dark, redissolved in 200 μL of HPLC-grade acetonitrile (Scharlab) and filtered through a 0.45 μm pore size nylon membrane (Jet biofilm, Guangzhou, China). The uHPLC Thermo Scientific Dionex UltiMate 3000 Rapid Separation LC (RSLC) system with an autosampler thermostat (UltiMate® 3000 Rapid Separation Autosampler, Thermo Scientific, Waltham, USA) coupled to an Ion Trap Mass Spectrometer System amaZon SL (Bruker Daltonics Inc., Bremen, Germany) was used to detect and quantify AFB1. The stationary phase was a reversed-phase column C18 (100 mm × 2.1 mm, 2 μm; Agilent Technologies, USA) while the mobile phase consisted of a mix of two solvents: 0.1% formic acid-10 mM ammonium formate (A) and acetonitrile (B). Analysis was conducted in a gradient mode from 2 to 98%. MS detection of AFB1 was carried out using the precursor (313) and the quantitation (285) ions. The calibration line which relates peak area and concentration of working solutions of AFB1 displayed R2 > 0.99. The limit of detection (LOD) was 4 ppb, and the limit of quantification (LOQ) was 12 ppb. 2.4. Data mining analysis WEKA (Waikato Environment for Knowledge Analysis) was the free software used to process all the data mining of the experiments (http:// www.cs.waikato.ac.nz/ml/weka/). Descriptive and predictive techniques methods constitute the main tasks in data mining (García, 2013). Consequently, both groups of techniques were exploited in this research.
2. Materials and methods 2.1. Mould strains and inoculum preparation Four strains of AFB1 producers, two from A. flavus (CBS 573.65 and IBT 3696) and two from A. parasiticus (CECT 2681 and CECT 2688), were used in this study. The strains of A. flavus belonged to the Centraalbureau voor Schimmelcultures (CBS) fungal collection (Utrecht, The Netherlands) and the Type Culture Collection of the Department of Biotechnology from the Technical University of Denmark (IBT), whereas the two strains of A. parasiticus were from the Spanish Type Culture Collection (CECT, Valencia, Spain). To prepare the inoculum of each mould, the strains were grown on Malt Extract Agar (MEA) (Scharlab S.L., Spain) at 25 °C for 7 days. Five millilitres of phosphate-buffered saline (PBS) were spread on the agar plate containing spores and this solution was pipetted out from the agar plates and transferred to sterile Eppendorfs test tubes. The spore suspensions were quantified by using a Thoma counting chamber Blaubrand® (Brand, Wertheim, Germany) and adjusted to 106 spores/ mL.
2.4.1. Databases The data of AFB1 production of two A. flavus and two A. parasiticus strains at 10, 15, 20 and 25 °C and 0.95, 0.90 and 0.85 aw in MBA and DHA obtained by Peromingo et al. (2016) were utilised to conduct this study. Database data from no AFB1 production when no growth of moulds occurred (i.e. at 10 °C or with 2 days of incubation) were also included in the database. The parameters used were: incubation days, temperature, aw and AFB1 production. From the initial database of 1170 records with 4 attributes (4 × 1170 = 4680 values), (912 records on raw data with all the parameters analysed). Given that 258 records did not provide the AFB1 production data (1 × 258 = 258 values), these missing values were inferred by applying Multiple Linear Regression (MLR). The inferred data represent a 5.51% data from the initial database. Thus, deductive techniques of data mining allowed the deduction of the unknown values for all analysed parameters.
2.2. Preparation of culture media and incubation conditions
2.4.2. Deductive techniques The deductive tasks were carried out by means of MLR (Hastie, Tibshirani, & Friedman, 2001). The dependent variable to be calculated was always unique and numerical, and this method allows the elimination of collinear attributes. Furthermore, regression techniques are considered the most appropriate way to predict values and to forecast values, as it allows inferring numerical data from the existing numerical values. For validating this procedure 10-fold cross validation was applied, where the dataset was divided into 10 partitions of equal size. One subset was performed each time and the remaining data were used for fitting the model. The process was sequentially repeated until all
To achieve the objectives of this study, the culture media meatbased agar (MBA) and dry-cured ham-based agar (DHA) were used. Both media were made by mixing 30 g/L of lyophilised fresh pork meat (MBA) or dry-cured ham (DHA), 20 g/L Bacto agar and 1000 mL water (Peromingo et al., 2016). The aw values of these two media were adjusted to 0.95, 0.90 and 0.85, following the procedure described by Peromingo et al. (2016). The agar plates were one-point centrally inoculated with 2 μL of inoculum. The treatments were incubated at 10, 15, 20 and 25 °C for up to 12 days to simulate the conditions found at 2
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subsets were tested. The correlation coefficient (R) was used to obtain the degree of confidence and feasibility of the deductive techniques following the rules given by Colton (Colton, 1974). In this way, correlation values between 0.75 and 1 indicate very good to excellent relationship, from 0.50 to 0.75 show moderate to good relationship, from 0.25 to 0.50 indicate fair degree of relationship, while values from 0 to 0.25 mean little or no relationship.
R=
n
+
1
i xi
n 0
ix
i
(yi
y )2
(Eq 3)
1 n
n i=1
(fi
yi )2
100
(Eq 4)
where fi is the predicted value and yi is the real value. 2.5. Model validation To validate the mathematical models obtained, two of the aflatoxigenic strains evaluated in this study (A. flavus CBS 573.65 and A. parasiticus CECT 2688) were inoculated onto real samples of dry-cured ham. Samples consisted of 16 cm2-surface pieces of commercial nonsterile dry-cured ham. First, they were submerged in 70% ethanol for 1 min and sterilised with UV light for 3 h. To simulate the evolution of aw during an industrial dry-cured ham processing, the pieces were separately placed in pre-sterilised receptacles with two relative humidity values that were kept constant at 94% and 85% after vapour–liquid equilibrium with saturated potassium sulphate and potassium chloride solutions, respectively. Fifty μL of each inoculum were inoculated onto each piece and then spread with a spatula. The samples were incubated during 15 days at 20 and 25 °C. Sampling was performed in triplicate. The extraction and quantification of AFB1 were conducted as described by Peromingo et al. (2016). 2.6. Statistical analysis
(Eq 1)
Values of AFB1 production from initial and validation set were compared by using one-way analysis of variance (ANOVA) following the general linear model procedure. Statistical analyses were performed by using the SPSS package software (v. 20.0) (IBM Co., New York, U.S.A.). The statistical significance was set at p ≤ 0.05.
where 0 is the intercept, and i is the weight for each independent variable (xi ). The method used to select attributes in the linear regressions was the M5. This method is a greedy algorithm that steps throughout the attributes, removing the one with the smallest standardized coefficient. The process iterates until no improvement is observed in the estimation of the error, and then, it performs another regression. If the obtained result improves the current result, the M5 method updates the selected attributes and, the attribute is dropped. This process is repeated until no attribute is dropped anymore (Kira & Rendell, 1992). Also, a ridge value of 1.0 × 10−4 was applied. The estimation procedure applied was 10fold cross validation, where the data was divided into 10 partitions of equal size. One subset is tested each time and the remaining data are used for fitting the model. The process is sequentially repeated until all subsets have been tested. On the other hand, IR estimates ordered values for a dependent variable as a function of one of the input parameters (Borge, 1985). Only the input parameter providing the best adjustment results will be selected. Finally, an interpolation function is determined (polynomial trend line) to compare the given dataset with original values in the database, obtaining the prediction equation (Equation (2)).
y=
y )2
RMSEP (%) =
2.4.4. Predictive techniques For the prediction task, the MLR and Isotonic Regression (IR) techniques were used. MLR represents linear relationship between a dependent variable and several independent variables, obtaining a linear regression equation (Equation (1)). This equation is used to predict future values (Hastie et al., 2001). 0
(fi
where fi is the predicted value, yi is the real value and y is the average value. Another method to corroborate the prediction results is the root mean square error of prediction (RMSEP %). This method was also used in the experiments. The RMSEP measures the relative difference between real values and predicted ones, and is calculated by equation (4).
2.4.3. Classification techniques A decision tree is a decision support tools that uses a tree-like graph or model of decisions and their possible categories (Drazin & Montag, 2012). In this way, this method generates classification models in form of tree structure with decision and leaf nodes. One of the most popular options is the C4.5 decision tree (Wu et al., 2008), which performs a decision model based on the information entropy concept. In the WEKA environment, the J48 decision tree corresponds to the implementation of the C4.5 tree. In this model, the attribute that most effectively splits the set of samples into subset is chosen at each node of the tree until the set of samples is classified. The splitting criterion is the information gain (difference in entropy values). A confidence factor of 0.5 and minimum bucket size of 30 were applied, these values were widely used for these parameters in previous studies (Caballero, Bevilacqua, & Amigo, 2019; Caballero et al., 2016a; Drazin & Montag, 2012).
y=
n i=1 n i=1
3. Results and discussion Mycotoxins represent the major concern from a food safety point of view associated with the growth of a mould population on dry-cured meat products. Among them, AFs, and specifically AFB1, are regarded as one of the most important mycotoxins in dry-cured meats (Pleadin et al., 2017). Markov et al. (2013) demonstrated the presence of AFB1 in some Croatian fermented meat products. Pleadin, Kovačević, and Perković (2015) reported AFB1 contamination in dry-fermented sausages with damaged casings at the ripening end-stages. Pleadin et al. (2017) showed significant AFB1 contamination of the regional speciality dry-fermented sausage ‘slavonski kulen’ after 12 month-ripening under controlled conditions. Therefore, it would be interesting to develop methods to determine the time when the toxigenic moulds may begin to produce this mycotoxin depending on the ripening conditions of dry-cured ham. Predictive mycology allows evaluating the growth of moulds in different foods under environmental conditions related to processing or storage so that the prediction of mycotoxin formation could be also conducted (García, Ramos, Sanchis, & Marín, 2009). Many studies have focused on developing predictive models to determine the growth of filamentous fungi under various environmental conditions (Mousa et al., 2013), but only a few have built models to predict the concentration of mycotoxins (Ioannidis, Kogkaki, Natskoulis, Nychas, & Panagou, 2015; Marín et al., 2012). The difficulty of developing
(Eq 2)
where i is the weight for each independent variable ( x i ). These independent variables are powered by the degree of the polynomial trend line (i). To quantify the goodness of the prediction was used the correlation coefficient (R), which is calculated by equation (3). The rules given by Colton were used to classify the goodness of the prediction process (Colton, 1974). 3
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methods able to predict the synthesis of mycotoxins is due to the fact that the same mould species can produce more than one mycotoxin or that the same mycotoxin can be produced by various mould species (Hussein & Brasel, 2001). In addition, given that there are differences in the production of mycotoxins by individual isolates, building reliable predictive mycology models should include a number of isolates to generate enough and trustable data (Aldar-García et al., 2018). This is the reason why the design and optimisation of validated mathematical models for the prediction of the amounts of mycotoxins produced by toxigenic fungi in food should be a primary objective in the area of food safety.
cured samples containing AFs in relation to the ecological conditions found during the ripening of such product. These results indicate that the application of the J48 decision tree on AFB1 amounts can be useful to estimate the amount of this toxic fungal metabolite according to time, temperature and aw during dry-cured ham ripening. 3.3. Prediction of the concentration of AFB1 in a dry-cured ham model system Two data mining techniques (MLR and IR) were applied in this study to predict the concentration of AFB1 synthesised by A. flavus and A. parasiticus when growing in conditions similar to those common for dry-cured ham ripening. For this purpose, three independent variables were used: incubation time, temperature and aw. Table 2 shows the correlation coefficient values for the prediction equations of AFB1 when the MLR and IR techniques were applied. For MLR the R value obtained was 0.816. This value was higher than those obtained after applying the IR technique (Table 2). These results were analysed taking into consideration the rules given by Colton (1974). Therefore, the R value when MLR was applied showed very good to excellent relationship between the independent variables (time, temperature, aw) and AFB1 amounts produced by A. flavus and A. parasiticus in a dry-cured ham model system. Furthermore, Table 2 shows the prediction equations for AFB1 amounts synthesised by aflatoxigenic species when the MLR and IR techniques were applied. One equation was obtained when applying the MLR technique while three equations, one for each independent variable (aw, temperature, time), were obtained after using the IR technique. These two data mining techniques were used to estimate some quality attributes in dry-cured meat products (Caballero, Antequera, Caro, Duran, & Perez-Palacios, 2016b; Caballero et al., 2018). However, the MLR and IR techniques were not previously used to predict the concentrations of AFB1 in a dry-cured meat product. On the other hand, several studies have estimated the production of mycotoxins in foods using other mathematical models. Ioannidis et al. (2015) obtained low R values (0.548) when estimating ochratoxin A production in a grape juice-based medium applying quadratic polynomial model. García (2013) used a primary model to predict the accumulation of AFB1 by A. flavus by applying the Luedeking-Piret equation and found that the formation of toxins did not show a clear correlation with growth under certain conditions. Yogendrarajah et al. (2016) used the Rosso cardinal model and Gibson's extended model, the latter being the best model to describe the combined effect of aw and temperature on the growth rate of A. flavus and A. parasiticus on peppercorns. However, the high variability of the mycotoxin produced in this food restricted the accuracy of this prediction. According to Aldar-García et al. (2018) predictive mycology studies should include more than one strain due to the high variability in the amounts of AFB1 produced by different isolates. For this, two strains each of common aflatoxigenic species (A. flavus and A. parasiticus) were inoculated on meat products to develop the prediction equations in this research. One of the most interesting aspects of data mining techniques is that they may be used to predict the moment of the beginning of AFB1 production. Therefore, these techniques may be utilised to develop equations for the prevention of AFB1 accumulation in meat products. For this purpose, only the MLR technique was applied, since it was necessary to use aw and temperature as independent variables. The R value obtained to predict the beginning of AFB1 production was 0.88. Table 3 shows the equation obtained to predict the beginning of AFB1 synthesis under experimental conditions. This equation is quite important since the knowledge of an approximate time in which a toxigenic fungus may begin to produce AFB1 at any given combination of aw and temperature allows making decisions to adjust the technological parameters of the ripening of dry-cured ham to avoid mycotoxin accumulation and the risks related to its presence.
3.1. Data mining for deduction of aflatoxin B1 quantities In order to obtain the best as well as robust results to predict the quantities of AFB1 in dry-cured ham, two AFB1-producing mould species were used. For this, deductive techniques of data mining were initially utilised to build a database from the data obtained by Peromingo et al. (2016). From this study, data were obtained from the amounts of AFB1 synthesised by the two strains each of A. flavus and A. parasiticus at 25 °C and 0.95, 0.90 and 0.85 aw in DHA and MBA taken every two days. However, samples incubated at 10, 15 and 20 °C at the three aw values evaluated were only taken at the end of the incubation period. Considering these data, there were some gaps in the records of the database, that is, unknown information for the attribute AFB1. Deductive techniques were used to estimate the AFB1 production in those days without sampling, which allow filling these gaps by including estimated values. From a total of 1170 records, only 258 lacked information on AFB1 production, and these gaps were inferred by applying MLR. The correlation between experimental and predicted data for AFB1 produced by Aspergillus spp. at different temperature and aw in drycured meat model systems showed high correlations (R > 0.99) for both parameters (data not shown). These data inferred were included in the successive analyses. 3.2. Classification for the aflatoxin B1 production Table 1 shows the percentage of correctly classified by applying the J48 decision tree on the AFB1 production at different days of incubation, considering temperature and aw. As shown on Table 1, high percentages of correct classification were achieved (76.6–95.9%). Fig. 1 shows the J48 decision tree for classifying synthesis of AFB1 as a function of temperature, incubation time and aw. In this figure, there are five different intervals to classify the samples, lower than the limit of detection (< LOD) (white colour), lower than 50 ng/g (light grey colour), between 50 and 200 ng/g (grey colour), between 200 and 500 ng/g (dark grey colour) and higher than 500 ng/g (black colour). The J48 decision tree achieves better accuracy and efficiency than other decision tree algorithms (Priyam, Abhijeeta, Rathee, & Srivastava, 2013). This technique was used to classify samples of dry-cured meat products as a function of salting stage (Caballero et al., 2016a). However, this is the first study where data mining is used to classify dryTable 1 Percentage of correct classification obtained by applying the J48 decision tree to classify aflatoxin B1 (AFB1) production at different days of incubation. Days
Correct classifications of AFB1
Total 2 4 6 8 10 12
85.13 95.89 88.57 81.97 85.25 80.33 76.56
4
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Fig. 1. J48 decision tree for classifying aflatoxin B1 (AFB1) production at different time, temperature and water activity (aw) during the ripening of dry-cured ham. The grey scale shows the concentration of AFB1; the darker the shade of grey, the greater the amount of AFB1.
Table 2 Equations to predict aflatoxin B1 amounts (ng/g) produced by Aspergillus spp. in dry-cured ham obtained by multiple linear regression (MLR) and isotonic regression (IR). Prediction technique
Independent variables
Correlation coefficient (R)
Prediction equation
MLR IR
Time, aw, temperature (°C) Time Temperature (°C)
0.816 0.697 0.687
aw
0.671
25.5114*days + 23.3995*temperature + 1789.1566*aw – 2196.9142 0.0145*day4 – 0.6655*day3 + 8.9571*day2 – 11.931*day + 0.0876 0.0003*temperature6 – 0.0201*temperature5 + 0.5893*temperature4 – 8.265*temperature3 + 55.571*temperature2 – 144.01*temperature + 0.0021 2957.6*aw4 – 4158.1*aw3 + 1729.4*aw2 – 207.59*aw + 1.7406
3.4. Validation of the predictive models
those redicted by the prediction equation. Whereas AFB1 was detected in DHA in two days of incubation at both temperatures (20 and 25 °C) and aw values (0.85 and 0.94), no AFB1 was obtained in dry-cured ham kept at 25 °C for 4 days (Table 4). This may be due to differences in the matrix used in each experiment. The composition of the substrate as well as the environmental conditions related to the processing of drycured meat products have a strong impact on the growth of aflatoxigenic aspergilli and the synthesis of AFs. On the other hand, the RMSEP ranged from 0.074% to 0.385% for all the models for Aspergillus spp. at different time, temperatures and aw. RMSEP values lower than 5% are regarded as appropriate (Hartemink & Minasny, 2016). The mathematical models showed higher standard deviations for the observed production of AFB1 which is also observed in its high RMSEP (0.385%). However, it still adequately fitted the experimental data. In the same way, the values of RMSEP were better (0.074%–0.188%) when the data for the prediction equation were
The developed models were validated with independent experimental data obtained from dry-cured ham. One strain each of A. flavus and A. parasiticus, were inoculated onto dry-cured ham pieces and incubated at conditions simulating ham ripening. Samples were taken in the middle and at the end of the incubation period. In order to corroborate the accuracy of the prediction equations obtained to estimate AFB1 production, R and RMSEP values were calculated from AFB1 concentrations determined in the inoculated drycured ham and those predicted by the prediction equations (Table 4). All the predicted mycotoxin concentrations showed correlation coefficients higher than 0.74 and prediction errors lower than 0.38, indicating the validity of the prediction. In our validation model, at any given combination of temperature, aw and time the concentrations obtained from the dry-cured ham samples were in general lower than
Table 3 Prediction equations to determine the time (days) when aflatoxin B1 (AFB1) produced by strains of Aspergillus spp. begins to be detected in dry-cured ham using a multiple linear regression (MLR). Prediction technique
Independent variables
Prediction equation
MLR
aw, temperature (°C)
- 0.9697*temperature – 30.1347*aw + 53.4398
5
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Table 4 Determined and predicted aflatoxin B1 (AFB1) concentration produced by Aspergillus flavus and A. parasiticus at different time, temperatures and aw values. Correlation coefficient (R) and Root Mean Square Error of Prediction (RMSEP) between values obtained in pieces of dry-cured ham and when applying the prediction equation for AFB1. Time (Days)
Temperature (°C)
aw
AFB1 determined in ham (ng/g ± SD)
Predicted AFB1 value (ng/g ± SD)
R
p
RMSEP (%)
4 4 8 8 4 4 11 11
20 20 20 20 25 25 25 25
0.85 0.94 0.85 0.94 0.85 0.94 0.85 0.94
< LOD 20.39 ± 8.98 < LOD 27.00 ± 16.05 28.01 ± 16.77 63.44 ± 30.71 215.48 ± 63.46 303.79 ± 276.42
< LOD 54.93 ± 0.01 < LOD 156.97 ± 0.01 10.90 ± 0.01 171.93 ± 0.01 189.48 ± 0.01 350.51 ± 0.01
– 0.816 – 0.842 0.834 0.878 0.812 0.744
– 0.333 – 0.193 0.850 0.204 0.457 0.198
– 0.314 – 0.385 0.188 0.074 0.076 0.134
LOD: Limit of detection.
obtained at 25 °C and both aw values. A study carried out to predict the growth of A. flavus in pistachio nuts using different kinetic models reached higher RMSEP values between 0.617% and 1.037% (Marín et al., 2012). Moreover, in case of to corroborate the accuracy of the values of the beginning of AFB1 synthesis in dry-cured ham samples of the validation set, R values higher than 0.75 and RMSEP lower than 0.200% were achieved for all cases.
Colton, T. (1974). Statistics in medicine. New York, New York, U.S.A.: Little Brown Co. Dantigny, P., Guilmart, A., & Bensoussan, M. (2005). Basis of predictive mycology. International Journal of Food Microbiology, 100, 187–196. https://doi.org/10.1016/j. ijfoodmicro.2004.10.013. Drazin, S., & Montag, M. (2012). Decision tree analysis using WEKA. Machine-Learning Project. Miami, Florida. U.S.A: University of Miami. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. Al Magazine, 17, 37–54. García, A. B. (2013). The use of data mining techniques to discover knowledge from animal and food data: Examples related to the cattle industry. Trends in Food Science & Technology, 29, 151–157. https://doi.org/10.1016/j.tifs.2012.10.004. García, D., Ramos, A. J., Sanchis, V., & Marín, S. (2009). Predicting mycotoxins in foods: A review. Food Microbiology, 26, 757–769. https://doi.org/10.1016/j.fm.2009.05. 014. Hartemink, A. E., & Minasny, B. (2016). Progress in soil science. Digital soil morphometrics. St. Gallen, Switzerland: Springer-Verlag. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining. Inference and prediction. New York, New York, U.S.A.: Springer-Verlag. Hussein, H. S., & Brasel, J. M. (2001). Toxicity, metabolism, and impact of mycotoxins on humans and animals. Toxicology, 167, 101–134. https://doi.org/10.1016/S0300483X(01)00471-1. IARC. (2002). Aflatoxins. Monographs on the evaluation of cacinogenic risks to human. IARC. International Agency for Research on Cancer, 82, 171–285 Lyon, France. Ioannidis, A. G., Kogkaki, E. A., Natskoulis, P. I., Nychas, G. J. E., & Panagou, E. Z. (2015). Modelling the influence of temperature, water activity and sodium metabisuphite on the growth and OTA production of Aspergillus carbonatius isolated from Greek wine grapes. Food Microbiology, 49, 12–22. https://doi.org/10.1016/j.fm.2015.01.006. Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. IX international conference on machine learning. United Kingdom: Aberdeen. Kotsonis, F. N., Burdock, G. A., & Flamm, W. G. (2001). Food toxicology. In C. D. Klaassen (Ed.). Casarett and doull's toxicology: The basic science of poisons (pp. 1049–1088). (6° edition). New York, U.S.A.: McGraw-Hill. Lahlali, R., Serrhini, M. N., & Jijakli, M. H. (2005). Studying and modelling the combined effect of temperature and water activity on the growth rate of Penicillium expansum. International Journal of Food Microbiology, 103, 315–322. https://doi.org/10.1016/j. ijfoodmicro.2005.02.002. Lozano-Ojalvo, D., Rodríguez, A., Bernáldez, V., Córdoba, J. J., & Rodríguez, M. (2013). Influence of temperature and substrate conditions on the omt-1 gene expression of Aspergillus parasiticus in relation to its aflatoxin production. International Journal of Food Microbiology, 166, 263–269. https://doi.org/10.1016/j.ijfoodmicro.2013.07. 011. Marín, S., Ramos, A. J., & Sanchis, V. (2012). Modelling Aspergillus flavus growth and aflatoxins production in pistachio nuts. Food Microbiology, 32, 378–388. https://doi. org/10.1016/j.fm.2012.07.018. Markov, K., Pleadin, J., Bevardi, M., Vahčić, N., Sokolić-Mihalak, D., & Frece, J. (2013). Natural occurrence of aflatoxin B1, ochratoxin A and citrinin in Croatian fermented meat products. Food Control, 34, 312–317. https://doi.org/10.1016/j.foodcont.2013. 05.002. Martín, A., Córdoba, J. J., Aranda, E., Córdoba, M. G., & Asensio, M. A. (2006). Contribution of a selected fungal population to the volatile compounds on dry–cured ham. International Journal of Food Microbiology, 110, 8–18. https://doi.org/10.1016/j. ijfoodmicro.2006.01.031. Mitchell, T. M. (1999). Machine learning and data mining. Communications of the ACM, 42, 30–36. https://doi.org/10.1145/2524713.2524722. Mousa, W., Ghazali, F. M., Jinap, S., Ghazali, H. M., & Radu, S. (2013). Modeling growth rate and assessing aflatoxins production by Aspergillus flavus as a function of water activity and temperature on polished and brown rice. Journal of Food Science, 78, 56–63. https://doi.org/10.1111/j.1750-3841.2012.02986.x. Núñez, F., Rodríguez, M. M., Bermúdez, E., Córdoba, J. J., & Asensio, M. A. (1996). Composition and toxigenic potential of the mould population on dry–cured Iberian ham. International Journal of Food Microbiology, 32, 185–197. https://doi.org/10. 4315/0362-028X-63.2.231. Peromingo, B., Rodríguez, A., Bernáldez, V., Delgado, J., & Rodríguez, M. (2016). Effect of temperature and water activity on growth and aflatoxin production by Aspergillus flavus and Aspergillus parasiticus on cured meat model systems. Meat Science, 122, 76–83. https://doi.org/10.1016/j.meatsci.2016.07.024.
4. Conclusion The results revealed that data mining may be used as a predictive tool to assess the AFB1 risk in dry-cured ham and the ripening time when the production of this mycotoxin begins in dry-cured ham. These findings can be used to propose control strategies to minimise mycotoxin contamination within the HACCP framework of dry-cured ham production. The data mining techniques, prediction equations and decision tree, described in this study could be used as reliable and accurate indicators of AFB1 contamination of dry-cured ham. In this way, identification of critical times affecting AFB1 production during ham ripening may allow monitoring related risks in relation to temperature and aw changes. This information may help to make informed decisions in an effort to mitigate the hazard related to aflatoxin in dry-cured ham. Acknowledgements This work has been funded by the Spanish Ministry of Economy and Competitiveness, “Junta de Extremadura” and FEDER (AGL201345729-P, AGL2016-80209-P, GR15108 and IB16089). B. Peromingo is recipient of a pre-doctoral fellowship (BES-2014-069484) from the Spanish Ministry of Economy and Competitiveness. D. Caballero thanks the “Junta de Extremadura” for the post-doctoral grant (PO17017). References Aldars-García, L., Berman, M., Ortiz, J., Ramos, A. J., & Marín, S. (2018). Probability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus. Food Microbiology, 72, 166–175. https://doi.org/10.1016/j.fm. 2017.11.015. Borge, L. (1985). Estimation and contrasts of hypothesis in the general linear model with inequality restrictionsDoctoral Thesis. Valladolid, Spain: University of Valladolid. Caballero, D., Antequera, T., Caro, A., Amigo, J. M., ErsbØll, B. K., Dahl, A. B., et al. (2018). Analysis of MRI by fractals for prediction of sensory attributes: A case study in loin. Journal of Food Engineering, 227, 1–10. https://doi.org/10.1016/j.jfoodeng. 2018.02.005. Caballero, D., Antequera, T., Caro, A., Duran, M. L., & Pérez-Palacios, T. (2016b). Data mining on MRI-Computational texture features to predict sensory characteristics in ham. Food and Bioprocess Technology, 9, 699–708. https://doi.org/10.1007/s11947015-1662-1. Caballero, D., Bevilacqua, M., & Amigo, J. M. (2019). Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame-retardants. Journal of Spectral Imaging, 8, a1. https://doi.org/10.1255/jsi.2019.a1. Caballero, D., Caro, A., Rodríguez, P. G., Durán, M. L., Ávila, M. M., Palacios, R., et al. (2016a). Modeling salt diffusion in Iberian ham by applying MRI and data mining. Journal of Food Engineering, 189, 115–122. https://doi.org/10.1016/j.jfoodeng.2016. 06.003.
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B. Peromingo, et al. Peromingo, B., Rodríguez, M., Núñez, F., Silva, A., & Rodríguez, A. (2018). Sensitive determination of cyclopiazonic acid in dry-cured ham using a QuEChERS method and UHPLC–MS/MS. Food Chemistry, 263, 275–282. https://doi.org/10.1016/j. foodchem.2018.04.126. Pleadin, J., Kovačević, D., & Perković, I. (2015). Impact of casing damaging on aflatoxin B1 concentration during the ripening of dry-fermented meat sausages. Journal of Immunoassay and Immunochemistry, 36, 655–666. https://doi.org/10.1080/ 15321819.2015.1032306. Pleadin, J., Zadravec, M., Brnic, D., Perkovic, I., Skrivanko, M., & Kovacevic, D. (2017). Moulds and mycotoxins detected in the regional speciality fermented sausage ‘slavonski kulen’ during a 1–year production period. Food Additives & Contaminants Part A: Chemistry, Analysis, Control, Exposure and Risk Assessment, 34, 282–290. https:// doi.org/10.1080/19440049.2016.1266395. Priyam, A., Abhijeeta, G. R., Rathee, A., & Srivastava, S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering Technology, 3, 334–337.
Rodríguez, A., Rodríguez, M., Martín, A., Núñez, F., & Córdoba, J. J. (2012). Evaluation of hazard of aflatoxin B1, ochratoxin A and patulin production in dry–cured ham and early detection of producing moulds by qPCR. Food Control, 27, 118–126. https://doi. org/10.1016/j.foodcont.2012.03.009. Schmidt–Heydt, M., Abdel–Hadi, A. M., Magan, N., & Geisen, R. (2009). Complex regulation of the aflatoxin biosynthesis gene cluster of Aspergillus flavus in relation to various combinations of water activity and temperature. International Journal of Food Microbiology, 135, 231–237. https://doi.org/10.1016/j.ijfoodmicro.2009.07.026. Wu, X., Kumar, V., Ross-Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1–37. https://doi. org/10.1007/s10115-007-0114-2. Yogendrarajah, P., Vermeulen, A., Jacxsens, L., Mavromichali, E., De Saeger, S., De Meulenaer, B., et al. (2016). Mycotoxin production and predictive modelling kinetics on the growth of Aspergillus flavus and Aspergillus parasiticus isolates in whole black peppercorns (Piper nigrum L.). International Journal of Food Microbiology, 228, 44–57. https://doi.org/10.1016/j.ijfoodmicro.2016.03.015.
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Application of data mining techniques to predict the production of aflatoxin B1 in dry-cured ham
T
Belén Peromingoa,1, Daniel Caballerob,c,1, Alicia Rodrígueza, Andrés Carob, Mar Rodrígueza,∗ a
Food Hygiene and Safety, Meat and Meat Products Research Institute. Faculty of Veterinary Science, University of Extremadura, Avda. de las Ciencias, s/n, ES-10003, Cáceres, Spain b Department of Computer Science, Meat and Meat Products Research Institute, University of Extremadura, Avda. de las Ciencias, s/n, ES-10003, Cáceres, Spain c Chemometrics and Analytical Technology, Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, DK-1958, Frederiksberg C, Denmark
ARTICLE INFO
ABSTRACT
Keywords: Dry-cured ham Aflatoxins Aspergillus spp. Data mining Prediction
Dry-cured ham may be contaminated with aflatoxin B1 (AFB1) produced by Aspergillus spp. Temperature and water activity (aw) are two key parameters that affect both ham ripening and AFB1 production. The objective of this study was to predict AFB1 production by Aspergillus parasiticus and Aspergillus flavus strains in conditions related to dry-cured ham ripening using data mining techniques. J48 decision tree, isotonic regression (IR), and multiple linear regression (MLR) were tested to (a) classify and predict AFB1 concentration as a function of different days, temperatures and aw values and (b) predict the beginning of AFB1 production as a function of different temperatures and aw values. For this, a model system based on a dry-cured ham-based medium was used. The percentage of correct classification was higher than 75%. R values to predict the concentration of AFB1 when applying MLR were 0.81, being higher than those obtained after using IR. The models developed were validated with experimental data obtained after inoculating samples of dry-cured ham with two aflatoxigenic strains. The predicted AFB1 concentration showed correlation coefficients ≥0.74 and prediction errors ≤0.38, confirming the feasibility of the prediction equations obtained. This information may help to make informed decisions to minimise the hazard posed by AFB1 in dry-cured ham.
1. Introduction Dry-cured ham is a traditional meat product ripened for about 12–48 months. During this period, moulds grow on the surface of the hams (Núñez, Rodríguez, Bermúdez, Córdoba, & Asensio, 1996). Although most of these moulds positively contribute to the sensorial properties of the final product (Martín, Córdoba, Aranda, Córdoba, & Asensio, 2006); some moulds may produce mycotoxins that persist in this meat product (Peromingo, Rodríguez, Núñez, Silva, & Rodríguez, 2018; Rodríguez, Rodríguez, Martín, Núñez, & Córdoba, 2012). Aflatoxins (AFs) are frequently detected in dry-cured ham. These mycotoxins, which are mainly produced by Aspergillus flavus and Aspergillus parasiticus (Schmidt–Heydt, Abdel–Hadi, Magan, & Geisen, 2009) possess mutagenic, immunotoxic, carcinogenic, and teratogenic effects on consumers (Kotsonis, Burdock, & Flamm, 2001). Among them, aflatoxin B1 (AFB1) was classified as group 1A carcinogen (IARC, 2002) due to its harmful effects on human being health.
Both mould growth and mycotoxin production in foods are influenced by various factors such as nutrient availability, solute concentration, competition with other microorganisms, pH, time, water activity (aw) and temperature (Lozano-Ojalvo, Rodríguez, Bernáldez, Córdoba, & Rodríguez, 2013), being the two late factors the most important ones (Dantigny, Guilmart, & Bensoussan, 2005). In addition, Peromingo, Rodríguez, Bernáldez, Delgado, and Rodríguez (2016), have shown that growth and AFs production by Aspergillus spp. are closely related to the temperature and aw values during the long ripening of hams. Therefore, the evaluation of the relationship between ripening conditions and AFs production should be well-established. Besides, assessing the time when toxigenic moulds will be able to produce AFs in hams is of paramount importance to discern if the initial mould growth could suppose a hazard due to AFs accumulation. The use of mathematical models for quantifying and predicting microbial behaviour may be useful to ensure food safety (Lahlali,
Corresponding author. E-mail address: [email protected] (M. Rodríguez). URL: http://higiene.unex.es/ (M. Rodríguez). 1 These authors contributed equally to this work. ∗
https://doi.org/10.1016/j.foodcont.2019.106884 Received 17 May 2019; Received in revised form 5 September 2019; Accepted 7 September 2019 Available online 07 September 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
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Serrhini, & Jijakli, 2005). In predictive microbiology, mathematical models are used to predict the growth of different microorganisms and subsequent toxin synthesis as well as to study their response against environmental factors. An increasing number of studies is now available dealing with the modelling approach to predict mould colonization and AFs accumulation in various food products and model systems (Marín, Ramos, & Sanchis, 2012; Mousa, Ghazali, Jinap, Ghazali, & Radu, 2013; Yogendrarajah et al., 2016). But so far no predictive models were developed to assess the probability that these mycotoxins, especially AFB1, will be produced in a cured meat model system simulating the ripening of dry-cured ham. Knowledge Discovery in Databases (KDD) techniques were proposed to identify hidden information from large datasets (Fayyad, PiatetskyShapiro, & Smyth, 1996). Certainly, KDD consist of several tasks, being data mining the main step of the whole process of knowledge discovery. Use of intensive computational methods for data analysis is nowadays possible because of the reduction in the costs of storage devices, the improvement in procedures for data processing, and the increase in computing power (Mitchell, 1999). To date, data mining techniques have been applied to food mycology to modelling the influence of temperature and aw on the growth and AFs production by various Aspergillus spp. (Aldars-García, Berman, Ortiz, Ramos, & Marín, 2018; Marín et al., 2012). However, no predictive data mining model has been developed yet to determine the probability of such mycotoxins being produced by two AFs-producing mould species in a ham model system. For this study, AFB1 production data from four Aspergillus spp. strains grown in conditions similar to those achieved during the ripening process of dry-cured ham obtained by Peromingo et al. (2016) were used. The objective of this study was to predict the AFB1 production by two strains each of A. parasiticus and A. flavus in conditions that match those for dry-cured ham ripening using data mining techniques. The validity of the developed methods was tested on real samples of dry-cured ham.
the different stages of dry-cured ham ripening. All experiments were performed in three replicates. 2.3. Aflatoxin B1 analysis The methodology proposed by Peromingo et al. (2016) for extraction and quantification of AFB1 from MBA and DHA was used in this study. Isolation of AFB1 from samples started from mixing them with 5 mL of chloroform (Panreac Quimica S.L.U., Barcelona, Spain) before shaking them at 150 rpm at room temperature overnight in an orbital shaker. Next, the extracts were completely dried in the dark, redissolved in 200 μL of HPLC-grade acetonitrile (Scharlab) and filtered through a 0.45 μm pore size nylon membrane (Jet biofilm, Guangzhou, China). The uHPLC Thermo Scientific Dionex UltiMate 3000 Rapid Separation LC (RSLC) system with an autosampler thermostat (UltiMate® 3000 Rapid Separation Autosampler, Thermo Scientific, Waltham, USA) coupled to an Ion Trap Mass Spectrometer System amaZon SL (Bruker Daltonics Inc., Bremen, Germany) was used to detect and quantify AFB1. The stationary phase was a reversed-phase column C18 (100 mm × 2.1 mm, 2 μm; Agilent Technologies, USA) while the mobile phase consisted of a mix of two solvents: 0.1% formic acid-10 mM ammonium formate (A) and acetonitrile (B). Analysis was conducted in a gradient mode from 2 to 98%. MS detection of AFB1 was carried out using the precursor (313) and the quantitation (285) ions. The calibration line which relates peak area and concentration of working solutions of AFB1 displayed R2 > 0.99. The limit of detection (LOD) was 4 ppb, and the limit of quantification (LOQ) was 12 ppb. 2.4. Data mining analysis WEKA (Waikato Environment for Knowledge Analysis) was the free software used to process all the data mining of the experiments (http:// www.cs.waikato.ac.nz/ml/weka/). Descriptive and predictive techniques methods constitute the main tasks in data mining (García, 2013). Consequently, both groups of techniques were exploited in this research.
2. Materials and methods 2.1. Mould strains and inoculum preparation Four strains of AFB1 producers, two from A. flavus (CBS 573.65 and IBT 3696) and two from A. parasiticus (CECT 2681 and CECT 2688), were used in this study. The strains of A. flavus belonged to the Centraalbureau voor Schimmelcultures (CBS) fungal collection (Utrecht, The Netherlands) and the Type Culture Collection of the Department of Biotechnology from the Technical University of Denmark (IBT), whereas the two strains of A. parasiticus were from the Spanish Type Culture Collection (CECT, Valencia, Spain). To prepare the inoculum of each mould, the strains were grown on Malt Extract Agar (MEA) (Scharlab S.L., Spain) at 25 °C for 7 days. Five millilitres of phosphate-buffered saline (PBS) were spread on the agar plate containing spores and this solution was pipetted out from the agar plates and transferred to sterile Eppendorfs test tubes. The spore suspensions were quantified by using a Thoma counting chamber Blaubrand® (Brand, Wertheim, Germany) and adjusted to 106 spores/ mL.
2.4.1. Databases The data of AFB1 production of two A. flavus and two A. parasiticus strains at 10, 15, 20 and 25 °C and 0.95, 0.90 and 0.85 aw in MBA and DHA obtained by Peromingo et al. (2016) were utilised to conduct this study. Database data from no AFB1 production when no growth of moulds occurred (i.e. at 10 °C or with 2 days of incubation) were also included in the database. The parameters used were: incubation days, temperature, aw and AFB1 production. From the initial database of 1170 records with 4 attributes (4 × 1170 = 4680 values), (912 records on raw data with all the parameters analysed). Given that 258 records did not provide the AFB1 production data (1 × 258 = 258 values), these missing values were inferred by applying Multiple Linear Regression (MLR). The inferred data represent a 5.51% data from the initial database. Thus, deductive techniques of data mining allowed the deduction of the unknown values for all analysed parameters.
2.2. Preparation of culture media and incubation conditions
2.4.2. Deductive techniques The deductive tasks were carried out by means of MLR (Hastie, Tibshirani, & Friedman, 2001). The dependent variable to be calculated was always unique and numerical, and this method allows the elimination of collinear attributes. Furthermore, regression techniques are considered the most appropriate way to predict values and to forecast values, as it allows inferring numerical data from the existing numerical values. For validating this procedure 10-fold cross validation was applied, where the dataset was divided into 10 partitions of equal size. One subset was performed each time and the remaining data were used for fitting the model. The process was sequentially repeated until all
To achieve the objectives of this study, the culture media meatbased agar (MBA) and dry-cured ham-based agar (DHA) were used. Both media were made by mixing 30 g/L of lyophilised fresh pork meat (MBA) or dry-cured ham (DHA), 20 g/L Bacto agar and 1000 mL water (Peromingo et al., 2016). The aw values of these two media were adjusted to 0.95, 0.90 and 0.85, following the procedure described by Peromingo et al. (2016). The agar plates were one-point centrally inoculated with 2 μL of inoculum. The treatments were incubated at 10, 15, 20 and 25 °C for up to 12 days to simulate the conditions found at 2
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subsets were tested. The correlation coefficient (R) was used to obtain the degree of confidence and feasibility of the deductive techniques following the rules given by Colton (Colton, 1974). In this way, correlation values between 0.75 and 1 indicate very good to excellent relationship, from 0.50 to 0.75 show moderate to good relationship, from 0.25 to 0.50 indicate fair degree of relationship, while values from 0 to 0.25 mean little or no relationship.
R=
n
+
1
i xi
n 0
ix
i
(yi
y )2
(Eq 3)
1 n
n i=1
(fi
yi )2
100
(Eq 4)
where fi is the predicted value and yi is the real value. 2.5. Model validation To validate the mathematical models obtained, two of the aflatoxigenic strains evaluated in this study (A. flavus CBS 573.65 and A. parasiticus CECT 2688) were inoculated onto real samples of dry-cured ham. Samples consisted of 16 cm2-surface pieces of commercial nonsterile dry-cured ham. First, they were submerged in 70% ethanol for 1 min and sterilised with UV light for 3 h. To simulate the evolution of aw during an industrial dry-cured ham processing, the pieces were separately placed in pre-sterilised receptacles with two relative humidity values that were kept constant at 94% and 85% after vapour–liquid equilibrium with saturated potassium sulphate and potassium chloride solutions, respectively. Fifty μL of each inoculum were inoculated onto each piece and then spread with a spatula. The samples were incubated during 15 days at 20 and 25 °C. Sampling was performed in triplicate. The extraction and quantification of AFB1 were conducted as described by Peromingo et al. (2016). 2.6. Statistical analysis
(Eq 1)
Values of AFB1 production from initial and validation set were compared by using one-way analysis of variance (ANOVA) following the general linear model procedure. Statistical analyses were performed by using the SPSS package software (v. 20.0) (IBM Co., New York, U.S.A.). The statistical significance was set at p ≤ 0.05.
where 0 is the intercept, and i is the weight for each independent variable (xi ). The method used to select attributes in the linear regressions was the M5. This method is a greedy algorithm that steps throughout the attributes, removing the one with the smallest standardized coefficient. The process iterates until no improvement is observed in the estimation of the error, and then, it performs another regression. If the obtained result improves the current result, the M5 method updates the selected attributes and, the attribute is dropped. This process is repeated until no attribute is dropped anymore (Kira & Rendell, 1992). Also, a ridge value of 1.0 × 10−4 was applied. The estimation procedure applied was 10fold cross validation, where the data was divided into 10 partitions of equal size. One subset is tested each time and the remaining data are used for fitting the model. The process is sequentially repeated until all subsets have been tested. On the other hand, IR estimates ordered values for a dependent variable as a function of one of the input parameters (Borge, 1985). Only the input parameter providing the best adjustment results will be selected. Finally, an interpolation function is determined (polynomial trend line) to compare the given dataset with original values in the database, obtaining the prediction equation (Equation (2)).
y=
y )2
RMSEP (%) =
2.4.4. Predictive techniques For the prediction task, the MLR and Isotonic Regression (IR) techniques were used. MLR represents linear relationship between a dependent variable and several independent variables, obtaining a linear regression equation (Equation (1)). This equation is used to predict future values (Hastie et al., 2001). 0
(fi
where fi is the predicted value, yi is the real value and y is the average value. Another method to corroborate the prediction results is the root mean square error of prediction (RMSEP %). This method was also used in the experiments. The RMSEP measures the relative difference between real values and predicted ones, and is calculated by equation (4).
2.4.3. Classification techniques A decision tree is a decision support tools that uses a tree-like graph or model of decisions and their possible categories (Drazin & Montag, 2012). In this way, this method generates classification models in form of tree structure with decision and leaf nodes. One of the most popular options is the C4.5 decision tree (Wu et al., 2008), which performs a decision model based on the information entropy concept. In the WEKA environment, the J48 decision tree corresponds to the implementation of the C4.5 tree. In this model, the attribute that most effectively splits the set of samples into subset is chosen at each node of the tree until the set of samples is classified. The splitting criterion is the information gain (difference in entropy values). A confidence factor of 0.5 and minimum bucket size of 30 were applied, these values were widely used for these parameters in previous studies (Caballero, Bevilacqua, & Amigo, 2019; Caballero et al., 2016a; Drazin & Montag, 2012).
y=
n i=1 n i=1
3. Results and discussion Mycotoxins represent the major concern from a food safety point of view associated with the growth of a mould population on dry-cured meat products. Among them, AFs, and specifically AFB1, are regarded as one of the most important mycotoxins in dry-cured meats (Pleadin et al., 2017). Markov et al. (2013) demonstrated the presence of AFB1 in some Croatian fermented meat products. Pleadin, Kovačević, and Perković (2015) reported AFB1 contamination in dry-fermented sausages with damaged casings at the ripening end-stages. Pleadin et al. (2017) showed significant AFB1 contamination of the regional speciality dry-fermented sausage ‘slavonski kulen’ after 12 month-ripening under controlled conditions. Therefore, it would be interesting to develop methods to determine the time when the toxigenic moulds may begin to produce this mycotoxin depending on the ripening conditions of dry-cured ham. Predictive mycology allows evaluating the growth of moulds in different foods under environmental conditions related to processing or storage so that the prediction of mycotoxin formation could be also conducted (García, Ramos, Sanchis, & Marín, 2009). Many studies have focused on developing predictive models to determine the growth of filamentous fungi under various environmental conditions (Mousa et al., 2013), but only a few have built models to predict the concentration of mycotoxins (Ioannidis, Kogkaki, Natskoulis, Nychas, & Panagou, 2015; Marín et al., 2012). The difficulty of developing
(Eq 2)
where i is the weight for each independent variable ( x i ). These independent variables are powered by the degree of the polynomial trend line (i). To quantify the goodness of the prediction was used the correlation coefficient (R), which is calculated by equation (3). The rules given by Colton were used to classify the goodness of the prediction process (Colton, 1974). 3
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methods able to predict the synthesis of mycotoxins is due to the fact that the same mould species can produce more than one mycotoxin or that the same mycotoxin can be produced by various mould species (Hussein & Brasel, 2001). In addition, given that there are differences in the production of mycotoxins by individual isolates, building reliable predictive mycology models should include a number of isolates to generate enough and trustable data (Aldar-García et al., 2018). This is the reason why the design and optimisation of validated mathematical models for the prediction of the amounts of mycotoxins produced by toxigenic fungi in food should be a primary objective in the area of food safety.
cured samples containing AFs in relation to the ecological conditions found during the ripening of such product. These results indicate that the application of the J48 decision tree on AFB1 amounts can be useful to estimate the amount of this toxic fungal metabolite according to time, temperature and aw during dry-cured ham ripening. 3.3. Prediction of the concentration of AFB1 in a dry-cured ham model system Two data mining techniques (MLR and IR) were applied in this study to predict the concentration of AFB1 synthesised by A. flavus and A. parasiticus when growing in conditions similar to those common for dry-cured ham ripening. For this purpose, three independent variables were used: incubation time, temperature and aw. Table 2 shows the correlation coefficient values for the prediction equations of AFB1 when the MLR and IR techniques were applied. For MLR the R value obtained was 0.816. This value was higher than those obtained after applying the IR technique (Table 2). These results were analysed taking into consideration the rules given by Colton (1974). Therefore, the R value when MLR was applied showed very good to excellent relationship between the independent variables (time, temperature, aw) and AFB1 amounts produced by A. flavus and A. parasiticus in a dry-cured ham model system. Furthermore, Table 2 shows the prediction equations for AFB1 amounts synthesised by aflatoxigenic species when the MLR and IR techniques were applied. One equation was obtained when applying the MLR technique while three equations, one for each independent variable (aw, temperature, time), were obtained after using the IR technique. These two data mining techniques were used to estimate some quality attributes in dry-cured meat products (Caballero, Antequera, Caro, Duran, & Perez-Palacios, 2016b; Caballero et al., 2018). However, the MLR and IR techniques were not previously used to predict the concentrations of AFB1 in a dry-cured meat product. On the other hand, several studies have estimated the production of mycotoxins in foods using other mathematical models. Ioannidis et al. (2015) obtained low R values (0.548) when estimating ochratoxin A production in a grape juice-based medium applying quadratic polynomial model. García (2013) used a primary model to predict the accumulation of AFB1 by A. flavus by applying the Luedeking-Piret equation and found that the formation of toxins did not show a clear correlation with growth under certain conditions. Yogendrarajah et al. (2016) used the Rosso cardinal model and Gibson's extended model, the latter being the best model to describe the combined effect of aw and temperature on the growth rate of A. flavus and A. parasiticus on peppercorns. However, the high variability of the mycotoxin produced in this food restricted the accuracy of this prediction. According to Aldar-García et al. (2018) predictive mycology studies should include more than one strain due to the high variability in the amounts of AFB1 produced by different isolates. For this, two strains each of common aflatoxigenic species (A. flavus and A. parasiticus) were inoculated on meat products to develop the prediction equations in this research. One of the most interesting aspects of data mining techniques is that they may be used to predict the moment of the beginning of AFB1 production. Therefore, these techniques may be utilised to develop equations for the prevention of AFB1 accumulation in meat products. For this purpose, only the MLR technique was applied, since it was necessary to use aw and temperature as independent variables. The R value obtained to predict the beginning of AFB1 production was 0.88. Table 3 shows the equation obtained to predict the beginning of AFB1 synthesis under experimental conditions. This equation is quite important since the knowledge of an approximate time in which a toxigenic fungus may begin to produce AFB1 at any given combination of aw and temperature allows making decisions to adjust the technological parameters of the ripening of dry-cured ham to avoid mycotoxin accumulation and the risks related to its presence.
3.1. Data mining for deduction of aflatoxin B1 quantities In order to obtain the best as well as robust results to predict the quantities of AFB1 in dry-cured ham, two AFB1-producing mould species were used. For this, deductive techniques of data mining were initially utilised to build a database from the data obtained by Peromingo et al. (2016). From this study, data were obtained from the amounts of AFB1 synthesised by the two strains each of A. flavus and A. parasiticus at 25 °C and 0.95, 0.90 and 0.85 aw in DHA and MBA taken every two days. However, samples incubated at 10, 15 and 20 °C at the three aw values evaluated were only taken at the end of the incubation period. Considering these data, there were some gaps in the records of the database, that is, unknown information for the attribute AFB1. Deductive techniques were used to estimate the AFB1 production in those days without sampling, which allow filling these gaps by including estimated values. From a total of 1170 records, only 258 lacked information on AFB1 production, and these gaps were inferred by applying MLR. The correlation between experimental and predicted data for AFB1 produced by Aspergillus spp. at different temperature and aw in drycured meat model systems showed high correlations (R > 0.99) for both parameters (data not shown). These data inferred were included in the successive analyses. 3.2. Classification for the aflatoxin B1 production Table 1 shows the percentage of correctly classified by applying the J48 decision tree on the AFB1 production at different days of incubation, considering temperature and aw. As shown on Table 1, high percentages of correct classification were achieved (76.6–95.9%). Fig. 1 shows the J48 decision tree for classifying synthesis of AFB1 as a function of temperature, incubation time and aw. In this figure, there are five different intervals to classify the samples, lower than the limit of detection (< LOD) (white colour), lower than 50 ng/g (light grey colour), between 50 and 200 ng/g (grey colour), between 200 and 500 ng/g (dark grey colour) and higher than 500 ng/g (black colour). The J48 decision tree achieves better accuracy and efficiency than other decision tree algorithms (Priyam, Abhijeeta, Rathee, & Srivastava, 2013). This technique was used to classify samples of dry-cured meat products as a function of salting stage (Caballero et al., 2016a). However, this is the first study where data mining is used to classify dryTable 1 Percentage of correct classification obtained by applying the J48 decision tree to classify aflatoxin B1 (AFB1) production at different days of incubation. Days
Correct classifications of AFB1
Total 2 4 6 8 10 12
85.13 95.89 88.57 81.97 85.25 80.33 76.56
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Fig. 1. J48 decision tree for classifying aflatoxin B1 (AFB1) production at different time, temperature and water activity (aw) during the ripening of dry-cured ham. The grey scale shows the concentration of AFB1; the darker the shade of grey, the greater the amount of AFB1.
Table 2 Equations to predict aflatoxin B1 amounts (ng/g) produced by Aspergillus spp. in dry-cured ham obtained by multiple linear regression (MLR) and isotonic regression (IR). Prediction technique
Independent variables
Correlation coefficient (R)
Prediction equation
MLR IR
Time, aw, temperature (°C) Time Temperature (°C)
0.816 0.697 0.687
aw
0.671
25.5114*days + 23.3995*temperature + 1789.1566*aw – 2196.9142 0.0145*day4 – 0.6655*day3 + 8.9571*day2 – 11.931*day + 0.0876 0.0003*temperature6 – 0.0201*temperature5 + 0.5893*temperature4 – 8.265*temperature3 + 55.571*temperature2 – 144.01*temperature + 0.0021 2957.6*aw4 – 4158.1*aw3 + 1729.4*aw2 – 207.59*aw + 1.7406
3.4. Validation of the predictive models
those redicted by the prediction equation. Whereas AFB1 was detected in DHA in two days of incubation at both temperatures (20 and 25 °C) and aw values (0.85 and 0.94), no AFB1 was obtained in dry-cured ham kept at 25 °C for 4 days (Table 4). This may be due to differences in the matrix used in each experiment. The composition of the substrate as well as the environmental conditions related to the processing of drycured meat products have a strong impact on the growth of aflatoxigenic aspergilli and the synthesis of AFs. On the other hand, the RMSEP ranged from 0.074% to 0.385% for all the models for Aspergillus spp. at different time, temperatures and aw. RMSEP values lower than 5% are regarded as appropriate (Hartemink & Minasny, 2016). The mathematical models showed higher standard deviations for the observed production of AFB1 which is also observed in its high RMSEP (0.385%). However, it still adequately fitted the experimental data. In the same way, the values of RMSEP were better (0.074%–0.188%) when the data for the prediction equation were
The developed models were validated with independent experimental data obtained from dry-cured ham. One strain each of A. flavus and A. parasiticus, were inoculated onto dry-cured ham pieces and incubated at conditions simulating ham ripening. Samples were taken in the middle and at the end of the incubation period. In order to corroborate the accuracy of the prediction equations obtained to estimate AFB1 production, R and RMSEP values were calculated from AFB1 concentrations determined in the inoculated drycured ham and those predicted by the prediction equations (Table 4). All the predicted mycotoxin concentrations showed correlation coefficients higher than 0.74 and prediction errors lower than 0.38, indicating the validity of the prediction. In our validation model, at any given combination of temperature, aw and time the concentrations obtained from the dry-cured ham samples were in general lower than
Table 3 Prediction equations to determine the time (days) when aflatoxin B1 (AFB1) produced by strains of Aspergillus spp. begins to be detected in dry-cured ham using a multiple linear regression (MLR). Prediction technique
Independent variables
Prediction equation
MLR
aw, temperature (°C)
- 0.9697*temperature – 30.1347*aw + 53.4398
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Table 4 Determined and predicted aflatoxin B1 (AFB1) concentration produced by Aspergillus flavus and A. parasiticus at different time, temperatures and aw values. Correlation coefficient (R) and Root Mean Square Error of Prediction (RMSEP) between values obtained in pieces of dry-cured ham and when applying the prediction equation for AFB1. Time (Days)
Temperature (°C)
aw
AFB1 determined in ham (ng/g ± SD)
Predicted AFB1 value (ng/g ± SD)
R
p
RMSEP (%)
4 4 8 8 4 4 11 11
20 20 20 20 25 25 25 25
0.85 0.94 0.85 0.94 0.85 0.94 0.85 0.94
< LOD 20.39 ± 8.98 < LOD 27.00 ± 16.05 28.01 ± 16.77 63.44 ± 30.71 215.48 ± 63.46 303.79 ± 276.42
< LOD 54.93 ± 0.01 < LOD 156.97 ± 0.01 10.90 ± 0.01 171.93 ± 0.01 189.48 ± 0.01 350.51 ± 0.01
– 0.816 – 0.842 0.834 0.878 0.812 0.744
– 0.333 – 0.193 0.850 0.204 0.457 0.198
– 0.314 – 0.385 0.188 0.074 0.076 0.134
LOD: Limit of detection.
obtained at 25 °C and both aw values. A study carried out to predict the growth of A. flavus in pistachio nuts using different kinetic models reached higher RMSEP values between 0.617% and 1.037% (Marín et al., 2012). Moreover, in case of to corroborate the accuracy of the values of the beginning of AFB1 synthesis in dry-cured ham samples of the validation set, R values higher than 0.75 and RMSEP lower than 0.200% were achieved for all cases.
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4. Conclusion The results revealed that data mining may be used as a predictive tool to assess the AFB1 risk in dry-cured ham and the ripening time when the production of this mycotoxin begins in dry-cured ham. These findings can be used to propose control strategies to minimise mycotoxin contamination within the HACCP framework of dry-cured ham production. The data mining techniques, prediction equations and decision tree, described in this study could be used as reliable and accurate indicators of AFB1 contamination of dry-cured ham. In this way, identification of critical times affecting AFB1 production during ham ripening may allow monitoring related risks in relation to temperature and aw changes. This information may help to make informed decisions in an effort to mitigate the hazard related to aflatoxin in dry-cured ham. Acknowledgements This work has been funded by the Spanish Ministry of Economy and Competitiveness, “Junta de Extremadura” and FEDER (AGL201345729-P, AGL2016-80209-P, GR15108 and IB16089). B. Peromingo is recipient of a pre-doctoral fellowship (BES-2014-069484) from the Spanish Ministry of Economy and Competitiveness. D. Caballero thanks the “Junta de Extremadura” for the post-doctoral grant (PO17017). References Aldars-García, L., Berman, M., Ortiz, J., Ramos, A. J., & Marín, S. (2018). Probability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus. Food Microbiology, 72, 166–175. https://doi.org/10.1016/j.fm. 2017.11.015. Borge, L. (1985). Estimation and contrasts of hypothesis in the general linear model with inequality restrictionsDoctoral Thesis. Valladolid, Spain: University of Valladolid. Caballero, D., Antequera, T., Caro, A., Amigo, J. M., ErsbØll, B. K., Dahl, A. B., et al. (2018). Analysis of MRI by fractals for prediction of sensory attributes: A case study in loin. Journal of Food Engineering, 227, 1–10. https://doi.org/10.1016/j.jfoodeng. 2018.02.005. Caballero, D., Antequera, T., Caro, A., Duran, M. L., & Pérez-Palacios, T. (2016b). Data mining on MRI-Computational texture features to predict sensory characteristics in ham. Food and Bioprocess Technology, 9, 699–708. https://doi.org/10.1007/s11947015-1662-1. Caballero, D., Bevilacqua, M., & Amigo, J. M. (2019). Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame-retardants. Journal of Spectral Imaging, 8, a1. https://doi.org/10.1255/jsi.2019.a1. Caballero, D., Caro, A., Rodríguez, P. G., Durán, M. L., Ávila, M. M., Palacios, R., et al. (2016a). Modeling salt diffusion in Iberian ham by applying MRI and data mining. Journal of Food Engineering, 189, 115–122. https://doi.org/10.1016/j.jfoodeng.2016. 06.003.
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