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ISSN 2573-1661 (Print) ISSN 2573-167X (Online) 

Vol. 1, No. 2, 2017 

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100 
 

Challenge Test Improvement: Analytical Costs and Time 

Optimization 

Marco Romani1* & Chiara Romani1 

1 Merieux Nutrisciences, Prato, Italy 

* Marco Romani, E-mail: marco.romani@mxns.com 

 

Received: October 1, 2017     Accepted: October 10, 2017     Online Published: October 18, 2017 

doi:10.22158/fsns.v1n2p100        URL: http://dx.doi.org/10.22158/fsns.v1n2p100 

 

Abstract 

The study’s aim was to develop a quantitative risk assessment model of Listeria monocytogenes in liver 

chicken paté. The model was performed using the Integrated Challenge Test (Italian Journal of Food 

Safety, Vol. 1 N. 6 2012) with the objective to reduce the analytical cost and time. The challenge test 

was carried out on 3 different batches stored at 12°C and inoculated with a mix of Listeria 

monocytogenes strains. Lactobacillus spp. and Listeria monocytogenes plate counts were performed 

daily on each sample until the stationary phase was reached by both populations. The challenge test 

results at 12°C were input in the Combase DMfit software to determine the growth parameters of 

Listeria monocytogenes and lactic flora which showed mutual interaction. Then, using the Combase 

Predictor for Listeria monocytogenes and the FSSP (Food Spoilage and Safety Predictor) software for 

lactic flora, the growth parameters were extrapolated at 4°C and 8°C. The growth parameters of both 

populations at 4°C, 8°C and 12°C were then used to apply the model in order to predict the maximum 

daily concentration of Listeria monocytogenes. Model results were assessed against the results of an 

additional challenge test conducted with the same strain mix inoculum in 3 different batches stored for 

4 days at 4°C, 4 days at 8°C and then 4 days at 12°C. The proposed model represents a reliable 

quantitative risk evaluation which provides realistic results with limited cost. 

Keywords 

Listeria monocytogenes, challenge test, anti-listerial activity, lactic acid bacteria, predictive 

microbiology 

 

1. Introduction 

The Integrated Challenge Test was created in response to ANSES guide (November 2008) to focus on 

its strengths and overcome its limitations. The first version of the Integrated Challenge Test (Italian 

Journal of Food Safety, Vol. 1, No. 6, December 2012) aimed to develop a quantitative risk assessment 

model of L. monocytogenes starting with experimental data only. This involved high analytical cost and 



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time. Therefore, the following steps consisted in providing a more accessible model without 

compromising the scientific strictness. The current version of the Integrated Challenge Test starts with 

experimental results, but uses predictive microbiology to extrapolate that data which, if obtained 

experimentally, would have greatly increased the cost and time. 

The model has been developed on a sample of liver chicken paté (RTE food) that, for the values of the 

chemical-physical parameters (pH and Aw) and the growth potential (> 0.5 log cfu/g), can support L. 

monocytogenes growth. This model is applicable also to the cooked meat products in which the lactic 

flora is predominant (example: sliced cooked ham, sliced mortadella, fresh sauces). 

 

2. Method 

The microbiological population of the product was mainly composed of lactic flora that was subjected to 

the following tests: agar well diffusion assay (Parente et al., 1994) and agar drop test (Paparella et al., 

1992). The first test aimed to evaluate the antilisterial activity; the second one determined the substances 

with antilisterial activity. Later a challenge test was conducted on three batches (three repetitions for 

batch). Selected L. monocytogenes strains, most meat isolates, were grown at 8°C to post exponential 

phase, mixed and inoculated in liver paté samples (approximately 1.6 log cfu/g, according to the ANSES 

guide, November 2008). The samples were incubated at 12°C and tested daily for L. monocytogenes 

plate count (UNI EN ISO 11290-02: 2005) and Lactic acid bacteria plate count (UNI EN ISO 15214: 

1998). 

Microbiological analysis were conducted until the stationary phase of both populations (1 week). The 

challenge test was conducted at 12°C because the time to reach by both population the stationary phase 

occurred in a short time (1 week). This allowed to speed the study, reducing costs significantly (at 4°C 

the lactic flora reaches the stationary phase after 20 days). L. monocytogenes and lactic flora growth data 

were then put in Combase DMfit software in order to determine the growth curves and parameters: lag 

phase (days), daily growth rate (log/day), beginning of the stationary phase (days) and concentration of 

the stationary phase (log cfu/g). Starting from the average growth parameters at 12°C, growth parameters 

(lag phase and growth rate) were extrapolated at 4°C and 8°C using two predictive software (ComBase 

Predictor and FSSP) and the formula of Baranyi Roberts relating to the physiological state of the 

microorganisms [physiological state = 1/10 (lag phase x growth rate)]. The experimental data at 12°C with those 

extrapolated at 4°C and 8°C were used to set the model which, taking also in consideration the difference 

time between L. monoctogenes and lactic flora stationary phase, aimed to define the maximum 

concentration reached by the pathogen in the liver chicken pate stored 4 days at 4°C, 4 days at 8°C and 4 

days at 12°C. 

The model was then statistical assessed (t-test) against real data coming from a challenge test carried out 

according to the same time-temperature profile: 4 days at 4°C, 4 days at 8°C and 4 days at 12°C. The 

t-test was applied to the averages of the experimental and predictive data (95% confidence limit). 

 



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3. Result 

Agar well diffusion assay showed that the indigenous lactic flora had antilisterial activity against L. 

monocytogenes strains used for inoculation. Agar drop test showed that such activity is supported by 

organic acids. In table 1 the averages of growth parameters of lactic flora and L. monocytogenes at 4°C, 

8°C and 12°C are reported. From Table 1 it can be observed that at 12°C L. monocytogenes achieved the 

stationary phase 0.43 days before lactic flora. This result with the other growth parameters permitted the 

development of the model. 

 

Table 1. Average Values of the Growth Parameters of L. monocytogenes (L.m.) and of Lactic Acid 

Bacteria (LAB) at the Temperatures of 4°C, 8°C, 12°C. The Table Shows the Values Used for the 

Development of the Model 

 LAB  

Lag 

phase 

days 

LAB 

Growth 

Rate 

log/day 

 LAB 

Stationary 

phase 

log cfu/g 

LAB 

Stationary 

phase 

days 

L.m. 

Lag 

phase 

days 

L.m. 

Growth 

Rate 

log/day 

L.m. 

Stationary 

phase 

log cfu/g 

L.m. 

Stationary 

phase 

days 

4°C 4,78 0,37 / / 5,62 0,15 / / 

8°C 1,95 0,92 / / 2,52 0,33 / / 

12°C 1,05 1,70 8,96 5,22 1,26 0,66 / 4,79 

 

The statistical comparison (T-test) between L. monocytogenes maximum concentration coming from the 

experimental challenge test and the predictive model (Table 2) showed a slight difference. The model 

underestimated the reality by little (<0.5 log), suggesting the possibility to correct the average predictive 

data (3.43 log ufc/g) with the limit of 95% confidence. Since the model underestimated the reality, the 

correction of the predictive data consisted in adding the upper limit of the confidence interval (-0.47 log). 

 

Table 2. Comparison of the Average of Predictive and Experimental Data Regarding the 

Maximum Concentration of L. monocytogenes among the Three Batches. The Lower Confidence 

Limit (LCL) and the Upper Confidence Limit (UCL) were also Calculated: Confidence Interval 

95% 

Challenge test 
Predictive data 

Log cfu/g 

Experimental data 

Log cfu/g 

1° batch, 1° rep. 3,42 3,74 

1° batch, 2° rep. 3,33 3,72 

1° batch, 3° rep. 3,47 4,15 

2° batch, 1° rep. 3,61 3,90 

2° batch, 2° rep. 3,54 3,80 



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2° batch, 3° rep. 3,46 3,95 

3° batch, 1° rep. 3,42 3,52 

3° batch, 2° rep. 3,39 3,70 

3° batch, 3° rep. 3,21 3,78 

Average 3,43a 3,74b 

Lower Confidence Limit (LCL) -0,23 log 

Upper Confidence Limit (UCL) -0,47 log 

 

4. Discussion 

The study shows that the proposed quantitative risk assessment model is very realistic because taking 

into consideration the characteristics of the food, as well as the growth parameters of lactic flora and of L. 

monocytogenes, it is possible to predict the maximum concentration of the pathogen very closely the real 

data. Also, the use of predictive microbiology for the extrapolation of data at temperatures at which the 

experimental test would be extended too much, gives the integrated challenge test a greater commercial 

value. Food companies can implement the integrated challenge test at an affordable cost which is 

immediately translated into a competitive advantage. The proposed model, in fact, is reliable and 

provides an accurate quantitative risk assessment with limited cost as the result of synergy between 

experimental and predictive data. Knowing the concentration of lactic flora and L. monocytogenes at any 

time of the shelf life, the model allows a determination of the maximum L. monocytogenes concentration. 

 

References 

Beaufort, Bergis, Lardeux, & Lombard. (2008, November). Technical guidance document on shelf life 

studies for Listeria monocytogenes in ready to eat foods. 

Colombo, S., Romani, M., Romani, C., & Matteini, P. (2012). Il Challenge test Integrato. Italian 

Journal of Food Safety, 1(6). 

Paparella, A., Ruocco, G., & Barbieri, B. (1992). Lattobacilli come inibitori della microflora delle carni 

fresche.  

Parente, E., Brienza, C., Moles, M., & Ricciardi, A. (1995). A comparison of methods for the 

measurement of bacteriocin activity. J. Microbiol. Meth., 22, 95-108. 

https://doi.org/10.1016/0167-7012(94)00068-I 

UNI EN ISO 11290-2/A1. 2005.  

UNI EN ISO 15214:1998. 1998.  

 

 


