- Email: [email protected]
Predictive model for inactivation of salmonella in infant formula during microwave heating processing
Accepted Manuscript Predictive model for inactivation of Salmonella in infant formula during microwave heating processing
Jéssica B. Portela, Pablo T. Coimbra, Leandro P. Cappato, Verônica O. Alvarenga, Rodrigo B.A. Oliveira, Karen S. Pereira, Denise.R.P. Azeredo, Anderson S. Sant’ Ana, Janaina S. Nascimento, Adriano G. Cruz PII:
S0956-7135(19)30214-2
DOI:
10.1016/j.foodcont.2019.05.006
Reference:
JFCO 6641
To appear in:
Food Control
Received Date:
06 March 2019
Accepted Date:
07 May 2019
Please cite this article as: Jéssica B. Portela, Pablo T. Coimbra, Leandro P. Cappato, Verônica O. Alvarenga, Rodrigo B.A. Oliveira, Karen S. Pereira, Denise.R.P. Azeredo, Anderson S. Sant’ Ana, Janaina S. Nascimento, Adriano G. Cruz, Predictive model for inactivation of Salmonella in infant formula during microwave heating processing, Food Control (2019), doi: 10.1016/j.foodcont. 2019.05.006
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ACCEPTED MANUSCRIPT 1
Predictive model for inactivation of Salmonella in infant formula during microwave heating
2
processing
3 Jéssica B. Portela1, Pablo T. Coimbra1, Leandro P. Cappato2, Verônica O. Alvarenga3,
4 5
Rodrigo B.A. Oliveira4, Karen S. Pereira5, Denise R. P. Azeredo1, Anderson S. Sant’ Ana3, Janaina S.
6
Nascimento1, Adriano G. Cruz1*
7 8 9
1
Instituto Federal de Educação, Ciência e Tecnologia do Rio de Janeiro (IFRJ),
Departamento de Alimentos, 20270-021, Rio de Janeiro, Brazil Universidade Federal Rural do Rio de Janeiro (UFRRJ), Instituto de Tecnologia (IT),
10
2
11
23890-000, Seropédica, Rio de Janeiro, Brazil
12
3
13
(FEA), 13083-862, Campinas, São Paulo; Brazil
14
4
15
Niterói, Rio de Janeiro, Brazil
16
5
17
21941909, Rio de Janeiro, Brazil
Universidade Estadual de Campinas (UNICAMP), Faculdade de Engenharia de Alimentos Universidade Federal Fluminense (UFF), Faculdade de Medicina Veterinária, 24230-340, Universidade
Federal
do
Rio
de
Janeiro
18 19 20 21 22 23 24 25
Runnin title: Pred. Model. Salm. Infant formula.
26
* Email:[email protected] (A.G. Cruz)
1
(UFRJ),
Escola
de
Química
(EQ)
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Abstract
29
This study aimed to study the behavior of Salmonella submitted to domestic
30
microwave through the use of predictive microbiology. The results showed reductions of
31
9.22, 9.59, 8.23, and 8.57 log CFU/ mL in Salmonella counts after exposure to microwave
32
heating at 20 W (750 s), 40 W (90 s), 60 W (120 s), and 80 W (120 s), respectively, with
33
a maximum temperature rise of 110.2 °C. For the primary inactivation model, a biphasic
34
profile was initially observed, obtaining a linear log behavior with the increase in power
35
values. Otherwise, the square root model was used for the secondary modeling, resulting
36
in the equation: √kmax = 0.0055 (P + 9.98). From the validation of the secondary model,
37
the MSE and R² presented a good fit for the model of Salmonella spp inactivation in infant
38
formulas by microwave heating. Overall, the models demonstrated efficacy to ensure the
39
safety of infant formulas, preventing Salmonella contamination and should be considered
40
considering a practical point of view.
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Key-words: lactary, Samonella, microwave heating, predictive model
42 43 44 45
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1. Introduction
48
Infant formula is used as a breast-milk substitute and is the only source of food for
49
infants up to first 6 months of life. The infant formula is a powder that can be
50
reconstituted and used to replace the human milk. This product is made by combined
51
ingredients, such as milk proteins, prebiotics, vegetable oils and mineral sources, among
52
other ingredients (Jardí Piñana, Aranda Pons, Bedmar Carretero, & Arija Val, 2015; Robert
53
M. Kent & Doherty, 2014; WHO/FAO, 2010; Villares, 2016).
54
The contamination of infant formula can occur at any point in the food chain, and the
55
main microorganisms associated with this contamination include Salmonella spp and
56
Cronobacter sakazakii (Cronobacter spp) (Akineden, Heinrich, Gross, & Usleber, 2017;
57
Angulo, Cahill, Wachsmuth, Costarrica, & Embarek, 2008; Obaidat, Alu'Datt, Bani Salman,
58
Obaidat, Al-Zyoud, Al-Saleh, et al., 2015). After powder reconstitution, the viable cells of
59
of theses microorganisms can grow and cause Salmonella and Cronobacter sakazakii
60
infections in childhood. Studies have shown that these microorganisms have been
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responsible for outbreaks associated with infant milk powder consumption worldwide
62
(Angulo, Cahill, Wachsmuth, Costarrica, & Embarek, 2008; Brouard, Espie, Weill,
63
Kerouanton, Brisabois, Forgue, et al., 2007; Cahill, Wachsmuth, Costarrica Mde, & Ben
64
Embarek, 2008; Centers for Disease Control and Prevention, 1993, 2002; Jarvis, 2005;
65
Kim, Kims, & Parks, 2004; Park, Seok, Choi, Kim, Lim, Yoon, et al., 2004; WHO/FAO,2006;
66
Rodríguez-Urrego, Herrera-León, Echeita-Sarriondia, Soler, Simon, & Mateo, 2010).
67
Salmonella infections present infective doses between 102 to 106 UFC/mL, these values
68
being dependent on the serotype and age of the individual, for example. According to
69
WHO/FAO (2006) in a cheese outbreak, it was estimated that the infective dose of
70
Salmonella Typhimurium was less than 10 cells. Although there are no data on the
71
infective dose for infants in infant formulas, however, data on different outbreaks 3
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involving Samonella in different foods indicate that the disease may occur at very low
73
doses. In addition, recent literature has reported the contamination of infant formulas by
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Bacillus cereus, besides the presence of enterotoxins in the product (Bursová, Necidová, &
75
Haruštiaková, 2017; Zhang, Feng, Xu, Liu, Shah, & Wei, 2016).
76
Infections and deaths have been associated with the ingestion of potential pathogens.
77
Viable microorganisms have been detected after reconstitution of the formulas even after
78
heat treatments, which shows the fails in the preparation of the formulas and the need for
79
improvements (HA & KANG, 2014). Predictive microbiology aims to demonstrate, through
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mathematical models, the microbial response to environmental factors, making the
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microbial behavior similarly reproducible under similar conditions. A large number of
82
studies and adaptations of this concept have arisen in the search for quality and safety
83
products, which has also been observed in milk dispensaries (Akineden, Heinrich, Gross, &
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Usleber, 2017; Tenenhaus-Aziza & Ellouze, 2015; Valdramidis, 2016).
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The use of microwave technology (MW) for commercial sterilization of products has
86
been recently approved by FDA (Tang, 2009), and widely used in food heating. One of the
87
issues in the process is due to heating of materials by the existing radiation, in which heat
88
penetrates directly into food, unlike water baths (Martins et al., 2019). Thus, in MW, food
89
is subjected to an intense heating, which affects the sensory and nutritional characteristics
90
of the product when compared to the conventional heating (Kent, Fitzgerald, Hill, Stanton,
91
& Ross, 2015). In addition, in MW, the heating system can be switched on or off
92
immediately, increasing the energy efficiency of the process. However, the main problem
93
associated with MW heating is the existence of uneven temperature distribution resulting
94
in the appearance of hot and cold regions throughout the product. The elimination of this
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problem is a crucial point for the use of this technology since it can affect the sensory
4
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characteristics and the microbiological stability of the product (Chandrasekaran,
97
Ramanathan, & Basak, 2013; Vadivambal & Jayas, 2010)
98
The use of MW has great importance in food preservation, leading to minimum
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sensory and nutritional changes, with microbiological safety, greater shelf life, besides
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minimizing undesirable biochemical effects (Cardello, Schutz, & Lesher, 2007; Khan,
101
Tango, Miskeen, Lee, & Oh, 2017; Sun, 2014). In turn, the predictive microbiology can be
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associated with new technological processes to obtain mathematical models capable of
103
safely providing a process for obtaining quality infant formulas. Thus, this study aims to
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evaluate the effect of domestic microwave heating of infant formulas on Salmonella
105
inactivation, an important pathogen associated with outbreaks in dairy products, in
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addition to determining a predictive model to describe this behavior.
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2. Materials and methods
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2.1 Salmonella strains
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Five Salmonella strains kindly provided by FIOCRUZ (Rio de Janeiro, Brazil) were
110
evaluated. The strains were Salmonella Typhi ATTC 19214 (n=1); Salmonella Enteritidis
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S064 (n=1); Salmonella Agona IOC1772/11 (n=1); Salmonella Anatum S156 (n=1); and
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Salmonella Senftenberg S087 (n=1). The pure cultures were kept at -20°C in TSB
113
(Trypticase Soy Broth, Himedia - India) containing 40% (w/ v) glycerol.
114
2.2 Inoculum and sample preparation
115
The strains were activated before each experiment in TSB and incubated at 37°C for
116
24 hours. For inoculum preparation, a colony was resuspended in 0.1% peptone water.
117
The cell concentration was adjusted to 0.5 MacFarland scale. The infant milk formula
118
(IMF) Aptamil 2 (Danone®, São Paulo, Brazil) used in this study was acquired in local
119
pharmacies. For all experiments, the formulas were prepared using sterile distilled water
120
according to the manufacturer's instruction (14.7 g of infant formula for 90 mL of 5
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previously boiled water) After reconstitution, 15 mL IMF was inoculated with Salmonella
122
suspension to reach a concentration of 109 CFU/mL.
123
2.3. Microwave heating
124
Microwave heating was performed in a domestic microwave oven (Electrolux, model
125
10311LBA106, voltage 127 V, power 1000 W, 60 Hz), a popular domestic microwave
126
equipment used Brazilian establishments, being one of the most commercialized in recent
127
years (Zoom, 2019) . An inoculated sample was placed into a sterile conical glass tube of
128
50 mL for the heat inactivation treatment. The microwave heating was evaluated for
129
different power levels (20 W - 750 s; 40 W - 360 s; 60 W – 120 s; and 80 W – 120 s)
130
(Table 1). The samples were aseptically placed in the center of the oven and the final
131
temperature was measured in the sample after each treatment. At each interval, the
132
samples were placed in an ice bath prior to microbiological characterization.
133
2.4 Bacterial enumeration
134
Before each experiment, an aliquot of contaminated IMF was sampled for Salmonella
135
counts. After microwave heating, 10 mL of sample was mixed with 90 mL of 0.1 %
136
peptone water and immersed in ice-bath to stop the residual heating. The sample was
137
homogenized manually for 5 minutes. After homogenization, 1 mL aliquots were ten-fold
138
serially diluted with 9 mL of 0.1 % peptone water, and then 0.1 mL was spread-plated into
139
TSA agar (Himedia, India). All plates were incubated at 37°C for 24 hours. Experiments
140
were carried out in duplicate.
141
2.5 Data analysis and model fitting for microwave power levels
142
Survival models were constructed as a function of the microwave power level, using
143
add-in GinaFit for Microsoft Excel v1.6 (Geeraerd, Valdramidis, & Van Impe, 2005), where
144
log CFU/mL was plotted against time for each experiment. To determine the best models
145
for each process, the standard correlation coefficient (R2) (Eq. I) was used along with MSE 6
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(Root Mean Square Error) (Eq. 2). Higher R2 values (R² > 0.97) and lower RMSE (RMSE <
147
0.35) are correlated with better model fit.
148 149
2 𝑅2 = 1 ― (∑𝑒𝑖 ∑(𝑦𝑖 ― 𝑦)²)
Eq. I
150 151
where ei is the error of the predictive data; yi is the predictive data, and y is the average
152
of predictive data
153 154
MSE =
∑(pred ― 𝑜𝑏𝑠)2
Eq. II
𝑛―𝑝
155 156
where pred and obs denote the predicted and observed microbial population, n is the
157
number of data points, and p is the number of parameters.
158
The curves were fitted according to the Biphasic + Shoulder model (Eq. III)
159
(Geeraerd, Valdramidis, and Van Impe, 2005), Log-Linear + Shoulder model (Eq. IV)
160
(Geeraerd, Herremans, and Van Impe, 2000) and Log-Linear model (Eq. V) (Bigelow,
161
1921).
162 163
{
𝑦 = 𝑦0 + 𝑙𝑜𝑔 [𝑓.𝑒 ― 𝑘𝑚á𝑥1 𝑡 + (1 ― 𝑓).𝑒 ― 𝑘𝑚á𝑥2 𝑡].𝑒
(
(𝑘𝑚á𝑥1𝑆𝐿) (1 + [𝑒
𝑘𝑚á𝑥1 𝑆𝐿
― 1].𝑒^( ― 𝑘𝑚á𝑥1𝑡)
)}
Eq. III
164 165
(
𝑁 = (𝑁0 ― 𝑁𝑟𝑒𝑠).𝑒 ― 𝑘𝑚𝑎𝑥𝑡
𝑒^(𝑘𝑚𝑎𝑥𝑆𝐼) 1 + (𝑒
𝑘𝑚𝑎𝑥𝑆𝐼
― 1).𝑒
)+ 𝑁
𝑘𝑚𝑎𝑥𝑡
𝑟𝑒𝑠
Eq. IV
166 167
Eq. V
𝑁 = 𝑁𝑜.𝑒 ―𝑘𝑡
7
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where N0 and N are the cell concentration (CFU/mL) in t0 and t(s), respectively; Sl is
169
the shoulder length (no decrease in cell count) (min); kmax is the maximum inactivation
170
rate (min-1); f is the fraction of the initial population characterized by the death rate kmax1,
171
and (1 − f) is the second sub-population with an inactivation rate kmax2.
172 173
To estimate the effects of the microwave power levels on the inactivation rate, the square root model (Ratkowsky, Olley, McMeekin, & Ball, 1982) was fitted (Eq. VI):
174 175
Eq. VI
𝑘𝑚𝑎𝑥 = 𝑏 (𝑃 ― 𝑃𝑚𝑖𝑛)
176 177
2.7 Model validation
178
In the biological validation process, 4 Salmonella serotypes (Typhi, Enteritidis, Agona,
179
Anatum, Steinberger), and 2 intermediary power levels (30 and 50 W) were used for
180
testing the model prediction. The predictive efficacy was determined by calculating the
181
model performance indices, Bias factor (Bf), and Accuracy factor (Af), (Eq. 7 and 8):
182
𝐵f = 10^ ∑log
183
𝐴f = 10^ ∑(log
[
( )⁄𝑛] 𝑝𝑟𝑒𝑑 𝑜𝑏𝑠
Eq. VII
[
( ))⁄𝑛]
Eq. VIII
𝑝𝑟𝑒𝑑 𝑜𝑏𝑠
184 185
where obs, pred, and n are the observed value, the predicted value, and the number of
186
observations, respectively.
187
3. Results
188
3.1 Effects of microwave heating on Salmonella Typhi inactivation
189
The inactivation of S. Typhi by the microwave heating is shown in Figure. 1.
190
Reductions of 9.22, 9.59, 8.23, and 8.57 log CFU/ mL were observed for the treatments at
191
20 W (750 s), 40 W (90 s), 60 W (120 s), and 80 W (120 s), respectively. Although the 8
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treatment at 20 W took longer, the samples resisted up to 750 s without apparent
193
degradation. At this power, a more significant effect on quantification was observed from
194
180 s, with a reduction of 3.72 log CFU/mL of the initial cell concentration. The
195
inactivation models and the kinetics parameters were presented in Table 1. For all
196
treatments, the models presented good adjustment, once high R² (R² > 0.97) and low
197
RMSE values (RMSE < 0.35) were obtained.
198
The fitted models showed the log linearity and non-log linearity behavior, as a function
199
of the power applied during the domestic microwave heating. At lower power levels, 20
200
and 40 W, data generated shoulder shape models, with no shoulder with the increase in
201
power to 60 and 80 W, and no tailing effect. According to Geeraerd, Herremans, and Van
202
Impe (2000), the presence of shoulder may be related to different factors, including the
203
food matrix, with a protective effect of proteins and lipids on the microbial cells, increasing
204
cell resistance to processing conditions, and the formation of clumps of bacteria,
205
conferring a protective effect.
206
At 20 W, a 4 D value of 228.8 s was observed, which emphasizes the presence of the
207
shoulder model and the resistance of the pathogen to the treatment. This is clearly
208
represented in Figure 1, which shows an inactivation curve with a much slower decay
209
when compared to the other treatments. In addition, at this power value, the best fit
210
presented a biphasic format, with two distinct inactivation rates, kmax1 and kmax2,
211
characterizing the presence of two subpopulations with different resistance mechanisms to
212
the treatment. As shown in the biphasic model in Table 1, the first subpopulation, less
213
resistant, had a higher inactivation rate (kmax1), where f represents this population
214
fraction. In contrast, the second subpopulation, represented by the fraction (1 - f), was
215
more resistant to the treatment, due to the lower kmax2 index.
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Figure 1 shows the behavior of the Salmonella population at 40 W. The curve was
217
adjusted by the Log-Linear + Shoulder model of Geeraerd, Herremans, and Van Impe
218
(2000), with the initial shoulder of the curve (about 22 s) similar to that obtained at 20 W
219
(about 20 s). In contrast, a great difference was observed for the inactivation rate, once
220
the 4 D value (50.4 ± 1.80 s) of the treatment at 40 W was about 4 times lower when
221
compared to the treatment at 20 W (228.8 ± 11.25 s). Generally, although the inactivation
222
rate increases with the increase in treatment intensity, this phenomenon was not observed
223
in the present research. When comparing the results of the inactivation rates at 40 W, 60
224
W, and 80 W, the kmax value at 40 W was higher than those at 60 and 80 W; in contrast,
225
the 4 D value at 40 W was higher due to the presence of the shoulder. At 60 W and 80 W,
226
the survival curves were described by the log-linear behavior, with 4D values of 45 and
227
32.1 s, respectively.
228
3.2 Secondary inactivation model and validation
229
From the kinetics inactivation data obtained in the primary models (Table 1), the
230
secondary square root models (Eq. IX) and (Eq. X) were developed to demonstrate the
231
correlation between the inactivation rate (Kmax) and power (W). For the secondary model,
232
a good fit was not achieved at 40 W, thus data were removed, and only data from the
233
other power levels were used, according to Table 2.
234
kmáx = (0.109412020127795) + (5.4750466004358E ― 3) ∗ Potência (W) Eq. IX
235 236
Eq. X
kmáx = 0.0055 (𝑃 + 9.98)
237 238
For validation of the secondary model, the correlation coefficient R2, the mean square
239
error (RMSE), the bias factor, and the accuracy were used to evaluate the adjustment. As
240
can be seen in Table 3, all evaluation parameters pointed to a good fit of the second 10
ACCEPTED MANUSCRIPT 241
model when using the results of S. Typhi serotype in the generation of the primary model,
242
with bias, accuracy, RMSE, and R2 values of 0.99, 1.10, 0.04, and 0.98, respectively. The
243
other serotypes used for model validation also showed good adjustments, with bias values
244
ranging from 0.95 to 1.05, accuracy from 1.06 to 1.10, RMSE from 0.03 to 0.05, and R2 of
245
0.98. Thus, the model can be used to predict the Salmonella behavior during heating of
246
infant formulas in lactaries using domestic microwave ovens and is an important tool to
247
guarantee the microbiological safety of the product.
248
4. Discussion
249
Several foodborne outbreaks have been associated with the presence of Salmonella
250
spp in foods, including infant formulas (Bermúdez-Aguirre & Corradini, 2012; Cahill,
251
Wachsmuth, Costarrica Mde, & Ben Embarek, 2008; Koseki, Nakamura, & Shiina, 2015;
252
Morlay, Piat, Mercey, & Roupioz, 2016; Van Asselt, Van der Fels-Klerx, Marvin, Van
253
Bokhorst-van de Veen, & Groot, 2017; WHO/FAO, 2003). However, no studies have been
254
found in literature about the effect of microwave heating on the Salmonella inactivation.
255
Thus, knowledge about the effect of microwaves on the inactivation rate of important
256
microorganisms involved in food outbreaks has a great importance in the actual scenario,
257
since this technology has been used frequently.
258
Some studies have been carried out in infant formulas involving other microorganisms,
259
mainly Cronobacter sakazakii. Kindle, Busse, Kampa, Meyer-Koenig, & Daschner (1996)
260
evaluated the effect of microwave heating on the inactivation of different microorganisms
261
(Pseudomonas aeroginosa, Klebsiella pneumonie, Escherichia coli, Staphylococcus aureus,
262
Candia albicans, Cronobacter sakazakii and Mycobacterium) in five infant milk formulas.
263
Those authors found a significant decrease in the microbial population after microwave
264
treatment, thus demonstrating the efficiency of the method. Other authors (Pina-Pérez,
265
Benlloch-Tinoco, Rodrigo, and Martinez, 2014) investigated the effectiveness of different 11
ACCEPTED MANUSCRIPT 266
microwave parameters (400 – 900 W varying from 0 to 120 s) for the inactivation of C.
267
sakazakii in reconstituted infant formula, and found a reduction of the initial C. sakazakii
268
population to undetectable levels (≥ 8 log cycles), in potencies varying from 800 to 900
269
W.
270
Concerning the Salmonella inactivation by microwave heating, this technique has been
271
considered as an important technology for Salmonella inactivation and food safety. Sung &
272
Kang (2014) evaluated the effect of microwave processing under various potencies (1.2,
273
1.8, 2.4, 3.6, and 4.8 kW) on the inactivation of Escherichia coli O157: H7, Salmonella
274
Typhimurium, and Listeria monocytogenes in salsa. Although the inactivation rates for the
275
microorganisms were not determined, the research showed that microwave can be an
276
important alternative for Salmonella inactivation in salsa, with no changes in the color
277
parameters. Similar results were observed by Song and Kang (2016), who reported that
278
the microwave (2, 4, and 6 kW) can be used as a control method to reduce Salmonella in
279
peanut butter without affecting the product's quality. Valero, Cejudo, & García-Gimeno
280
(2014) studied microwave heating and found a reduction of 4D in a few seconds at 70 °C
281
(300 W - 80 s, 450 W - 60 s, or 600 W/800 W - 40 s), which may be an alternative to
282
commercial establishments such as restaurants. In addition, the authors determined the
283
kinetic inactivation parameters from the linear log-shoulder model, with the presence of
284
shoulder in all potencies studied, opposite from the present results, which showed a linear
285
log reduction with the increase in the potency values. For the inactivation kinetics, the
286
authors found higher inactivation rates at 600 W when compared to 800 W, which was
287
also observed in the present study, with higher inactivation rates at 60 W when compared
288
to 80 W.
289
This phenomenon may have occurred due to the evaporation of part of water, which
290
may have led to a reduction in heat transfer in the sample. Song and Kang (2016) 12
ACCEPTED MANUSCRIPT 291
evaluated the effect of water activity (Aw = 0.30, 0.40, and 0.50) on the time x
292
temperature profile during microwave heating (2, 4, and 6 kW) in peanut butter, and
293
found that the lower the Aw, the lower heating rates. In addition, the kinetics inactivation
294
of Salmonella, E.coli, and Listeria monocytogenes was lower at lower Aw values, showing
295
that Aw is an important factor during microwave heating, directly affecting the inactivation
296
rate (Song & Kang, 2016).
297
Overall, due the increased use of microwave heating in lactaries, our findings present
298
relevance for food industry and bring interesting findings that enable a better
299
understanding about a more useful usage of this emerging technology for people who
300
work in these establishments. In this sense, the predictive models presented here
301
presented practical and should be taken in consideration.
302 303
5. Conclusion
304
The present results allowed the characterization of the behavior of Salmonella in the
305
form of contaminated infant milk formulas submitted to microwave heating, which is a
306
common procedure used in lactary centers. The treatment at 20 W was characterized by a
307
processing time greater than 750 s, but was efficient in the elimination of the pathogen,
308
with a reduction of 9.26 log CFU/mL. For the other potencies (40, 60, and 80 W) the
309
microorganism resisted from 90 to 120 s, indicating a poor survival of this microorganism.
310
Based on the good performance of the treatments, the predictive models were
311
effective for Salmonella inactivation, in view of the resistance of this agent in food, which
312
guarantees an effective food safety against possible contamination by this microorganism.
313 314 315 13
ACCEPTED MANUSCRIPT 316 317 318 319 320 321 322
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323
Akineden, Ö., Heinrich, V., Gross, M., & Usleber, E. (2017). Reassessment of Cronobacter
324
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325
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Angulo, F. J., Cahill, S. M., Wachsmuth, I. K., Costarrica, M. d. L., & Embarek, P. K. B.
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(2008). Powdered infant formula as a source of Salmonella infection in infants.
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329
Bermúdez-Aguirre, D., & Corradini, M. G. (2012). Inactivation kinetics of Salmonella spp.
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V., & de Valk, H. (2007). Two consecutive large outbreaks of Salmonella enterica
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serotype Agona infections in infants linked to the consumption of powdered infant
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formula. Pediatric Infection Disease Journal, 26, 148-152.
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Jarvis, C. (2005). Fatal Enterobacter sakazakii infection associated with powdered infant
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Kim, S., Kims, Y.-S., & Parks, J. Y. (2004). Salmonella enterica serovar London infections associated with consumption of infant formula. Yonsei medical journal, 45, 43-48. Kindle, G., Busse, A., Kampa, D., Meyer-Koenig, U., & Daschner, F. (1996). Killing activity of microwaves in milk. Journal of Hospital Infection, 33, 273-278.
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Koseki, S., Nakamura, N., & Shiina, T. (2015). Comparison of desiccation tolerance among
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Listeria monocytogenes, Escherichia coli O157:H7, Salmonella enterica, and
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Martins, C.P.C., Cavalcanti, R.N., Couto, S.M., Moraes, J., Esmerino, E.A., Silva, M.C.,
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Morlay, A., Piat, F., Mercey, T., & Roupioz, Y. (2016). Immunological detection of
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assay. Letters in Applied Microbiology, 62, 459-465.
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Obaidat, M. M., Alu'Datt, M. H., Bani Salman, A. E., Obaidat, H. M., Al-Zyoud, A. A., Al-
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Saleh, O. K., & Abu al'anaz, B. (2015). Inactivation of nondesiccated and desiccated
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Cronobacter Sakazakii and Salmonella spp. at low and high inocula levels in
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reconstituted infant milk formula by vanillin. Food Control, 50, 850-857.
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Park, J. K., Seok, W. S., Choi, B. J., Kim, H. M., Lim, B. K., Yoon, S. S., Kim, S., Kim, Y. S.,
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& Park, J. Y. (2004). Salmonella enterica serovar London infections associated with
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sakazakii Inactivation by Microwave Processing. Food and bioprocess technology, 7,
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Ratkowsky, D. A., Olley, J., McMeekin, T. A., & Ball, A. (1982). Relationship between
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Rodríguez-Urrego, J., Herrera-León, S., Echeita-Sarriondia, A., Soler, P., Simon, F., &
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Mateo, S. (2010). Nationwide outbreak of Salmonella serotype Kedougou associated
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with infant formula, Spain, 2008. Eurosurveillance, 15, 19582.
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Song, W.-J., & Kang, D.-H. (2016). Inactivation of Salmonella Senftenberg, Salmonella
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Typhimurium and Salmonella Tennessee in peanut butter by 915 MHz microwave
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heating. Food Microbiology, 53, 48-52.
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Sun, D.-W. (2014). Emerging technologies for food processing: Elsevier.
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Sung, H.-J., & Kang, D.-H. (2014). Effect of a 915 MHz microwave system on inactivation
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of Escherichia coli O157:H7, Salmonella Typhimurium, and Listeria monocytogenes
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in salsa. LWT - Food Science and Technology, 59, 754-759.
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Microwave Heating of Food Materials—A Review. Food and bioprocess technology,
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3, 161-171.
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Valdramidis, V. (2016). 1 - Predictive Microbiology. In Modeling in Food Microbiology, (pp. 1-15). Oxford: Elsevier.
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Valero, A., Cejudo, M., & García-Gimeno, R. M. (2014). Inactivation kinetics for Salmonella
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Enteritidis in potato omelet using microwave heating treatments. Food Control, 43,
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175-182.
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Van Asselt, E. D., van der Fels-Klerx, H. J., Marvin, H. J. P., van Bokhorst-van de Veen, H.,
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& Groot, M. N. (2017). Overview of Food Safety Hazards in the European Dairy
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Supply Chain. Comprehensive Reviews in Food Science and Food Safety, 16, 59-75.
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Villares, J. M. M. (2016). Prebiotics in Human Milk and in Infant Formulas A2 - Watson,
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WHO/FAO. (2006). Enterobacter sakazakii and Salmonella in powdered infant formula.
World Health Organization: Geneva. WHO (2010). International code of marketing of breast-milk substitutes. World Health
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Zhang, Z., Feng, L., Xu, H., Liu, C., Shah, N. P., & Wei, H. (2016). Detection of viable
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enterotoxin-producing Bacillus cereus and analysis of toxigenicity from ready-to-eat
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foods and infant formula milk powder by multiplex PCR. Journal of Dairy Science,
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99, 1047-1055.
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443
https://www.zoom.com.br/microondas/deumzoom/melhor-microondas. Accessed in
444
2019.
19
05
may
ACCEPTED MANUSCRIPT 12.00
Log N (UFC/mL)
10.00
8.00
6.00
4.00
2.00
0.00 0
100
200
300
400
500
600
700
Time (s) 20W
40W
60W
80W LINEAR
Figure 1. Survival curves for S. Typhi in in infant milk formula treated with microwave heating at 20 W (●), 40 W (▲), 60 W (■) and 80 W (x).
800
ACCEPTED MANUSCRIPT Predictive model for inactivation of Salmonella in infant formula in
1
2
microwave heating processing;
3
Initial biphasic profile with a linear log behavior was observed;
4
Excellent bias, accuracy, RMSE, and R2 values founds at the validation
5
step.
6
Table 1. Kinetics parameters obtained to survival data of Salmonella in infant milk treated by microwave heating* Power (W)
20
Models
Geeraerd, Valdramidis, and Van Impe (2005), Biphasic+ Shoulder
Parameters f
0.99 ± 0.002
Kmax1
0.04 ± 0.005
Kmax2
0.02 ± 0.005
Log10(N0)
10.71 ± 0.090
Sl (shoulder length)
40
60
Geeraerd, Herremans, and Van Impe (2000), LogLinear + Shoulder
Bigelow (1921), LogLinear Regression
Kmax
Time 4D (s)
R²
RMSE
228.8 ± 11.25
0.992 ± 0.001
0.118± 0.016
50.4 ± 1.80
0.986 ± 0.002
20.4 ± 8.16 0.3 ± 0.01
Log10(N0)
10.17 ± 0.045
Sl (shoulder length)
21.92 ± 0.86
Kmax
0.2 ± 0.01 45.0 ± 0.00
Log10(N0)
9.05 ± 0.075
0.990 ± 0.002
0.359± 0.047
0.086± 0.017
80
Bigelow (1921), LogLinear Regression
Kmax
0.28 ± 0.005 32.1 ± 0.35
Log10(N0)
8.67 ± 0.150
* Results are expressed in mean ± standard deviation. Analysis performed in triplicate.
0.979 ± 0.002
0.273± 0.023
Table 2. Parameters of the secondary model. Power (W)
kmax
√kmax
4D
log 4D
20
0.045
0.2121
228.75
2.36
60
0.21
0.4583
45
1.65
80
0.285
0.5338
32.18
1.51
Table 3. Data resulting from the validation of the secondary model Sorotype
Bias
Accuracy
RMSE
R²
Typhi
0.99
1.10
0.04
0.98
Enteritidis
0.98
1.09
0.04
0.98
Agona
0.95
1.06
0.03
0.98
Anatum
1.05
1.16
0.05
0.98
Steinberger
0.98
1.08
0.03
0.98
Jéssica B. Portela, Pablo T. Coimbra, Leandro P. Cappato, Verônica O. Alvarenga, Rodrigo B.A. Oliveira, Karen S. Pereira, Denise.R.P. Azeredo, Anderson S. Sant’ Ana, Janaina S. Nascimento, Adriano G. Cruz PII:
S0956-7135(19)30214-2
DOI:
10.1016/j.foodcont.2019.05.006
Reference:
JFCO 6641
To appear in:
Food Control
Received Date:
06 March 2019
Accepted Date:
07 May 2019
Please cite this article as: Jéssica B. Portela, Pablo T. Coimbra, Leandro P. Cappato, Verônica O. Alvarenga, Rodrigo B.A. Oliveira, Karen S. Pereira, Denise.R.P. Azeredo, Anderson S. Sant’ Ana, Janaina S. Nascimento, Adriano G. Cruz, Predictive model for inactivation of Salmonella in infant formula during microwave heating processing, Food Control (2019), doi: 10.1016/j.foodcont. 2019.05.006
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ACCEPTED MANUSCRIPT 1
Predictive model for inactivation of Salmonella in infant formula during microwave heating
2
processing
3 Jéssica B. Portela1, Pablo T. Coimbra1, Leandro P. Cappato2, Verônica O. Alvarenga3,
4 5
Rodrigo B.A. Oliveira4, Karen S. Pereira5, Denise R. P. Azeredo1, Anderson S. Sant’ Ana3, Janaina S.
6
Nascimento1, Adriano G. Cruz1*
7 8 9
1
Instituto Federal de Educação, Ciência e Tecnologia do Rio de Janeiro (IFRJ),
Departamento de Alimentos, 20270-021, Rio de Janeiro, Brazil Universidade Federal Rural do Rio de Janeiro (UFRRJ), Instituto de Tecnologia (IT),
10
2
11
23890-000, Seropédica, Rio de Janeiro, Brazil
12
3
13
(FEA), 13083-862, Campinas, São Paulo; Brazil
14
4
15
Niterói, Rio de Janeiro, Brazil
16
5
17
21941909, Rio de Janeiro, Brazil
Universidade Estadual de Campinas (UNICAMP), Faculdade de Engenharia de Alimentos Universidade Federal Fluminense (UFF), Faculdade de Medicina Veterinária, 24230-340, Universidade
Federal
do
Rio
de
Janeiro
18 19 20 21 22 23 24 25
Runnin title: Pred. Model. Salm. Infant formula.
26
* Email:[email protected] (A.G. Cruz)
1
(UFRJ),
Escola
de
Química
(EQ)
ACCEPTED MANUSCRIPT 28
Abstract
29
This study aimed to study the behavior of Salmonella submitted to domestic
30
microwave through the use of predictive microbiology. The results showed reductions of
31
9.22, 9.59, 8.23, and 8.57 log CFU/ mL in Salmonella counts after exposure to microwave
32
heating at 20 W (750 s), 40 W (90 s), 60 W (120 s), and 80 W (120 s), respectively, with
33
a maximum temperature rise of 110.2 °C. For the primary inactivation model, a biphasic
34
profile was initially observed, obtaining a linear log behavior with the increase in power
35
values. Otherwise, the square root model was used for the secondary modeling, resulting
36
in the equation: √kmax = 0.0055 (P + 9.98). From the validation of the secondary model,
37
the MSE and R² presented a good fit for the model of Salmonella spp inactivation in infant
38
formulas by microwave heating. Overall, the models demonstrated efficacy to ensure the
39
safety of infant formulas, preventing Salmonella contamination and should be considered
40
considering a practical point of view.
41
Key-words: lactary, Samonella, microwave heating, predictive model
42 43 44 45
2
ACCEPTED MANUSCRIPT 47
1. Introduction
48
Infant formula is used as a breast-milk substitute and is the only source of food for
49
infants up to first 6 months of life. The infant formula is a powder that can be
50
reconstituted and used to replace the human milk. This product is made by combined
51
ingredients, such as milk proteins, prebiotics, vegetable oils and mineral sources, among
52
other ingredients (Jardí Piñana, Aranda Pons, Bedmar Carretero, & Arija Val, 2015; Robert
53
M. Kent & Doherty, 2014; WHO/FAO, 2010; Villares, 2016).
54
The contamination of infant formula can occur at any point in the food chain, and the
55
main microorganisms associated with this contamination include Salmonella spp and
56
Cronobacter sakazakii (Cronobacter spp) (Akineden, Heinrich, Gross, & Usleber, 2017;
57
Angulo, Cahill, Wachsmuth, Costarrica, & Embarek, 2008; Obaidat, Alu'Datt, Bani Salman,
58
Obaidat, Al-Zyoud, Al-Saleh, et al., 2015). After powder reconstitution, the viable cells of
59
of theses microorganisms can grow and cause Salmonella and Cronobacter sakazakii
60
infections in childhood. Studies have shown that these microorganisms have been
61
responsible for outbreaks associated with infant milk powder consumption worldwide
62
(Angulo, Cahill, Wachsmuth, Costarrica, & Embarek, 2008; Brouard, Espie, Weill,
63
Kerouanton, Brisabois, Forgue, et al., 2007; Cahill, Wachsmuth, Costarrica Mde, & Ben
64
Embarek, 2008; Centers for Disease Control and Prevention, 1993, 2002; Jarvis, 2005;
65
Kim, Kims, & Parks, 2004; Park, Seok, Choi, Kim, Lim, Yoon, et al., 2004; WHO/FAO,2006;
66
Rodríguez-Urrego, Herrera-León, Echeita-Sarriondia, Soler, Simon, & Mateo, 2010).
67
Salmonella infections present infective doses between 102 to 106 UFC/mL, these values
68
being dependent on the serotype and age of the individual, for example. According to
69
WHO/FAO (2006) in a cheese outbreak, it was estimated that the infective dose of
70
Salmonella Typhimurium was less than 10 cells. Although there are no data on the
71
infective dose for infants in infant formulas, however, data on different outbreaks 3
ACCEPTED MANUSCRIPT 72
involving Samonella in different foods indicate that the disease may occur at very low
73
doses. In addition, recent literature has reported the contamination of infant formulas by
74
Bacillus cereus, besides the presence of enterotoxins in the product (Bursová, Necidová, &
75
Haruštiaková, 2017; Zhang, Feng, Xu, Liu, Shah, & Wei, 2016).
76
Infections and deaths have been associated with the ingestion of potential pathogens.
77
Viable microorganisms have been detected after reconstitution of the formulas even after
78
heat treatments, which shows the fails in the preparation of the formulas and the need for
79
improvements (HA & KANG, 2014). Predictive microbiology aims to demonstrate, through
80
mathematical models, the microbial response to environmental factors, making the
81
microbial behavior similarly reproducible under similar conditions. A large number of
82
studies and adaptations of this concept have arisen in the search for quality and safety
83
products, which has also been observed in milk dispensaries (Akineden, Heinrich, Gross, &
84
Usleber, 2017; Tenenhaus-Aziza & Ellouze, 2015; Valdramidis, 2016).
85
The use of microwave technology (MW) for commercial sterilization of products has
86
been recently approved by FDA (Tang, 2009), and widely used in food heating. One of the
87
issues in the process is due to heating of materials by the existing radiation, in which heat
88
penetrates directly into food, unlike water baths (Martins et al., 2019). Thus, in MW, food
89
is subjected to an intense heating, which affects the sensory and nutritional characteristics
90
of the product when compared to the conventional heating (Kent, Fitzgerald, Hill, Stanton,
91
& Ross, 2015). In addition, in MW, the heating system can be switched on or off
92
immediately, increasing the energy efficiency of the process. However, the main problem
93
associated with MW heating is the existence of uneven temperature distribution resulting
94
in the appearance of hot and cold regions throughout the product. The elimination of this
95
problem is a crucial point for the use of this technology since it can affect the sensory
4
ACCEPTED MANUSCRIPT 96
characteristics and the microbiological stability of the product (Chandrasekaran,
97
Ramanathan, & Basak, 2013; Vadivambal & Jayas, 2010)
98
The use of MW has great importance in food preservation, leading to minimum
99
sensory and nutritional changes, with microbiological safety, greater shelf life, besides
100
minimizing undesirable biochemical effects (Cardello, Schutz, & Lesher, 2007; Khan,
101
Tango, Miskeen, Lee, & Oh, 2017; Sun, 2014). In turn, the predictive microbiology can be
102
associated with new technological processes to obtain mathematical models capable of
103
safely providing a process for obtaining quality infant formulas. Thus, this study aims to
104
evaluate the effect of domestic microwave heating of infant formulas on Salmonella
105
inactivation, an important pathogen associated with outbreaks in dairy products, in
106
addition to determining a predictive model to describe this behavior.
107
2. Materials and methods
108
2.1 Salmonella strains
109
Five Salmonella strains kindly provided by FIOCRUZ (Rio de Janeiro, Brazil) were
110
evaluated. The strains were Salmonella Typhi ATTC 19214 (n=1); Salmonella Enteritidis
111
S064 (n=1); Salmonella Agona IOC1772/11 (n=1); Salmonella Anatum S156 (n=1); and
112
Salmonella Senftenberg S087 (n=1). The pure cultures were kept at -20°C in TSB
113
(Trypticase Soy Broth, Himedia - India) containing 40% (w/ v) glycerol.
114
2.2 Inoculum and sample preparation
115
The strains were activated before each experiment in TSB and incubated at 37°C for
116
24 hours. For inoculum preparation, a colony was resuspended in 0.1% peptone water.
117
The cell concentration was adjusted to 0.5 MacFarland scale. The infant milk formula
118
(IMF) Aptamil 2 (Danone®, São Paulo, Brazil) used in this study was acquired in local
119
pharmacies. For all experiments, the formulas were prepared using sterile distilled water
120
according to the manufacturer's instruction (14.7 g of infant formula for 90 mL of 5
ACCEPTED MANUSCRIPT 121
previously boiled water) After reconstitution, 15 mL IMF was inoculated with Salmonella
122
suspension to reach a concentration of 109 CFU/mL.
123
2.3. Microwave heating
124
Microwave heating was performed in a domestic microwave oven (Electrolux, model
125
10311LBA106, voltage 127 V, power 1000 W, 60 Hz), a popular domestic microwave
126
equipment used Brazilian establishments, being one of the most commercialized in recent
127
years (Zoom, 2019) . An inoculated sample was placed into a sterile conical glass tube of
128
50 mL for the heat inactivation treatment. The microwave heating was evaluated for
129
different power levels (20 W - 750 s; 40 W - 360 s; 60 W – 120 s; and 80 W – 120 s)
130
(Table 1). The samples were aseptically placed in the center of the oven and the final
131
temperature was measured in the sample after each treatment. At each interval, the
132
samples were placed in an ice bath prior to microbiological characterization.
133
2.4 Bacterial enumeration
134
Before each experiment, an aliquot of contaminated IMF was sampled for Salmonella
135
counts. After microwave heating, 10 mL of sample was mixed with 90 mL of 0.1 %
136
peptone water and immersed in ice-bath to stop the residual heating. The sample was
137
homogenized manually for 5 minutes. After homogenization, 1 mL aliquots were ten-fold
138
serially diluted with 9 mL of 0.1 % peptone water, and then 0.1 mL was spread-plated into
139
TSA agar (Himedia, India). All plates were incubated at 37°C for 24 hours. Experiments
140
were carried out in duplicate.
141
2.5 Data analysis and model fitting for microwave power levels
142
Survival models were constructed as a function of the microwave power level, using
143
add-in GinaFit for Microsoft Excel v1.6 (Geeraerd, Valdramidis, & Van Impe, 2005), where
144
log CFU/mL was plotted against time for each experiment. To determine the best models
145
for each process, the standard correlation coefficient (R2) (Eq. I) was used along with MSE 6
ACCEPTED MANUSCRIPT 146
(Root Mean Square Error) (Eq. 2). Higher R2 values (R² > 0.97) and lower RMSE (RMSE <
147
0.35) are correlated with better model fit.
148 149
2 𝑅2 = 1 ― (∑𝑒𝑖 ∑(𝑦𝑖 ― 𝑦)²)
Eq. I
150 151
where ei is the error of the predictive data; yi is the predictive data, and y is the average
152
of predictive data
153 154
MSE =
∑(pred ― 𝑜𝑏𝑠)2
Eq. II
𝑛―𝑝
155 156
where pred and obs denote the predicted and observed microbial population, n is the
157
number of data points, and p is the number of parameters.
158
The curves were fitted according to the Biphasic + Shoulder model (Eq. III)
159
(Geeraerd, Valdramidis, and Van Impe, 2005), Log-Linear + Shoulder model (Eq. IV)
160
(Geeraerd, Herremans, and Van Impe, 2000) and Log-Linear model (Eq. V) (Bigelow,
161
1921).
162 163
{
𝑦 = 𝑦0 + 𝑙𝑜𝑔 [𝑓.𝑒 ― 𝑘𝑚á𝑥1 𝑡 + (1 ― 𝑓).𝑒 ― 𝑘𝑚á𝑥2 𝑡].𝑒
(
(𝑘𝑚á𝑥1𝑆𝐿) (1 + [𝑒
𝑘𝑚á𝑥1 𝑆𝐿
― 1].𝑒^( ― 𝑘𝑚á𝑥1𝑡)
)}
Eq. III
164 165
(
𝑁 = (𝑁0 ― 𝑁𝑟𝑒𝑠).𝑒 ― 𝑘𝑚𝑎𝑥𝑡
𝑒^(𝑘𝑚𝑎𝑥𝑆𝐼) 1 + (𝑒
𝑘𝑚𝑎𝑥𝑆𝐼
― 1).𝑒
)+ 𝑁
𝑘𝑚𝑎𝑥𝑡
𝑟𝑒𝑠
Eq. IV
166 167
Eq. V
𝑁 = 𝑁𝑜.𝑒 ―𝑘𝑡
7
ACCEPTED MANUSCRIPT 168
where N0 and N are the cell concentration (CFU/mL) in t0 and t(s), respectively; Sl is
169
the shoulder length (no decrease in cell count) (min); kmax is the maximum inactivation
170
rate (min-1); f is the fraction of the initial population characterized by the death rate kmax1,
171
and (1 − f) is the second sub-population with an inactivation rate kmax2.
172 173
To estimate the effects of the microwave power levels on the inactivation rate, the square root model (Ratkowsky, Olley, McMeekin, & Ball, 1982) was fitted (Eq. VI):
174 175
Eq. VI
𝑘𝑚𝑎𝑥 = 𝑏 (𝑃 ― 𝑃𝑚𝑖𝑛)
176 177
2.7 Model validation
178
In the biological validation process, 4 Salmonella serotypes (Typhi, Enteritidis, Agona,
179
Anatum, Steinberger), and 2 intermediary power levels (30 and 50 W) were used for
180
testing the model prediction. The predictive efficacy was determined by calculating the
181
model performance indices, Bias factor (Bf), and Accuracy factor (Af), (Eq. 7 and 8):
182
𝐵f = 10^ ∑log
183
𝐴f = 10^ ∑(log
[
( )⁄𝑛] 𝑝𝑟𝑒𝑑 𝑜𝑏𝑠
Eq. VII
[
( ))⁄𝑛]
Eq. VIII
𝑝𝑟𝑒𝑑 𝑜𝑏𝑠
184 185
where obs, pred, and n are the observed value, the predicted value, and the number of
186
observations, respectively.
187
3. Results
188
3.1 Effects of microwave heating on Salmonella Typhi inactivation
189
The inactivation of S. Typhi by the microwave heating is shown in Figure. 1.
190
Reductions of 9.22, 9.59, 8.23, and 8.57 log CFU/ mL were observed for the treatments at
191
20 W (750 s), 40 W (90 s), 60 W (120 s), and 80 W (120 s), respectively. Although the 8
ACCEPTED MANUSCRIPT 192
treatment at 20 W took longer, the samples resisted up to 750 s without apparent
193
degradation. At this power, a more significant effect on quantification was observed from
194
180 s, with a reduction of 3.72 log CFU/mL of the initial cell concentration. The
195
inactivation models and the kinetics parameters were presented in Table 1. For all
196
treatments, the models presented good adjustment, once high R² (R² > 0.97) and low
197
RMSE values (RMSE < 0.35) were obtained.
198
The fitted models showed the log linearity and non-log linearity behavior, as a function
199
of the power applied during the domestic microwave heating. At lower power levels, 20
200
and 40 W, data generated shoulder shape models, with no shoulder with the increase in
201
power to 60 and 80 W, and no tailing effect. According to Geeraerd, Herremans, and Van
202
Impe (2000), the presence of shoulder may be related to different factors, including the
203
food matrix, with a protective effect of proteins and lipids on the microbial cells, increasing
204
cell resistance to processing conditions, and the formation of clumps of bacteria,
205
conferring a protective effect.
206
At 20 W, a 4 D value of 228.8 s was observed, which emphasizes the presence of the
207
shoulder model and the resistance of the pathogen to the treatment. This is clearly
208
represented in Figure 1, which shows an inactivation curve with a much slower decay
209
when compared to the other treatments. In addition, at this power value, the best fit
210
presented a biphasic format, with two distinct inactivation rates, kmax1 and kmax2,
211
characterizing the presence of two subpopulations with different resistance mechanisms to
212
the treatment. As shown in the biphasic model in Table 1, the first subpopulation, less
213
resistant, had a higher inactivation rate (kmax1), where f represents this population
214
fraction. In contrast, the second subpopulation, represented by the fraction (1 - f), was
215
more resistant to the treatment, due to the lower kmax2 index.
9
ACCEPTED MANUSCRIPT 216
Figure 1 shows the behavior of the Salmonella population at 40 W. The curve was
217
adjusted by the Log-Linear + Shoulder model of Geeraerd, Herremans, and Van Impe
218
(2000), with the initial shoulder of the curve (about 22 s) similar to that obtained at 20 W
219
(about 20 s). In contrast, a great difference was observed for the inactivation rate, once
220
the 4 D value (50.4 ± 1.80 s) of the treatment at 40 W was about 4 times lower when
221
compared to the treatment at 20 W (228.8 ± 11.25 s). Generally, although the inactivation
222
rate increases with the increase in treatment intensity, this phenomenon was not observed
223
in the present research. When comparing the results of the inactivation rates at 40 W, 60
224
W, and 80 W, the kmax value at 40 W was higher than those at 60 and 80 W; in contrast,
225
the 4 D value at 40 W was higher due to the presence of the shoulder. At 60 W and 80 W,
226
the survival curves were described by the log-linear behavior, with 4D values of 45 and
227
32.1 s, respectively.
228
3.2 Secondary inactivation model and validation
229
From the kinetics inactivation data obtained in the primary models (Table 1), the
230
secondary square root models (Eq. IX) and (Eq. X) were developed to demonstrate the
231
correlation between the inactivation rate (Kmax) and power (W). For the secondary model,
232
a good fit was not achieved at 40 W, thus data were removed, and only data from the
233
other power levels were used, according to Table 2.
234
kmáx = (0.109412020127795) + (5.4750466004358E ― 3) ∗ Potência (W) Eq. IX
235 236
Eq. X
kmáx = 0.0055 (𝑃 + 9.98)
237 238
For validation of the secondary model, the correlation coefficient R2, the mean square
239
error (RMSE), the bias factor, and the accuracy were used to evaluate the adjustment. As
240
can be seen in Table 3, all evaluation parameters pointed to a good fit of the second 10
ACCEPTED MANUSCRIPT 241
model when using the results of S. Typhi serotype in the generation of the primary model,
242
with bias, accuracy, RMSE, and R2 values of 0.99, 1.10, 0.04, and 0.98, respectively. The
243
other serotypes used for model validation also showed good adjustments, with bias values
244
ranging from 0.95 to 1.05, accuracy from 1.06 to 1.10, RMSE from 0.03 to 0.05, and R2 of
245
0.98. Thus, the model can be used to predict the Salmonella behavior during heating of
246
infant formulas in lactaries using domestic microwave ovens and is an important tool to
247
guarantee the microbiological safety of the product.
248
4. Discussion
249
Several foodborne outbreaks have been associated with the presence of Salmonella
250
spp in foods, including infant formulas (Bermúdez-Aguirre & Corradini, 2012; Cahill,
251
Wachsmuth, Costarrica Mde, & Ben Embarek, 2008; Koseki, Nakamura, & Shiina, 2015;
252
Morlay, Piat, Mercey, & Roupioz, 2016; Van Asselt, Van der Fels-Klerx, Marvin, Van
253
Bokhorst-van de Veen, & Groot, 2017; WHO/FAO, 2003). However, no studies have been
254
found in literature about the effect of microwave heating on the Salmonella inactivation.
255
Thus, knowledge about the effect of microwaves on the inactivation rate of important
256
microorganisms involved in food outbreaks has a great importance in the actual scenario,
257
since this technology has been used frequently.
258
Some studies have been carried out in infant formulas involving other microorganisms,
259
mainly Cronobacter sakazakii. Kindle, Busse, Kampa, Meyer-Koenig, & Daschner (1996)
260
evaluated the effect of microwave heating on the inactivation of different microorganisms
261
(Pseudomonas aeroginosa, Klebsiella pneumonie, Escherichia coli, Staphylococcus aureus,
262
Candia albicans, Cronobacter sakazakii and Mycobacterium) in five infant milk formulas.
263
Those authors found a significant decrease in the microbial population after microwave
264
treatment, thus demonstrating the efficiency of the method. Other authors (Pina-Pérez,
265
Benlloch-Tinoco, Rodrigo, and Martinez, 2014) investigated the effectiveness of different 11
ACCEPTED MANUSCRIPT 266
microwave parameters (400 – 900 W varying from 0 to 120 s) for the inactivation of C.
267
sakazakii in reconstituted infant formula, and found a reduction of the initial C. sakazakii
268
population to undetectable levels (≥ 8 log cycles), in potencies varying from 800 to 900
269
W.
270
Concerning the Salmonella inactivation by microwave heating, this technique has been
271
considered as an important technology for Salmonella inactivation and food safety. Sung &
272
Kang (2014) evaluated the effect of microwave processing under various potencies (1.2,
273
1.8, 2.4, 3.6, and 4.8 kW) on the inactivation of Escherichia coli O157: H7, Salmonella
274
Typhimurium, and Listeria monocytogenes in salsa. Although the inactivation rates for the
275
microorganisms were not determined, the research showed that microwave can be an
276
important alternative for Salmonella inactivation in salsa, with no changes in the color
277
parameters. Similar results were observed by Song and Kang (2016), who reported that
278
the microwave (2, 4, and 6 kW) can be used as a control method to reduce Salmonella in
279
peanut butter without affecting the product's quality. Valero, Cejudo, & García-Gimeno
280
(2014) studied microwave heating and found a reduction of 4D in a few seconds at 70 °C
281
(300 W - 80 s, 450 W - 60 s, or 600 W/800 W - 40 s), which may be an alternative to
282
commercial establishments such as restaurants. In addition, the authors determined the
283
kinetic inactivation parameters from the linear log-shoulder model, with the presence of
284
shoulder in all potencies studied, opposite from the present results, which showed a linear
285
log reduction with the increase in the potency values. For the inactivation kinetics, the
286
authors found higher inactivation rates at 600 W when compared to 800 W, which was
287
also observed in the present study, with higher inactivation rates at 60 W when compared
288
to 80 W.
289
This phenomenon may have occurred due to the evaporation of part of water, which
290
may have led to a reduction in heat transfer in the sample. Song and Kang (2016) 12
ACCEPTED MANUSCRIPT 291
evaluated the effect of water activity (Aw = 0.30, 0.40, and 0.50) on the time x
292
temperature profile during microwave heating (2, 4, and 6 kW) in peanut butter, and
293
found that the lower the Aw, the lower heating rates. In addition, the kinetics inactivation
294
of Salmonella, E.coli, and Listeria monocytogenes was lower at lower Aw values, showing
295
that Aw is an important factor during microwave heating, directly affecting the inactivation
296
rate (Song & Kang, 2016).
297
Overall, due the increased use of microwave heating in lactaries, our findings present
298
relevance for food industry and bring interesting findings that enable a better
299
understanding about a more useful usage of this emerging technology for people who
300
work in these establishments. In this sense, the predictive models presented here
301
presented practical and should be taken in consideration.
302 303
5. Conclusion
304
The present results allowed the characterization of the behavior of Salmonella in the
305
form of contaminated infant milk formulas submitted to microwave heating, which is a
306
common procedure used in lactary centers. The treatment at 20 W was characterized by a
307
processing time greater than 750 s, but was efficient in the elimination of the pathogen,
308
with a reduction of 9.26 log CFU/mL. For the other potencies (40, 60, and 80 W) the
309
microorganism resisted from 90 to 120 s, indicating a poor survival of this microorganism.
310
Based on the good performance of the treatments, the predictive models were
311
effective for Salmonella inactivation, in view of the resistance of this agent in food, which
312
guarantees an effective food safety against possible contamination by this microorganism.
313 314 315 13
ACCEPTED MANUSCRIPT 316 317 318 319 320 321 322
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may
ACCEPTED MANUSCRIPT 12.00
Log N (UFC/mL)
10.00
8.00
6.00
4.00
2.00
0.00 0
100
200
300
400
500
600
700
Time (s) 20W
40W
60W
80W LINEAR
Figure 1. Survival curves for S. Typhi in in infant milk formula treated with microwave heating at 20 W (●), 40 W (▲), 60 W (■) and 80 W (x).
800
ACCEPTED MANUSCRIPT Predictive model for inactivation of Salmonella in infant formula in
1
2
microwave heating processing;
3
Initial biphasic profile with a linear log behavior was observed;
4
Excellent bias, accuracy, RMSE, and R2 values founds at the validation
5
step.
6
Table 1. Kinetics parameters obtained to survival data of Salmonella in infant milk treated by microwave heating* Power (W)
20
Models
Geeraerd, Valdramidis, and Van Impe (2005), Biphasic+ Shoulder
Parameters f
0.99 ± 0.002
Kmax1
0.04 ± 0.005
Kmax2
0.02 ± 0.005
Log10(N0)
10.71 ± 0.090
Sl (shoulder length)
40
60
Geeraerd, Herremans, and Van Impe (2000), LogLinear + Shoulder
Bigelow (1921), LogLinear Regression
Kmax
Time 4D (s)
R²
RMSE
228.8 ± 11.25
0.992 ± 0.001
0.118± 0.016
50.4 ± 1.80
0.986 ± 0.002
20.4 ± 8.16 0.3 ± 0.01
Log10(N0)
10.17 ± 0.045
Sl (shoulder length)
21.92 ± 0.86
Kmax
0.2 ± 0.01 45.0 ± 0.00
Log10(N0)
9.05 ± 0.075
0.990 ± 0.002
0.359± 0.047
0.086± 0.017
80
Bigelow (1921), LogLinear Regression
Kmax
0.28 ± 0.005 32.1 ± 0.35
Log10(N0)
8.67 ± 0.150
* Results are expressed in mean ± standard deviation. Analysis performed in triplicate.
0.979 ± 0.002
0.273± 0.023
Table 2. Parameters of the secondary model. Power (W)
kmax
√kmax
4D
log 4D
20
0.045
0.2121
228.75
2.36
60
0.21
0.4583
45
1.65
80
0.285
0.5338
32.18
1.51
Table 3. Data resulting from the validation of the secondary model Sorotype
Bias
Accuracy
RMSE
R²
Typhi
0.99
1.10
0.04
0.98
Enteritidis
0.98
1.09
0.04
0.98
Agona
0.95
1.06
0.03
0.98
Anatum
1.05
1.16
0.05
0.98
Steinberger
0.98
1.08
0.03
0.98