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Modeling of Byssochamys nivea and Neosartorya fischeri inactivation in papaya and pineapple juices as a function of temperature and soluble solids content
Accepted Manuscript Modeling of Byssochamys nivea and Neosartorya fischeri inactivation in papaya and pineapple juices as a function of temperature and soluble solids content Poliana B.A. Souza, Keilane F. Poltronieri, Verônica O. Alvarenga, Daniel Granato, Angie D.D. Rodriguez, Anderson S. Sant’Ana, Wilmer E.L. Peña PII:
S0023-6438(17)30240-2
DOI:
10.1016/j.lwt.2017.04.021
Reference:
YFSTL 6159
To appear in:
LWT - Food Science and Technology
Received Date: 25 January 2017 Revised Date:
3 April 2017
Accepted Date: 8 April 2017
Please cite this article as: Souza, P.B.A., Poltronieri, K.F., Alvarenga, Verô.O., Granato, D., Rodriguez, A.D.D., Sant’Ana, A.S., Peña, W.E.L., Modeling of Byssochamys nivea and Neosartorya fischeri inactivation in papaya and pineapple juices as a function of temperature and soluble solids content, LWT - Food Science and Technology (2017), doi: 10.1016/j.lwt.2017.04.021. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Modeling of Byssochamys nivea and Neosartorya fischeri inactivation in
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papaya and pineapple juices as a function of temperature and soluble solids
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content
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Poliana B.A. Souza1, Keilane F. Poltronieri1, Verônica O. Alvarenga2, Daniel Granato3, Angie
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D. D. Rodriguez4, Anderson S. Sant’Ana2*, Wilmer E.L. Peña1,4
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Department of Rural Engineering. Federal University of Espírito Santo. Alegre, ES - Brazil.
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Department of Food Science, Faculty of Food Engineering, University of Campinas,
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Campinas, SP - Brazil.
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Brazil.
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Department of Food Technology, Federal University of Viçosa, Viçosa, MG - Brazil.
Abbreviated running title: Modeling heat-resistant molds inactivation.
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Department of Food Engineering. State University of Ponta Grossa, Ponta Grossa, PR –
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*Corresponding author: A.S.Sant’Ana ([email protected]). Rua Monteiro Lobato, 80.
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Campinas, SP, Brazil. Phone: +55(19)3521-2174.
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ACCEPTED MANUSCRIPT Abstract
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This study aimed to model the inactivation of B. nivea and N. fischeri ascospores in pineapple
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and papaya juices as influenced by temperature (78, 80, 85, 90 and 92 ºC) and soluble solids
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concentration (10, 13, 20, 27 and 30 °Brix). First, a primary model was used to fit the Weibull
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model to inactivation data obtained from a combination of temperature and soluble solids
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concentration and to calculate δ (time for the first decimal reduction) and p (shape parameter).
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Then, a secondary model was used to describe how the inactivation kinetic parameters of
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these fungi in pineapple and papaya juices varied with the changes in temperature and soluble
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solids concentration. The shape parameter (p) was fixed for each strain and at temperature and
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soluble solids concentration studied. The results indicated that both linear and quadratic
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effects of temperature as well as the interaction between temperature and total soluble solids
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were deemed significant on δ value for both B. nivea and N. fischeri (except for B. nivea in
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papaya juice). This study contributes to the field by bringing new predictive models
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describing the influence and interactions of mild temperature conditions and soluble solids
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contents of fruit juices on the inactivation kinetics of heat-resistant fungi.
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Key-words: Predictive microbiology, food spoilage, fruit juice, thermal processing, Weibull model.
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1)
Introduction Fungi such as Penicillium, Aspergillus, Alternaria are the main microorganisms
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associated with spoilage of a wide variety of foods (Pitt and Hocking, 1999). Regardless of
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this, these genera mainly include species that are not able to tolerate harsh food processing
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conditions. On the other hand, some species of Byssochlamys, Neosartorya, Talaromyces and
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Eupenicillium, comprise fungi presenting high chemical and heat resistances (Suresh et al.,
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1996, Tournas, 1994, Valik and Pieckova, 2001). Therefore, these fungi are of major
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relevance for the stability of thermally processed foods, such as fruit juices and purees.
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The thermal and chemical resistances of B. nivea, B. fulva, N. fischeri, T. flavus are
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related to the presence of structures known as ascospores, which confers the ability to survive
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after heating at least at 80°C per 30 minutes (Kotzekidou, 1997, Pitt and Hocking, 1999,
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Tournas, 1994). As ascospores are exposed to sub lethal temperature conditions, they are
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activated, germinate and can multiply on the product during storage. This can further lead to
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spoilage and extensive economic losses for the industry (Slongo and Aragao, 2006). Several
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D values (the time at a specific temperature needed to cause one log cycle reduction in the
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population of a target microorganism) or inactivation kinetic parameters for heat-resistant
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fungi have been reported in the literature (Engel and Teuber, 1991, Delgado et al., 2012a,b,
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Rajashekhara et al., 1996, Sant’Ana et al., 2008, Tournas and Traxler, 1994). D-values at
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90°C ranging from <2 minutes to 6 minutes, for example, have been found for different
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species of heat-resistant fungi. It is known that D-values and other inactivation kinetic
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parameters may vary with substrata, pH, soluble solids contents, water activity, presence of
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preservatives, among other factors (Engel and Teuber, 1991, Delgado et al., 2012a,b,
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Rajashekhara et al., 1996, Tournas and Traxler, 1994).
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ACCEPTED MANUSCRIPT Because of their high heat-resistance, ascospores of the species B. nivea and N. fischeri
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were used as targets of fruit juices thermal processes for several years (Eicher, 2002,
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Sant’Ana et al., 2008). However, the importance of heat-resistant fungi as targets of thermal
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processing of acidic foods declined in the last decades with the emergence of Alicyclobacillus,
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an acidothermophilic sporeforming bacterium presenting D-values higher than those reported
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for heat-resistant fungi (Mcknight et al., 2011, Spinelli et al., 2009, 2010). Because of the
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high heat-resistance of Alicyclobacillus spores, several industries have applied thermal
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processes that can reach up to 115°C/15-30 seconds in order to produce shelf-stable fruit
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juices. Although thermal processing at these time and temperature conditions will ensure the
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inactivation of heat-resistant fungi in fruit juices (Tribst et al., 2009), the over concern with
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Alicyclobacillus has led to a lack of interest on heat-resistant fungi. Nonetheless, the
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importance of heat-resistant fungi as fruit juice spoilers should not be underestimated because
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these microorganisms can pose shelf-stability problems in mild thermally processed fruit
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juices and acidic foods. This gains importance in an era of consumer concerns about
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ultraprocessed foods (Moubarac et al., 2012) and willingness to purchase foods not subjected
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to intense processing (Ragaert et al., 2004). Additionally to spoilage problems, heat-resistant
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fungi such as B. nivea and B. fulva can produce mycotoxins, such as patulin, potentially
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posing a threat for food safety (Sant’Ana et al., 2008, Sant’Ana et al., 2010). In the scenario
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described above, the importance of heat-resistant fungi for the microbiological quality and
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safety of fruit juices is then revisited.
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Several studies regarding the inactivation kinetics of heat-resistant fungi can be found in
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the literature (Engel and Teuber, 1991, Delgado et al., 2012a, Rajashekhara et al., 1996,
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Sant’Ana et al., 2008, Tournas and Traxler, 1994). Regardless of this, studies dealing with the
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modeling of combined factors and their interactions on thermal resistance of heat-resistant
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fungi are scarce. It is known that temperature and soluble solids contents are two major 4
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al., 2009). Therefore, the objective of this study were to determine the inactivation kinetic
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parameters of B. nivea and N. fischeri ascospores in pineapple and papaya juices and to assess
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the impact of temperature and soluble solids concentration on these parameters through a
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secondary modeling approach.
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Material and Methods
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2.1) Microorganisms and preparation of suspensions of spores
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B. nivea LB01 and N. fischeri LB11 isolated from fruit juices and belonging to the
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culture collection of the Laboratory of Quantitative Food Microbiology at the University of
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Campinas, SP, Brazil, were used in this study. The fungi were grown on Potato Dextrose
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Agar (PDA, Himedia Laboratories, Mumbai, India) at 30°C for seven days. Then, the colonies
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were washed with sterile distilled water. Roux bottles containing 180 mL of Malt Extract
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Agar (MEA, Difco Laboratories, Detroit, MI, USA) were inoculated with 0.5 mL following
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incubation at 30°C for 30 days. The suspension of ascospores were obtained as previously
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described by Sant’Ana et al. (2009) and further stored at 4ºC until used. The concentration of
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ascospores was determined after activation at 80°C for 10 minutes, followed by serial
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dilutions in sterile 0.1% peptone water and plating in MEA. The concentration of the
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ascospores suspensions was adjusted at 107 ascospores/mL (Delgado et al., 2009a,b).
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2.2) Preparation of Fruit Juices
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Commercial pineapple (pH 3.7 and 16 °Brix) and papaya (pH 3.9 and 13 °Brix) juices
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free of preservatives were used in the experiments. Soluble solids concentration values (10,
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13, 20, 27 and 30 °Brix) were adjusted using sucrose or sterile distilled water. The soluble
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solids concentration was measured using a refractometer (model Abbe, Atago, Tokyo, Japan). 5
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The juices were subjected to thermal treatment at 105 ºC for 10 min to inactivate any potential
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contaminants (Sant’Ana et al., 2009).
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2.3) Determination of B. nivea and N. fischeri ascospores heat resistance in pineapple and
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papaya juices at different temperatures and soluble solid concentrations
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Estimation of thermal inactivation kinetics of B. nivea and N. fischeri ascospores in
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pineapple and papaya juices was performed in thermal death tubes (TDT, 8 mm external
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diameter, 6 mm internal diameter and 1 mm wall thickness). The TDT tubes were filled with
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1 mL of pineapple and papaya juices at different soluble solid concentrations and 1 mL of the
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ascospore suspension, resulting in a final concentration of 106 ascospores/mL. The procedures
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for determining heat resistance were those previously described by Sant’Ana et al. (2009).
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In order to investigate the influence of interactions between temperature and soluble
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solids on thermal inactivation kinetics of B. nivea and N. fischeri ascospores, a central
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composite design for two factors was used. The ranges of the factors were 78, 80, 85, 90 and
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92 ºC, for temperature, and 10, 13, 20, 27 and 30 °Brix, for soluble solids concentration. The
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conditions studied comprise the range of temperature and soluble solids which mild-processed
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fruit juices are subjected during processing and commercialization (Tables 1 and 2 contain the
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experimental design).
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2.4) Modeling of B. nivea and N. fischeri ascospores inactivation in pineapple and papaya
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juices as a function of temperature and soluble solid concentration
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2.4.1) Primary modeling
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As the inactivation kinetics data was found to mainly follow a nonlinear trend, the
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Weibull model (Equation 1) described by Mafart et al. (2002) was chosen to fit the data of
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survival of heat-resistant molds studied herein (primary modeling). The GinaFiT software 6
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(Geeraerd et al., 2005) was used to fit the model to the data and to estimate the main
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parameters of inactivation of the Weibull model, i.e., δ and p. Equation 1
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Where: N (population at time t), N0 (initial population), t (time), δ (time for the first
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decimal reduction) and p (shape parameter). The δ (unity is time) represents the probability
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distribution describing the time interval for failure to occur (microbial death). The p value
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(which is dimensionless) describes the curvature of the microbial survival along the time. If
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p=1 the susceptibility of microbial cells does not change throughout the time, while for p>1
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and p<1, the microbial cells become more and less susceptible throughout the time (van
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Boekel, 2002). The standard deviation and R2 values were obtained for models dealing with
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inactivation of B. nivea and N. fischeri ascospores in the different conditions assessed.
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2.4.2) Secondary modeling
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A response surface model was used to describe the effect of temperature and soluble
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solids concentration (independent variables) on the δ parameter (response variable) for both
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B. nivea and N. fischeri ascospores. For this purpose, a two-factor central composite, rotatable
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design added with cube and central points were used, totalizing 11 experiments for each
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microorganism (Tables 1 and 2).
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Experimental data were fit into a quadratic model and the interaction between factors was assessed according to Equation 2:
Equation 2
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Where δ represents the time, expressed in minutes for the first decimal reduction,
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b0...b5 are the regression coefficients obtained by multiple regression analysis, SS is the 7
ACCEPTED MANUSCRIPT concentration of soluble solids, expressed in °Brix, and T is the temperature given in °C. The
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equations provided for each microorganism for each type of juice were obtained using real
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values instead of using coded values. As the shape parameter (p) is also needed to describe
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the inactivation kinetics by the Weibull model (Mafart et al., 2002), single p values for each
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fungal strain heated at specific conditions (juice, temperature and soluble solid concentration)
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were estimated from Equation 1. This was done through an iterative approach using solver
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ability of Excel as previously described in Mafart et al. (2002) and Sant’Ana et al. (2008).
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The secondary model built based on Equation 2 and the respective p values obtained through
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the iterative procedure described above allow the estimation of the F-value of the process
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(Equation 3) (Mafart et al., 2002) for each fungal strain heated at specific conditions studied:
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Equation 3.
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Where: F-value= the relative time required to inactivate the population of a target
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microorganism under specified conditions, n= ratio of decimal reduction, δ*= time for the first
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decimal reduction at reference temperature (T*).
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The quality of the RSM model was assessed by analysis of variances (ANOVA), and
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the regression equation contained only the significant coefficients (p < 0.10). Additionally,
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the percentage of the variability explained by the model was calculated and expressed by the
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determination coefficient (R2) and adjusted determination coefficient (R2adj). For all inferential
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tests, an α = 10% was used to assess statistically different results (Granato et al., 2014).
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3)
Results and Discussion
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The experimental survivor curves for B. nivea and N. fischeri at specific conditions
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studied according to Tables 1 and 2 were characterized by a slightly deviation of the log8
ACCEPTED MANUSCRIPT linear inactivation kinetics. Several studies on the thermal inactivation of heat-resistant fungi
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(Delgado et al., 2012a, Gressoni and Massaguer, 2003, Houbraken et al., 2006, Rajashekhara
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et al., 2000) have reported that the heat inactivation of these microorganisms followed a
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nonlinear inactivation kinetics. However, in most of these studies, the linearization method of
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Alderton Snell (1970) was used as an alternative for estimation of inactivation kinetics.
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Despite this, in the present study data was dealt with through the Weibull model (Mafart et
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al., 2002) using the GinaFiT add-in for Excel (Geeraerd et al., 2005). Even when the
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deviation of the inactivation kinetics is not strongly pronounced, the use of models that
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properly take into account that pattern of microbial death is preferably, as it can avoid issues
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in estimating thermal processing requirements for a specific food. In this sense, the first
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indication of the fitness of the data to a model is the R2 value. The R2 value is the adequate
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measure of the percentage of entire variation of results described by the model (Draper and
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Smith, 1998). As seen in Tables 1 and 2, R2 values were always above 0.96 indicating an
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excellent fitting of the Weibull model to the data.
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As the Weibull model properly fitted the data, the kinetic parameters for inactivation of
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B. nivea and N. fischeri in pineapple and papaya juices as a function of temperature and
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soluble solids were further determined (Tables 1 and 2). In general, δ values were very close
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for B. nivea and N. fischeri in both juices, except for those experiments performed at 80 °C
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with pineapple juice containing 13 and 27 °Brix (Tables 1 and 2), respectively. Nonetheless,
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regardless of the juice, it can be seen that the concentration of soluble solids affected the δ
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values. Generally, an increase of ºBrix of the juice resulted in an increase of δ-value (Tables 1
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and 2). It is known that the greater the concentration of soluble solids in the heating medium
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the more protected the ascospores of heat-resistant fungi are (Baglioni, 1998), which has
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markedly impacts on the design of thermal processing.
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inactivation, such as temperature and soluble solids content, relies on the fact that industry
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aims at a diversification of their products (such as juices with different and high soluble solids
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contents) besides healthy appeals. It is known that thermal resistance of fungi such as N.
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fischeri and B. nivea is highly influenced by soluble solids content and temperature (Beuchat,
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1986, Tournas, 1994). Therefore, estimating the impact of temperature and soluble solids
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interactions on the inactivation kinetics of N. fischeri and B. nivea under conditions studied is
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of relevance. Table 3 shows the results of the ANOVA and the coefficients obtained by
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multiple regression analysis (RSM). Both linear and quadratic effects of temperature as well
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as the interaction between temperature and total soluble solids were deemed significant on δ
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value for both B. nivea and N. fischeri (except for B. nivea in papaya juice) (Table 3). The
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linear effects presented a higher quantitative impact on δ values as compared to quadratic
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effects. While the increase in temperature decreased the δ values, a higher concentration of
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soluble solids increased the value of this parameter. On the other hand, the quadratic effect of
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temperature contributed significantly (p < 0.10) for the increase of δ.
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The regression coefficients shown in Table 3 were obtained using the real values in the
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multiple regression analysis. These polynomial mathematical models describe the effects of
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temperature and soluble solids concentration on the time for the first decimal reduction (δ) of
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both N. fischeri and for B. nivea in pineapple and papaya juices, respectively. These equations
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were obtained after removing the non-significant effects (p > 0.10) as they do not provide an
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impact on the time for the first decimal reduction of the studied fungi. In this study, single p-
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values (shape parameter) were obtained for each heat resistant studied using the procedures
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described in Mafart et al. (2002) and Sant’Ana et al. (2008). The average p-values estimated
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for B. nivea in pineapple and papaya juices at the conditions shown in Table 1 were 0.79 and
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ACCEPTED MANUSCRIPT 1.04, respectively. For N. fischeri, the average p-values estimated for heating in pineapple and
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papaya juices were 1.00 and 1.07, respectively. The estimation of fixed p-values and their use
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with δ obtained through polynomial models (Table 3) is proposed for the first time and allow
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the calculation of F-values using Equation 3. Therefore, in those cases in which microbial
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death does not follow log-linear kinetics and secondary models taking into account
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intrinsic/extrinsic factors are built, this approach can be used for determination of F-values.
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This is needed because p-values are not strongly correlated with temperature (Mafart et al.,
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2002), while the contrary is observed with δ, which is influenced by heating temperature
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(Couvert et al., 2005).
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Regarding the statistical quality of the fitted models, it is necessary to assess the t-
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values, p-values and R2 for each proposed multiple regression model. The models were highly
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significant (p < 0.001) and the R2 values ranged between 0.88 and 0.99, indicating that the
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models were able to explain more than 88% of the variability of the data, thus validating their
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ability to explain the effect of temperature and soluble solids concentration on the time for the
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first decimal reduction of N. fischeri and B. nivea in pineapple and papaya juices. It is
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noteworthy that microbiological data present naturally a considerable variation within assays,
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so if a mathematical model that is able to explain more than 70% of the experimental data is
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attained, it is possible to assume that this model has a good predictive ability (Granato et al.,
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2010, Peña et al., 2014).
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Figure 1 presents the three-dimensional response surfaces for the four multiple
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regression models. As we can observed in these surfaces, an increase in the temperature from
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78 to 92 ºC caused a decrease of up to 10 times in the δ value, for both B. nivea and N.
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fischeri in papaya juice. In the case of pineapple juice, this decrease was more pronounced,
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mainly for B. nivea (Figures 1a and 1c). According to the results, it is not possible to point
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that one genus of fungi was more heat-resistant than the other was. In fact, thermal resistance
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of heat-resistant fungi seems to vary with the strain (Tournas, 1994). This study contributes to the field by bringing new predictive models describing the
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influence and interactions of mild temperature conditions and soluble solids contents of fruit
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juices on the inactivation kinetics of heat-resistant fungi. In addition, a strategy to use δ-
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derived from secondary models and their combination with fixed p-values (p-values of a
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strain) is proposed. The models built herein are of relevance in a context in which consumers
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are seeking for a variety of foods and beverages subjected to less intense processing. It should
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be highlighted that producers of shelf-stable fruit juices have been forced in the last years to
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increase processing temperature (as high as 116 °C, for example) aiming at inactivating
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Alicyclobacillus spores. Thus, the temperature range studied herein (78 - 92 ºC) represent
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conditions that are not sufficient for inactivation of Alicyclobacillus (Spinelli et al., 2009)
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(currently, the main target of fruit juice industries). Nonetheless, this temperature range can
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lead to shelf-stable fruit juices if combined with refrigeration or other technologies. It is
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noteworthy to mention that Alicyclobacillus does not grow at temperatures below 20 °C
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(Spinelli et al., 2009) and as such, thermal processing could merely be focused in the
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inactivation of heat-resistant fungi while refrigeration could efficiently control the growth of
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Alicyclobacillus. Regardless of this fact, it should be highlighted that continuous efforts must
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be done in order to reduce the chances of fruit contamination by ascopores of heat-resistant
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fungi.
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Acknowledgements
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The authors show their gratitude to Conselho Nacional de Desenvolvimento Cientifico e
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Tecnológico (CNPq) (Process 400806/2013-4 and 302763/2014-7), Coordenação de
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Aperfeiçoamento de Pessoal de Nível Superior (CAPES/PNPD – D. Granato) and Fundação 12
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de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for the financial support of projects
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undertaken at Laboratory of Quantitative Food Microbiology, University of Campinas.
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4)
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Moubarac, J., Martins, A. P. B., Claro, R. M., Levy, R. B., Cannon, G., & Monteiro, C. A.,
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Peña, W. E. L., de Andrade, N. J., Soares, N. F. F., Alvarenga, V. O., Rodrigues Junior, S.,
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Granato, D., Zuniga, A.D.G., Sant'Ana, A.S., 2014. Modelling bacillus cereus adhesion on
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stainless steel surface as affected by temperature, pH and time. International Dairy Journal
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34, 153-158.
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Pitt, J. I.; Hocking, A. D., 1999. Fungi and food spoilage. 2. ed. Gaithersburg: Aspen, p.592.
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Ragaert, P., Verbeke, W., Devlieghere, F., & Debevere, J., 2004. Consumer perception and
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choice of minimally processed vegetables and packaged fruits. Food Quality and Preference
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Rajashekhara, E., Suresh, E.R. & Ethiraj, S., 1996. Influence of different heating media on
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thermal resistance of Neosartorya fischeri isolated from papaya fruit. Journal of Applied
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Bacteriology 81, 337-340.
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Rajashekhara, E., Suresh, E. R., Ethiraj, S., 2000. Modulation of thermal resistance of
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ascospores of Neosartorya fischeri by acidulants and preservatives in mango and grape fruit,
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Food Microbiology 17, 269-275.
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Sant'Ana, A.S.; Rosenthal, A.; Massaguer, P.R., 2008. The fate of patulin in apple juice
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processing: A review. Food Research International 41, 441-453.
Brazilian
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juices:
Isolation,
genotypic
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continuous pasteurization on the inactivation of Byssochlamys fulva ascospores in clarified
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apple juice, Journal of Applied Microbiology 107, 197-209.
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Sant’Ana, A.S.; Simas, R.C.; Almeida, C.A.A.; Cabral, E.C.; Rauber, R.H.; Mallmann, C.A.;
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Eberlin, M.N.; Rosenthal, A.; Massaguer, P.R., 2012. Influence of package, type of apple
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juice and temperature on the production of patulin by Byssochlamys nivea and Byssochlamys
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fulva. International Journal of Food Microbiology 173, 299-302.
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Slongo, A.P.; Aragão, G.M.F., 2006. Factors affecting the thermal activation of Neosartorya
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fischeri in pineapple and papaya nectars. Brazilian Journal of Microbiology 37, 266-270.
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Spinelli, A. C. N. F., Sant'Ana, A. S., Rodrigues Jr., S., & Massaguer, P. R., 2009. Influence
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of different filling, cooling, and storage conditions on the growth of Alicyclobacillus
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acidoterrestris CRA7152 in orange juice. Applied and Environmental Microbiology 75, 7409-
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7416.
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Spinelli, A. C. N. F., Sant'Ana, A. S., Pacheco-Sanchez, C. P., & Massaguer, P. R., 2010.
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Influence of the hot-fill water-spray-cooling process after continuous pasteurization on the
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number of decimal reductions and on Alicyclobacillus acidoterrestris CRA 7152 growth in
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orange juice stored at 35 °C. International Journal of Food Microbiology 137, 295-298.
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Suresh, E.R., Ethiaraj, S., Jayaram, H.L., 1996. Heat resistance of Neosartorya fischeri
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isolated from grapes. Journal of Food Science and Technology 33, 76-78.
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Tournas, V., 1994. Heat-resistant fungi of importance to the food and beverage industry.
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Critical Reviews in Microbiology 20, 243-263.
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Tournas, V., Traxler, R.W., 1994. Heat resistance of a Neosartorya fischeri strain isolated
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from pineapple juice frozen concentrate. Journal of Food Protection 57, 814-816.
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Valík, L.; Piecková, E., 2001. Growth modeling of heat-resistant fungi: the effect of water
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activity. International Journal of Food Microbiology 63, 11-17.
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Tables
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Table 1: Experimental design and data concerning the inactivation of B. nivea ascospores in pineapple and papaya juices as a function of temperature and soluble solids.
Papaya juice
Pineapple juice
p ± sd*
132.2 ± 48.9 168.9 ± 41.1 27.0 ± 16.9 44.0 ± 7.6 3.6 ± 0.7
0.81 ± 0.16 0.87 ± 0.13 0.78 ± 0.28 1.07 ± 0.15 0.57 ± 0.06
7.2 ± 1.7 1.5 ± 0.7 454.4 ± 144.7 27.5 ± 10.3 37.2 ± 12.3 35.4 ± 5.9
0.69 ± 0.12 0.72 ± 0.18 0.90 ± 0.20 0.88 ± 0.19 0.90 ± 0.21 0.84 ± 0.09
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δ ± sd* (min)
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-1 -1 -1 1 0 -1.41 0 1.41 1 -1 1 1 1.41 0 -1.41 0 0 0 0 0 0 0 *sd=standard deviation.
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Temperature Soluble solids
Real values Temperature Soluble (ºC) solids (°Brix) 80 13 80 27 85 10 85 30 90 13 90 27 92 20 78 20 85 20 85 20 85 20
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R
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R2
δ ± sd* (min)
p ± sd*
0.98 0.99 0.96 0.99 0.99
301.4 ± 93.2 317.5 ± 68.7 27.2 ± 5.9 48.1 ± 6.8 10.4 ± 1.8
1.23 ± 0.36 1.24 ± 0.27 0.80 ± 0.10 0.69 ± 0.06 1.44 ± 0.27
0.96 0.98 0.99 0.99 0.98
0.99 0.98 0.98 0.98 0.98 0.99
10.9 ± 2.8 2.9 ± 0.3 374.5 ± 34.7 46.2 ± 1.9 41.3 ± 7.5 40.9 ± 7.6
1.32 ± 0.38 0.94 ± 0.08 0.80 ± 0.05 1.07 ± 0.04 0.93 ± 0.13 0.92 ± 0.13
0.97 0.99 0.99 0.99 0.99 0.99
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Table 2: Experimental design and data concerning the inactivation of N. fischeri ascospores in pineapple and papaya juices as a function of temperature and
Papaya juice
Pineapple juice δ ± sd* (min)
p ± sd*
0.76 ± 0.12 0.86 ± 0.14 0.76 ± 0.11 1.27 ± 0.34 1.09 ± 0.11 1.04 ± 0.31 0.83 ± 0.06 0.62 ± 0.02 1.2 ± 0.08 1.21 ± 0.20 1.33 ± 0.05
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225.4 ± 61.9 338.4 ± 75.7 35.8 ± 7.4 46.5 ± 13.2 3.9 ± 0.5 6.3 ± 1.7 0.8 ± 0.1 424.0 ± 24.7 41.6 ± 3.2 41.0 ± 7.9 44.9 ± 1.9
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Temperature Soluble solids
Real values Temperature Soluble (ºC) solids (°Brix) 80 13 80 27 85 10 85 30 90 13 90 27 92 20 78 20 85 20 85 20 85 20
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0.99 0.99 0.99 0.97 0.99 0.97 0.99 0.99 0.99 0.99 0.99
δ ± sd* (min)
p ± sd*
146.6 ± 56.1 183.4± 72.2 37.3 ± 5.4 48.5 ± 3.0 3.3 ± 0.8 5.8 ± 1.7 0.7 ± 0.2 426.9 ± 102.2 39.3 ± 3.8 44.8 ± 13.8 38.7 ± 4.2
1.09 ± 0.27 1.19 ± 0.36 1.17 ± 0.13 0.97 ± 0.06 0.54 ± 0.09 1.18 ± 0.39 0.46 ± 0.07 1.31 ± 0.26 0.69 ± 0.05 1.16 ± 0.32 1.13 ± 0.10
R2 0.98 0.97 0.99 0.99 0.99 0.96 0.99 0.98 0.99 0.97 0.99
Table 3: Regression coefficients and statistical parameters of the polynomial models for δ (min) for the ACCEPTED MANUSCRIPT inactivation of N. fischeri and B. nivea ascospores in pineapple and papaya juices.
Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) Soluble solid (°Brix)(Q) 1L by 2L R2 adj R2 p-value (model)
p-value
-95% CI
+95% CI
0.001 0.001 0.001
23177.99 -660.01 3.01
28806.90 -530.18 3.77
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B. nivea - pineapple 654.12 39.74 15.09 -39.44 0.09 38.40
25992.44 -595.09 3.39
6.51
4.81
0.041
3.30
59.31
-0.25 -0.24 0.88 0.75 <0.001
0.04 0.07
-5.77 -3.21
0.029 0.085
-0.44 -0.55
-0.06 0.08
<0.001 <0.001 <0.001
25122.96 -632.68 3.14
28120.72 -562.04 3.56
0.031
0.18
1.46
<0.001 <0.001 <0.001
26169.99 -655.52 3.47
28460.93 -602.68 3.78
B. nivea - papaya 348.36 76.42 8.21 -72.77 0.05 69.35
26621.84 -597.36 3.35 0.82
0.15
0.97 0.96 <0.001 27315.46 -629.10 3.62
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N. fischeri - pineapple 266.22 102.60 6.14 -102.45 0.04 100.82
67.06
2.65
25.32
0.002
55.67
78.46
0.06 -0.79 0.99 0.98 <0.001
0.02 0.03
3.38 -26.33
0.078 0.001
-0.02 -0.92
0.14 -0.66
<0.001 <0.001 <0.001
21739.80 -580.10 2.81
25407.53 -495.51 3.30
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Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) Soluble solid (°Brix)(Q) 1L by 2L R2 adj R2 p-value (model)
t-value
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Standard error
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Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) Soluble solid (°Brix)(Q) 1L by 2L R2 adj R2 p-value (model)
Regression coefficient
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Parameter
23573.66 -537.80 3.06
N. fischeri - papaya 426.22 55.31 9.83 -54.71 0.06 53.12
29.90
4.24
7.05
0.020
11.65
48.14
-0.20 -0.24 0.90 0.82 <0.001
0.03 0.05
-7.13 -5.10
0.019 0.036
-0.32 -0.45
-0.08 -0.04
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Figure captions
Figure 1: Response surface plots showing the inactivation of N. fischeri and B. nivea as a function of temperature and soluble solids in pineapple (A,C) and papaya juices
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Research Highlights
Inactivation of B.nivea and N.fischeri in pineapple and papaya juice was studied Temperature, T, (78-92ºC) and soluble solids, SS, (10-30°Brix) were factors
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Linear and quadratic effects of T, SS and their interaction were significant on δ Fixed p-values for each strain were obtained and may allow estimation of F-
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S0023-6438(17)30240-2
DOI:
10.1016/j.lwt.2017.04.021
Reference:
YFSTL 6159
To appear in:
LWT - Food Science and Technology
Received Date: 25 January 2017 Revised Date:
3 April 2017
Accepted Date: 8 April 2017
Please cite this article as: Souza, P.B.A., Poltronieri, K.F., Alvarenga, Verô.O., Granato, D., Rodriguez, A.D.D., Sant’Ana, A.S., Peña, W.E.L., Modeling of Byssochamys nivea and Neosartorya fischeri inactivation in papaya and pineapple juices as a function of temperature and soluble solids content, LWT - Food Science and Technology (2017), doi: 10.1016/j.lwt.2017.04.021. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Modeling of Byssochamys nivea and Neosartorya fischeri inactivation in
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papaya and pineapple juices as a function of temperature and soluble solids
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content
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Poliana B.A. Souza1, Keilane F. Poltronieri1, Verônica O. Alvarenga2, Daniel Granato3, Angie
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D. D. Rodriguez4, Anderson S. Sant’Ana2*, Wilmer E.L. Peña1,4
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Department of Rural Engineering. Federal University of Espírito Santo. Alegre, ES - Brazil.
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Department of Food Science, Faculty of Food Engineering, University of Campinas,
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Campinas, SP - Brazil.
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Brazil.
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Department of Food Technology, Federal University of Viçosa, Viçosa, MG - Brazil.
Abbreviated running title: Modeling heat-resistant molds inactivation.
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Department of Food Engineering. State University of Ponta Grossa, Ponta Grossa, PR –
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*Corresponding author: A.S.Sant’Ana ([email protected]). Rua Monteiro Lobato, 80.
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Campinas, SP, Brazil. Phone: +55(19)3521-2174.
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ACCEPTED MANUSCRIPT Abstract
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This study aimed to model the inactivation of B. nivea and N. fischeri ascospores in pineapple
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and papaya juices as influenced by temperature (78, 80, 85, 90 and 92 ºC) and soluble solids
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concentration (10, 13, 20, 27 and 30 °Brix). First, a primary model was used to fit the Weibull
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model to inactivation data obtained from a combination of temperature and soluble solids
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concentration and to calculate δ (time for the first decimal reduction) and p (shape parameter).
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Then, a secondary model was used to describe how the inactivation kinetic parameters of
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these fungi in pineapple and papaya juices varied with the changes in temperature and soluble
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solids concentration. The shape parameter (p) was fixed for each strain and at temperature and
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soluble solids concentration studied. The results indicated that both linear and quadratic
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effects of temperature as well as the interaction between temperature and total soluble solids
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were deemed significant on δ value for both B. nivea and N. fischeri (except for B. nivea in
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papaya juice). This study contributes to the field by bringing new predictive models
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describing the influence and interactions of mild temperature conditions and soluble solids
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contents of fruit juices on the inactivation kinetics of heat-resistant fungi.
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Key-words: Predictive microbiology, food spoilage, fruit juice, thermal processing, Weibull model.
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1)
Introduction Fungi such as Penicillium, Aspergillus, Alternaria are the main microorganisms
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associated with spoilage of a wide variety of foods (Pitt and Hocking, 1999). Regardless of
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this, these genera mainly include species that are not able to tolerate harsh food processing
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conditions. On the other hand, some species of Byssochlamys, Neosartorya, Talaromyces and
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Eupenicillium, comprise fungi presenting high chemical and heat resistances (Suresh et al.,
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1996, Tournas, 1994, Valik and Pieckova, 2001). Therefore, these fungi are of major
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relevance for the stability of thermally processed foods, such as fruit juices and purees.
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The thermal and chemical resistances of B. nivea, B. fulva, N. fischeri, T. flavus are
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related to the presence of structures known as ascospores, which confers the ability to survive
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after heating at least at 80°C per 30 minutes (Kotzekidou, 1997, Pitt and Hocking, 1999,
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Tournas, 1994). As ascospores are exposed to sub lethal temperature conditions, they are
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activated, germinate and can multiply on the product during storage. This can further lead to
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spoilage and extensive economic losses for the industry (Slongo and Aragao, 2006). Several
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D values (the time at a specific temperature needed to cause one log cycle reduction in the
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population of a target microorganism) or inactivation kinetic parameters for heat-resistant
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fungi have been reported in the literature (Engel and Teuber, 1991, Delgado et al., 2012a,b,
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Rajashekhara et al., 1996, Sant’Ana et al., 2008, Tournas and Traxler, 1994). D-values at
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90°C ranging from <2 minutes to 6 minutes, for example, have been found for different
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species of heat-resistant fungi. It is known that D-values and other inactivation kinetic
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parameters may vary with substrata, pH, soluble solids contents, water activity, presence of
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preservatives, among other factors (Engel and Teuber, 1991, Delgado et al., 2012a,b,
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Rajashekhara et al., 1996, Tournas and Traxler, 1994).
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ACCEPTED MANUSCRIPT Because of their high heat-resistance, ascospores of the species B. nivea and N. fischeri
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were used as targets of fruit juices thermal processes for several years (Eicher, 2002,
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Sant’Ana et al., 2008). However, the importance of heat-resistant fungi as targets of thermal
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processing of acidic foods declined in the last decades with the emergence of Alicyclobacillus,
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an acidothermophilic sporeforming bacterium presenting D-values higher than those reported
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for heat-resistant fungi (Mcknight et al., 2011, Spinelli et al., 2009, 2010). Because of the
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high heat-resistance of Alicyclobacillus spores, several industries have applied thermal
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processes that can reach up to 115°C/15-30 seconds in order to produce shelf-stable fruit
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juices. Although thermal processing at these time and temperature conditions will ensure the
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inactivation of heat-resistant fungi in fruit juices (Tribst et al., 2009), the over concern with
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Alicyclobacillus has led to a lack of interest on heat-resistant fungi. Nonetheless, the
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importance of heat-resistant fungi as fruit juice spoilers should not be underestimated because
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these microorganisms can pose shelf-stability problems in mild thermally processed fruit
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juices and acidic foods. This gains importance in an era of consumer concerns about
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ultraprocessed foods (Moubarac et al., 2012) and willingness to purchase foods not subjected
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to intense processing (Ragaert et al., 2004). Additionally to spoilage problems, heat-resistant
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fungi such as B. nivea and B. fulva can produce mycotoxins, such as patulin, potentially
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posing a threat for food safety (Sant’Ana et al., 2008, Sant’Ana et al., 2010). In the scenario
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described above, the importance of heat-resistant fungi for the microbiological quality and
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safety of fruit juices is then revisited.
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Several studies regarding the inactivation kinetics of heat-resistant fungi can be found in
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the literature (Engel and Teuber, 1991, Delgado et al., 2012a, Rajashekhara et al., 1996,
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Sant’Ana et al., 2008, Tournas and Traxler, 1994). Regardless of this, studies dealing with the
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modeling of combined factors and their interactions on thermal resistance of heat-resistant
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fungi are scarce. It is known that temperature and soluble solids contents are two major 4
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al., 2009). Therefore, the objective of this study were to determine the inactivation kinetic
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parameters of B. nivea and N. fischeri ascospores in pineapple and papaya juices and to assess
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the impact of temperature and soluble solids concentration on these parameters through a
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secondary modeling approach.
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2)
Material and Methods
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2.1) Microorganisms and preparation of suspensions of spores
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B. nivea LB01 and N. fischeri LB11 isolated from fruit juices and belonging to the
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culture collection of the Laboratory of Quantitative Food Microbiology at the University of
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Campinas, SP, Brazil, were used in this study. The fungi were grown on Potato Dextrose
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Agar (PDA, Himedia Laboratories, Mumbai, India) at 30°C for seven days. Then, the colonies
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were washed with sterile distilled water. Roux bottles containing 180 mL of Malt Extract
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Agar (MEA, Difco Laboratories, Detroit, MI, USA) were inoculated with 0.5 mL following
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incubation at 30°C for 30 days. The suspension of ascospores were obtained as previously
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described by Sant’Ana et al. (2009) and further stored at 4ºC until used. The concentration of
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ascospores was determined after activation at 80°C for 10 minutes, followed by serial
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dilutions in sterile 0.1% peptone water and plating in MEA. The concentration of the
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ascospores suspensions was adjusted at 107 ascospores/mL (Delgado et al., 2009a,b).
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2.2) Preparation of Fruit Juices
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Commercial pineapple (pH 3.7 and 16 °Brix) and papaya (pH 3.9 and 13 °Brix) juices
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free of preservatives were used in the experiments. Soluble solids concentration values (10,
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13, 20, 27 and 30 °Brix) were adjusted using sucrose or sterile distilled water. The soluble
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solids concentration was measured using a refractometer (model Abbe, Atago, Tokyo, Japan). 5
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The juices were subjected to thermal treatment at 105 ºC for 10 min to inactivate any potential
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contaminants (Sant’Ana et al., 2009).
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2.3) Determination of B. nivea and N. fischeri ascospores heat resistance in pineapple and
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papaya juices at different temperatures and soluble solid concentrations
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Estimation of thermal inactivation kinetics of B. nivea and N. fischeri ascospores in
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pineapple and papaya juices was performed in thermal death tubes (TDT, 8 mm external
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diameter, 6 mm internal diameter and 1 mm wall thickness). The TDT tubes were filled with
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1 mL of pineapple and papaya juices at different soluble solid concentrations and 1 mL of the
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ascospore suspension, resulting in a final concentration of 106 ascospores/mL. The procedures
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for determining heat resistance were those previously described by Sant’Ana et al. (2009).
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In order to investigate the influence of interactions between temperature and soluble
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solids on thermal inactivation kinetics of B. nivea and N. fischeri ascospores, a central
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composite design for two factors was used. The ranges of the factors were 78, 80, 85, 90 and
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92 ºC, for temperature, and 10, 13, 20, 27 and 30 °Brix, for soluble solids concentration. The
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conditions studied comprise the range of temperature and soluble solids which mild-processed
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fruit juices are subjected during processing and commercialization (Tables 1 and 2 contain the
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experimental design).
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2.4) Modeling of B. nivea and N. fischeri ascospores inactivation in pineapple and papaya
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juices as a function of temperature and soluble solid concentration
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2.4.1) Primary modeling
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As the inactivation kinetics data was found to mainly follow a nonlinear trend, the
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Weibull model (Equation 1) described by Mafart et al. (2002) was chosen to fit the data of
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survival of heat-resistant molds studied herein (primary modeling). The GinaFiT software 6
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(Geeraerd et al., 2005) was used to fit the model to the data and to estimate the main
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parameters of inactivation of the Weibull model, i.e., δ and p. Equation 1
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Where: N (population at time t), N0 (initial population), t (time), δ (time for the first
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decimal reduction) and p (shape parameter). The δ (unity is time) represents the probability
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distribution describing the time interval for failure to occur (microbial death). The p value
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(which is dimensionless) describes the curvature of the microbial survival along the time. If
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p=1 the susceptibility of microbial cells does not change throughout the time, while for p>1
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and p<1, the microbial cells become more and less susceptible throughout the time (van
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Boekel, 2002). The standard deviation and R2 values were obtained for models dealing with
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inactivation of B. nivea and N. fischeri ascospores in the different conditions assessed.
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2.4.2) Secondary modeling
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A response surface model was used to describe the effect of temperature and soluble
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solids concentration (independent variables) on the δ parameter (response variable) for both
164
B. nivea and N. fischeri ascospores. For this purpose, a two-factor central composite, rotatable
165
design added with cube and central points were used, totalizing 11 experiments for each
166
microorganism (Tables 1 and 2).
168 169 170
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Experimental data were fit into a quadratic model and the interaction between factors was assessed according to Equation 2:
Equation 2
171
Where δ represents the time, expressed in minutes for the first decimal reduction,
172
b0...b5 are the regression coefficients obtained by multiple regression analysis, SS is the 7
ACCEPTED MANUSCRIPT concentration of soluble solids, expressed in °Brix, and T is the temperature given in °C. The
174
equations provided for each microorganism for each type of juice were obtained using real
175
values instead of using coded values. As the shape parameter (p) is also needed to describe
176
the inactivation kinetics by the Weibull model (Mafart et al., 2002), single p values for each
177
fungal strain heated at specific conditions (juice, temperature and soluble solid concentration)
178
were estimated from Equation 1. This was done through an iterative approach using solver
179
ability of Excel as previously described in Mafart et al. (2002) and Sant’Ana et al. (2008).
180
The secondary model built based on Equation 2 and the respective p values obtained through
181
the iterative procedure described above allow the estimation of the F-value of the process
182
(Equation 3) (Mafart et al., 2002) for each fungal strain heated at specific conditions studied:
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183
Equation 3.
184
Where: F-value= the relative time required to inactivate the population of a target
186
microorganism under specified conditions, n= ratio of decimal reduction, δ*= time for the first
187
decimal reduction at reference temperature (T*).
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The quality of the RSM model was assessed by analysis of variances (ANOVA), and
189
the regression equation contained only the significant coefficients (p < 0.10). Additionally,
190
the percentage of the variability explained by the model was calculated and expressed by the
191
determination coefficient (R2) and adjusted determination coefficient (R2adj). For all inferential
192
tests, an α = 10% was used to assess statistically different results (Granato et al., 2014).
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193 194
3)
Results and Discussion
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The experimental survivor curves for B. nivea and N. fischeri at specific conditions
196
studied according to Tables 1 and 2 were characterized by a slightly deviation of the log8
ACCEPTED MANUSCRIPT linear inactivation kinetics. Several studies on the thermal inactivation of heat-resistant fungi
198
(Delgado et al., 2012a, Gressoni and Massaguer, 2003, Houbraken et al., 2006, Rajashekhara
199
et al., 2000) have reported that the heat inactivation of these microorganisms followed a
200
nonlinear inactivation kinetics. However, in most of these studies, the linearization method of
201
Alderton Snell (1970) was used as an alternative for estimation of inactivation kinetics.
202
Despite this, in the present study data was dealt with through the Weibull model (Mafart et
203
al., 2002) using the GinaFiT add-in for Excel (Geeraerd et al., 2005). Even when the
204
deviation of the inactivation kinetics is not strongly pronounced, the use of models that
205
properly take into account that pattern of microbial death is preferably, as it can avoid issues
206
in estimating thermal processing requirements for a specific food. In this sense, the first
207
indication of the fitness of the data to a model is the R2 value. The R2 value is the adequate
208
measure of the percentage of entire variation of results described by the model (Draper and
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Smith, 1998). As seen in Tables 1 and 2, R2 values were always above 0.96 indicating an
210
excellent fitting of the Weibull model to the data.
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As the Weibull model properly fitted the data, the kinetic parameters for inactivation of
212
B. nivea and N. fischeri in pineapple and papaya juices as a function of temperature and
213
soluble solids were further determined (Tables 1 and 2). In general, δ values were very close
214
for B. nivea and N. fischeri in both juices, except for those experiments performed at 80 °C
215
with pineapple juice containing 13 and 27 °Brix (Tables 1 and 2), respectively. Nonetheless,
216
regardless of the juice, it can be seen that the concentration of soluble solids affected the δ
217
values. Generally, an increase of ºBrix of the juice resulted in an increase of δ-value (Tables 1
218
and 2). It is known that the greater the concentration of soluble solids in the heating medium
219
the more protected the ascospores of heat-resistant fungi are (Baglioni, 1998), which has
220
markedly impacts on the design of thermal processing.
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ACCEPTED MANUSCRIPT The knowledge of the interactions of highly important factors affecting microbial
222
inactivation, such as temperature and soluble solids content, relies on the fact that industry
223
aims at a diversification of their products (such as juices with different and high soluble solids
224
contents) besides healthy appeals. It is known that thermal resistance of fungi such as N.
225
fischeri and B. nivea is highly influenced by soluble solids content and temperature (Beuchat,
226
1986, Tournas, 1994). Therefore, estimating the impact of temperature and soluble solids
227
interactions on the inactivation kinetics of N. fischeri and B. nivea under conditions studied is
228
of relevance. Table 3 shows the results of the ANOVA and the coefficients obtained by
229
multiple regression analysis (RSM). Both linear and quadratic effects of temperature as well
230
as the interaction between temperature and total soluble solids were deemed significant on δ
231
value for both B. nivea and N. fischeri (except for B. nivea in papaya juice) (Table 3). The
232
linear effects presented a higher quantitative impact on δ values as compared to quadratic
233
effects. While the increase in temperature decreased the δ values, a higher concentration of
234
soluble solids increased the value of this parameter. On the other hand, the quadratic effect of
235
temperature contributed significantly (p < 0.10) for the increase of δ.
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The regression coefficients shown in Table 3 were obtained using the real values in the
237
multiple regression analysis. These polynomial mathematical models describe the effects of
238
temperature and soluble solids concentration on the time for the first decimal reduction (δ) of
239
both N. fischeri and for B. nivea in pineapple and papaya juices, respectively. These equations
240
were obtained after removing the non-significant effects (p > 0.10) as they do not provide an
241
impact on the time for the first decimal reduction of the studied fungi. In this study, single p-
242
values (shape parameter) were obtained for each heat resistant studied using the procedures
243
described in Mafart et al. (2002) and Sant’Ana et al. (2008). The average p-values estimated
244
for B. nivea in pineapple and papaya juices at the conditions shown in Table 1 were 0.79 and
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ACCEPTED MANUSCRIPT 1.04, respectively. For N. fischeri, the average p-values estimated for heating in pineapple and
246
papaya juices were 1.00 and 1.07, respectively. The estimation of fixed p-values and their use
247
with δ obtained through polynomial models (Table 3) is proposed for the first time and allow
248
the calculation of F-values using Equation 3. Therefore, in those cases in which microbial
249
death does not follow log-linear kinetics and secondary models taking into account
250
intrinsic/extrinsic factors are built, this approach can be used for determination of F-values.
251
This is needed because p-values are not strongly correlated with temperature (Mafart et al.,
252
2002), while the contrary is observed with δ, which is influenced by heating temperature
253
(Couvert et al., 2005).
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Regarding the statistical quality of the fitted models, it is necessary to assess the t-
255
values, p-values and R2 for each proposed multiple regression model. The models were highly
256
significant (p < 0.001) and the R2 values ranged between 0.88 and 0.99, indicating that the
257
models were able to explain more than 88% of the variability of the data, thus validating their
258
ability to explain the effect of temperature and soluble solids concentration on the time for the
259
first decimal reduction of N. fischeri and B. nivea in pineapple and papaya juices. It is
260
noteworthy that microbiological data present naturally a considerable variation within assays,
261
so if a mathematical model that is able to explain more than 70% of the experimental data is
262
attained, it is possible to assume that this model has a good predictive ability (Granato et al.,
263
2010, Peña et al., 2014).
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Figure 1 presents the three-dimensional response surfaces for the four multiple
265
regression models. As we can observed in these surfaces, an increase in the temperature from
266
78 to 92 ºC caused a decrease of up to 10 times in the δ value, for both B. nivea and N.
267
fischeri in papaya juice. In the case of pineapple juice, this decrease was more pronounced,
268
mainly for B. nivea (Figures 1a and 1c). According to the results, it is not possible to point
11
ACCEPTED MANUSCRIPT 269
that one genus of fungi was more heat-resistant than the other was. In fact, thermal resistance
270
of heat-resistant fungi seems to vary with the strain (Tournas, 1994). This study contributes to the field by bringing new predictive models describing the
272
influence and interactions of mild temperature conditions and soluble solids contents of fruit
273
juices on the inactivation kinetics of heat-resistant fungi. In addition, a strategy to use δ-
274
derived from secondary models and their combination with fixed p-values (p-values of a
275
strain) is proposed. The models built herein are of relevance in a context in which consumers
276
are seeking for a variety of foods and beverages subjected to less intense processing. It should
277
be highlighted that producers of shelf-stable fruit juices have been forced in the last years to
278
increase processing temperature (as high as 116 °C, for example) aiming at inactivating
279
Alicyclobacillus spores. Thus, the temperature range studied herein (78 - 92 ºC) represent
280
conditions that are not sufficient for inactivation of Alicyclobacillus (Spinelli et al., 2009)
281
(currently, the main target of fruit juice industries). Nonetheless, this temperature range can
282
lead to shelf-stable fruit juices if combined with refrigeration or other technologies. It is
283
noteworthy to mention that Alicyclobacillus does not grow at temperatures below 20 °C
284
(Spinelli et al., 2009) and as such, thermal processing could merely be focused in the
285
inactivation of heat-resistant fungi while refrigeration could efficiently control the growth of
286
Alicyclobacillus. Regardless of this fact, it should be highlighted that continuous efforts must
287
be done in order to reduce the chances of fruit contamination by ascopores of heat-resistant
288
fungi.
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Acknowledgements
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The authors show their gratitude to Conselho Nacional de Desenvolvimento Cientifico e
292
Tecnológico (CNPq) (Process 400806/2013-4 and 302763/2014-7), Coordenação de
293
Aperfeiçoamento de Pessoal de Nível Superior (CAPES/PNPD – D. Granato) and Fundação 12
ACCEPTED MANUSCRIPT 294
de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for the financial support of projects
295
undertaken at Laboratory of Quantitative Food Microbiology, University of Campinas.
296 297
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4)
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acidoterrestris CRA7152 in orange juice. Applied and Environmental Microbiology 75, 7409-
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number of decimal reductions and on Alicyclobacillus acidoterrestris CRA 7152 growth in
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Suresh, E.R., Ethiaraj, S., Jayaram, H.L., 1996. Heat resistance of Neosartorya fischeri
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isolated from grapes. Journal of Food Science and Technology 33, 76-78.
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Tournas, V., 1994. Heat-resistant fungi of importance to the food and beverage industry.
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Tournas, V., Traxler, R.W., 1994. Heat resistance of a Neosartorya fischeri strain isolated
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Tables
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Table 1: Experimental design and data concerning the inactivation of B. nivea ascospores in pineapple and papaya juices as a function of temperature and soluble solids.
Papaya juice
Pineapple juice
p ± sd*
132.2 ± 48.9 168.9 ± 41.1 27.0 ± 16.9 44.0 ± 7.6 3.6 ± 0.7
0.81 ± 0.16 0.87 ± 0.13 0.78 ± 0.28 1.07 ± 0.15 0.57 ± 0.06
7.2 ± 1.7 1.5 ± 0.7 454.4 ± 144.7 27.5 ± 10.3 37.2 ± 12.3 35.4 ± 5.9
0.69 ± 0.12 0.72 ± 0.18 0.90 ± 0.20 0.88 ± 0.19 0.90 ± 0.21 0.84 ± 0.09
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δ ± sd* (min)
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-1 -1 -1 1 0 -1.41 0 1.41 1 -1 1 1 1.41 0 -1.41 0 0 0 0 0 0 0 *sd=standard deviation.
EP
Temperature Soluble solids
Real values Temperature Soluble (ºC) solids (°Brix) 80 13 80 27 85 10 85 30 90 13 90 27 92 20 78 20 85 20 85 20 85 20
AC C
Coded values
R
2
R2
δ ± sd* (min)
p ± sd*
0.98 0.99 0.96 0.99 0.99
301.4 ± 93.2 317.5 ± 68.7 27.2 ± 5.9 48.1 ± 6.8 10.4 ± 1.8
1.23 ± 0.36 1.24 ± 0.27 0.80 ± 0.10 0.69 ± 0.06 1.44 ± 0.27
0.96 0.98 0.99 0.99 0.98
0.99 0.98 0.98 0.98 0.98 0.99
10.9 ± 2.8 2.9 ± 0.3 374.5 ± 34.7 46.2 ± 1.9 41.3 ± 7.5 40.9 ± 7.6
1.32 ± 0.38 0.94 ± 0.08 0.80 ± 0.05 1.07 ± 0.04 0.93 ± 0.13 0.92 ± 0.13
0.97 0.99 0.99 0.99 0.99 0.99
ACCEPTED MANUSCRIPT
Table 2: Experimental design and data concerning the inactivation of N. fischeri ascospores in pineapple and papaya juices as a function of temperature and
Papaya juice
Pineapple juice δ ± sd* (min)
p ± sd*
0.76 ± 0.12 0.86 ± 0.14 0.76 ± 0.11 1.27 ± 0.34 1.09 ± 0.11 1.04 ± 0.31 0.83 ± 0.06 0.62 ± 0.02 1.2 ± 0.08 1.21 ± 0.20 1.33 ± 0.05
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225.4 ± 61.9 338.4 ± 75.7 35.8 ± 7.4 46.5 ± 13.2 3.9 ± 0.5 6.3 ± 1.7 0.8 ± 0.1 424.0 ± 24.7 41.6 ± 3.2 41.0 ± 7.9 44.9 ± 1.9
TE D
-1 -1 -1 1 0 -1.41 0 1.41 1 -1 1 1 1.41 0 -1.41 0 0 0 0 0 0 0 *sd=standard deviation.
EP
Temperature Soluble solids
Real values Temperature Soluble (ºC) solids (°Brix) 80 13 80 27 85 10 85 30 90 13 90 27 92 20 78 20 85 20 85 20 85 20
AC C
Coded values
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soluble solids.
R
2
0.99 0.99 0.99 0.97 0.99 0.97 0.99 0.99 0.99 0.99 0.99
δ ± sd* (min)
p ± sd*
146.6 ± 56.1 183.4± 72.2 37.3 ± 5.4 48.5 ± 3.0 3.3 ± 0.8 5.8 ± 1.7 0.7 ± 0.2 426.9 ± 102.2 39.3 ± 3.8 44.8 ± 13.8 38.7 ± 4.2
1.09 ± 0.27 1.19 ± 0.36 1.17 ± 0.13 0.97 ± 0.06 0.54 ± 0.09 1.18 ± 0.39 0.46 ± 0.07 1.31 ± 0.26 0.69 ± 0.05 1.16 ± 0.32 1.13 ± 0.10
R2 0.98 0.97 0.99 0.99 0.99 0.96 0.99 0.98 0.99 0.97 0.99
Table 3: Regression coefficients and statistical parameters of the polynomial models for δ (min) for the ACCEPTED MANUSCRIPT inactivation of N. fischeri and B. nivea ascospores in pineapple and papaya juices.
Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) Soluble solid (°Brix)(Q) 1L by 2L R2 adj R2 p-value (model)
p-value
-95% CI
+95% CI
0.001 0.001 0.001
23177.99 -660.01 3.01
28806.90 -530.18 3.77
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B. nivea - pineapple 654.12 39.74 15.09 -39.44 0.09 38.40
25992.44 -595.09 3.39
6.51
4.81
0.041
3.30
59.31
-0.25 -0.24 0.88 0.75 <0.001
0.04 0.07
-5.77 -3.21
0.029 0.085
-0.44 -0.55
-0.06 0.08
<0.001 <0.001 <0.001
25122.96 -632.68 3.14
28120.72 -562.04 3.56
0.031
0.18
1.46
<0.001 <0.001 <0.001
26169.99 -655.52 3.47
28460.93 -602.68 3.78
B. nivea - papaya 348.36 76.42 8.21 -72.77 0.05 69.35
26621.84 -597.36 3.35 0.82
0.15
0.97 0.96 <0.001 27315.46 -629.10 3.62
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31.30
5.54
N. fischeri - pineapple 266.22 102.60 6.14 -102.45 0.04 100.82
67.06
2.65
25.32
0.002
55.67
78.46
0.06 -0.79 0.99 0.98 <0.001
0.02 0.03
3.38 -26.33
0.078 0.001
-0.02 -0.92
0.14 -0.66
<0.001 <0.001 <0.001
21739.80 -580.10 2.81
25407.53 -495.51 3.30
AC C
Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) Soluble solid (°Brix)(Q) 1L by 2L R2 adj R2 p-value (model)
t-value
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Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) R2 adj R2 p-value (model)
Standard error
TE D
Mean/Interc. (1)Temperature (ºC)(L) Temperature (ºC)(Q) (2)Soluble solid (°Brix)(L) Soluble solid (°Brix)(Q) 1L by 2L R2 adj R2 p-value (model)
Regression coefficient
EP
Parameter
23573.66 -537.80 3.06
N. fischeri - papaya 426.22 55.31 9.83 -54.71 0.06 53.12
29.90
4.24
7.05
0.020
11.65
48.14
-0.20 -0.24 0.90 0.82 <0.001
0.03 0.05
-7.13 -5.10
0.019 0.036
-0.32 -0.45
-0.08 -0.04
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Figure captions
Figure 1: Response surface plots showing the inactivation of N. fischeri and B. nivea as a function of temperature and soluble solids in pineapple (A,C) and papaya juices
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Research Highlights
Inactivation of B.nivea and N.fischeri in pineapple and papaya juice was studied Temperature, T, (78-92ºC) and soluble solids, SS, (10-30°Brix) were factors
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Linear and quadratic effects of T, SS and their interaction were significant on δ Fixed p-values for each strain were obtained and may allow estimation of F-
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values