The inhibitory effects of spice essential oils and rapidly prediction on the growth of Clostridium perfringens in cooked chicken breast

Journal Pre-proof The inhibitory effects of spice essential oils and rapidly prediction on the growth of Clostridium perfringens in cooked chicken breast Yaodi Zhu, Yangyang Ma, Jiaye Zhang, Miaoyun Li, Longgang Yan, Gaiming Zhao, Yanxia Liu, Yanyan Zhang PII:

S0956-7135(19)30567-5

DOI:

https://doi.org/10.1016/j.foodcont.2019.106978

Reference:

JFCO 106978

To appear in:

Food Control

Received Date: 3 July 2019 Revised Date:

26 October 2019

Accepted Date: 31 October 2019

Please cite this article as: Zhu Y., Ma Y., Zhang J., Li M., Yan L., Zhao G., Liu Y. & Zhang Y., The inhibitory effects of spice essential oils and rapidly prediction on the growth of Clostridium perfringens in cooked chicken breast, Food Control (2019), doi: https://doi.org/10.1016/j.foodcont.2019.106978. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2019 Published by Elsevier Ltd.

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The inhibitory effects of spice essential oils and rapidly prediction on the growth

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of Clostridium perfringens in cooked chicken breast

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Yaodi Zhu#1, Yangyang Ma#1, Jiaye Zhang1, Miaoyun Li*1, Longgang Yan1, Gaiming

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Zhao1, Yanxia Liu1, Yanyan Zhang2

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1. College of Food Science and Technology, Henan Agricultural University, No.95

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Wenhua Road, Zhengzhou, 450002, P.R. China

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2. Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou

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University of Light Industry, Zhengzhou, P. R. China

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#.The authors contributed equally to this study.

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*Corresponding author. Tel.: 86-371-63558150; fax: 86-371-63558150.

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E-mail: [email protected] (Miaoyun Li)

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1

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Abstract

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In this study, the effects of spices essential oils on the growth of C. perfringens

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were investigated. The in vitro inhibitory activities and minimum inhibitory

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concentrations (MIC) of different essential oils on C. perfringens were determined

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using the Oxford cup and two-fold dilution method. And two models (the parameter-

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adjusted Gompertz kinetic model, and least square support vector machine (LS-SVM)

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model) were used to rapidly predict the relative growth/survival of C. perfringens in

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cooked chicken breast under different essential oil concentrations. The results

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indicated that cinnamon essential oil exhibited a notable inhibitory effect on C.

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perfringens in vitro. Moreover, cinnamon essential oil had the lowest inhibitory

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concentration, and the growth of C. perfringens ceased completely at 19.12 mg/mL

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and 22.72 mg/mL, respectively. By comparing, the LS-SVM model is the optimum

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model. The prediction accuracy of the LS-SVM model was greater than 0.99, and the

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degree of fitting is higher than adjusted Gompertz kinetic model. The results present

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illustrate that the model using LS-SVM has high prediction accuracy and can be

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employed to predict the number of C. perfringens under different essential oils. This

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work could contribute to control and rapid assessment of food safety hazards of C.

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perfringens during meat processing.

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Key words: Clostridium perfringens; Spice essential oils; Inhibitory effects;

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Prediction model; Cooked chicken breast

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2

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1. Introduction

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Clostridium perfringens (C. perfringens) spores are ubiquitously present in the

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environment, and can be found in the intestinal tracts of humans and animals, soil,

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water, and many ingredients and raw materials used to make meat and poultry

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products (London and Payne et al., 2017). It is known for forming capsules and

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producing a large amount of gas through the breakdown of carbohydrates in muscles

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and connective tissues(Makino and Xu et al., 2019). Moreover, the spores of C.

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perfringens can survive during food preservation processes, and upon germination

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outgrowth, it can cause food spoilage as well as safety risks (Lindstrom and

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Heikinheimo et al., 2011). Therefore, it is not only highly pathogenic, but can also

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result in great economic losses for the meat industry (Grass and Gould et al., 2013;

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May and Polkinghorne et al., 2016).

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C. perfringens has been widely detected in ready-to-eat cooked meat products in

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China (Koziel and Kukier et al., 2019). Due to the difficulties in sterilization,

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handling and controlling C. perfringens in meat products, many developed countries

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have considered C. perfringens as an important factor for formulating HACCP and

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related food safety policies for meat products (Acheson and Bell et al., 2016). Though

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being one of the research hotspots on foodborne pathogenic bacteria (Alnoman and

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Udompijitkul et al., 2017; Li and Lillehoj et al., 2017), at present, the control of C.

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perfringens in foods is mainly achieved by microbiological testing of products. This is

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not only time-consuming and laborious, but can also suffer from frequent delays in

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obtaining test results. Thus, rapid predicting and efficient inhibiting of C. perfringens

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are of great importance to completely guarantee food safety.

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Spice essential oils are aromatic oils extracted from spice plants, which are safe,

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natural and non-toxic with the antimicrobial activity and health benefits (Chan and

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Gan et al., 2018). Therefore, it holds broad prospects of spice essential oils for further

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development to replace antibiotics for the inhibitory effects against various bacteria.

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So far, extensive researches have been done. For example, it was shown that clove,

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forsythia and cinnamon essential oils had clear inhibitory activities against bacteria,

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yeasts and molds (Renyong and Weichang et al., 2008). And thyme, cinnamon, clove,

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almond, Ligusticum striatum and marjoram essential oils were found to exert strong

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and comprehensive inhibitory effects against the 25 strains tested (Dorman and Deans,

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2004; Costa and Bounatirou et al., 2006). Besides, it was reported that addition of

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oregano essential oil greatly influenced the proliferation of S. typhimurium and

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prolonged the shelf life of fresh meat (Skandamis and Nychas, 2002). However, there

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are few studies about the inhibition of C. perfringens by spice essential oils. In China,

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various spices (star anise, dictamnus, laurel, malabathrum, etc.) are often added

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during the manufacturing process of sauced and braised meat products (Renyong and

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Weichang et al., 2008). Therefore, investigation into the inhibitory effects of spice

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essential oils on the growth of C. perfringens is of great significance.

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Predictive microbiology is a science that directly assesses food safety by

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predicting the growth, survival and death of microorganisms, which combining the

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knowledge of food science, microbiology and statistics with the aid of computers and

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mathematical models (Akkermans and Nimmegeers et al., 2018; Gonzalez and Possas

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et al., 2018). Investigating the proliferation characteristics and prediction models of C.

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perfringens could provide a reference for the control of this pathogen in meat.

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Modified Gompertz model is widely applicable and can best describe the growth of

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bacteria under different temperature in food products (Zwietering and Jongenburger et 4

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al., 1990).. Support vector machines (SVM) is developed by Vapnikhave’s group

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(Vapnik, 1998) which been successfully applied in classification and regression.

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Details of the least square support vector machine (LS-SVM) method were reported

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previously elsewhere. SVMs or support vector networks were introduced to

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nonlinearly map input vectors to a very high dimensional feature space in which a

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linear decision surface is available. The details of them can be found in literature

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(Wang and Zhang et al., 2019). The LS-SVM, which is a common prediction model

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for small data sets, considers equality type constraints instead of inequalities as in the

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classic SVM approach. This reformulation greatly simplifies a problem such that the

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LS-SVM solution follows directly from solving a set of linear equations rather than

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from a convex quadratic program (Anuragi and Sisodia, 2019). Finding an optimal

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kinetic model for predicting the survival of C. perfringens is beneficial to directly

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assess food safety.

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Herein, the antibacterial effectiveness of a range of essential oils (cinnamon,

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black pepper, wormwood, fennel and ginger) against C. perfringens (standard strain

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ATCC 13124 and isolated strain C1) and the MIC were determined. Furthermore, the

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optimal prediction model for C. perfringens survival was identified and selected by

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comparing the parameter-adjusted Gompertz survival kinetics model, and LS-SVM.

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This work might help to provide technical support for the control and rapid prediction

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of C. perfringens during meat processing.

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2. Materials and methods

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2.1 Preparation of spice essential oils

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The essential oils, including cinnamon (cinnamal dehyde as the main ingredient),

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black pepper (pinene as the main ingredient), fennel (anethol as the main ingredient), 5

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ginger (citronellal as the main ingredient) and wormwood (thujone and camphor as

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the main ingredients), were purchased from Shanghai Moellhausen International

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Trade Co., Ltd. Spices were irradiated at 42 kGy for 6 h to ensure their sterility. After

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distillation and extraction, the concentration of refining essential oil was 0.88 g/ mL.

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Tween-80 was used as an emulsifier, and the five types of essential oils were all

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thoroughly mixed uniformly in a ratio of oil: tween-80: sterile distilled water 4:1:5,

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respectively. Essential oil suspensions of different concentrations (Table 1) were

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prepared according to analysis of the minimum inhibitory concentrations (MIC)

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results.

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Insert Table. 1 goes here 2.2 Strains and media

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In this paper, through sampling of 249 samples in Henan Province (roast chicken,

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marinated chicken legs, salt pheasant, etc.), the C. perfringens was screened in 42

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samples (Table.S1). Moreover, most of them were identified as C. perfringens C1,

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which indicating the representive and universal of C1. Strain of C. perfringens ATCC

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1312 was from China general Microbiological cultural collection center (CGMCC).

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The C1 strain of C. perfringens was isolated and identified from vacuum- packed bag

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of salted chicken in the microbiology laboratory of the College of Food Science and

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Technology of Henan Agricultural University.

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Culture media used included the following: tryptose sulfite cycloserine (TSC)

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agar, fluid thioglycollate (FTG) medium, sulfite polymyxin sulfadiazine (SPS) agar

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medium (Beijing Luqiao Biotechnology) and tryptic soy agar (TSA) medium (Beijing

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Aobox Biotechnology).

6

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2.3 Antibacterial activity assay

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The Oxford cup method was used to determine the antibacterial activity of the

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essential oils (Vasilijevic and Mitic-Culafic et al., 2019). A sample (200 µL) of the

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initial bacterial suspension was pipetted onto TSA medium and streaked evenly three

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times with an applicator. Oxford cups of a uniform size were placed on bacterial agar

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plates using sterile tweezers, followed by the addition of 100 µL essential oil

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suspension (352 mg/mL). After standing for 5 to 10 min, the plates were

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anaerobically cultured at a constant temperature (37 °C) for 24 h. Three parallel

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assays and a blank control were performed. During the incubation, the size of the

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inhibition zones was observed, photographed and recorded. The antibacterial activity

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of different essential oils on C. perfringens were determined by measuring the

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diameter of inhibition zones (criteria for rating the antibacterial effect: inhibition zone

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diameter > 20 mm was extremely sensitive, 15 ~20 mm was highly sensitive, 10~15

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mm was moderately sensitive, 7~9 mm was slightly sensitive, and absence of

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inhibition zone was insensitive).

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2.4 Determination of the minimum inhibitory concentrations (MIC)

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The two-fold dilution method was used to determine the MIC of spice essential

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oils on C. perfringens. The initial concentration of essential oil was 352 mg/mL.

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Firstly, 1 mL of FTG liquid medium was added to 15 test tubes labeled 1 to 15

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respectively. Then, 1 mL of essential oil was added to tube 1 and thoroughly mixed,

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and 1 mL of the mixture from tube 1 was transferred to tube 2. In the same way, serial

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dilutions were performed for tubes 1 to 12. Tube 13 was the positive control. As a

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blank control for determining whether the spice essential oil was contaminated, 1 mL

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of the mixture was pipetted from tube 12 to tube 14. Tube 15 was the Tween-80 7

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control. Afterwards, a 100 µL sample of the initial bacterial culture was added to test

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tubes 1-13 and 15, respectively, followed by thorough mixing. The test tubes were

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placed in an anaerobic environment and cultured at 37 °C for 18-24 h. Thereafter, a

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200 µL sample from each tube was evenly streaked onto TSA media, and the plates

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were anaerobically cultured at 37 °C for 18-24 h followed by observation of results. C.

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perfringens appeared as white colonies with irregular edges on the TSA plates, and

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the highest essential oil concentration that corresponded to absence of bacterial

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growth on plates was the MIC. All the experiments were repeated three times.

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2.5 Meat preparation

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Chicken breast was purchased at local supermarkets and held at 0-4 °C until used.

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A sample was used to check for the absence of natural contamination by C.

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perfringens. After adding 3500 mL of water, each 1.5 kg of chicken breast was

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simmered at high heat (1600 W) for 5 min and at low heat (800 W) for 90 min. Then,

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the cooked chicken breast was pulled into strips according to the texture, sterilized by

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steam for 15 min, and dispensed (60 g) into bags for use. Samples of the essential oil

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and the bacterial suspension were added, followed by homogenization for 120 s in a

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homogenizer to allow extensive contact between the essential oil, bacterial suspension

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and the chicken breasts. Finally, the mixture was clamped with a sealing clip, placed

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in an anaerobic workstation, and cultured at 37 °C for 18 h.

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2.6 Bacterial counting

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The samples were diluted with 0.1% peptone water at 1:100 (g/mL), and then

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200 µL of samples were added to the TSC plates for microbial testing. Three parallel

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tests were performed for each gradient. After streaking and anaerobic incubation at

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37 °C for 18 h, colonies of C. perfringens on the plates were inspected and counted. 8

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2.7 C. perfringens survival kinetics model

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C. perfringens survival kinetics model was constructed using Log N or Ln (N/N0)

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as the y-axis and the essential oil concentration as the x-axis, which reflecting the

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relationship between the surviving counts of C. perfringens and the essential oil

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concentration. The survival curve of C. perfringens under different essential oil

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concentrations was fitted according to the parameter-adjusted Gompertz model, and

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LS-SVM model. The fitted results of the models were compared and the inactivation

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kinetics model of C. perfringens with different essential oil concentrations was

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established.

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(1) Construction of the parameter-adjusted Gompertz model

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i Gompertz model:

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Y = N + A ∗ exp{− exp[((B ∗ 2.718）/ a ∗ (C − X ) + 1]}

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In Eq (1), X is the essential oil concentration (mg/mL); Y is the C. perfringens

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counts (CFU/g) when the essential oil concentration was X mg/mL; A is the relative

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maximum inhibition rate; B is the essential oil concentration required to achieve the

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relative maximum inhibition rate (mg/mL); C is the essential oil concentration when

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the tangent line intersects the X-axis at the maximum inhibition rate; a is the constant;

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N0 is the initial value of C. perfringens (CFU/g).

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ii Expression of the parameter-adjusted Gompertz model:

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log

N = C exp( − exp( A + B t )) − C exp( − exp( A)) N0

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(1)

(2)

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Where N0 is the initial value of C. perfringens (CFU/g); N is the C. perfringens

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counts (CFU/g) when the essential oil concentration was X mg/mL; A, B and C are as

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described of above; t is the constant.

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(2) LS-SVM model construction and algorithm optimization

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LS-SVM is a data analysis method that requires a small number of data set for

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training and validation. In order to build an LS-SVM model, the first step was to

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import training data. The second step was to enter the commands type = ‘function

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estimation’ and kernel = ‘RBF_kernel.’ Here, the Gaussian radial basis function (RBF)

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is a nonlinear kernel widely used in nonlinear problems as a kernel function to reduce

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computational complexity of training procedures. The third step was the initialization

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of the model by ‘initlssvm’ function. The details of them can be found in literature.

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For a LS-SVM classifier, in the primal space it takes the form:

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y ( x) = sign ( wT x + b )

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where b is a real constant. For nonlinear classification, the LS-SVM classifier in

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the dual space takes the form:

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N  y ( x) = sign  ∑ ak yk ( x, xk ) + b   k =1 

(3)

(4)

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where ak are positive real constants and b is a real constant, in general, K (x, xi)=

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〈ϕ(x), ϕ(xi) 〉 , and ϕ(x) is the nonlinear map from original space to the high-

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dimensional space. For function estimation, the LS-SVM model takes the form. When

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radial basic function (RBF) kernels are used, two tuning parameters (γ, σ2) are added.

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The regularization parameter gam (γ) determines the tradeoff between minimizing the

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training error and minimizing model complexity. The parameter sig2 (σ2) is the 10

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bandwidth and implicitly defines the nonlinear mapping from input space to some

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high dimensional feature space. Kernel function can map the data which are not

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linearly separable in input space into a higher dimensional feature space, where

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becomes linearly separable.

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2.8 Reliability evaluation of C. perfringens survival kinetics model

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By comparing the correlation coefficient (R2) (Eq (5)) and the root mean square

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error of prediction (RMSEP) (Eq (6)) values from different models, the optimal

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prediction model for an inactivation kinetics model of C. perfringens in cooked

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chicken breast with different essential oil concentrations was identified. Accuracy

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factor (Af) and bias factor (Bf) were calculated and examined to compare the

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parameter-adjusted Gompertz model, and LS-SVM model against the experimental

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data. Among them, Af was used to check the fluctuation range of the predicted value.

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Bf was used to check the difference between predicted and measured values. The

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specific calculation formulas were shown in Eq (7) and (8), respectively.

∑ =1− ∑

n

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R

2

i =1 n

RMSEP =

( y′i − yi ) 2

(5)

( y′i − y ) 2 i =1

∑

n i =1

( yi − yi′)

(6)

n

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A f = 10[∑ N pre − N obs ] / m

(7)

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B f = 10[∑ N pre − N obs ] / m

(8)

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In Eq (5) and (6), y

' i

is the predicted value of the ith sample; y is the measured i

value of the ith sample; n is the number of samples. In Eq (7) and (8), Npre is the 11

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predicted value of the microbiological counts. Nobs is the measured value of the

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microbiological counts. m is the time of the test.

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2.9 Data analysis

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The MATLAB 2016 software was used to perform calculations of the parameter-

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adjusted Gompertz model and LS-SVM model, and to fit the survival curves of C.

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perfringens against different essential oils in cooked chicken breast.

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3. Results and Discussion

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3.1 The antibacterial activity of spice essential oils

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The antibacterial activity of different essential oils on two C. perfringens strains

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were determined using the Oxford Cup method. It could be seen that the five different

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spice essential oils had good bacteriostatic effects on the ATCC 13124. The addition

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of cinnamon and wormwood resulted in the strongest inhibitory effects (large

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inhibition zones > 20 mm); while the antibacterial effects of black pepper, fennel and

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ginger essential oils on C. perfringens were weaker (the inhibition zones 10~15 mm).

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In comparison, for the isolated strain C1, cinnamon, wormwood and black pepper

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essential oils showed strong inhibitory effects (the inhibition zones 15~20 mm), while

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fennel and ginger essential oils demonstrated weaker inhibitory effects (the inhibition

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zones 10~15 mm). These results indicated that the antibacterial activities on C.

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perfringens were in the order of cinnamon, wormwood, black pepper, fennel and

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ginger essential oil, which were from strong to weak (Table. 2). It was in accordance

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with the observation that spices oil show varying levels of antimicrobial activity.

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Insert Table. 2 goes here 3.2 The MIC of different spice essential oils on C. perfringens 12

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Based on the results presented in section 3.1, the MIC of different essential oils

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on C. perfringens were determined. For the strain ATCC 13124, the concentration of

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cinnamon essential oil was determined in test tubes 1~12, respectively. The solution

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of the 14th test tube (control group) was clarified, which showed the cinnamon

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essential oil was not contaminated. However, the solution of test tubes 15 (Tween-80

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control group) and 13 (positive growth control group) were feculent, which showed

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the growth of C. perfringen. In addition, the solution of Tween-80 did not

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demonstrate inhibiting effect. In order to gain further insight into the results, the

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growth of C. perfringens in different concentrations of essential oils were determined

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by plate cultural methods (Table. 3). The results indicated that cinnamon essential oil

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exhibited the strongest inhibitory activity against the standard strain ATCC 13124,

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with a MIC of 2.75 mg/mL. And the MIC of the spice essential oils on the strain

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ATCC 13124 showed the following increasing order: cinnamon essential oil < black

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pepper essential oil < wormwood essential oil < fennel essential oil < ginger essential

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oil. In comparison, the MIC against strain C1 showed the following increasing order:

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cinnamon essential oil < wormwood essential oil < black pepper essential oil < fennel

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essential oil < ginger essential oil. The results indicated that cinnamon essential oil

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exhibited the strongest inhibitory activity against both the two strains of C.

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perfringens, with the MIC of 2.75 mg/mL and 5.5 mg/mL, respectively. And the

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antibacterial activities of black pepper and wormwood essential oils were relatively

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lower.

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Inset Table. 3 goes here 3.3 Construction of parameter-adjusted Gompertz model

13

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As the concentration of different spice essential oils increased, the surviving

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count of C. perfringens decreased, and when the concentration increased to a certain

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value, growth of C. perfringens ceased. To rapidly obtain the survival state of C.

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perfringens at different concentrations, the parameter-adjusted Gompertz model and

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LS-SVM model were used for nonlinear fitting to plot the survival curve of C.

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perfringens in cooked chicken breast. According to the above results, cinnamon, black

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pepper and wormwood essential oils, which had obvious inhibitory effects, were

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selected and constructed the survival prediction model of C. perfringens.

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The best fitted equations with the parameter-adjusted Gompertz model were

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shown in Table. 4, and the survival curves of C. perfringens at different essential oil

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concentrations were obtained (Fig. 1). For the strain ATCC 13124 (Fig. 1 a ~ c), with

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the concentration of cinnamon essential oil increasing, the C. perfringens declined

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reached the maximum at 5.5 mg/mL. Then the number of C. perfringens tends to

300

stabilize. The prediction curve of black pepper was similar with the decline of C.

301

perfringens reaching to the maximum at 42 mg/mL. While for the wormwood

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essential oil, the declining of C. perfringens reached the maximum at 30 mg/mL to

303

44 mg/mL. The results showed that the prediction model with cinnamon essential oil

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was the optimum model, and R2 and RMSEP values were 0.8912 and 0.3041 log

305

CFU/g, respectively.

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As to the C1, as the concentration of different spice essential oils increased, the

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surviving count of C. perfringens gradually decreased (Fig. 1 d ~ f). It was

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noteworthy that the trend of prediction curve decline was obvious for cinnamon

309

essential oil. This indicated that cinnamon essential oil exerted the most pronounced

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inhibitory effect on C. perfringens proliferation. And it was shown that the prediction 14

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model with added cinnamon essential oil was the optimum model, and R2 and RMSEP

312

values were 0.8979, 0.2918 log CFU/g, respectively (Table. 5).

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Fig. 1

314

Inset Table. 4 goes here

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3.4 Construction of LS-SVM prediction model

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Before implementing LS-SVM, the tuning parameters must be tuned. In this

317

paper, the tuning parameters are regularization parameter and decision variable

318

related to RBF (γ and σ2). These starting values are then passed to another

319

optimization algorithm which is the simplex method to perform a fine-tuning step.

320

Then the model performance is estimated using leave-one-out crossvalidation. The

321

tuning process must be repeated for each case. After finding the suitable values of

322

tuning parameters, the model parameters (γ and σ2) can be obtained. The values of γ

323

and σ2 calculated were 19611.28 and 0.062, respectively.

324

Prediction for the survival of C. perfringens at different essential oil

325

concentrations using the LS-SVM model were shown in Fig. 2. For the ATCC 13124,

326

when the concentration of essential oil was ≤ 5.5 mg/mL, with three essential oil

327

concentration increasing, the number of C. perfringens decreased. Among them, the

328

rate of descent was the biggest with the cinnamon essential oil. When the cinnamon

329

essential oil concentration was ≥ 15 mg/mL, the curve tended to stabilize for the C1.

330

For ATCC 13124, When the cinnamon essential oil concentration was ≥ 10 mg/mL,

331

the curve tended to stabilize. The minimum surviving counts of the ATCC 13124 and

332

the C1 were 4.13 lg(CFU)/g and 4.05 lg(CFU)/g under wormwood essential oil,

333

respectively. The results showed that for each concentration range, the survival of C. 15

334

perfringens with cinnamon essential oil was lower than that with the other two

335

essential oils. What is more, for the cinnamon essential oil, the R2 values of prediction

336

model were 0.9975 and 0.9802, respectively. The RMSEP were 0.2854 lg(CFU)/g,

337

0.2619 lg(CFU)/g, respectively.

338

339

Fig. 2 3.5 Comparison of statistical indices

340

The parameter-adjusted Gompertz mode, and LS-SVM model were compared

341

against the experiment data. In general, the calculated values of the Bf for LS-SVM

342

model close to 1 indicated no systematic bias. The calculated performance indices (R2,

343

RMSEP, Af and Bf) were shown in Table. 5. It could be seen that the prediction

344

accuracy of the LS-SVM model was greater than 0.99, and the degree of fitting was

345

highest. For the ATCC 13124 and the C1, the optimum prediction models based on

346

LS-SVM were cinnamon essential oil. For the ATCC 13124, the Af and Bf were

347

1.0101, 1.001, respectively. For the C1, the Af and Bf were 1.0122, 1.0002,

348

respectively. Therefore, it was inferred that cinnamon essential oil exhibited the

349

lowest inhibitory concentration and the strongest antibacterial activity on C.

350

perfringens. And also all the evaluation results of the prediction model constructed by

351

LS-SVM were best. Hence, in this study, LS-SVM model was selected to predict the

352

numbers of C. perfringens under different essential oil concentrations.

353

354

Inset Table. 5 goes here

4.Conclusion

355

This study confirmed the potential inhibition of spice essential oils on the C.

356

perfringens in cooked chicken breast. It was shown that cinnamon essential oil had 16

357

the lowest inhibitory concentration and the strongest inhibitory effects, followed by

358

wormwood essential oil, while ginger essential oil displayed the weakest. In addition,

359

through evaluation and comparison, it was shown that LS-SVM model could

360

accurately predict the C. perfringens survival against essential oils. The R2 values of

361

prediction model were 0.9975 and 0.9802, respectively. The RMSEP were 0.2854

362

lg(CFU)/g, 0.2619 lg(CFU)/g, respectively. This work demonstrates the feasibility of

363

rapid prediction on the growth of C. perfringens against spice essential oils, and it was

364

expected to provide some technical support for the control of C. perfringens during

365

the manufacture and processing of meat products.

366

Acknowledgment

367

This work was financially supported by the National Natural Science Fundation

368

of China (31571856, 31801671), the Henan Science and Technology Major Project

369

(NO. 161100110800), Science and technology innovation talent support program of

370

Henan province (NO. 18HASTIT036), Henan Key Laboratory of Cold Chain Food

371

Quality and Safety Control (CCFQ2018-YB-13), and Innovation Fund of doctoral

372

scientific research of Henan agricultural university (NO. 30601677).

373 374

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20

TABLE 1 The concentration of essential oil added to cooked chicken breast for C. perfringens Essential oil type Cinnamon

Wormwood

Black pepper

Fennel

Ginger

Essential oil suspensions of different concentrations (mg/mL) ATCC 13124 0.6875, 1.375, 2.75, 5.5, 11, 15, 19 ,22, 44 5.5, 11, 22, 30, 38, 44, 55, 66, 77, 88, 176 2.75, 5.5, 11, 22, 44, 55, 66, 77, 88 5.5, 11, 22, 30, 38, 44, 55, 66, 77, 88, 176

C1 1.375, 2.75, 5.5, 11, 22, 28, 34, 40, 44

2.75, 5.5, 11, 22, 30, 38, 44, 88

5.5, 11, 22, 44, 55, 66, 77, 88

2.75, 5.5, 11, 22, 44, 55, 66, 77, 88, 176

11, 22, 44, 88, 110, 132,

5.5, 11, 22, 44, 88, 110, 132, 154, 176,

154, 176, 352, 704

220, 264, 308, 352

TABLE 2. The inhibitory zone diameters of different essential oils on C. perfringens The inhibition zone diameters (mm) Strains type

cinnamon

wormwood

black pepper

fennel

ginger

ATCC13124

31.00

22.81

20.99

15.82

13.65

C1

21.07

19.71

24.25

16.33

15.54

Note: The criteria for rating the antibacterial effect: inhibition zone diameter > 20 mm was rated extremely sensitive, 15 ~20 mm was rated highly sensitive, 10~15 mm was rated moderately sensitive, 7~9 mm was rated slightly sensitive, and absence of inhibition zone was rated insensitive.

TABLE 3. The MIC of different essential oils on C. perfringens MIC of different essential oils on C. perfringens （mg/mL） Spices essential oil

ATCC 13124

C1

Cinnamon

2.75

5.5

Wormwood

22

11

Black pepper

11

11

Fennel

22

11

Ginger

88

22

TABLE 4. The best fitted equation of the parameter-adjusted Gompertz model for different C. perfringens strains Essential

ATCC 13124

C1

f(x) = 8.0414+2663.0155*exp(-

f(x)= 8.4695+1623.7487*exp(-

exp(2.1522+0.0416*x))-

exp(2.0607+0.0099*x))-

2663.0155*exp(-exp(2.1522))

1623.7487*exp(-exp(2.0607))

f(x) = 7.8842+1643.4920*exp(-

f(x) = 8.4728+30.9437*exp(-exp(-

exp(2.0143+0.0046*x))-

2.0501+0.0030*x))- 30.9437*exp(-

1643.4920*exp(-exp(2.0143))

exp(-2.0501))

f(x) = 8.1903+44.6171*exp(-exp(-

f(x) = 9.7731+172.7651*exp(-

oil type

Cinnamon

Black pepper

Wormwood 5.8174+0.0424*x))- 44.6171*exp(exp(-5.8174))

exp(1.3339+0.0020*x))172.7651*exp(-exp(1.3339))

Table 5. The evaluation indices of prediction model of C. perfringens under different essential oils

Model evaluation parameters

C1

cinnamon

black pepper

wormwood

cinnamon

black pepper

wormwood

0.8912

0.8663

0.8216

0.8979

0.8568

0.8763

0.3041

0.3675

0.4932

0.2918

0.3726

0.3528

Af

1.0147

1.0156

1.0306

1.0261

1.0215

1.0215

Bf

1.0002

1.0011

1.0006

1.0010

1.0003

1.0003

R2

0.9975

0.9765

0.9389

0.9802

0.9482

0.9734

0.2854

0.3223

0.4037

0.2619

0.3368

0.4536

Af

1.0101

1.0111

1.0101

1.0122

1.0134

1.0322

Bf

1.0001

1.0001

1.0002

1.0002

1.0003

1.0004

R2 parameter-adjusted

ATCC 13124

RMSEP (lg(CFU)/g)

Gompertz model

RMSEP LS-SVM model

(lg(CFU)/g)

+ +

7 Number of C.perfringens (lgCFU·g-1)

Number of C.perfringens (lgCFU·g-1)

7.5 7

6.5

+ +

6

+

5.5

+

5

+ 5

10

+

+

5.5

20

40

10

20

30

Essential oil concentration (mg·mL ) d. Cinnamon essential oil -1

5

+ +

40

20

60

Essential oil concentration (mg·mL-1) c.Wormwood essential oil

++

7

+ 6

+ 5

+ +

98

+ +

7

6

+

5

4

+

3

3 0

+

10

+

4.5

6

0

60

+++

4 5

+

Essential oil concentration (mg·mL ) b.Black pepper essential oil

Number of C.perfringens (lgCFU·g-1)

6.5

+

7

-1

89

+

+

3 0

+ ++ +

+

4

3

15

Essential oil concentration (mg·mL ) a.Cinnamon essential oil

Number of C.perfringens (lgCFU·g-1)

++

6

-1

6

8

+ 0

7

+

+

4.5

7.5 8

9

4

5

8.5

+

Number of C.perfringens (lgCFU·g-1)

8

8

Number of C.perfringens (lgCFU·g-1)

8.5

0

50

100

Essential oil concentration (mg·mL ) e.Black pepper essential oil -1

0

10

20

30

Essential oil concentration (mg·mL-1) f.Wormwood essential oil

Fig.1 Effect of different concentrations of essential oils on the growth of C.perfringens (ATCC13124, C1)

8.5

10

9

-ATCC13124 predicted value -C1 predicted value

8

9

-Measured value

8

7.5

7

6.5

6

5.5

Number of C.perfringens (lgCFU·g-1)

Number of C.perfringens (lgCFU·g-1)

Number of C.perfringens (lgCFU·g-1)

8

7

6

5

3

4.5 0

10

20

Essential oil concentration (mg·mL-1) a.Cinnamon essential oil

6

5

4

4

5

7

3 0

20

40

Essential oil concentration (mg·mL-1) b.Wormwood essential oil

0

20

40

60

80

Essential oil concentration (mg·mL-1) c.Black pepper essential oil

Fig. 2 The correlation of measured values and predicted values by LS-SVM method under different essential oil concentration

Highlights:

The effect of spices essential oils on the survival of C. perfringens were studied; The in vitro inhibitory activities and MIC of spices essential oils were studied; The cinnamon essential oil exhibited a notable inhibitory effect on C. perfringens; The rapidly prediction model of the survival of C. perfringens is proposed; LS-SVM is the optimal model for predicting the survival of C. perfringens