Accepted Manuscript Rapid and standardized methods for detection of foodborne pathogens and mycotoxins on fresh produce F. Yeni, S. Acar, Ö.G. Polat, Y. Soyer, H. Alpas PII:
S0956-7135(13)00661-0
DOI:
10.1016/j.foodcont.2013.12.020
Reference:
JFCO 3621
To appear in:
Food Control
Received Date: 14 June 2013 Revised Date:
2 December 2013
Accepted Date: 17 December 2013
Please cite this article as: YeniF., AcarS., PolatÖ.G., SoyerY. & AlpasH., Rapid and standardized methods for detection of foodborne pathogens and mycotoxins on fresh produce, Food Control (2014), doi: 10.1016/j.foodcont.2013.12.020. 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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1
Food Control
2 RAPID AND STANDARDIZED METHODS FOR DETECTION OF FOODBORNE
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PATHOGENS AND MYCOTOXINS ON FRESH PRODUCE
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F. YENĐa,b, S. ACARb, Ö.G. POLATb, Y. SOYERb, H. ALPASb
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a
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Turkey
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b
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Department of Earth System Sciences, Middle East Technical University, 06800, Ankara,
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Department of Food Engineering, Middle East Technical University, 06800, Ankara, Turkey
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Due to the increase in consumption of fresh produce regarding to the health demand in the last
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decades, a considerable portion of foodborne outbreaks has been trackbacked to contaminated
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fresh produce, which have appeared as highly possible vehicles for foodborne outbreaks
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nowadays. Delays in detection of pathogens and mycotoxins on fresh produce hindered the
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trace-back investigations in finding the source and revealed the urgent need of rapid and
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reliable methods. In the frame of this review, we summarized available fast, reliable and
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standardized methods (conventional, molecular, rapid and recently developed methods) used
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for detection of the most common foodborne pathogens and mycotoxins which are the most
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likely causative agents of outbreaks caused by contaminated fresh produce.
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Keywords: Detection, Rapid methods, Foodborne pathogens, Mycotoxins, Fresh produce
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Corresponding author. Tel: +90 (312) 210 56 18, fax: +90 (312) 210 27 67
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E-mail address:
[email protected] (H. Alpas).
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1. Introduction
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28 In the last few decades, volume of international trade in food items rapidly increased
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while whole agro-food markets has globalized which resulted in food safety problems due to
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different safety practices of countries in all around the world. On the other hand, since 1980s,
32
consumption of fresh produce has been in an upward trend due to the consumers demanding
33
healthy food (FAOSTAT, 2012; Huang, 2004). Moreover, this demand continues through the
34
year and compensated by greater export volume while increasing the risk of contamination
35
during prolonged duration of storage and transportation stages (Lynch, Tauxe, & Hedberg,
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2009). This trend resulted in elevated numbers of foodborne outbreaks caused by
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contaminated fresh produce in the recent years in all around the world. For instance, in the
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period November 2010 - November 2012; 5191 people were infected with foodborne
39
pathogens on raw fresh produce items and consequently 95 people died due to the outbreaks
40
in European countries, USA, Canada and Japan (CDC, 2013). According to data published by
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European Food Safety Authority (EFSA), food of non-animal origin, which mainly comprised
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of processed and non-processed fresh produce items, is responsible for 10% of the total
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foodborne outbreaks, 26% of the cases, 35% of the hospitalizations and 46% of the deaths
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between 2007-20011 in Europe (Member States of European Union, Norway and
45
Switzerland) (Cerroni et al., 2010). On the other hand, 5630 people infected, 859 people
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hospitalized and 11 people died due to consumption of fresh produce including food items
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which were contaminated with foodborne pathogens between 2006-2010 in USA (CDC,
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2013) . In terms of mycotoxins, although number of outbreaks have declined as a result of
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strict limits set by national and international regulatory agencies, there can be occasional
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outbreaks due to inappropriate storage environmental conditions in the underdeveloped
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countries (Adams & Moss, 2006). In the last ten years, two outbreaks occurred Kenya and
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Brazil while 837 people were intoxicated and 157 people died because of mycotoxin
53
containment in cereals (Elizaquivel, Sanchez, & Aznar, 2012; Lima et al., 2010). Also, in the
54
USA, 70 people affected 41 people hospitalized and 3 people died due to in mushrooms and
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pineapple containing mycotoxin between 2001-2010 (CDC, 2013).
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In the light of these outbreak experiences, fresh produce has been accepted as a highly
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possible vehicle for foodborne outbreaks and routinely monitored for pathogen and
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mycotoxin contamination during the trace-back investigations (Lynch et al., 2009). The trace-
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back investigations ascertained the prevalence of foodborne pathogens and mycotoxins (e.g.
60
as aflatoxins, ochratoxin A, citrinin and patulin) amongst the major sources of recent
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outbreaks. For instance, Salmonella enterica, Staphylococcus aureus, pathogenic Escherichia
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coli, Listeria monocytogenes, Clostridium spp, Shigella spp. and Yersinia spp. are responsible
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for 42% of the total outbreaks occurred while pathogenic E. coli species account for the great
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majority of hospitalizations and death between 2007-2011 (Cerroni et al., 2010). These seven
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pathogens caused 99 outbreaks via fresh produce items between 2006-2010 in USA (CDC,
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2013). On the other hand, although occurrence of outbreaks caused by mycotoxin
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containment have been prevented almost completely due to strict national and international
68
regulations, mycotoxins still pose a risk to food of plant origin (especially to nuts, seeds and
69
spices among fresh produce) via occasional outbreaks due to inappropriate storage and
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environmental conditions (Adams & Moss, 2006; Forsythe, 2010). Therefore, it is evident that
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efforts to prevent all the outbreaks could not have been completely successful due to time-
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consuming process of available detection methods used in routine screening practices.
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Screening practices by microbiological testing formerly applied only to finished
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products in order to prevent outbreaks, however, modern surveillance systems aims to ensure
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food safety by preventing or minimizing contamination of food items along the food chain
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beginning with the production of raw materials (Forsythe, 2010). Preventing contamination of
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fresh produce is of vital importance in several ways. One critical point is that fresh produce
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items are often consumed raw and not exposed to enough postharvest treatment or cooking to
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eliminate or reduce pathogens before consumption (Rambo & Pillai, 2011; Ribot, Hyytia-
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Trees, & Cooper, 2008). Moreover, as the produce items are essential raw materials for food
81
manufacturers (Carlin, 2007), potential serious consequences of contaminated produce items
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in terms of mixed or prepared food products should also be taken into account. Also,
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contamination of the produce items in the production phase or in early stages of postharvest
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handling will increase the risk of cross contamination and spread of pathogens locally,
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nationally and internationally due to wider range of distribution of contaminated produce
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(Gorny, 2006; Hoofar et al., 2011). Recent outbreaks supported this argument as the trace-
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back investigations demonstrated that the produce items are often contaminated during the
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production phase at a single produce company. On the other hand, if contamination occurs,
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complete elimination of foodborne pathogens and mycotoxins from the produce is not always
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possible (J. Bennett & Klich, 2003; Parish et al., 2003).
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When contamination occurs in any stage of food supply chain and it can not be
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eliminated before the produce item becomes available for consumption, detecting the
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contaminated products appears as the best option in order to prevent any public health issue.
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Because the fresh produce items have a short shelf life and they are consumed instantly,
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sampling and detection of contaminated products must be considerably rapid and reliable.
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Many outbreaks caused by contaminated fresh produce items reveal the urgent need of using
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rapid methods in national and international surveillance systems. More recently, due to the
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errors and delays in detection of the source of the outbreak, the E.coli O104:H4 outbreak in
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Europe 2011 became the biggest foodborne outbreak occurred in Europe (Sprenger et al., 2011; WHO, 2011).
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There are general standard methods for determination of the foodborne pathogens and
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mycotoxins in food products including conventional methods such as culture and microscopic
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methods, chemical and biological methods (immunological, molecular genetic methods, gel
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diffusion), there are also more rapid methods which were recently developed including
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physical methods (biosensors, impedance, microcalorimetry, flow cytometry, biosys
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instrument) and bioassays (Jay, Loessner, & Golden, 2005). While antibody and nucleic acid
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based rapid assays prevailed over the methods which use basic technologies during 1990s
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while real-time PCR (rtPCR), microarrays, and biosensors have emerged as recent
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developments in the field of pathogen testing market in the 2000s (Mattingly, Butman, Plank,
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Durham, & Robison, 1988). Therefore, current surveillance systems have been changed from
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culture-confirmed microscopic methods to rapid and commercial non-culture methods in
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order to prevent food-borne diseases (Jones & Gerner-Smidt, 2012). However, all of the
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recently emerged methods can not be applied to fresh produce and the available methods used
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for determination and enumeration of pathogens on raw produce are generally modifications
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of the ones developed and validated for processed food products of plant origin (Beuchat,
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2006).
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As a natural consequence of the problems mentioned above, using rapid and reliable
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methods is of vital importance in the surveillance studies in order to prevent the outbreaks, to
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detect these organisms in the early warning and notification systems and to trace back the
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source pathogens. In the context of this paper we have reviewed the conventional analytical
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methods, which are widely used to detect foodborne pathogens and mycotoxins on fresh
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produce as well as more rapid methods including validated rapid commercial protocols and
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developments in the available molecular and emerging methods.
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124 2. Conventional Methods
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Validation is essential for standardization of a method as well as extent its usage in all
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around the world. Many national and some international institutions validate developed
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methods due to the sensitivity and specificity of the method which are approximately at the
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level of 95% concerning the methods available for foodborne pathogens (Forsythe, 2010).
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Methods for detection of foodborne pathogens and mycotoxins on fresh produce validated by
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International Organization for Standardization (ISO) are used with minor adaptations in the
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food control and reference laboratories of almost every country. However, some countries
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develop their own methods or revalidates available methods according to their own legal
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requirements. For instance, European Union (EU) countries prefer to use the protocols, which
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are modified from ISO methods by European Committee for Standardization (CEN), on the
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other hand Bacteriological Analytical Manual of United States Food and Drug Administration
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(FDA-BAM) is used as a guideline in public health laboratories in the USA. Conventional
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methods validated by these institutions were listed in the Table 1.
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The conventional methods listed in Table 1 are comprised of culture-based methods
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for detection and enumeration of foodborne pathogens and high-pressure liquid
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chromatography (HPLC) based methods are used for determination and identification of
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mycotoxins. Among the conventional methods validated by FDA, CEN and ISO, solely
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culture-based methods are available for Salmonella, Y. enterocolitica, L. monocytogenes, C.
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perfringens, and coagulase-positive staphylococci including S. aureus. Moreover, some other
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molecular techniques are also available for detection of foodborne pathogens together with
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culture-based methods. For instance, real-time PCR is used for determination of E. coli
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(STEC) O157, O111, O26, O103 and O145 serogroups in the methods validated by CEN/ISO
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and DNA hybridization technique is used for detection and enumeration of Shigella in the
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method validated by FDA.
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In terms of biological toxins, immunological, molecular and chromatographic
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techniques form the basis of validated methods. Concerning Staphylococcal enterotoxins;
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toxins are extracted via chromatographic techniques, detected and identified via enzyme-
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linked immunosorbent assay (ELISA) and Enzyme linked fibrinolytic assay (ELFA)
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techniques according to the methods approved by FDA. Likewise, botulinum toxins are
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detected via ELISA technique and mouse bioassay and groups of toxins identified via PCR
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according to the methods approved by FDA.
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On the other side, HPLC appears as the basic technique for determination of
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mycotoxins among the conventional methods available for fresh produce. All of the methods
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validated by CEN/ISO and FDA are based on this technique with minor differences such as
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with immunoaffinity column clean-up and post-column devrivatization, or fluorescence
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detection.
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2.1 Sampling and isolation procedures In a routine sampling process for fresh produce items possibly contaminated with
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pathogens, solid fresh produce items are transported to laboratory rapidly in chilled or frozen
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state in order to prevent growth and death of microorganisms and then generally 25g of
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sample is taken with aseptic tools and diluted to 1:10 dilution (Stewart & Gendel, 1998b).
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After sampling, there are some basic steps to isolate the target organism from the food
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namely, homogenization for solid samples, pre-enrichment for recovery of injured cells,
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enrichment for suppression of non-target organisms, plating with selective, non-selective or
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semi- selective agars for distinguishing target pathogen and ensuring the purity of the isolates,
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respectively (Forsythe, 2010). Enrichment steps for pathogens and extraction and
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concentration steps for toxins in food samples is essential prior to identification of pathogens
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and toxins in order to meet the sensitivity levels of available detection methods (Mattingly et
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al., 1988).
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Sampling of food items for analysis of mycotoxins is considerably different from the
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strategies applied for foodborne pathogens. Sampling step is the main reason of erroneous
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results along the detection procedure of mycotoxins due to inhomogeneous distribution of
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mycotoxins and highly contaminated spots in lots of food items. Therefore quantification of
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mycotoxin containment of food products generally can not be calculated with the exact
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certainty (Reiter, Zentek, & Razzazi, 2009). However, sampling procedures of mycotoxin
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producing fungi are performed almost in the manner with the protocols applied for bacterial
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foodborne pathogens.
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Following the sampling step, mycotoxins are extracted from the food item via organic
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solvents and interfering substances are removed in the clean-up step in chromatographic
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techniques (Reiter et al., 2009). After the clean-up step, high performance liquid
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chromatography (HPLC) thin layer chromatography (TLC) is used as conventional methods
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in order to quantify the mycotoxin containment in fresh produce items.
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Although the above mentioned procedure is simple, food scientists face with more
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complicated problems because of inherent properties of fresh produce samples. In addition to
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differences in size, shape and surface morphology of fresh produce precluding the possibility
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of applying a single standardized procedure for sampling in order to detect and enumerate
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foodborne pathogens, there are also difficulties associated with homogenized, blended or
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macerated tissues during preparation of the samples such as the inhibitory effects of organic
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acids in many fruits or some other antimicrobial compounds naturally available in tissues of
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vegetables, herbs and spices (Beuchat, 2006; Mattingly et al., 1988).
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Moreover, because fresh produce is often consumed as mixed food products such as
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salads or garnish, determining the contaminated source is a challenge for investigators in the
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situation of a fresh produce related outbreak (Berger et al., 2010). However, sampling stage
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during the trace-back investigations of an outbreak is relatively less complicated because
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there is often a suspected food product. On the other hand, food items are randomly sampled
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in all stages of a food supply chain during routine screening practices according to the
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ultimate goal of modern surveillance systems. However, the sample may not be representative
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of the food analyzed in random sampling because of the presence of different species in a
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sample and homogeneity of the solid food samples (Forsythe, 2010).
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2.2 Detection procedures
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Subsequent to isolation steps, mainly biochemical identification tests are used for
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detection of foodborne pathogens in conventional methods (Forsythe, 2010). Among the
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culture-based conventional methods, standard plate count is the longest available detection
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and enumeration method (Fung, 2006). Although this method is simple and widely used for
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decades, it requires a large amount of laboratory equipment, labor and time. As an alternative
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to standard plate count, more rapid and simpler culture-based enumeration methods
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introduced such as viable cell counts, differential counts, pathogen counts and some
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commercial methods including spiral plating, the isogrid system, petrifilm method, redigel
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system in the last 25 years (Fung, 2006). Also, incorporation of chromogenic and fluorogenic
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substrates into culture media can be regarded as recent developments in the biochemical
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identification of pathogenic microorganisms (Mattingly et al., 1988). In terms of mycotoxins, there are many protocols based on chromatographic
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techniques, mass spectrometry, immunological methods and biosensors applied for detection
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of mycotoxins in cereals and diary products (Maragos & Busman, 2010; Prieto-Simon &
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Campas, 2009; Reiter et al., 2009; Shephard et al., 2012), however, the only conventional
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methods available for detection of mycotoxin containment in fresh produce are thin layer
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chromatography (TLC) and high performance liquid chromatography.
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2.3 Advantages & disadvantages of conventional detection methods Culture-based methods and chromatographic methods constitute the large majority of
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the conventional methods, which were validated and standardized for international use for
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detection of foodborne pathogens and mycotoxins. The culture-based conventional methods
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are the basic tools used for detection of foodborne pathogens in all around the world for their
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reliability in efficiency, sensitivity to target organism, and application to a wide range of food
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matrices (e.g. all food ad feed stuff) and environments of food production and handling
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whereas more rapid protocols based on recent developments in food science can be applicable
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for a limited food matrix. Moreover, conventional methods are still regarded as a more
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reliable option for conformation of the obtained results in emergency situations. For instance,
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the false-negative results of rapid methods may cause expansion of the outbreak when there is
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a suspicion of contamination, or the false-positive results of rapid methods may cause a delay
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in finding the real source when there is an ongoing epidemiological investigation of an
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foodborne outbreak (Hoofar et al., 2011).
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Beyond these advantages, culture-based conventional isolation and detection methods
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require a considerable amount of laboratory equipment, large amounts of medium, and several
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days to detect the target pathogens mainly because of multiple enrichment steps. In addition
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to these disadvantages, laboratory personal should be trained to prepare the samples and
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interpret the results of the culture-based conventional methods (Forsythe, 2010; Stewart &
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Gendel, 1998b). Likewise, HPLC and TLC based methods requires considerable amount of
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time, labor and instrumentation cost although these techniques are widely used in all around
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the world due to their high sensitivity and reliability (Prieto-Simon & Campas, 2009).
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Therefore, more rapid and reliable methods such as molecular and immunological
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methods and bioassays are needed to be routinely used for screening practices of foodborne
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pathogens and mycotoxins in order to prevent public health issues by considering the speed
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and amount of transnational movement of fresh produce items in the modern agro-food
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markets.
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3. Molecular methods
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Emerging pathogens, which are not detectable by conventional microbiological
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methods, are growing in importance and time plays a large role in industrial processes
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(Lofstrom, Krause, Josefsen, Hansen, & Hoorfar, 2009). As a result of that; there is an
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increase of nucleic acid (DNA and RNA)-based assays for the differentiation and
261
identification of foodborne pathogens. Due to rapid and reliable detections and
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differentiations of foodborne pathogens, DNA methods including polymerase chain reaction
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(PCR), pulsed-field gel electrophoresis, ribotyping, plasmid typing, randomly amplified
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polymorphic DNA (RAPD), restriction fragment length polymorphism (RFLP) are generally
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used. There are some automated versions of these methods and some can be applied in kits
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that enable to recover pure DNA. PCR- based methods have been the widely known and used
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method among them.
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268 3.1. Sampling and isolation procedures
DNA isolation from food samples is a crucial step in all molecular detection methods.
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First, the sample should be kept in an appropriate temperature for a determined time period.
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After storage, the sample is extracted in a buffer system and then the supernatant is usually
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treated with proteinase K and CTAB solution (headecyltrimethylammonium bromide with
274
NaCl). DNA is precipitated, the purity of DNA is checked, which is done generally by gel
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electrophoresis methods (Naravaneni & Jamil, 2005). Lastly, the extracted DNA is used for
276
PCR and other molecular methods. For DNA extraction, there are also commercially available
277
kits such as QIAamp DNA Stool Mini kit (Qiagen) (Fukushima, Tsunomori, & Seki, 2003).
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After DNA extraction, the procedure specific to method is applied depending on the type of
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molecular technique.
280 3.2. Detection procedures
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PCR is a highly effective application that uses enzymes to amplify a single colony of
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DNA by 106-fold in a few hours. It can differentiate single-nucleotide polymorphisms (SNPs)
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and also can be designed as multiplex tests. Thus it is able to detect several pathogens
285
depending on specific DNA regions of organisms. Recently, it has been used to detect
286
foodborne bacterial pathogens such as viable E. coli O157:H7, Salmonella and
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L. monocytogenes cells in fresh-cut vegetables.(Elizaquivel et al., 2012).
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Real-time PCR (rtPCR) differs from conventional PCR in various ways; such as
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results are obtained (i) in real time thus there is no need of gel-based detection, (ii) in a very
290
short time since the amplification cycles are shorter and (iii) by using many different kind of
291
heating equipment, illumination source and detectors (Kubista & Zoric, 2005). SYBR Green
292
(a cyanine dye) I assay is the most economic and easiest one among other PCR assays. SYBR
293
Green I is a fluorescent dye to which double stranded DNA binds specifically. When the
294
amount of double stranded amplicon increases, the intensity of the SYBR Green I
295
fluorescence increases and it is monitored by rtPCR throughout amplification. In 2003, 17
296
species of foodborne and waterborne pathogens (enteroinvasive Escherichia coli,
297
enteropathogenic
298
enteroaggregative E. coli, Salmonella spp., Shigella spp., Yersinia enterocolitica, Yersinia
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pseudotuberculosis, Campylobacter jejuni, Vibrio cholera, Vibrio parahaemolyticus, Vibrio
300
vulnificus, Aeromonas spp., Staphylococcus aureus, Clostridium perfringens, and Bacillus
301
cereus) in stools are tested by SYBR Green Light Cycler PCR by identifying their melting
302
temperatures (Fukushima et al., 2003). At the end, the detection limit was calculated to be 105
303
cells/g, but there was a need of overnight enrichment. Another SYBR Green assay is
304
performed to detect Salmonella in alfalfa sprouts, milk and ground beef with the combination
305
of IMS (immunomagnetic separation) before the PCR and detection degree was 1.5 cells/26 g
306
of sample in 13 hours (Mercanoglu & Griffiths, 2005). Since the identification of amplicons is
307
based on only primers and melting temperature; sequence specificity cannot be detected. Thus
308
a combination with specific probes is usually required to get over the problems of mispriming
309
and genetic variations. Another illumination source is the TaqMan process that uses
310
sequence-specific probe with a fluorescent reporter and quencher dye. Firstly the probe binds
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to a specific sequence and then Taq polymerase cleaves the bound by separating the reporter
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from quencher during amplification proceeds. The intensity of the fluorescence, which
313
changes according to the number of amplicons, is measured. A TaqMan quantitative real-time
314
PCR is developed to detect only live Salmonella cells and the improved rtPCR was found to
315
give fast and accurate results for viable Salmonella detection for spinach, tomatoes, jalapeno
316
and serrano peppers (Gonzalez-Escalona et al., 2009). The other rtPCR assay uses molecular
317
beacon technique in which sequence-specific probes having hair-pin or stem-loop
318
oligonucleotide are used. When the probes -consisting of a fluorophore and a quencher-
319
hybridizes to a specific target, reporter separates from the quencher and fluorescence intensity
320
increases. Molecular beacon-PCR is studied to detect the presence of Salmonella spp. with
321
fresh-cut produce such as cantaloupe, mixed-salad, cilantro, and alfalfa sprouts and as a result,
322
1–4 cfu/PCR reaction could be detected (Liming & Bhagwat, 2004). A similar result was
323
obtained in a Listeria monocytogenes study that is performed on artificially inoculated fresh-
324
cut produce such as cantaloupe and mixed salad. At the end, 4 to 7 cfu/25 g of artificially
325
contaminated produce could be detected (Liming, Zhang, Meng, & Bhagwat, 2004).
326
Fluorescence resonance energy transfer (FRET) rtPCR which uses energy transfer between
327
the donor and acceptor molecules has also been studied for detection of pathogens in food
328
samples. With the help of high-throughput automated DNA extraction, 32 food specimens
329
were processed and assayed in less than 2 hours and as a result, a lower limit of 1.5 × 102 and
330
1.5 × 105 CFU/mL without pre-enrichment was achieved (Olsen, Gibbins, & Grayson, 2009).
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Multiplex PCR, on the other hand, is the simultaneous amplification of more than one
332
target sequence in a single reaction (Henegariu, Heerema, Dlouhy, Vance, & Vogt, 1997).
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Differently from individual rtPCR applications, fewer reactions are run, thus it conserves the
334
expensive reagents such as dNTPs, enzymes, primers etc. Therefore, at the end, the economic
335
value of multiplex PCR becomes lower than individual PCR. When sample amount is limited,
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multiplexing permits more targets to be analyzed using a single aliquot of sample material,
337
thus it preserves limited amount samples that should be tested for detection of several
338
pathogens. In a different manner from individual PCR methods, multiplex PCR uses data,
339
which has an improved quality, since the target of interest is normalized to the endogenous
340
control within the same aliquot of the sample, which at the end, allows for increased
341
reliability. But, to use a successful multiplex PCR, one should be careful about: relative
342
concentration of primers, PCR buffer concentration, balance between magnesium chloride
343
and deoxinucleotide concentrations, cycling temperatures and amounts of template DNA and
344
Taq DNA polymerase (Markoulatos, Siafakas, & Moncany, 2002). The sensitivity of
345
multiplex PCR is studied on artificially inoculated cleaned and packed spinach and lettuce
346
and it was observed that it was able to detect 0.9, 1.8 and 4.8 CFU/reaction of E. coli
347
O157:H7, Salmonella, and Sta. aureus, respectively, corresponding to 103 CFU g−1 each
348
(Elizaquivel & Aznar, 2008).
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Nucleic acid sequence based amplification (NASBA) differs from PCR application as
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there is no need of a thermal cycler and also it is performed in isothermal conditions
351
(Compton, 1991). It is usually carried out to amplify RNA and cDNA (Lauri & Mariani,
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2009). NASBA has the capability to detect viable microorganisms in different kind of
353
samples including environmental and food matrices (Chan & Fox, 1999; Cook, 2003).
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The BAX system (DuPont, Qualicon, Wilmington, DE), a commercial polymerase
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chain reaction-based instrumentation, provides processes up to 96 unique samples within four
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hours after sample preparation (Bailey, 1998; Silbernagel, Jechorek, Carver, Barbour, &
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Mrozinski, 2003). And the results are useable as soon as the following day and are distinctly
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displayed on screen with a simple positive or negative report. It is available to be used for
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screening Salmonella, E. coli O157:H7, L. monocytogenes, etc. (Bhagwat, 2003; Stewart &
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Gendel, 1998a). Sample preparations are based on the standard protocols for each food type.
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Samples are then heated in a lysis reagent solution to separate the bacterial cell wall and
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release the DNA. PCR tablets, which contain all the reagents necessary for PCR plus
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fluorescent dye, are hydrated with lysed sample and processed in the cycler/detector. Within a
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few hours, the PCR amplifies a DNA fragment that is specific to the target. The amplified
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DNA generates a fluorescent signal, which the BAX® system uses to analyze the results
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which are then displayed as simple positive or negative symbols (Becker, Jordan, &
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Holzapfel, 2005). BAX system was studied with retail sprouts and mushrooms for
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contamination with Escherichia coli O157:H7, Salmonella, Listeria spp., and Listeria
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monocytogenes in 2003. Failure of the method is measured as 0.7% and thus reagent failures
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and the inhibition of PCR by plant compounds were defined as uncommon. It was observed
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that the sensitivity of the test is not consistent among food types and microorganisms. Thus, it
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was concluded that test sensitivity is affected by the type of produce involved and is probably
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related to the growth of pathogens in the resuscitation and enrichment media (Strapp, Shearer,
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& Joerger, 2003).
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3.3. Advantages & disadvantages of molecular detection methods
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Molecular methods allow for sensitive and rapid detection of several pathogens. The
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results can be obtained in shorter times as in a day such as in real time multiplex PCR (de
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Boer, Ott, Kesztyus, & Kooistra-Smid, 2010). The sensitivity of a molecular method can be
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set and improved by designing new primers, probes, using optimum conditions etc. thus, these
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methods are available to any changes regarding to the requirements of detection level of
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pathogens (Yang et al., 2002). Also, the molecular methods give reliable, high-throughput,
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reproducible and specific results (Klein, 2002; Lindstrom et al., 2001). In addition, species
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identification, quantification and subtyping can be performed together with species detection
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during molecular methods, so they allow for further data on the phylogenetic characteristics
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of the strains identified (Amagliani, Omiccioli, Brandi, Bruce, & Magnani, 2010; Girones et
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al., 2010).
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The positive result obtained a molecular method can be only regarded as presumptive
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and must be confirmed by standard methods. Thus, a molecular method alone cannot be used
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to detect the source of a foodborne outbreak or identify the pathogen. Also, the most rapid
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methods lack of sufficient sensitivity and specificity for director testing, foods still need to be
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culture-enriched before analysis. In addition, molecular methods are food and microorganism
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dependent. Besides, these methods can be used to detect cell, but cannot be used to detect the
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toxin occurrence.
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Despite the advantages of PCR such as being sensitive, commercially available; PCR
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applications have some drawbacks. For instance, PCR is negatively affected by the complex
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structure of food and thus its sensitivity decreases depending on food types. Also, food may
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include some PCR inhibitors (J. M. Perez et al., 2003; Vaneechoutte & Van Eldere, 1997)
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which in the end influence amplification efficiency or primer binding. Especially foods that
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have high fat or protein content may cause such decrease in efficiency. Thus, culture
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enrichment becomes a necessity to come up with this problem. Similarly, the other issue in
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PCR application is the DNA purification step. It is performed in PCR assays to fix
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amplification efficiency. Extraction step is crucial and thus should be done in a careful
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manner to control all the components and variables that influence PCR results. Internal
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amplification controls are thus required (Hoorfar et al., 2003) which includes primers that are
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specific to a non-target DNA. For instance, a PCR result bringing out only the 16S rDNA
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amplicon with the primers specific for 16S rDNA would represent an effective extraction.
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408 409
4. Recently developed detection methods
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410 The general requirements of a recent detection method are (i) increased specificity, (ii)
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high-throughput results, (iii) increased reliability, (iv) being applicable in all international
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laboratories, (v) having protocols that permits standardization, (vi) rapid technique, (vii) low
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cost compared to its alternatives. Considering the demands, immunology-based methods and
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several types of biosensors are the new developing techniques for detection of foodborne
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pathogens (Velusamy, Arshak, Korostynska, Oliwa, & Adley, 2010).
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4.1. Sampling and isolation procedures
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The sampling protocol does even not exist for recently developed detection methods.
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The suspensions (containing peptone water or washing water of a produce) gathered from
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homogenized sample or only adjoining the sensor to vegetable/fruit surface is sufficient
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(Ercole, Del Gallo, Mosiello, Baccella, & Lepidi, 2003; Li et al., 2010; Ogunjimi &
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Choudary, 1999).
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4.2. Detection procedures
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For immunodetection, which is based on antigen-antibody binding selection, several
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types of antibodies can be used; conventional and heavy chain antibodies, as well as
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polyclonal, monoclonal or recombinant antibodies. For instance, detection of Listeria
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monocytogenes can be performed via polyclonal antibodies (Feldsine, Lienau, Forgey, &
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Calhoon, 1997; Jung, Frank, & Brackett, 2003) and via monoclonal antibodies (Mattingly et
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al., 1988), but for Salmonella detection, monoclonal antibodies (Schneid, Ludtke, Diel, &
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Aleixo, 2005) have been used. Polyclonal antibodies have low cost and can be prepared
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quickly compared to its alternatives but it has low specificity and abundance (Leonard et al.,
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2003). Monoclonal antibodies, on the other hand, have been found to be more specific and
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thus used in an extended range of foodborne pathogens such as Listeria monocytogenes,
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Salmonella spp., Staphylococcus aureus, Escherichia coli O157, and Shigella. But it has some
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disadvantages too, requiring skilled workers, specialized specimen and high cost.
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Immunological detection methods have been studied on various techniques such as enzyme
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immunoassay (EIA) (Borck, Stryhn, Ersboll, & Pedersen, 2002), enzyme-linked
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immunosorbent assay (ELISA) (R. Bennett, 2005), immunochromatography (ICG) strip test
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(Shim et al., 2007), immunomagnetic separation (Hudson, Lake, Savill, Scholes, &
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McCormick, 2001) etc.
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ELISA (enzyme-linked immunosorbent assay) is the most prominent one among other
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types of immunological pathogen detection methods. It integrates the specificity of antibodies
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and the sensitivity of simple enzyme assays by using antibodies or antigens connected to an
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enzyme (Lazcka, Del Campo, & Munoz, 2007). The enzymes used in ELISA may differ, but
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the general ones are alkaline phosphatase, horseradish peroxidase (HRP) and beta-
448
galactosidase.
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Campylobacter jejuni is achieved with horseradish peroxidase enzyme by labelling the
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antibody for these pathogens that is performed by sandwich ELISA (Chemburu, Wilkins, &
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Abdel-Hamid, 2005).
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Biosensors, being a rapid method compared to its alternatives (culture-based methods,
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molecular methods and immunological methods), are analytical instruments that works by
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trapping a molecule as a reactive surface in close distance to a transducer. Transducers, which
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can be in the form of piezoelectric crystals, electrochemical and optical devices, and acoustic
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waves, convert the binding of analyze to the capturing molecule into measurable signal
457
(Velusamy et al., 2010). The main benefits of biosensor technology are its specificity,
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sensitivity, reliability, portability, real time analysis and simplicity of the operation (D'Souza,
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2001). The classification of biosensors can be done based on their bio receptor and transducer
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types. Enzymes, nucleic acid-based molecules, antibodies, cell/cellular components,
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biomimetic molecules and bacteriophages can be used as a bio receptor for biosensors. And
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for transducer element, optical (Fourier transform infrared –F-TIR-, Raman spectroscopy,
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fiber optics, surface plasmon resonance –SPR-, etc.), electrochemical (amperometrics,
464
potentiometric, conductiometric, etc.) and mass-based (piezoelectric, magnetoelastic, etc.)
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technologies are studied so far.
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Electrochemical methods integrated with magnetic separation were experienced to
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detect Salmonella Typhimurium (Che, Yang, Li, Paul, & Slavik, 1999) and Escherichia coli
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(F. G. Perez, Mascini, Tothill, & Turner, 1998). The methods were completed in less than
469
2 hours. And the detection limit was 5×103 cell ml-1 for the Salmonella and 105 cell ml-1 for
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the E. coli. Ercole have studied the application of an polyclonal antibody based biosensor for
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the detection of Escherichia coli cells in different six packages of commercial ready to use
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(RTU) vegetable salads, consisting of rucola, lettuce, carrots and three samples of mixed salad
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(Ercole et al., 2003). The detection limit of the study was 10 cells/ml and the detection time
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was 10-20 times smaller than conventional methods, thus antibody based biosensor detection
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was designated as a sensitive and rapid technique. In a similar study, a novel biosensor
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grounded on electrochemical sandwich immunoassay for Escherichia coli O157:H7 has been
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tried on detection in fresh produce such as lettuce, alfalfa sprouts, and strawberries
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(Muhammad-Tahir & Alocilja, 2004). The detection limit of proposed biosensor was
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estimated to be 81 cfu ml−1 and the time for biosensor detection was as short as 6 min.
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Similarly, Salmonella Typhimurium detection has been studied on fresh tomato surfaces using
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phage-based magnetoelastic (ME) biosensors which is made up of a ME resonator platform
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coated with filamentous E2 phage (Li et al., 2010). It was concluded that the biosensor was
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able to detect 5 × 102 cfu ml-1 and higher concentration of Salmonella on tomato surface in 30
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min.
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4.3. Advantages & disadvantages of recently developed detection methods
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Immunological methods have the capability of detection of bacterial cells, spores,
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viruses and also toxins (especially mycotoxins) (Iqbal et al., 2000). And also they use rapid
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and more robust techniques compared to molecular detection methods (Velusamy et al.,
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2010).
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The sensitivity of biosensor detection methods are similar to conventional methods
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(Ercole et al., 2003; Muhammad-Tahir & Alocilja, 2004). The assay time is decreased to
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hours or even minutes compared to conventional methods which require days to detect a
494
foodborne pathogen.
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The immunological-based detection is less specific and sensitive than nucleic acidbased detection.
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Biosensors are promising techniques when their rapid and sensitive detection are taken
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into consideration but research and improvement is required to become a reliable and usable
499
alternative (Lazcka et al., 2007). There are too many types of biosensors, thus there is also a
500
need of standardization for the protocols to be specific to different pathogen species and food
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types. Subjects like being useable in all laboratories, low maintenance, continuous operation
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and cost effectiveness should also be needed to be considered.
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6. Conclusions
505 The key role of rapid and reliable methods have been emphasized by the
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epidemiological studies of the major outbreaks occurred in the recent years including the
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E.coli O104:H4 outbreak in 2011. These outbreaks entailed emerging of new rapid
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commercial methods. However, the regulatory agencies and the food industry have been
510
facing the dilemma of choosing the right method in screening practices and in emergency
511
situations: using the rapid methods as screening tools despite the threat of false-negative
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results, or using the conventional methods for investigations of foodborne outbreak despite
513
long time requirements. At this point, validation of commercial methods is essential and only
514
a few international institutions perform this task.
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Consequently, it is evident that the process of outbreak investigation should be
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shortened in order to reduce the pathogen spread and find the source of contamination by
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standardized rapid methods. Extended use of these standardized methods in all around the
518
world depends on their acceptance by national and international agencies. Therefore, it is
519
crucial that the process of validation should be accelerated and the number of international
520
institutions validating these methods should be increased in order to have rapid standardized
521
detection methods that can be used worldwide. Thus, the ultimate goal in the near future
522
should be developing a rapid and reliable detection method which can be preferred to time
523
consuming conventional methods and can be used as a standard method in all around the
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world.
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Acknowledgement
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The research leading to these results has received funding from the European Union
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Seventh Framework Programme (FP7/2007-2013) under grant agreement no: 261752 "Plant
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and Food Biosecurity" (PLANTFOODSEC).
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Adams, M., & Moss, M. (2006). Food Microbiology (2nd ed.). Cambridge, UK: The Royal Society of Chemistry. Amagliani, G., Omiccioli, E., Brandi, G., Bruce, I. J., & Magnani, M. (2010). A multiplex magnetic capture hybridisation and multiplex Real-Time PCR protocol for pathogen detection in seafood. Food Microbiology, 27(5), 580-585. Bailey, J. S. (1998). Detection of Salmonella cells within 24 to 26 hours in poultry samples with the polymerase chain reaction BAX system. J Food Prot, 61(7), 792-795. Becker, B., Jordan, S., & Holzapfel, W. H. (2005). Rapid and specific detection of Listeria monocytogenes in smoked salmon with BAX (R)-PCR. Food Control, 16(8), 717-721. Bennett, J., & Klich, M. (2003). Mycotoxins. Clinical Microbiology Reviews, 16(3), 497-516. Bennett, R. (2005). Staphylococcal enterotoxin and its rapid identification in foods by enzyme-linked immunosorbent assay-based methodology. Journal of Food Protection, 68(6), 1264-1270. Berger, C., Sodha, S., Shaw, R., Griffin, P., Pink, D., Hand, P., & FRANKEL, G. (2010). Fresh fruit and vegetables as vehicles for the transmission of human pathogens. Environmental Microbiology, 12(9), 2385-2397. Beuchat, L. (2006). Sampling, Detection, and Enumeration of Pathogenic and Spoilage Microorganisms. In G. Sapers, J. Gorny & A. Yousef (Eds.), Microbiology of Fruits and Vegetables (1st ed., pp. 543-564). Boca Raton: CRC Press. Bhagwat, A. A. (2003). Simultaneous detection of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella strains by real-time PCR. Int J Food Microbiol, 84(2), 217-224. Borck, B., Stryhn, H., Ersboll, A. K., & Pedersen, K. (2002). Thermophilic Campylobacter spp. in turkey samples: evaluation of two automated enzyme immunoassays and conventional microbiological techniques. Journal of Applied Microbiology, 92(3), 574-582. Carlin, F. (2007). Fruits and Vegetables. In M. Doyle & L. Beuchat (Eds.), Food Microbiology: Fundamentals and Frontiers (3rd ed., pp. 157-170). Washington DC: ASM Press. CDC, Centers for Disease and Prevention (2013). Foodborne Outbreak Online Database (FOOD) http://wwwn.cdc.gov/foodborneoutbreaks/ Accessed 21.04.2013. Cerroni, M. P., Barrado, J. C., Nobrega, A. A., Lins, A. B., da Silva, I. P., Mangueira, R. R., da Cruz, R. H., Mendes, S. M., & Sobel, J. (2010). Outbreak of beriberi in an Indian population of the upper Amazon region, Roraima State, Brazil, 2008. Am J Trop Med Hyg, 83(5), 1093-1097. Chan, A. B., & Fox, J. D. (1999). NASBA and other transcription-based amplification methods for research and diagnostic microbiology. Reviews in Medical Microbiology, 10(4), 185-196. Che, Y. H., Yang, Z. P., Li, Y. B., Paul, D., & Slavik, M. (1999). Rapid detection of Salmonella typhimurium using an immunoelectrochemical method coupled with immunomagnetic separation. Journal of Rapid Methods and Automation in Microbiology, 7(1), 47-59.
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RI PT
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RI PT
Chemburu, S., Wilkins, E., & Abdel-Hamid, I. (2005). Detection of pathogenic bacteria in food samples using highly-dispersed carbon particles. Biosens Bioelectron, 21(3), 491499. Compton, J. (1991). Nucleic-Acid Sequence-Based Amplification. Nature, 350(6313), 91-92. Cook, N. (2003). The use of NASBA for the detection of microbial pathogens in food and environmental samples. Journal of Microbiological Methods, 53(2), 165-174. D'Souza, S. F. (2001). Microbial biosensors. Biosens Bioelectron, 16(6), 337-353. de Boer, R. F., Ott, A., Kesztyus, B., & Kooistra-Smid, A. M. D. (2010). Improved Detection of Five Major Gastrointestinal Pathogens by Use of a Molecular Screening Approach. Journal of Clinical Microbiology, 48(11), 4140-4146. Elizaquivel, P., & Aznar, R. (2008). A multiplex RTi-PCR reaction for simultaneous detection of Escherichia coli O157 : H7, Salmonella spp. and Staphylococcus aureus on fresh, minimally processed vegetables. Food Microbiology, 25(5), 705-713. Elizaquivel, P., Sanchez, G., & Aznar, R. (2012). Quantitative detection of viable foodborne E. coli O157:H7, Listeria monocytogenes and Salmonella in fresh-cut vegetables combining propidium monoazide and real-time PCR. Food Control, 25(2), 704-708. Ercole, C., Del Gallo, M., Mosiello, L., Baccella, S., & Lepidi, A. (2003). Escherichia coli detection in vegetable food by a potentiometric biosensor. Sensors and Actuators BChemical, 91(1-3), 163-168. FAOSTAT, Food and Agriculture Organization of the United Nations (2012). FAO Statistical Database http://faostat.fao.org/ Accessed 15.12.2012. Feldsine, P. T., Lienau, A. H., Forgey, R. L., & Calhoon, R. D. (1997). Assurance polyclonal enzyme immunoassay for detection of Listeria monocytogenes and related Listeria species in selected foods: Collaborative study. Journal of Aoac International, 80(4), 775-790. Forsythe, S. (2010). The Microbiology of Safe Food (2nd ed.). UK: Blackwell Publishing Ltd. Fukushima, H., Tsunomori, Y., & Seki, R. (2003). Duplex real-time SYBR green PCR assays for detection of 17 species of food- or waterborne pathogens in stools. Journal of Clinical Microbiology, 41(11), 5134-5146. Fung, D. (2006). Rapid Detection of Microbial Contaminants. In G. Sapers, J. Gorny & A. Youssef (Eds.), Microbiology of Fruits and Vegetables (1st ed., pp. 565-594). Boca Raton: CRC Press. Girones, R., Ferrus, M. A., Alonso, J. L., Rodriguez-Manzano, J., Calgua, B., Correa, A. D., Hundesa, A., Carratala, A., & Bofill-Mas, S. (2010). Molecular detection of pathogens in water - The pros and cons of molecular techniques. Water Research, 44(15), 43254339. Gonzalez-Escalona, N., Hammack, T. S., Russell, M., Jacobson, A. P., De Jesus, A. J., Brown, E. W., & Lampel, K. A. (2009). Detection of Live Salmonella sp Cells in Produce by a TaqMan-Based Quantitative Reverse Transcriptase Real-Time PCR Targeting invA mRNA. Applied and Environmental Microbiology, 75(11), 3714-3720. Gorny, J. (2006). Microbial Contamination of Fresh Fruits and Vegetables. In G. Sapers, J. Gorny & A. Yousef (Eds.), Microbiology of Fruits and Vegetables (1st ed., pp. 3-32). Boca Raton: CRC Press. Henegariu, O., Heerema, N. A., Dlouhy, S. R., Vance, G. H., & Vogt, P. H. (1997). Multiplex PCR: Critical parameters and step-by-step protocol. Biotechniques, 23(3), 504-511. Hoofar, J., Cahill, S., Clarke, R., Barker, G., Fazil, A., Wong, D., & Feng, P. (2011). The Public Health, Industrial, and Global Significance of Rapid Microbiological Food
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Testing. In H. J (Ed.), Rapid Detection, Characterization, and Enumaration of Foodborne Pathogens (pp. 1-12). Washington: ASM Press. Hoorfar, J., Cook, N., Malorny, B., Wagner, M., De Medici, D., Abdulmawjood, A., & Fach, P. (2003). Making internal amplification control mandatory for diagnostic PCR. J Clin Microbiol, 41(12), 5835. Huang, S. H., U.S. Department of Agriculture (USDA) - Economic Research Service (2004). Global Trade Patterns in Fruits and Vegetables 1-83, http://www.ers.usda.gov/media/320504/wrs0406_1_.pdf Accessed 05.01.2013. Hudson, J. A., Lake, R. J., Savill, M. G., Scholes, P., & McCormick, R. E. (2001). Rapid detection of Listeria monocytogenes in ham samples using immunomagnetic separation followed by polymerase chain reaction. J Appl Microbiol, 90(4), 614-621. Iqbal, S. S., Mayo, M. W., Bruno, J. G., Bronk, B. V., Batt, C. A., & Chambers, J. P. (2000). A review of molecular recognition technologies for detection of biological threat agents. Biosensors & Bioelectronics, 15(11-12), 549-578. Jay, J., Loessner, M., & Golden, D. (2005). Modern Food Microbiology (7th ed.). New York: Springer Science+Business Media. Inc. Jones, T., & Gerner-Smidt, P., (2012). Nonculture Diagnostic Tests for Enteric Diseases Emerging Infectious Diseases, 18, 513-514, http://wwwnc.cdc.gov/eid/article/18/3/111914_article.htm Accessed 30.11.2012. Jung, Y. S., Frank, J. F., & Brackett, R. E. (2003). Evaluation of antibodies for immunomagnetic separation combined with flow cytometry detection of Listeria monocytogenes. Journal of Food Protection, 66(7), 1283-1287. Klein, D. (2002). Quantification using real-time PCR technology: applications and limitations. Trends in Molecular Medicine, 8(6), 257-260. Kubista, M., & Zoric, N. (2005). Real-time PCR platforms. In F. J. & P. M. (Eds.), Encyclopedia of Diagnostic Genomics and Proteomics (pp. 1052-1059). New York, N.Y.: Marcel Dekker. Lauri, A., & Mariani, P. O. (2009). Potentials and limitations of molecular diagnostic methods in food safety. Genes and Nutrition, 4(1), 1-12. Lazcka, O., Del Campo, F. J., & Munoz, F. X. (2007). Pathogen detection: a perspective of traditional methods and biosensors. Biosens Bioelectron, 22(7), 1205-1217. Leonard, P., Hearty, S., Brennan, J., Dunne, L., Quinn, J., Chakraborty, T., & O'Kennedy, R. (2003). Advances in biosensors for detection of pathogens in food and water. Enzyme and Microbial Technology, 32(1), 3-13. Li, S. Q., Li, Y. G., Chen, H. Q., Horikawa, S., Shen, W., Simonian, A., & Chin, B. A. (2010). Direct detection of Salmonella typhimurium on fresh produce using phagebased magnetoelastic biosensors. Biosensors & Bioelectronics, 26(4), 1313-1319. Lima, H. C., Porto, E. A., Marins, J. R., Alves, R. M., Machado, R. R., Braga, K. N., de Paiva, F. B., Carmo, G. M., Silva e Santelli, A. C., & Sobel, J. (2010). Outbreak of beriberi in the state of Maranhao, Brazil: revisiting the mycotoxin aetiologic hypothesis. Trop Doct, 40(2), 95-97. Liming, S. H., & Bhagwat, A. A. (2004). Application of a molecular beacon - real-time PCR technology to detect Salmonella species contaminating fruits and vegetables. International Journal of Food Microbiology, 95(2), 177-187. Liming, S. H., Zhang, Y., Meng, J., & Bhagwat, A. A. (2004). Detection of Listeria monocytogenes in fresh produce using molecular beacon - Real-time PCR technology. Journal of Food Science, 69(8), M240-M245.
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Lindstrom, M., Keto, R., Markkula, A., Nevas, M., Hielm, S., & Korkeala, H. (2001). Multiplex PCR assay for detection and identification of Clostridium botulinum types A, B, E, and F in food and fecal material. Applied and Environmental Microbiology, 67(12), 5694-5699. Lofstrom, C., Krause, M., Josefsen, M. H., Hansen, F., & Hoorfar, J. (2009). Validation of a same-day real-time PCR method for screening of meat and carcass swabs for Salmonella. BMC Microbiol, 9, 85. Lynch, M., Tauxe, R., & Hedberg, C. (2009). The growing burden of foodborne outbreaks due to contaminated fresh produce: risks and opportunities. Epidemiol. Infetect., 137, 307-315. Maragos, C., & Busman, M. (2010). Rapid and advanced tools for mycotoxin analysis: a review. Food Additives and COntaminants, 27(5), 688-700. Markoulatos, P., Siafakas, N., & Moncany, M. (2002). Multiplex polymerase chain reaction: A practical approach. Journal of Clinical Laboratory Analysis, 16(1), 47-51. Mattingly, J. A., Butman, B. T., Plank, M. C., Durham, R. J., & Robison, B. J. (1988). Rapid Monoclonal Antibody-Based Enzyme-Linked Immunosorbent-Assay for Detection of Listeria in Food-Products. Journal of the Association of Official Analytical Chemists, 71(3), 679-681. Mercanoglu, B., & Griffiths, M. W. (2005). Combination of Immunomagnetic separation with real-time PCR for rapid detection of salmonella in milk, ground beef, and alfalfa sprouts. Journal of Food Protection, 68(3), 557-561. Muhammad-Tahir, Z., & Alocilja, E. C. (2004). A disposable biosensor for pathogen detection in fresh produce samples. Biosystems Engineering, 88(2), 145-151. Naravaneni, R., & Jamil, K. (2005). Rapid detection of food-borne pathogens by using molecular techniques. Journal of Medical Microbiology, 54(1), 51-54. Ogunjimi, A. A., & Choudary, P. V. (1999). Adsorption of endogenous polyphenols relieves the inhibition by fruit juices and fresh produce of immuno-PCR detection of Escherichia coli O157:H7. FEMS Immunol Med Microbiol, 23(3), 213-220. Olsen, E. V., Gibbins, C. S., & Grayson, J. K. (2009). Real-Time FRET PCR Assay for Salmonella enterica Serotype Detection in Food. Military Medicine, 174(9), 983-990. Parish, M. E., Beuchat, L. R., Suslow, T. V., Harris, L. J., Garrett, E. H., Farber, J. N., & Busta, F. F. (2003). Methods to Reduce/Eliminate Pathogens from Fresh and FreshCut Produce. Comprehensive Reviews in Food Science and Food Safety, 2(Supplement s1), 161-173. Perez, F. G., Mascini, M., Tothill, I. E., & Turner, A. P. F. (1998). Immunomagnetic separation with mediated flow injection analysis amperometric detection of viable Escherichia coli O157. Analytical Chemistry, 70(11), 2380-2386. Perez, J. M., Cavalli, P., Roure, C., Renac, R., Gille, Y., & Freydiere, A. M. (2003). Comparison of Four Chromogenic Media and Hektoen Agar for Detection and Presumptive Identification of Salmonella Strains in Human Stools. Journal of Clinical Microbiology, 41(3), 1130-1134. Prieto-Simon, B., & Campas, M. (2009). Immunochemical tools for mycotoxin detection in food. Monatshefte fur Chemie, 140, 915–920. Rambo, C., & Pillai, S. (2011). Pathogen Testing in Fresh Produce and Irrigation Water. In J. Hoofar (Ed.), Rapid detection, characterization, and enumaration of foodborne pathogens (1st ed.). Washington: ASM Press
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Reiter, E., Zentek, J., & Razzazi, E. (2009). Review on sample preparation strategies and methods used for the analysis of aflatoxins in food and feed. Molecular Nutrition & Food Research, 53, 508-524. Ribot, E. M., Hyytia-Trees, E., & Cooper, K. (2008). PulseNet and Emerging Foodborne Pathogens. In C. Wilson (Ed.), Microbial Food Contamination (2nd ed., pp. 3-29). Boca Raton: CRC Press. Schneid, A. D., Ludtke, C. B., Diel, C., & Aleixo, J. A. G. (2005). Production and caracterization of monoclonal antibodies for the detection of Salmonella enterica in chicken meat. Brazilian Journal of Microbiology, 36(2), 163-169. Shephard, G. S., Berthiller, F., Burdaspal, P. A., Crews, C., Jonker, M. A., Krska, R., MacDonald, S., Malone, R., Maragos, C., Sabino, M., Solfrizzo, M., Egmond, H. P. V., & Whitaker, T. B. (2012). Developments in mycotoxin analysis: an update for 2010-2011. World Mycotoxin Journal, 5(5), 3-30. Shim, W. B., Choi, J. G., Kim, J. Y., Yang, Z. Y., Lee, K. H., Kim, M. G., Ha, S. D., Kim, K. S., Kim, K. Y., Kim, C. H., Ha, K. S., Eremin, S. A., & Chung, D. H. (2007). Production of monoclonal antibody against Listeria monocytogenes and its application to immunochromatography strip test. J Microbiol Biotechnol, 17(7), 1152-1161. Silbernagel, K., Jechorek, R., Carver, C., Barbour, W. M., & Mrozinski, P. (2003). Evaluation of the BAX system for detection of Salmonella in selected foods: collaborative study. J AOAC Int, 86(6), 1149-1159. Sprenger, M., Coulombier, D., Duncan, B., Edwards, K., Kreisel, U., Kokki, M., Mos, J., Struelens, M., & Takkinen, J. (2011). Understanding the 2011 EHEC/STEC outbreak in Germany. In ICAAC Conference. Chicago. Stewart, D., & Gendel, S. M. (1998a). Specificity of the BAX polymerase chain reaction system for detection of the foodborne pathogen Listeria monocytogenes. J AOAC Int, 81(4), 817-822. Stewart, D., & Gendel, S. M. (1998b). Specificity of the BAX polymerase chain reaction system for detection of the foodborne pathogen Listeria monocytogenes. Journal of Aoac International, 81(4), 817-822. Strapp, C. M., Shearer, A. E. H., & Joerger, R. D. (2003). Survey of retail alfalfa sprouts and mushrooms for the presence of Escherichia coli O157 : H7, Salmonella, and Listeria with BAX, and evaluation of this polymerase chain reaction-based system with experimentally contaminated samples. Journal of Food Protection, 66(2), 182-187. Vaneechoutte, M., & Van Eldere, J. (1997). The possibilities and limitations of nucleic acid amplification technology in diagnostic microbiology. J Med Microbiol, 46(3), 188194. Velusamy, V., Arshak, K., Korostynska, O., Oliwa, K., & Adley, C. (2010). An overview of foodborne pathogen detection: In the perspective of biosensors. Biotechnology Advances, 28(2), 232-254. WHO, World Health Organization (2011). A public health review of the Enterohemorrhagic Escherichia coli outbreak in Germany 9, http://www.euro.who.int/ Accessed 18.07.2013. Yang, S., Lin, S., Kelen, G. D., Quinn, T. C., Dick, J. D., Gaydos, C. A., & Rothman, R. E. (2002). Quantitative multiprobe PCR assay for simultaneous detection and identification to species level of bacterial pathogens. Journal of Clinical Microbiology, 40(9), 3449-3454.
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Table 1 – Standardized Conventional Methods for Detection of Foodborne Pathogens and
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Mycotoxins on Fresh Produce
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Chapter 4, 2002 Chapter 4A, 2011 culture-based horizontal methods for detection and enumeration of pathogenic E. coli except EHEC of serotype O157:H7
E. coli O157
Staphylococcus
EN ISO 10273:2003 culture-based horizontal method for detection
ISO 10273:2003 culture-based horizontal method for detection
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L. monocytogenes
EN ISO 16654:2001 culture-based horizontal method for detection EN ISO 21567:2004 culture-based horizontal method for detection
ISO 7251:2005 culture-based horizontal method for detection and enumeration ISO 16654:2001 culture-based horizontal method for detection ISO 21567:2004 culture-based horizontal method for detection
EN ISO 11290-1:1996 EN ISO 11290-2:1998 EN ISO 11290-1:1996/A1:2004 EN ISO 11290-2:1998/A1:2004 culture-based horizontal methods for detection and enumeration EN ISO 6888-3:2003 EN ISO 6888-3:2003/AC:2005 culture-based horizontal method for enumeration and detection of coagulase-positive staphylococci including S. aureus
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Y. enterocolitica
Chapter 6, 2001 culture-based horizontal method & DNA hybridization method for detection and enumeration Chapter 8, 2007 culture based horizontal method for detection and enumeration Chapter 10, 2011 culture-based horizontal method for detection and enumeration
Chapter 12, 2001 culture based horizontal method for detection and enumeration of S. aureus and/or its enterotoxins
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Shigella
Chapter 13A, 2011 extraction of enterotoxins by chromatographic techniques & culture-based enumeration method & commercial test kits using
ISO ISO 6579:2002 ISO/NP 6579-1 ISO/PRF TS 6579-2 culture-based horizontal methods for detection, enumeration ISO/PRF TS 13136 rt PCR-based horizontal method for detection of E.coli (STEC) and O157, O111, O26, O103 and O145 serogroups
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E. coli
CEN EN ISO 6579:2002 EN ISO 6579:2002/AC:2006 EN ISO/TS 6579-2:2012 culture-based horizontal method for detection and enumeration CEN ISO/TS 13136:2012 rt PCR-based horizontal method for detection of E.coli (STEC) and O157, O111, O26, O103 and O145 serogroups
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FDA/BAM Chapter 5, 2011 culture-based horizontal method for detection and enumeration
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Pathogen Salmonella
ISO 11290-1:1996 ISO 11290-2:1998 culture-based horizontal method for detection and enumeration
ISO 6888-3:2003 culture-based horizontal method for enumeration and detection of coagulasepositive staphylococci
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Ochratoxin A
Chapter 18, 2001 TLC or HPLC method for determination of ochratoxin
ISO 7937:2004 culture-based horizontal method for detection and enumeration
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EN 14123:2007 HPLC method with immunoaffinity column clean-up (IAC) and post-column devrivatization (PCD) for determination of aflatoxin B1 and the sum of aflatoxin B1, B2, G1 and G2 in hazelnuts, peanuts, pistachios, figs EN ISO 16050:2011 reverse-phase HPLC method with IAC and PCD for determination of aflatoxin B1 and the sum of aflatoxin B1, B2, G1 and G2 in cereals, nuts, and derived products EN 15829:2010 HPLC method with IAC and FD for determination in currants, raisins, sultanas, mixed dried fruit and dried figs
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Aflatoxin
EN ISO 7937:2004 culture-based horizontal method for detection and enumeration
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C. botulinum
Chapter 16, 2001 culture based horizontal method for detection and enumeration of C. perfringens and/or its enterotoxins Chapter 17, 2001 mouse bioassay or ELISA-based technique for detection of botulinum toxins & PCR based method for identification of C. botulinum Chapter 18, 2001 TLC or HPLC method for determination of aflatoxins
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immunoenzymatic techniques for detection and identification of staphylococcal enterotoxins
ISO 16050:2003 reverse-phase HPLC method with IAC and PCD for determination of aflatoxin B1 and the sum of aflatoxin B1, B2, G1 and G2 in cereals, nuts, and derived products