Quantitative human exposure model to assess the level of glucosinolates upon thermal processing of cruciferous vegetables

Accepted Manuscript Quantitative human exposure model to assess the level of glucosinolates upon thermal processing of cruciferous vegetables Uma Tiwari, Eimile Sheehy, Dilip Rai, Michael Gaffney, Paul Evans, Enda Cummins PII:

S0023-6438(15)00241-8

DOI:

10.1016/j.lwt.2015.03.088

Reference:

YFSTL 4562

To appear in:

LWT - Food Science and Technology

Received Date: 6 November 2014 Revised Date:

20 March 2015

Accepted Date: 22 March 2015

Please cite this article as: Tiwari, U., Sheehy, E., Rai, D., Gaffney, M., Evans, P., Cummins, E., Quantitative human exposure model to assess the level of glucosinolates upon thermal processing of cruciferous vegetables, LWT - Food Science and Technology (2015), doi: 10.1016/j.lwt.2015.03.088. 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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Quantitative human exposure model to assess the level of glucosinolates upon thermal processing

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of cruciferous vegetables

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4 Uma Tiwaria, Eimile Sheehyb , Dilip Raic, Michael Gaffneyc, Paul Evansb , Enda Cumminsa*

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Belfield, Dublin 4, Ireland.

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School of Chemistry and Chemical Biology, University College Dublin, Belfield, Dublin 4, Ireland.

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Teagasc Food Research Centre, Ashtown, Dublin 15, Ireland.

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School of Biosystems Engineering, Agriculture and Food Science Centre, University College Dublin,

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*Corresponding author: E-mail: [email protected], Ph: +353-1-7167476

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Abstract

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This study aims to model the level of glucosinolates (Gls) in cruciferous vegetables (Cv) following

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thermal processing and to evaluate subsequent human exposure based on the dietary survey for Irish,

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(Irl), European Prospective Investigation into Cancer and Nutrition (EPIC) consumers and US adult

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consumers. Four Cv (broccoli, Brussels sprouts, cabbage and cauliflower) are evaluated to assess the

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level of Gls following thermal degradation and leaching (during blanching and boiling) using

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mathematical simulation methods. The model predicted that the raw Cv contained a high amount of

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Gls (1.56, 5.11, 2.48, 1.88 µmol /g FW for broccoli, Brussels sprouts, cabbage and cauliflower,

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respectively) compared to blanched and boiled counterparts with a degradation of up to ~18 to 36 and

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~50 to 76%, respectively. A sensitivity analysis highlighted the negative impact of longer processing

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time (e.g. boiling) on human exposure levels to Gls with an average correlation coefficient of -0.27

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(males) and -0.28 (females) for Irl/EPIC/US consumers. This study increases awareness of the

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influence of process stages on Gls in Cv (minimally processed) to optimise exposure and maximise

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human health benefits.

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Keywords: Cruciferous vegetables; Glucosinolates; Processing; Health benefits, simulation model.

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1. Introduction

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Over the past few decades, the relative association between increased fruit and vegetable consumption

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and a lower risk of disease has been well documented (World Health Organisation (WHO), 2003). This

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association may provide consumers with long-term sustainable health benefits (WHO, 2003).

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Additionally, WHO (1990) recognise that a daily intake of 400 g/d or 5 × 80 g/serving of fruit and

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vegetables may reduce the risk of several chronic diseases, including cancer and cardiovascular

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diseases. Epidemiological studies have linked high intake of cruciferous vegetables with an effective

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reduction in chronic diseases, mainly due to the presence of bioactive components such as

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glucosinolates (Herr & Büchler, 2010). Cruciferous vegetables (Cv) including broccoli, Brussels

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sprouts, cabbage and cauliflower contain high levels of glucosinolates (Verkerk et al., 2009; Tiwari &

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Cummins, 2013). Glucosinolates (Gls) are naturally occurring sulphur-containing secondary plant

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metabolites mostly found in the tissues of cruciferous plants, and are classified in to aliphatic, indole

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and aromatic groups. Generally, Gls are unstable compounds which form degradation products upon

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hydrolysis due to the action of enzyme myrosinase, i.e. catalyses the cleavage of glucose to form an

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unstable aglycone intermediate, which undergoes spontaneous degradation to form isothiocyanates

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(ITCs), organic thiocyanates and nitriles. ITCs are a major biological active derivative that possess

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anti-cancerous properties and inhibits cell proliferation by reducing the susceptibility to carcinogens

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(Talalay & Fahey, 2001).

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Available literature has indicated that the level of Gls in Cv are widely influenced by various

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processing conditions (Song & Thornalley 2007; Volden et al., 2008). The duration and magnitude of

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thermal exposure on Cv is of vital importance in initiating the denaturation of the enzyme activity

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(Rungapamestry, Duncan, Fuller & Ratcliffe, 2006), which causes subsequent loss of Gls due to

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leaching of intact glucosinolates during the cooking process (Bongoni, Steenbekkers, Verkerk, van

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Boekel & Dekker, 2013). Minimal processing (eg. cutting, chopping) including chewing or crushing of

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Cv causes significant damage to the cell wall with subsequent release of the myrosinase enzyme and

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hydrolysis of the GIs (Shapiro, Fahey, Wade, Stephenson & Talalay, 1998). The hydrolysis of the 3

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enzyme, and subsequent release of Gls derivatives (ITCs) (known for their cancer-reducing

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properties), is also mediated by the microflora of the human gut, but will vary with the level of fresh

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and/or processed Cv consumed (Conaway et al., 2000). The development of mathematical models can

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be effective in estimating likely human exposure levels to health promoting compounds and evaluating

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the influence of process stages (Tiwari and Cummins, 2008). Considering the potential therapeutic

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effect of Cv, the objective of this study was to model and quantify the level of Gls in Cv upon thermal

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processing and to evaluate subsequent human exposure levels.

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67 2. Model development

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A schematic of the conceptual model for processing of cruciferous vegetables and the subsequent

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exposure level is shown in Figure 1, with model inputs and details provided in Table 1.

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2.1 Glucosinolates level in cruciferous vegetables

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The level of Gls in cruciferous plants may vary with cultivars, environmental and agronomic

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conditions (Padilla, Velasco, de Haro & Ordás, 2007). The total Gls level in selected cruciferous

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vegetables used in the model ranged from 0.48 – 2.63 µmol/g FW for broccoli (Bro), 0.17 –9.40

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µmol/g FW for Brussels sprouts (Brs) while for cabbages (Cab) and cauliflower (Caul) it ranged from

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0.10 – 4.68 and 0.14 – 3.34 µmol/g FW, respectively (Table 1). The variations in the initial level of

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total Gls are represented as probabilistic distributions (best-fit from literature data) as shown in Table

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1.

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2.2 Thermal processing

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Cruciferous vegetables are commonly consumed after suitable thermal processing (e.g. blanching,

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boiling and steaming etc.). Processing of cruciferous vegetables has an impact on the level of

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phytochemicals and its biological activities (Verkerk, van der Gaag, Dekker & Jongen, 1997). Dekker,

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Hennig & Verkerk (2009) demonstrated varying thermal stability of glucosinolates at 100 oC for 4

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cruciferous or brassica vegetables using degradation kinetics, thus indicating the stable nature of the

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compound during processing. To model the variance in thermal stability of cruciferous vegetables, a

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triangular distribution was fitted to the degradation rate constant from the dataset of Dekker et al.

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(2009), (see Table 1). Gls degradation was captured by looking at different thermal processes i.e.

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blanching time (Blt) and/or boiling time (Bolt) compared to initial level in raw Cv. To capture the

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variability of Blt a triangular distribution with a minimum of 0, most likely of 3 minutes and maximum

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of 5 minutes was used. Similarly, a triangular distribution with 0 (minimum), 10 (most likely) and 20

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minutes (maximum) was used in the model to capture the variability of Bolt (Table 1). These

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processing conditions for blanching and boiling time were chosen to mimic the domestic processing of

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vegetables. Eq 1 illustrates the general degradation kinetics which was employed to model the

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variation in degradation level of ‘GlsCv’ with ‘Blt’ or ‘Boilt’.

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dA dA = − kA ⇒ = − kdt dt A

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Where ‘A’ is the concentration of ‘GlsCv’, ‘k’ (min-1) is the degradation rate constant and ‘t’ is time

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(min). By rearranging and integrating the Eq 1, the equation can be written as follows:

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[Eq 1]

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A  ln t  = − kt ⇒ A = A0 e − kt ⇒ GlsCv × e ( − kGlsCv × Blt or Bolt )  A0 

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Where, at a given time ‘t’, the final concentraion is given as ‘A’ and ‘A0’ is the initial concentration

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of‘GlsCv’ at time 0. ‘GlsCv’ (µmol/g FW) is the initial level of glucosinolates from selected crucifeous

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vegetables (Bro, Brs, Cab and Caul), using a best-fit distribution for selected cruciferous vegetables

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(see section 2.1); ‘Blt’ and ‘Bolt’ represent the blanching and boiling time (minutes); and ‘k’ is the

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degradation rate constant. k (min-1) which is fitted to a triangular distribution for Bro, Brs, Cab and

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Caul based on data of Dekker et al. (2009). Table 1 details various inputs for the frequency

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distributions and mathematical calculations used to model the degradation rate constant.

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[Eq 2]

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2.3 Leaching 5

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Leaching of Gls occurs due to the cell lysis and breakdown of intact cells in the Cv during processing

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(Oerlemans et al., 2006; Bongoni, Steenbekkers, Verkerk, van Boekel & Dekker et al., 2013). The

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amount of Gls leaching during processing of Cv depends on the ratio of cooking water: vegetable

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and/or the size of the vegetable cutting (Sarvan, Verkerk, van Boekel & Dekker, 2014). The

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degradation in Gls level followed by leaching in the cooking water was modelled using a leaching

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kinetic model as described by Sarvan, Verkerk, Dekker (2012). The variability in the leaching kinetic

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rate constant for various Cv were fitted to a triangular distributions based on data of Sarvan et al

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(2014) and was used in the model as shown in Table 1.

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2.4 Bioavailability (Bv)

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The bioavailability of Gls is measured by the mercapturic acid pathway which acts as an indicator to

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measure the bioavailability of breakdown products that transmit health benefits in humans (Mithen,

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Dekker, Verkerk, Rabot & Johnson, 2000). Sulforaphane (a major ITC in broccoli) is conjugated with

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glutathione and metabolised via mercapturic pathway to be excreted as N-acetylcysteine S-conjugates

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(Gasper et al., 2005). Conaway et al. (2000) noted that the ingestion of steamed broccoli accounted for

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a low (10%) excretion of ITCs compared to the consumption of fresh broccoli which accounted for

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32% excretion of ITCs in male subjects. Vermeulen, van den Berg & Freidig (2006) observed a higher

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bioavailability of ITCs via urinary excretion post consumption of raw cruciferous vegetables (~61%)

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compared to cooked vegetables (~10%). Vermeulen, Klopping-Ketelaars, van den Berg & Vaes (2008)

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also demonstrated a higher bioavailability of sulforaphane following the intake of raw broccoli (~11

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times) compared to cooked broccoli. To account for the bioavailability of Gls in raw (BvRCv) and

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processed (BvPCv) cruciferous vegetables, a lognormal distribution was fitted to the dataset of

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Vermeulen et al. (2006; 2008). Therefore, to capture variation around the mean and standard deviation

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values for both raw and processed cruciferous vegetables, a uniform distribution was fitted separately

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for the mean values and standard deviation values using the minimum and maximum values from the

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datasets of Vermeulen et al. (2006; 2008) (Table 1). Additionally, a correlation matrix was applied to 6

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correlate the two uniform distributions (i.e. a distribution for mean values is correlated to the

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distribution for standard deviation values) for both BvRCv and BvPCv. The correlation function returns

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the distribution with a correlation coefficient of 0.79 for BvRCv and 0.76 for BvPCv, demonstrating a

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strong correlation between the selected distributions (Table 1).

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140 2.5 Human exposure level

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Daily exposure to Cv was assessed for Irish adults (18–64 yrs) with body weight of 82.9 ± 13.3 kg

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(males) and 67.5 ± 12.5 kg (females) using the consumption database obtained from the Irish

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Universities Nutritional Alliance survey (IUNA, 2011). A detailed survey on dietary intake of

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cruciferous vegetable consumption data (g/day) for adults (35–74 years) from 10 European countries

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were adopted from Agudo et al. (2002) as part of the prospective investigation into cancer and

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nutrition (EPIC) projects. The average body weight for EPIC cohorts (10 countries including 19

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centres) for adults ranged from 60–71.8 kg for females and 76.8–83.3 kg for males (Slimani et al.,

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2002). Based on WHO’s recommendation (i.e. 400 g of fruit and vegetables per day), the American

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Institute for Cancer Research (AICR, 2012) estimated at least 3 serving portions /wk (i.e. ~ 240 g/wk

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of broccoli /cruciferous vegetables) as the new American plate (for US dietary intake) to reduce the

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risk of chronic diseases. The US Anthropometric survey (National Center for Health Statistics, 2012),

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indicated that for US adults (i.e. 20 years and over) the average body weight for males and females are

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88.7 ± 0.46 kg and 75.4 ± 0.35 kg , respectively.

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To compare the intake of Cv on a weekly basis as AICR estimates, the consumption rates for Irl and

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EPIC consumers were converted to weekly intake. The human exposure model used a triangular

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distribution with a minmum intake of 80 g/wk (1 portion /wk), 240 g/wk (most likely of 3 portion /wk)

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and maximum intake of 400 g/wk (i.e 5 portion /wk) of broccoli / cruciferous vegetables to calculate

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the exposure level for Irl/EPIC/US adults. A discrete function was used to model the variation in type

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of Cv (Bro / Brs / Cab /Caul) intake by consumers with an equal probability of 25%. The exposure

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level of processed Cv was compared with that unprocessed Cv. The resultant Gls exposure to 7

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Irl/EPIC/US adult consumers following consumption of processed Cv was compared with unprocessed

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(raw) Cv exposure as shown in Eq.3 (Table 1). Equation 3 illustrates the general formula to calculate

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the exposure level “EL” (µmol/kg bw /wk) for Irl/EPIC/US adult consumption of processed Cv

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compared to raw Cv.

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[( RCv or BlCv or BolCv ) × ( Irl FMi i ) or ( EPIC FMi i ) or (US FMi i )]  EL =  × [( BvRCv / 100) or( BvPCv / 100)]  [Eq 3] M bw M bw Mi ( Irl Fbw ) or ( EPIC Fbw ) or (US Fi )  

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Where RCv : Gls content in raw Cv (µmol/g FW) , BlCv: Gls content in blanched Cv, BolCv: Gls content

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in boiled Cv, BvRCv or BvPCv: bioavailability (%) for raw and processed Cv, Irl : Irish, EPIC:

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European Prospective Investigation into Cancer and Nutrition, US: United States, Mi or Fi represents

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the male and female intake of Cv (g/wk) and Mbw or Fbw represents the male and female body weight

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(kg bw).

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Full or partial validation can be carried out on a model and is an essential step to ensure that the model

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predictions are realistic and also checks the model validity with experimental data (Tiwari et al., 2010).

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In many instances full validation is unfeasible due to limited time and resources to carry out the

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necessary repeated comparisons between model outputs and real data. In this study a parallel

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experimental partial validation study was conducted to assess the level of Gls in raw and in thermally

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processed broccoli (vBro). Sixty four broccoli samples namely “Belstar” or “Fiesta” cultivars grown in

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varying agricultural management (conventional or organic soil) were analysed for initial level of Gls

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content as reported by Hernández-Hierro et al. (2012). The initial Gls concentration in raw broccoli

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samples (n = 64) varied with a mean and standard deviation of 1.52 ± 0.30 µmol/g FW and was fitted

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to a lognormal distribution based on the best-fit distribution (Anderson-Darling statistic, A-D= 0.43).

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The broccoli samples were thermally processed (steaming, sous vide, boiling and grilling) to 8

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investigate the impact of processing on level of Gls degradation. The input parameters for the partial

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validation study were the same as that of the baseline model except with varying bioavailability (raw

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and processed) for broccoli (Table 2).

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The model input parameters for both the baseline and partial validation study coupled with the

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mathematic calculations were combined onto an Excel spreadsheet. The simulation was performed

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using Latin hypercube sampling and run for 10,000 iterations using @Risk add-on package (Palisade

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Software, Newfield, NY, USA).

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194 3. Results and Discussion

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Table 3 summarises the simulated level of Gls with subsequent human exposure to Gls from the

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weekly intake of cruciferous vegetables.

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3.1 Glucosinolates levels in raw cruciferous vegetables

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The simulated level of Gls in raw and thermally processed selected Cv ranged from 0.48 to 2.63 µmol

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/g FW for broccoli, 0.17 to 9.40 µmol /g FW for Brussels sprouts, 0.10 to 4.68 µmol /g FW for

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cabbage and 0.14 to 3.34 µmol /g FW for cauliflower (Figure 2). The predicted Gls level in broccoli,

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Brussels sprouts, cabbage and cauliflower showed a wide variation in the level, in line with literature

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sources. For instance, Song, Morrison, Botting & Thornalley (2005), reported total Gls content ranging

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from 0.56 to 0.78 µmol /g FW for broccoli, 0.09 to 0.26 µmol /g FW for Brussels sprouts, 0.08 to 0.19

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for cauliflower and 0.06 to 0.14 µmol /g FW for cabbage respectively. Similarly, Meyer and Adam

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(2008), observed total Gls for broccoli ranged from 1.23 to 1.66µmol /g FW and 2.04 to 2.08 µmol /g

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FW for cabbage cultivars (calculated based on fresh weight). The reported variations in Gls levels are

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largely due to various factors including growing conditions, cultivars and agronomic practices.

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3.2 Changes in Gls level due to thermal degradation and leaching

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Following boiling (up to 20 minutes) of broccoli and cauliflower, the mean Gls content reduced by

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~76% (combined effects of thermal degradation and leaching) compared to the initial level. It is worth

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noting that ~80% of this reduction is due to the effects of leaching, highlighting the importance of

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leaching in addition to the thermal degradation process in reducing Gls levels. Likewise, the model

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showed ~50% reduction in Gls for both Brussels sprouts and cabbage (combined effects of thermal

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degradation and leaching) (Figure 2; Table 3). Comparatively, for blanching of Cv (up to 5 minutes),

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the model predicted a reduction of Gls content by ~36% (combined effects) for broccoli and

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cauliflower (94% of this was due to the leaching process alone). Similarly, ~20% Gls reduction was

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observed following the leaching of Gls from both blanched Brussels sprouts and blanched cabbage

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(Figure 2; Table 3).

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The variation in degradation level highlights the significance of an increase in processing time on the

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level of Gls in Cv which compares well with the published literature. For example, Cieślik et al (2007)

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observed about a 23% degradation after 3 minutes blanching and nearly 55% reduction in Gls content

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following boiling of cruciferous vegetables for 15 minutes. Song and Thornalley (2007) also

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demonstrated a significant loss of the Gls compound (> 75%) upon boiling of cruciferous vegetables

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for 30 minutes. The degradation of Gls depends on the degree of thermal treatment which may lead to

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partial or complete inactivation of myrosinase and cause leaching of Gls in cooking water by rupturing

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the cell wall of the plant tissue (Cieślik et al., 2007). The leaching of Cv depends on the

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thermostability of Gls and cell wall matrix subjected to processing. During processing, the Gls content

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are influenced by its external environment (pH and salt ions etc), which may increase the uptake of

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water and reduce the cohesiveness of cell wall matrix (Sarvan, Verkerk, van Boekel & Dekker, 2014).

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This may account for the significant variation in the leaching quantity for different Cv.

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3.3 Partial validation model for Gls level in broccoli

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The partial validation model demonstrated that the steaming process showed a minimal effect on Gls

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level of broccoli compared to sous vide, boiling and grilling (Figure 3). Approximately, a 5% and 10

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27% reduction was observed following steaming of broccoli and Sous vide processed broccoli,

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respectively when compared to unprocessed broccoli. The validation model showed a significant

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reduction in the level of Gls with boiling (~40%) and grilling (~70%) of broccoli following processing

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(Table 3). The steaming process of Cv may reduce the loss of Gls, probably due to fact that no or

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minimal leaching occurs during steam cooking compared to other blanching or boiling processes.

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Likewise, Conaway et al. (2000) reported no significant difference in mean total Gls level when

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compared to fresh and steamed broccoli up to 15 minutes The model predictions on the Gls level for

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various thermal processing (steaming, sous vide, boiling, grilling) are within the range of the

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validation band shown in Figure 3.

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3.4 Exposure to glucosinolates following weekly intake of Cv

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Chemical changes that take place during processing stages may significantly influence the Gls level in

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processed (blanched and boiled) Cv (broccoli or Brussels sprouts or cabbage or cauliflower) and

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therefore affect exposure following the consumption of processed Cv. The model predicted a high

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level of Gls exposure for adult consumers if the Cv are consumed fresh (unprocessed), however this is

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not the usual practice, therefore the exposure level for raw Cv were used on a comparison basis (Table

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4). The mean weekly exposure level of Gls (blanched and boiled Cv) for Irish adults was found to be

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0.28 and 0.50 µmol/kg.bw/wk and 0.15 and 0.27 µmol/kg.bw/wk for males and females, respectively.

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Similar a low exposure level was estimated for EPIC adults, whereas US adult consumers showed a

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higher mean level of exposure to Gls of 0.63 and 0.74 µmol/kg.bw/wk (blanched Cv) and 0.33 and

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0.39 µmol/kg.bw/wk (boiled Cv) for both males and females, respectively (Table 4). Likewise, the

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validation exposure model showed a high range of Gls exposure following intake of steamed Cv (i.e.

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broccoli) for adult consumers (Irl/EPIC/US) compared to other processed Cv.

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Both blanched and boiled Cv exposure are significantly influenced by the Gls bioavailability in the

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processed cruciferous vegetables. As expected, the model predictions for the exposure to Gls in cooked

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vegetables are in accordance with the scientific literature which shows that the processing of 11

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vegetables reduces the bioavailability of the Gls (Conway et al., 2000) when compared to the

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consumption of raw vegetables. The bioavailability of processed Cv were predicted to be about 22

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times lower compared to the consumption of raw Cv. The probable reason for the low Gls exposure

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level may be due to the inactivation of myrosinase activity with different thermal treatments of Cv

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(Rungapamestry et al., 2008). Additionally, the cooking regime was shown to reduce the activity of

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enzyme hydrolysis and if processed Cv are consumed, it may further delay the release of breakdown

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products in the gut (Rouzaud et al., 2004), thus influencing the bioavailability of Gls. The inactivation

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of enzymes occurs predominantly during long cooking (i.e. >3 minutes) of Cv and thus leading to a

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severe degradation of the Gls derivatives (i.e. cooked Cv), and further metabolism of Gls derivatives in

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the human body may limit the bioavailability of Gls derivatives for health benefits (eg. anticarinogenic

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properties) (Herr and Büchler, 2010). However, the hydrolysis activity of enzyme myrosinase also

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depends on the type of cultivar and the corresponding bioavailability of Gls derivatives which also

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vary with individual subjects (Verkerk et al., 2009).

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The sensitivity analysis (Figure 4) highlights the critical factors in the model influencing the final Gls

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exposure from various cruciferous vegetables. The analysis indicated the importance of the

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bioavailability factor for Gls in raw (BvRCv) and processed Cv (BvPCv) with an average correlation

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coefficient of 0.60 for males and 0.63 for females irrespective of consumer (Irl/EPIC/US). The analysis

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also identifies the importance of Cv intake with a high correlation coefficient (average of Bro, Brs,

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Cab and Caul) of 0.41 (males) and 0.37 (females) for Irl/EPIC/US adults consumers. Increasing the

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intake of Cv on a weekly basis may have a greater impact on exposure to Gls derivatives, (which in

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turn may lower the incidence of cancer (Agudo et al., 2002)), however the cooking process requires

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attention due to the degradation of Gls content in Cv.

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Increased the boiling time of Cv showed a negative impact on the level of Gls for Irl/EPIC/US

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consumers with an average correlation coefficient of -0.27 (males) and -0.28 (females), respectively. 12

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Conversely, no significant influence of blanching time was shown by the sensitivity analysis, this may

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be due to the short treatment time which reduces the rapid inactivation of myrosinase enzyme and

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increase the Gls exposure. Moreover, increased thermal treatment (boiling) of Cv inactivates the entire

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enzyme activity and leaches Gls in to the cooking water, which in turn may influence the sensorial

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quality of the boiled product. A short cooking time or blanching of Cv may trigger an enzymatic

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activity which has a beneficial effect on anticancer properties without comprising the texture of the

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vegetables (Verkerk et al., 2009).

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Conclusion

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This study was developed to assess the changes in the level of Gls in Cv based products following

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different thermal processes using predictive modelling techniques with a subsequent human exposure

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model coupled with a partial validation study. The simulation model indicates that thermal processing

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(blanching and boiling), degradation kinetics and leaching kinetics have a major impact on the level of

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Gls in Cv based products. Consumption of processed (blanched and boiled) Cv indicated a low mean

304

weekly intake of Gls for Irl/ EPIC/ US adult consumers. However, the partial validation model

305

observed a higher level of exposure following consumption of steamed Cv compared to boiling, sous

306

vide and grilling process. The sensitivity analysis highlighted the importance of bioavailability of Cv

307

for Irl/ EPIC/ US consumers. Likewise, the analysis also pointed out the negative influence of a longer

308

processing time (boiling) compared to blanching (short time boiling) of Cv. The model also highlights

309

the importance of short cooking time for Cv to reduce the damage to the tissues without altering the

310

nutritional benefits of the breakdown products of Gls. However, it is important to understand the

311

catalytic activity of the enzyme and the bioavailability of breakdown products that may vary with the

312

Gls hydrolysis activity in the microflora of the gut, this requires further study. The partial validation

313

study, conducted to observe the influence of steaming, sous vide, boiling and grilling thermal process

314

on broccoli (Irl cultivar), showed that a minimal reduction of Gls was estimated for steamed Cv

315

compared to other processes. The model simulated the influence of various processing effects in a 13

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cooking module, which is critical in estimating the final intake level and subsequent bioavailability of

317

glucosinolates. The model facilitates an analysis of factors influencing Gls in Cv based products and

318

therefore facilitates the identification of strategies to maximise exposure to such compounds with

319

potential human health benefits.

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320 Acknowledgements

322

This study is funded by the Department of Agriculture and Food through the Network and Team

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Building Initiative of the Food Institutional Research Measure (FIRM Ref. Num. 06/NITARFC6). The

324

Network is integrated by Irish Phytochemical Food Network: Tracing phytochemical from farm to

325

fork.

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5

References

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Agudo, A., Slimani, N., Ocké, M.C., Naska, A., Miller, A.B., Kroke, A et al. (2002). Consumption of vegetables, fruit and other plant foods in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts from 10 European countries. Public Health Nutrition, 5 (6B): 1179 – 11 96. AICR (2012) The New American Plate. American Institute for Cancer Research. http://www.aicr.org/new-american-plate/reduce_diet_new_american_plate_portion.html (accessed on 5th Feb, 2015). Bongoni, R., Steenbekkers, L.P.A., Verkerk, R., van Boekel, M.A.J.S & Dekker, M. (2013). Studying consumer behaviour related to the quality of food: A case on vegetable preparation affecting sensory and health attributes. Trends in Food Science & Technology, 33:139–145. Carlson, D.A., Daxenbichler, M.E., VanEtten, C.H., Kwolek, W.F & Williams. P.H. (1987). Glucosinolates in Crucifer Vegetables: Broccoli, Brussels Sprouts, Cauliflower, Collards, Kale, Mustard Greens, and Kohlrabi. Journal of the American Society for Horticultural Science, 112(1):173-178. Cieślik, E., Leszczyńska, T., Filipiak-Florkiewicz., A., Sikora, E., & Pisulewski, P.M. (2007). Effects of some technological processes on glucosinolate contents in cruciferous vegetables. Food Chemistry, 105(3), 976 – 9 81. Conaway, C.C., Getahun, S.M., Liebes, L.L., Pusateri, D.J., Topham, D.K et al. (2000). Disposition of glucosinolates and sulforaphane in humans after ingestion of steamed and fresh broccoli. Nutrition and Cancer, 38(2), 168 – 178. Dekker, M., Hennig, K., & Verkerk, R. (2009). Differences in thermal stability of glucosinolates in five Brassica vegetables. Czech Journal of Food Sciences, 27 (S1), S85–S88. Delonga, K., Redonikovic, R.I., Dragovic-Uzelac, V., Mrkic,V., & Vorkapic-Furac,J. (2007). Distribution of glucosinolates in some raw and processed Brassica vegetables grown in Croatia, Acta Alimentaria, 36 (2), 207–216. Gasper, A.V., Al-Janobi, A., Smith, J.A., Bacon, J.R., Fortun, P., Atherton, C et al. (2005). Glutathione S-transferase M1 polymorphism and metabolism of sulforaphane from standard and highglucosinolate broccoli. The American Journal of Clinical Nutrition, 82(6), 1283–1291.

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Slimani, N., Kaaks, R., Ferrari, P., Casagrande, C., Clavel-Chapelon, F., Lotze, G et al. (2002). European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study: rationale, design and population characteristics. Public Health Nutrition, 5(6B), 1125 – 1145. Song, L. J., & Thornalley, P. J. (2007). Effect of storage, processing and cooking on glucosinolate content of Brassica vegetables. Food Chemical Toxicology, 45(2), 216–224. Song,L., Morrison, J.J., Botting, N.P., & Thornalley, P. J. (2005). Analysis of glucosinolates, isothiocyanates, and amine degradation products in vegetable extracts and blood plasma by LC– MS/MS. Analytical Biochemistry, 347, 234–243. Talalay, P., & Fahey, J.W. (2001). Phytochemicals from cruciferous plants protect against cancer by modulating carcinogen metabolism. Journal of Nutrition, 131, 3027S–3033S. Tian, Q., Rosselot, R.A., & Schwartz, S.J. (2005). Quantitative determination of intact glucosinolates in broccoli, broccoli sprouts, Brussels sprouts, and cauliflower by high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry. Analytical Biochemistry, 343, 93–99. Tiwari, U. and Cummins, E. (2008). A predictive model of the effects of genotypic, pre-and postharvest stages on barley β-glucan levels, Journal of the Science of Food and Agriculture, 88, 2277–2287. Tiwari, U., & Cummins, E. (2013). Factors influencing levels of phytochemicals in selected fruit and vegetables during pre-and post-harvest food processing operations, Food Research International, 50 (2), 497–506. Tiwari, U., Cummins, E., Sullivan, P., O’Flaherty, J., Brunton, N., Gallagher, E. (2010). Probabilistic methodology for assessing changes in the level and molecular weight of barely β-glucan during bread baking, Food Chemistry, 124 (4) 1567–1576. Verkerk, R., Schreiner, M., Krumbein, A., Ciska, E., Holst, B., Rowland, I et al. (2009). Glucosinolates in Brassica vegetables: the influence of the food supply chain on intake, bioavailability and human health. Molecular Nutrition & Food Research,53 (2), S219 – S265. Verkerk, R., van der Gaag, M.S., Dekker, M., & Jongen, W.M.F. (1997). Effects of processing conditions on glucosinolates in cruciferous vegetables. Cancer letter, 114, 193 – 194. Vermeulen, M., Klopping-Ketelaars, I. W. A. A., van den Berg, R., Vaes, W. H. J. (2008). Bioavailability and kinetics of sulforaphane in humans after consumption of cooked versus raw broccoli. Journal of Agriculture and Food Chemistry, 56, 10505–10509. Vermeulen, van den Berg, R. Freidig, A.P. (2006). Association between consumption of cruciferous vegetables and condiments and excretion in urine of isothiocyanate mercapturic acids. Journal of Agriculture and Food Chemistry, 54, 5350– 5358. Volden, J., Bengtsson,G.B., & Wicklund, T. (2009). Glucosinolates, L-ascorbic acid, total phenols, anthocyanins, antioxidant capacities and colour in cauliflower (Brassica oleracea L. ssp. botrytis); effects of long-term freezer storage. Food Chemistry, 112, 967–976. Volden, J., Wicklund, T., Verkerk, R., & Dekker, M. (2008). Kinetics of changes in glucosinolate concentrations during long-term cooking of white cabbage (Brassica oleracea L. ssp. capitata f. alba). Journal of Agricultural and Food Chemistry, 26, 56(6), 2068–2073. WHO (1990) Diet, nutrition and the prevention of chronic diseases. (Technical Report Series No. 797). World Health Organization, Geneva. WHO (2003) WHO Technical Report Series 916. Diet, Nutrition and the Prevention of Chronic Diseases. WHO: Geneva.

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Table 1. Input parameters for baseline exposure model for glucosinolates in cruciferous vegetables Description /Units Distribution Gls level in raw Cruciferous vegetables (Cv), µmol/g FW

Lognormal [4.32 ± 2.28]

Cabbage (Cab)

Lognormal [2.62 ± 5.80]

Cauliflower (Caul)

Lognormal [1.86 ± 1.39]

RI PT

Brussels sprouts (Brs)

SC

Lognormal [1.49 ± 0.69]

Carlson et al. (1987); Conaway et al. (2000); Song & Thornalley (2007); Dekker et al. (2009); Oliviero et al.(2012) Carlson et al. (1987); Tian et al. (2005);Song and Thornalley (2007); Dekker et al. (2009) Oerlemans et al. (2006); Delonga et al. (2007); Song and Thornalley (2007); Volden et al. (2008a); Dekker et al. (2009) Carlson et al. (1987); Tian et al. (2005); Song and Thornalley (2007); Volden et al.(2009)

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Broccoli (Bro)

References

Processing time (min)

Triangular [0,3,5]

Cieślik et al. (2007); Volden et al. (2008b)

Boling time (Bolt)

Triangular [0,10,20]

Dekker et al. (2009)

EP

Blanching time (Blt)

Thermal degradation (Td, kt × 10-2 min -1) of Gls level in Cv -2

-2

-2

-2

-

Triangular [0.1×10 , 1.5×10 ,5 ×10 ] ; [2.1×10 , 4.9×10 2 ,6.8×10-2] ; [2.1×10-2, 4.9×10-2,6.8×10-2]; [0.1×10-2,1.5×102 ,5×10-2]

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1 2

ktBro ; ktBrs ; ktCab ; ktCaul

Dekker et al. (2009) (rate constant for cauliflower were assumed similar to that of broccoli)

Changes in Gls due to Td Blanching of Cv, µmol/g FW Boiling of Cv, µmol/g FW

TdBlBro = Bro × e ( − kt Bro × Blt ) ; TdBlBrs = Brs × e ( − kt Brs × Blt ) ; TdBlCab = Cab × e ( − ktCab × Blt ) ; TdBlCaul = Caul × e ( − kt Caul × Blt ) ( − kt Bro × Bolt )

( − kt Brs × Bolt )

TdBolBro = Bro × e ; TdBolBrs = Brs × e ; ( − kt Cab × Bolt ) ( − kt Caul × Bolt ) TdBolCab = Cab × e ; TdBolCaul = Caul × e

first order kinetics equation

Leaching d (kl min -1) of Gls 1

ACCEPTED MANUSCRIPT

Triangular [0.02, 0.15,0.30] ; [0.02, 0.04,0.05]; [0.02, 0.04,0.09]; [0.02, 0.15,0.30]

klBro ; klBrs ; klCab ; klCaul

Sarvan et al. (2014); (rate constant for cauliflower were assumed similar to that of broccoli)

Changes in Gls due to leaching

RI PT

BlBro = Td Bl Bro × e ( − klBro ×Blt ) ; BlBrs = Td Bl Brs × e ( − kl Brs × Blt ) ;

Blanched Cv, µmol/g FW

BlCab = Td BlCab × e ( − klCab × Blt ) ; BlCaul = Td BlCaul × e ( − klCaul × Blt ) BolBro = Td Bol Bro × e ( − kl Bro × Bolt ) ; BolBrs =

Td Bol Brs × e ( − klBrs × Bolt ) ; BolCab = Td BolCab × e ( − klCab × Bolt ) ; BolCaul

= Td BolCaul × e ( − klCaul × Bolt ) Bioavailability (Bv) , %

SC

Boiled Cv, µmol/g FW

Lognormal [Uniform(21 to 81) , Uniform (2.1 to 44), Correlation matrix (1 : 0.79)] Bv of processed Cv Lognormal [Uniform(1.7 to 21) , Uniform (2.3 to 6.7), (BvPCv) Correlation matrix (1 : 0.76)] Cruciferous vegetable intake Irish -Male intake, (IrlLognormal [17 ± 25, Truncated (0, 69)]e Mi), g/d Irish-Male body weight, Lognormal [82.9 ± 13.3] (Irl-Mbw), Kg Irish –Female intake, (IrlLognormal [24 ± 30,Truncated (0, 82)]e Fi), g/d Irish-Female body Lognormal [67.5 ± 12.5] weight, (Irl-Fbw), Kg EPIC-Male body weight, EPIC-Mbw =Discrete Uniform [lognormal (m1,s1),…. (EPIC-Mbw), Kg lognormal (m19,s19)] c a EPIC-Male intake , EPIC-Mi =Discrete Uniform [lognormal (m1,s1),…. (EPICMi), g/d lognormal (m19,s19)]c,n EPIC-Female body EPIC-Fbw =Discrete Uniform [lognormal (m1,s1),…. lognormal (m19,s19)] c weight, (EPIC-Fbw), Kg EPIC-Female intake b, EPIC-Fi =Discrete Uniform [lognormal (m1,s1),…. (EPIC-Fi), g/d lognormal (m19,s19)] c,n

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Bv of raw Cv (BvRCv)

US-Male intake (US-Mi), g/wk

first order kinetics equation

Vermeulen et al. (2006); Vermeulen et al. (2008)

IUNA (2011)

Slimami et al. (2002) Agudo et al. (2002) Slimami et al. (2002) Agudo et al. (2002)

Triangular [80, 240,400]

AICR (2012)

US-Male body weight (US-Mbw), Kg

Lognormal [88.7 ± 0.48]

National Center for Health Statistics. Vital Health Stat (2012)

US-Female intake (USFi), g/wk

Triangular [80, 240,400]

AICR (2012)

Lognormal [75.4 ± 0.35]

National Center for Health Statistics. Vital Health Stat (2012)

US-Female body weight (US-Fbw), Kg

2

ACCEPTED MANUSCRIPT

Exposure Level (EL)f (µmol/kg bw /wk) Blanched Cv, µmol/g BlCv = Discrete [BlBro: BlBrs: BlCab:BlCaul; 0.25:0.25:0.25:0.25] FW

ELIrl-M (or) ELIrl-F ; ELEPIC-M (or) ELEPIC-F ; ELUS-M (or) ELUS-F for Bolcv

BolCv = Discrete [BolBro: BolBrs: BolCab:BolCaul; 0.25:0.25:0.25:0.25]

[( BolCv ) × ( Irl FMi i ) or ( EPIC FMi i ) or (US FMi i )]  × [( BvPCv / 100) ]   M bw M bw Mi ( Irl Fbw ) or ( EPIC Fbw ) or (US Fi )  

EPIC: European Prospective Investigation into Cancer and Nutrition; Irl: Ireland; IUNA: Irish Universities Nutrition Alliance; AICR: American Institute for Cancer Research a,b EPIC- mean Male or female intake from each of 19 EPIC centres using a lognormal distribution, and the variation in the body weight was model using a discrete uniform distribution. c m1 …m19 and s1 …s19 represents mean and standard deviation of EPIC centres. d rate constant distribution for leaching kinetics of broccoli were limited to Raphanin, Glucobrassicin and 4-Methoxy-glucobrassicin (Sarvan et al., 2014) e represent intake of Cv (gram per week) f

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3 4 5 6 7 8 9 10 11

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Boiled Cv, µmol/g FW

[( BlCv ) × ( Irl FMi i ) or ( EPIC FMi i ) or (US FMi i )]  × [( BvPCv / 100) ]   M bw M bw Mi ( Irl Fbw ) or ( EPIC Fbw ) or (US Fi )  

SC

ELIrl-M (or) ELIrl-F ; ELEPIC-M (or) ELEPIC-F ; ELUS-M (or) ELUS-F for Blcv

M M M EL of processed Cv is compared with unprocessed (raw ) Cv : [( RCv ) × ( IrlF ) or ( EPIC F ) or (USF )] i

 

( Irl

) or ( EPIC

i

M bw Fbw

i

Mi Fi

) or (US )

i

 × [( BvRCv / 100) ]  

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i

i

M bw Fbw

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Table 2. Summary of input parameters based on the partial validation study Description /Units

Distribution

Gls level in Broccoli (vBro), µmol/g FW

Lognormal [1.52 ± 0.30, Truncate(0, )]

References Hernandez-Hierro et al. (2012) – experimental study

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14 15

Processing time (min) of Broccoli Steaming (St) ; Sous vide 100oC , 10 min ; <80oC , 10 min (fixed value) (Sv) Boiling (Bol) ; Grilling (Gr) 100oC , 10 min ; 100oC , 10 min (fixed value) Thermal degradation (Td) Td StvBro factor Td StvBro= {vBro × Triangular [0.76,0.97,1.12]}

Experimental study

Td SvvBro factor Td BolvBro factor Td GrvBro factor

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SC

Experimental study Td SvvBro ={vBro × Triangular [0.53,0.73,0.94]} Reduction factor Td BolvBro ={vBro × Triangular [0.44,0.58,0.78]} obtained from triplicate experimental Td GrvBro = {vBro × Triangular [0.09,0.287,0.51]} data; best-fit distribution

Bioavailability (Bv) , %

Lognormal [42.5 ± 15.24, Truncate(0, )]

Bv of processed vBro (BvPvBro)

Lognormal [2.0 ± 1.79, Truncate(0, )]

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Bv of raw vBro (BvRvBro)

Vermeulen et al. (2006); Vermeulen et al. (2008)

Exposure Level (EL) , µmol/kg bw /wk

Same as baseline model parameters

EP

[( PvBro ) × ( IrlFMi i ) or ( EPICFMi i ) or (USFMi i )]  × [( BvPvBro / 100) ]   M bw M bw Mi ( IrlFbw ) or ( EPIC Fbw ) or (USFi )  

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Processed Broccoli (StvBro , SvvBro , BolvBro , GrvBro) Footnotes same as Table 1

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Table 3. Simulated mean Gls level in various cruciferous vegetables following thermal degradation and leaching

Parameters

Raw (µmol/g FW)

Blanched Cv Td and leaching (%)

Boiled Cv Td and leaching (%)

Baseline model

Brussels sprouts

5.11 (1.14 to 9.41)

↓ 11.40%(5.11 to 4.53) and ↓ 19.43% (5.11 to 4.12)

Cabbage

2.48 (0.34 to 5.13)

↓ 6.42% (2.48 to 2.32) and ↓ 17.80% (2.48 to 2.04)

Cauliflower

1.88 (0.31 to 3.71)

↓ 5.62 % (1.88 to 1.77) and ↓ 35.96 % (1.88 to 1.20)

↓ 70.44% (1.52 to 0.45)

↓ 35.38% (5.11 to 3.30) and ↓ 53.33% (5.11 to 2.38)

↓ 21.72% (2.48 to 1.94) and ↓ 49.99% (2.48 to 1.24)

EP

↓ 18.99% (1.88 to 1.52) and ↓ 75.65% (1.88 to 0.46)

Broccoli (partial ↓ 40% (1.52 to 1.52 (1.09 to 2.06) validation model) 0.91) Footnotes are same as in Table 1. # (mean ±95% CI) ; Td: thermal degradation

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20 21 22

↓ 26.68% (1.52 to 1.12)

↓ 18.93 % (1.56 to 1.26) and ↓ 75.66% (1.56 to 0.38)

SC

↓ 5.62 %(1.56 to 1.47) and ↓ 35.98 % (1.56 to 1.00)

Grilled Cv Td and leaching (%)

M AN U

1.56 (0.49 to 2.65) #

Sous vide Cv Td and leaching (%)

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Broccoli

Steamed Cv Td and leaching (%)

RI PT

18 19

↓ 5 % (1.52 to 1.45)

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Table 4. Simulated human exposure to Gls (mean ±95% CI) following consumption of processed Cv compared to level in raw Cv. Blanched

Boiled

0.28 (0.01 to 1.15)

0.15 (0.13 × 10-2 to 0.66)

3.04 (0.07 to 11.71)

0.50 (0.01 to 1.99)

0.27(0.27 × 10-2 to 1.18)

EPIC (Mexp)

2.23 (0.06 to 7.92)

0.37 (0.01 to 1.34)

0.20 (0.23 × 10-2 to 0.79)

EPIC (Fexp)

2.76 (0.11 to 9.75)

0.45 (0.02 to 1.61)

0.24 (0.38 × 10-2 to 0.96)

US (Mexp)

3.85 (0.17 to 12.99)

0.63 (0.03 to 2.21)

0.33 (0.01 to 1.27)

US (Fexp)

4.52 (0.20 to 15.22)

0.74 (0.03 to 2.55)

0.39 (0.01 to 1.50)

0.19 (0.03 to 0.57)

EPIC (Mexp)

0.99 (0.19 to 2.11)

EPIC (Fexp)

1.22 (0.39 to 2.48)

US (Mexp)

1.73 (0.77 to 3.03)

US (Fexp)

2.30 (0.97 to 4.22)

Footnotes same as Table 1

0.05 (0.48 × 10-2 to 0.15)

0.01 (0.12 × 10-2 to 0.03)

0.02 (0.20 × 10-2 to 0.05)

0.01 (0.15 × 10-2 to 0.04)

0.05 (0.01 to 0.11)

0.08 (0.01 to 0.17)

0.06 (0.01 to 0.14)

0.06 (0.02 to 0.13)

0.10 (0.03 to 0.21)

0.08 (0.02 to 0.16)

0.09 (0.04 to 0.16)

0.14 (0.06 to 0.25)

0.11 (0.05 to 0.20)

0.12 (0.05 to 0.22)

0.19 (0.07 to 0.35)

0.14 (0.06 to 0.27)

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Irl (Fexp)

0.06 (0.62 × 10-2 to 0.20)

0.04 (0.39 × 10-2 to 0.13)

EP

0.76 (0.08 to 2.43)

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Irl (Mexp)

Sous vide

Grilled

SC

Irl (Fexp)

Partial validation model (µmol/kg bw /wk)

26

Steamed

RI PT

Parameters Raw Baseline model (µmol/kg bw /wk) Irl (Mexp) 1.71 (0.03 to 6.99)

M AN U

23 24 25

0.01 (0.12 × 102 to 0.04) 0.32 × 10-2 (0.04 × 10-2 to 0.01) 0.02 (0.27 × 102 to 0.04) 0.02 (0.01 to 0.04) 0.03 (0.01 to 0.05) 0.04 (0.01 to 0.08)

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EP

Figure 1. Flow diagram for processing and exposure assessment of glucosinolates in cruciferous vegetables

AC C

10 11 12 13 14 15

TE D

M AN U

SC

RI PT

1 2 3 4 5 6 7 8 9

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(a)

(c) 0.6

= 1.56

BlanchedBro = 1.00

2.0

BoiledBro

BoiledCab

= 0.38

1.5

0.3 1.0

0.1

0.0

0.0 1.0

2.0

3.0

(b)

(d) 0.35

RawBrs

0.30

2.0

= 5.11

BoiledBrs

4.0

6.0

RawCaul

BlanchedBrs = 4.12

8.0

= 1.88

BlanchedCaul = 1.20

= 2.38

BoiledCaul

1.5

= 0.46

TE D

0.25

2.0

0.20

1.0

0.15 0.10

0.5

0.05 0.00

EP

Probability Density

0.0

4.0

= 1.24

SC

0.2 0.5

= 2.48

BlanchedCab = 2.04

0.5 0.4

0.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

AC C

0.0

16 17 18 19 20

RawCab

RI PT

RawBro

M AN U

Probability Density

2.5

Glucosinolates level (µmol/g, FW)

0.0

1.0

2.0

3.0

4.0

5.0

Glucosinolates level (µmol/g, FW)

Figure 2. Simulated (baseline model) glucosinolates level following blanching and boiling process of (a) Broccoli (Bro), (b) Brussels sprouts (Brs), (c) Cabbage (Cab) and (d) Cauliflower (Caul).

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2.5

RawvBro

= 1.52

SteamedvBro = 1.45 Sous videvBro = 1.12 = 0.91

GrilledvBro

= 0.45

1.0 0.5 0.0 0.5

1.0

1.5

2.0

2.5

TE D

0.0

SC

1.5

BoiledvBro

M AN U

Probability Density

2.0

RI PT

21 22

Glucosinolates level (µmol/g, FW)

EP

Figure 3. Glucosinolates level (partial validation model) in broccoli following thermal processing compared to the measured values (band)

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23 24 25 26 27

Measured values (band) : ◊ : Raw ; ∆: Steamed; ○: Sous vide; □: boiled and × : Grilled broccoli

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28

Gls level in Cabbage (µmol/g FW)

7

Gls level in Cauliflower (µmol/g FW)

6

Gls level in Brussels sprouts (µmol/g FW)

5

Gls level in Broccoli (µmol/g FW)

4

Leaching

3

Blanching time (Blt)

2

Boiling time (Bolt)

1

-0.35

-0.25

-0.15

-0.05

SC

8

M AN U

Cv intake (g/wk)

0.05

TE D

9

0.15

EP

Processed Cv Bioavailability (BvPCv)

RI PT

10

Raw Cv Bioavailability (BvRCv)

0.25

Irl (Mexp) Irl (Fexp) Irl (Mexp)

EPIC (Mexp) Irl (Fexp) EPIC(Fexp) (Mexp) EPIC EPIC (Fexp) AICR (Fexp) US (Mexp) AICR (Mexp)

US (Fexp)

0.35

0.45

0.55

0.65

0.75

29 30

AC C

Correlation Coefficient

EPIC: European Prospective Investigation into Cancer and Nutrition; Irl: Ireland; US: United States Mexp: male and Fexp: female exposure assessment

Figure 4. Sensitivity analysis for adult exposure to Gls in cruciferous vegetables (Cv)

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1

Highlights:
Short blanching of cruciferous vegetables minimises the loss of glucosinolates.

3


The importance of thermal degradation and leaching of glucosinolates is highlighted

4


A sensitivity analysis highlighted the importance of glucosinolate bioavailability

5


The study increases awareness of the influence of process stages on glucosinolates.

AC C

EP

TE D

M AN U

SC

RI PT

2

1