Content uploaded by Shikha Saha
Author content
All content in this area was uploaded by Shikha Saha on Nov 20, 2017
Content may be subject to copyright.
The contribution of alliaceous and cruciferous vegetables to dietary
sulphur intake
Joanne F. Doleman
a
, Katrijn Grisar
a
, Lena Van Liedekerke
a
, Shikha Saha
a
, Mark Roe
b
, Henri S. Tapp
c
,
Richard F. Mithen
a,
⇑
a
Food & Health Programme, Institute of Food Research, Norwich, UK
b
Food Databanks, Institute of Food Research, Norwich NR4 7UA, UK
c
Analytical Sciences Unit, Institute of Food Research, Norwich NR4 7UA, UK
article info
Article history:
Received 9 June 2016
Received in revised form 17 March 2017
Accepted 17 April 2017
Available online 18 April 2017
Keywords:
Sulphur
Sulphur amino acids (SAA)
Sulphate
Diet diary
Duplicate diet
abstract
Despite its importance in many areas of human metabolism, there are no recommended daily intake
guide lines for sulphur. It is generally assumed that most dietary sulphur originates from intake of
methionine and cysteine. We estimated sulphur intake from food diaries, and validated the results with
the use of a duplicate diet analyses. Sulphur intake estimations were highly correlated with that obtain
through an elemental analysis of duplicate diets, with a mean ± sd daily intakes of 956 ± 327.9 mg esti-
mated from diet diary analyses and 935 ± 329.9 mg estimated by a duplicate diet analyses. Sulphur intake
from alliaceous and cruciferous vegetables contributed up to 42% of total sulphur intake. Daily intake
estimation comparisons through diet diary analyses and duplicate diet for other elements showed good
agreement, except for sodium and zinc, in which analyses of 24 h diet dairies overestimated intake by
35% and 52%, respectively.
Ó2017 The Authors. Published by Elsevier Ltd. This is an open access article underthe CC BY license (http://
creativecommons.org/licenses/by/4.0/).
1. Introduction
It is generally considered that most dietary sulphur is derived
from the sulphur-containing amino acids (SAA), methionine and
cysteine, with other forms of organic sulphur, such as the spe-
cialised metabolites that accumulate in alliaceous and cruciferous
vegetables contributing a very small percentage to total sulphur
intake. While there are recommended daily allowance (RDA) for
SAA, based upon the requirements to maintain nitrogen balance,
there is not a RDA or an ‘adequate intake estimation’ for sulphur,
despite its importance in many aspects of metabolism (Nimni,
Han, & Cordoba, 2007). This lack of recommended intakes is
reflected in current methods of dietary analyses that do not pro-
vide an estimate of sulphur intake, and do not attempt to identified
important sources of sulphur in the diet other than SAA.
The likely major sources of sulphur in the diet other than SAA
are inorganic sulphate in drinking water and foods, and the
sulphur-containing specialised metabolites that are characteristic
of Allium vegetables, such as onions, leeks and garlic, and crucifer-
ous vegetables, such as cabbages, kales and broccoli. The levels of
sulphate in tap water varies around the world, with low concentra-
tions measured in tap water from the Netherlands (below 260 m/L)
and higher levels in central Canada (up to 22 mmol/L). A water
quality survey of British tap water suppliers measured a mean sul-
phate concentration of 620 mmol/L, and a maximum of 2458 mmol/
L(Powell, Bailey, & Jolly, 1987). Sulphate ingestion has been of
interest due to the potential importance of sulphate reducing bac-
teria (SRB) in the colon, and their association with ulcerative coli-
tis, irritable bowel disease, and Crohn’s disease (Salonen, de Vos, &
Palva, 2010), and inorganic sulphur is required for synthesis of 3
0
-
phosphoadenosine-5
0
-phosphosulfate required for detoxification
of endogenous metabolites and xenobiotics. The consumption of
both alliaceous and cruciferous vegetables have been associated
with health benefits (McNaughton & Marks, 2003; Nicastro, Ross,
& Milner, 2015), including a reduced risk of developing various
cancers including breast, prostate, lung, pancreatic and gastroin-
testinal (Herr & Buchler, 2010), cardio vascular disease (Zhang
et al., 2011), reduced risk of type-2 diabetes (Kurotani et al.,
2013), and protection from neurodegenerative disease (Giacoppo
et al., 2015). For cruciferous vegetables, these health benefits are
associated with the consumption of glucosinolates, the sulphur-
rich compounds that accumulate in cruciferous vegetables, and
their metabolic derivatives (Armah et al., 2015;Maheo et al.,
1997;Yamagishi & Matsui, 2016). Likewise, for alliaceous vegeta-
http://dx.doi.org/10.1016/j.foodchem.2017.04.098
0308-8146/Ó2017 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
⇑
Corresponding author at: Food and Health Programme, Institute of Food
Research, Norwich Research Park, Norwich NR4 7UA, UK.
E-mail address: richard.mithen@ifr.ac.uk (R.F. Mithen).
Food Chemistry 234 (2017) 38–45
Contents lists available at ScienceDirect
Food Chemistry
journal homepage: www.elsevier.com/locate/foodchem
bles, these putative health benefits are attributed to S-2-propenyl-
l-cysteine S-oxide and other related cysteine S-oxides (Block, 1992)
which have been shown to inhibit cell proliferation and induce cell
death of cancerous cells (Borlinghaus, Albrecht, Gruhlke,
Nwachukwu, & Slusarenko, 2014).
In this paper, we provide a method for evaluating sulphur
intake based upon food diary analyses and validate this with the
use of a duplicate diary analyses. Subsequently, we provide an esti-
mation of the proportion of dietary sulphur that comes from non-
SAA sources. The study was extended further to look at daily
intakes for other commonly consumed minerals including sodium,
calcium, iron, zinc, magnesium, phosphorous and potassium and
the validity of current intake estimations from diet diary analysis.
2. Materials & methods
2.1. Analysis of commonly consumed foods for sulphur content
Thirty-two commonly consumed foods were purchased for sul-
phur analysis including fruits and vegetables, meat, fish, pasta, rice
and eggs. Fruits and vegetables were analysed raw and included
spring onions, red onions, brown onions, onions, leek, garlic, pars-
nip, potato, sweet potato, green pepper, courgette, carrot, tomato,
sugar snap peas, dwarf beans, Braeburn apple, lettuce, wild rocket,
radish, savoy cabbage, sweetheart cabbage, curly leaf kale, broccoli
and cauliflower (frozen). Cooked ham, plain flour, white bread and
minced beef (20% fat) were analysed as purchased. Chicken breast
fillets and boneless cod fillets were pan fried without fat prior to
analysis. Long grain rice, spaghetti pasta and medium free range
eggs were boiled prior to analysis. All samples were weighed,
freeze dried and weighed again prior to grinding to a fine powder.
Freeze dried samples were sent to Eurofins UK (Eurofins Food Test-
ing UK Ltd, i54 Business Park, Valiant Way, Wolverhampton, WV9
5 GB) for analysis of amino acids and minerals, in which the sul-
phur, sodium, potassium, calcium, magnesium, phosphorus, iron
and zinc content of each sample was measured. Eighteen of the
vegetable samples were also analysed in-house using established
methods for glucosinolate content (Gasper et al., 2005).
For analysis of sulphate, 25 mg PVPP (polyvinylpolypirrolidone;
washed and ground) was soaked in 1 ml H
2
O, overnight at 4 °C. 20–
30 mg ground, frozen sample was added and mixed and shaken for
1 h at 4 °C. The sample was incubated for 15 min at 95 °C, cen-
trifuged 15 min at 4 °C and supernatant filtered by PVDF syringe
filter into an HPLC vial for analysis by LC-MS with a Agilent 1100
HPLC system and a mass spectroscopy detector (Agilent Technolo-
gies, Waldbronn, Germany). Samples were eluted at 0.5 ml/min
with a gradient mobile phase. Mobile Phase A was 200 mM ammo-
nium acetate buffer at pH 4.0 and B was 60% acetonitrile. The gra-
dient started at 5% solution A increasing over 15 min to 90% A and
finally re-equilibrated to 5% A for 5 min. Sulphate was monitored
using mass spectrometry in selective ion monitor mode (m/
z= 97) in negative polarity with electrospray ionization. The quan-
tification was performed using matrix match calibration curve.
2.2. Sulphur database construction
An on-line literature search was conducted to identify sources
of published food composition data for the sulphur content in
foods. One key paper, two databases and five book supplements
were identified that contained sulphur content information and
these were imported into our own sulphur database containing
1222 different foods from the different reference sources
(FoodStandardsAgency, 2008; Gnagnarella, Salvini, 2008; Holland,
Unwin, & Buss, 1988, 1989, 1991, 1992; Masters & McCance,
1939; PublicHealthEngland, 2015). To aid navigation through the
database, the foods were grouped into the following eleven cate-
gories: vegetables, fruits and nuts, cereals, meat, fish, dairy, bever-
ages, condiments, fats, cooked dishes and miscellaneous foods.
2.3. Computational analysis software
DietPlan6 (http://www.foresoft.co.uk) is a computer pro-
gramme for the nutrition analysis of recipes, meals, menus and
personal weighed-intake food diaries and contains approximately
5500 types of UK foods and allows other national food data tables
to be imported and more foods to be added by the user. The data-
base also includes Dietary Reference Values (Ashwell, 1991) Food
Portion Sizes (MAFF, Ministry of Agriculture, & F. a. F, 1993) and
Food Labelling Data (EU Regulation 1169/2011). For each of the dif-
ferent foods contained within dietary intervention diaries used in
this study a new sulphur database code was developed that incor-
porated all the existing DietPlan6 nutritional information supple-
mented with the sulphur content from the newly developed
database.
2.4. Validation of sulphur database
The consistency of the collected sulphur data was compared
between the different data sources. Few differences were identified
and where differences arose we chose the sulphur data to use in
the database on a case by case basis. Where data was available
from more than one source, the value used was a combination of
the most up to date and/or the most frequently quoted data in
the source documents. Recipes from a previous publication
(Masters & McCance, 1939) were compared to recent recipes to
ensure they were still produced using the same kinds of ingredi-
ents to maintain validity of the historic sulphur data prior to inclu-
sion in the database.
2.5. Diet diaries
Diet diaries from the BASH human dietary intervention study
(rec ref 12/EE/0313 clinical trial Number NCT01929564), which
was carried out in collaboration between the Institute of Food
Research and University of Reading, were used for dietary analysis.
Detailed instructions describing how to complete the diet diary
were provided, including photographs of food portion sizes and
drinking vessels and their volumes, as well as details of how to
record both processed meals and homemade recipes. Participants
were instructed to record the details of all meals, snacks and drinks
consumed, estimating or measuring the quantity of each item and
the time of consumption. Participants were encouraged to be as
accurate as possible when completing the dairies and were pro-
vided with an example completed diary.
In total, 41 one-day diet diaries from 32 different individuals
were coded for the computational analysis of calories and nutrients
and sulphur content in DietPlan6 software using the sulphur data-
base codes developed specifically for this analysis. Dishes, recipes
and commercial food were broken down into their individual
ingredients. Daily sulphur intake was estimated and used to iden-
tify, low, moderate and high consumers of sulphur.
2.6. Duplicate diet analysis
Eighteen diet diaries encompassing low, moderate and higher
consumers of sulphur, as identified from the initial 41 diet diaries,
were selected. The recorded food items were purchased from local
supermarkets and stored as appropriate, until required. Using the
exact cooking methods and quantities consumed, the total food
and drink intake for the 24-h period was recreated and blended
to produce a homogeneous sample to represent each diet diary.
J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45 39
Table 1
Sulphur content in mmoles/g dry weight of 32 commonly consumed foods, portioned to show the proportion derived from the sulphur amino acids methionine and cysteine and other sulphur (including sulphate). The sulphur
partitioning of sulphur containing metabolites of 18 commonly consumed vegetables including tryptophan and methionine derived glucosinolates, sulphur amino acids cysteine and methionine, sulphate and other sulphur is also
included. (– indicates not measured).
Sulphur amino acids
Cysteine &
Methionine mmoles/
g dry weight (% of
total sulphur)
Other sulphur
including sulphate
mmoles/g dry
weight (% of total
sulphur)
Sulphur partitioning of sulphur containing metabolites in 18 commonly consumed vegetables
Sulphate mmoles/g
dry weight (%)
Met derived glucosinolate
(mmoles/g dry weight)
Trp derived glucosinolate
(mmoles/g dry weight)
Other sulphur excluding
glucosinolates (mmoles/g dry weight)
Spring onion 23.6 (26.5) 65.3 (73.5) 17.1 (19.2) 0.0 0.0 48.2
Red onion 12.7 (10.5) 108.6 (89.5) – – – –
Brown onion 9.3 (12.0) 67.7 (88.0) 6.6 (8.6) – – –
Leek 27.5 (22.7) 93.5 (77.3) 16.2 (13.4) 0.0 0.0 77.3
Garlic 26.5 (10.5) 225.8 (89.5) 2.4 (1.0) 0.0 0.0 223.4
Parsnip 12.8 (62.7) 7.6 (37.3) 10.6 (51.7) 0.0 0.0 0.0
Potato 20.9 (54.4) 17.5 (45.6) 13.0 (34.0) 0.0 0.0 4.5
Sweet potato 23.8 (80.8) 5.7 (19.2) 8.8 (29.8) 0.0 0.0 0.0
Green pepper 19.5 (34.4) 37.2 (65.6) – – – –
Courgette 43.1 (72.8) 16.1 (27.2) 11.7 (19.7) 0.0 0.0 4.4
Carrot 13.0 (42.1) 17.8 (57.9) 19.0 (61.7) 0.0 0.0 0.0
Tomato 25.1 (59.3) 17.3 (40.7) 13.6 (32.1) 0.0 0.0 3.7
Sugar snap peas 26.3 (44.7) 32.6 (55.3) 19.5 (33.0) 0.0 0.0 13.2
Dwarf beans 32.5 (61.4) 20.5 (38.6) 11.7 (22.1) 0.0 0.0 8.8
Lettuce 26.1 (51.0) 25.1 (49.0) 18.9 (37.0) 0.0 0.0 6.2
Wild rocket 82.4 (14.9) 469.1 (85.1) 319.9 (58.0) 126.6 1.8 149.2
Radish 17.8 (49.4) 18.3 (50.6) 27.0 (74.7) 19.6 33.2 0.0
Savoy cabbage 40.7 (21.6) 148.0 (78.4) – – – –
Cauliflower 44.3 (23.3) 145.6 (76.7) – – – –
Broccoli 56.1 (20.4) 218.5 (79.6) 87.7 (31.9) 24.2 10.3 130.8
Sweetheart cabbage 28.5 (28.9) 70.1 (71.1) 26.0 (26.4) 14.6 5.7 44.1
Curly leaf kale 49.4 (16.0) 259.5 (84.0) 196.7 (63.7) 17.9 6.8 62.8
Braeburn apple 3.8 (84.2) 0.7 (15.8) – – – –
Cooked ham 140.2 (80.0) 35.1 (20.0) – – – –
Minced beef 117.9 (74.7) 39.9 (25.3) – – – –
Chicken breast 247.6 (97.4) 6.6 (2.6) – – – –
Cod 288.8 (85.0) 51.1 (15.0) – – – –
Eggs 232.6 (96.8) 7.8 (3.2) – – – –
Long grain rice 31.2 (91.9) 2.8 (8.1) – – – –
Spaghetti 37.8 (88.5) 4.9 (11.5) – – – –
Plain flour 35.2 (91.8) 3.1 (8.2) – – – –
White bread 31.8 (45.0) 38.9 (55.0) – – – –
40 J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45
Diet samples were stored at 20 °C prior to inorganic and moisture
content analysis by Eurofins Food Testing Laboratory (Wolver-
hampton, UK).
2.7. Statistical analysis
Statistical analyses were performed using Minitab version
17.2.1, and Matlab (version 8.5), with the Statistics and Machine
Learning Toolbox (version 10.0). Differences between duplicate
diet analysis and DietPlan6 estimations were compared using
paired t-tests and presented using Bland Altman plots (Bland &
Altman, 1986). Linear trends in the differences were assessed from
the significance of the Pearson correlation between the sum and
difference of the two estimates.
3. Results and discussion
3.1. Sulphur partitioning in commonly consumed foods
Thirty-two commonly consumed foods were analysed for total
sulphur content and the proportion of sulphur from SAA, and a
subset of 18 vegetables were also analysed for the contribution
of sulphur from glucosinolates and sulphate (Table 1). As expected
the proportion of SAA was very high in fish, chicken and minced
beef (74–97%). SAA are also the predominant source of sulphur
compounds in rice, pasta and eggs. In alliaceous vegetables, a
low proportion of the total sulphur in the vegetables was in the
form of SAA (e.g. garlic 10.5%, red onions 10.5%), whereas for star-
chy vegetables the proportion of total sulphur in the form of SAA
was much higher (e.g. sweet potato 80.8%, parsnip 62.7%). In wild
rocket, radish, broccoli, sweetheart cabbage and curly leaf kale a
proportion of the total sulphur content was from glucosinolates
with the contribution ranging from 7.4% in curly leaf kale to
59.3% in radish. Sulphate contribution ranged from 1.0% (garlic)
to 61.7% (carrot) of the total sulphur.
3.2. Sulphur database construction and estimating dietary sulphur
intakes
The sulphur intake determined from analysis of each 24-h food
diary analysed ranged from 390 mg to 1414 mg total sulphur con-
sumed in a 24-h period, and enabled the identification of low
(<750 mg/day), moderate (between 750 and 1250 mg/day) and
high sulphur intake (>1250 mg/day) diets across the 41 diet dairies
analysed.
3.3. Sources of sulphur in the diet
Diet diary analysis enabled us to identify the sources of sulphur
in the diet. We categorised the sources into eight groups which
were meat, fish, dairy, vegetables, fruits, wheat and starchy foods,
dishes (e.g. prepared foods such as stock cubes); and miscellaneous
(foods that did not easily fit into any of the other categories e.g.
eggs, coffee, wine). All 41 diet diaries were analysed and the speci-
fic sources of sulphur in each diary determined. The total sulphur
from meat and fish consumption contributed up to 458 mg
(38.2%) and 884 mg (62.5%) respectively of total sulphur intake.
Wheat and starchy foods contributed up to 594 mg (46.9%); dairy
contributed up to 426 mg (35.3%) and vegetables contributed a
maximum of 359 mg (37.9%) of the total sulphur intake (Table 2).
We were particularly interested in the sulphur intake originating
from cruciferous and alliaceous vegetables consumption and as
such performed an additional analysis to investigate what propor-
tion of the sulphur intake came from these vegetables. Cruciferous
and alliaceous vegetables combined contributed up to 41.7% of the
total sulphur intake. The highest contribution from cruciferous
vegetables was 39.9% and the maximum contribution from allia-
ceous vegetables in any one diet diary analysed was 7.3% (Table 2).
In 20 of the 41 diet diaries analysed cruciferous and alliaceous veg-
etable consumption represented more than 50% of the total sul-
phur consumed from all vegetables.
3.4. Validity of the sulphur data in the historic databases
Of the 32 different commonly consumed foods selected for sul-
phur analyses, including vegetables, fruit, meat, fish, pasta and rice,
28 were present in the sulphur database we constructed (absent
were garlic, courgette, sugar snap peas and wild rocket). Much of
the data collated in the sulphur database originated from the old
components dataset of McCance and Widdowson’s ‘The Composi-
tion of Foods Integrated Dataset’ (PublicHealthEngland, 2015),
the accuracy of this historical sulphur data was checked against
the current sulphur content of various foods and vegetables by per-
forming an in-house sulphur analysis of 32 commonly consumed
foods. Samples were prepared and analysed as described in the
materials and methods section for total sulphur content. The sul-
phur content of the analysed foods was compared to that in the
sulphur database. In most cases, we could show good agreement
between our in-house sulphur data and that in the collated sulphur
database (Fig. 1).
Differences between the two sources of data were evident for
onions, spring onions and sweetheart cabbage. The difference in
the data for sweetheart cabbage was not unexpected as we were
only able to obtain an average cabbage sulphur content and not a
specific value for sweetheart cabbage. The data we obtained for
Table 2
Summary data table showing the mean sulphur intake (mg), % of total sulphur intake, intake range and the standard deviation across 8 food categories used in the analysis of
41 24-h diet diaries analysed using DietPlan6 with the sulphur database incorporated. The contribution of sulphur from the consumption of cruciferous and /or alliaceous
vegetables is also included.
Food Category Mean sulphur intake, mg Mean % of total sulphur intake Range mg (%) Standard Deviation
Meat 142.1 15.8 0–458 (0–38.2) 135.5
Fish 93.1 10.4 0–884 (0–62.5) 200.5
Dairy 130.7 14.5 0–426 (0–35.3) 98.6
Vegetables 149.6 16.7 0–359 (0–37.9) 101.4
Fruits 22.2) 2.5 0–189 (0–18.6) 32.7
Wheat and starchy foods 152.8 17.0 0–584 (0–46.9) 119.1
Dishes 66.0 7.3 0–516 (0–62.0) 121.4
Other 141.9 15.8 4–559 (0.5–48.0) 125.9
Total sulphur consumed 898.3 361–1417 296.2
Cruciferous & alliaceous vegetables 67.3 7.5 0–335 (0–41.7) 79.9
Alliaceous vegetables 9.7 1.1 0–41 (0–7.3) 13.2
Cruciferous vegetables 57.6 6.4 0–320 (0–39.9) 78.2
J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45 41
the sulphur content of the onions and spring onion by our in-house
analysis was lower than expected (1.9 and 2.85 mg/g dry weight
respectively), and this was also lower than recently published data
in which the sulphur content of 9 different onion varieties was
tested and ranged from 3.5 to 5.1 mg/g dry weight (Lee, Yoo,
Jifon, & Patil, 2009). The low sulphur content in the in-house anal-
ysis of onions and spring onions could be due to seasonal variation
and the variety tested in this instance. The historic sulphur data for
onions and spring onions, which aligned with the recently pub-
lished data on the sulphur content of onions, was used in the sul-
phur database (Lee et al., 2009). Although some differences were
observed, there did not appear to be any obvious systematic bias
indicating that the sulphur data included in the database was rep-
resentative of foods currently available to consumers.
3.5. Duplicate diet analysis
Duplicate diet analysis enabled us to assess the validity of the
sulphur intake estimates compared to the historical sulphur data
used in diet diary analysis. Of the 41 diet diaries analysed a sub-
group of 18 diet diaries that encompassed 6 low, 6 moderate and
6 high sulphur intakes were analysed by duplicate diet analysis
as described in the materials and methods section. These analytical
data were compared to the sulphur intakes calculated from diet
diary analysis using the Bland Altman method (Fig. 2). There was
a strong positive linear association between the two methods
(r= 0.73) and little evidence of a difference between the two meth-
ods (bias = 20.2 mg; P= 0.73; Table 3) or of any systematic trend in
the differences against the mean (P= 0.97; Table 3). Total sulphur
Fig. 1. Sulphur content of 28 commonly consumed foods from the sulphur database versus Eurofins UK analysis of IFR in-house prepared samples. The sulphur content in mg
per 100 g fresh weight of each food tested was comparable to that in the sulphur database, although some differences are observed. There does not appear to be any obvious
systematic bias in the analysis.
42 J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45
intake estimated by diet diary analysis across the 41 diet diaries
ranged from 11.3–44.2 mmol/day and from the 18 duplicate diets
the total dietary sulphur ranged from 16.1–48.3 mmol/day, thus
showing little difference in the range of sulphur intake estimated
between the two methods.
The total daily sulphur intake measured by both methods in this
study is comparable to the daily sulphur intake estimations
proposed by Florin and Ingenbleek et al. (Florin, Neale, Gibson,
Christl, & Cummings, 1991; Ingenbleek & Kimura, 2013). In other
studies a moderately high total sulphur intake has been reported
Fig. 2. Sulphur intake estimates of 18 24-h dietary intake diaries using DietPlan6 and duplicate diet analysis. Left panel, Bland and Altman plot (Bland & Altman, 1986)of
the differences, DietPlan6 – duplicate diary, against the mean of the two estimates. Also shown is the bias given as the mean difference and the 95% limits of agreement (mean
difference ± 2 standard deviation). Central panel, scatterplot of DietPlan6 estimates against duplicate diary values. Also shown is the Pearson correlation between the two
methods and the line of unity. Right panel, boxplots summarising the distribution of the two estimates.
Table 3
Summary statistics for a selection of constituents estimated during the diet diary analysis by DietPlan6 and by duplicate diet analysis.
Constituent nDietPlan6 Duplicate diet DIFF: DP6-DD Trend
Mean ±SD Mean ±SD Mean ±SD P diff P trend
Sulphur (mg) 18 956 ±327.9 935 329.9 20.2 240.2 0.7251 0.9726
Sodium (mg) 18 2500 ±1752 1764 1295 736 612 0.0001 0.0003
Calcium (mg) 18 890 ±378.7 955 400.4 65 299.8 0.3707 0.7576
Iron (mg) 18 14.33 ±5.446 13.47 6.696 0.86 4.355 0.4129 0.2173
Magnesium (mg) 18 322.6 ±96.65 339.2 98.44 16.6 47.92 0.1610 0.8795
Phosphorus (mg) 18 1485 ±460.1 1349 458.6 136 290.8 0.0625 0.9835
Potassium (mg) 18 3466 ±1237 3785 1149 319 758 0.0918 0.6297
Zinc (mg) 18 11.07 ±4.724 8.36 3.110 2.71 2.791 0.0007 0.0079
n, sample size; SD, standard deviation; DIFF: DP6-DD, paired difference in the estimates by DietPlan6 and duplicated diary analysis; P diff, significance of difference based on
paired t-test; P trend, significance of linear trend based on the correlation between the sum and difference of the two estimates; mg, milligram.
Fig. 3. Sodium intake estimates of 18 24-h dietary intake diaries using DietPlan6 and duplicate diet analysis. Left panel, Bland and Altman plot (Bland & Altman, 1986) of the
differences, DietPlan6 – duplicate diary, against the mean of the two estimates. Also shown is the bias given as the mean difference and the 95% limits of agreement (mean
difference ± 2 standard deviation). Central panel, scatterplot of DietPlan6 estimates against duplicate diary values. Also shown is the Pearson correlation between the two
methods and the line of unity. Right panel, boxplots summarising the distribution of the two estimates.
J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45 43
to be 38.2 mmol/day (high 42.2 mmol/day & very high 57.6 mmol/-
day) (Curno, Magee, Edmond, & Cummings, 2008). Using these fig-
ures a portion of the diet diaries in this study falls into the
moderately high and high total sulphur intake category, however
none of the diet diaries analysed could be classified as very high
sulphur intake.
3.6. Analysis of sodium in the diet using duplicate diet and diet diary
analyses
As both the duplicate diet analysis and diet diary analysis pro-
vided information on other minerals we also investigated the
agreement between the two methods for dietary sodium (Fig. 3)
and various other minerals (Table 3). For sodium, there was a
strong positive linear association between the two methods
(r= 0.963). However, DietPlan6 showed a tendency to produce pro-
portionately higher estimates as compared to duplicate diets (ratio
of standard deviations, DP6/DD = 1.35, significance of linear trend,
P= 0.0003). There was also strong evidence of a positive bias (mean
difference = 736 mg, P= 0.0001). The dataset used for sodium con-
tent of foods in diet diary analysis was McCance and Widdowson’s
‘The Composition of Foods’ integrated dataset from 2008
(FoodStandardsAgency, 2008). In recent years’ links between
sodium intake and health have led the food industry to lower the
salt content of processed foods and it was therefore possible that
the sodium data used within the diet diary analysis could underes-
timate current intake.
In March 2015, an updated food composition dataset was pub-
lished by the UK Department of Health (PublicHealthEngland,
2015) and has since been included as an update for DietPlan (Diet-
Plan7). We therefore developed new dietary codes for the foods
consumed in the diet diaries that included both the sulphur con-
tent as previously used, and the updated sodium values from the
2015 food composition dataset. The sodium intake as estimated
by duplicate diet and DietPlan7 analysis was compared as before.
Overall the data between the two methods was more comparable
than with the older dataset for sodium intake, although there
was still strong evidence of a positive bias (mean differ-
ence = 311 mg, P= 0.0041) and proportionately higher estimates
(ratio of standard deviations, DP7/DD = 1.21, significance of linear
trend, P= 0.0015). Summary plots for the comparison between
DietPlan7 and duplicate diaries are provided as Supplementary
figures.
High sodium intake has been linked to increased risk of high
blood pressure and cardiovascular disease (He & MacGregor,
2015). Although the average intake of sodium in the UK is on a
downward trend, it was still 76 per cent above the recommended
maximum Reference Nutrient Intake of 2.4 g/day in 2014 and in
this study was found to be 2.5 g/day on average by diet diary anal-
ysis (Table 3). As such there has been an active approach to the
reduction of salt added to foods in recent years (Webster, Trieu,
Dunford, & Hawkes, 2014) and it is therefore important to use
up-to-date information on sodium content of foods in dietary anal-
ysis calculations. The change in salt added to processed foods is
undergoing such a rapid change that even the updated databases
in Dietplan7 (from 2015 data) may still be overestimating dietary
sodium intake, which may explain the disparity between the diet
record estimates and duplicate diet analysis.
3.7. Analysis of calcium, iron, magnesium, phosphorous, potassium
and zinc in the diet using duplicate diet and diet diary analyses
Calcium, iron, magnesium, phosphorous, potassium and zinc
intake were also estimated for the 18 diet diaries and compared
to duplicate diet analysis. Table 3 shows there was little evidence
of bias for calcium, iron and magnesium (P> 0.10), and weak evi-
dence of bias for phosphorous (P= 0.063) and potassium
(P= 0.092). For zinc, there was strong evidence of a positive bias
(mean difference = 2.7 mg, P = 0.0007), and to overestimate (ratio
of standard deviations DP6/DD = 1.52, significance of linear trend
P= 0.008) by diet diary analysis. Compound figures showing Bland
and Altman, scatter and boxplots for these minerals are provided
as Supplementary figures.
Caution is also advised when using dietary analysis software for
the estimation of other mineral intake such as iron. Many foods
that may contain added minerals, e.g. breakfast cereals, food com-
position data is often the average data for a range of similar prod-
ucts and the actual amount of fortificant consumed can be brand
dependant.
4. Conclusions
As expected, high levels of sulphur were obtained from con-
sumption of meat and fish, mainly in the form of SAA. However,
the analysis of sulphur content and partitioning in commonly con-
sumed foods and vegetables undertaken in this study indicates as
much as 89.5% of the total sulphur consumed in a typical diet may
not be derived from SAA. Significant sulphur intake was also
achieved from consumption of wheat and starchy foods, dairy
and vegetables. If only SAA intake is monitored the significant con-
tribution to total sulphur intake from these other foods will be dis-
regarded. Alliaceous and cruciferous vegetables are shown to be an
important source of sulphur in the diet.
The results of this study suggest there is no requirement for
mass re-analysis of the composition of sulphur in foods and that
existing data can be used with consumption data for estimation
of sulphur intake. We also found evidence that dietary intake anal-
ysis overestimated the amount of sodium consumed which may be
a consequence of the compositional data not keeping pace with the
trend of manufacturing food with a reduced salt content. Total sul-
phur intake estimated from dietary intake diaries is comparable to
that from duplicate diet analysis and can include a breakdown of
the sulphur amino acid, sulphate and sulphur containing metabo-
lites consumed.
Conflicts of interest
None
Authorship contributions
Joanne F Doleman: formulation of research question, prepara-
tion of the sulphur database, design of study, analysis of diet dia-
ries, data analysis and writing of article.
Shikha Saha: analysis of food samples.
Katrijn Grisar: preparation of the sulphur database, analysis of
diet diaries.
Lena Van Liedekerke: preparation of the sulphur database, anal-
ysis of diet diaries.
Henri S Tapp: data analysis.
Mark Roe: input to data for sulphur database, assistance in diet
diary analysis, contribution to structure, content and writing of
article.
Richard F Mithen: formulation of research question, design of
study, contribution to structure, content and writing of article.
Funding
The study was supported by a strategic programme grant to IFR
from the UK Biotechnology and Biological Sciences Research Coun-
cil [BB/J004545/1].
44 J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45
Acknowledgements
The authors would like to thank Dr Charlotte N Armah of IFR, Dr
Christos Derdemezis and Dr Julie A Lovegrove of University of
Reading for the use of the diet diaries from the BASH study.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.foodchem.2017.
04.098.
References
Armah, C. N., Derdemezis, C., Traka, M. H., Dainty, J. R., Doleman, J. F., Saha, S., ...
Mithen, R. F. (2015). Diet rich in high glucoraphanin broccoli reduces plasma
LDL cholesterol: Evidence from randomised controlled trials. Molecular
Nutrition and Food Research, 59(5), 918–926.
Ashwell, M. (1991). 4: The COMA Report on Dietary Reference Values. Nutrition
Bulletin, 16(3), 132–135.
Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement
between two methods of clinical measurement. Lancet, 1(8476), 307–310.
Block, E. (1992). The Organosulfur Chemistry of the Genus Allium – Implications for
the Organic-Chemistry of Sulfur. Angewandte Chemie-International Edition in
English, 31(9), 1135–1178.
Borlinghaus, J., Albrecht, F., Gruhlke, M. C., Nwachukwu, I. D., & Slusarenko, A. J.
(2014). Allicin: Chemistry and biological properties. Molecules, 19(8),
12591–12618.
Curno, R., Magee, E. A., Edmond, L. M., & Cummings, J. H. (2008). Studies of a urinary
biomarker of dietary inorganic sulphur in subjects on diets containing 1–38
mmol sulphur/day and of the half-life of ingested 34SO
4
(2-). European Journal of
Clinical Nutrition, 62(9), 1106–1115.
Florin, T., Neale, G., Gibson, G. R., Christl, S. U., & Cummings, J. H. (1991). Metabolism
of dietary sulphate: absorption and excretion in humans. Gut, 32(7), 766–773.
FoodStandardsAgency. (2008). The McCance and Widdowson’s Composition of
Foods integrated dataset. In Public Health England.
Gasper, A. V., Al-Janobi, A., Smith, J. A., Bacon, J. R., Fortun, P., Atherton, C., ... Mithen,
R. F. (2005). Glutathione S-transferase M1 polymorphism and metabolism of
sulforaphane from standard and high-glucosinolate broccoli. American Journal
of Clinical Nutrition, 82(6), 1283–1291.
Giacoppo, S., Galuppo, M., Montaut, S., Iori, R., Rollin, P., Bramanti, P., & Mazzon, E.
(2015). An overview on neuroprotective effects of isothiocyanates for the
treatment of neurodegenerative diseases. Fitoterapia, 106, 12–21.
Gnagnarella P, Salvini S, & M., P. (2008). Food Composition Database for
Epidemiological Studies in Italy. In vol. Version 1.2015.
He, F. J., & MacGregor, G. A. (2015). Salt and sugar: their effects on blood pressure.
Pflugers Archiv, 467(3), 577–586.
Herr, I., & Buchler, M. W. (2010). Dietary constituents of broccoli and other
cruciferous vegetables: implications for prevention and therapy of cancer.
Cancer Treatment Reviews, 36(5), 377–383.
Holland, B., Unwin, I. D., & Buss, D. H. (1988). Cereals and Cereal Products. (Vol.
Third supplement to 4th edition of McCance and Widdowson’s The Composition
of Foods). Nottingham: The Royal Society of Chemistry.
Holland, B., Unwin, I. D., & Buss, D. H. (1989). Milk Products and Eggs (Vol. Fourth
supplement to 4th edition of McCance and Widdowson’s The Composition of
Foods). Cambridge: The Royal Society of Chemistry.
Holland, B., Unwin, I. D., & Buss, D. H. (1991). Vegetables, Herbs and Spices (Vol.
Fifth supplement to 4th edition of McCance and Widdowson’s The Composition
of Food). Cambridge: The Royal Society of Chemistry.
Holland, B., Unwin, I. D., & Buss, D. H. (1992). Fruit and Nuts. (Vol. First supplement
to 5th edition of McCance and Widdowson’s The Composition of Foods.).
Cambridge: The Royal Society of Chemistry.
Ingenbleek, Y., & Kimura, H. (2013). Nutritional essentiality of sulfur in health and
disease. Nutrition Reviews, 71(7), 413–432.
Kurotani, K., Nanri, A., Goto, A., Mizoue, T., Noda, M., Kato, M., ... Japan Public Health
Center-based Prospective Study (2013). Vegetable and fruit intake and risk of
type 2 diabetes: Japan Public Health Center-based Prospective Study. British
Journal of Nutrition, 109(4), 709–717.
Lee, E. J., Yoo, K. S., Jifon, J., & Patil, B. S. (2009). Characterization of shortday onion
cultivars of 3 pungency levels with flavor precursor, free amino acid, sulfur, and
sugar contents. Journal of Food Science, 74(6), C475–C480.
MAFF, Ministry of Agriculture, F. a. F (1993). Food portion sizes (2nd ed.). London:
Her Majesty’s Stationery Office.
Maheo, K., Morel, F., Langouet, S., Kramer, H., Le Ferrec, E., Ketterer, B., & Guillouzo,
A. (1997). Inhibition of cytochromes P-450 and induction of glutathione S-
transferases by sulforaphane in primary human and rat hepatocytes. Cancer
Research, 57(17), 3649–3652.
Masters, M., & McCance, R. A. (1939). CLIX. The sulphur content of foods.
Biochemistry Journal, 33(8), 1304–1312.
McNaughton, S. A., & Marks, G. C. (2003). Development of a food composition
database for the estimation of dietary intakes of glucosinolates, the biologically
active constituents of cruciferous vegetables. British Journal of Nutrition, 90(3),
687–697.
Nicastro, H. L., Ross, S. A., & Milner, J. A. (2015). Garlic and onions: their cancer
prevention properties. Cancer Prevention Research (Philadelphia), 8(3), 181–189.
Nimni, M. E., Han, B., & Cordoba, F. (2007). Are we getting enough sulfur in our diet?
Nutrition and Metabolism (London), 4,24.
Powell, R., Bailey, R. J., & Jolly, P. K. (1987). Trace Elelments in British Tap Water
Supplies. In http://dwi.defra.gov.uk/research/completed-research/reports/
dwi0007.pdf: Department of the Environment.
PublicHealthEngland. (2015). The McCance and Widdowson’s Composition of Foods
integrated dataset. In Foods standards agency.
Salonen, A., de Vos, W. M., & Palva, A. (2010). Gastrointestinal microbiota in irritable
bowel syndrome: Present state and perspectives. Microbiology, 156(Pt 11),
3205–3215.
Webster, J., Trieu, K., Dunford, E., & Hawkes, C. (2014). Target salt 2025: A global
overview of national programs to encourage the food industry to reduce salt in
foods. Nutrients, 6(8), 3274–3287.
Yamagishi, S. I., & Matsui, T. (2016). Protective role of sulphoraphane against
vascular complications in diabetes. Pharmaceutical Biology, 1–11.
Zhang, X., Shu, X. O., Xiang, Y. B., Yang, G., Li, H., Gao, J., ... Zheng, W. (2011).
Cruciferous vegetable consumption is associated with a reduced risk of total
and cardiovascular disease mortality. American Journal of Clinical Nutrition, 94
(1), 240–246.
J.F. Doleman et al. / Food Chemistry 234 (2017) 38–45 45
