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molecules
Article
Carbon and Nitrogen Isotope Ratios of Food and
Beverage in Brazil
Luiz A. Martinelli 1, *, Gabriela B. Nardoto 2, Maria A. Z. Perez 1, Geraldo Arruda Junior 1,
Fabiana C. Fracassi 1, Juliana G. G. Oliveira 1, Isadora S. Ottani 1, Sarah H. Lima 1,
Edmar A. Mazzi 1, Taciana F. Gomes 1, Amin Soltangheisi 1, Adibe L. Abdalla Filho 1,
Eduardo Mariano 1, Fabio J. V. Costa 3, Paulo J. Duarte-Neto 4, Marcelo Z. Moreira 1and
Plinio B. Camargo 1
1Laboratory of Isotope Ecology, Center for Nuclear Energy in Agriculture, University of São Paulo, Av.
Centenário, 303, São Dimas, Piracicaba CEP 13416-000, SP, Brazil; mazperez@cena.usp.br (M.A.Z.P.);
garruda@cena.usp.br (G.A.J.); fffracass@cena.usp.br (F.C.F.); jgiovann@cena.usp.br (J.G.G.O.);
isadoraottani@hotmail.com (I.S.O.); sarahlima0723@gmail.com (S.H.L.); eamazzi@cena.usp.br (E.A.M.);
tgomes@cena.usp.br (T.F.G.); soltangheise@gmail.com (A.S.); adibefilho@cena.usp.br (A.L.A.F.);
dumariano@gmail.com (E.M.); mmoreira@cena.usp.br (M.Z.M.); pbcamargo@cena.usp.br (P.B.C.)
2Ecology Department, Institute of Biological Sciences, University of Brasília, Asa Norte,
Brasília CEP 70910-900, Brazil; gbnardoto@gmail.com
3National Institute of Criminalistics, Federal Police, Asa Sul, Brasília CEP 70610-200, Brazil;
mr.f.bio@gmail.com
4
Department of Statistics and Informatics, Rural Federal University of Pernambuco, R. Manuel de Medeiros,
35, Dois Irmãos, Recife CEP 52171-050, Brazil; pjduarteneto@gmail.com
*Correspondence: martinelli@cena.usp.br; Tel.:+55-193-429-4074
Academic Editors: Nives Ogrinc and Federica Camin
Received: 27 January 2020; Accepted: 6 March 2020; Published: 24 March 2020
Abstract:
Several previous studies on targeted food items using carbon and nitrogen stable isotope
ratios in Brazil have revealed that many of the items investigated are adulterated; mislabeled or even
fraud. Here, we present the first Brazilian isotopic baseline assessment that can be used not only in
future forensic cases involving food authenticity, but also in human forensic anthropology studies.
The
δ13
C and
δ15
N were determined in 1245 food items and 374 beverages; most of them made in
Brazil. The average
δ13
C and
δ15
N of C
3
plants were
−
26.7
±
1.5%, and 3.9
±
3.9%, respectively,
while the average
δ13
C and
δ15
N of C
4
plants were
−
11.5
±
0.8%and 4.6
±
2.6%, respectively. The
δ13
C and
δ15
N of plant-based processed foods were
−
21.8
±
4.8%and 3.9
±
2.7%, respectively. The
average
δ13
C and
δ15
N of meat, including beef, poultry, pork and lamb were -16.6
±
4.7%, and 5.2
±
2.6%, respectively, while the
δ13
C and
δ15
N of animal-based processed foods were
−
17.9
±
3.3%
and 3.3
±
3.5%, respectively. The average
δ13
C of beverages, including beer and wine was
−
22.5
±
3.1%. We verified that C-C
4
constitutes a large proportion of fresh meat, dairy products, as well as
animal and plant-based processed foods. The reasons behind this high proportion will be addressed
in this study.
Keywords: processed foods; staple foods; photosynthesis metabolism; isotopes; Brazil
Molecules 2020,25, 1457; doi:10.3390/molecules25061457 www.mdpi.com/journal/molecules
Molecules 2020,25, 1457 2 of 18
1. Introduction
Rapid industrialization and urbanization together with changes in lifestyles are among the factors
responsible for changes in eating habits, especially the consumption of highly processed foods [
1
]. The
access, mainly by urban populations, to an immense variety of processed food products triggered the
so-called “supermarket era” [
2
]. Diversification as a marketing strategy has resulted in a vast range of
food products in the last two decades. However, consumers may be duped into buying food items
without being aware of their quality and integrity [3].
Brazil is one of the world’s main food consumers which makes the country a vast market for food
fraud and adulteration through the large quantity of foods produced, exported, or imported [
4
]. In
the Brazilian territory, complex supply chains of foods with animal origin, such as milk and dairy
products, are the main targets of food fraud and adulterations, followed by vegetable oils, especially
olive oil, considered as high-value products. Meat and fish, as well as their respective by-products,
were also involved in some food fraud and adulteration [
4
]. Recently there has been a couple of
scandals refocusing national attention to food adulteration. The “Ouro Branco” and “Carne Fraca”
operations conducted by the Brazilian Federal Police revealed the addition of illegal substances in milk
and beef, respectively, to increase business profits. The operations received public attention due to the
health harm risks that those adulterations inflicted.
Since sophisticated fraudsters have been generally capable of mimicking the physical and chemical
compositions of targeted foods, there is a need to address and prevent food fraud and adulteration [
4
].
Food safety might be also compromised in these cases. In this context, a current tool of choice in food
forensics is stable isotope analysis, which can be a rapid and cost-effective method to detect fraud [
3
].
Several studies on targeted food items using carbon and nitrogen stable isotope ratios in Brazil revealed
that many of the food items investigated are adulterated, mislabeled or even fraudulent [
5
–
8
]. In
this regard, carbon isotope ratio analyses have become a useful tool to track the abundances of C
3
and C
4
food sources in human and animal diets [
9
]. The primary photosynthetic producers support
hundreds of millions of heterotrophic species like humans. The C
4
photosynthetic pathway evolved in
plants 24–35 million years ago as a response to the decrease in CO
2
and increase in O
2
atmospheric
concentrations [
10
]. By the competitive advantage of these plants in warm environments, extensive
grasslands were formed, mainly in low latitudes [11].
Several large terrestrial herbivores, like bovine, equid and other ungulates, followed by top
carnivores, and, finally, hominids, have adapted to these new ecosystems [
12
]. The presence of
hominids in grasslands was remarkable as such in human evolution [
10
]. The importance of C
4
plants
became even more evident when humans started agriculture, since in this endeavor, C
4
plants like
maize (Zea mays L.), sorghum (Sorghum bicolor L.), pearl millet (Pennisetum glaucum L.) and sugarcane
(Saccharum spp.) were among the species selected by humans to be cultivated [
13
]. Maize had such an
impact in the Americas that in few centuries, the human populations changed from a C
3
to a C
4
-based
diet [
14
]. Furthermore, with the Columbian exchanges, C
4
plants were disseminated throughout the
old and new continents [15–17].
Even with this competitive advantage, there is still a large dominance of C
3
species, almost four
times more than C
4
species. The European Union (EU) and other temperate regions preferentially
grow C
3
rather than C
4
plants, which modifies their food carbon isotopic composition. As an example,
Martinelli et al. [
18
] observed that hamburgers in countries that feed cattle preferentially with C
4
grass
or maize (e.g., Brazil, Mexico, and USA) have less negative
δ13
C values in comparison to countries
with cattle preferentially fed with C3plants (e.g., the EU).
Unlike carbon, of which the main source for terrestrial plants is atmospheric CO
2
, nitrogen can
be acquired from both the atmosphere through biological fixation and through the soil. After the
Haber–Bosch process became a viable method at industrial scale, commercial crops also commenced to
take up nitrogen from mineral fertilizers. Before the large-scale use of these fertilizers, crops had relied
mainly on manures, organic sources of nitrogen generated by animals. Due to the multiple nitrogen
sources for plant uptake, interpreting the nitrogen isotopic ratio (
15
N/
14
N) in plants is not an easy
Molecules 2020,25, 1457 3 of 18
task [
19
,
20
]. However, the use of fertilizers with
δ15
N values around 0%can be distinguished from the
use of manures with more positive
δ15
N values [
21
,
22
]. Thus, as a first approach,
δ15
N of crops may be
an indicative of the application of synthetic or organic fertilizers for improving plant growth (e.g., [
23
]).
After being incorporated into the plant tissue, nitrogen moves up along the food web with
preferential loss of
14
N related to
15
N along the way. Consequently, the organisms positioned higher in
the food chain tend to be more enriched in
15
N than the organisms positioned on the bottom of the
chain, the so-called “trophic isotopic discrimination” [
24
]. Therefore, the nitrogen isotope ratio has
been a useful tool to place organisms along the food chain, including humans. Vegetarians and vegans
tend to be depleted in 15N in comparison with omnivorous humans [25].
Carbon and nitrogen isotopic ratios also have the potential to serve as tracers of dietary habits and
migration in humans and animals [
26
,
27
]. Consequently, the isotopic analysis of human remains such
as hair, nails, bones or teeth, allows the retrieval of information regarding a person’s diet to reconstruct
dietary habits (e.g., [
28
,
29
]) or to determine various regions where the decedents have been fed through
time, which gives evidences of past mobility patterns [30].
Previous studies performed in Brazil have found that large urban populations have a relatively
homogenous diet regardless of the geographic region of the country [
25
,
31
]. Based on the stable isotope
composition of fingernails, these authors also found that plants with a C
4
photosynthetic metabolism
(sugarcane, maize and pastures) constitute the major part of the diet in these large urban centers. Based
on this fact, we hypothesize here that there is a pervasive presence of C-C
4
in Brazilian food items
although the staple foods in Brazil are rice (Oriza sativa L.), beans (Phaseolus spp. and Vigna sp.), and
cassava (Manihot esculenta L.), plants that follow the C3photosynthetic metabolism.
In this context, we developed the first Brazilian baseline assessment using carbon and nitrogen
stable isotope ratios of about one thousand food items considered essential to test the above hypothesis.
We also generated forensic data supporting food authenticity and traceability. As the assessment
included in natura as well as processed foods, it might also become a powerful baseline for tracking
human movement under the “supermarket diet” trend in Brazil.
2. Results
The complete dataset containing the
δ13
C and
δ15
N values of every single sample of foods and
beverages is available in the Supplementary Material.
2.1. Plants Used as Food
Considering all samples of in natura plants, there were two main distinct groups observed: those
following the C
3
photosynthesis metabolism and those using the C
4
pathway (Figure 1a). There were
also some plants which follow the crassulacean acid metabolism (CAM), such as pineapple (Ananas
comosus L.), and their
δ13
C is intermediate between C
3
and C
4
plants (Figure 1a). The average
δ13
C
of C
3
plants was
−
26.7%(n=434), varying from
−
30.7 to
−
22.5%, while the average
δ13
C of C
4
plants was
−
11.5%(n=47), varying from
−
14.2 to
−
10.2%(Figure 1a), which is a highly significant
difference (p<0.01). The values in the CAM plants varied from −15.3 to −14.1%, with an average of
−
14.8%(n=3; Figure 1a). The
δ15
N of in natura plants had a large variation from
−
4.4%up to more
than 18.4%(Figure 1b). However, most of them had positive values. The average for C
3
plants was
3.9%, which does not differ from the δ15N of C4plants (4.5%; Figure 1b).
Molecules 2020,25, 1457 4 of 18
Then, according to the FAO classification, plants were grouped into C
3
-cereals, C
4
-cereals, fruits,
pulses, tubers and vegetables. Except for C
4
-cereals, the average
δ13
C values of these groups ranged
from
−
30.7 to
−
14.1%(Figure 2a). The main
δ13
C difference was between C
4
-cereals and the other
plant groups (p<0.01). On the other hand,
δ15
N values of oilseeds (
−
0.3%) and pulses (2.4%) were
significantly less positive (p<0.01) in comparison with other the groups. In addition, nuts (17.3%)
had more positive
δ15
N values (p<0.01) than vegetables, tubers, fruits, and C
3
-cereals, whose average
δ15N values varied between −4.4%and 15.9%(Figure 2b).
Figure 1. Frequency histogram of C (a) and 5N (b) of in natura plants used as food in Brazil. The
green bars represent C4 plants, the blue bars represent CAM plants, and the red bars represent C3
plants.
(a)
(
b
)
Figure 1.
Frequency histogram of
δ13
C (
a
) and
δ15
N (
b
)of in natura plants used as food in Brazil.
The green bars represent C
4
plants, the blue bars represent CAM plants, and the red bars represent
C3plants.
2.2. Meats
Beef had the least negative
δ13
C values (p<0.01) among different types of meat, close to the
δ13
C
of C
4
plants. The
δ13
C of lamb, pork, and poultry were also more negative than that of beef (p<0.01)
(Figure 3a). On the other hand, freshwater wild fish had the most negative
δ13
C values (p<0.01). In
contrast,
δ13
C values in farmed-raised tilapia (Oreochromis niloticus) were similar to those values of
pork and poultry (Figure 3a). In addition, marine fish had a similar
δ13
C, but it was more negative
(p<0.01) than pork and poultry due to the reasons which will be discussed later.
Molecules 2020,25, 1457 5 of 18
Figure 2. Box-whisker of C (a) and 5N (b) of in natura plants used as food in Brazil grouped
according to the FAO classification. C-C3: cereals that follow the C3 photosynthetic metabolism (wheat
and rice); C-C4: cereals that follow the C4 photosynthetic pathway (maize); Fru: fruits; Nuts: nuts;
Pulse: pulses; S-Oil: oilseeds; Tuber: tubers; Veg: vegetables. See the Supplementary Material for food
items included in each of these groups.
2.2. Meats
Beef had the least negative 13C values (p < 0.01) among different types of meat, close to the 13C
of C4 plants. The 13C of lamb, pork, and poultry were also more negative than that of beef (p < 0.01)
(Figure 3a). On the other hand, freshwater wild fish had the most negative 13C values (p < 0.01). In
contrast, 13C values in farmed-raised tilapia (Oreochromis niloticus) were similar to those values of
pork and poultry (Figure 3a). In addition, marine fish had a similar 13C, but it was more negative (p
< 0.01) than pork and poultry due to the reasons which will be discussed later.
The 15N of animal protein consumed in southeast Brazil varied considerably among different
products (Figure 3b). The most positive 15N values were observed in wild freshwater and marine
fish (p < 0.01); however, 15N of farmed raised tilapia was only more positive (p < 0.01) than pork and
poultry (Figure 3b).
Figure 2.
Box-whisker of
δ13
C (
a
) and
δ15
N (
b
) of in natura plants used as food in Brazil grouped
according to the FAO classification. C-C
3
: cereals that follow the C
3
photosynthetic metabolism (wheat
and rice); C-C
4
: cereals that follow the C
4
photosynthetic pathway (maize); Fru: fruits; Nuts: nuts;
Pulse: pulses; S-Oil: oilseeds; Tuber: tubers; Veg: vegetables. See the Supplementary Material for food
items included in each of these groups.
Molecules 2020,25, 1457 6 of 18
Figure 3. Box-whisker of C (a) and 5N (b) of raw meats used as food in Brazil grouped as follows:
beef, bushmeat (Bush), farm-raised fish (F-Farm), freshwater fish (F-Fres), marine fish (F-Mar), dairy,
lamb, pork, and poultry.
2.3. Processed Food
The isotope values of processed foods are summarized in the tables due to the large number of
food items (Tables 1 and 2). Plant-based processed foods have a wide range of 13C values (Table 1).
Food items with C3-like values are flours from wheat (Triticum spp.) and cassava, noodles and pasta.
Alongside these products, cocoa (Theobroma cacao L.) powder and bitter chocolate had also 13C values
resembling C3 plants, as well as vegetal fat (Table 1). Conversely, sugarcane-derived sugar, fruit,
jams, juice-powders, chocolate-drink powders, and industrial puddings had
Table 1. Descriptive statistics* of 13C and 15N of plant-based processed foods.
Figure 3.
Box-whisker of
δ13
C (
a
) and
δ15
N (
b
) of raw meats used as food in Brazil grouped as follows:
beef, bushmeat (Bush), farm-raised fish (F-Farm), freshwater fish (F-Fres), marine fish (F-Mar), dairy,
lamb, pork, and poultry.
The
δ15
N of animal protein consumed in southeast Brazil varied considerably among different
products (Figure 3b). The most positive
δ15
N values were observed in wild freshwater and marine
fish (p<0.01); however,
δ15
N of farmed raised tilapia was only more positive (p<0.01) than pork and
poultry (Figure 3b).
2.3. Processed Food
The isotope values of processed foods are summarized in the tables due to the large number of
food items (Tables 1and 2). Plant-based processed foods have a wide range of
δ13
C values (Table 1).
Food items with C
3
-like values are flours from wheat (Triticum spp.) and cassava, noodles and pasta.
Alongside these products, cocoa (Theobroma cacao L.) powder and bitter chocolate had also
δ13
C values
resembling C
3
plants, as well as vegetal fat (Table 1). Conversely, sugarcane-derived sugar, fruit,
jams, juice-powders, chocolate-drink powders, and industrial puddings had typical
δ13
C values of C
4
Molecules 2020,25, 1457 7 of 18
plants (Table 1). Other products are clearly a mixture of C
3
and C
4
plants in different proportions. For
instance, chocolate bars (bitter, milk, and white) have different
δ13
C values according to the contents of
cocoa, milk and sugar. Chocolate powder for cooking, with an average
δ13
C of
−
19%, represents a
mixture of chocolate and C-C4sugar.
Table 1. Descriptive statistics* of δ13C and δ15 N of plant-based processed foods.
δ13C (%). Mean SD Median IQR Min Max n
Cake −20.4 1.6 −20.2 2.4 −22.8 −18.2 11
Chocolate-bitter −26.5 2.2 −26.1 2.0 −31.0 −24.3 19
Chocolate-drink −14.4 1.1 −14.0 0.9 −17.2 −13.0 18
Chocolate-milk −22.0 0.9 −22.2 1.3 −23.4 −20.1 28
Chocolate-powder −18.8 2.3 −19.8 2.1 −20.8 −14.8 6
Chocolate-white −21.9 0.8 −21.7 0.8 −23.8 −20.9 10
Cocoa powder −27.8 2.3 −28.3 0.7 −29.1 −20.5 12
Cookie −23.0 0.7 −23.1 0.8 −23.7 −21.4 12
Fat −28.9 2.0 −30.1 2.6 −30.5 −25.4 8
Flour −26.1 0.8 −26.0 0.9 −27.5 −23.9 20
Jam −15.5 4.7 −13.5 2.8 −26.5 −11.6 33
Juice-powder −13.0 1.1 −12.7 1.5 −14.7 −11.8 10
Noodles −26.9 0.4 −27.1 0.8 −27.3 −26.2 9
Oil −29.5 1.3 −30.0 1.5 −30.8 −27.4 7
Others −17.7 4.5 −17.9 7.6 −24.2 −10.9 19
Pasta −25.7 0.8 −26.2 1.3 −26.4 −24.6 5
Pudding −12.8 2.3 −11.4 3.2 −16.1 −11.3 6
Sauce −20.3 5.6 −20.0 9.9 −29.8 −12.3 27
Seasoning −18.0 2.8 −19.5 3.3 −20.3 −13.7 5
Snack −22.5 4.8 −21.0 8.8 −27.9 −17.0 9
Soda −12.1 0.5 −11.9 0.4 −13.0 −11.7 5
Soup powder −21.8 2.1 −22.6 1.0 −23.3 −17.7 6
Stock −23.8 2.5 −24.6 2.4 −25.7 −21.0 3
Sugar −12.2 0.3 −12.2 0.4 −13.2 −11.8 15
δ15N (%)Mean SD Median IQR Min Max n
Cake 3.7 0.3 3.6 0.1 3.3 4.3 11
Chocolate-bitter 5.9 0.7 5.8 0.7 4.4 7.3 19
Chocolate-drink 5.2 0.6 5.4 0.9 3.6 6.0 18
Chocolate-milk 5.8 0.4 5.8 0.5 5.1 6.8 28
Chocolate-powder 5.8 0.8 5.9 0.3 4.3 6.7 6
Chocolate-white 6.0 0.6 5.9 0.4 4.9 6.8 10
Cocoa powder 5.9 0.9 6.1 1.2 3.8 6.9 12
Cookie 3.7 0.9 3.8 1.1 2.4 5.1 12
Fat 2.5 2.4 3.8 2.1 −0.2 4.0 8
Flour 5.2 3.4 3.6 5.5 0.9 10.7 20
Jam 4.5 2.0 4.8 2.8 0.3 8.4 33
Juice-powder −0.4 2.5 0.5 1.1 −5.8 1.8 10
Noodles 3.2 0.2 3.2 0.2 2.9 3.5 9
Oil 2.9 5.1 2.9 3.6 −0.7 6.5 7
Others 2.7 2.3 2.5 2.6 −0.8 7.7 19
Pasta 3.3 0.6 3.4 0.7 2.4 4.0 5
Pudding 5.9 0.1 5.9 0.1 5.8 5.9 6
Sauce 2.2 2.1 1.5 3.6 −1.4 5.8 27
Seasoning −2.7 2.4 −3.1 3.2 −5.1 0.7 5
Snack 2.9 0.5 2.9 0.4 2.1 3.7 9
Soda -a- - - - - -
Soup powder 2.0 1.3 2.0 0.8 0.0 4.0 6
Stock −3.1 3.7 −3.7 3.7 −6.4 0.9 3
Sugar - - - - - - -
* SD: standard deviation; IQR: inter quartile range; Min: minimum value; Max: maximum value.
a
Not determined.
Molecules 2020,25, 1457 8 of 18
Table 2. Descriptive statistics* of δ13C and δ15 N of animal-based processed foods.
δ13C (%)Mean SD Median IQR Min Max n
Cheese −16.0 2.0 −15.7 0.9 −22.4 −14.1 14
Cream cheese −18.6 1.2 −18.6 1.5 −20.3 −17.4 5
Dehydrated stock cube −19.5 5.2 −21.4 9.0 −25.8 −12.3 10
Ice cream −21.0 1.1 −21.4 1.1 −21.8 −19.1 6
Jello −12.0 0.4 −12.0 0.5 −12.5 −11.5 6
Lard −19.9 2.8 −19.8 4.7 −23.4 −16.6 8
Processed meat −16.8 0.9 −16.9 1.0 −18.5 −14.6 25
Sauce −19.0 5.2 −19.6 8.1 −27.3 −13.5 11
Seasoning −18.2 3.7 −17.1 5.8 −23.5 −14.8 6
Soup powder −20.1 2.0 −20.5 3.2 −22.6 −16.6 12
Yogurt −17.5 2.0 −17.3 1.7 −21.4 −14.3 13
δ15N (%)Mean SD Median IQR Min Max n
Cheese 5.9 1.2 5.9 1.8 4.2 7.9 14
Cream cheese 5.4 0.4 5.5 0.3 4.8 5.7 5
Dehydrated stock cube −4.3 1.2 −4.5 0.9 −6.0 −2.0 10
Ice cream 5.9 0.2 6.0 0.2 5.5 6.1 6
Jello 7.8 0.9 8.3 1.4 6.5 8.6 6
Lard 4.3 1.1 4.0 1.0 3.3 5.9 8
Processed meat 4.2 1.7 3.8 2.8 1.8 6.7 25
Sauce 2.5 2.7 2.3 3.0 −1.8 6.9 11
Seasoning −2.3 2.2 −3.1 3.4 −4.5 0.6 6
Soup powder 1.5 1.8 1.6 2.6 −2.3 4.4 12
Yogurt 5.1 0.7 5.1 1.2 4.1 6.5 13
* SD: standard deviation; IQR: inter quartile range; Min: minimum value; Max: maximum value.
The
δ15
N values of plant-based processed foods varied from 2%to 6%in most of the cases
(Table 1). Products derived from wheat like flour, noodles, pasta, and cookies had
δ15
N values
resembling those of in natura wheat grain (see supplementary material). The exceptions are seasonings
and stocks that had more negative
δ15
N (p<0.01), which differed greatly from their feedstock,
suggesting a high degree of isotopic discrimination in these products (Table 1).
Among processed foods of animal origin, dairy products followed the
δ13
C values of milk,
however, some dairy products, such as heavy cream and ice cream, tended to have more negative
δ13
C values compared to milk, although this difference was not significant (Table 2). Processed meat
products, like hot dogs, sausages, salami, ham, and lard followed the
δ13
C of pork meat (Table 2). The
same pattern was observed for jello made mainly of bovine collagen (Table 2). Dehydrated meat stock
cubes had an average
δ13
C value of
−
19.5%(Table 2). By looking at the ingredients of these cubes
on their labels, it is difficult to discern whether these
δ13
C values resemble the presence of animal fat
tending to have more negative
δ13
C values in comparison with the meat itself or if they resemble the
addition of vegetable oils of C
3
plants with also more negative
δ13
C values. The same conclusion is
applicable to meat-based soup powders, sauces, and seasonings with
δ13
C values close to the stock
cubes (Table 2).
The
δ15
N values of dairy, processed meat, and jello were similar to those values for milk, pork and
beef, respectively, implying that there was a small isotopic discrimination during processing (Table 2).
On the other hand, meat-based seasonings, primarily dehydrated meat stock cubes, meat-based
seasoning had a significantly less positive
δ15
N value (p<0.01) related to other types of meat (Table 2),
therefore suggesting a strong isotopic discrimination during processing and/or a significant amount of
plant- rather than meat-derived ingredients (Table 1).
As a classical method of data presentation in stable isotope studies,
δ13
C-
δ15
N biplot (
δ
-space) is
a bidimensional space, giving information about resources consumed by an organism and also the
bioclimatic context in each such organism developed, the so-called “isotopic niche” as defined by
Molecules 2020,25, 1457 9 of 18
Newsome et al. [
32
]. There is a clear separation in the
δ
-space between in natura food plants and most
animal proteins (Figure 4a). Processed foods of animal origin resemble raw meat and poultry in the
δ
-space (Figure 4b), while processed foods of plant origin seems to have C
4
and C
3
plants in variable
proportions as ingredients (Figure 4c).
Figure 4. Biplot of 13C vs. 15N of: (a) plants (yellow triangle), and animal protein (yellow circle). A:
C3-cereal; B: C4-cereal; C: fruit; D: pulse; E: tuber; F: vegetable. 1: beef; 2: bushmeat; 3: poultry; 4: egg;
Figure 4.
Biplot of
δ13
C vs.
δ15
N of: (
a
) plants (yellow triangle), and animal protein (yellow circle). A:
C
3
-cereal; B: C
4
-cereal; C: fruit; D: pulse; E: tuber; F: vegetable. 1: beef; 2: bushmeat; 3: poultry; 4: egg;
5: freshwater fish; 6: lamb; 7: marine fish; 8: milk; 9: pork; 10: farmed fish (tilapia). (
b
) plants (yellow
triangle), animal protein (yellow circle), processed food of animal origin (small grey circle). (
c
) plants
(yellow triangle), animal protein (yellow circle), processed food of plant origin (small grey circle). In
order to facilitate visualization, only processed foods with δ15N>0%were included in (b,c).
Molecules 2020,25, 1457 10 of 18
2.4. Beverages
The average
δ13
C of wine samples was
−
23.1%(n =291) with a wide range of values, from a
minimum of
−
28.4%to a maximum of
−
14.8%(Figure 5a). The average
δ13
C of beer samples was
−
20.9%(n=78), having a bimodal frequency distribution, with values concentrating around
−
27%
and around
−
19%(Figure 5a). A few samples of soda were also analyzed just to confirm the heavy
presence of sugar. The average
δ13
C of five soda samples was
−
11.7%, with a standard deviation of
only 0.5%(Figure 5b).
Figure 5. Frequency histogram (a) box-whisker (b) of C of alcoholic beverages in Brazil. The blue
bars represent wine samples, the green bars represent cachaça samples, and the red bars represent beer
samples.
3. Discussion
The 13C of C3 and C4 plants used as food were in the range expected considering other studies
with native plants [33–37]. Overall, crops are cultivated in a way to avoid shadow and maximize light
exposition [38]. Conversely, leaves in a forest must struggle to maximize their exposition to sun light.
Consequently, crops also have high evaporative demands, which prompts stomata closing to avoid
Figure 5.
Frequency histogram (
a
) box-whisker (
b
) of
δ13
C of alcoholic beverages in Brazil. The blue
bars represent wine samples, the green bars represent cachaça samples, and the red bars represent
beer samples.
Molecules 2020,25, 1457 11 of 18
3. Discussion
The
δ13
C of C
3
and C
4
plants used as food were in the range expected considering other studies
with native plants [
33
–
37
]. Overall, crops are cultivated in a way to avoid shadow and maximize
light exposition [38]. Conversely, leaves in a forest must struggle to maximize their exposition to sun
light. Consequently, crops also have high evaporative demands, which prompts stomata closing to
avoid water losses [
36
]. As photosynthesis pumps CO
2
out from the stomata, pi/pa (i.e., intercellular to
ambient CO
2
partial pressure) tend to decrease, leading to an increase in the plant
δ13
C of crops in
relation to forest leaves. This difference is obvious in Figure 6a, showing a frequency distribution of
δ13
C values of C
3
crops in this study in relation to
δ13
C of tree leaves of the Brazilian Atlantic forest
(Martinelli et al. under review). There is not much overlap between
δ13
C of crops and forest besides a
difference of ~6%between them (Figure 6a).
water losses [36]. As photosynthesis pumps CO2 out from the stomata, pi/pa (i.e., intercellular to
ambient CO2 partial pressure) tend to decrease, leading to an increase in the plant 13C of crops in
relation to forest leaves. This difference is obvious in Figure 6a, showing a frequency distribution of
13C values of C3 crops in this study in relation to 13C of tree leaves of the Brazilian Atlantic forest
(Martinelli et al., under review). There is not much overlap between 13C of crops and forest besides
a difference of ~6‰ between them (Figure 6a).
There was large variability in the 15N of domesticated plants, from −4‰ to 18‰ (Figure 6b).
This trend was expected since plants take up nitrogen from several sources depending on its
availability and the climate, and each source has a different nitrogen isotopic ratio [19]. In
domesticated plants, in addition to natural sources, soil amendments, such as mineral and organic
nitrogen fertilizers, also have to be considered [21]. The primary source of reactive nitrogen before
the advent of the Haber–Bosch process (which allowed the production of synthetic fertilizers) was
atmospheric nitrogen which becomes available for organisms through biological fixation [39]. The
15N of the nitrogen fixed is ~0‰ and as mineral nitrogen fertilizers are also synthesized by using
atmospheric nitrogen, the 15N of such fertilizers is also close to 0‰ [22]. Therefore, plants capable of
nitrogen fixation or amended with mineral nitrogen fertilizers tend to have less positive 15N values
compared to other plants. This explains why some domesticated plants like pulses (nitrogen-fixing
plants of the Fabaceae family) have less positive 15N values compared to other domesticated plants
in this study [40].
Figure 6. Frequency histogram of C (a) and 5N (b) comparing native tree leaves of the Atlantic
Forest (blue bars) with domesticated plants used as food in Brazil (red bars).
Huelsemann et al. [41], based on a large survey of the literature, showed that the average 15N
of vegetables under the influence of mineral fertilizers was 3.1‰, increasing to 6.8‰ and 9.8‰ in
vegetables fertilized with organic and animal-derived manure, respectively. The average 15N of
vegetables found here was 4.2‰, which is more positive than the value found by Huelsemann et al.
[41] for plants that received mineral fertilizers (Table 3), but less positive than those fertilized with
organic manure. The reasons for this difference are difficult to pinpoint, mainly because several
Figure 6.
Frequency histogram of
δ13
C (
a
) and
δ15
N (
b
) comparing native tree leaves of the Atlantic
Forest (blue bars) with domesticated plants used as food in Brazil (red bars).
There was large variability in the
δ15
N of domesticated plants, from
−
4%to 18%(Figure 6b).
This trend was expected since plants take up nitrogen from several sources depending on its availability
and the climate, and each source has a different nitrogen isotopic ratio [
19
]. In domesticated plants,
in addition to natural sources, soil amendments, such as mineral and organic nitrogen fertilizers,
also have to be considered [
21
]. The primary source of reactive nitrogen before the advent of the
Haber–Bosch process (which allowed the production of synthetic fertilizers) was atmospheric nitrogen
which becomes available for organisms through biological fixation [
39
]. The
δ15
N of the nitrogen
fixed is ~0%and as mineral nitrogen fertilizers are also synthesized by using atmospheric nitrogen,
the
δ15
N of such fertilizers is also close to 0%[
22
]. Therefore, plants capable of nitrogen fixation or
amended with mineral nitrogen fertilizers tend to have less positive
δ15
N values compared to other
plants. This explains why some domesticated plants like pulses (nitrogen-fixing plants of the Fabaceae
family) have less positive δ15N values compared to other domesticated plants in this study [40].
Huelsemann et al. [
41
], based on a large survey of the literature, showed that the average
δ15
N
of vegetables under the influence of mineral fertilizers was 3.1%, increasing to 6.8%and 9.8%in
vegetables fertilized with organic and animal-derived manure, respectively. The average
δ15
N of
vegetables found here was 4.2%, which is more positive than the value found by Huelsemann et
al. [
41
] for plants that received mineral fertilizers (Table 3), but less positive than those fertilized
with organic manure. The reasons for this difference are difficult to pinpoint, mainly because several
nitrogen sources available for domesticated plants. However, it is likely that the following hypotheses
can explain this divergence: (i) the more positive
δ15
N values of tropical soils relative to temperate
Molecules 2020,25, 1457 12 of 18
soils [
42
]; and (ii) a higher amount of nitrogen fertilizer is used in more developed countries than in
Brazil [25].
Table 3.
Comparison of average
δ15
N (%) of domesticated plants reported this study and those in the
meta-analysis of Huelsemann et al. (2015) [
41
]. Plants were grouped following the FAO classification.
The values represent mean ±standard deviation.
FAO Group This Study Huelsemann et al. (2015) [41]
δ15N (%)nδ15 N (%)n
Cereal C33.7 ±2.8 67 3.2 ±1.3 >128
Cereal C44.5 ±2.6 47 3.3 ±1.9 57
Fruit 4.3 ±3.0 81 4.6 ±2.4 >891
Nuts 17.3 ±0.8 10 2.7 ±2.1 310
Pulse 2.4 ±1.7 38 1.1 ±1.8 109
Tuber 4.6 ±2.8 45 3.1 ±3.4 134
Vegetable * 4.2 ±3.5 150 3.1 ±3.0 >990
* Amended with mineral nitrogen fertilizer.
Bovine, poultry and processed pork meat, together with dairy products and eggs, are the most
important sources of animal proteins for Brazilians [
43
]. Most cattle herds in Brazil are free-range and
C
4
grass-fed, especially those of the genus Brachiaria. The average
δ13
C of 51 samples of these grasses
collected in the Amazon region was
−
12%, and the average
δ15
N was equal to 2.0%(Martinelli,
unpublished data). Poultry and pork in Brazil are raised intensively and fed by a feed composed of
maize and soy (Glycine max (L.) Merrill) that have the following isotopic composition:
δ13
C=
−
16.7%,
and
δ15
N=0.6%[
9
]. Consequently, Brazilian beef, poultry and pork (Figure 4a), as well as dairy
products such as milk, heavy cream, yogurt and cheese, and also all types of processed meat products
like ham, salami, sausages, hot dogs, among others, are rich in C-C
4
(Figure 4b). Although lamb is
not widely consumed in Brazil, the C-C
4
is also present in this meat, but less than beef, poultry and
pork (Figure 4a). In comparison with other countries, the McDonald’s hamburger made in Brazil had
the largest amount of C-C
4
[
18
]. The same pattern was found by Osorio et al. [
44
], who analyzed dry
defatted meat from Europe and US. Even in comparison with Chinese defatted beef, which is raised on
maize and C3plants, C-C4in Brazilian beef is likely higher than Chinese beef [45].
Most of the forage species used to feed dairy cattle in Europe are C
3
plants and the addition
of maize in their diet is being considered as a deviation from the traditional milk production [
46
].
Therefore, milk produced in Europe also tends to have more negative
δ13
C values compared to
Brazilian milk, although a direct comparison is not possible because the milk fat was not removed in
our study [47].
C
4
-C is also found in Argentinian milk, but not at the same proportions which exists in Brazilian
milk, because in addition to maize, C
3
plants like soy, cotton (Gossypium hirsutum L.) seeds, alfalfa
(Medicago sativa L.) and oats (Avena sativa L.) are also used to feed dairy cattle in the former country [
48
].
As milk is the basic dairy ingredient, all dairy products in Brazil tend to have a higher C
4
content in
comparison with dairy products from other countries. The same is true for processed pork meats like
ham, which, in Brazil, has a larger proportion of C-C
4
than dry cured ham raised in Spain [
49
] and
Italy [
50
]. A similar pattern was observed for Brazilian lamb relative to lamb raised in Europe or in
some parts of South Africa [51,52].
The presence of C-C
4
in animal proteins was even observed in farm-raised fish (Figure 4a),
especially tilapia, where the production is growing in the country and is widely available in grocery
stores [
53
]. The fish food which is being used to raise farm-raised fish species is also based on maize
and soy, and consequently, the average
δ13
C of farm-raised tilapia is
−
18.2%(Figure 4a). Marine fish
also have
δ13
C values resembling animals fed with C
4
plants (Figure 3a), and the reason is the fact that
marine phytoplankton, which are the base of oceanic food chains, have average
δ13
C values close to
Molecules 2020,25, 1457 13 of 18
−
21%[
54
], resulting in
δ13
C values of oceanic food chains close to the values resembling a mixture
of C
3
and C
4
-C in terrestrial ecosystems. In contrast, in freshwater systems, the average
δ13
C of the
phytoplankton in Brazil is more negative than −30%[55], which results in the most negative δ13C in
freshwater wild fish in comparison with all types of meat (Figure 4a).
Wild fish, either from freshwater or from the ocean, had the most positive
δ15
N values among
all types of animal-derived foods (Figure 4a). This is explained by the fact that the food chain in
rivers and in the ocean is complex, including several trophic degrees, which increases the isotopic
trophic discrimination regarding nitrogen and, therefore, the
15
N enrichment of the food [
56
].
δ15
N
in farm-raised fish like tilapia was less positive than wild fish since the available food chain of a
farm system is based on fish foods produced from maize and soy (Figure 4a). Coletta et al. [
9
] also
observed that the
δ15
N of free-range chicken was more positive than barn-raised chicken, probably
because free-range chicken feed on soil invertebrates. This trend was also observed here between wild
animals (bushmeat) and domesticated ones since bushmeat had a more positive
δ15
N (p<0.05) than
any domesticated animal (Figure 4). We hypothesize that the animal protein produced from large-scale
operations tends to have lower
δ15
N than wild-animal protein since agriculture is a simplification of
natural ecosystems (which have more a complex food chain [
57
]), therefore leading to a low “trophic
discrimination”. In addition, soy, a nitrogen-fixing plant that has a low
δ15
N value, is extensively used
as animal feed [58].
Among plant-based processed foods, excluding those that are wheat-based like pasta, noodles,
crackers, and snacks, C-C
4
is present in variable amounts in several products, including those for
which C-C
4
occurrence is somehow unexpected (Figure 4c). For instance, commercial fruit jam and
preserves have much more C-C
4
related to C-C
3
in their compositions. C-C
4
can also be found in large
quantities in soup powders, juice powders, sugary beverages, baby formulas, and spices like cumin
(Cuminum cyminum L.), saffron (Crocus sativus L.), and garam masala (an India-style blend of ground
spices), which are mixed with a fine maize flour (termed in Brazil as fub
á
) (Figure 4b). Maize is also
present in large quantities in beers made by the largest brewer companies in Brazil (Figure 5). Most
Brazilian wines have at least 25% C-C
4
due to the use of sugarcane as an adjunct in the grape (Vitis
vinifera) fermentation process [
6
]. Other products also have suspiciously high contents of C-C
4
, among
them, sauces like mustard and ketchups. Perhaps the most iconic example of the heavy presence of
C-C
4
in Brazilian processed foods is soy sauce (shoyu), which has a dominant proportion of maize
rather than soy or wheat (both are traditional ingredients of the classic recipe) [7].
A question arises about the reasons for such pervasive use of C-C
4
by the Brazilian food industry.
We mention here that the cost of C-C
4
in Brazil is overall cheaper than C-C
3
. Brazil is mostly a tropical
country where the growth of C
4
plants is favored by a high light incidence [
11
,
12
,
59
]. Therefore, there
is a climatic control in the geographical distribution of C
4
plants and it seems that this distribution
can be tracked by certain types of foods. This is the case of hamburgers made by a global fast-food
company. The country-level
δ13
C of patties grouped by latitude clearly showed that there was an
inverse correlation between the proportion of C-C
4
and latitude, meaning that countries in the tropical
belt had more C-C4than countries located in higher latitudes [18].
In Brazil, among agricultural commodities, two C
4
plants are very important: sugarcane and
maize. Brazil is one of the largest producers of sugar from sugarcane in the world. Brazil was the top
sugar producer in 2014, producing ~37 million Mg of sugar [
60
]. Brazil is also an important producer
of maize in the global food market. In 2017, Brazil was the third largest producer of this crop after the
USA and China, producing almost 100 million Mg [
61
]. Therefore, sugar in Brazil is a relatively cheap
product which is being widely used by the population and by the food industry. In some remote rural
communities of the country, such as those in the Amazon region, sugar is used as a source of energy in
some periods of the year during which food is in short supply [62].
The price of maize grain in Brazil is lower than rice, soy, and wheat [
58
] and consequently, it is
widely used to feed animals in Brazil as well as in processed foods. For instance, in 2017, Brazil was
the second largest producer of chicken meat (almost 40 million Mg), and beef (almost 10 million Mg);
Molecules 2020,25, 1457 14 of 18
and the fifth largest producer of pig meat, with almost 4 million Mg of meat. Chickens and pigs in the
country are fed a combining ratio of maize and soy, with maize as the dominant proportion [
9
] and
most Brazilian cattle herds are fed with C
4
forage grasses, mainly of the genus Brachiaria. Therefore,
although raw meat is a relatively expensive item for Brazilians, any meat included in their diets would
be a source of C-C
4
(Figure 4a,b). On the other hand, processed meat, especially pork sausage, termed
calabresa in Brazil, has become a cheap alternative for the low-income population, primarily in areas
where refrigeration is not available, such as isolated communities of the Amazon region [
62
]. Coupled
with the abundance of cheap C-C
4
in the country, there is also the fact that the Brazilian food legislation
is usually very flexible regarding the use of ingredients, allowing the use of maize or sugarcane (sources
of C-C4) instead of C3-derived plants in products such as wine [6] and soy sauce [7].
By investigating a significant number of food items, we believe that it is fair to conclude that
although the traditional staple foods of Brazilians are composed mainly of plants that follow the
photosynthetic C
3
metabolism (e.g., rice, beans, and cassava), C-C
4
plants have a higher degree of
pervasiveness in the Brazilian food system (Figure 4), leading to high unaware consumption of these
plants by Brazilians (especially processed food [
63
]), which is ultimately reflected in the more positive
δ13C tissue values found in the national population, confirming our initial hypothesis.
Finally, addressing and preventing food fraud and adulteration requires not only enforcement of
regulatory systems but also more sampling, monitoring, and development of cost-effective methods
contributing to fraud detection [
4
]. In this sense, understanding and interpreting carbon and nitrogen
isotope ratios of both plant and animal based food items together with the use of the raw isotope
database would support actions and investigations regarding food adulteration and might assist the
prevention of food fraud in the Brazilian territory.
4. Methods
4.1. Sampling Protocol
We analyzed the stable carbon and nitrogen abundances in 1619 samples (804 were composed
of in natura vegetal and animal products, while 815 samples were processed foods). Of the total,
1245 and 374 were food items and beverages, respectively. Most of the samples were bought during
2015–2019 in grocery stores located in Piracicaba, a municipality in the Southeast Region of Brazil
with a population of ~400,000 inhabitants. In natura animal samples include the following species:
cattle, chicken, pig, lamb, freshwater and marine fish, and bushmeat (feral pig and peccary). In natura
vegetal samples include cereals, fruits, pulses, tubers, and vegetables grouped according to the FAO
classification. For processed food, the classification proposed by Monteiro et al. [
64
] was followed.
Processed foods with animal origin included dairy, cured meat, meat stock, and lard. Dairy products
included yogurt, milk cream, ice cream, cheese, and butter. Cured meat included sausages, ham, hot
dogs, salami, mortadella, and canned cook pork. Meat stock is defined here as industrially reduced
meat stock made from beef, poultry, and pork (Figure 1). Plant-based processed food included: flour,
oil and fat, sweeties, chocolates, sauces and soups. “Flour” included the following plants: cassava,
maize and soy. “Fats” included margarine and coconut oil. “Sweeties” included a diverse variety of
products: corn (maize) flakes, cereal bars, cookies, cakes, pastries, jams, and puddings. “Chocolate”
included chocolate bars and chocolate powders mixed with milk (similar to powders used to prepare
hot chocolate). “Sauces and soups” included: dehydrated soups, salad sauces, mustard, ketchup, soy
sauce (shoyu), and Worcestershire sauce. Finally, in the group defined as “Others”, the following items
were included: powder spices, like cumim, urucum, saffron, and sweeties like marshmallow, Japanese
soy-based products like miso paste, instant misoshiro powder, baby formula, and pre-cooked polenta.
4.2. Isotopic Analysis
Solid foods were oven-dried at 60
◦
C to a constant weight. Subsequently, samples were finely
ground to facilitate homogenization. Processed foods were composed of several ingredients and cookies
Molecules 2020,25, 1457 15 of 18
with chocolate chips and cookies stuffed with cream were ground together. After homogenization,
1–2 mg of sample was transferred to a tin capsule for further elemental and isotopic analysis. Beverages
like soda, beer, and wine were directly placed in tin capsules.
The isotope ratios of carbon (
13
C/
12
C) and nitrogen (
15
N/
14
N) of each sample were determined
using a continuous-flow isotope ratio mass spectrometer (Delta Plus, ThermoFisher Scientific, Bremen,
Germany) coupled to an elemental analyzer (CHN-1110, Carlo Erba, Rodano, Italy) at the Laboratory
of Isotope Ecology of the Center for Nuclear Energy in Agriculture, University of São Paulo.
Carbon and nitrogen isotope compositions were calculated as
δ(%)= [Rsample/Rstandard )−1i×1000
where Ris the ratio of
13
C/
12
C or
15
N/
14
N. Stable isotope ratios were measured using an
internationally recognized standard and relative to a laboratory standard. Thus, we used the
25-(Bis(5-tert-butyl-2-benzo-oxazol-2-yl) thiophene (BBOT; C
26
H
26
N
2
O
2
S; Fisons Instruments Inc.,
Danvers, MA, USA) as an international standard while fine-milled sugarcane leaves were used as a
laboratory standard.
4.3. Statistical Analysis
Descriptive statistics (mean, standard deviation, median, inter quartile range, and minimum and
maximum values) were used to report the
δ13
C and
δ15
N values of food and beverage samples. To test
the differences between food items, ANOVA was used because carbon and nitrogen isotope values
followed a normal distribution. The post-hoc Tukey test was used to pinpoint specific differences
between food items. Statistical analyses were performed in R (version 3.6.3, The R Foundation for
Statistical Computing, Boston, MA, USA) and RStudio (version 1.2.5019, RStudio, Vienna, Austria)
using the “multcomp” package [65].
Supplementary Materials:
Spreadsheet:
δ13
C and
δ15
N values of every single sample of foods and beverages
analyzed in this study.
Author Contributions:
Conceptualization, L.A.M. and G.B.N.; Data curation, M.A.Z.P., G.A.J., F.C.F., J.G.G.O.,
I.S.O., S.H.L., E.A.M. and P.B.C.; Formal analysis, M.A.Z.P., G.A.J., F.C.F., J.G.G.O., I.S.O., S.H.L., P.J.D.-N.
and M.Z.M.; Methodology, L.A.M.; Writing—original draft, L.A.M., G.B.N., T.F.G., A.S., A.L.A.F. and E.M.;
Writing—review and editing, E.M., T.F.G., A.S., A.L.A.F., F.J.V.C., P.J.D.-N., M.Z.M. and P.B.C. All authors have
read and agreed to the published version of the manuscript.
Funding: São Paulo Research Foundation (FAPESP), grant number #2011/50345-9.
Acknowledgments:
We would like to thank Julia Gerds for her kind assistance during early stages of the study
(FAPESP project, grant number #2011/50345-9).
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Monteiro, C.A.; Moura, E.C.; Conde, W.L.; Popkin, B.M. Public health reviews socioeconomic status and
obesity in adult populations of developing countries: A review. Bull. World Health Organ.
2004
,82, 940–946.
[PubMed]
2.
Popkin, B.M.; Gordon-Larsen, P. The nutrition transition: Worldwide obesity dynamics and their determinants.
Int. J. Obes. Relat. Metab. Disord. 2004,28, S2–S9. [CrossRef] [PubMed]
3.
Carter, J.; Chesson, L.A. Food Forensics: Stable Isotopes as a Guide to Authenticity and Origin, 1st ed.; CRC Press:
Boca Raton, FL, USA, 2017.
4.
Tibola, C.S.; da Silva, S.A.; Dossa, A.A.; Patr
í
cio, D.I. Economically motivated food fraud and adulteration in
Brazil: Incidents and alternatives to minimize occurrence. J. Food Sci. 2018,83, 2028–2038. [CrossRef]
5.
Mardegan, S.F.; Andrade, T.M.B.; de Sousa Neto, E.R.; de Castro Vasconcellos, E.B.; Martins, L.F.B.;
Mendonça, T.G.; Martinelli, L.A. Stable carbon isotopic composition of Brazilian beers-A comparison between
large- and small-scale breweries. J. Food Compos. Anal. 2013,29, 52–57. [CrossRef]
Molecules 2020,25, 1457 16 of 18
6.
Martinelli, L.A.; Moreira, M.Z.; Ometto, J.P.H.B.; Alcarde, A.R.; Rizzon, L.A.; Stange, E.; Ehleringer, J.R.
Stable carbon isotopic composition of the wine and CO
2
-bubbles of sparkling wines: Detecting C
4
sugar
additions. J. Agric. Food Chem. 2003,51, 2625–2631. [CrossRef]
7.
Morais, M.C.; Pellegrinetti, T.A.; Sturion, L.C.; Sattolo, T.M.S.; Martinelli, L.A. Stable carbon isotopic
composition indicates large presence of maize in Brazilian soy sauces (shoyu). J. Food Compos. Anal.
2018
,70,
18–21. [CrossRef]
8.
Ducatti, R.; Pinto, J.P.D.A.N.; Sartori, M.M.P.; Ducatti, C. Quantification of soy protein using the isotope
method (δ13C and δ15 N) for commercial brands of beef hamburger. Meat Sci. 2016,122, 97–100. [CrossRef]
9.
Coletta, L.D.; Pereira, A.L.; Coelho, A.A.D.; Savino, V.J.M.; Menten, J.F.M.; Correr, E.; Frana, L.C.;
Martinelli, L.A. Barn vs. free-range chickens: Differences in their diets determined by stable isotopes.
Food Chem. 2012,131. [CrossRef]
10. Sage, R.F. The evolution of C4photosynthesis. New Phytol. 2004, 341–370. [CrossRef]
11.
Still, C.J.; Berry, J.A.; Collatz, G.J.; Defries, R.S. Global distribution of C
3
and C
4
vegetation: Carbon cycle
implications. Global Biogeochem. Cycles 2003,17, 1006. [CrossRef]
12.
Sage, R.F.; Stata, M. Photosynthetic diversity meets biodiversity: The C
4
plant example. J. Plant Physiol.
2015
,
172, 104–119. [CrossRef]
13.
Perry, L.; Sandweiss, D.H.; Piperno, D.R.; Rademaker, K.; Malpass, M.A.; Vera, P.D. Early maize agriculture
and interzonal interaction in. Nature 2006,440, 76–79. [CrossRef]
14.
van der Merwe, N.J.; Tschauner, H. C
4
Plants and the development of human societies. In C
4
Plant Biology;
Sage, R.F., Monson, K.M., Eds.; CRC Press: San Diego, CA, USA, 1999; pp. 509–549.
15.
Andrews, J. Diffusion of Mesoamerican food complex to southeastern Europe. Geogr. Rev.
1993
,83, 194–204.
[CrossRef]
16.
Zerjal, C.M.T.; Dumas, V.C.F.; Bedoya, D.M.C. Out of America: Tracing the genetic footprints of the global
diffusion of maize. Theor. Appl. Genet. 2013,126, 2671–2682.
17.
Cherniwchan, J.; Moreno-cruz, J. Maize and precolonial Africa. J. Dev. Econ.
2019
,136, 137–150. [CrossRef]
18.
Martinelli, L.A.; Nardoto, G.B.; Chesson, L.A.; Rinaldi, F.D.; Ometto, J.P.H.B.; Cerling, T.E.; Ehleringer, J.R.
Worldwide stable carbon and nitrogen isotopes of Big Mac
®
patties: An example of a truly “glocal” food.
Food Chem. 2011,127, 1712–1718. [CrossRef]
19. Högberg, P. 15N natural abundance in soil-plant systems. New Phytol. 1997,137, 179–203. [CrossRef]
20. Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 2001,16, 153–162. [CrossRef]
21.
Yoneyama, T.; Kouno, K.; Yazaki, J. Variation of natural N abundance of crops and soils in Japan with special
reference to the effect of soil conditions and fertilizer application. Soil Sci. Plant Nutr.
1990
,36, 667–675.
[CrossRef]
22.
Bateman, A.S.; Kelly, S.D. Fertilizer nitrogen isotope signatures. Isot. Environ. Health Stud.
2007
,43, 237–247.
[CrossRef]
23.
Nishida, M. Natural
15
N abundance can aid the discrimination of organic and conventional rice. JARQ
2018
,
52, 173–180. [CrossRef]
24.
Minagawa, M.; Wada, E. Stepwise enrichment of N along food chains; Further evidence and the relation
between delta 15N and animal age. Geochim. Cosmochim. Acta 1984,48, 1135–1140. [CrossRef]
25.
Nardoto, G.B.; Silva, S.; Kendall, C.; Ehleringer, J.R.; Chesson, L.A.; Ferraz, E.S.B.; Moreira, M.Z.;
Ometto, J.P.H.B.; Martinelli, L.A. Geographical patterns of human diet derived from stable-isotope analysis
of fingernails. Am. J. Phys. Anthropol. 2006,131, 137–146. [CrossRef] [PubMed]
26.
DeNiro, M.J.; Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim.
Cosmochim. Acta 1978,42, 495–506. [CrossRef]
27.
DeNiro, M.J.; Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim.
Cosmochim. Acta 1981,45, 341–351. [CrossRef]
28.
Sponheimer, M.; Alemseged, Z.; Cerling, T.E.; Grine, F.E.; Kimbel, W.H.; Leakey, M.G.; Lee-Thorp, J.A.;
Manthi, F.K.; Reed, K.E.; Wood, B.A.; et al. Isotopic evidence of early hominin diets. Proc. Natl. Acad. Sci.
USA 2013,110, 10513–10518. [CrossRef]
29.
Minagawa, M. Reconstruction of human diet from
σ13
C and
σ15
N in contemporary Japanese hair: A stochastic
method for estimating multi-source contribution by double isotopic tracers. Appl. Geochem.
1992
,7, 145–158.
[CrossRef]
Molecules 2020,25, 1457 17 of 18
30.
Thompson, A.H.; Chesson, L.A.; Podlesak, D.W.; Bowen, G.J.; Cerling, T.E.; Ehleringer, J.R. Stable isotope
analysis of modern human hair collected from Asia (China, India, Mongolia, and Pakistan). Am. J. Phys.
Anthropol. 2010,141, 440–451. [CrossRef]
31.
Nardoto, G.B.; Murrieta, R.S.S.; Prates, L.E.G.; Adams, C.; Garavello, M.E.P.E.; Schor, T.; Moraes, A.;
Rinaldi, F.D.; Gragnani, J.G.; Moura, E.A.F.; et al. Frozen chicken for wild fish: Nutritional transition in the
Brazilian Amazon region determined by carbon and nitrogen stable isotope ratios in fingernails. Am. J. Hum.
Biol. 2011,23, 642–650. [CrossRef]
32.
Newsome, S.D.; Maartinez del Rio, C.; Bearhop, S.; Phillips, D.L. A niche for isotopic ecology. Front. Ecol.
Environ. 2007,5, 429–436. [CrossRef]
33. Brescia, M.A.; Di Martino, G.; Guillou, C.; Reniero, F.; Sacco, A.; Serra, F.; Orabona, V. Differentiation of the
geographical origin of durum wheat semolina samples on the basis of isotopic composition. Rapid Commun.
Mass Spectrom. 2002,16, 2286–2290. [CrossRef] [PubMed]
34.
Kelly, S.; Baxter, M.; Chapman, S.; Rhodes, C.; Dennis, J.; Brereton, P. The application of isotopic and elemental
analysis to determine the geographical origin of premium long grain rice. Eur. Food Res. Technol.
2002
,214,
72–78. [CrossRef]
35.
Suzuki, Y.; Chikaraishi, Y.; Ogawa, N.O. Geographical origin of polished rice based on multiple element and
stable isotope analyses. Food Chem. 2008,109, 470–475. [CrossRef] [PubMed]
36.
Luo, D.; Dong, H.; Luo, H.; Xian, Y.; Wan, J.; Guo, X.; Wu, Y. The application of stable isotope ratio analysis to
determine the geographical origin of wheat. Food Chem. 2015,174, 197–201. [CrossRef] [PubMed]
37.
Chung, I.; Kim, J.; Lee, K.; Park, S.; Lee, J.; Son, N.; Jin, Y.; Kim, S. Geographic authentication of Asian rice
(Oryza sativa L.) using multi- elemental and stable isotopic data combined with multivariate analysis. Food
Chem. 2018,240, 840–849. [CrossRef] [PubMed]
38.
Araus, L.; Ferrio, J.P.; Voltas, J. Agronomic conditions and crop evolution in ancient Near East Agriculture.
Nat. Commun. 2014,5, 3953. [CrossRef]
39.
Shearer, G.; Kohl, D.H.; Virginia, R.A.; Bryan, B.A.; Skeeters, J.L.; Nilsen, E.T.; Sharifi, M.R.; Rundel, P.W.
Estimates of N
2
-fixation from variation in the natural abundance of
15
N in Sonoran desert ecosystems.
Oecologia 1983,56, 365–373. [CrossRef]
40.
Szpak, P.; Longstaffe, F.J.; Millaire, J.; White, C.D. Large variation in nitrogen isotopic composition of a
fertilized legume. J. Archaeol. Sci. 2014,45, 72–79. [CrossRef]
41.
Huelsemann, F.; Koehler, K.; Braun, H.; Schaenzer, W.; Flenker, U. Human dietary
δ15
N intake: Representative
data for principle food items. Am. J. Phys. Anthropol. 2013,152, 58–66. [CrossRef]
42.
Martinelli, L.A.; Piccolo, M.C.; Townsend, A.R.; Vitousek, P.M.; Cuevas, E.; McDowell, W.; Robertson, G.P.;
Santos, O.C.; Treseder, K. Nitrogen stable isotopic composition of leaves and soil: Tropical versus temperate
forests. Biogeochemistry 1999,46, 45–65. [CrossRef]
43.
da Costa Louzada, M.L.; Ricardo, C.Z.; Steele, E.M.; Levy, R.B.; Cannon, G.; Monteiro, C.A. The share of
ultra-processed foods determines the overall nutritional quality of diets in Brazil. Public Health
2017
,21,
94–102.
44.
Osorio, M.T.; Moloney, A.P.; Schmidt, O.; Monahan, F.J. Multielement Isotope Analysis of bovine muscle
for determination of international geographical origin of meat. J. Agric. Food Chem.
2011
,59, 3285–3294.
[CrossRef] [PubMed]
45.
Zhao, Y.; Zhang, B.; Chen, G.; Chen, A.; Yang, S.; Ye, Z. Recent developments in application of stable isotope
analysis on agro-product authenticity and traceability. Food Chem.
2014
,145, 300–305. [CrossRef] [PubMed]
46.
Camin, F.; Perini, M.; Colombari, G.; Bontempo, L.; Versini, G.; Wiley, J. Influence of dietary composition on
the carbon, nitrogen, oxygen and hydrogen stable isotope ratios of milk. Rapid Commun. Mass Spectrom.
2008,22, 1690–1696. [CrossRef] [PubMed]
47.
Camin, F.; Bontempo, L.; Perini, M.; Piasentier, E. Stable isotope ratio analysis for assessing the authenticity
of food of animal origin. Compr. Rev. Food Sci. Food Saf. 2016,15, 868–877. [CrossRef]
48.
Gribo, J.; Baroni, M. V; Horacek, M.; Wunderlin, D.A.; Monferran, M.V. Multielemental +isotopic fingerprint
enables linking soil, water, forage and milk composition, assessing the geographical origin of Argentinean
milk. Food Chem. 2019,283, 549–558. [CrossRef] [PubMed]
49.
Gonz
á
lez-Martin, I. Use of isotope analysis to characterize meat fromIberian-breed swine. Meat Sci.
1999
,52,
437–441. [CrossRef]
Molecules 2020,25, 1457 18 of 18
50.
Perini, M.; Camin, F.; S
á
nchez, J.; Piasentier, E. Effect of origin, breeding and processing conditions on the
isotope ratios of bioelements in dry-cured ham. Food Chem. 2013,136, 1543–1550. [CrossRef]
51.
Camin, F.; Bontempo, L.; Heinrich, K.; Horacek, M.; Kelly, S.D.; Schlicht, C.; Thomas, F.; Monahan, F.J.;
Hoogewerff, J.; Rossmann, A. Multi-element (H, C, N, S) stable isotope characteristics of lamb meat from
different European regions. Anal. Bioanal. Chem. 2007,389, 309–320. [CrossRef]
52.
Erasmus, S.W.; Muller, M.; Butler, M.; Hoffman, L.C. The truth is in the isotopes: Authenticating regionally
unique South African lamb. Food Chem. 2018,239, 926–934. [CrossRef]
53.
Castilho-Barros, L.; Owatari, M.S.; Mouriño, J.L.P.; Silva, B.C.; Sei, W.Q. Economic feasibility of tilapia culture
in southern Brazil: A small-scale farm model. Aquaculture 2020,515. [CrossRef]
54.
Hardt, F.A.S.; Cremer, M.J.; Tonello Junior, A.J.; Bellante, A.; Buffa, G.; Buscaino, G.; Mazzola, S.; Barreto, A.S.;
Martinelli, L.A.; Zuppi, G.M. Use of carbon and nitrogen stable isotopes to study the feeding ecology of
small coastal cetacean populations in southern Brazil. Biota Neotrop. 2013,13, 90–98. [CrossRef]
55.
Benedito-Cecilio, E.; Araujo-lima, C.M.; Forsberg, B.R.; Bittencourt, M.M.; Martinelli, L.C. Carbon sources of
Amazonian fisheries. Fish. Manag. Ecol. 2000,7, 305–315. [CrossRef]
56.
Newsome, S.D.; Clementz, M.T.; Koch, P.L. Using stable isotope biogeochemistry to study marine mammal
ecology. Mar. Mammal Sci. 2010,26, 509–572. [CrossRef]
57.
Phalan, B.; Balmford, A.; Green, R.E.; Scharlemann, J.P.W. Minimising the harm to biodiversity of producing
more food globally. Food Policy 2011,36, S62–S71. [CrossRef]
58.
Garrett, R.D.; Lambin, E.F.; Naylor, R.L. Land Use Policy Land institutions and supply chain configurations
as determinants of soybean planted area and yields in Brazil. Land Use Policy 2013,31, 385–396. [CrossRef]
59.
Collatz, G.J.; Berry, J.A.; Clark, J.S. Effects of climate and atmospheric CO
2
partial pressure on the global
distribution of C4 grasses: Present, past, and future. Oecologia 1998,114, 441–454. [CrossRef]
60.
FAOSTAT. Crops Processed. Available online: http://www.fao.org/faostat/en/#data/QD (accessed on 26
February 2020).
61.
FAOSTAT. Crops. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 26 February 2020).
62.
Murrieta, R. O dilema do papa-chib
é
: Consumo alimentar, nutriç
ã
o e pr
á
ticas de intervenç
ã
o na Ilha de
Ituqui, baixo Amazonas, Pará.Rev. Antropol. 1998,42, 1–33. [CrossRef]
63.
Schor, T.; Azenha, G.S. Ribeirinho Food Regimes, Socioeconomic inclusion and unsustainable development
of the Amazonian floodplain. EchoGéo2017,41, 1–14. [CrossRef]
64.
Monteiro, C.A.; Levy, R.B. A new classification of foods based on the extent and purpose of their processing.
Cad. Saude Publica 2010,26, 2039–2049. [CrossRef] [PubMed]
65.
Hothorn, T.; Bretz, F.; Westfall, P. Simultaneous inference in general parametric models. Biom. J.
2008
,50,
346–363. [CrossRef] [PubMed]
Sample Availability: Samples of the compounds are available from the authors.
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