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foods
Article
NMR Characterization of Ten Apple Cultivars from the
Piedmont Region
Giacomo Di Matteo 1, Mattia Spano 1, Cristina Esposito 2, Cristina Santarcangelo 2, Alessandra Baldi 3,
Maria Daglia 2,4 , Luisa Mannina 1, Cinzia Ingallina 1, * and Anatoly P. Sobolev 5
Citation: Di Matteo, G.; Spano, M.;
Esposito, C.; Santarcangelo, C.; Baldi,
A.; Daglia, M.; Mannina, L.; Ingallina,
C.; Sobolev, A.P. NMR
Characterization of Ten Apple
Cultivars from the Piedmont Region.
Foods 2021,10, 289. https://doi.org/
10.3390/foods10020289
Academic Editor: Luca Laghi
Received: 30 December 2020
Accepted: 25 January 2021
Published: 1 February 2021
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Attribution (CC BY) license (https://
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4.0/).
1Department of Chemistry and Technology of Drugs, Sapienza University of Rome, Piazzale Aldo Moro 5,
00185 Rome, Italy; giacomo.dimatteo@uniroma1.it (G.D.M.); mattia.spano@uniroma1.it (M.S.);
luisa.mannina@uniroma1.it (L.M.)
2Department of Pharmacy, University of Naples Federico II, 80138 Naples, Italy;
cristina.esposito@unina.it (C.E.); cristina.santarcangelo@unina.it (C.S.); maria.daglia@unina.it (M.D.)
3Tefarco Innova, Parco Area delle Scienze 27/A—Campus, 43124 Parma, Italy;
alessandra.baldi.alimenti@gmail.com
4International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
5Institute for Biological Systems, Magnetic Resonance Laboratory “Segre-Capitani”, CNR,
Via Salaria Km 29.300, 00015 Monterotondo, Italy; anatoly.sobolev@cnr.it
*Correspondence: cinzia.ingallina@uniroma1.it
Abstract:
The metabolite profile of ten traditional apple cultivars grown in the Piedmont region
(Italy) was studied by means of nuclear magnetic resonance spectroscopy, identifying an overall
number of 36 compounds. A more complete assignment of the proton nuclear magnetic resonance
(
1
H NMR) resonances from hydroalcoholic and organic apple extracts with respect to literature data
was reported, identifying fructose tautomeric forms, galacturonic acid,
γ
-aminobutyric acid (GABA),
p-coumaroyl moiety, phosphatidylcholine, and digalactosyldiacylglycerol. The chemical profile of
each apple cultivar was defined by thorough quantitative NMR analysis of four sugars (fructose,
glucose, sucrose, and xylose), nine organic acids (acetic, citric, formic, citramalic, lactic, malic, quinic,
and galacturonic acids), six amino acids (alanine, asparagine, aspartate, GABA, isoleucine, and
valine), rhamnitol, p-coumaroyl derivative, phloretin/phloridzin and choline, as well as
β
-sitosterol,
fatty acid chains, phosphatidylcholine, and digalactosyldiacylglycerol. Finally, the application
of PCA analysis allowed us to highlight possible differences/similarities. The Magnana cultivar
showed the highest content of sugars, GABA, valine, isoleucine, and alanine. The Runsécultivar
was characterized by high amounts of organic acids, whereas the Gamba Fina cultivar showed a
high content of chlorogenic acid. A significant amount of quinic acid was detected in the Carla
cultivar. The knowledge of apple chemical profiles can be useful for industries interested in specific
compounds for obtaining ingredients of food supplements and functional foods and for promoting
apple valorization and preservation.
Keywords: malus domestica; local apple cultivars; NMR; metabolite profile; PCA; food ingredients
1. Introduction
Apples are the third most produced fruits in the world [
1
] after bananas and wa-
termelons, consumed in the human diet as raw or dried products, juice, paste, jam and
syrups. From a nutritional point of view, apples are a low-fat (less than 1%) and low-protein
food (less than 1%) [
2
], whereas sugars represent about 10% of total apple weight, with
fructose being the most abundant sugar (6%). Micronutrients, such as minerals (mainly
potassium, phosphorus, calcium, and magnesium), vitamins (mainly vitamin C), and sec-
ondary metabolites (such as phenolic compounds [
3
,
4
]) are also present. Moreover, apples
represent an important source of pectin, a gelling agent obtained from apple pomaces, used
for many food industrial productions [5,6].
Foods 2021,10, 289. https://doi.org/10.3390/foods10020289 https://www.mdpi.com/journal/foods
Foods 2021,10, 289 2 of 22
In 2018, more than 86 million tons of apples were produced worldwide, with China
being the main producer (39 million tons). Italy is the sixth largest producer in the world
with 2.4 million tons in 2018 [
1
]. In many Italian regions, local apple cultivars, used and
consumed by the local population, represent an important crop, especially in terms of the
local economy. In particular, Alto Adige (912.757 tons in 2020) and Trentino (496.783 tons
in 2020) are the most important growing areas of high-quality apples throughout Europe,
despite the fact that the cultivation area is not particularly large. However, Piedmont rep-
resents today the third largest Italian producer, contributing 225.281 tons of apples to the
national production and with an increase of +13% with respect to 2019. Indeed, although
Piedmont apple cultivation is not well-known, the story of Piedmont apple growing dates
back to the Middle Ages, when the monastic orders cultivated and improved the varieties
that had survived barbarian invasions [
7
–
9
]. By the early 20th century, Piedmont was home
to thousands of varieties, but with the advent of industrial agriculture, decisions with great
impact were made regarding selection. The market preferred more productive and attrac-
tive apple cultivars, characterized by a bigger size, more attractive appearance, and less
delicate flavor, leading to the loss of traditional Italian apple cultivars [
10
], which survived
only in local and niche areas. Today, a turnaround can be observed—the rediscovery of
traditional and ancient cultivars that often represent more environmentally sustainable
cultivation than commercial ones [
11
], maintaining both biodiversity and the historical and
cultural links [
12
]. In this scenario, deep characterization and valorization of this heritage
is fundamental in order to preserve the special quality of these fruits and to avoid the loss
of precious and useful germplasms [12].
Apples’ flavor, consistency, taste and, health-related properties strictly depend on the
fruit’s chemical composition and on the balance between apple component levels. For
instance, each organic acid gives rise to a particular acidity sense, and each sugar has its own
sweetness level. Apple chemical composition has been largely investigated using targeted
chromatographic techniques, such as high-performance liquid chromatography (HPLC) to
determine phenolic content [
13
–
16
] and gas chromatography (GC) for the determination of
sugars, polyols, sugar phosphates, organic acids, sterols, polyphenols [
17
], fatty acids [
18
],
and other aromatic compounds [
19
,
20
]. Untargeted proton nuclear magnetic resonance
(
1
H NMR) methodologies [
21
] have focused mainly on apple juice matrixes [
19
,
22
,
23
] to
study the chemical composition of new cultivars [
24
], cultivars with different resistances
against fungi attacks [
25
], and commercial cultivars from Japan and New Zealand [
26
].
1
H high field NMR spectroscopy has already shown to be a valuable tool to investigate
local typical products, such as red sweet peppers [
27
], white celery [
28
], tomatoes [
29
], and
olive oils [
30
]. To date, ancient apple cultivars from Piedmont have been investigated in
term of sensory parameters, nutritional aspects [
12
,
31
], and genetic characterization [
32
]
through the application of standard analytical techniques. Both Donno et al. and Contessa
et al. highlighted superior nutritional traits of ancient Piedmont apple cultivars, with
a generally high content of organic acids, sugars, and total phenolic compounds [
31
].
However, deep chemical characterization by means of advanced analytical methodologies
has never been performed.
In this paper, the hydroalcoholic and organic extracts of ten traditional apple cultivars
grown in the Piedmont region (northwest Italy) were investigated by means of the NMR
methodology to determine their apple metabolite profiles. The application of principal
component analysis (PCA) to NMR data allowed us to summarize and highlight the
differences and similarities between the selected apple cultivars. Indeed, mathematical
tools, better known as chemometrics, are widely used for metabolomic data analysis. In
particular, unsupervised methods are utilized to summarize, explore, and discover clusters
or trends in the data without a priori attribution of any class membership [33].
The knowledge of chemical apple profile can be extremely important for the introduc-
tion of local products in national and international markets and for industries interested in
specific apple compounds.
Foods 2021,10, 289 3 of 22
2. Materials and Methods
2.1. Sampling
Ten apple cultivars (Malus domestica) typical of Piedmont region, Italy (Figure 1) were
collected in the period of their complete maturity, specific for every cultivar, as reported
in Table 1. The apples were provided by three farms: “Azienda Agricola Melamangio”,
“Scuola Malva Arnaldi”, and “Azienda Agricola Turaglio”. In particular the “Azienda
Agricola Melamangio” farm, located in Odalengo Piccolo, provided the variety Canditina
(A1); “Scuola Malva Arnaldi”, located in Bibiana, provided five varieties: Grigia di Torriana
(A2), Magnana (A3), Runsé(A4), Carla (A5) and Gamba Fina (A6); “Azienda Agricola
Turaglio”, located in Cavour, provided four varieties: Ross Giambon (A7), Dominici (A8),
Calvilla (A9) and Grenoble (A10). Some features of the ten cultivars, such as maturity time,
size, peel tactility and color, pulp consistency, and color, are reported in [7–9].
Foods 2021, 10, x FOR PEER REVIEW 5 of 25
Figure 1. Apple cultivars from the Piedmont region.
2.2. Chemicals
Deuterated water (D
2
O) 99.97% D, methanol-D4 99.80% D, chloroform-D 99.80% D +
0.03% tetramethylsilane (TMS), and 3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium
salt (TSP) were purchased from Euriso–Top (Saclay, France). Anhydrous potassium
phosphate dibasic (K₂HPO₄) and anhydrous potassium phosphate monobasic (KH₂PO₄)
were purchased from Sigma–Aldrich (St. Louis, MO, USA). Methanol (HPLC-grade) and
chloroform (HPLC-grade) were purchased from Carlo Erba Reagenti (Milan, Italy).
Millipore grade water was purchased from Tito Menichelli S.r.l. (Rome, Italy).
2.3. Sample Preparation
In the sample preparation step, apple skin and pulp and seeds were removed. Seven
apples were sampled for each cultivar. The apples were first washed with water and
Na
2
CO
3
to eliminate every dirt residue. A clove was taken from each apple and
subsequently shredded with a ceramic knife. To control the oxidation during preparation,
the samples were cut in an ice bath. All the samples were transferred to the falcon and
lyophilized for 7 days. All apples were cut into eight parts, and seven pieces (one from
each apple of the cultivar) were chopped together into smaller pieces. Each sample was
then freeze-dried and pulverized with mortar and pestle. The freeze-drying process
promotes water removal, thereby reducing the oxidation process.
2.4. Extraction Procedure for NMR Analysis
Extracts for NMR analysis were obtained following the protocol previously
described [35] with some modifications. Powder (0.5 g) was sequentially added with a 3
mL methanol/chloroform (2:1 v/v) mixture, containing 1 mL of chloroform and 1.8 mL of
Millipore-grade water, which was shaken slightly after each solvent addition. The
Figure 1. Apple cultivars from the Piedmont region.
2.2. Chemicals
Deuterated water (D
2
O) 99.97% D, methanol-D4 99.80% D, chloroform-D 99.80% D +
0.03% tetramethylsilane (TMS), and 3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium salt
(TSP) were purchased from Euriso–Top (Saclay, France). Anhydrous potassium phosphate
dibasic (K
2
HPO
4
) and anhydrous potassium phosphate monobasic (KH
2
PO
4
) were pur-
chased from Sigma–Aldrich (St. Louis, MO, USA). Methanol (HPLC-grade) and chloroform
(HPLC-grade) were purchased from Carlo Erba Reagenti (Milan, Italy). Millipore grade
water was purchased from Tito Menichelli S.r.l. (Rome, Italy).
Foods 2021,10, 289 4 of 22
Table 1. Characteristics (maturity time, peel, pulp and size) of the ten investigated apple cultivars [7–9,34].
N◦Cultivar Maturity Time Peel Pulp Size
A1 Canditina Late October Smooth, green with red
overcolour
Juicy,
white/green Medium
A2 Grigia di
Torriana Mid/late October Rough, green Soft, white Medium
A3 Magnana Late October/early
November
Smooth/rough,
white/green with red
overcolour
Soft,
white/green Medium
A4 RunséLate October/early
November Smooth, red Juicy,
white/pink Medium
A5 Carla Mid/late September Smooth, yellow with
red/orange overcolour Soft, white Medium/small
A6 Gamba Fina Early/Mid October Smooth, yellow/green
with red overcolour Soft, white Medium/small
A7 Ross Giambon Mid October Smooth, yellow/green Juicy, white Large
A8 Dominici Mid October
Rough, yellow/green with
red overcolour Crisp, white Large
A9 Calvilla Mid October Smooth, green with red
overcolour
Crisp and juicy,
white/green Medium
A10 Grenoble Late October Rough, green Crisp,
white/green Small
2.3. Sample Preparation
In the sample preparation step, apple skin and pulp and seeds were removed. Seven
apples were sampled for each cultivar. The apples were first washed with water and
Na
2
CO
3
to eliminate every dirt residue. A clove was taken from each apple and sub-
sequently shredded with a ceramic knife. To control the oxidation during preparation,
the samples were cut in an ice bath. All the samples were transferred to the falcon and
lyophilized for 7 days. All apples were cut into eight parts, and seven pieces (one from each
apple of the cultivar) were chopped together into smaller pieces. Each sample was then
freeze-dried and pulverized with mortar and pestle. The freeze-drying process promotes
water removal, thereby reducing the oxidation process.
2.4. Extraction Procedure for NMR Analysis
Extracts for NMR analysis were obtained following the protocol previously de-
scribed [
35
] with some modifications. Powder (0.5 g) was sequentially added with a
3 mL methanol/chloroform (2:1 v/v) mixture, containing 1 mL of chloroform and 1.8 mL of
Millipore-grade water, which was shaken slightly after each solvent addition. The obtained
emulsion was stored at 4
◦
C for 40 min and then centrifuged at 4200
×
gfor 15 min at
4◦C.
The organic and hydroalcoholic phases were separated, and the residual pellet was
extracted again using half of the solvent volumes to ensure complete extraction of the
soluble metabolites. A slow N
2
flow was used for drying. The obtained extracts were
stored at −20 ◦C until analysis.
2.5. Metabolic Profile by NMR Analysis
Hydroalcoholic and organic extracts were analyzed using a Bruker AVANCE
600 spectrometer (Bruker, Milan, Italy) at 28
◦
C operating at 600.13 MHz (proton fre-
quency) and equipped with a Bruker multinuclear z-gradient 5 mm probe head. The
temperature for NMR spectral analysis (28
◦
C) was chosen according to the internal labora-
tory protocol that assures temperature maintenance within (
±
0.1
◦
C) limits and is close to
the experimental conditions reported in most databases. Each dried hydroalcoholic extract
was solubilized in 1 mL of D
2
O; 0.2 mL of the obtained solution was mixed with
0.5 mL
of 400 mM phosphate buffer (pH 7.4)/D
2
O containing 2 mM solution of TSP (internal
standard) and transferred into a 5 mm NMR tube. The use of TPS as internal standard
Foods 2021,10, 289 5 of 22
does not interfere with the protein in apple extracts, as the balk of apple proteins is not
easily soluble in water [
36
].
1
H spectra (Bruker pulse sequence zgpr) of hydroalcoholic
extracts were acquired with 200 transients, a recycle delay of 5 s, an acquisition time of
2.28 s,
a 90
◦
flip angle pulse of 14
µ
s, and 32K data points. The water signal was suppressed
using solvent pre-saturation. Each dried organic extract was solubilized in 0.7 mL of the
CDCl
3
/CD
3
OD (2:1 v/v) mixture and transferred into a 5 mm NMR tube that was flame
sealed.
1
H spectra (Bruker pulse sequence zg) of the organic extracts were acquired with
128 transients, a recycle delay of 5 s, an acquisition time of 1.82 s, a 90
◦
flip angle pulse of
10.5 µs, and 32K data points.
Two-dimensional (2D) NMR experiments (
1
H-
1
H TOtal Correlated SpectroscopY
(TOCSY),
1
H-
13
C Heteronuclear Single Quantum Coherence (HSQC), and
1
H-
13
C Het-
eronuclear Multiple Bond Correlation (HMBC) were performed on each hydroalcoholic
and organic extract under the experimental conditions previously reported [29].
In order to evaluate the repeatability of the protocol and to ensure the complete
extraction of the soluble metabolites, all dry matter after freeze-drying was recovered, and
the entire procedure (from the extraction to NMR analysis) was carried out in triplicate.
In Table 2, the signals of the identified metabolites in the hydroalcoholic extracts
1
H
NMR spectra are reported. Among them, the 26 selected signals marked with asterisks
were integrated using the Bruker TOPSPIN 1.3 software and normalized with respect to
the methyl group signal of TSP (0.00 ppm), set to 100. The quantified metabolites were
expressed in mg/100 g of the dried sample ±SD (standard deviation) (Table S1).
Table 2.
Metabolites identified in the 600.13 MHz proton nuclear magnetic resonance (
1
H NMR),
1
H-
1
H TOCSY,
1
H-
13
C HSQC, and
1
H-
13
C HMBC spectra of Bligh–Dyer hydroalcoholic extracts of apple
in 0.7 mL of phosphate buffer/D2O at 28 ◦C. Asterisks (*) indicate signals selected for integration.
Compound Assignment 1H (ppm) Multiplicity
(J(Hz))
13C (ppm)
Carbohydrates
α-D-Fructofuranose C-2 105.5
CH-3 4.13 * 83.0
CH-4 4.00 77.2
CH-5 4.07 82.4
β-D-Fructofuranose C-2 102.6
CH-3 4.12 * 76.6
CH-4 4.12 * 75.5
CH-5 3.84 81.7
CH2-6,603.83; 3.68
β-D-Fructopyranose CH2-1,103.57; 3.72 64.9
C-2 99.2
CH-3 3.80 68.6
CH-4 3.90 70.7
CH-5 4.01 70.2
CH2-6,603.72; 4.03 64.5
α-Xylose CH-1 5.20 * d a(3.8) 93.4
CH-2 3.55
CH-3 3.65
β- Xylose CH-1 4.59 * d (8.0) 97.7
CH-2 3.54
CH-3 3.69
α-Glucose CH-1 5.24 * d (3.8) 93.2
CH-2 3.55 72.2
CH-3 3.73 73.7
CH-4 3.42 70.3
CH-5 3.84 72.8
β-Glucose CH-1 4.66 * d (8.0) 97.0
Foods 2021,10, 289 6 of 22
Table 2. Cont.
Compound Assignment 1H (ppm) Multiplicity
(J(Hz))
13C (ppm)
CH-2 3.25
dd
b
(9.4; 7.9)
75.1
CH-3 3.51 76.9
CH-4 3.43 70.7
CH-5 3.49 76.9
CH2-6,603.90; 3.75 61.8
Sucrose CH-1
(glucose) 5.42 * d (3.8) 93.3
CH-2 3.57 72.0
CH-3 3.77 73.7
CH-4 3.47 70.1
CH-5 3.85 73.5
C-2 (fructose) 104.8
CH-3 4.22 d (8.8) 77.3
Rhamnitol CH31.28 * d (6.4) 20.0
CH-2 3.88 68.2
CH-3 3.61 74.3
Organic acids
Acetic acid α-CH31.92 * s c
Citric acid α,γ-CH 2.54 * d (15.2) 46.7
α’,γ’-CH 2.67 46.7
β-C 76.5
1,5-COOH 180.8
6-COOH 183.0
Formic acid HCOOH 8.46 * s
Citramalic acid β-CH31.33 * s 26.5
β-CH22.44 d (15.8) 47.7
β’-CH22.74 d (15.8) 47.7
α-C 75.5
1,4-COOH 184.2
Lactic acid β-CH31.33 * d (7.0) 21.4
α-CH 4.12 69.8
COOH 183.4
Malic acid α-CH 4.30 * dd (9.9; 3.2) 71.6
β-CH 2.67 dd (15.4; 3.2) 43.9
β’-CH 2.37 dd (15.4; 9.9) 43.9
Quinic acid C-1 78.3
CH2-2, 201.88 *; 2.06 dd (13.5;
10.8); m 41.9
CH-3 4.03 md68.0
CH-4 3.57 m 76.2
CH-5 4.16 m 71.3
α-Galacturonic acid CH-1 5.31 d (3.8) 93.1
CH-2 3.80
CH-3 3.90
CH-4 4.29
CH-5 4.41* d (1.2) 72.5
COOH 177.1
Amino acids
Alanine α-CH 3.80 51.5
β-CH31.49 * d (7.3) 17.3
COOH 176.8
Asparagine α-CH 4.05 52.3
β,β’-CH22.88 *; 2.96 dd (7.4; 16.9);
dd (4.3; 12.6) 35.6
COOH 175.5
Aspartate β,β’-CH22.70; 2.81 * dd(3.7; 17.4)
γ-Aminobutyrate α-CH22.30 * t e(7.4) 35.3
Foods 2021,10, 289 7 of 22
Table 2. Cont.
Compound Assignment 1H (ppm) Multiplicity
(J(Hz))
13C (ppm)
β-CH21.90 24.7
γ-CH23.01
Isoleucine γ-CH31.01 * d (7.1)
Valine γ-CH30.99 * d (7.1)
γ’-CH31.05 d (7.1)
Miscellaneous metabolites
Chlorogenic acid CH2-2 2.20
CH-3 5.33 m 72.2
CH2-6 2.04
CH-106.42 * d (16.0) 115.8
CH-207.67 d (16.0) 147.3
CH-307.22 d (2.0) 116.3
CH-606.98 116.7
CH-707.15 123.8
CH2-6, 601.97;2.05 m 38.5
Choline N(CH3)3+ 3.21 * s 55.1
p-Coumaric acid
derivative CH-2,6 7.62 * d (8.8) 131.9
CH-3,5 6.97
CH=CH 7.79; 6.51 d (16.1)
Phloretin/Phloridzin CH-2,6 7.16 d (8.3) 130.7
CH-3,5 6.85 * d (8.3) 116.5
CH-30,506.19; 6.24 s 96.6; 97.3
β-CH22.92 31.0
ad = doublet; bdd = double doublet; cs = singlet; dm = multiplet; et = triplet.
The integral areas of the 7 selected signals in the 1H NMR spectra of organic extracts
(Table 3) were measured using the Bruker TOPSPIN 1.3 software and normalized with
respect to the integral (I
FA
) of the
α
-CH
2
group signal of all fatty acids (2.30 ppm), set
to 100. The molar % values
±
SD of fatty acids,
β
-sitosterol, phosphatidylcholine, and
digalactosyldiacylglycerol were calculated with consideration of the number of equivalent
protons using the following equations:
%STE = 100(0.66ISTE/Itot) (1)
%TRI = 100(0.5ITRI/Itot) (2)
%DI = 100(IDI/Itot) (3)
%MONO = 100(IUNS −2IDI −1.5ITRI)/Itot (4)
%SAT = 100(IFA −IDI −0.5ITRI −%MONO)/Itot (5)
%PC = 100(4IPC/9Itot) (6)
%DGDG = 100(4IDGDG/Itot) (7)
where %
STE
, %
TRI
, %
DI
, %
MONO
, %
SAT
, %
PC
, and %
DGDG
are the molar % of
β
-sitosterol, tri-
unsaturated fatty acids, di-unsaturated fatty acids, mono-unsaturated fatty acids, saturated
fatty acids, phosphatidylcholine, and digalactosyldiacylglycerol, respectively. I
STE
, I
TRI
,
I
DI
, I
UNS
, I
FA
, I
PC
, and I
DGDG
are integrals, whereas I
tot
is calculated according to the
following equation:
Itot = IFA + 0.66ISTE (8)
Foods 2021,10, 289 8 of 22
Table 3.
Metabolites identified in the 600.13 MHz
1
H NMR,
1
H-
1
H TOCSY,
1
H-
13
C HSQC, and
1
H-
13
C HMBC spectra (28
◦
C) of Bligh–Dyer organic extracts of apple in CDCl
3
/CD
3
OD (2:1 v/v)
mixture at 28
◦
C. Asterisks (*) indicate signals selected for integration. For the integration of total
fatty acids (I
FA
), the region of 2.22–2.35 was considered. For the integration of total unsaturated fatty
acids (IUNS), the region of 5.25–5.384 was considered.
Compound Assignment 1H (ppm) Multiplicity
(J(Hz))
13C (ppm)
Oleic fatty chain COO 174.4
(C18:1 ∆9)CH2-2 2.30 34.6
CH2-3 1.58 m a25.3
CH2-4,7 1.30 m 29.5
CH2-8 2.01 m 27.6
CH=CH 9,10 5.31 m 130.6
CH2-11 2.01 m 27.6
CH2-12,15 1.33–1.30 m 29.4–30.2
CH2-16 1.28 m 31.6
CH2-17 1.26 m 23.0
CH3-18 0.84 tb14.4
Linoleic fatty chain COO 174.4
(C18:2 ∆9,12)CH2-2 2.30 34.6
CH2-3 1.58 m 25.3
CH2-4,7 1.32–1.28 m 29.5
CH2-8 2.02 m 27.6
CH= 9 5.34 m 130.6
CH= 10 5.31 m 128.6
CH2-11 2.73 * (IDI) t (6.8) 26.0
CH= 12 5.31 m 128.6
CH= 13 5.34 m 130.6
CH2-14 2.02 m 27.6
CH2-15 1.29 m 29.4
CH2-16 1.29 m 31.6
CH2-17 1.23 m 23.0
CH3-18 0.85 t 14.4
Linolenic fatty chain COO 174.4
(C18:3 ∆9,12,15)CH2-2 2.30 34.9
CH2-3 1.58 m 25.3
CH2-4,7 1.30 m 29.5
CH2-8 2.03 m 27.6
CH= 9 5.34 m 130.6
CH= 10 5.30 m 128.6
CH211 2.77 * (ITRI) t (6.2) 26.0
CH=CH 12,13 5.30 m 128.6
CH2-14 2.77 * (ITRI) t (6.2) 26.0
CH= 15 5.28 m 127.4
CH= 16 5.34 m 132.2
CH2-17 2.03 m 20.9
CH3-18 0.94 t (7.6) 14.4
Saturated fatty acids COO 174.4
CH2-2 2.28 34.6
CH2-3 1.58 m 25.3
CH21.28–1.22 m 29.6-32.0
CH2n-1 1.26 22.9
CH3n 0.84 t 14.4
Diacylglycerol moiety CH-sn 2 5.06 72.5
CH-sn 1 4.15, 4.33 62.5
CH-sn 3 3.65 61.0
Foods 2021,10, 289 9 of 22
Table 3. Cont.
Compound Assignment 1H (ppm) Multiplicity
(J(Hz))
13C (ppm)
β-Sitosterol CH3-18 0.66 * (ISTE) s c12.2
Squalene CH3-a 1.56 16.3
CH3-b 1.64 25.8
CH-c 5.07 m 124.8
CH2-d 2.02 27.4
CH2-e 1.96 40.1
1,2-Diacyl-sn-glycero-3-
phosphatidylcholine N(CH3)3+ 3.22 * (IPC) s 54.5
CH2N+ 3.75 66.4
CH2OP 4.45 60.6
CH-sn 2 5.06 72.5
CH-sn 1 4.15, 4.33 62.5
CH-sn 3 3.65 61.0
Digalactosyldiacylglycerol
CH”-1 4.87 * (IDGDG)dd(3.8) 99.8
CH”-2 3.77 69.2
CH”-3 3.69 70.6
CH”-4 3.91 70.2
CH’-1 4.19 104.5
CH’-2,3 3.51–3.53
CH’-4 3.90
CH-sn 2 5.06 72.5
CH-sn 1 4.15, 4.33 62.5
CH-sn 3 3.65 61.0
am = multiplet; bt = triplet; cs = singlet; dd = doublet.
The amount of each quantified metabolite in organic extracts is reported in Table S2.
2.6. Multivariate Statistical Analysis
Principal component analysis (PCA) was carried out on 30 selected variables corre-
sponding to 23 metabolites from the hydroalcoholic extract and 7 from the organic extract.
In the case of fructose, glucose, and xylose, the sum of their alpha and beta anomeric forms
instead of the individual isomer content was used in statistics. Before statistical analysis,
the data were preprocessed using autoscaling: all of the variables were mean centered, and
each variable was divided by its standard deviation. Principal component analysis was
carried out using SIMCA software (version 12).
3. Results
3.1. Assignments of Aqueous and Organic Extracts
The
1
H and
13
C NMR spectra assignments apple aqueous (Figure 2) and organic
(Figure 3) extracts of apple were carried out using 2D NMR experiments, standard com-
pound addition, and literature data [
24
–
26
,
37
]. A more complete spectral assignment
(Table 2) of the aqueous extracts with respect to literature data was obtained, identifying
fructose tautomeric forms, galacturonic acid, GABA, and p-coumaroyl moiety. Fructose
tautomeric forms, namely
α
-D-fructofuranose and
β
-D-fructopyranose, were identified by
means of their diagnostic
1
H NMR signals and 2D experiments. In particular, the presence
of
α
-D-fructofuranose was suggested by its characteristic signal at 4.13 ppm due to the
CH-3 proton.
1
H-
1
H TOCSY experiment allowed us to identify the correlation with the
CH-4 proton (4.00 ppm), whereas the
1
H-
13
C HMBC experiment showed the correlation
of the CH-3 proton with the C-2 carbon at 105.5 ppm, typical of ketoses. Analogously,
β
-D-fructopyranose was recognized by the signal of the CH-5 proton at 4.01 ppm, and the
diagnostic spin system detected by the
1
H-
1
H TOCSY experiment allowed us to identify
the other protons of this sugar, namely CH-3 (3.80 ppm), CH-4 (3.90 ppm), and CH
2
-6,6
0
(3.72, 4.03 ppm). C-2 carbon at 99.2 ppm was also identified by means of the
1
H-
13
C HMBC
Foods 2021,10, 289 10 of 22
map. The presence of
α
-galacturonic moiety was suggested by the diagnostic chemical
shifts of the spin systems in the
1
H-
1
H TOCSY experiment, the coupling constants, and
the correlations in the
1
H-
13
C HMBC experiment. In particular, the doublet at 4.41 ppm
with a J coupling constant of 1.2 Hz due to CH-5 proton indicates single coupling between
H-5 and the equatorial H-4. The
1
H-
1
H TOCSY experiment allowed us to identify the other
protons of this metabolite, namely CH-4 (4.29 ppm), CH-3 (3.90 ppm), CH-2 (3.80 ppm),
and CH-1 (5.31 ppm, doublet with J = 3.80 Hz). The correlation of the CH-5 proton with
carboxylic carbon at 177.1 ppm was confirmed by the 1H-13C HMBC map.
Foods 2021, 10, x FOR PEER REVIEW 12 of 25
Figure 2. 600 MHz
1
H NMR spectrum of hydroalcoholic extract from apple fruit (var. Magnana) in
a 400 mM phosphate buffer (pH 7.4)/D
2
O mixture with 2mM of 3-(trimethylsilyl)-propionic-
2,2,3,3-d4 acid sodium salt (TSP). (A) Entire spectrum. (B) Low field region: 1, formic acid; 2, p-
coumaric acid derivative; 3, phloretin/phloridzin. (C) Middle field region: 4, chlorogenic acid; 5,
sucrose; 6, α-glucose; 7, α-xylose; 8, β -glucose; 9, β-xylose; 10, α-galacturonic acid; 11, malic acid;
12, D-fructofuranose, (D) High field region: 13, choline; 14, aspartate; 15, asparagine; 16, citric acid;
Figure 2.
600 MHz
1
H NMR spectrum of hydroalcoholic extract from apple fruit (var. Magnana) in a 400 mM phosphate
buffer (pH 7.4)/D
2
O mixture with 2mM of 3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium salt (TSP). (
A
) Entire spectrum.
(
B
) Low field region: 1, formic acid; 2, p-coumaric acid derivative; 3, phloretin/phloridzin. (
C
) Middle field region: 4,
chlorogenic acid; 5, sucrose; 6,
α
-glucose; 7,
α
-xylose; 8,
β
-glucose; 9,
β
-xylose; 10,
α
-galacturonic acid; 11, malic acid; 12,
D-fructofuranose, (
D
) High field region
:
13, choline; 14, aspartate; 15, asparagine; 16, citric acid; 17,
γ
-aminobutyrate; 18,
acetic acid; 19, quinic acid; 20, alanine; 21, lactic acid; 22, citramalic acid; 23, rhamnitol; 24, isoleucine; 25, valine.
Foods 2021,10, 289 11 of 22
Foods 2021, 10, x FOR PEER REVIEW 14 of 25
Figure 3.
1
H NMR spectrum at 600 MHz of organic extract from apple fruit (var. Magnana) in the CDCl
3
/CD
3
OH 2:1 (v/v)
mixture. (A) Entire spectrum. (B) Middle field region: 1, total unsaturated fatty acids; 2, digalactosyldiacylglycerol; 3, 1,2-
diacyl-sn-glycero-3-phosphatidylcholine. (C) High field region: 4, linolenic fatty chain; 5, linoleic fatty chain; 6, total fatty
acids; 7, β-sitosterol.
3.2. Metabolite Profiles of Apple Cultivars
In this section, each class of compounds is discussed separately, and a comparison of
the ten cultivars is conducted.
3.2.1. Sugars and Polyols
The highest content of total sugars was measured in cultivars A9, A7, A3, and A2,
whereas the lowest content was found in cultivars A1 and A6 (Figure 4). In particular,
fructose, glucose and, sucrose were the most abundant sugars in all apple cultivars (Figure
Figure 3. 1
H NMR spectrum at 600 MHz of organic extract from apple fruit (var. Magnana) in the CDCl
3
/CD
3
OH 2:1 (v/v)
mixture. (
A
) Entire spectrum. (
B
) Middle field region: 1, total unsaturated fatty acids; 2, digalactosyldiacylglycerol; 3,
1,2-diacyl-sn-glycero-3-phosphatidylcholine. (
C
) High field region
:
4, linolenic fatty chain; 5, linoleic fatty chain; 6, total
fatty acids; 7, β-sitosterol.
The presence of GABA was confirmed by its typical triplet at 2.30 ppm (J= 7.4 Hz)
assigned to
α
-CH
2
protons. The diagnostic spin systems identified in the
1
H-
1
H TOCSY
experiment allowed us to observe the correlations with
β
-CH
2
and
γ
-CH
2
protons at
1.90 and 3.01 ppm, respectively.
The diagnostic spin systems identified in the
1
H-
1
H TOCSY experiment suggested
the presence of p-coumaroyl moiety due to the correlation between H-2/H-6 equivalent
Foods 2021,10, 289 12 of 22
aromatic signals at 7.62 ppm and H-3/H-5 equivalent signals at 6.97 ppm (J= 8.8 Hz
corresponding to ortho position) and the correlation between the double bond protons at
7.79 and 6.51 ppm with J= 16.1 Hz corresponding to trans configuration of the double bond.
The low intensity of these signals—and, therefore, the low concentration of the correspond-
ing compound—does not allow further correlations to be observed in the 2D experiments to
complete the structural assignment. p-Coumaroyl quinic acid is the principal p-coumaroyl
ester previously identified in apples using the targeted HPLC methodology [
13
,
38
,
39
].
Therefore, the identified p-coumaroyl moiety can be bound to quinic acid; however, the
esterified quinic acid moiety was not detected due to the signal overlapping.
It was not possible to distinguish phloretin from its glycosides (such as phloridzin)
using the available signals from aromatic protons region. Unfortunately, the signals
expected for
β
-glucose moiety were not observed due to the strong overlapping with
markedly more intense signals of other carbohydrates. The orto- and meta- protons of the
para-substituted aromatic ring, a common moiety for all phloretin derivatives, gave rise to
the signals at 7.16 and 6.85 ppm, respectively.
In Table 2, the assignment of four sugars (fructose, glucose, sucrose, and xylose), nine
organic acids (acetic, citric, formic, citramalic, lactic, malic, quinic, and galacturonic acids),
six amino acids (alanine, asparagine, aspartate, GABA, isoleucine, and valine), rhamnitol,
p-coumaroyl derivative, phloretin/phloridzin, and choline is reported.
A more complete assignment of organic extracts of apples (Table 3) with respect
to the literature data was obtained, reporting the assignment of glycerogalactolipid and
glycerophospholipid polar heads. The presence of digalactosyldiacylglycerol (DGDG), the
most abundant galactolipid in apples [
40
], was suggested by the characteristic doublet
(J= 3.8 Hz) at 4.87 ppm due to the equatorial CH”-1 proton of the external galactose
ring [
41
]. The
1
H-
1
H TOCSY experiment allowed us to identify other protons of the ring,
namely CH-2” (3.77 ppm), CH-3” (3.69 ppm), and CH-4” (3.91 ppm). Analogously, the
CH-1
0
proton of the internal DGDG galactose was also assigned at 4.19 ppm, together with
the CH-20/CH-30and CH-40protons (3.50–3.53 and 3.90 ppm, respectively).
Methyl groups of phosphatidylcholine N
+
-(CH
3
)
3
moiety were detected by the di-
agnostic
1
H and
13
C signals at 3.22 ppm at 54.5 ppm, respectively. Moreover, the
1
H-
1
H
TOCSY experiment allowed us to identify proton correlation between the CH
2
OP group at
4.45 ppm (13C 60.6 ppm) and the CH2N+protons at 3.75 ppm (13C 66.4 ppm).
In Table 3, the assignment of
β
-Sitosterol, fatty acid chains, phosphatidylcholine, and
digalactosyldiacylglycerol is also reported.
3.2. Metabolite Profiles of Apple Cultivars
In this section, each class of compounds is discussed separately, and a comparison of
the ten cultivars is conducted.
3.2.1. Sugars and Polyols
The highest content of total sugars was measured in cultivars A9, A7, A3, and A2,
whereas the lowest content was found in cultivars A1 and A6 (Figure 4). In particular, fruc-
tose, glucose and, sucrose were the most abundant sugars in all apple cultivars
(Figure 5A).
As expected [
18
], fructose turned out to be the main sugar in all samples, with the highest
content detected in cultivars A5 and A7 and the lowest content in A1. The highest value
of glucose content was measured in cultivar A2, whereas the lowest content ( more than
three times less) was identified in sample A6. According to literature data [
18
,
42
,
43
], the
fructose-to-glucose ratio was higher than 1.7 in all of the analyzed samples. Moreover, the
fructose/glucose, fructose/sucrose, and sugar/acid weight ratios were calculated (Table 4).
Cultivar A3 showed the highest sucrose content, whereas the lowest content was observed
in A5 (more than four times less). Xylose was present in low concentrations in all of the
cultivars, with cultivar A5 showing the highest content, whereas A1 showed the lowest
content (more than six times). The highest content of rhamnitol, a polyol, was measured in
cultivar A10, whereas the lowest content was found in A7.
Foods 2021,10, 289 13 of 22
Foods 2021, 10, x FOR PEER REVIEW 14 of 24
5A). As expected [18], fructose turned out to be the main sugar in all samples, with the
highest content detected in cultivars A5 and A7 and the lowest content in A1. The highest
value of glucose content was measured in cultivar A2, whereas the lowest content ( more
than three times less) was identified in sample A6. According to literature data [18,42,43],
the fructose-to-glucose ratio was higher than 1.7 in all of the analyzed samples. Moreover,
the fructose/glucose, fructose/sucrose, and sugar/acid weight ratios were calculated (Ta-
ble 4). Cultivar A3 showed the highest sucrose content, whereas the lowest content was
observed in A5 (more than four times less). Xylose was present in low concentrations in
all of the cultivars, with cultivar A5 showing the highest content, whereas A1 showed the
lowest content (more than six times). The highest content of rhamnitol, a polyol, was
measured in cultivar A10, whereas the lowest content was found in A7.
Figure 4. Bar charts of the total content of (A) sugars, (B) organic acids, (C) amino acids, (D) sugar/acid ratio, identified
and quantified (mg/100 g of DW ± SD) in the 1H NMR spectra of hydroalcoholic extracts of apples.
Figure 4.
Bar charts of the total content of (
A
) sugars, (
B
) organic acids, (
C
) amino acids, (
D
) sugar/acid ratio, identified
and quantified (mg/100 g of DW ±SD) in the 1H NMR spectra of hydroalcoholic extracts of apples.
Table 4.
Fructose/glucose, fructose/sucrose, and sugar/acid weight ratios in the ten studied Pied-
mont region apple cultivars and in three commercial cultivars (calculated using literature data).
Cultivar Reference Fru/Glc Fru/Suc Sugar/Acid
Golden
Delicious [18] 2.0 2.3 34
Golden
Delicious [44] 2.9 1.8
Golden
Delicious [45] 3.6 1.9 60
Fuji [18] 1.7 2.8 42
Fuji [44] 15.0 2.0
Fuji [45] 1.9 3.0 90
Jonagold [18] 1.7 2.6 31
Jonagold [44] 7.3 1.1
Jonagold [45] 3.2 1.8 127
A1 Present work 2.3 2.4 57
A2 Present work 1.8 3.2 61
A3 Present work 3.6 1.3 44
A4 Present work 3.5 3.1 43
A5 Present work 2.9 6.5 69
A6 Present work 4.2 2.0 42
A7 Present work 3.6 2.1 49
A8 Present work 3.8 2.3 37
A9 Present work 4.3 1.5 37
A10 Present work 3.2 1.3 38
Foods 2021,10, 289 14 of 22
Foods 2021, 10, x FOR PEER REVIEW 15 of 24
Figure 5.
Bar charts of the metabolites identified and quantified (mg/100 g of DW
±
SD) in the
1
H NMR spectra of
hydroalcoholic extracts of apples. (
A
) Sugars and polyols, (
B
) organic acids, (
C
) amino acids, (
D
) miscellaneous metabolites.
Phloretin/phloridzin and p-coumaroyl derivative contents are expressed as phloretin equivalents and p-coumaric acid
equivalents, respectively.
Foods 2021,10, 289 15 of 22
3.2.2. Organic Acids
The highest total organic acids content (Figure 4) was found in cultivars A9 and A10,
whereas the lowest amount was observed in cultivar A1 (more than two times less). In
particular, malic acid was the most abundant acid in apples. The highest concentrations
of malic acid were found in samples A9 and A10, whereas the lowest concentration was
observed in A5 (Figure 5B). Cultivar A5 presented by far the highest content of quinic acid,
whereas the lowest content (almost seven times less) was found in sample A8. The highest
content of galacturonic acid was measured in cultivar A2, whereas it was not detected
in sample A1. Cultivar A9 showed the highest amount of citric acid, whereas the lowest
content was measured in A1 (more than three times less). Citramalic acid was not detected
in sample A5; conversely, the highest amount was measured in sample A1. Cultivars A4
and A8 showed the highest content of lactic acid, whereas the lowest content was found
in A10. Sample A10 was also characterized by the lowest amount of acetic acid, whereas
cultivar A5 showed the highest amount. Finally, formic and acetic acids were found in very
low concentrations, below 2 mg/100 g.
3.2.3. Amino Acids
The content of free amino acids (total and those of individual components) was
extremely variable among the different cultivars (Figure 5C). The highest total amino acid
content was found in cultivars A8 and A10, whereas the lowest amount was observed in
cultivars A1, A7, and A5 (Figure 4). In particular, cultivar A3 showed the highest amount of
GABA, valine, and isoleucine, and, together with cultivar A10, alanine. Asparagine content
varied from 280 mg/100 g in A10 to less than 1 mg/100 g (below the limit of detection) in
A1. Aspartate was not found in cultivars A1, A6, or A7, and GABA was not detected in
cultivar A1.
3.2.4. Miscellaneous
Cultivar A6 showed the highest amount of chlorogenic acid, whereas cultivar A3
the lowest amount (5 times less). A2, A5, and A10 showed by far the highest amounts
of phloretin/phloridzin, whereas the lowest amount was observed in cultivar A1. The
highest amount of p-coumaroyl derivatives was detected in cultivar A1, whereas the lowest
amount was found in A10. The highest amount of choline was measured in cultivars A2
and A3, whereas the lowest amount was found in samples A1 and A9 (Figure 5D).
3.2.5. β-Sitosterol
This molecule was found in appreciable concentrations in all of the analyzed cultivars.
In particular, the highest value was measured in cultivar A2, whereas the lowest amount
was identified in A1 (Figure 6).
3.2.6. Fatty Acids
The content of unsaturated fatty acids (TOT UFA) was higher than the content of
saturated fatty acids (TOT SFA) in all of the ten cultivars. Di-unsaturated fatty acids (DUFA)
were the most abundant unsaturated fatty acids in all of the samples, the highest level
being measured in cultivar A2 and the lowest in A1. Sample A1 was also characterized
by the lowest value of mono-unsaturated fatty acids (MUFA) and the highest value of tri-
unsaturated fatty acids (TUFA). Cultivar A4 showed an opposite trend, having the highest
amount of MUFA and the lowest amount of TUFA. Finally, the highest concentration of
TOT SFA was found in cultivar A1, whereas the lowest concentration was measured in
sampleA8 (Figure 6).
Foods 2021,10, 289 16 of 22
Foods 2021, 10, x FOR PEER REVIEW 18 of 25
detection) in A1. Aspartate was not found in cultivars A1, A6, or A7, and GABA was not
detected in cultivar A1.
3.2.4. Miscellaneous
Cultivar A6 showed the highest amount of chlorogenic acid, whereas cultivar A3 the
lowest amount (5 times less). A2, A5, and A10 showed by far the highest amounts of
phloretin/phloridzin, whereas the lowest amount was observed in cultivar A1. The
highest amount of p-coumaroyl derivatives was detected in cultivar A1, whereas the
lowest amount was found in A10. The highest amount of choline was measured in
cultivars A2 and A3, whereas the lowest amount was found in samples A1 and A9 (Figure
5D).
3.2.5.β-Sitosterol
This molecule was found in appreciable concentrations in all of the analyzed
cultivars. In particular, the highest value was measured in cultivar A2, whereas the lowest
amount was identified in A1 (Figure 6).
Figure 6. Bar charts of the metabolites (molar % ± SD) identified and quantified in the 1H NMR spectra of organic extracts
of apples.
3.2.6. Fatty Acids
The content of unsaturated fatty acids (TOT UFA) was higher than the content of
saturated fatty acids (TOT SFA) in all of the ten cultivars. Di-unsaturated fatty acids
(DUFA) were the most abundant unsaturated fatty acids in all of the samples, the highest
level being measured in cultivar A2 and the lowest in A1. Sample A1 was also
characterized by the lowest value of mono-unsaturated fatty acids (MUFA) and the
highest value of tri-unsaturated fatty acids (TUFA). Cultivar A4 showed an opposite
trend, having the highest amount of MUFA and the lowest amount of TUFA. Finally, the
highest concentration of TOT SFA was found in cultivar A1, whereas the lowest
concentration was measured in sampleA8 (Figure 6).
Figure 6.
Bar charts of the metabolites (molar %
±
SD) identified and quantified in the
1
H NMR spectra of organic extracts
of apples.
3.2.7. Polar Lipids
The highest content of phosphatidylcholine (PC) was observed in cultivar A10,
whereas the lowest content was measured in A5. Cultivar A2 showed the highest concen-
tration of digalactosyldiacylglycerol (DGDG), whereas the lowest content was observed in
sample A3 (Figure 6).
3.3. Multivariate Statistycal Analysis (PCA)
The overall analysis of the histograms made it possible to find characteristic features
relative to each cultivar. Moreover, PCA applied to all of the NMR variables allowed us to
highlight possible cultivar similarities and differences (Figure 7).
Cultivar A1 (Canditina) was observed to be well separated from all of the others for
the high content of citramalic acid, p-coumaroyl moiety, TUFA, and TOT SFA, and low
amounts of xylose, fructose, sucrose, malic acid, citric acid, alanine, and MUFA. Notably,
galacturonic acid, asparagine, aspartate, and GABA were not detected.
Carla, cultivar A5, was characterized by high contents of fructose, xylose, and organic
acids (such as quinic acid, lactic acid, acetic acid, and formic acid) and low amounts of
sucrose, chlorogenic acid, malic acid, amino acids (namely, asparagine aspartate, and
alanine), and malic and galacturonic acids. Citramalic acid was not detected.
Gamba Fina, cultivar A6, was characterized by high content of MUFA and chlorogenic
acid and low amounts of fructose, glucose, and citramalic acid. Aspartate was not detected.
Ross Giambon, cultivar A7, was characterized by high fructose and MUFA contents
and low amounts of rhamnitol and asparagine. The level of aspartate was beyond the limit
of detection.
Foods 2021,10, 289 17 of 22
Foods 2021, 10, x FOR PEER REVIEW 20 of 25
Figure 7. PCA applied to NMR data of the ten apple cultivars. (A) Sample scores and (B) loadings. PC1 and PC2 represent
30.0% and 19.8% of the total variance, respectively.
Cultivar A1 (Canditina) was observed to be well separated from all of the others for
the high content of citramalic acid, p-coumaroyl moiety, TUFA, and TOT SFA, and low
amounts of xylose, fructose, sucrose, malic acid, citric acid, alanine, and MUFA. Notably,
galacturonic acid, asparagine, aspartate, and GABA were not detected.
Carla, cultivar A5, was characterized by high contents of fructose, xylose, and organic
acids (such as quinic acid, lactic acid, acetic acid, and formic acid) and low amounts of
sucrose, chlorogenic acid, malic acid, amino acids (namely, asparagine aspartate, and
alanine), and malic and galacturonic acids. Citramalic acid was not detected.
Figure 7.
PCA applied to NMR data of the ten apple cultivars. (
A
) Sample scores and (
B
) loadings. PC1 and PC2 represent
30.0% and 19.8% of the total variance, respectively.
Runsé, cultivar A4, was characterized by high amounts of organic acids and MUFA.
Grigia di Torriana, cultivar A2, showed high amounts of glucose, galacturonic acid,
choline, DUFA, and DGDG and a low level of citramalic acid.
Calvilla, cultivar A9, was characterized by high levels of sucrose, organic acids, DUFA
and PC.
Dominici, cultivar A8, showed high contents of rhamnitol, lactic acid, asparagine,
aspartate, isoleucine, and MUFA and a low concentration of quinic acid.
Foods 2021,10, 289 18 of 22
The Magnana (A3) and Grenoble (A10) cultivars, situated nearby on the PCA score
plot (PC1 > 4, Figure 3a), were characterized by high levels of sucrose, rhamnitol, malic
acid, asparagine, alanine, and PC and low content of p-coumaroyl moiety.
4. Discussion
The present data on the chemical composition of traditional apple cultivars of the
Piedmont region can be compared with those of some of the most widely cultivated
apple cultivars in the world [
46
,
47
], such as Golden Delicious, Fuji, Granny Smith, and
Jonagold [
18
,
44
,
45
]. Taking into account the fact that the data reported in the literature are
related to apple juice composition (usually expressed as g/L or g/kg FW), while our data
are related to dried tissue (expressed as mg/100 g of DW), it is clear that only the ratios
between the components and not the absolute values can be directly compared.
The sugar content in apple fruit is very important, especially for diabetic patients who
adapt their insulin intake in relation to the carbohydrate content of each food. Table 4
shows the ratios of fructose/glucose and fructose/sucrose calculated using the literature
data [
18
,
44
,
45
] for the most popular apple cultivars (Golden Delicious, Fuji, and Jonagold)
in comparison with our data on Piedmont region cultivars. It is noteworthy that among
three different studies of the same apple cultivars, there is a lack of agreement regarding
sugar content. The studies only agree on the fact that fructose seemed to be the most
abundant in all apple cultivars, whereas the relative content of glucose and sucrose was
highly variable. The fructose/glucose ratio was always higher than 1.7 for all apple
cultivars, whereas the upper limit can be as high as 15 (calculated for Fuji according to
literature data [
44
]). In the case of Piedmont cultivars, the fructose/glucose ratio was in
the range of 4.3 (A9)–1.8 (A2). The fructose/sucrose ratio was less variable, ranging from
1.1 (Jonagold, [
44
]) to 3.0 (Fuji, [
45
]). The corresponding values for Piedmont cultivars are
inside this range, except for one (6.5), corresponding to cultivar A5 with the lowest sucrose
content and a high fructose level.
The sugar-to-acid ratio is another important parameter indicating the taste and fla-
vor of apples. In particular, a high level of the sugar/acid ratio is related to higher
sweetness [
48
,
49
]. Again, the literature data on the sugar-to-acid ratio in Golden Delicious,
Fuji, and Jonagold are not consistent—see Table 4. For example, for the Jonagold cultivar,
values from 31 [
18
] to 127 [
45
] were reported. In our study, the cultivars with the highest
sugar/acid ratio were Canditina (A1), Grigia di Torriana (A2), and Carla (A5) due to a low
level of total organic acid, whereas a lower sugar/acid ratio was observed for the Dominici
(A8), Calvilla (A9), and Grenoble (A10) cultivars.
The content of chlorogenic acid, one of the most abundant polyphenols in apple, has
been reported to range from 34.9 (Golden Delicious) to 39.4 mg/100 g of DW (Jonagold) in
the pulp of commercial cultivars [
45
]. Almost all Piedmont cultivars (except A3 and A10)
were generally characterized by a higher content of chlorogenic acid with the maximum
observed in Gamba Fina A6 (89 mg/100 g DW).
The fatty acid composition observed in the analyzed apple cultivars was similar to
that of the literature data [
18
]. In particular, DUFA was by far the main class of fatty acids,
followed by saturated fatty acids, MUFA, and TUFA. A high percentage of DUFA has been
reported for Golden Delicious and Jonagold (more than 50 mol %) [
11
], whereas among the
Piedmont cultivars, the highest DUFA content (45 mol%) was observed in Canditina A1.
The identification of some characteristic metabolites of apples, such as phloridzin/
phloretin, rhamnitol, and citramalic acid, is particularly noteworthy. [
26
,
33
,
50
,
51
] Their
content is largely variable both in the analyzed cultivars and in others described in the
literature [
18
,
25
,
26
,
52
]. These molecules are also widely studied to assign their role in the
human body and in apple biosynthetic pathways.
Phloridzin as a polyphenol compound with antioxidant activity [
53
] shows some
health benefits; in particular, it can be useful in the prevention of the type 2 diabetes
mellitus [
54
,
55
] by reducing intestinal sugar uptake. Rhamnitol has been considered as a
dietary biomarker in relation to the consumption of apples [
56
] and also as an important
Foods 2021,10, 289 19 of 22
metabolite for the geographical discrimination of apple varieties with different geographical
origins [
26
]. Finally, citramalic acid has been studied for its contribution to the development
of anthocyanin in apple skin [57] and for its correlation with the storage of apples [58].
5. Conclusions
The presented results show that every local variety has its own chemical profile
responsible for the sensorial, nutritional, and health-related properties, and they may be
used as sources of specific substances that are utilized as ingredients of health products,
such as food supplements, functional foods, and cosmetics. For instance, the Gamba Fina
(A6) cultivar showed a significant content of chlorogenic acid with recognized properties
against metabolic syndrome disorder [
59
,
60
], and the Grigia di Torriana (A2), Carla (A5),
and Grenoble (A10) cultivars, with their high content of phloretin and phloridzin, could be
considered very interesting for their possible properties against insulin resistance [
54
,
55
,
61
].
From a nutritional point of view, being rich in sugars, the Magnana cultivar (A3) can be used
to prepare apple juice with no added sugars. Moreover, as the morphological characteristics
(especially of the medium/small fruits) make these apple cultivars unattractive to the
market for direct consumption, most of them could be successfully employed for the
preparation of ingredients of nutraceutical products with high added value.
Although further studies should be performed to gain further understanding of the
genetic and environmental basis that leads to these peculiar chemical compositions and the
accumulation of polyphenols or nutrients, the reported data could be useful for national
and international information systems to reinforce food and agriculture sectors, giving
producers and industries accurate information regarding local food peculiarities [62].
Supplementary Materials:
The following are available online at https://www.mdpi.com/2304-8
158/10/2/289/s1, Table S1: Metabolite content in the hydroalcoholic extract of the analyzed apple
cultivars from the Piedmont region (mg/100 g
±
SD); Table S2: Metabolite content in the organic
extract of the analyzed apple cultivars from the Piedmont region (molar % ±SD).
Author Contributions:
Conceptualization, L.M. and A.P.S.; methodology, M.S., G.D.M.; software,
G.D.M.; validation, A.P.S., C.I. and L.M.; formal analysis, C.E., C.S.; investigation, M.S., G.D.M.;
resources, C.I.; data curation, A.P.S.; writing—original draft preparation, M.S., G.D.M. and A.P.S.;
writing—review and editing, M.D., A.B.; visualization, C.I.; supervision, A.P.S. and L.M. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Dipartimento di Chimica e Tecnologie del Farmaco and Italian
Ministry of Education, Universities and Research-Dipartimenti di Eccellenza-L. 232/2016.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
The authors wish to thank “Azienda Agricola Melamangio”, “Scuola Malva
Arnaldi”, and “Azienda Agricola Turaglio” for supplying apple samples, as well as Clara Bonifacio
and Cinzia Pizzo for their contribution to the identification and collection of ancient cultivars.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
References
1. FAO FAOSTAT Data. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 18 May 2020).
2. U.S. Department of Agriculture Food Data Central. Available online: https://fdc.nal.usda.gov/ (accessed on 18 May 2020).
3.
Lee, K.W.; Kim, Y.J.; Kim, D.O.; Lee, H.J.; Lee, C.Y. Major Phenolics in Apple and Their Contribution to the Total Antioxidant
Capacity. J. Agric. Food Chem. 2003,51, 6516–6520. [CrossRef] [PubMed]
4.
Francini, A.; Sebastiani, L. Phenolic compounds in apple (Malus x domestica borkh.): Compounds characterization and stability
during postharvest and after processing. Antioxidants 2013,2, 181–193. [CrossRef]
5.
Wikiera, A.; Mika, M.; Grabacka, M. Multicatalytic enzyme preparations as effective alternative to acid in pectin extraction.
Food Hydrocoll. 2015,44, 156–161. [CrossRef]
Foods 2021,10, 289 20 of 22
6.
Güzel, M.; Akpınar, Ö. Valorisation of fruit by-products: Production characterization of pectins from fruit peels.
Food Bioprod. Process. 2019,115, 126–133. [CrossRef]
7.
Caruso, T.; Ciarmiello, L.F.; Cutino, I.; Malvolti, M.E.; Murri, G.; Piccirillo, P. Atlante dei Fruttiferi Autoctoni Italiani; Crea—Centro
di Frutticoltura: Roma, Italy, 2016; Volume III, pp. 1276–1285.
8.
Arnaldi, S.M. Antiche VarietàPiemontesi, le Mele. Available online: https://www.antichevarietapiemontesi.it/mele/ (accessed
on 6 August 2020).
9.
Bounous, G. Antiche Cultivar di Melo in Piemonte. Piemonte, R., Ed.; 2006. Available online: http://hdl.handle.net/2318/15310
(accessed on 18 May 2020).
10.
Bounous, G.; Melo, D.G.I. Collana Coltura & Cultura; Script, A.R.T., Servizi, E.S.P.A., Eds.; Bayer Crop Science: Bologna, Italy, 2008;
pp. 82–111.
11.
Cerutti, A.K.; Bruun, S.; Donno, D.; Beccaro, G.L.; Bounous, G. Environmental sustainability of traditional foods: The case of
ancient apple cultivars in Northern Italy assessed by multifunctional LCA. J. Clean. Prod. 2013,52, 245–252. [CrossRef]
12.
Donno, D.; Beccaro, G.L.; Mellano, M.G.; Torello Marinoni, D.; Cerutti, A.K.; Canterino, S.; Bounous, G. Application of sensory,
nutraceutical and genetic techniques to create a quality profile of ancient apple cultivars. J. Food Qual.
2012
,35, 169–181. [CrossRef]
13.
Ramirez-Ambrosi, M.; Abad-Garcia, B.; Viloria-Bernal, M.; Garmon-Lobato, S.; Berrueta, L.A.; Gallo, B. A new ultrahigh
performance liquid chromatography with diode array detection coupled to electrospray ionization and quadrupole time-of-
flight mass spectrometry analytical strategy for fast analysis and improved characterization of phenolic compounds in ap.
J. Chromatogr. A 2013,1316, 78–91. [CrossRef]
14.
Mari, A.; Tedesco, I.; Nappo, A.; Russo, G.L.; Malorni, A.; Carbone, V. Phenolic compound characterisation and antiproliferative
activity of “Annurca” apple, a southern Italian cultivar. Food Chem. 2010,123, 157–164. [CrossRef]
15.
Guo, J.; Yue, T.; Yuan, Y.; Wang, Y. Chemometric Classification of Apple Juices According to Variety and Geographical Origin
Based on Polyphenolic Profiles. J. Agric. Food Chem. 2013,61, 6949–6963. [CrossRef]
16.
Bai, L.; Guo, S.; Liu, Q.; Cui, X.; Zhang, X.; Zhang, L.; Yang, X.; Hou, M.; Ho, C.T.; Bai, N. Characterization of nine polyphenols in
fruits of Malus pumila Mill by high-performance liquid chromatography. J. Food Drug Anal. 2016,24, 293–298. [CrossRef]
17.
Ferruzza, S.; Natella, F.; Ranaldi, G.; Murgia, C.; Rossi, C.; Trošt, K.; Mattivi, F.; Nardini, M.; Maldini, M.; Giusti, A.M.; et al.
Nutraceutical improvement increases the protective activity of broccoli sprout juice in a human intestinal cell model of gut
inflammation. Pharmaceuticals 2016,9, 48. [CrossRef] [PubMed]
18.
Wu, J.; Gao, H.; Zhao, L.; Liao, X.; Chen, F.; Wang, Z.; Hu, X. Chemical compositional characterization of some apple cultivars.
Food Chem. 2007,103, 88–93. [CrossRef]
19.
Iaccarino, N.; Varming, C.; Petersen, M.A.; Viereck, N.; Schütz, B.; Toldam-Andersen, T.B.; Randazzo, A.; Engelsen, S.B. Ancient
danish apple cultivars—A comprehensive metabolite and sensory profiling of apple juices. Metabolites
2019
,9, 139. [CrossRef]
[PubMed]
20.
Aprea, E.; Gika, H.; Carlin, S.; Theodoridis, G.; Vrhovsek, U.; Mattivi, F. Metabolite profiling on apple volatile content based on
solid phase microextraction and gas-chromatography time of flight mass spectrometry. J. Chromatogr. A
2011
,1218, 4517–4524.
[CrossRef] [PubMed]
21.
Sobolev, A.P.; Thomas, F.; Donarski, J.; Ingallina, C.; Circi, S.; Cesare Marincola, F.; Capitani, D.; Mannina, L. Use of NMR
applications to tackle future food fraud issues. Trends Food Sci. Technol. 2019,91, 347–353. [CrossRef]
22.
Belton, P.S.; Delgadillo, I.; Gil, A.M.; Roma, P.; Casuscelli, F.; Colquhoun, I.J.; Dennis, M.J.; Spraul, M. High-field proton NMR
studies of apple juices. Magn. Reson. Chem. 1997,35, 52–60. [CrossRef]
23.
Berregi, I.; del Campo, G.; Caracena, R.; Miranda, J.I. Quantitative determination of formic acid in apple juices by 1H NMR
spectrometry. Talanta 2007,72, 1049–1053. [CrossRef] [PubMed]
24.
Eisenmann, P.; Ehlers, M.; Weinert, C.H.; Tzvetkova, P.; Silber, M.; Rist, M.J.; Luy, B.; Muhle-Goll, C. Untargeted NMR
spectroscopic analysis of the metabolic variety of new apple cultivars. Metabolites 2016,6, 29. [CrossRef]
25.
Sciubba, F.; Di Cocco, M.E.; Gianferri, R.; Capuani, G.; De Salvador, F.R.; Fontanari, M.; Gorietti, D.; Delfini, M. Nuclear Magnetic
Resonance-Based Metabolic Comparative Analysis of Two Apple Varieties with Different Resistances to Apple Scab Attacks.
J. Agric. Food Chem. 2015,63, 8339–8347. [CrossRef]
26.
Tomita, S.; Nemoto, T.; Matsuo, Y.; Shoji, T.; Tanaka, F.; Nakagawa, H.; Ono, H.; Kikuchi, J.; Ohnishi-Kameyama, M.; Sekiyama, Y.
A NMR-based, non-targeted multistep metabolic profiling revealed l-rhamnitol as a metabolite that characterised apples from
different geographic origins. Food Chem. 2015,174, 163–172. [CrossRef]
27.
Sobolev, A.P.; Mannina, L.; Capitani, D.; Sanzò, G.; Ingallina, C.; Botta, B.; Fornarini, S.; Crestoni, M.E.; Chiavarino, B.;
Carradori, S.; et al.
A multi-methodological approach in the study of Italian PDO “Cornetto di Pontecorvo” red sweet pepper.
Food Chem. 2018,255, 120–131. [CrossRef] [PubMed]
28.
Ingallina, C.; Capitani, D.; Mannina, L.; Carradori, S.; Locatelli, M.; Di Sotto, A.; Di Giacomo, S.; Toniolo, C.; Pasqua, G.;
Valletta, A.; et al.
Phytochemical and biological characterization of Italian “sedano bianco di Sperlonga” Protected Geographical
Indication celery ecotype: A multimethodological approach. Food Chem. 2020,309, 125649. [CrossRef] [PubMed]
29.
Ingallina, C.; Sobolev, A.P.; Circi, S.; Spano, M.; Giusti, A.M.; Mannina, L. New hybrid tomato cultivars: An NMR-based chemical
characterization. Appl. Sci. 2020,10, 1887. [CrossRef]
30.
D’Imperio, M.; Dugo, G.; Alfa, M.; Mannina, L.; Segre, A.L. Statistical analysis on Sicilian olive oils. Food Chem.
2007
,102, 956–965.
[CrossRef]
Foods 2021,10, 289 21 of 22
31. Contessa, C.; Botta, R. Comparison of physicochemical traits of red-fleshed, commercial and ancient apple cultivars. Hortic. Sci.
2016,43, 159–166. [CrossRef]
32.
Cavanna, M.; Marinoni, D.T.; Bounous, G.; Botta, R. Genetic diversity in ancient apple germplasm from northwest Italy. J. Hortic.
Sci. Biotechnol. 2008,83, 549–554. [CrossRef]
33.
Mannina, L.; Sobolev, A.P.; Viel, S. Liquid state 1H high field NMR in food analysis. Prog. Nucl. Magn. Reson. Spectrosc.
2012
,66,
1–39. [CrossRef]
34.
Arnaldi, S.M. Piante di melo di Antiche Varietàpiemontesi. Available online: http://www.scuolamalva.it/wp-content/uploads/
2016/06/Descrizione-piante-melo-TRIO.pdf (accessed on 6 August 2020).
35.
Ingallina, C.; Sobolev, A.P.; Circi, S.; Spano, M.; Fraschetti, C.; Filippi, A.; Di Sotto, A.; Di Giacomo, S.; Mazzoccanti, G.;
Gasparrini, F.; et al. Cannabis sativa L. Inflorescences from Monoecious Cultivars Grown in Central Italy: An Untargeted
Chemical Characterization from Early Flowering to Ripening. Molecules 2020,25, 1908. [CrossRef]
36.
Zhu, D.; Shen, Y.; Wei, L.; Xu, L.; Cao, X.; Liu, H.; Li, J. Effect of particle size on the stability and flavor of cloudy apple juice.
Food Chem. 2020,328, 126967. [CrossRef]
37.
Vermathen, M.; Marzorati, M.; Baumgartner, D.; Good, C.; Vermathen, P. Investigation of different apple cultivars by high
resolution magic angle spinning NMR. A feasibility study. J. Agric. Food Chem. 2011,59, 12784–12793. [CrossRef]
38.
Vrhovsek, U.; Rigo, A.; Tonon, D.; Mattivi, F. Quantitation of polyphenols in different apple varieties. J. Agric. Food Chem.
2004
,
52, 6532–6538. [CrossRef] [PubMed]
39.
Panzella, L.; Petriccione, M.; Rega, P.; Scortichini, M.; Napolitano, A. A reappraisal of traditional apple cultivars from Southern
Italy as a rich source of phenols with superior antioxidant activity. Food Chem. 2013,140, 672–679. [CrossRef] [PubMed]
40.
Sugawara, T.; Miyazawa, T. Separation and determination of glycolipids from edible plant sources by high-performance liquid
chromatography and evaporative light-scattering detection. Lipids 1999,34, 1231–1237. [CrossRef] [PubMed]
41.
Ingallina, C.; Spano, M.; Sobolev, A.P.; Esposito, C.; Santarcangelo, C.; Baldi, A.; Daglia, M.; Mannina, L. Characterization of Local
Products for Their Industrial Use : The Case of Italian Potato Cultivars Analyzed by Untargeted and Targeted Methodologies.
Foods 2020,9, 1216. [CrossRef] [PubMed]
42.
Ticha, A.; Salejda, A.M.; Hyšpler, R.; Matejicek, A.; Paprstein, F.; Zadak, Z.; Cukrów, W.S. Jabłkach Ró˙
znych Odmian I Ich Wpływ
Na Cechy Sensoryczne. Zywn. Nauk. Technol. Jakosc/Food. Sci. Technol. Qual. 2015,22, 137–150. [CrossRef]
43.
Hermann, K.; Bordewick-Dell, U. Fructose in different apple varieties. Implications for apple consumption in persons affected by
fructose intolerance. Ernährungs Umschau 2018,65, 48–52. [CrossRef]
44.
Hecke, K.; Herbinger, K.; Veberiˇc, R.; Trobec, M.; Toplak, H.; Štampar, F.; Keppel, H.; Grill, D. Sugar-, acid- and phenol contents in
apple cultivars from organic and integrated fruit cultivation. Eur. J. Clin. Nutr. 2006,60, 1136–1140. [CrossRef]
45.
Kim, I.; Ku, K.H.; Jeong, M.C.; Kwon, S., II; Lee, J. Metabolite profiling and antioxidant activity of 10 new early- to mid-season
apple cultivars and 14 traditional cultivars. Antioxidants 2020,9, 443. [CrossRef]
46.
Agricultural Marketing Resource Center Apples. Available online: https://www.agmrc.org/commodities-products/fruits/
apples (accessed on 6 August 2020).
47.
Produce Report Global Trends in Apple Innovation. Available online: https://www.producereport.com/article/global-trends-
apple-innovation (accessed on 6 August 2020).
48.
Petkovsek, M.M.; Stampar, F.; Veberic, R. Parameters of inner quality of the apple scab resistant and susceptible apple cultivars
(Malus domestica Borkh.). Sci. Hortic. 2007,114, 37–44. [CrossRef]
49.
Colaric, M.; Veberic, R.; Stampar, F.; Hudina, M. Evaluation of peach and nectarine fruit quality and correlations between sensory
and chemical attributes. J. Sci. Food Agric. 2005,85, 2611–2616. [CrossRef]
50.
Hulme, A.C. The isolation of L-citramalic acid from the peel of the apple fruit. Biochim. Biophys. Acta
1954
,14, 36–43. [CrossRef]
51.
Ehrenkranz, J.R.L.; Lewis, N.G.; Kahn, C.R.; Roth, J. Phlorizin: A review. Diabetes. Metab. Res. Rev.
2005
,21, 31–38. [CrossRef]
[PubMed]
52.
Escarpa, A.; González, M.C. High-performance liquid chromatography with diode-array detection for the determination of
phenolic compounds in peel and pulp from different apple varieties. J. Chromatogr. A 1998,823, 331–337. [CrossRef]
53. Boyer, J.; Liu, R.H. Apple phytochemicals and their health benefits. Nutr. J. 2004,3, 1–15. [CrossRef]
54.
Niederberger, K.E.; Tennant, D.R.; Bellion, P. Dietary intake of phloridzin from natural occurrence in foods. Br. J. Nutr.
2020
,123,
942–950. [CrossRef]
55.
Kumar, S.; Sinha, K.; Sharma, R.; Purohit, R.; Padwad, Y. Phloretin and phloridzin improve insulin sensitivity and enhance
glucose uptake by subverting PPAR
γ
/Cdk5 interaction in differentiated adipocytes. Exp. Cell Res.
2019
,383, 111480. [CrossRef]
56.
Posma, J.M.; Garcia-Perez, I.; Heaton, J.C.; Burdisso, P.; Mathers, J.C.; Draper, J.; Lewis, M.; Lindon, J.C.; Frost, G.;
Holmes, E.; et al.
Integrated Analytical and Statistical Two-Dimensional Spectroscopy Strategy for Metabolite Identification: Application to Dietary
Biomarkers. Anal. Chem. 2017,89, 3300–3309. [CrossRef]
57.
Noro, S.; Kudo, N.; Kitzuwa, T. Differences in Sugars and Organic Acids between Red and Yellow Apple Cultivars at Time of
Coloring, and Effect of Citramalic Acid on Development of Anthocyanin. J. Jpn. Soc. Hortic. Sci. 1988,57, 381–389. [CrossRef]
58.
Rudell, D.R.; Mattheis, J.P.; Curry, E.A. Prestorage ultraviolet-white light irradiation alters apple peel metabolome. J. Agric.
Food Chem. 2008,56, 1138–1147. [CrossRef]
59.
Santana-Gálvez, J.; Cisneros-Zevallos, L.; Jacobo-Velázquez, D.A. Chlorogenic Acid: Recent advances on its dual role as a food
additive and a nutraceutical against metabolic syndrome. Molecules 2017,22, 358. [CrossRef]
Foods 2021,10, 289 22 of 22
60.
Finamore, A.; Roselli, M.; Donini, L.M.; Brasili, D.E.; Rami, R.; Carnevali, P.; Mistura, L.; Pinto, A.; Giusti, A.M.; Mengheri, E.
Supplementation with Bifidobacterium longum Bar33 and Lactobacillus helveticus Bar13 mixture improves immunity in elderly
humans (over 75 years) and aged mice. Nutrition 2019,63, 184–192. [CrossRef] [PubMed]
61.
Boutaoui, N.; Zaiter, L.; Benayache, F.; Benayache, S.; Cacciagrano, F.; Cesa, S.; Secci, D.; Carradori, S.; Giusti, A.M.;
Campestre, C.; et al.
Atriplex mollis Desf. aerial parts: Extraction procedures, secondary metabolites and color analysis. Molecules
2018,23, 1962. [CrossRef] [PubMed]
62.
Toledo, Á.; Burlingame, B. Biodiversity and nutrition: A common path toward global food security and sustainable development.
J. Food Compos. Anal. 2006,19, 477–483. [CrossRef]
