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<figure><img src="image_112.png" /></figure>

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_113.png" /></figure>

Article

# Untargeted and Targeted Metabolomic Profiling of Australian Indigenous Fruits

Vuanghao Lim<sup>1,2,*</sup>, Sara Ghorbani Gorji<sup>1</sup>, Venea Dara Daygon<sup>1</sup> and Melissa Fitzgerald<sup>1,*</sup>

Received: 18 February 2020; Accepted: 11 March 2020; Published: 19 March 2020

**Abstract:** Selected Australian native fruits such as Davidson's plum, finger lime and native pepperberry have been reported to demonstrate potent antioxidant activity. However, comprehensive metabolite profiling of these fruits is limited, therefore the compounds responsible are unknown, and further, the compounds of nutritional value in these native fruits are yet to be described. In this study, untargeted and targeted metabolomics were conducted using the three fruits, together with assays to determine their antioxidant activities. The results demonstrate that targeted free and hydrolysed protein amino acids exhibited high amounts of essential amino acids. Similarly, important minerals like potassium were detected in the fruit samples. In antioxidant activity, Davidson's plum reported the highest activity in ferric reducing power (FRAP), finger lime in antioxidant capacity (ABTS), and native pepperberry in free radical scavenging (DPPH) and phosphomolybdenum assay. The compounds responsible for the antioxidant activity were tentatively identified using untargeted GC×GC-TOFMS and UHPLC-QqQ-TOF-MS/MS metabolomics. A clear discrimination into three clusters of fruits was observed using principal component analysis (PCA) and partial least squares (PLS) analysis. The correlation study identified a number of compounds that provide the antioxidant activities. GC×GC-TOFMS detected potent aroma compounds of limonene, furfural, and 1-R-α-pinene. Based on the untargeted and targeted metabolomics, and antioxidant assays, the nutritional potential of these Australian bush fruits is considerable and supports these indigenous fruits in the nutraceutical industry as well as functional ingredients for the food industry, with such outcomes benefiting Indigenous Australian communities.

**Keywords:** Davidson's plum; finger lime; native pepperberry; antioxidant; amino acids; metabolomics; GC×GC-TOFMS; UHPLC-QqQ-TOF-MS/MS; bush fruit

## 1. Introduction

Australia is famous for its rich diversity of native plant foods, which are also known as bush tucker, or bush food. There are about 6500 types of bush foods, and only a handful have been commercialised, and are considered to be worth about $18–25 million to the Australian economy [1,2]. Among these, there are about 2400 native fruits found in Queensland alone. In the stocktake published by the Australian native food industry, [joint collaboration of the Australian Native Food Industry Limited (ANFIL) and Rural Industries Research and Development Corporation (RIRDC) now AgriFutures Australia], emphasis has focussed on twelve key crops for further development. In the list, several native fruits have been identified, such as Davidson's plum, desert limes, quandong, lemon aspen, riberry, muntries, finger lime, kakadu plum, and native pepperberry [3]. These native edible fruits possess health benefits and can be used in applications such as functional foods and nutraceuticals, contributing to the emerging commercialisation in pharmaceutical industries. In the present work,

<footnote n="1">¹ School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia; s.ghorbanigorji@uq.edu.au (S.G.G.); v.daygon@uq.edu.au (V.D.D.)</footnote>

<footnote n="2">² Integrative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200, Penang, Malaysia</footnote>

<footnote>* Correspondence: vlim@usm.my (V.L.); m.fitzgerald2@uq.edu.au (M.F.)</footnote>
three fruits were selected for in depth metabolomic profiling based on future potential: Davidson's plum, native pepperberry, and finger lime.

Davidson's plum (*Davidsonia pruriens* F. Muell) belongs to the Davidsoniaceae family, and it grows mostly in north-east Queensland in areas like coastal and upland rainforests (Djirbalngan, Yidinjdji, Djabuganjdi, Kuku-yalanji Nations). There are two other varieties; *D. johnsonii*, which is found in the south-east Queensland and New South Wales (Bundjalung Nation) while *D. jerseyana*, cultivates in the northern New South Wales (Bundjalung and Gumbainggir Nations) [4]. The tree can reach up to 20 m high [5]. The fruit is a purple plum described as tasting intensely sour [6], due to a high amount of acid with very little sugar to counteract it [6]. The fruit is rich in flavonoids, vitamins, minerals and several other important secondary metabolites, such as anthocyanins as well as proanthocyanidins [4]. Few preliminary studies have proclaimed that the extracts of Davidson's plum fruit inhibited in vitro cancer cells, metabolic syndrome enzymes, and contained high antioxidant activity [7]. Belonging to the Rutaceae family, finger lime, *Citrus australasica* var. *sanguinea* is also called Rainforest Pearl, found in the rainforests of Queensland (Bundjalung Nation) and northern part of New South Wales (Gumbainggir Nation) [8,9]. The fruits come in various shapes and sizes, and a range of colours including purple, green, yellow and pink. The native finger lime cultivated in Australia is one of the seven citrus species with 'caviar like' appearance of the fruit pulp. In general, finger lime is rich in vitamins, minerals and terpenes, such as limonene [10]. The bioactivities and phytoconstituents of finger lime are not well-established with reported properties limited to antioxidant and anti-inflammatory activity [9,11]. Native pepperberry [Tasmannia lanceolata (Poir.) A.C. Smith], which belongs to the family of Winteraceae grows at highland areas in Tasmania (Palawa Nation) and southeastern Australia (Boonwurrung, Woiworung, Jaitmatang, Bidwell, Yuin, Ngarigo Nations). As a native shrub tree, the fruit is black (dark purple) in colour and contains many tiny black seeds [12]. Native pepperberry has been ethnopharmacologically used as an ailment to treat stomach discomfort, and as an antifungal for skin diseases by Indigenous people, and it has scientifically proven to inhibit in vitro platelet aggregation, microbial activity, as well as antioxidant capacity [13–15]. Sesquiterpene polygodial, a major phytoconstituent which is mostly found in the oil of native pepperberry makes the spicy and pungent flavour [14]. Additionally, other major secondary metabolites like guaiol, calamenene, hexacosanal, drimenol and linalool were also reported to possess antimicrobial properties [12,16].

In order to support the medical, nutritional and food significance of these bushfoods, it is important to identify the functional compounds and understand their activities. In this regard, the identification of phytoconstituents from various analyses is emphasised, considering the synergistic or antagonistic activity of the metabolite-metabolite interactions for certain bioactivities. Plant metabolomics provides the tools necessary to analyse and potentially identify all the metabolites that possess bioactive properties. Analytical platforms, such as ultra-high performance liquid chromatography mass spectrometry (UHPLC-MS), have been widely used in plant science for metabolomics applications to identify and quantify compounds [17,18]. The comprehensive profiling and metabolomics studies of bush fruits are important in reaping better insight into commercial viability of these fruits. Antioxidant activity has been reported for the fruits [7,11,14,15,19], however the correlation of the activity with bioactive compounds through metabolomics approaches has not been reported. Therefore, this study was conducted to bridge the gap for the discovery of antioxidant-based active metabolites together with comprehensive profiling of Davidson's plum, finger lime and native pepperberry. In the current study, profiling of the fruits was conducted using targeted applications such as mineral analysis and amino acid analysis, and untargeted applications for semi-polar and aromatic compounds. Multivariate data analysis (MVDA), both supervised and un-supervised was applied to assess the association and discrimination of the compounds in the fruit samples. Data comparison of antioxidant scavenging activities was carried out together with total phenolic, flavonoids and flavonols content. The correlation between identified compounds and antioxidant activity was then conducted to identify potential bioactive markers in the fruit samples, as a mean of comprehensive findings for the fruits to be used in the nutraceutical industry.
## 2. Results

### 2.1. Untargeted Metabolic Profiling Using GC×GC-TOFMS and UHPLC-QqQ-TOF-MS/MS

The aromatic compounds in the powdered fruits were analysed by two-dimensional gas chromatography time of flight mass spectrometry (GC × GC-TOFMS), and approximately 616 peaks were found from each sample. The samples were screened using ChromaTOF software for the presence of common components. A total of 604 compounds were tentatively identified in the samples of fruits based on the library match searching data in NIST 11 v 2.0 and our in-house library. The metabolite profiles were compared in the PCA scores plots by submitting the combined Davidson's plum, finger lime and native pepperberry triplicate samples as shown in Figure 1a. Clustering of the scores was observed in three groups based on the three types of fruits. Two PCs were found with the greatest eigenvalues recorded at 55.7% and 25.7% of the total variance. The discrimination of the samples in three clusters indicates the aroma components in these fruits were different.

<figure><img src="image_114.png" /><figcaption>**Figure 1.** The principal component analysis of Davidson's plum (DP), finger lime (FL) and native pepperberry (NP) using (a) GC×GX-TOFMS; (b) UHPLC-QqQ-TOF-MS/MS; and (c) loading score plot representing compounds using UHPLC-QqQ-TOF-MS/MS. Compounds are coloured to indicate the relative density of peak areas.</figcaption></figure>
The untargeted metabolic profile of the fruit samples obtained from the negative mode of ultrahigh-performance liquid chromatography triple quadrupole-time-of-flight mass spectrometry/mass spectrometry (UHPLC-QqQ-TOF-MS/MS) provided 1166 compounds (after data processing), with 542 annotated compounds tentatively identified by MS1 and MS2 matchings; 478 by MS1 and 64 by MS2 matching only. The PCA analysis dataset of the 3 types of fruits provided distinct differences of clustering patterns as shown in Figure 1b. Noticeably, PC1 indicated 56.1% deviation between the 3 fruits with finger lime clustering on the right side, whereas native pepperberry was observed towards the negative quadrant of PC1. The PCA was further analysed in loading plot to observe the discrimination of the compounds (Figure 1c).

## 2.2. Antioxidant Activity Using DPPH, ABTS, FRAP and Phosphomolybdenum Assays

The fruit samples were subjected to in vitro antioxidant activity using different spectrophotometric assays as shown in Table 1. From the table, finger lime exhibited the strongest capability in ABTS, however the lowest in DPPH assay despite the same radical scavenging activity. In reducing power ability, Davidson's plum reported the highest, while finger lime and native pepperberry did not show any significant difference. Phosphomolybdate method was later investigated for total antioxidant activity and expressed as gallic acid equivalent (µmol/gDW). The activity was found to decrease in the order of native pepperberry > finger lime > Davidson's plum.

## 2.3. Total Phenolic Content (TPC), Total Flavonoid Content (TFC) and Total Flavonol Content (TFIC)

Further investigation was conducted for TPC, TFC and TFIC. Native pepperberry exhibited a significantly higher amount of TPC, TFC and TFIC compared to finger lime, as shown in Table 1. Finger lime showed the lowest gallic acid and quercetin equivalent for all the assays. These assays showed a similar trend for the samples, with native pepperberry the highest, followed by Davidson's plum, and then finger lime.

## 2.4. Correlation Between Antioxidant Activity And Compounds in The GCMS Dataset

A supervised multivariate data, partial least squares (PLS) was applied to fathom the relationship between antioxidant activity and the fruit extracts. Based on Figure S1 in Supplementary Materials, the model showed cumulative R²X = 0.864 and R²Y = 0.886, indicating good fitness and high predictability (>0.5). In this analysis, the X-variables denote the aroma compounds and Y-variables are the antioxidant activity. Both PC1 and PC2 explained 86.4% of the variation in the aroma compounds showing discrimination in the compounds for antioxidant activity, with Y-variables recorded at 98%. In order to determine the aroma compounds that may contribute to the antioxidant activity, variable importance in projection (VIP) was carried out. The potential bioactive aroma compounds were chosen from the variables with VIP values of greater than 2.5. A total of 21 aroma compounds were sorted and the details are tabulated in Table 2. The identified aroma compounds were categorised into 6 different groups, namely, terpenes, aldehydes, terpenoids, furans, isoprenoids, and alkanes.

<table><caption>**Table 1.** In vitro antioxidant activity of the 3 fruit samples. Data is expressed in dry weight, DW and presented as mean values ± standard deviation (n = 3). Different superscript letters within each column indicate significant (p < 0.01) difference between samples.</caption><thead><tr><th>Samples</th><th>ABTS<br>(GAEµmol/gDW)</th><th>DPPH<br>(GAEµmol/gDW)</th><th>FRAP<br>(µmol<br>Fe²⁺/gDW)</th><th>Phosphomolybdenum<br>(GAEµmol/gDW)</th><th>TPC<br>(GAEµmol/gDW)</th><th>TFC (QTE<br>µmol/gDW)</th><th>TFIC (QTE<br>(µmol/gDW)</th></tr></thead><tbody><tr><td>DP</td><td>21.92 ± 1.60<sup>a</sup></td><td>97.38 ± 3.93<sup>b</sup></td><td>500.38 ± 64.32<sup>c</sup></td><td>52.71 ± 5.40<sup>a</sup></td><td>113.58 ± 14.20<sup>b</sup></td><td>11.31 ± 0.52<sup>a</sup></td><td>5.55 ± 0.08<sup>b</sup></td></tr><tr><td>FL</td><td>62.73 ± 0.55<sup>c</sup></td><td>17.23 ± 2.04<sup>a</sup></td><td>46.16 ± 3.74<sup>a</sup></td><td>114.89 ± 3.25<sup>b</sup></td><td>63.46 ± 1.10<sup>a</sup></td><td>10.59 ± 0.97<sup>a</sup></td><td>0.56 ± 0.18<sup>a</sup></td></tr><tr><td>NP</td><td>22.06 ± 3.43<sup>a</sup></td><td>119.49 ± 2.72<sup>c</sup></td><td>48.48 ± 3.23<sup>a</sup></td><td>291.94 ± 2.23<sup>c</sup></td><td>134.82 ± 11.08<sup>b</sup></td><td>35.86 ± 3.99<sup>b</sup></td><td>9.99 ± 0.21<sup>c</sup></td></tr></tbody></table>

ABTS: 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid); DPPH: 2,2-diphenyl-1-picrylhydrazyl; FRAP: Ferric Reducing of Antioxidant Power Assay; TPC: Total Phenolic Content; TFC: Total Flavonoid Content; TFIC: Total Flavonol Content; DP: Davidson's plum; FL: Finger lime; NP: Native pepperberry; GA: Gallic acid equivalent; QTE: Quercetin equivalent.
<table><caption>**Table 2.** Aroma compounds identified by variable importance in projection (VIP) > 2.5 selection method for Davidson's plum, finger lime and native pepperberry.</caption><thead><tr><th>Peak No.</th><th>Compounds</th><th>Group (VIP Score)</th><th>Retention Time (1t<sub>R</sub>, 2t<sub>R</sub>)<sup>a</sup></th><th>CAS No.</th><th>Mf<sup>b</sup></th><th>Exact Mass</th><th>Level<sup>c</sup></th></tr></thead><tbody><tr><td>526</td><td>1-methyl-4-(prop-1-en-2-yl) cyclohex-1-ene [Limonene]</td><td>Terpene (7.01)</td><td>769.55, 0.72</td><td>138-86-3</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>329</td><td>1-R-α-Pinene</td><td>Terpene (6.34)</td><td>716.15, 637.29</td><td>7785-70-8</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>498</td><td>Furfural</td><td>Aldehyde (5.58)</td><td>577.36, 1.28</td><td>98-01-1</td><td>C<sub>5</sub>H<sub>4</sub>O<sub>2</sub></td><td>96.08</td><td>L1</td></tr><tr><td>508</td><td>Hexanal</td><td>Aldehyde (5.12)</td><td>503.75, 0.69</td><td>66-25-1</td><td>C<sub>6</sub>H<sub>12</sub>O</td><td>100.15</td><td>L1</td></tr><tr><td>437</td><td>1-methyl-4-(1-methylethylidene)-cyclohexene [Terpinolene]</td><td>Terpenoid (4.74)</td><td>972.00, 0.98</td><td>586-62-9</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>410</td><td>γ-Terpine</td><td>Terpene (4.70)</td><td>1072.61, 1.18</td><td>99-85-4</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>599</td><td>Terpinen-4-ol</td><td>Terpenoid (4.49)</td><td>1038.69, 1.00</td><td>562-74-3</td><td>C<sub>10</sub>H<sub>18</sub>O</td><td>154.24</td><td>L2</td></tr><tr><td>397</td><td>2,2-dimethyl-3-methylidenebicyclo[2.2.1]heptane [Camphene]</td><td>Terpenoid (4.28)</td><td>953.92, 1.06</td><td>79-92-5</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>326</td><td>α-Phellandrene</td><td>Terpene (3.78)</td><td>778.35, 0.70</td><td>99-83-2</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>342</td><td>1-methyl-4-(1-methylethenyl)-benzene [p-Cymenene]</td><td>Terpene (3.63)</td><td>960.45, 0.88</td><td>1195-32-0</td><td>C<sub>10</sub>H<sub>12</sub></td><td>132.20</td><td>L2</td></tr><tr><td>493</td><td>2-ethylfuran</td><td>Furan (3.55)</td><td>367.27, 0.88</td><td>3208-16-0</td><td>C<sub>6</sub>H<sub>8</sub>O</td><td>96.12</td><td>L2</td></tr><tr><td>281</td><td>1-methyl-4-(1-methylethyl)-7-Oxabicyclo[2.2.1]heptane</td><td>Alkane (3.38)</td><td>757.76, 0.68</td><td>470-67-7</td><td>C<sub>10</sub>H<sub>18</sub>O</td><td>154.24</td><td>L2</td></tr><tr><td>1</td><td>(-)-Carvone</td><td>Terpenoid (3.11)</td><td>1170.77, 1.10</td><td>6485-40-1</td><td>C<sub>10</sub>H<sub>14</sub>O</td><td>150.21</td><td>L2</td></tr><tr><td>331</td><td>α-Terpineol</td><td>Terpenoid (3.05)</td><td>1072.61, 1.18</td><td>98-55-5</td><td>C<sub>10</sub>H<sub>18</sub>O</td><td>154.24</td><td>L2</td></tr><tr><td>527</td><td>1-menthone</td><td>Terpenoid (2.90)</td><td>1018.87, 0.87</td><td>10458-14-7</td><td>C<sub>10</sub>H<sub>18</sub>O</td><td>154.24</td><td>L2</td></tr><tr><td>364</td><td>4-methylene-1-(1-methylethyl)-Bicyclo[3.1.0]hexane, [Sabinene]</td><td>Terpene (2.89)</td><td>799.78, 0.70</td><td>3387-41-5</td><td>C<sub>10</sub>H<sub>16</sub></td><td>136.23</td><td>L2</td></tr><tr><td>399</td><td>Caryophyllene</td><td>Isoprenoid (2.82)</td><td>1399.12, 0.71</td><td>87-44-5</td><td>C<sub>15</sub>H<sub>24</sub></td><td>204.35</td><td>L2</td></tr><tr><td>569</td><td>Pentanal</td><td>Aldehyde (2.81)</td><td>382.87, 0.67</td><td>110-62-3</td><td>C<sub>5</sub>H<sub>10</sub>O</td><td>86.13</td><td>L2</td></tr><tr><td>272</td><td>5-isopropenyl-2-methylcyclopent-1-enecarboxaldehyde</td><td>Terpenoid (2.78)</td><td>1275.00, 0.95</td><td>3865-09-6</td><td>C<sub>10</sub>H<sub>14</sub>O</td><td>150.21</td><td>L2</td></tr><tr><td>271</td><td>4-methylene-5-hexenal</td><td>Aldehyde (2.52)</td><td>629.52, 0.90</td><td>17844-21-2</td><td>C<sub>7</sub>H<sub>10</sub>O</td><td>110.15</td><td>L2</td></tr><tr><td>469</td><td>Dodecane</td><td>Alkanes (2.51)</td><td>984.85, 0.61</td><td>112-40-3</td><td>C<sub>12</sub>H<sub>26</sub></td><td>170.33</td><td>L1</td></tr></tbody></table>

## 2.5. Correlation Between Antioxidant Activity And Compounds in The LCMS Dataset

With regard to the clear variance defined by the PCA (Figure 1b) and antioxidant activity (Table 2), a supervised multivariate data analysis, i.e., partial least squares (PLS) was utilised to organise and distinguish the samples according to their MS dataset. The correlation of the antioxidant activity and identified compounds was conducted by setting ABTS, DPPH, FRAP and phosphomolybdenum assays as Y variables, while the identified compounds were assigned as the X variables (Figure 2). In this experiment, a clear discrimination was achieved between Davidson's plum, finger lime and native pepperberry with good discriminant model indicators, R<sup>2</sup>Y at 0.968 with the goodness-of-prediction

<footnote><sup>a</sup> 1t<sub>R</sub>: First dimension retention time, 2t<sub>R</sub>: Second dimension retention time; <sup>b</sup> Mf: Molecular formula; <sup>c</sup> Level: Level of identification based on the guidelines [20]. L1—level 1 identified through authentic chemical standards; L2—putatively identified compounds through library matching.</footnote>
value, Q² at 0.965. The robustness of the PLS model on the antioxidant activity for discriminating the identified compounds was confirmed by the results of permutation tests (Figure S2 in Supplementary Materials). The correlation of identified compounds with antioxidant activity showed discrimination of the fruits with scattered Y variables as shown in Figure 2b. Y variables of DPPH and FRAP are seen in the right quadrant of PC1 (X = 47.5%; Y = 55.1%), whereas the ABTS and phosphomolybdenum assays are towards the left. PC2 accounts for 49.5% of X variables and 43.3% in Y variables.

<figure><img src="image_115.png" /><figcaption>**Figure 2.** (a) Partial least square (PLS) score plot derived from UHPLC-QqQ-TOF-MS/MS on 3 fruit sample. (b) PLS biplot plots showing correlation between identified compounds with antioxidant activity. X = compounds, Y = antioxidant activity. (c) The variable importance in the projection (VIP) values (>1.5) represented by red coloured bars, (1.0–1.5) in green while blue coloured bars are VIP values (<1.0). The coloured compounds in (b) are VIP values >1.5.</figcaption></figure>
Compounds that are responsible for the separation in Figure 2b were observed through variable importance in projection (VIP) selection approach. In this model, we set the VIP values at greater than 1.5 and loadings correlation coefficient [p Nature) values above 0.5 to be significant for sample separation in the PLS model. From the analysis, a total of 44 compounds displayed good VIP values as listed in Table 3, with 7 unknown compounds. From the VIP scores, sugars, flavonoids and terpenes were the most important classes of compounds for antioxidant activity, followed by other phenolic compounds. Next, in order to better understand the distinctive incidence in antioxidant activity, we provide a fold-change analysis on the compounds (LogFC, Table 3). This analysis aims to correlate the significant value changes between the two group means. In this analysis, the fold-change was set at two, and any numbers that surpassed the threshold were considered significant. In particular, finger lime and native pepperberry showed the highest series of fold change with the fold-change distributions ranging from 3 to 10 times higher compared to Davidson's plum and finger lime. Nevertheless, Davidson's plum and native pepperberry only exhibited 5 significant fold-change of putatively identified compounds, ie. isovitexin, quercetagetin, racemosic acid, quercetin 3-[rhamnosyl-(1->2)-alpha-L-arabinopyranoside], and [3-[2-(3-hydroxy-5-methoxyphenyl)ethyl]phenyl]oxidanesulfonic acid, with the last compound also exhibited the highest fold-change (17.55).

## 2.6. Targeted Free And Protein Amino Acid Profiling Using UHPLC-MS

In total, 11 free amino acids were found in Davidson's plum, 19 in finger lime and 14 in native pepperberry (Table 4). Interestingly, essential amino acid, lysine, exhibited the highest free amino acid for all the three fruit samples with Davidson's plum (74.11%), finger lime (53.74%), and native pepperberry (67.88%). Other amino acids such as isoleucine, cystine and histidine are relatively high among the samples. Most of the free amino acids were detected in finger lime, and included aromatic amino acids, phenylalanine, tyrosine and tryptophan; sulfur-containing amino acids, cysteic acid, taurine and cystine; and non-essential amino acids, arginine, aspartic acid, glutamic acid, glycine, proline, and serine, but not alanine. Trace amounts of some free amino acids (<0.7%) were detected in native pepperberry compared to lysine, and isoleucine.

Analysis of the protein amino acids showed that Davidson's plum exhibited 10 amino acids, and both finger lime and native pepperberry reported all the protein amino acids (Table 4). Similar to the profile of free amino acids, the three fruit samples demonstrated that lysine was the highest amount of hydrolysed protein amino acid, followed by isoleucine indicating a significant difference between the samples for isoleucine. lysine is significantly higher in Davidson's plum compared to both finger lime and native pepperberry, in which the content is insignificant. Different trends of amino acids were observed for the three samples, where the percentage of amino acids in Davidson's plum ranged from 55.53% (lysine) to 0.08% (valine), finger lime, from 45.185% (lysine) to 0.235% (norleucine), and native pepperberry, from 37.07% (lysine) to 0.18% (cysteic acid).

Un-supervised, PCA of the free and hydrolyed protein amino acids was later conducted to observe the association of the amino acids with the fruits (Figure S3 in Supplementary Materials). From the biplots, finger lime (positive PC1) is discriminated from Davidson's plum and native pepperberry (negative PC1) and most of the free amino acids are associated with finger lime. In contrast, hydrolysed protein amino acids showed clustering of finger lime with native pepperberry and separating from Davidson's plum with negative PC1. Almost all the amino acids were associated with finger lime and native pepperberry except lysine, cysteine and isoleucine.
<table><caption>**Table 3.** PLS modelling of discriminant putatively identified compounds by variable importance in the projection (VIP) selection method based on antioxidant activity. The list of VIP scores are provided together with Log fold-change (LogFC) values.</caption><thead><tr><th>No</th><th>Var ID</th><th>VIP Compounds</th><th>Class</th><th>VIP Score</th><th>p corr</th><th>LogFC (DP vs. FL)</th><th>LogFC (DP vs. NP)</th><th>LogFC [FL vs. NP]</th></tr></thead><tbody><tr><td>1</td><td>1842</td><td>β-D-Glucuronopyranosyl(1-&gt;3)-a-D-galacturonopyranosyl(1-&gt;2)-L-rhamnose</td><td>Oligosaccharides</td><td>1.70</td><td>-9.67 × 10<sup>3</sup></td><td>-5.83</td><td>-</td><td>16.85</td></tr><tr><td>2</td><td>1845</td><td>Dicaffeoylquinic acid</td><td>Quinic acids and derivatives</td><td>1.70</td><td>-9.67 × 10<sup>3</sup></td><td>-5.79</td><td>-</td><td>16.81</td></tr><tr><td>3</td><td>1846</td><td>Formononetin 7-(6'-malonyl)glucoside</td><td>Isolavonoid O-glycosides</td><td>1.69</td><td>-9.67 × 10<sup>3</sup></td><td>-5.73</td><td>-</td><td>16.75</td></tr><tr><td>4</td><td>2010</td><td>Octotamine</td><td>Aminopyrimidines and derivatives</td><td>1.66</td><td>-9.67 × 10<sup>3</sup></td><td>-4.83</td><td>-</td><td>15.85</td></tr><tr><td>5</td><td>268</td><td>Quercetagetin</td><td>Flavonoid-7-O-glycosides</td><td>1.66</td><td>-9.39 × 10<sup>3</sup></td><td>-4.94</td><td>-7.61</td><td>8.36</td></tr><tr><td>6</td><td>2447</td><td>2′′-O-Acetylrutin</td><td>Flavonoid-3-O-glycosides</td><td>1.65</td><td>-9.67 × 10<sup>3</sup></td><td>-4.69</td><td>-</td><td>15.71</td></tr><tr><td>7</td><td>2172</td><td>Quercetin 3-[rhamnosyl(1-&gt;2)-alpha-L-arabinopyranoside]</td><td>Flavonoid-3-O-glycosides</td><td>1.64</td><td>9.92 × 10<sup>3</sup></td><td>3.69</td><td>6.88</td><td>-7.83</td></tr><tr><td>8</td><td>820</td><td>Suaveolenine</td><td>3-alkylindoles</td><td>1.64</td><td>-9.67 × 10<sup>3</sup></td><td>-4.43</td><td>-</td><td>15.44</td></tr><tr><td>9</td><td>1797</td><td>Kurzichalcolactone B</td><td>2'-Hydroxychalcones</td><td>1.63</td><td>-9.67 × 10<sup>3</sup></td><td>-4.23</td><td>-</td><td>15.25</td></tr><tr><td>10</td><td>1789</td><td>Syringetin-3-O-glucoside</td><td>Flavonoid-3-O-glycosides</td><td>1.63</td><td>-9.67 × 10<sup>3</sup></td><td>-4.16</td><td>-</td><td>15.18</td></tr><tr><td>11</td><td>310</td><td>3-(5-methoxy-2,2-dimethyl-1-benzopyran-8-yl)-3-oxopropanoic acid</td><td>2,2-dimethyl-1-benzopyran</td><td>1.62</td><td>-9.67 × 10<sup>3</sup></td><td>-4.07</td><td>-</td><td>15.09</td></tr><tr><td>12</td><td>1085</td><td>Bakkenolide D</td><td>Terpene lactones</td><td>1.62</td><td>-9.67 × 10<sup>3</sup></td><td>-3.87</td><td>-</td><td>14.89</td></tr><tr><td>13</td><td>712</td><td>3-Feruloyl-1,5-quinolactone</td><td>Coumarins and derivatives</td><td>1.61</td><td>-9.67 × 10<sup>3</sup></td><td>-3.81</td><td>-</td><td>14.83</td></tr><tr><td>14</td><td>1857</td><td>6-[7-Acetoxy-5-chloro-3-[(1E,3E)-3,5-dimethyl-1,3-heptadien-1-yl]-7-methyl-6,8-dioxo-7,8-dihydro-2(6H)-isoquinolinyl]norleucine</td><td>Amino acid derivatives</td><td>1.61</td><td>-9.67 × 10<sup>3</sup></td><td>-3.64</td><td>-</td><td>14.66</td></tr><tr><td>15</td><td>1592</td><td>5,7-dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-3-[(3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]loxy]-4H-chromen-4-one</td><td>Flavonoid-3-O-glycosides</td><td>1.60</td><td>-9.67 × 10<sup>3</sup></td><td>-3.49</td><td>-</td><td>14.51</td></tr><tr><td>16</td><td>1189</td><td>Unknown</td><td>-</td><td>1.60</td><td>-9.67 × 10<sup>3</sup></td><td>-3.47</td><td>-</td><td>14.49</td></tr><tr><td>17</td><td>819</td><td>Unknown</td><td>-</td><td>1.60</td><td>-9.67 × 10<sup>3</sup></td><td>-3.44</td><td>-</td><td>14.46</td></tr><tr><td>18</td><td>1188</td><td>farnochrol</td><td>Terpene lactones</td><td>1.59</td><td>-9.67 × 10<sup>3</sup></td><td>-3.40</td><td>-</td><td>14.42</td></tr><tr><td>19</td><td>581</td><td>Perilloside B</td><td>Terpene glycosides</td><td>1.59</td><td>-9.66 × 10<sup>3</sup></td><td>-3.37</td><td>-</td><td>14.39</td></tr><tr><td>20</td><td>1847</td><td>Racemosic acid</td><td>Phenolic glycosides</td><td>1.59</td><td>-8.95 × 10<sup>3</sup></td><td>-4.28</td><td>-9.18</td><td>6.12</td></tr><tr><td>21</td><td>2341</td><td>Kaempferol 3-[2′′′-acetyl-alpha-L-arabinopyranosyl(1-&gt;6)-galactoside]</td><td>Flavonoid-3-O-glycosides</td><td>1.58</td><td>-9.67 × 10<sup>3</sup></td><td>-3.16</td><td>-</td><td>14.18</td></tr></tbody></table>
<table><caption>Table 3. Cont.</caption><thead><tr><th>No</th><th>Var ID</th><th>VIP Compounds</th><th>Class</th><th>VIP Score</th><th>p corr)</th><th>LogFC (DP vs. FL)</th><th>LogF[DP vs. NP]</th><th>LogFC [FL vs. NP]</th></tr></thead><tbody><tr><td>22</td><td>1851</td><td>Dracunculifoside G</td><td>Coumaric acids and derivatives</td><td>1.58</td><td>-9.66 x 10<sup>3</sup></td><td>-3.12</td><td>-</td><td>14.14</td></tr><tr><td>23</td><td>583</td><td>Betuloside</td><td>Fatty acyl glycosides of mono- and disaccharides</td><td>1.57</td><td>-9.66 x 10<sup>3</sup></td><td>-2.83</td><td>-</td><td>13.85</td></tr><tr><td>24</td><td>2404</td><td>Isosakuranetin-7-O-methesperidoside</td><td>Flavanone</td><td>1.56</td><td>-9.66 x 10<sup>3</sup></td><td>-2.76</td><td>-</td><td>13.78</td></tr><tr><td>25</td><td>2000</td><td>6-(benzoyloxy)-1-(hexopyranosyloxy)-1,4a,5,6,7,7a-hexahydro-5-hydroxy-7-methyl-cyclopentenal[γ]pyran-4-carboxylic acid</td><td>Carboxyl</td><td>1.56</td><td>-9.67 x 10<sup>3</sup></td><td>-2.68</td><td>-</td><td>13.69</td></tr><tr><td>26</td><td>413</td><td>Unknown</td><td>-</td><td>1.55</td><td>-9.66 x 10<sup>3</sup></td><td>-2.52</td><td>-</td><td>13.54</td></tr><tr><td>27</td><td>1304</td><td>Unknown</td><td>-</td><td>1.55</td><td>-9.67 x 10<sup>3</sup></td><td>-2.35</td><td>-</td><td>13.37</td></tr><tr><td>28</td><td>1802</td><td>Pungiolide A</td><td>Xanthanolides</td><td>1.54</td><td>-9.67 x 10<sup>3</sup></td><td>-2.28</td><td>-</td><td>13.30</td></tr><tr><td>29</td><td>817</td><td>Speradine A</td><td>Isoindolones</td><td>1.54</td><td>-9.67 x 10<sup>3</sup></td><td>-2.25</td><td>-</td><td>13.27</td></tr><tr><td>30</td><td>1228</td><td>Isovitexin</td><td>Flavone</td><td>1.53</td><td>-9.49 x 10<sup>3</sup></td><td>-1.85</td><td>-3.22</td><td>9.65</td></tr><tr><td>31</td><td>2016</td><td>6-methoxy-7-[3,4,5-trihydroxy-6-[3,4,5-trihydroxy-6-methyloxan-2-yl)]oxymethyl[loxoan-2-yl]loxychromen-2-one</td><td>Ketone</td><td>1.53</td><td>-9.66 x 10<sup>3</sup></td><td>-1.94</td><td>-</td><td>12.96</td></tr><tr><td>32</td><td>2388</td><td>Unknown</td><td>-</td><td>1.53</td><td>-9.66 x 10<sup>3</sup></td><td>-1.90</td><td>-</td><td>12.92</td></tr><tr><td>33</td><td>726</td><td>4-Methylumbelliferyl glucuronide</td><td>Coumarins and derivatives</td><td>1.52</td><td>-9.67 x 10<sup>3</sup></td><td>-1.77</td><td>-</td><td>12.78</td></tr><tr><td>34</td><td>1697</td><td>3,4,5-trihydroxy-6-[3,4,5-trihydroxy-2-[3-(4-methoxyphenyl)-2-oxopropanoyl]phenoxyl]oxane-2-carboxylic acid</td><td>Flavonoid O-glycosides</td><td>1.52</td><td>-9.81 x 10<sup>3</sup></td><td>-1.12</td><td>-</td><td>11.36</td></tr><tr><td>35</td><td>1949</td><td>Unknown</td><td>-</td><td>1.52</td><td>-9.67 x 10<sup>3</sup></td><td>-1.70</td><td>-</td><td>12.72</td></tr><tr><td>36</td><td>582</td><td>3-Hydroxy-4-isopropylbenzyl alcohol 3-glucoside</td><td>Terpene glycosides</td><td>1.52</td><td>-9.66 x 10<sup>3</sup></td><td>-1.70</td><td>-</td><td>12.72</td></tr><tr><td>37</td><td>2256</td><td>Unknown</td><td>-</td><td>1.52</td><td>-9.65 x 10<sup>3</sup></td><td>-1.80</td><td>-</td><td>12.81</td></tr><tr><td>38</td><td>552</td><td>{3-[2-(3-hydroxy-5-methoxyphenyl)ethyl]phenyl]loxxidanesulfonic acid</td><td>Stilbenes</td><td>1.52</td><td>7.05 x 10<sup>3</sup></td><td>6.53</td><td>17.55</td><td>-</td></tr><tr><td>39</td><td>1020</td><td>Picraquassioside A</td><td>Phenolic glycosides</td><td>1.52</td><td>-9.66 x 10<sup>3</sup></td><td>-1.65</td><td>-</td><td>12.67</td></tr><tr><td>40</td><td>834</td><td>O-[β-D-Apifuranosyl-(1-6)-β-D-glucopyranoside]</td><td>O-glycosyl compounds</td><td>1.51</td><td>-9.67 x 10<sup>3</sup></td><td>-1.62</td><td>-</td><td>12.64</td></tr><tr><td>41</td><td>1939</td><td>Longipedulin A</td><td>Hydrolyzable tannins</td><td>1.50</td><td>-9.66 x 10<sup>3</sup></td><td>-1.43</td><td>-</td><td>12.45</td></tr><tr><td>42</td><td>441</td><td>10,11-epoxycurvularin</td><td>Argyl alkyl ketones</td><td>1.50</td><td>-9.67 x 10<sup>3</sup></td><td>-1.36</td><td>-</td><td>12.38</td></tr><tr><td>43</td><td>727</td><td>Moschamine</td><td>N-acylserotonin</td><td>1.50</td><td>-9.67 x 10<sup>3</sup></td><td>-1.34</td><td>-</td><td>12.36</td></tr><tr><td>44</td><td>2131</td><td>Unknown</td><td>-</td><td>1.50</td><td>-9.66 x 10<sup>3</sup></td><td>-1.32</td><td>-</td><td>12.34</td></tr></tbody></table>

Var ID: Peak number; p corr): correlation coefficient; DP: Davidson's plum; FL: Finger time; NP: Native pepperberry.
## 2.7. Targeted Minerals and Heavy Metals Profiling using ICP-OES

Here, we targeted 18 minerals and heavy metals for the fruit samples and the analysed data are shown in Figure S4 in Supplementary Materials. The heat map shows the relative concentration of all the minerals and heavy metals in each sample. Minerals (Mn, Na, Fe, Zn) are abundantly distributed in native pepperberry compared to Davidson's plum and finger lime. However, Davidson's plum contained elevated levels of Mg (816.1 ± 7.4 mg/kg) and Al (114.2 ± 1.2 mg/kg) while finger lime with higher levels of Ca (1390.1 ± 35.8 mg/kg) and P (870.63 ± 24.2 mg/kg) than any other elements analysed in the study (Table S1 in Supplementary Materials). From the statistical analysis, 9 elements varied significantly (*p* < 0.05), i.e., Al, Ca, Fe, K, Mg, Mn, Na, P, and Zn as shown in Figure 3 and represented in box and whiskers plot. Interestingly, the relative amount of Al, K, and Mg were highest in Davidson's plum compared to native pepperberry and finger lime. Al, Ca, Mg and P were moderately high in native pepperberry except for potassium. Finger lime exhibited a relatively low amount of most elements, but did show an amount of Ca and P compared to the other fruits.

<figure><img src="image_116.png" /><figcaption>**Figure 3.** Box and whisker plots of nine elements varied significantly. DP: Davidson's plum, FL: Finger lime, NP: Native pepperberry. The Y-axis of box and whisker plots indicates the amount in mg/kg.</figcaption></figure>
### 3. Discussion

Davidson's plum, finger lime and native pepperberry are among Australian native foods that are important to be classified as functional foods due to vast biologically active primary and secondary metabolites, especially terpenes, and flavonoids. Recently, with the inclusion of Indigenous foods in the food industry, natural antioxidants from bush fruits have gained expanding attention due to the growing demand for novel flavours, new functional compounds, and clean labelling. These fruits are rich in polyphenolic compounds, which are prominent natural antioxidants. The identification of aroma compounds using GC×GC-TOFMS provides an important factor in discriminating the clusters of the fruit samples. Correlation between antioxidant activity and aroma compounds exhibited abundance in terpenoid and terpene groups such as limonene, which generally contributes to the fruity smell due to its low odour threshold. Similar with previous studies, limonene was relatively high in finger lime, showing one of the major volatile compounds compared to other terpene groups [2,9,10]. The abundance of terpenes and terpenoids in the fruit samples suggest that they are suitable for processing of the fruits into jam and chutneys or even other food products with pleasant aroma and colour [9].

In the current study, we also conducted a correlation between bioactive compounds with antioxidant activity of the fruits using LCMS metabolomics. Through the comprehensive metabolite profiling, the results of various scavenging activities along with TPC, TFC and TFIC are presented in Table 2. In relation to the reported studies, Davidson's plum exhibited proportionate amount of total phenolic content with damson plum, *Prunus domestica* subsp. *Insititia* L. (124.32 GAE µmol/gDW) cultivated in France [21], but three times higher (38.20 GAE µmol/gDW) compared with the same plum planted in Serbia [22]. In general, bush fruits are rich in phenolics, terpenes and flavonoids, forming the vital class of compounds for scavenging activities in human body [14]. Each antioxidant assay provides different data, therefore four types of antioxidant assays were conducted in order to completely evaluate the efficacy of the powdered fruit extracts. Generally, different fruits exhibited different strengths in their respective antioxidant assays when compared with gallic acid. The antioxidant capacity changes with in vitro assays depending on the affinities of the active compounds present in the fruits [23]. The outcome of this study is also synergistic with the TPC, TFC and TFIC activities with their respective antioxidant activity. Thus, these findings support the antioxidant function of the fruits, which is in good agreement with previous results [2, 11, 14, 24, 25].

The relative variability of the compounds in the fruits was measured using MVDA to characterise the tentative identification of peaks that are bio-actively related to antioxidant activity. PCA and PLS were employed with clear discrimination of the score plots among the fruits. The correlation of the compounds with antioxidant activity (DPPH, ABTS, FRAP and phosphomolybdenum) was conducted using a PLS biplot to reveal the dominant compounds that may be responsible for the activities. The search of the compounds that attributed to antioxidant activity was conducted through the VIP values greater than 1.5 (Table 3). Therefore, from the findings of both GC and LC chromatographic analyses, the identified notable compounds were highly possible for the antioxidant activity. Some aroma compounds such as limonene, furfural, α-pinene, terpinen-4-ol, and γ-terpinene and polar compounds like dicaffeoylquinic acid, quercetagetin, and 2'-O-acetylrutin have been reported to possess potent antioxidant activity [22–32].

In addition to the secondary compounds identified, the fruits, especially finger lime and native pepperberry, are rich in amino acids. Nevertheless, most of the essential amino acids were present in the samples and can therefore act as a functional food in dietary supplements [33]. Though lysine is well represented in some vegetable species [34,35], it is absent in many plants compared to other amino acids and this is a disadvantage for vegans. According to the Academy of Nutrition and Dietetics (AND), vegetarians are recommended to consume an array of plant-based food that are rich in protein in order to meet nutritional and health requirements [36]. Interestingly, lysine exhibited the highest amount in the fruits, which is suitable for vegetarians as well as those who are allergic to beans and legumes [37].
Minerals and trace elements in fruits are acquired and transported naturally for biological
processes in the plants. However, some of these minerals this could be toxic to humans when they
occur at particular levels. Instead of posing a threat, mineral nutrients under the normal level assist
in mitigating toxicity caused by heavy metals. In our mineral study, the ANOVA statistical analysis
highlighted nine important minerals from the heatmap (Figure S4 in Supplementary Materials) of
the fruit samples. As the major mineral in human dietary intake, potassium contributed the highest
percentage for all the samples ranging from 52–68% showing a good source of potassium, especially
for Davidson's plum (68%). The requirement of potassium by humans is greater than 100 mg/day and
with this fact, it is anticipated that the contribution of these fruits to dietary intake will grow in future.
The potassium levels of Davidson's plum (6877 ± 138 mg/kg), and finger lime (6697 ± 98 mg/kg) found
in this study are higher than potassium levels in any Colombian fruits [38], and subtropical fruits
grown in Spain, such as custard apple, avocado, mango, banana, papaya, persimmon and starfruit [39].
The amount of phosphorus (835 ± 49 mg/kg), calcium (788 ± 37 mg/kg) and magnesium (723 ± 23
mg/kg) were relatively high in native pepperberry, compared to jujube fruits grown in China [40]. Bush
fruits have been reported for rich sources of calcium and magnesium with an added advantage because
both minerals are important in forming DNA and are especially important in repairing damaged DNA.
They are equally crucial in segregating the chromosomes during DNA synthesis [19].

Taken together, all the data indicate that these three native fruits are excellent sources of important
compounds, that offer nutritional value to consumers. For the food industry, these fruits offer potential
to extend shelf-life naturally, and increase the functional value of the food, enabling the industry
to make health claims about the food. This information could also assist with the development of
Indigenous businesses to supply these high value foods to the food industry. In addition, this study
illustrates that the reason for different antioxidants values from the different methods is based on the
nature and classes of the secondary compounds much more than any of the other compounds.

**4. Materials and Methods**

**4.1. Chemicals and Reagents**

All organic solvents were purchased from Fisher Scientific (Pittsburgh, PA, USA) unless stated
otherwise. Ammonium ferrous sulfate, sulfuric acid, aluminium chloride, sodium nitrate, sodium
hydroxide, sodium acetate, aluminium hexahydrate, ammonium molybdate, sodium phosphate,
sodium nitrite, quercetin, gallic acid, glacial acetic acid, 2,4,6-Tris(2-pyridyl)-s-triazine (TPTZ),
2,2-Di(4-tert-octylphenyl)-1-picrylhydrazyl (DPPH), potassium persulfate, formic acid, 2,2''-azinobis
(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), were purchased from Sigma-Aldrich
(St. Louis, MO, USA).

**4.2. Plant Materials**

Davidson's plum, native pepperberry, and finger lime and were purchased from Taste Australia
Bush Food Shop (Queensland, Australia). The samples were pulverised into fine powder using
TissueLyser II (Qiagen, Tokyo, Japan). The pulverised samples were extracted with solvent or water
(specified in respective method) in triplicates for chemical assays.

**4.3. GC×GC-TOFMS Analysis**

The aroma in fruits was comprehensively analysed using static headspace extraction coupled
with separation by two-dimensional gas chromatography time-of-flight mass spectrometry (GC ×
GC-TOFMS, LECO Pegasus 4D, Castle Hill, Australia). Briefly, sample (500 mg) was weighed into 20
mL silicon capped GC headspace vials (Restek, Germany) and kept at –80 °C until further analysis.
2.5 mL headspace syringe was used to collect 1.5 mL of sample headspace after sample agitation
of 10 min at 80 °C. An empty vial was used as blank and quality assurance (QA) standard was
prepared by mixing all the samples. The program settings, conditions and parameters are provided
in Table S2 in Supplementary Materials [41]. LECO ChromaTOF 4.50 software was used to process the GC×GC-TOFMS data for pre-processing baseline correction and identification was conducted by library matching (NIST 11 v2.0) and from authentic reference standards created in an in-house library. The similarity of ≥80% with the NIST library was defined as putative identification (when standards were not available) [42–44].

## 4.4. UHPLC-QqQ-TOF-MS/MS Analysis

The pulverised fruits powder (10 mg) from 3 plant species were dissolved in methanol (600 µL) and vortexed for 15 min at 30 °C. The mixture was then centrifuged at 28,500× g for 15 min and an aliquot of each supernatant (100 µL) was transferred into new Eppendorf tubes. Millipore water was added in ratio 3:1 to the supernatant. The mixture was vortexed and filtered through a 0.2 µm PTFE filter into an autosampler vial. For pooled biological quality control (PBQC) samples, 5 µL of each sample was taken and vortexed for 1 min. For the blank, 100 µL of Millipore water was used. The UHPLC-QqQ-TOF-MS/MS analysis was performed using Shimadzu Nexera UHPLC system (Kyoto, Japan; LC-30AD pump, SIL-30AC autosampler and CTO-30A column oven) equipped with Shimadzu Q-TOFMS-9030 detector. Separation of the sample analytes was conducted on a Shimadzu Velox C18 (2.1 × 100 mm, 1.8 µm, part number 227-32007-03, Shimadzu, Kyoto, Japan). The mobile phase consisted of A (0.1% [v/v] formic acid in acetonitrile) and B (0.1% [v/v] formic acid in water). The flow of the solvent gradient flow was set as follows: 97% A for 0–0.75 min, 5% A for 0.75–13 min, 97% A for 13–16 min. The sample injection volume was 1 µL with consistent flow rate at 0.4 mL/min. The column temperature and auto-sampler were set at 40 °C. Negative ionisation mode was operated for mass spectrometry analysis equipped with electrospray ionisation (ESI) source with collision energy set at 70 eV. The MS data were collected from *m/z* 70–700 Da; nebulisation gas at 3 L/min; source temperature, 120 °C; and desolvation temperature, 200 °C. The MS data were centroided and acquired with Lab Solutions software version 5.80. The raw data files were exported to Lab Solutions Insight software in LCD (*.lcd) format for pre-processing, correction of retention time, and baseline. The raw files were also converted to mzML format for peak discrimination, filtering and alignment using MS-DIAL [45,46].

## 4.5. Targeted Analysis of Free Amino Acids

### 4.5.1. Extraction of Free Amino Acids from Fruit Samples

The samples were weighed (20 mg) in Eppendorf tube. Methanol (500 µL) was added and vortexed. The mixture was centrifuged at 30,000× g for 5 min, then the supernatant was transferred into a 2 mL tube. The precipitate was resuspended in 500 µL of Millipore water and vortexed. Next, the mixture was centrifuged at the same speed for 5 min. The methanol and water extract were combined and filtered through a 0.2 µm PTFE filter.

### 4.5.2. Free Amino Acid Derivatisation

The free amino acids were derivatised using AccQ.FluorTM reagent according to the Waters AccQ.TagTM pre-column derivatisation procedure [47]. Briefly, 35 µL of borate buffer was put into a tube (2 mL). 5 µL of sample was then mixed and vortexed for several seconds. Ten µL of reconstituted derivatisation reagent were then admixed to the buffered samples and immediately vortexed. Next, the mixture was left at room temperature for 1 min. After that, the samples were heated in a heating block for 10 min at 55 °C to finalise the derivatisation. The derivatised free amino acids were then transferred to autosampler vial for analysis. PBQC samples were prepared by pooling 5 µL from each sample, vortexed for 1 min and derivatised using the same method as mentioned previously.
#### 4.5.3. Standards

Standard amino acid mix solution (Batch. No SLBS2232V; Sigma-Aldrich, St. Louis, MO, USA) was used for the identification and quantification of amino acids. The standards were prepared in serial dilutions of 40% stock solutions to 20%, 10%, 5%, 2.5% and 1.25%. Derivatisation of the standard amino acids were conducted according to the methods explained in Section 4.5.2. As for blank, 5 µL of Millipore water was used to replace sample at the beginning step, then followed by the derivatisation steps mentioned in Section 4.5.2.

#### 4.5.4. UHPLC-MS Conditions

The analysis of amino acid derivatives was analysed on a Shimadzu Nexera UHPLC system (Kyoto, Japan; LC-30AD pump, SIL-30AC autosampler and CTO-30A column oven) equipped with Shimadzu MS-2020 detector. Waters Acquity UPLC™ BEH C₁₈ column (2.1 × 100 mm, 1.7 µm, part number 186003837, Waters, Milford, MA, USA) was used for the chromatographic separation at consistent temperature of 55 °C. The MS parameters were set as follows: Acquisition mode, SIM (refer Table S3 in Supplementary Materials); detector voltage, 0.1 V; interface voltage, 2.5 V; ionisation mode, positive; heat block temperature, 500 °C; interface temperature, 350 °C; nebulising gas flow, 1.5 L/min; injection volume, 10 µL. The mobile phase consists of A: 0.1% formic acid (v/v) in Millipore water, and B: 0.1% formic acid (v/v) in acetonitrile. The flow rate was set at 0.7 mL/min based on the gradient profile: initial-0.54 min (0-0.1% B); 0.54-5.74 min (0.1-15% B); 5.74-8.74 min (15-21.2% B); 8.74-10.50 min (21.2-59.6% B); 10.50-11.50 min (59.6% B); 11.50-12.00 min (59.6-0.1% B) and finally at 0.1% B until 13 min. The interconnected cleaning purge was set within 1 min (rinsing speed 35 µL/sec), and equilibrium was repeated for 5 min at initial conditions. The whole cycle time took 13 min to complete before the next injection.

### 4.6. Targeted Analysis of Protein Amino Acids

#### 4.6.1. Sample Digestion

Fruit samples (20 mg each pulverised) were digested in a glass vessel containing 2 mL of 6 N HCl and 0.1% of phenol. The glass vessels were flushed with N₂ before sealing. Samples were hydrolysed at 110 °C for 20 h. Next, the samples were filtered using 0.2-µm filter, and the filtrate was neutralised with freshly prepared 6 N NaOH solution [48].

#### 4.6.2. Protein Amino Acid Derivatisation

The protein amino acid derivatisation, PBQC and standards were prepared according to the methods mentioned in Sections 4.5.2 and 4.5.3.

#### 4.6.3. UHPLC-MS Analysis

The standards and derivatised protein amino acid were injected and analysed according to the UHPLC-MS conditions mentioned in Section 4.5.4.

### 4.7. Targeted Analysis of Minerals and Heavy Metals

#### 4.7.1. Sample Preparation

The fruits samples were weighed (100 mg) into new and clean 15 mL digestion tubes. To each tube, concentrated nitric acid (2.0 mL) was added and samples were pre-digested overnight. They were then placed into a digestion rack of Hotblock® Digester SC100 Digestion System (Environmental Express, Vernon Hills, IL, USA). The samples were digested for 1 h at 100 °C. Upon completion, the samples were cooled, and then water added to bring the volume to 15 mL. The samples were shaken and centrifuged for 5 min [49].
### 4.7.2. ICP-OES Analysis

The operating conditions for inductively coupled plasma optical emission spectrometer (ICP-OES) were performed according to the parameters: RF incident power, 1000 W; plasma argon flow rate, 15.0 mL/min; auxillary argon flow rate, 1.50 mL/min; nebulizer argon flow rate, 0.75 mL/min; mist chamber, tracey and nebulizer SeaSpray, 2 mL/min flow rate. The wavelength measured for each element is as follows: Al 396.152 nm, As 188.980 nm, Ca 422.673 nm, Cd 214.439 nm, Co 238.892 nm, Cr 267.716 nm, Cu 324.754 nm, Fe 238.204 nm, K 766.491 nm, Mg 279.553 nm, Mn 257.610 nm, Mo 202.032 nm, Na 588.995 nm, Ni 231.604 nm, P 213.618 nm, Pb 220.353 nm, S 181.972 nm, and Zn 213.857 nm.

### 4.8. Determination of in Vitro Antioxidant Activity

The DPPH, FRAP, ABTS and phosphomolybdenum activities of the fruit samples were evaluated by the methods reported by [44,50,51], respectively. The ABTS, DPPH and phosphomolybdenum assays were reported as gallic acid equivalents (GAE µmol/gDW), whereas FRAP as Fe²⁺ equivalents (Fe²⁺ µmol/gDW).

### 4.9. Total Phenolic, Flavonoid and Flavonol Contents

Spectrophotometric technique was used for total phenolic, flavonoid and flavonol contents and were quantified following the protocol of [52,53]. Total phenolic contents were expressed as gallic acid equivalents (GAE µmol/gDW), whereas total flavonoid and flavonol contents were presented as quercetin equivalents (QTE µmol/gDW).

### 4.10. Data Processing and Analysis

Data for bioactivities were presented as mean ± SD for all triplicate analysis and a one-way analysis of the variance (ANOVA) was carried out using SPSS Statistics version 25 (IBM Corp, Armonk, NY, USA). The mean comparisons were conducted using the post-hoc Tukey's (HSD) multiple comparison test. Values with *p* < 0.05 were considered statistically significant.

The processed data from GC×GC-TOFMS were analysed using SIMCA-P software version 15 (Umeå, Sweden) for multivariate data analysis (MVDA). In order to access the clustering and trends of the comprehensive depiction of the fruit samples, principal component analysis (PCA) was used. Partial least squares (PLS) chemometric method was conducted to further analyse the correlation of antioxidant activity with volatile compounds in the fruit samples [41,44]. The variables were pareto scaled for PCA and PLS analyses. Afterwards, the variables selection namely VIP was used to select those notable volatile compounds (VIP > 2.5).

For UHPLC-QqQ-TOF-MS/MS data, the variables were pareto scaled and log transformed for PCA and PLS to lower heteroscedasticity and asymmetry in the statistical distribution [44]. In order to tentatively identify the compounds, the acquired raw data in mzML format were processed using MS-DIAL by comparing the MS/MS spectra with those in the spectral library. VIP was applied to identify the most significant compounds contributing to the antioxidant activity [54]. Compounds with VIP score > 1.5 were maintained for further elaboration using methods from MetaboAnalyst 4.0, an open source web-based tools for metabolomics data analysis [55].

For targeted MVDA of amino acids, both targeted free and hydrolysed amino acids were pareto scaled and log transformed. The heatmap and box and whiskers were generated using MetaboAnalyst 4.0 [55].

## 5. Conclusions

We have successfully reported that the Australian bush fruits, Davidson's plum, finger lime and native pepperberry are rich in terpenes, phenolics, flavonoids, flavonols, minerals, essential and non-essential free and hydrolysed protein amino acids, and functional bioactive compounds, which are promising fruits to be commercialised in the nutraceutical industry and food industry. We found that
the fruits are an abundant source of antioxidant compounds (sugars, terpenes, flavonoids etc.) and that may serve as the source of natural antioxidants in food products or as new medicines. The use of metabolomics in correlation to the primary and secondary metabolites as well as the minerals would render an opportunity to obtain more potential bioactivities of the Australian bush fruits as functional food in nutraceutical industry. We also show that when reporting antioxidant activity, it is important to use more than one method to obtain a true understanding of the activity.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/3/114/s1, Table S1: Targeted analysis of 18 minerals and heavy metals from Davidson's plum (DP), finger lime (FL) and native pepperberry (NP). Results are expressed as mean values (mg/DWkg) ± SD (n = 3). Superscript letters within each column indicate statistically significant (*p* < 0.05; Tukey test), Table S2: GC×GC-TOF-MS parameters for comprehensive profiling of fruit samples, Table S3: Amino acids' single ion monitoring for mass spectrometry detector, Figure S1: (a) Partial least square (PLS) score plot based on GC×GX-TOFMS data. (b) PLS biplot plots showing correlation between identified aroma compounds with antioxidant activity. X = compounds, Y = antioxidant activity, Figure S2: Permutation test of the PLS model based on UHPLC-QqQ-TOF-MS/MS data (a) ABTS; (b) DPPH; (c) FRAP; (d) Phosphomolybdenum assays, Figure S3: Biplot association of (a) free amino acid; (b) hydrolysed protein amino acid in Davidson's plum (DP), finger lime (FL) and native pepperberry (NP), Figure S4: Heat map of 18 mineral nutrients and heavy metals found in the fruit samples.

**Author Contributions:** Conceptualization, M.F. and V.L.; methodology, V.L. and S.G.G.; software, V.L. and S.G.G.; validation, V.L., S.G.G., V.D.D. and M.F.; formal analysis, V.L., S.G.G.; investigation, V.L., S.G.G., V.D.D.; resources, M.F.; data curation, V.L.; writing—original draft preparation, V.L.; writing—review and editing, V.L., S.G.G., V.D.D., M.F.; visualization, M.F.; supervision, M.F.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** V.L. would like to thank Universiti Sains Malaysia and Ministry of Higher Education, Malaysia for the SLAB Scholarship under the Post-Doctoral program. We also thank Shimadzu Australia for access to some equipment and assistance with data analysis.

**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.

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<figure><img src="image_117.png" /></figure>

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<figure><img src="image_118.png" /></figure>

Article

# Non-Targeted Metabolite Profiles and Sensory Properties Elucidate Commonalities and Differences of Wines Made with the Same Variety but Different Cultivar Clones

Álvaro Cuadros-Inostroza<fnref n="1" />, Claudio Verdugo-Alegría<fnref n="2" />, Lothar Willmitzer<fnref n="3" />,
Yerko Moreno-Simunovic<fnref n="2" />,* and José G. Vallarino<fnref n="2" />,∗

Received: 28 April 2020; Accepted: 22 May 2020; Published: 28 May 2020

**Abstract:** Grapes, one of the oldest agricultural crops, are cultivated to produce table fruits, dried fruits, juice, and wine. Grapevine variety is composed of clones that share common morphological traits. However, they can differ in minor genetic mutations which often result in not only notorious morphological changes but also in other non-visible sensorial distinctive attributes. In the present work, we identified three *Vitis vinifera* cv. Pinot noir clones grown under identical field conditions that showed different grape cluster types. Here, sensorial analysis together with non-targeted metabolite profiles by Ultra High performance Liquid Chromatography (UPLC) couples to Ultra High Resolution Mass Spectrometry (FT-ICR-MS) of wines elaborated from the three different grape cluster types was studied with the aim of (i) finding sensorial differences among these three types of wines, and, if there were, (ii) determining the molecular features (metabolites) associated with these sensorial attributes by a multivariate statistical approach.

**Keywords:** metabolomics; wine; clones; *Vitis vinifera*; sensory analysis

## 1. Introduction

With several thousand varieties, the *Vitis* genus is characterized by high levels of genetic diversity. The *Vitis* international variety catalogue identifies more than 21 thousand names of varieties, including 12.250 for *Vitis vinifera*. However, we have to consider that this number includes also synonyms and homonyms [1]. Moreover, the actual number of vine varieties for *V. vinifera* species is estimated at 6000 [2]. Additionally, vines are one of the most important crops in the world with a total surface area planted reaching 7.4 million hectares (Mha and world-wine market reaching 31.3 bn EUR in 2018. The variety Pinot noir is the 6th most widely planted red grape variety worldwide [3]. This cultivar is particularly well adapted to cool growing areas and can be grown at higher latitudes in comparison to other varieties. In addition, the Pinot noir variety is appreciated by the quality of its fresh and fruity monovarietal wine.

Wine can be considered a complex product whose quality is attributable to its chemical composition, which is highly influenced by grape variety [4], viticultural practices [5,6], fermentation conditions [7], vineyard geographical location that is related to soil and climate characteristics [8–10], technical conditions of wine-making [11], and the quality of the grapes used for its production [12,13]. Determination of grape quality is important as grower payment per ton of fruit. Aroma is one of the

<footnote n="1">¹ metaSysX GmbH, 14476 Potsdam-Golm, Germany; Inostroza@metasysx.com</footnote>

<footnote n="2">² Centro Tecnológico de la Vid y el Vino, Universidad de Talca, Av. Lircay s/n, 3460000 Talca, Maule, Chile; cverdugo@utalca.cl</footnote>

<footnote n="3">³ Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476 Potsdam-Golm, Germany; willmitzer@mpimp-golm.mpg.de</footnote>

<footnote>* Correspondence: ymoreno@utalca.cl (Y.M.-S.); vallarino@uma.es (J.G.V.)</footnote>
most important factors in determining wine character and quality [14]. Another equally important wine quality attribute is mouthfeel. Aroma descriptors include red berries, smoky, herbal, and woody, which represent what a wine smells like; while taste is described by attributes such as astringency, bitter, acidity, and sweetness. These characteristics are the result of complex interactions among volatile and non-volatile compounds. So far, the contributions of these attributes to wine are determined by a tasting panel, who must be rigorous and well trained using an appropriate and standardized vocabulary.

Metabolomic approaches can take a snapshot of the current biochemical status and are useful to compare varieties and evaluate changes in metabolic pools. In this regard, metabolomics-based approaches can reveal several thousand signals of candidate biomarkers providing high sensitivity and good resolution for wine authentication purposes. Recently, there have been a number of reports utilizing high-throughput techniques focusing on wine or grape metabolomes to discriminate wines from distinct varieties, fermentation conditions, geographical origin, as well as different qualities [15–26]. Moreover, most of the current analysis of wine with high-throughput analytical technologies contributes to a volatile profiling using different multivariate techniques [17,18,21,27]. However, very little is reported on non-targeted and non-volatile metabolite profiles as a basis [19,20,22,26,28,29]. Furthermore, to the best of our knowledge, none of the non-volatile biomarkers associated with wine quality (sensory properties) have been targeted to assist or confirm the rating score provided by wine panelists.

Due to the unstudied phenotypic cluster variation observed in an established clonal-commercial vineyard, the scope of this work is to use the combination of sensorial analysis and non-targeted metabolite profiling by ultra-high-performance liquid chromatography coupled to Ultra-High-Resolution Mass Spectrometry (UPLC-FT-ICR-MS) to determine whether both platforms have the power to distinguish between three red wines made using the different cluster types obtained each from three *V. vinifera* cv. Pinot noir putative-clones grown under identical field conditions. In addition, if this is feasible, we are also interested in identifying molecular features which contribute most to the distinction between the different sensory properties of the three types of red wines assessed by an expert panel. Therefore, the selected molecular features can be used as biomarkers for certification or assessment sensorial analysis.

## 2. Results

### 2.1. *Viticultural Characteristics of Three Pinot Noir Grape Cluster Types*

Different fruit size was observed between different cluster types (Figure 1). Grape cluster type iii exhibited a significantly greater berry size in comparison to grape cluster type i, while grape cluster type ii showed an intermediate phenotype, with large, medium, and small grape sizes. Therefore, different ratios between skin and fresh weight were produced by the different types of berries as expected. Although berry size was different, it is apparent that this resulted in an unaltered soluble solids content (Brix), total acidity, and pH of grape berries (Table 1). In wines made from the three grape cluster types, no differences in the alcohol content, residual sugar content, total acidity, and pH were observed.
<figure><img src="image_119.png" /></figure>

<table><caption>**Figure 1.** (A). A comparison of the three Pinot noir grape cluster types. Scale bar = 2 cm. (B). Berry diameter at the ripe stage. Values are mean ± standard deviation (n = 50).</caption><caption>(B)</caption><thead><tr><th rowspan="2">Diameter (cm)</th><th colspan="3">Frequency of grape diameter per cluster type (%)</th></tr><tr><th>Cluster type I</th><th>Cluster type II</th><th>Cluster type III</th></tr></thead><tbody><tr><td><strong>1.4</strong></td><td></td><td></td><td>13.1 ± 0.7</td></tr><tr><td><strong>1.2</strong></td><td>4.1 ± 0.8</td><td>18.2 ± 0.6</td><td>48.3 ± 1.1</td></tr><tr><td><strong>1</strong></td><td></td><td>22.2 ± 0.9</td><td>39.2 ± 1.6</td></tr><tr><td><strong>0.8</strong></td><td>45.2 ± 1.1</td><td>34.4 ± 1.8</td><td></td></tr><tr><td><strong>0.6</strong></td><td>34.3 ± 0.8</td><td>18.1 ± 1.5</td><td></td></tr><tr><td><strong>0.4</strong></td><td>17.1 ± 0.9</td><td>8.3 ± 0.4</td><td></td></tr></tbody></table>

<table><caption>**Table 1.** Grape and wine analysed in the experiment including some basic compositional parameters. Values are mean ± standard deviation (n = 50 and n = 6 for grapes and wines, respectively.</caption><thead><tr><th rowspan="2">Season</th><th rowspan="2">Cluster Type</th><th colspan="3">Grapes</th><th colspan="3">Wines</th></tr><tr><th>Average ± SD</th><th></th><th>Average ± SD</th><th></th><th></th></tr></thead><tbody><tr><td rowspan="6">Type i</td><td><sup>1</sup>Brix</td><td>24.8 ±</td><td>2.92</td><td>Alcohol (% vol.)</td><td>14.4 ±</td><td>0.31</td></tr><tr><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>7.5 ±</td><td>1.11</td><td>Residual sugar (gr/L)</td><td>1.3 ±</td><td>0.17</td></tr><tr><td>pH</td><td>3.0 ±</td><td>0.11</td><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>4.3 ±</td><td>0.27</td></tr><tr><td>Malic acid (mg/L)</td><td>3455.2 ±</td><td>78.82</td><td>pH</td><td>3.4 ±</td><td>0.01</td></tr><tr><td>Tartaric acid (mg/L)</td><td>5471.1 ±</td><td>61.23</td><td>Tartaric acid (mg/L)</td><td>1352.1 ±</td><td>45.89</td></tr><tr><td>Total phenolics (mg GAE)</td><td>539.7 ±</td><td>33.31</td><td>Total phenolics (mg GAE)</td><td>1825.1 ±</td><td>23.44</td></tr><tr><td rowspan="6">Type ii</td><td><sup>1</sup>Brix</td><td>23.7 ±</td><td>0.38</td><td>Alcohol (% vol.)</td><td>13.9 ±</td><td>0.12</td></tr><tr><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>6.9 ±</td><td>0.38</td><td>Residual sugar (gr/L)</td><td>1.3 ±</td><td>0.31</td></tr><tr><td>pH</td><td>3.1 ±</td><td>0.04</td><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>4.1 ±</td><td>0.08</td></tr><tr><td>Malic acid (mg/L)</td><td>3322.4 ±</td><td>86.71</td><td>pH</td><td>3.4 ±</td><td>0.04</td></tr><tr><td>Tartaric acid (mg/L)</td><td>5480.5 ±</td><td>33.11</td><td>Tartaric acid (mg/L)</td><td>1423.8 ±</td><td>54.98</td></tr><tr><td>Total phenolics (mg GAE)</td><td>576.1 ±</td><td>47.14</td><td>Tartaric acid (mg/L)</td><td>2679.1 ±</td><td>57.34</td></tr><tr><td rowspan="6">Type iii</td><td><sup>1</sup>Brix</td><td>20.9 ±</td><td>1.73</td><td>Total phenolics (mg GAE)</td><td>1870.1 ±</td><td>42.32</td></tr><tr><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>7.5 ±</td><td>0.99</td><td>Alcohol (% vol.)</td><td>12.6 ±</td><td>0.26</td></tr><tr><td>pH</td><td>3.1 ±</td><td>0.09</td><td>Residual sugar (gr/L)</td><td>1.4 ±</td><td>0.15</td></tr><tr><td>Malic acid (mg/L)</td><td>3089.3 ±</td><td>67.32</td><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>3.7 ±</td><td>0.33</td></tr><tr><td>Tartaric acid (mg/L)</td><td>5190.2 ±</td><td>45.33</td><td>pH</td><td>3.4 ±</td><td>0.12</td></tr><tr><td>Total phenolics (mg GAE)</td><td>557.9 ±</td><td>52.41</td><td>Tartaric acid (mg/L)</td><td>2499.2 ±</td><td>71.10</td></tr><tr><td rowspan="6">Type i</td><td><sup>1</sup>Brix</td><td>23.0 ±</td><td>0.67</td><td>Total phenolics (mg GAE)</td><td>1735.2 ±</td><td>33.42</td></tr><tr><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>5.5 ±</td><td>0.82</td><td>Alcohol (% vol.)</td><td>13.5 ±</td><td>0.89</td></tr><tr><td>pH</td><td>3.2 ±</td><td>0.11</td><td>Residual sugar (gr/L)</td><td>1.3 ±</td><td>0.10</td></tr><tr><td>Malic acid (mg/L)</td><td>3333.3 ±</td><td>66.62</td><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>4.4 ±</td><td>0.27</td></tr><tr><td>Tartaric acid (mg/L)</td><td>5379.3 ±</td><td>58.32</td><td>pH</td><td>3.4 ±</td><td>0.12</td></tr><tr><td>Total phenolics (mg GAE)</td><td>522.1 ±</td><td>31.23</td><td>Tartaric acid (mg/L)</td><td>2598.5 ±</td><td>58.72</td></tr><tr><td rowspan="6">Type ii</td><td><sup>1</sup>Brix</td><td>24.0 ±</td><td>1.07</td><td>Total phenolics (mg GAE)</td><td>1799.4 ±</td><td>35.09</td></tr><tr><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>6.0 ±</td><td>1.01</td><td>Alcohol (% vol.)</td><td>13.1 ±</td><td>0.65</td></tr><tr><td>pH</td><td>3.1 ±</td><td>0.15</td><td>Residual sugar (gr/L)</td><td>1.3 ±</td><td>0.25</td></tr><tr><td>Malic acid (mg/L)</td><td>3322.3 ±</td><td>88.10</td><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>4.2 ±</td><td>0.08</td></tr><tr><td>Tartaric acid (mg/L)</td><td>5380.2 ±</td><td>30.22</td><td>pH</td><td>3.4 ±</td><td>0.04</td></tr><tr><td>Total phenolics (mg GAE)</td><td>518.9 ±</td><td>48.80</td><td>Tartaric acid (mg/L)</td><td>1376.2 ±</td><td>88.78</td></tr><tr><td rowspan="6">Type iii</td><td><sup>1</sup>Brix</td><td>23.7 ±</td><td>0.77</td><td>Total phenolics (mg GAE)</td><td>2683.9 ±</td><td>66.91</td></tr><tr><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>5.5 ±</td><td>0.30</td><td>Alcohol (% vol.)</td><td>12.3 ±</td><td>0.35</td></tr><tr><td>pH</td><td>3.2 ±</td><td>0.12</td><td>Residual sugar (gr/L)</td><td>1.3 ±</td><td>0.15</td></tr><tr><td>Malic acid (mg/L)</td><td>3101.3 ±</td><td>65.31</td><td>Total Acidity (HSO<sub>4</sub> gr/L)</td><td>3.8 ±</td><td>0.22</td></tr><tr><td>Tartaric acid (mg/L)</td><td>5060.2 ±</td><td>82.23</td><td>pH</td><td>3.3 ±</td><td>0.09</td></tr><tr><td>Total phenolics (mg GAE)</td><td>513.1 ±</td><td>50.22</td><td>Tartaric acid (mg/L)</td><td>1246.8 ±</td><td>86.34</td></tr></tbody></table>
## 2.2. Influence of Different Pinot Noir Grape Cluster Types on Sensory Evaluation of Wines

A paired comparison test was carried out to evaluate whether trained panelists were able to note organoleptic differences in taste and aroma between wines made from the three grape cluster types. The sensory data were the mean values of all panelists who took part in every season under evaluation. Results showed significant differences among the three classes of wines for all the sensory properties assessed (Figure 2). It is important to note that no significant differences were observed when data from the two seasons were compared. As revealed in Figure 2, the aroma of wines made with grapes from cluster type i was mainly characterized by red berries, stone fruits, caramel, and coffee/chocolate attributes. By contrast, the herbal and vegetal attributes were especially high in the wine elaborated with grape cluster type iii. The other attributes that varied significantly between samples were spice, woody, and smoky which presented high scores in both samples of wine type i and type ii compared with wine made of grape cluster type iii. In addition, we observed that the taste attributes, acidity, and astringency notes were higher in the wines from grape cluster type i and cluster type ii, while sweetness and bitterness attributes were lower. By contrast, in wine obtained from grape cluster type iii, sweetness and bitterness attributes were enhanced by reduced acidity and astringency. Also, when the panelists were asked about their general impression of these wines, again those made with grapes from cluster type i were the best evaluated. In contrast, larger grapes seem to generate wines with less relative balance, body, and complex attributes. Panelists were also asked to score the wines from 0 to 100 points in terms of enological potential to be used in wine blend and also about their commercial value if those wines were bottled and sold as monovarietal wines. As before, those wines made with cluster type i received the best scores. Statistically significant differences were observed between the qualifications of the wines made with all different cluster types. Overall, given these sensory attributes for the three wine types, the panelists classified as the most well-rounded and with better enological/commercial potential the wine made from grape cluster type i.

<figure><img src="image_120.png" /><figcaption>**Figure 2.** Sensory analysis. Graph of the mean sensory ratings of the three Pinot noir wines studied. Ten experienced Pinot noir wine-makers conducted Duo-Trio test trials to determine the overall difference between wines samples.</figcaption></figure>

## 2.3. Untargeted Metabolite Profiles of Three Pinot Noir Wines

Given the differences in the sensory analysis of the three types of wines, we were next interested in determining whether the unrestricted metabolomics analysis of these types of wines allowed us to significantly associate non-volatile metabolites with specific aromatic and taste attributes. To this
end, an exhaustive untargeted analysis of secondary metabolite profiling was conducted on wines produced from the three grape cluster types. Wines from the two seasons were analysed using a UPLC-FT-ICR-MS in positive and negative ionization mode. Based on visual inspection of the raw total ion chromatograms, the wine samples showed clear differences depending on the grape cluster types (data not shown). The data sets were examined by principal component analysis (PCA) (Figure 3A), with two principal components explaining 50.11% and 60.72% of the overall variance of the detected features in positive and negative mode, respectively. The PCA revealed two distinct patterns. The analysis highlighted a sharp season-dependent trend (PC1), while PC2 showed clear differences that were evident between the three types of wines from the same season. This result strongly indicates that the PCA-derived separation was not based only on the presence/absence of grape cluster type specific features but also on quantitative differences within the metabolites present in all wines.

<figure><img src="image_121.png" /><figcaption>**Figure 3.** Score plots of all detected *m/z* features of the three Pinot noir wines from two different seasons in positive and negative mode investigated by (A) principal component analysis (PCA) and (B) the latent variables (LVs) in first discriminant analysis (sPLS-DA).</figcaption></figure>

In light of such results, linear discriminant analysis (sPLS-DA) was used as a supervised classification technique [30] to develop models capable of predicting the classification of wines according to the grape cluster type and harvest year. A plot of the two first discriminant variates (derived from all *m/z* features measured in either positive or negative ionization mode) is presented in Figure 3B. When the discrimination was based on harvest year, a similar pattern was observed compared with the unsupervised PCA (Figure 3A). However, when grape cluster type was considered
as a variable, we observed a clear separation into three groups according to the grape cluster type independent of harvest year (Figure 3B).

## 2.4. Multivariate Discrimination of Three Pinot Noir Wines Based on Metabolic Profiling and Identification of Multiple Metabolic Features as Biomarkers of Wine Quality

Since the quality of the three wine types were scored differently based on their sensory evaluation, we next constructed sparse Partial Least Squares (sPLS) regression models in order to identify *m/z* features (biomarkers) related to sensory attributes and that also distinguished different harvest years and cluster types. An sPLS model was generated for each dependent variable (wine attributes), and the normalized intensity levels of the molecular features were used as predictors. The relative importance of each independent variable (*m/z* feature) is measured in terms of the VIP (variable importance in projection) scores; the higher the VIP scores, the greater the association of the analysed *m/z* feature with respect to the dependent variable (sensory attribute). The *m/z* features with the highest VIP score (≥ 1.0) are displayed in Table S1. The identified molecular features were then subjected to compound annotation.

Annotation of metabolites based on high resolution mass spectrometry accurate mass provides an initial indication of the potential identification. A match to a potential molecular formula, as defined by the metabolomics standards initiative (MSI), corresponds to a level 2-based identification. Level 3 identification is for *m/z* features that match multiple molecular formulas but within a single class of metabolites, while unknown features are classified as level 4 [31]. In order to classify compounds with higher confidence, we also used the MS2 spectral data (unit mass and peak ratio match) for identification. When MS2 spectral data were not captured for a given metabolite, data acquired with previous grape and wine samples analysed under identical conditions were considered, together with published data. Where a given compound ionized in negative and positive modes and different adduct species were observed for each, the MS2 spectra data for each ion species were manually assessed and compared.

We were able to tentatively identify a total of 26 molecular features with a VIP score higher than 1.0 that exhibited a significant contribution to explain the variation between harvest years. In concordance with the results obtained in the sensorial analysis with respect to the lack of discrimination of wines made from the same cluster type but in different seasons, any of those features were in common with the features found to be associated with all measured sensory attributes.

To better visualize the molecular features that significantly contribute to specific sensory attributes, we performed an association study using networks (Figure 4, Table S1). In each network, sensory attributes and molecular features are represented as nodes of different size (large and small) and are color coded by compound category. An edge represents an association between nodes if the corresponding VIP coefficient is greater than 1.0. The edge thickness and color indicate the strength of the association; darker and thicker lines mean stronger associations. Network analysis emphasized links both between and within the various sensory attributes. From the analysis for sensory evaluation associated with aroma and taste attributes (a total of 15 sensory attributes were addressed; spice, woody, smoky, coffee/chocolate, vegetal, animal, herbal, red berries, stone fruits, nuts, caramel, astringency, acidity, bitterness, and sweetness), we observed a total of 65 polar *m/z* features that displayed good VIP score as listed in Table S2. Following the identification process, 22 secondary metabolites were tentatively assigned to specific compound categories, eight flavonols and flavanols, eight anthocyanins, one hydroxycinnamic acid derivative, two ellagitannins, two cyanogenic glucosides, two classified as phenols, and 43 other classes (Figure 4, Table S2).
<figure><img src="image_122.png" /><figcaption>**Figure 4.** Visualization of sensory attribute-molecular feature correlations in Pinot noir wines. (A) Network considering aroma attributes and (B) taste attributes. Notes are as follows: sensory attributes are represented as large circles, while molecular features are represented as small circles and are color coded by compound category. The edge thickness and color indicate the strength of the association; darker and thicker lines mean stronger associations (only those that showed a VIP coefficient > 1.0).</figcaption></figure>

Interestingly, the network associated with aroma attributes (Figure 4A) revealed markedly higher connectivity than the network for taste attributes (Figure 4B), suggesting that aroma attributes in wine may represent a higher level of complexity as compared with taste attributes. Comparative network analysis also suggested that molecular features associated with taste attributes were largely modular, while features associated with aroma attributes showed more complex and interconnected associations between specific features. The aroma attributes network analysis revealed five interesting clusters, one of which included red berries, stone fruit, and caramel attributes, another involved spice
and woody characteristics, while two other clusters contained herbaceous characters (herbal and vegetal) and coffee/chocolate, smoky, and animal, respectively. Interestingly, we observed a strong link between nut attributes and the two aroma attributes, coffee/chocolate and red berries. For network involving taste attributes, we observed specific *m/z* features strongly associated with the four tested taste attributes. Here, we observed an association between sweetness and bitterness attributes, while acidity and astringency displayed non-clear association with the other analysed attributes. Interestingly, the link between sweetness and bitterness attributes was through four *m/z* features; two of them were annotated as anthocyanins (named catechylpyranopeonidin-3-O-glucose and pyranopeonidin 3-O-coumaroyl-glucose), although in general, Pinot noir wines have been described to have lower anthocyanin concentrations [32].

**3. Discussion**

The quality of a wine is known to directly correlate with the quality of the grapes used for its production [13]. Therefore, quality and character of a wine should correlate directly with the chemical composition of grape [33].

The starting point of this study was due to an interesting observation made by the grower in an established vineyard, in which a Pinot noir clone grown under the same mesoclimate and treated using the same viticultural practices produced three different grape cluster types (Figure 1).

High-throughput metabolomics facilitates dissecting a phenotype at the metabolite level, potentially allowing a more holistic perspective in monitoring and gaining information on the winemaking process, and thus, this approach can assist in the evaluation of sensorial attributes and can potentially help the improvement of several aspects of wine quality.

The first aim of the present study was to test if a comparative metabolome and sensorial analyses of three Pinot noir wines made by three different grape cluster types were able to discriminate wine types. If this was feasible, we were also interested in better understanding the diversity in the sensory attributes of these wines. For that, we tested a multivariate statistical method to identify metabolites or molecular patterns (biomarkers) related to different sensory attributes, which can be used to assist the sensory analysis postulated by wine expert panelists.

The wine made from grape cluster type iii revealed significant organoleptic differences from the other two wine types. In particular, we observed that wine produced by grape cluster type iii was found to have greater scores for vegetal and herbal aromas. On the other hand, wine types i and ii showed differences by displaying more canned fruity characteristics (red berries, stone fruits, and caramel). Moreover, wine type i was the most intense in woody and coffee/chocolate characters. This strong discrimination between herbaceous and fruity attributes within wines produced from the same grape variety has been previously reported [34–37]. In these studies, the authors also indicated that the descriptive profile of Cabernet Sauvignon wines displayed a dichotomy between herbaceous and fruity characteristics.

With respect to the taste attributes, wine obtained from grape cluster iii was classified as having a reduced mouthfeel in terms of astringency and acidity. It was also noted that wine produced from grape cluster type iii had a noticeable increase in bitterness and sweetness. High levels of organic acids have been associated with an increased perception of astringency. Furthermore, this perception is also pH dependent [38]. Here, although we did not observe significant differences in total acidity and pH, we detected differences in the level of malic acid in wine made from grape cluster type iii compared to the other types. Moreover, astringency of high-molecular-weight polyphenolics (tannins) can be enhanced by the presence of organic acids [38,39]. Therefore, this observation agrees with the hypothesis that different concentrations of tannins in wine type iii can contribute to the observed differences in astringency. Additionally, we observed increased ethanol levels in wine types i and ii, although non-significant, which can have a greater effect on perceived astringency than the difference between acid and high-molecular-weight polyphenol levels [40]. Similarly, a high ethanol level is described to increase the perception of bitterness [41–43]. However, we observed that bitterness was
perceived to be higher in wine type iii, although previous studies suggest that the bitterness perception
can be suppressed by the sweetness [41]. However, we observed that wine type iii was classified
with higher values for sweetness and bitterness attributes, suggesting that these sensory attributes
cannot reflect the same chemical properties when comparing different cultivars. Consistent with this
hypothesis, we observed no significant changes in residual sugar levels when comparing the three
wine types, although it has been described that levels of sugars in wine correlate with the sweetness
perception [44].

In view of the fact that wine is a really complex matrix and the levels of individual metabolites are
not necessarily as important as their interaction with other compounds, the use of metabolomics allows a
holistic perspective in monitoring several aspects of wine quality. Metabolomics have been successfully
applied to discriminate and classify wines according to grape variety, geographic origin, age or
winemaking practice [15–26]. Therefore, we decided to further use mass-spectrometry metabolomics
to investigate if we could establish an association between non-volatile metabolites or features and
specific sensory attributes related to wine quality.

For this purpose, wines produced by the three grape cluster types and classified differently
according to their quality by wine experts were analysed using UPLC-FT-ICR-MS. This analysis
revealed that the different wine types underwent several changes in secondary metabolite concentration,
mainly phenolic compounds. We focused on these classes of metabolites since they are important
chemical components of wines and can strongly influence the final organoleptic perception [45-47].
The content and presence of these classes of metabolites alone enabled us to discriminate between years
as demonstrated by PCA, in which the two analysed seasons can be clearly distinguished, in agreement
with previously published studies on other classes of wines [19]. In addition, this analysis was also able
to classify wines according to the grape cluster types used for winemaking as shown in the sPLS-DA
model (see Figure 3B). In this study, we have described that the putative biomarkers discriminating
the wine quality are related to changes in their abundance. Therefore, our results reveal that our
metabolite profiling provided sufficient discriminatory power to differentiate wine according to its
quality. We were then interested in identifying potential biomarkers associated with sensory wine
attributes as scored by a panel of experts. To that end, we applied sparse Partial Least Squares (sPLS)
regression models using the experts' scores as dependent variables. From the metabolite-attribute
networks constructed from the sPLS models, we observed that the network associated with aroma
attributes revealed markedly higher connectivity than the network for taste attributes, suggesting that
aroma attributes in wine may represent a higher level of complexity as compared with taste attributes
(Figure 4). After that, we focused our attention on the molecular features that were strongly associated
with specific sensory attributes (VIP > 10) to assess whether we were able to putatively annotate them.

We found that specific anthocyanins were strongly associated with different aroma attributes
(Figure 4 and Table S1). These pigments can be grouped as glycosides (i.e., monoglucosylated
anthocyanins), acylated pigments (i.e., acylated anthocyanin monoglucosides, in which the acylated
group can be *p*-coumaric acid, caffeic acid or acetic acid), and anthocyanin-derived pigments
(i.e., pyranoanthocyanins and tannin-anthocyanin dimers). The Pinot noir anthocyanin profile
is unique as it lacks (except in traces) acylated anthocyanins [48,49]. In this study, we found
pyranopeonidin-3-O-glc to be associated with caramel, vegetal, herbal, coffee/chocolate, spice, woody,
and smoky attributes, while catechylpyranopeonidin-3-O-glc was associated only with the nut character.
It has been described that these chemical compounds are produced in wines during fermentation and
the aging process [50,51]. Furthermore, two other studies have demonstrated that pyranoanthocyanins
can also be found in red grapes postharvest [52,53]. In addition to these anthocyanin compounds,
we identified pelargonidin 3-O-glc to be strongly associated with the animal attribute (VIP = 28.603).
Interestingly, this anthocyanin has been detected at a very low concentration in comparison to other
anthocyanins in red and pink grape berries [54]. This finding can suggest that pelargonidin 3-O-glc
is a clear candidate to be a chemical marker associated with the animal attribute in Pinot noir wines.
In most *f* *V. vinifera* varieties, anthocyanins are known to be present only in the skin of red grape
cultivars and together with the co-pigments or anthocyanins-related pigments are responsible for the red colour shown by red wine [55]. Anthocyanins are odorless molecules which can conflict our result. However, anthocyanins are known to modulate the formation of polymeric pigments [56] which are adducts formed during winemaking that provide mouthfeel properties [57]. In addition, anthocyanin-derived pigments have precursors molecules of different chemistries, including vinyl- and ethyl-phenols [58] or tannins [59], and we cannot discard their influence on aroma.

Resveratrol is a non-flavonoid compound in the stilbenoid class. In particular, the biomedical literature focuses attention on *trans*-resveratrol (3,4,5-trihydroxy-*trans*-stilbene) because it shows a strong antioxidant capacity and it has been positively associated with health effects, for instance, in cardiovascular disease and colon and breast cancers [60,61]. In wine, this compound has been found to be present around 10 times higher in red varieties than in white varieties [62]. Piceid, the β-D-glucoside form of resveratrol (resveratrol 3-O-β-D-glucoside), is also found in wines [63] and has been physiologically described as important as *trans*-resveratrol [64,65]. Interestingly, our data revealed that the piceid level in wine was associated with coffee/chocolate. We are unaware of any previous research that showed how the piceid level influenced wine quality; however, it has been described that a high level of *trans*-resveratrol did not alter the flavor or aroma profile of Cabernet Sauvignon [66].

Following the same strategy to identify chemical compounds associated with mouthfeel attributes, we focused our interest on those features with a VIP score > 10. We observed that the sPLS model associated with taste was less dense that the model from aroma characters. The model clearly showed that the compounds responsible for the discrimination between the four tested characters (sweetness, bitterness, acidity, and astringency), at least from the putative annotated features, are mainly represented by anthocyanins, flavanols, and flavanols species (Figure 4). It is well understood that monomeric flavan-3-ols and oligomeric tannins, particularly catechin and epicathechin species, are associated with astringency perception of red wines [41,67,68]. Interestingly, we identified catechin (VIP = 17.19) that correlated with the astringency attribute. Therefore, we can suggest that the level of catechin can act as a chemical marker for the astringency attribute in Pinot noir wines. However, the level of variation of this metabolite that can be associated with this sensory character will need further investigation.

From this study, we can demonstrate that wine quality in term of sensory analysis is a complex process in which the individual level of volatile and non-volatile compounds but also their interaction play an important role. Here, we presented a metabolic pipeline methodology allowing

(i) efficient discrimination of different wine quality

(ii) identification of *m/z* features associated with specific sensory attributes.

However, further research on the validation of the identified *m/z* features (putative annotated and non-annotated) related to aroma and taste characters is needed to release metabolic biomarkers to assist panelists with wine quality discrimination.

# 4. Materials and Methods

## 4.1. Chemicals

All solvents were of High Performance Liquid Chromatography (HPLC) grade and were obtained from VWR International. Other chemicals were of the highest purity grade available and were obtained from Sigma-Aldrich. Water was of Milli-Q quality (Millipore Corp., Bedford, MA, USA).

## 4.2. *Experimental Design*

An established commercial vineyard located in Casablanca valley, Chile (33°18'59'' S 71°27'30'' W) was used in this study. The grapevines were clonally multiplied, and the field was established seven years before this study. The experiments were performed on *V. vinifera* cv. Pinot noir during the 2008/2009 and 2009/2010 seasons. Vine spacing was 1.2 m between vines and 2.2 m between rows.
Vine management was performed by the grower according to the regional appropriate viticultural practices for this *V. vinifera* cultivar. The vines were cane pruned for a vertical shoot-positioning trellis system. No rain occurred after fruit set. Irrigation was applied to vines in order to maintain midday leaf water potential between -0.6 and -0.8 MPa throughout the season. Considering the phenotypic cluster difference among plants in the field, each experimental unit consisted of a few interior vineyard panels in a randomized complete block design. Individual grapevine plants and cluster samples were selected at random from throughout a vineyard block at the ripe stage.

### 4.3. Wine Samples

Clusters from the different selected blocks were used to make microvinifications. Wines were made from each selection group according to standard winemaking practices (Centro Tecnológico de la Vid y el Vino, Universidad de Talca) with four biological and three technical replicates. Each replicate consisted of 100 ± 1 kg of hand-harvested fruit collected from the field-selected blocks. Grapes were destemmed, and the berries were inoculated with yeast (*Saccharomyces bayanus*, EC-1118, Lallemand, 1620 Rue Préfontaine, Montréal, QC H1W 2N8, Canada) at a rate of 20 g hL⁻¹ in stainless steel fermenters. Cap management was performed twice per day by manual punch-downs. The fermentation temperature was maintained between 22 °C and 23 °C at a density of 990 g/L. After alcoholic fermentation, wines were pressed and racked into carboys and inoculated with Enoferm Alpha (Lallemand, 1620 Rue Préfontaine, Montréal, QC H1W 2N8, Canada) to start malolactic fermentation. At the end of malolactic fermentation, K₂S₂O₅ was added to 30 ppm of SO₂ in the wine. In addition, a cold stabilization at 2 °C was performed before bottling. Bottling and capping were performed manually.

### 4.4. Wine Sensory Analysis

Duo-Trio test trials were conducted to determine the overall difference between wines samples. Ten experienced Pinot noir wine-makers, highly trained and experienced as Pinot noir enologists, formed the wine panelists (four females and six males between the ages of 30 and 58). To minimize carry-over of flavors, the panelists were asked to rinse their mouth with water between samples, and the test was repeated on different days. One month after bottling, the wines were evaluated.

### 4.5. Metabolite Measurements

Chromatographic separation, mass spectrometric measurements, and data analysis were performed using a Waters Acquity Ultra High Resolution Liquid Chromatography (UPLC) system (Waters, Milford, MA, USA) using an HSS T3 C₁₈ reverse phase column (100 × 2.1 mm i.d., 1.8 µm particle size; Waters) which operated at a temperature of 40 °C. The mobile phases consisted of 0.1% formic acid in water (Solvent A) and 0.1% formic acid in acetonitrile (Solvent B). The flow rate of the mobile phase was 400 µL/min, and 2 µL of sample was loaded per injection. The gradient was: 0–1 min isocratic flow at 99% A, 1–12 min linear gradient from 95% to 65% A, 12–13.5 min linear gradient from 65% to 20% A, 13.5–14.5 min linear gradient from 20% to 1% A, 14.5–16 min isocratic flow at 1% A, 16–17 min linear gradient from 1% to 95% A, and 17 to 20 min isocratic flow at 95% A, to re-equilibrate the column before to the next sample could be injected. The UPLC was connected to an Ultra High Resolution Mass Spectrometry (FT-ICR) via a TriVersa NanoMate (Advion, Ithaca, NY, USA). The spectra were recorded alternating between full-scan and all ion-fragmentation-scan modes, covering a mass range from 100 to 1500 m/z using the LTQ FT-ICR-Ultra Mass Spectrometer (Thermo-Fisher, Bremen, Germany). The resolution was set to 35,000, and the maximum scan time was set to 250 ms. The sheath gas was set to a value of 50, while the auxiliary gas was set to 20. The transfer capillary temperature was set to 250 °C, while the heater temperature was adjusted to 350 °C. MS spectra were recorded from minute 0 to 19 of the UPLC gradient. All samples were randomized prior to mass spectrometric analyses to avoid any experimental drifts. A number of internal standards (chloramphenicol, corticosterone, and ampicillin), added to each sample just prior
to injection, were used to control experimental variability. One month after bottling, the wines were
chemically evaluated.

4.6. *Metabolite Annotation and Statistical Analysis*

Pre-processing of raw chromatograms was performed using Expressionist Refiner MS 10.0 (GeneData; http://www.genedata.com) with an established workflow. This workflow included baseline correction, removal of chemical noise, and chromatogram alignment. As output, a list of molecular features, that is, a retention time and a *m/z* ratio pair, and a data matrix containing relative intensities for each feature and for each chromatogram were obtained. All further computations, data manipulations, and plot generation were carried out using the R programming language (http://www.r-project.org).

The data matrix was further filtered for low intense features whose mean intensity across all samples was less than 0.1% of the highest average. Moreover, features with more than 30% missing values were also removed as well as features that eluted before 0.45 min. After filtering, the *m/z* feature data were normalized by sample volume and sample median intensity. Due to machine sensitivity variation across different measurement runs, each feature was normalized by dividing its intensity in a sample by the median intensity across all measurements within a batch in order to compensate for said effect.

Principal component analysis (PCA) was performed in R using the package pcaMethods [69] using five components and unit variance scaling. Sparse partial least squares (sPLS) and Sparse partial least squares discriminant analysis (sPLS-DA) were performed using the package mixOmics [70]. The sPLS-DA was used for categorical variables (season and cluster type) while the sPLS was used for quantitative data (sensorial attributes). In both cases, the metabolite levels were considered predictors. For each response variable (categorical or quantitative), a single sPLS/sPLS-DA model was established. To determine the optimal number of components and variables of a given model, we searched the parameter space spanned by 1 to 12 (3 for sPLS-DA) components and 50, 100, 200, 300, 500, 1000 selected variables. For each such component/variable combination, 100 iterations of 5-fold cross-validation rounds were tested and the pair that resulted in the lowest (sPLS-DA) or highest R2 (sPLS) classification error was taken as the optimal parameters. Once an optimal number of components and variables was determined for each response variable, we computed the respective sPLS/sPLS-DA model and using this, we obtained the variable importance in projection (VIP) coefficient for each metabolite. Finally, we used the VIP coefficients to generate networks in which a node represents either a molecular feature or a sensorial parameter, and an edge was drawn if the respective VIP coefficient was greater than zero. All networks manipulations were performed using the igraph package [71].

Molecular features were putatively annotated by searching the *m/z* value against the KEGG compound database. A maximum tolerance of 5 ppm was allowed, considering the following potential known adducts: M+H, M+Na, M+NH₄ for positive mode and M-H, M+AcOH-H for negative mode. A custom Python script was written for this task. The MS/MS fragmentation of the metabolites was compared with candidate molecules found in databases and verified with earlier literature on similar compounds, especially when the presence of the metabolite was reported in grapes and wines.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/6/220/s1, Table S1. Table of all features associated with specific attributes (VIP score > 1.0). Table S2. Identification of all molecular features that significantly contribute to specific sensory attributes (VIP score > 1.0).

**Author Contributions:** J.G.V., Y.M.-S. and L.W. conceptualized the study. J.G.V. and C.V.-A. carried out the field trials and sensory analysis. J.G.V. and Á.C.-I. processed samples and performed the data analysis. J.G.V. wrote the manuscript which was proofread, corrected, and approved by all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by an INNOVA-CORFO-Chile grant and by the Max Planck Society, Germany.

**Acknowledgments:** We would like to thank Agricola Santa Sara, Casablanca Valley, Chile, for providing the grape samples for this study. Also, we would like to thank Änne Michaelis, Max Planck Institute of Molecular Plant Physiology, for her help with wine chromatography.
**Conflicts of Interest:** The authors declare no conflict of interest.

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