Identification and Validation of Sensory-Active Compounds from Data

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Identification and validation of sensory-active compounds from data-driven research: A flavoromics approach Ian Ronningen, Michelle Miller, Youlin Xia, and Devin G. Peterson J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 19 May 2017 Downloaded from http://pubs.acs.org on May 20, 2017

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Journal of Agricultural and Food Chemistry

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Identification and validation of sensory-active

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compounds from data-driven research: A

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flavoromics approach

4 Ian Ronningen1, Michelle Miller2, Youlin Xia2, Devin G Peterson1,*

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*

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*

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2015 Fyffe Rd, Columbus, OH 43210

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Corresponding author: Devin G Peterson

Current address: 317 Parker Building, Food Science & Technology, The Ohio State University,

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Corresponding Author Email: [email protected]

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= Department of Food Science, University of Minnesota, MN 55108

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= MNMR Center, University of Minnesota, MN 55455

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ACS Paragon Plus Environment

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Keywords: flavoromics, MVA, untargeted, chemical fingerprinting, flavor, modulation

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ABSTRACT

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In the current study, significant LC/MS features (retention time_m/z) derived from multivariate

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analysis (MVA) models developed using untargeted chemical fingerprinting to characterize

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flavor changes in extracts of citrus fruits related to aging, were further isolated and analyzed for

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sensory impact, followed by structural elucidation of flavor actives. The top ten statistical

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features from two MVA approaches, partial least squares data analysis (PLS-DA) and Random

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Forrest (RF), were purified to approximately 70% via multi-dimensional liquid chromatography-

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mass directed fractionation to screen for sensory activity. When added to an orange flavor model

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system (aroma isolate from orange juice with sucrose and citric acid) 50-60% of the isolates

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were reported by the panel to cause a sensory change. From the subset of the actives identified,

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two compounds were selected, based on statistical relevance, that were further purified to >97%

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for identification (MS, NMR) and for sensory descriptive analysis (DA). The compounds were

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identified as Nomilin glucoside and a novel ionone glucoside.

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recombination orange model indicated both compounds statistically suppressed the perceived

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intensity of the ‘orange character’ attribute, whereas the novel ionone glycoside also decreased

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the intensity of the floral character while increasing the green bean attribute intensity.

DA evaluation in the


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Introduction:

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Modern methods of flavor analysis typically utilize targeted techniques such as gas

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chromatography-olfactory or liquid chromatography-taste directed fractionation to characterize

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and understand the sensory attributes of foodstuffs1–7. These techniques have, without question,

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advanced our understanding of the compounds that contribute to the flavor of a food. Although

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targeted methods have been successful, information is potentially overlooked as only compounds

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that directly produce a sensory response would be included, ignoring any modulating activity and

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potential ingredient interactions8,9,10. Discriminatory untargeted chemical fingerprinting coupled

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with MVA methods have facilitated significant advances in both plant and biological science by

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relating inputs to changes in pathways and altered expression11–14. Applying similar hypothesis

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seeking methods in food research to understand the role inputs have on food quality can similarly

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be applied to advance flavor discovery and facilitate successful food formulation. With advances

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in instrumental analysis technology more data is available, providing an opportunity to discover

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contextual relationships by statistical modeling, which can lead to better understanding inputs

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and their impact on biological outcomes13,15. The application of untargeted methods to flavor

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discovery should consist of a systematic workflow that includes the identification of relevant

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chemical changes in food systems that are further validated by sensory recombination analysis.

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Chemically profiling complex systems, such as food, necessitates a large but directed data pool

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be developed with robust and systematic analytics aimed to a biological outcome (sensory,

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appearance, nutrition, etc.).

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discriminatory methods is a lack of extensive LC/MS libraries which are typically utilized for

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unknown identification, thereby limiting scientific outcomes of data driven research16,17,18.

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Consequently an untargeted analytical platform for flavor research should include three

A current challenge for untargeted chemical profiling-MVA


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important steps: characterization, modeling and validation/compound identification (Figure 1).

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Rigorous application of these steps enables improved scientific progress by linking appropriate

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inputs with sensory outcomes, where the generated hypothesis is validated through additional

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testing.

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In a previous phase of the current study, two oranges and one hybrid orange citrus fruit ethanol

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extracts aged over 6 days were chemically profiled by LC/MS and high quality MVA models

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were developed to predict sample age which also corresponded to changes in flavor profiles

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observed by a trained sensory panel19. The experimental design emphasized common chemistry

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changes in diverse biological systems in order to find commonalities, which has shown success

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in identifying predictors of phenotypes in complex biological systems before20. To these ends

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modeling was directed to understand aging across the citrus platform, and mined for features

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with statistical relation to aging. The current work is focused on the ‘additional testing’ step,

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specifically to isolate statistically significant features identified from the MVA models

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developed for age classification, further evaluate the sensory relevance of these features, and

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subsequently identify compounds with sensory activity. Thus this work contextualizes a prior

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experiment to define flavor changes in aged citrus extracts by discriminatory LC/MS profiling

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with descriptive modeling by conducting an additional systematic sensory recombination

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validation step.

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Materials and Methods:

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Chemicals and Food Samples. Navel, Valencia and Mineola citrus fruit, not from concentrate

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orange juice (Tropicana), from concentrate orange juice (Kemps), canned green beans (Green


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Giant), were sourced from a local market (Saint Paul, MN). Food grade citric acid, Optima

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grade methanol was sourced from Fischer Scientific (Waltham, Massachusetts), while nanopure

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water was produced in house using a Barnstead nanopure system (Thermo Scientific,

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Massachusetts). LC-MS grade Acetonitrile was sourced from Burdick & Jackson (Honeywell,

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Michigan). 200 proof USP ethanol was sourced from Decon Labs (King of Prussia, PA). Butyl

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4-hydroxybenzoate, formic acid, food grade caffeine, food grade alum, food grade citric acid,

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deuterium oxide (D2O), and methanol-d4 were sourced from Sigma Aldrich (St. Louis, Missouri).

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Orange Sample Extract. Oranges were extracted as previously described19,21. In brevity oranges

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were thinly sliced (70% for each feature, based on TIC peak area

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analysis. All isolates were subsequently screened for sensory activity.

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Orange Flavor Recombination Model Base.

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recombination sensory analysis. A volatile orange aroma isolate was obtained by Solvent

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Assisted Flavor Evaporation (SAFE)22 from a commercial orange juice (Tropicana) diluted with

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5% by weight food grade 200 proof ethanol. The resultant distillate was adjusted to 8% w/w

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sucrose and 0.05% w/w citric acid to create the tasting model base.

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Precaution Taken for Sensory Analysis of Food Fractions and Taste Compounds. All fractions

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from food samples for taste analysis, prior to sensory testing, were liberated from solvent by

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rotary evaporation and were subsequently freeze-dried twice. GC/MS or 1H NMR revealed that

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fractions treated by the above protocol are free of solvents and suitable for sensory analysis.

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Approval of the sensory evaluation protocol was granted by the Ethics Committee, University of

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Minnesota (IRB #1505E70948).

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Medium Purity (>70%) Feature Sensory Same/Different Test. Recombination sample analysis of

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each second-dimension LC purified feature was evaluated by a trained sensory panel (n=6).

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Each feature was added individually to the orange flavor recombination base at approximate

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concentrations to the citrus extract aged for 96 h (based on equivalent LC/MS peak area).

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Panelists were presented pairs of 10 ml samples consisting of a recombination model with the

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select feature and without (the model base). Panelists evaluated each pair and indicated whether

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the samples were the same or different.

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Third dimension LC purification. Two features m/z 383.1727 and 693.283 were selected and

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further purified using the same LC/MS system for the first dimension fractionation but equipped

A tasting solution was prepared for sample


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a Waters Charged Surface Hybrid flouro-phenyl column (5 µm, 10 mm x150 mm) maintained at

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40 oC. Initial gradient was 90% solvent A (Water with 0.1% formic acid), 10% solvent B

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(methanol with 0.1% formic acid) which was held constant for 4 min, raised to 40% solvent B at

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18 min and 95% solvent B at 25 min, followed by a one minute column wash and a 4 minute re-

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equilibration at 10% solvent B.

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system to check purity. Each compound purity was confirmed at >97% based on TIC peak area

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analysis. Purity was additionally confirmed via 1H NMR.

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Quantitative Analysis of Isolates in Aged Orange Extracts (LC/MS/MS). Known quantities of the

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high purity compounds (m/z 383.172 and 693.283) and butyl 4-hydroxybenzoate (100 mg/L) as

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an internal standard were added to a 0 day old citrus extract to calculate the compound recovery.

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For each sample 500 µL of extract was diluted to 10% ethanol and introduced to a 96 well

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Supelco 100 mg DSC-C18 96 well plate. Wells were washed with 5% ACN/95% water and

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eluted with 100 µL of ACN with further dilution with 66 µL. A Waters I-class FTN sample

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manager and flow binary solvent manager were coupled to a Waters Xevo G2 Q-TOF. A Waters

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BEH C18 (2.1 x 50 mm) was kept at 45 °C in a Waters Column Manager. A flow rate of 0.55

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mL/min was used with initial gradient conditions of 3% acetonitrile (ACN) and 97% Water

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(0.1% Formic Acid), which was held for 0.5 min. A linear gradient and raised ACN content to

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15% at 1.5 min, 45% ACN at 8 min followed by a 1 min column wash (100% ACN) and re-

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equilibration. Electrospray Spray ionization was run in negative mode with source temperature

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of 120 °C, desolvation temperature of 350 °C, capillary set to 1.75 kV, sample cone of 25 V.

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The MS parameters were as follows: MSMS channels were set to 383.1727 221.1178 m/z

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[M-C6H11O5]˙m/z (15 eV) and 693.2770 m/z 161.0446 m/z [Glc-] (15 eV). Five-point

The resultant isolates were re-injected on the UPLC-MS-tof


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standard curves were generated with good linearity (r2 > 0.97), (1-100 mg/L for 693.) and (0.5-

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40 mg/L for 383).

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High Purity Feature Sensory Evaluation – Descriptive Analysis. Eight panelists were recruited

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from the Department of Food Science and Nutrition at the University of Minnesota and had

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previous experience on descriptive analysis panels. A lexicon was developed over 4 one-hour

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sessions using fresh and aged samples prepared similarly to the original isolate, not from

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concentrate orange juice, and from concentrate orange juice. The panel generated the following

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terms and agreed on the following references; sweet (sugar solution 8% w/w), sour (0.05% w/w

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citric acid solution), bitter (0.08% w/w caffeine solution), astringent (0.6 g/L alum solution),

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orange character (not-from concentrate orange juice (Tropicana)), orange peel (freshly cut navel

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orange peel), cooked (from concentrate orange juice (Kemps)), green grassy, herbal like (freshly

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minced grass), green bean (canned green beans (Green Giant)).

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Panelists underwent 8 one-hour long training sessions to familiarize themselves with references,

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samples and align terminology. Reproducibility of panelists was found to be within one unit

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between replicate evaluations and across the panel. The attributes were rated using a 10-point

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scale, with citric acid references available for cross modal matching (3 = 0.019%. 5 = 0.05%, 10

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= 0.14% citric acid w/v). Five mL of sample was presented to panelists in duplicate across two

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different sessions. Three digit randomized codes were used to blind sample identity.

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vacumetrics snuffer foam nose clip was used to isolate taste attributes, while retronasal attributes

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were evaluated without the nose clip.

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The panel evaluated the orange flavor model base and the two recombinant samples. Compound

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693.283 was added to the recombination model base at 44 mg/L, which is consistent with the 144

A


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h aged sample. Compound 383.114 was added in at 1.2 mg/L due to limited quantity but was

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consistent with the 48 h aged sample19. Data was collected using Compusense Cloud software

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(Compusense Inc., Guelph, Ontario, Canada), and samples were presented in a randomized order

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in duplicate, over two sessions. Data exported in CSV format for analysis through R. Analysis

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was conducted using an initial two way-ANOVA and further post-hoc analysis via Dunnett’s test

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with the recombination model base as the control sample.

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Compound Public Library Searches. Accurate mass (reported to a sub 2 ppm accuracy) was

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used to generate an elemental composition and potential chemical formula. Both accurate mass

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and chemical formula were used to search a number of databases including; Metlin (Scripps

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Institute), Dictionary of Natural Products (Taylor & Francis Group), The Human Metabolome

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Database (The Metabolomics Innovation Center), FooDB (The Metabolomics Innovation

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Center), ChemSpider (Royal Society of Chemistry), Universal Natural Products Database

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(Peking Database).

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Nuclear magnetic resonance spectroscopy (NMR). Feature m/z 383 was dissolved in deuterium

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oxide (D2O) and analyzed by a Bruker Avance III 700 MHz NMR equipped with a 1.7 mm TCI

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probe. Feature 693 was dissolved in Methanol-d4 and analyzed using a Bruker 900 US2 NMR

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equipped with a 5 mm TCI probe. Data processing was performed using Bruker TopSpin 2.1.

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Spectral Data. Compound m/z 383 β-d-glucopyranosyl substituted 1-oxo-∝-ionone (Figure 2).

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MS-TOF: m/z 383.1727 ([M-H]-, 2 ppm), 365.1620 ([M-H2O]-, 2ppm), 221.1178 ([M-

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C6H11O5]˙, 2ppm), 163.1128 ([C6H11O5]˙, 2 ppm). 1H NMR (700 MHz): δ 1.02[s,3H, H-

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C(2C, 3C, 4C, 12C)], 1.08 [s, 3H, H-C(2C, 3C, 4C, 13C)], 1.98 [s, 3H, H-C(1C, 4C, 5C, 6C,

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7C)], 3.08[ d J=9.3 Hz, 1H, H-C(1C ,2C, 3C, 5C, 6C, 7C, 8C, 11C, 12C)], 3.40 [m, 1H, H-


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C(1"C, 3"C)], 3.41 [t, J=8.5 Hz, 1H, H-C(3"C, 5"C, 6"C)], 3.47 [t, J=8.8 Hz, 1H, H-C(4"C,

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6"C)], 3.52 (t, J=8.5 Hz, 1H, H-C(2"C, 4"C)], 4.53 [d, J=6.9 Hz, 1H, H-C(2"C, 3"C, 5"C)], 6.10

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[s, 1H, H-C(2C, 4C, 7C, 11C), 6.45 (d, J=15.8 Hz, 1H, H-C(3C, 4C, 5C, 7C, 9C, 10C)], 7.00 [dd,

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J=15.8, 9.3 Hz, 1H, H-C(3C, 4C, 5C, 8C, 9C)], 2.21 [d, J= 16.8 Hz, 1H, H-C(1C, 3C, 4C, 12C,

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13C)], 2.53 [d, J=16.8 Hz, 1H, H-C(1C, 3C, 4C, 12C, 13C)], 3.72[ m, 1H, H-C(4"C, 5"C)],

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3.92[d, J=12.7 Hz, 1H, H-C(4"C, 5"C)]. 4.73 [d, J=8.0 Hz, H-C(1"C, 9C)], 4.88 [d, J= 8.0 Hz,

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1H, H-C(1"C, 9C)]. 13C NMR (175MHz):δ 25.6 [C-11], 28.5 [C-13], 29.3 [C-12], 39.1 [C-3],

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49.3 [C-2], 57.6 [C-4], 63.3 [C-6”], 72.2 [C-4”], 75.0 [C-10], 75.4 [C-2”], 78.2 [C-3”], 78.7[5”],

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104.8 [1”], 128.1 [C-6], 131.8 [C-8], 148.9 [C-7], 168.2 [C-5], 201.3 [C-9], 207.0 [C-1].

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Compound m/z 693, Nomilin 17-O-beta-D-glucopyranoside (Figure 2): MS-TOF: m/z 693.2770

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([M-H]-, 2 ppm), 179.0564 ([Glc-], 2 ppm), 161.0446([Glc-], 2 ppm). 1H NMR (900 MHz,

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CD3OD): δ 0.79 [s, 3H, H-C(5C, 7C, 9C, 14C)], 1.01 [s, 3H, H-C(1C, 5C, 9C)], 1.37 [s, 6H, H-

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C(4C, 5C, 25bC)], 1.37 [s, 6H, H-C(12C, 13C, 14C, 17C)], 1.52 [s, 3H, H-C(4C, 5C, 25aC)],

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1.64 [m, 2H, H-C(9C)], 1.40 [m, 1H, C-H(9C)], 2.11 [m, 1H, H-C (9C, 14C, 18C)], 1.65 [m,

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2H, H-C (9C, 14C, 18C)], 2.14 [ s, 3H, H-C[OAc(CO)], 2.64 [dd, J= 12.0, 7.7 Hz, 1H, H-C (8C,

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11C, 13C, 19C, 24C)], 2.89 [s, 1H, H-C(16C)], 2.92 [dd, J= 20.3, 7.4 Hz, 1H, H-C (4C, 5C, 7C,

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10C)], 2.38 [dd, J=20.3, 12.0 Hz, 1H, H-C(4C, 5C, 7C, 10C)], 3.04 [m, 1H, H-C(4C, 6C, 19C,

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25bC)], 3.15 [dd, J= 15.9, 7.7 Hz, 1H, H-C (1C, 3C, 10C)], 3.19 [d, J=8.5 Hz, 1H, H-C (1”C,

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3”C)], 3.26 [t, J= 9.3 Hz, 1H, H-C (5”C)], 3.28 [m, 1H, H-C(4”C)], 3.41 [t, J=9.3 Hz, 1H, H-C

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(2”C, 4”C)], 3.46 [d, J=15.9 Hz, 1H, H-C (1C, 3C, 10C)], 3.54 [dd, J= 12.3, 5.6 Hz, 1H], 3.70

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[dd, J= 12.3, 2.0 Hz, 1H], 4.41 [d, J=8 Hz, 1H, H-C(17C)], 4.84 [d, J=7.7 Hz, 1H, H-C (3C,

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5C)], 5.39 [s, 1H, H-C (8C, 12C, 13C, 21C, 23C, 1”)], 6.62 [s, 1H, H-C(22C)], 7.43 [s, 1H, H-C

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(20C, 21C, 23C)], 7.7 [s, 1H, H-C(20C, 21C, 23C)]. 13C NMR (175 MHz): δ 15.2 [C-19], 17.8


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[C-11], 22.1 [C-24], 22.8 [OAc(Me)], 23.4 [C-25b], 27.2[C-18], 29.3 [C-12], 34.1 [C-25a],

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37.5[C-2], 42.5 [C-6], 44.7 [C-9], 45.5 [C-10], 46.7 [C-13], 48.6 [C-5], 53.8 [C-8], 62.6 [C-

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15], 63.2 [C-6”], 72.36 [C-4”], 72.44 [C-14], 74[C-1], 76.3 [C-2”], 78.2 [C-5”], 78.5 [C-3”], 81.6

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[C-17], 90.1 [C-4], 106.8 [C-1”], 114.9 [C-21], 127.4 [C-20], 144.2 [C-22], 145 [C-23], 175.7

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[OAc(CO)], 177.4 [C-3], 177.5 [C-16], 218.4 [C-7].

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Results and Discussion:

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LC/MS molecular features identified to characterize aging of a citrus fruit ethanol extract by

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untargeted chemical fingerprinting methods coupled with discriminatory MVA were further

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evaluated to define the cause and effect, and in essence move from hypothesis seeking to

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hypothesis testing.

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matches were evident, and therefore to better characterize these unknown compounds and to test

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the hypothesis that the age-related compounds identified via two MVA modeling approaches

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PLS-DA (projection methods) and RF (machine learning) have sensory significance, isolation

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from the food system was required to conduct sensory recombination testing. To manage the

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experimental workload, the top ten statistically significant features from both PLS-DA and RF

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were selected and are shown in Table 1. As expected, given the unique modeling algorithms, a

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different list of the top 10 significant features was identified between PLS-DA and RF; the

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Variable of Importance (VIP) provides a metric for feature contribution to the model and

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generally features with values > 1 are considered significant contributors to the model. Seven of

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out the top ten features for RF were not significant for the PLS-DA model whereas six of the top

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ten features for PLS-DA were not significant for the RF model (Table 1).

Based on public MS (accurate mass) libraries, no logical food related


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Due to the complexity of food systems and the high purity requirements for compound

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confirmation, the targeted LC isolates from the food extract were initially screened for sensory

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impact in an orange model system at moderate purity (>70%) for sample throughput efficiency.

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Samples that were reported to have sensory impact were subsequently purified to meet the higher

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purity requirements for structural elucidation and validation of sensory activity. From the total

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of 20 LC isolates evaluated, 11 were reported to impact the sensory profile when compared to a

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control orange model sample (without test compound, Table 1), providing a clue to the

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effectiveness of data driven methods for flavor discovery and the application of such analytical

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approaches in supplementing more traditional methods of flavor analysis. Evaluations were

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conducted in a ‘complex’ orange model system, that consisted of an aroma isolate from fresh

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commercial orange juice and known basic tastants (sugar and citric acid), to evaluate potential

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contextual interactions among flavor stimuli.

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Among the l1 (of 20) compounds that caused a sensory change when evaluated in a

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recombinant orange juice model (Table 1), a sub-group of two compounds were selected for

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further purification to validate sensory impact and subsequent identification by MS and NMR.

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These two compounds were selected for multiple reasons. First, both compounds reported

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overall the highest VIP scores for both the PLS-DA and RF models. Second, both compounds

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were able to be purified to a high level (>97%) after a third dimensional of LC separation and in

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adequate quantify for NMR analysis. Third, both compounds showed strong positive linear

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trends for all three citrus fruit samples with age (0, 48, 96, and 144 h). Feature 2.541_383.114

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for Mineola: y = 49x + 45.4, R² = 0.881, for Valencia: y = 63.7x + 28.9, R² = 0.971, for Navel: y

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= 48.59x + 27.68 R² = 0.977. Feature 3.145_693.283 for Mineola: y = 218.26x + 14.12 R² =

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0.974, Valencia: y = 227.56x + 39.92, R² = 0.988, y = 233x + 34 R² = 0.928.

During aging,


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compounds would be expected to be generated as well as be degraded.

The current

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recombination models for sensory impact focused on compounds generated during aging.

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Descriptive analysis (DA) for each compound, 383.114 and 693.283, at high purity (>97%)

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in the recombinant orange juice model along with the orange juice model base is shown in Table

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2. The panel evaluated sweet, sour, bitter tastes, the feeling of astringency, and orange character,

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orange peel, cooked, floral and green bean for aroma. During sensory training and lexicon

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development higher levels of bitter, astringency, cooked and green bean were associated with

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aged orange extract samples and from commercial concentrate orange juice. Whereas higher

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intensities of orange character, orange peel, floral and sweet were associated with the un-aged

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orange extract samples and from commercial not from concentrate orange juice. These

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observations were in general agreement of the descriptive analysis panel for each recombination

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model compared to the orange model base solution (Table 2). Notably, both compounds 383.114

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and 693.283 (generated during aging) resulted in suppressed orange character while the

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compound 383.114 additionally enhanced the green bean intensity and suppressed the floral

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intensity of the recombinants. Interestingly, both non-volatile compounds had a significant

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impact on aroma attributes, which was unlikely due to their direct volatility given the molecular

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weight. Two panelists (due to limited sample) form the trained panel also evaluated both the

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high purity 383.114 and 693.283 in water at the same concentration as in the recombinants, no

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sensory (aroma or taste attributes) were observed. Furthermore, both compounds did not show

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an impact on sweet, sour or bitter of the orange model recombinants (Table 2). While the impact

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of non-volatile compounds (at concentrations below taste activity) on aroma perception many not

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be simply interpreted, others have demonstrated how taste stimuli at sub-threshold levels can


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impact aroma perception23. Increasing evidence supports the role of multisensory integration

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and higher order interactions on flavor perception24.

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The DA sensory results show how compounds generated with age impacted the orange

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model solution by accentuating “aged” flavor attributes. Additionally, with no reported taste or

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aroma activity, data driven untargeted chemical fingerprinting discriminatory MVA methods

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were able to identify compounds that may be eliminated by other targeted screening methods that

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typically remove analytes if there is not a direct response. All of these outcomes combine to

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make a case for MVA to be included in flavor discovery workflows as a complimentary

310

approach to targeted methods.

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Based on the high purity sensory validation that both 383.114 and 693.283 were flavor

312

active, the structures were further elucidated.

Each compound was characterized through

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multidimensional NMR experiments combined with information from MSn. Compound 383.114

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was reported to be a novel ionone glycoside (structure shown in Figure 2). The presence of a

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sugar was confirmed by 2D 1H-1H TOCSY, which aligns with the 163 m/z loss found in MSn

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experiments. The unique chemical shift positions of 1”C and 6”C pinpoint the anomeric (104.8

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ppm) and exocyclic hydroxymethyl (63.3 ppm) carbon, respectively. The b-d-glucopyranosyl

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was confirmed using the 1H chemical shifts and coupling constants and are consistent with

319

literature values25,26. Using the 2D TOCSY, HMQC and HMBC, all other 1H and 13C chemical

320

shifts of the sugar were assigned. From HMBC, HMQC and 1D 1H, the aglycone portion of 383

321

contains 13 carbons and 17 hydrogens. 3C is a quaternary carbon with strong HMBC cross

322

peaks to protons attached to 12C, 13C, 2C and 4C; thus, it is reasonable to assume that 3C is

323

directly linked to all four of these carbons. 12C and 13C are methyl groups, while 2C has two

324

protons, and 4C has one proton. 1C, a carbonyl carbon, is strongly correlated to 2H’/2H”,


15


Page 16 of 27

325

making it likely that 1C is adjacent to 2C indicating the presence of a 1C(=O)-2C(2H’/2H”)-

326

3C(12C, 13C)-4C(H) fragment. Since 4C is a tertiary carbon, it is either a branch point or a ring

327

closure. Based on their large 13C chemical shifts, 5C, 6C, 7C and 8C are carbon-carbon double

328

bonds. All four of these carbons had strong HMBC cross peaks to 4H, which pointed to 4C

329

being located between these two pairs of carbon double bonds. 7H and 8H share a 1H-1H J

330

coupling constant of 15.8 Hz. This value is found only when protons across a carbon double

331

bond are in a trans conformation; hence, 7C=8C cannot be in a ring structure. 7H also shares a

332

1H-1H J coupling constant of 9.3 Hz with 4H, so 7C is adjacent to 4C. In the 1H 1D spectrum,

333

peak 6H is a singlet, consequently 6C cannot be attached to 4C; 5C is. Methyl group 11H

334

exhibits the strongest HMBC cross peak to 5C, providing evidence that 11C is directly linked to

335

5C suggesting the presence of a linear fragment 8C(8H)=7C(7H)-4C(4H) as well as either linear

336

or cyclic 6C(6H)=5C(11C)-4C-3C(12C,13C)-2C(2H’/2H”)-1C(=O) fragment. There are weak

337

HMBC peaks correlating 11H to 1C, 6H to 2C, and 2H” to 6C: if this portion of 383 was linear,

338

these atoms would all be separated by 6 bonds, which is highly improbable; therefore, this

339

fragment is a ring, with 6C adjacent to 1C. Interestingly, there is no HMBC signal correlating

340

6H with 1C and 5C. This can be explained by the fact that in a planar structure 2JCH coupling

341

constants are about 1-2 Hz, while 3JCH are closer to 7.5 Hz27. Since the HMBC was collected

342

with the long range JCH parameter set to 6 Hz, it follows that cross peaks are present for 3JCH,

343

but not 2JCH. The only carbons left were to assign are 9C, a carbonyl carbon and 10C, which

344

has two protons and because of its chemical shift (75.0 ppm) is mostly likely attached to an

345

oxygen. 10H’/10H” are the only aglycone atoms correlated to 1”C, the sugar anomeric proton,

346

as well as to 9C, which is correlated with 7H and 8H. Thus, the connection from the sugar to the

347

ring with 4C is complete: sugar-O-10C(10H’/10H”)-9C(=O)-8C(8H)=7C(7H)-4C(4H). Here,


16

Page 17 of 27


348

the carbon chemical shifts of the aglycone of 383 are calculated28. The congruence of the real

349

measured chemical shifts, with this simulation further confirmed the structure determination.

350

The second compound, 693.283 was determined to be nomilin glucoside, shown in Figure 2.

351

Existence of a sugar is confirmed by 2D 1H-1H TOCSY. The anomeric 1″H (4.41 ppm) and

352

exocyclic hydroxymethyl 6″H′/6″H″ protons are easily identified by their carbon δ. Assignment

353

of the remaining sugar atoms was accomplished using 2D TOCSY, HSQC and HMBC. The

354

anomeric proton of the sugar is correlated to 17C via HMBC, while TOCSY reveals that 17H is

355

part of a coupling network with 21H, 22H and 23H. Furthermore, 17H shows HMBC cross

356

peaks with 12C and 13C. Taken together, this indicates that 17C is the connection between three

357

substructures; the carbohydrate, a component that includes 20-23C, and a third that contains 12C

358

and 13C. Based on δ, 22C (144.2 ppm) and 23C (145.0 ppm) are double-bonded carbons

359

attached to an oxygen, while 20C (127.4) and 21C (114.9) are double-bonded and/or members of

360

an aromatic ring. This suggests that 20-23C may be a furan, which was confirmed using

361

generated spectra for a substituted furan29. 1H and 2H’/2H” are correlated on TOCSY, and all

362

three exhibit an HMBC cross peak with 3C (177.4 ppm), a carboxyl carbon. 1C (74.0 ppm) is

363

connected to an oxygen, one proton and two carbons, while 2C (37.5 ppm) is linked to two

364

protons and two carbons. Moreover, 1H and 2H” share 3JHH of 7.7 Hz, proving 1C and 2C are

365

adjacent. Additional HMBC correlations are 1H to 5C, 2H’/2H” to 10C and 19H methyl group to

366

1C, of which only 10C is quaternary. Establishing 10C as the next position in the backbone,

367

linked to methyl group 19C, 5C, and one other carbon. 7C (218.4 ppm) is a carbonyl between

368

two other chain-elongating carbons. From HMBC, 24H methyl also has cross peaks with 8C, 9C

369

and 14C. Rings 13C-14C, 10C-9C, and 4C-O-3C are linked together via 8C and 9C, and 5C and

370

10C, respectively. 15H and 16C are correlated, 16C (177.5 ppm) is a carboxyl carbon, and the


17


Page 18 of 27

371

peak 15H is a singlet. The fragment O-14C-15C(15H)-16C(=O, O) is attached to the ring

372

containing 14C. The final atom attached to 15C (62.6 ppm), is an oxygen. A three member ring

373

of O-14C-15C fits the correlations. Rings 13C-14C, 10C-9C, and 4C-O-3C are linked together

374

via 8C and 9C, and 5C and 10C, respectively. 15H and 16C are correlated, 16C (177.5 ppm) is a

375

carboxyl carbon, and the peak 15H is a singlet. The fragment O-14C-15C(15H)-16C(=O, O) is

376

attached to the ring containing 14C. The final atom attached to 15C (62.6 ppm), is an oxygen.

377

Nomilin glucoside was originally identified and is known to be highly prominent in the seeds

378

of citrus products30, and has been illustrated to have no direct flavor activity as an individual

379

compound but upon the loss of the glucoside, the resultant nomilin developed flavor activity

380

being described as bitter or astringent30–33.

381

In summary, this work illustrates a systematic untargeted chemical profiling approach of

382

characterizing, modeling and validating an observed flavor trait and presents an effective

383

workflow to identify outcomes and in this case identify novel compounds that are associated

384

with the aging of citrus extracts19,21. MVA provides a foundation to more thoroughly understand

385

biological systems by creating new links between chemical drivers and biological outcomes.

386

The use of untargeted methods in addition to traditional targeted methods provides

387

complementary information to better understand contextual relationships important to biological

388

outcomes.

389

integrate into targeted methods and compliment traditional workflows for flavor discovery and

390

prediction.

Validating statistically identified compounds sets up future research to better

391


18

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392 393 394 395

Cited Literature: (1)

Acree, T. E.; Barnard, J.; Cunningham, D. G. A procedure for the sensory analysis of gas chromatographic effluents. Food Chem. 1984, 14 (4), 273–286.

396 397

(2)

Grosch, W. Detection of potent odorants in foods by aroma extract dilution analysis. Trends Food Sci. Technol. 1993, 4 (3), 68–73.

398 399 400

(3)

O. Frank; H. Ottinger, and; Hofmann*, T. Characterization of an Intense Bitter-Tasting 1H,4H-Quinolizinium-7-olate by Application of the Taste Dilution Analysis, a Novel Bioassay for the Screening and Identification of Taste-Active Compounds in Foods. 2000.

401 402 403

(4)

Ottinger, H.; Bareth, A.; Hofmann, T. Characterization of Natural “Cooling” Compounds Formed from Glucose and l -Proline in Dark Malt by Application of Taste Dilution Analysis. J. Agric. Food Chem. 2001, 49 (3), 1336–1344.

404 405 406

(5)

Charve, J.; Chen, C.; Hegeman, A. D.; Reineccius, G. a. Evaluation of instrumental methods for the untargeted analysis of chemical stimuli of orange juice flavour. Flavour Fragr. J. 2011, 26 (6), 429–440.

407 408 409

(6)

Buettner, A.; Schieberle, P. Evaluation of aroma differences between hand-squeezed juices from Valencia late and Navel oranges by quantitation of key odorants and flavor reconstitution experiments. J. Agric. Food Chem. 2001, 49 (5), 2387–2394.

410 411

(7)

Hasegawa, S.; Miyake, M. Biochemistry and biological functions of citrus limonoids. Food Rev. Int. 2009.

412 413

(8)

Keast, R. S. J. Modification of the bitterness of caffeine. Food Qual. Prefer. 2008, 19 (5), 465–472.

414 415

(9)

De Araujo, I. E.; Rolls, E. T.; Velazco, M. I.; Margot, C.; Cayeux, I. Cognitive modulation of olfactory processing. Neuron 2005, 46 (4), 671–679.

416 417

(10)

Kurihara, Y. Characteristics of antisweet substances, sweet proteins, and sweetness‐ inducing proteins. Crit. Rev. Food Sci. Nutr. 2009.

418 419 420

(11)

Kell, D. B.; Oliver, S. G. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 2004, 26 (1), 99–105.

421 422

(12)

Kitchin, R. Big Data, new epistemologies and paradigm shifts. Big Data Soc. 2014, 1 (1), 2053951714528481.

423 424

(13)

Myers, C. L.; Troyanskaya, O. G. Context-sensitive data integration and prediction of biological networks. Bioinformatics 2007, 23 (17), 2322–2330.

425 426

(14)

Friedman, N. I. R.; Linial, M.; Nachman, I. Using Bayesian Networks to Analyze Expression Data. Proc. Fourth Annu. Int. Conf. Comput. Mol. Biol. 2000, 7, 601–620.

427

(15)

Lex, A.; Partl, C.; Kalkofen, D.; Streit, M.; Gratzl, S. Europe PMC Funders Group


19


428 429

Page 20 of 27

Entourage : Visualizing Relationships between Biological Pathways using Contextual Subsets. 2014, 19 (12), 2536–2545.

430 431

(16)

Daszykowski, M.; Walczak, B. Use and abuse of chemometrics in chromatography. TrAC - Trends Anal. Chem. 2006, 25 (11), 1081–1096.

432 433 434 435

(17)

Gan, H. H.; Soukoulis, C.; Fisk, I. Atmospheric pressure chemical ionisation mass spectrometry analysis linked with chemometrics for food classification - A case study: Geographical provenance and cultivar classification of monovarietal clarified apple juices. Food Chem. 2014, 146, 149–156.

436 437 438

(18)

Lee, S. M.; Kwon, G. Y.; Kim, K.-O.; Kim, Y.-S. Metabolomic approach for determination of key volatile compounds related to beef flavor in glutathione-Maillard reaction products. Anal. Chim. Acta 2011, 703 (2), 204–211.

439 440

(19)

Ronningen, I. Untargeted Flavoromics to Identify Flavor Active Compounds, University of Minnesota, 2016.

441 442 443

(20)

Tautenhahn, R.; Patti, G. J.; Kalisiak, E.; Miyamoto, T.; Schmidt, M.; Lo, F. Y.; McBee, J.; Baliga, N. S.; Siuzdak, G. metaXCMS: second-order analysis of untargeted metabolomics data. Anal. Chem. 2011, 83 (3), 696–700.

444 445 446

(21)

Ronningen, I. G.; Peterson, D. G. Application of untargeted LC/MS techniques (Flavoromics) to identify changes related to freshness of food. In ACS Symposium Series; 2015; Vol. 1191, pp 269–277.

447 448 449

(22)

Engel, W.; Bahr, W.; Schieberle, P. Solvent assisted flavour evaporation - a new and versatile technique for the careful and direct isolation of aroma compounds from complex food matrices. Eur. Food Res. Technol. 1999, 209 (3–4), 237–241.

450 451

(23)

Dalton, P.; Doolittle, N.; Nagata, H.; Breslin, P. A. S. The merging of the senses: integration of subthreshold taste and smell. Nat. Neurosci. 2000, 3 (5), 431–432.

452

(24)

Spence, C. The multisensory perception of flavour. Psychologist 2010, 23 (9), 720–723.

453 454

(25)

Jens Ø. Duus; Charlotte H. Gotfredsen, and; Bock*, K. Carbohydrate Structural Determination by NMR Spectroscopy: Modern Methods and Limitations†. 2000.

455 456 457

(26)

Bubb, W. A. NMR spectroscopy in the study of carbohydrates: Characterizing the structural complexity. Concepts Magn. Reson. Part A Bridg. Educ. Res. 2003, 19 (1), 1– 19.

458

(27)

Cazes, J. Analytical Instrumentation Handbook. 2004, 340.

459 460

(28)

Banfi, D.; Patiny, L. nmrdb.org: Resurecting and processing NMR Spectra online. Chimia (Aarau). 2008, 64 (2), 280–281.

461 462

(29)

Yuri Binev; Maria M. B. Marques, and; Aires-de-Sousa*, J. Prediction of 1H NMR Coupling Constants with Associative Neural Networks Trained for Chemical Shifts. 2007.

463

(30)

Bennett, R. D.; Hasegawa, S.; Herman, Z. Glucosides of acidic limonoids in citrus.


20

Page 21 of 27


464

Phytochemistry 1989, 28 (10), 2777–2781.

465 466

(31)

Hasegawa, S.; Bennett, R.; Herman, Z.; Fong, C.; Ou, P. Limonoid glucosides in citrus. Phytochemistry 1989, 28 (6), 1717–1720.

467 468

(32)

Rouseff, R. L. Nomilin, a new bitter compound in grapefruit juice. J. Agric. Food Chem. 1982, 30 (3).

469 470

(33)

Herman, Z.; Fong, C. H.; Hasegawa, S. Biosynthesis of limonoid glucosides in navel orange. Phytochemistry 1991, 30 (5), 1487–1488.

471 472

ACKNOWLEDGMENT

473

The authors would like to acknowledge the financial support provided by the Flavor Research

474

and Education Center at The Ohio State University and its supporting members.

475 476 477 478 479 480 481 482 483 484


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485

Table 1. Same/Different sensory screening evaluation of top 10 statistical features identified by

486

partial least squares (PLS) or Random Forrest (RF) at medium purity in a recombinant orange

487

model system

Feature (Retention time_m/z) 2.54_383.114 3.15_693.283 4.90_191.091 2.20_295.063 2.45_413.121 2.19_413.121 1.50_541.174 6.93_563.239 2.38_ 661.265 5.48_457.256 3.07_684.307 1.66_148.972 2.24_473.248 1.75_213.002 3.73_360.194 2.70_295.063 2.31_931.351 3.80_581.133 3.71_723.262 2.06_1063.33

Observed sensory change by > 4 of 6 panelista Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No No

Modeling Variable of Importance (VIP) 2.4 (PLS), 2.8 (RF) 2.0 (PLS), 2.6 (RF) 1.5 (PLS), 2.4 (RF) 0

2 X435.158 p < 0.001 ≤0

>0

3 X437.228.1 p = 0.001 ≤ 0

>0

Node 4 (n = 19)

Node 5 (n = 16)

1

0.8 0.6

0.4

0.4

0.2

0.2

0

Chemical Fingerprinting

T1

T2

T3

Node 7 (n = 12)

1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0 Fresh

Sample Aging

Node 6 (n = 13)

1

0.8 0.6

0.2

0 Fresh

T1

T2

T3

0 Fresh

T1

T2

T3

Fresh

T1

T2

T3

Statistical Modeling

O O O

O

HO

OH OH

OH

Compound Identification

Sensory Analysis

MS Guided Fractionation


27

Identification and Validation of Sensory-Active Compounds from Data

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