Identification of Aging-Associated Food Quality Changes in Citrus

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Identification of Aging-Associated Food Quality Changes in Citrus Products Using Untargeted Chemical Profiling Ian Ronningen, and Devin G. Peterson J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b04450 • Publication Date (Web): 19 Dec 2017 Downloaded from http://pubs.acs.org on December 20, 2017

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Journal of Agricultural and Food Chemistry

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Identification of Aging Associated Food Quality

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Changes in Citrus Products Using Untargeted

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Chemical Profiling

4 5 Ian G. Ronningen1 and Devin G. Peterson1,*

6 7 8

1

Department of Food Science, University of Minnesota, MN 55108

9

*

Corresponding author

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*

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

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University, 2015 Fyffe Rd, Columbus, OH 43210

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*

Corresponding Author Email: [email protected]

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ACS Paragon Plus Environment


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Abstract

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Chemometric techniques have seen wide application in biological and medical sciences,

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but are still developing in the food sciences. This study illustrated the use of untargeted

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LC/MS chemometric methods to identify features (retention time_m/z) associated with

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food quality changes as products age (freshness). Extracts of three citrus fruit varietals

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aged over four time points that corresponded to noted changes in sensory attributes, were

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chemical profiled and modeled by two discriminatory multivariate statistical techniques,

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projection partial least squares data analysis (PLS-DA) and machine learning random

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forest (RF). Age-associated compounds across the citrus platform were identified.

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Varietal was treated as a nuisance variable to emphasize aging chemistry, and further

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variable selection using age-related piecewise model generation and meta filtering to

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emphasize features associated with general aging chemistry common to all the citrus

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extracts. The identified features were further replicated in a validation study to illustrate

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the validity and persistence of these markers for applications in citrus food platforms.

28 29 30 31

Key Words: flavoromics, untargeted chemical profiling, MVA, flavor, freshness


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Introduction

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A core aim of flavor chemistry research is to understand the molecular basis for the

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sensory properties of foodstuffs. Traditionally methods of flavor identification have

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relied on ‘targeted’ analytical approaches that screen individual compounds and select

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those with flavor activity1–3. While these focused, reductionist analytical approaches have

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advanced our understanding of the compounds that contribute to food flavor, they are

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also inherently limited in scope as compounds are evaluated individually (in isolation)

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and out of context. Flavor is a complex sensation derived from a multitude of stimuli

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from different sensory systems such as aroma (olfactory), taste (gustatory), and

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tactile/irrigation/temperature (chemestetic). Consequently, analytical methods based on

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singular compound evaluation can overlook drivers of flavor perception by ignoring

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potential interactions among stimuli. Untargeted chemical profiling methods would

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therefore provide a further basis to characterize flavor compounds/interactions. These

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approaches typically utilize orders of magnitude more data compared to targeted methods

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and are much more likely to identify interacting data features4,5.

47 48

Recent advancements in many scientific fields have benefited from inclusive data driven

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‘omic’ research methods6–9. Untargeted chemical profiling methods are positioned as a

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complementary method to the more traditional targeted approaches utilized in the

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biological sciences10. For example, the use of metabolomics in plant physiology has

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progressed the understanding of phenotypes by increasing understanding of contextual

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relationships4. Paralleling advances seen in other fields with complex interrelated

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outcomes, Reineccius et al11 discussed the term “Flavoromics” as a new frontier in flavor



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research, initially suggesting the use of proven workflows seen in metabolomics and

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other “omics” fields. Although this term has been present for a number of years, the field

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has yet to see substantial growth as well as applications still lack a sensory verification

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aspect or have a limited scope12,13. Emphasis has been placed on differentiating samples

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without establishing causative relationships for the identified statistical features14. Even

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as computational resources and analytical technology have progressed, untargeted

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approaches often conclude investigations using only principle component analysis to

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make conclusions whereas traditional targeted methods provide significantly stronger

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conclusions15.

64 65

Combining untargeted chemical fingerprinting methods with multivariate analysis allows

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for the inclusion of as many data points as possible, providing a hypothesis seeking

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approach which relates inputs (e.g. compounds) to an output (i.e. flavor). Within flavor

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research, data driven approaches has been applied to instrumental data for sensory panel

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prediction12,16,17. The ability to gain novel information from flavor analysis while also

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generating models for future prediction of flavor relevant outcomes is a major motivator

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to apply untargeted chemometric methodology to food systems. Other applications in

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food aim to understand the geographical region or type of food being analyzed14,18–20.

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While there has been an increase in the application of untargeted chemometric based

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research, there is still limited validation of the identified statistical features and

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translatability.

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The current study is the first phase of a two-part investigation to apply

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discriminatory untargeted chemical profiling methods to identify compounds

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related to flavor quality changes as products age (freshness). The first phase

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focuses on chemical characterization, statistical methods of analyses and modeling

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techniques for compound selection, whereas the second phase focused on sensory

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validation and compound identification 21. In this current paper, discriminatory

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untargeted liquid chromatography-mass spectrometry-time of flight (LC-MS-tof)

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chemical fingerprinting methods coupled with multivariate analysis (MVA) were utilized

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to characterize chemical changes in citrus extracts during storage to identify compounds

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associated with flavor quality changes as the products aged (freshness). LC/MS

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techniques were selected to provide further characterization (novel insights) of the

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chemical changes of oranges related to the flavor attributes of oranges. Prior research on

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identification of orange citrus flavor has primarily emphasized volatile aroma

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characterization22,23. Two modeling methods, projection partial least squares (PLS) and

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machine learning random forest (RF) were utilized, with further variable selection using

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age-related piecewise model generation and meta filtering techniques. Finally, the

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statistically significant chemical data was validated in a repeated experiment.

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Materials and methods

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Sample Preparation. Three common citrus hybrid fruit products were sourced form the

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domestic market. Navel Oranges, Mineola Tangerines, and Valencia Oranges were

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washed, rinsed, and then cut to be 97%) by multi-dimensional

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LC fractionation and subsequently identified by TOF MS/MS and NMR, as ionone

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glycoside and nomilin glucoside, respectively. Furthermore, when each compound was

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added to fresh orange samples at quantities identified in aged sample, a trained sensory

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panel reported the flavor attributes changed and mimicked that of the aged product.

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Interestingly both compounds themselves in water did not report any sensory activity and

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would not be expected to be identified by traditional targeted methods1–3. To the best of

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the authors knowledge, this is a first report to identify and validate persistent LC/MS

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chemical features (unknowns) using statistical directed methods linked to sensory

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significance.

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In summary, this work illustrates the utility of flavoromics (untargeted analysis) for

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identifying data trends related to a biological (flavor) outcome and an approach to

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establish those relationships as persistent through repeated modeling. Additionally, this

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work illustrates how modeling can emphasize aging through treating varietal differences

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a nuisance variable to identify chemistry related to a ‘global’ citrus platform and to

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identify flavor-active compounds.

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ACKNOWLEDGMENT

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

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

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468 Table 1. Variable of Importance metrics for the identified statistical features for both PLS and Random Forrest (RF) modeling approaches in the initial and validation experiment Feature

Initial PLS

Initial RF

Validation PLS

Validation RF

(RT_M/ Z)

VIP

VIP

VIP

VIP

2.541_383.114

2.4

2.8

1.65

1.98

2.193_413.121

2.11

2.2

1.27

1.25

3.145_693.283

1.99

2.6

1.23

1.76

2.454_413.121

1.81

2.4

1.35

1.82

6.93_563.2393

1.62

0

1.02

1

4.909_191.091

1.52

2.4

NA

0

3.07_684.3070

1.5

0

1.07

1

2.38_ 661.2653

1.4

1

1.4

1

1.50_541.1749

1.33

0

0.99

0

5.48_457.2563

1.09

1.2

1.05

0

2.208_295.063

0.3

2.2

2.15

2.06

469



470

Figure Captions

471

Figure 1. Unsupervised PCA of chemical fingerprinting of citrus extracts (n=20 for each

472

varietal).

473 474

Figure 2. PLS-DA models of sample age for data subsets of each of the varietals; A.

475

Mineola (R2X= 0.571, R2Y=0.988, Q2=0.923), B. Navel (R2X=0.631, R2Y=0.988,

476

Q2=0.915), C. Valencia (R2X=0.632, R2Y=0.989, Q2=0.923)

477 478

Figure 3. PLS-DA of citrus aging with classification of sample age rather than varietal

479

and age; R2Y= 0.95 and a Q2 of 0.981.

480 481

Figure 4. Variable of importance for the top ten most features in the Random Forest

482

analysis, the model out of bag error was 2.38% and generated with piecewise

483

optimization of parameters.

484


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Navel

Mineola

Valencia

485 486

Figure 1. Unsupervised PCA of chemical fingerprinting of citrus extracts (n=20 for each

487

varietal).

488



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A. Mineola Day 4

Day 6

Day 2

Day 0

B. Navel Day 2 Day 4 Day 6

Day 0

C. Valencia Day 2 Day 6 Day 4

Day 0

489 490

Figure 2. PLS-DA models of sample age for data subsets of each of the varietals; A.

491

Mineola (R2X= 0.571, R2Y=0.988, Q=0.923), B. Navel (R2X=0.631, R2Y=0.988,

492

Q2=0.915), C. Valencia (R2X=0.632, R2Y=0.989, Q2=0.923).


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Day 2 Day 6 Day 0 Day 4 493 494

Figure 3. PLS-DA of citrus aging with classification of sample age rather than varietal

495

and age; R2Y= 0.95 and a Q2 of 0.981.

496 497 498 499 500



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2.541_383.114 3.145_693.283 2.311_337.076 2.454_413.121 4.909_191.091 1.662_148.972 3.381_419.204 2.497_295.063 0.225_326.921 2.208_295.063 2.193_413.121 2

2.1

2.2

2.3 2.4 2.5 2.6 Mean Decrease In Accuracy

2.7

2.8

2.9

501 502

Figure 4. Variable of importance for the top ten most features in the Random Forest

503

analysis, the model out of bag error was 2.38% and generated with piecewise

504

optimization of parameters.

505 506 507 508 509 510 511 512 513 514


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TOC

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Identification of Aging-Associated Food Quality Changes in Citrus

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