Subscriber access provided by READING UNIV
Article
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
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 29
Journal of Agricultural and Food Chemistry
1
Identification of Aging Associated Food Quality
2
Changes in Citrus Products Using Untargeted
3
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
10
*
Current address: 317 Parker Building, Food Science & Technology, The Ohio State
11
University, 2015 Fyffe Rd, Columbus, OH 43210
12
*
Corresponding Author Email:
[email protected] 13
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
14
Abstract
15
Chemometric techniques have seen wide application in biological and medical sciences,
16
but are still developing in the food sciences. This study illustrated the use of untargeted
17
LC/MS chemometric methods to identify features (retention time_m/z) associated with
18
food quality changes as products age (freshness). Extracts of three citrus fruit varietals
19
aged over four time points that corresponded to noted changes in sensory attributes, were
20
chemical profiled and modeled by two discriminatory multivariate statistical techniques,
21
projection partial least squares data analysis (PLS-DA) and machine learning random
22
forest (RF). Age-associated compounds across the citrus platform were identified.
23
Varietal was treated as a nuisance variable to emphasize aging chemistry, and further
24
variable selection using age-related piecewise model generation and meta filtering to
25
emphasize features associated with general aging chemistry common to all the citrus
26
extracts. The identified features were further replicated in a validation study to illustrate
27
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
ACS Paragon Plus Environment
Page 2 of 29
Page 3 of 29
Journal of Agricultural and Food Chemistry
32
Introduction
33
A core aim of flavor chemistry research is to understand the molecular basis for the
34
sensory properties of foodstuffs. Traditionally methods of flavor identification have
35
relied on ‘targeted’ analytical approaches that screen individual compounds and select
36
those with flavor activity1–3. While these focused, reductionist analytical approaches have
37
advanced our understanding of the compounds that contribute to food flavor, they are
38
also inherently limited in scope as compounds are evaluated individually (in isolation)
39
and out of context. Flavor is a complex sensation derived from a multitude of stimuli
40
from different sensory systems such as aroma (olfactory), taste (gustatory), and
41
tactile/irrigation/temperature (chemestetic). Consequently, analytical methods based on
42
singular compound evaluation can overlook drivers of flavor perception by ignoring
43
potential interactions among stimuli. Untargeted chemical profiling methods would
44
therefore provide a further basis to characterize flavor compounds/interactions. These
45
approaches typically utilize orders of magnitude more data compared to targeted methods
46
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
49
‘omic’ research methods6–9. Untargeted chemical profiling methods are positioned as a
50
complementary method to the more traditional targeted approaches utilized in the
51
biological sciences10. For example, the use of metabolomics in plant physiology has
52
progressed the understanding of phenotypes by increasing understanding of contextual
53
relationships4. Paralleling advances seen in other fields with complex interrelated
54
outcomes, Reineccius et al11 discussed the term “Flavoromics” as a new frontier in flavor
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
55
research, initially suggesting the use of proven workflows seen in metabolomics and
56
other “omics” fields. Although this term has been present for a number of years, the field
57
has yet to see substantial growth as well as applications still lack a sensory verification
58
aspect or have a limited scope12,13. Emphasis has been placed on differentiating samples
59
without establishing causative relationships for the identified statistical features14. Even
60
as computational resources and analytical technology have progressed, untargeted
61
approaches often conclude investigations using only principle component analysis to
62
make conclusions whereas traditional targeted methods provide significantly stronger
63
conclusions15.
64 65
Combining untargeted chemical fingerprinting methods with multivariate analysis allows
66
for the inclusion of as many data points as possible, providing a hypothesis seeking
67
approach which relates inputs (e.g. compounds) to an output (i.e. flavor). Within flavor
68
research, data driven approaches has been applied to instrumental data for sensory panel
69
prediction12,16,17. The ability to gain novel information from flavor analysis while also
70
generating models for future prediction of flavor relevant outcomes is a major motivator
71
to apply untargeted chemometric methodology to food systems. Other applications in
72
food aim to understand the geographical region or type of food being analyzed14,18–20.
73
While there has been an increase in the application of untargeted chemometric based
74
research, there is still limited validation of the identified statistical features and
75
translatability.
76
ACS Paragon Plus Environment
Page 4 of 29
Page 5 of 29
Journal of Agricultural and Food Chemistry
77
The current study is the first phase of a two-part investigation to apply
78
discriminatory untargeted chemical profiling methods to identify compounds
79
related to flavor quality changes as products age (freshness). The first phase
80
focuses on chemical characterization, statistical methods of analyses and modeling
81
techniques for compound selection, whereas the second phase focused on sensory
82
validation and compound identification 21. In this current paper, discriminatory
83
untargeted liquid chromatography-mass spectrometry-time of flight (LC-MS-tof)
84
chemical fingerprinting methods coupled with multivariate analysis (MVA) were utilized
85
to characterize chemical changes in citrus extracts during storage to identify compounds
86
associated with flavor quality changes as the products aged (freshness). LC/MS
87
techniques were selected to provide further characterization (novel insights) of the
88
chemical changes of oranges related to the flavor attributes of oranges. Prior research on
89
identification of orange citrus flavor has primarily emphasized volatile aroma
90
characterization22,23. Two modeling methods, projection partial least squares (PLS) and
91
machine learning random forest (RF) were utilized, with further variable selection using
92
age-related piecewise model generation and meta filtering techniques. Finally, the
93
statistically significant chemical data was validated in a repeated experiment.
94 95
Materials and methods
96
Sample Preparation. Three common citrus hybrid fruit products were sourced form the
97
domestic market. Navel Oranges, Mineola Tangerines, and Valencia Oranges were
98
washed, rinsed, and then cut to be 97%) by multi-dimensional
365
LC fractionation and subsequently identified by TOF MS/MS and NMR, as ionone
366
glycoside and nomilin glucoside, respectively. Furthermore, when each compound was
367
added to fresh orange samples at quantities identified in aged sample, a trained sensory
368
panel reported the flavor attributes changed and mimicked that of the aged product.
369
Interestingly both compounds themselves in water did not report any sensory activity and
370
would not be expected to be identified by traditional targeted methods1–3. To the best of
371
the authors knowledge, this is a first report to identify and validate persistent LC/MS
372
chemical features (unknowns) using statistical directed methods linked to sensory
373
significance.
374
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
375
In summary, this work illustrates the utility of flavoromics (untargeted analysis) for
376
identifying data trends related to a biological (flavor) outcome and an approach to
377
establish those relationships as persistent through repeated modeling. Additionally, this
378
work illustrates how modeling can emphasize aging through treating varietal differences
379
a nuisance variable to identify chemistry related to a ‘global’ citrus platform and to
380
identify flavor-active compounds.
381 382
ACKNOWLEDGMENT
383
The authors would like to acknowledge the financial support provided by the Flavor
384
Research and Education Center at The Ohio State University and its supporting members.
385
ACS Paragon Plus Environment
Page 18 of 29
Page 19 of 29
Journal of Agricultural and Food Chemistry
386
Cited Literature
387
(1)
Ottinger, H.; Bareth, A.; Hofmann, T. Characterization of Natural “Cooling”
388
Compounds Formed from Glucose and l -Proline in Dark Malt by Application of
389
Taste Dilution Analysis. J. Agric. Food Chem. 2001, 49 (3), 1336–1344.
390
(2)
O. Frank; and H. Ottinger; Hofmann*, T. Characterization of an Intense Bitter-
391
Tasting 1H,4H-Quinolizinium-7-olate by Application of the Taste Dilution
392
Analysis, a Novel Bioassay for the Screening and Identification of Taste-Active
393
Compounds in Foods. 2000.
394
(3)
395 396
of gas chromatographic effluents. Food Chem. 1984, 14 (4), 273–286. (4)
397 398
(5)
Myers, C. L.; Troyanskaya, O. G. Context-sensitive data integration and prediction of biological networks. Bioinformatics 2007, 23 (17), 2322–2330.
(6)
401 402
Fiehn, O. Metabolomics – the link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48 (1), 155–171.
399 400
Acree, T. E.; Barnard, J.; Cunningham, D. G. A procedure for the sensory analysis
Davies, H. A role for “omics” technologies in food safety assessment. Food Control 2010, 21 (12), 1601–1610.
(7)
Buczynski, M. W.; Dumlao, D. S.; Dennis, E. A. Thematic Review Series:
403
Proteomics. An integrated omics analysis of eicosanoid biology. J. Lipid Res.
404
2009, 50 (6), 1015–1038.
405
(8)
Roux, A.; Lison, D.; Junot, C.; Heilier, J.-F. Applications of liquid
406
chromatography coupled to mass spectrometry-based metabolomics in clinical
407
chemistry and toxicology: A review. Clin. Biochem. 2011, 44 (1), 119–135.
408
(9)
Trygg, J.; Holmes, E.; Lundstedt, T. Chemometrics in metabonomics. J. Proteome
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
409 410
Res. 2007, 6 (2), 469–479. (10)
411 412
2014, 1 (1), 2053951714528481. (11)
413 414
Kitchin, R. Big Data, new epistemologies and paradigm shifts. Big Data Soc.
Reineccius, G. Flavoromics- the next frontier? Abstr. Pap. 235th ACS Natl. Meet. 2008.
(12)
Charve, J.; Chen, C.; Hegeman, A. D.; Reineccius, G. a. Evaluation of
415
instrumental methods for the untargeted analysis of chemical stimuli of orange
416
juice flavour. Flavour Fragr. J. 2011, 26 (6), 429–440.
417
(13)
Majcher, M.; Siger, A.; Kaczmarek, A.; Gracka, A.; Jele, H. H. Flavoromics
418
approach in monitoring changes in volatile compounds of virgin rapeseed oil
419
caused by seed roasting. J. Chromatogr. A 2016, 1428, 292–304.
420
(14)
421 422
Aishima, T.; Nakai, S. Pattern Recognition of GC Profiles for Classification of Cheese Variety. 1987, 52 (4), 1985–1988.
(15)
Lindinger, C.; Pollien, P.; Labbe, D.; Rytz, A.; Juillerat, M. A.; Blank, I.
423
Prediction of the overall sensory profile of espresso coffee by online headspace
424
measurement using proton transfer reaction mass spectrometry (PTR-MS). Dev.
425
Food Sci. 2006, 43 (Flavour Science: Recent Advantages and Trends), 497–500.
426
(16)
Togari, N.; Kobayashi, A.; Aishima, T. Relating sensory properties of tea aroma to
427
gas chromatographic data by chemometric calibration methods. Food Res. Int.
428
1995, 28 (5), 485–493.
429
(17)
Tikunov, Y.; Bovy, a G.; Hall, R. D. Flavour Metabolomics : Holistic Approaches
430
in Flavour Research Versus Targeted Approaches in Flavor Research. Expr.
431
Multidiscip. Flavour Sci. Sci. 2008, 573–580.
ACS Paragon Plus Environment
Page 20 of 29
Page 21 of 29
432
Journal of Agricultural and Food Chemistry
(18)
Mehl, F.; Marti, G.; Boccard, J.; Debrus, B.; Merle, P.; Delort, E.; Baroux, L.;
433
Raymo, V.; Velazco, M. I.; Sommer, H.; et al. Differentiation of lemon essential
434
oil based on volatile and non-volatile fractions with various analytical techniques:
435
a metabolomic approach. Food Chem. 2014, 143, 325–335.
436
(19)
Lee, S. M.; Kwon, G. Y.; Kim, K.-O.; Kim, Y.-S. Metabolomic approach for
437
determination of key volatile compounds related to beef flavor in glutathione-
438
Maillard reaction products. Anal. Chim. Acta 2011, 703 (2), 204–211.
439
(20)
Ochi, H.; Naito, H.; Iwatsuki, K.; Bamba, T.; Fukusaki, E. Metabolomics-based
440
component profiling of hard and semi-hard natural cheeses with gas
441
chromatography/time-of-flight-mass spectrometry, and its application to sensory
442
predictive modeling. J. Biosci. Bioeng. 2012, 113 (6), 751–758.
443
(21)
Ronningen, I.; Miller, M.; Xia, Y.; Peterson, D. G. Identification and Validation of
444
Sensory-Active Compounds from Data-Driven Research: A Flavoromics
445
Approach. J. Agric. Food Chem. 2017, acs.jafc.7b00093.
446
(22)
447 448
Fresh and Processed Orange Juices. J. Agric. Food Chem 1990, 38 (4), 1048–1052. (23)
449 450
Perez-Cacho, P. R.; Rouseff, R. L. Fresh squeezed orange juice odor: a review. Crit. Rev. Food Sci. Nutr. 2008, 48 (7), 681–695.
(24)
451 452
Nisperos-carriedo, M.; Shaw, P. E. Comparison of Volatile Flavor Components in
Wold, S.; Sjostrom, M.; Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109–130.
(25)
Tautenhahn, R.; Patti, G. J.; Kalisiak, E.; Miyamoto, T.; Schmidt, M.; Lo, F. Y.;
453
McBee, J.; Baliga, N. S.; Siuzdak, G. metaXCMS: second-order analysis of
454
untargeted metabolomics data. Anal. Chem. 2011, 83 (3), 696–700.
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
455
(26)
Liland, K. H.; Snipen, L.; Mehmood, T.; Liland, K. H.; Snipen, L.; Sæbø, S. A
456
review of variable selection methods in Partial Least Squares Regression A review
457
of variable selection methods in Partial Least Squares Regression. Chemom. Intell.
458
Lab. Syst. 2012, No. 118, 62–69.
459
(27)
Wold, S.; Trygg, J. The PLS method -- partial least squares projections to latent
460
structures -- and its applications in industrial RDP ( research , development , and
461
production ). PLS Ind. RPD Prague 2004, 1 (June), 1–44.
462
(28)
Menze, B. H.; Kelm, B. M.; Masuch, R.; Himmelreich, U.; Bachert, P.; Petrich,
463
W.; Hamprecht, F. A. A comparison of random forest and its Gini importance with
464
standard chemometric methods for the feature selection and classification of
465
spectral data. 2009, 16, 1–16.
466
(29)
Breiman, L. Random forests. Mach. Learn. 2001, 45 (1), 5–32.
467
ACS Paragon Plus Environment
Page 22 of 29
Page 23 of 29
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 24 of 29
Page 25 of 29
Journal of Agricultural and Food Chemistry
Navel
Mineola
Valencia
485 486
Figure 1. Unsupervised PCA of chemical fingerprinting of citrus extracts (n=20 for each
487
varietal).
488
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 26 of 29
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).
ACS Paragon Plus Environment
Page 27 of 29
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 28 of 29
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
ACS Paragon Plus Environment
Page 29 of 29
515
Journal of Agricultural and Food Chemistry
TOC
516
ACS Paragon Plus Environment