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Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and chemometric analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour

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Abstract

A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with chemometric analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %.

Workflow of targeting discriminatory compounds by use of FCMS and chemometric analysis

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References

  1. Fung TT, Hu FB, Pereira MA, Liu S, Stampfer MJ, Colditz GA, Willett WC (2002) Whole-grain intake and the risk of type 2 diabetes: a prospective study in men. Am J Clin Nutr 76(3):535–540

    CAS  Google Scholar 

  2. Lutsey PL, Jacobs DR, Kori S, Mayer-Davis E, Shea S, Steffen LM, Szklo M, Tracy R (2007) Whole grain intake and its cross-sectional association with obesity, insulin resistance, inflammation, diabetes and subclinical CVD: The MESA Study. Br J Nutr 98(02):397–405

    Article  CAS  Google Scholar 

  3. Maki KC, Beiseigel JM, Jonnalagadda SS, Gugger CK, Reeves MS, Farmer MV, Kaden VN, Rains TM (2010) Whole-grain ready-to-eat oat cereal, as part of a dietary program for weight loss, reduces low-density lipoprotein cholesterol in adults with overweight and obesity more than a dietary program including low-fiber control foods. J Am Diet Assoc 110(2):205–214

    Article  CAS  Google Scholar 

  4. Albertson A, Joshi N (2014) Whole grain consumption and associations with body weight measures in the United States: results from NHANES 2009–10 and the new USDA Food Patterns Equivalents Database (810.23). FASEB J 28(1 Supplement):810.823

    Google Scholar 

  5. McKeown NM, Meigs JB, Liu SM, Wilson PWF, Jacques PF (2002) Whole-grain intake is favorably associated with metabolic risk factors for type 2 diabetes and cardiovascular disease in the Framingham Offspring Study. Am J Clin Nutr 76(2):390–398

    CAS  Google Scholar 

  6. Kristensen M, Toubro S, Jensen MG, Ross AB, Riboldi G, Petronio M, Bugel S, Tetens I, Astrup A (2012) Whole Grain Compared with Refined Wheat Decreases the Percentage of Body Fat Following a 12-Week, Energy-Restricted Dietary Intervention in Postmenopausal Women. J Nutr 142(4):710–716. doi:10.3945/jn.111.142315

    Article  CAS  Google Scholar 

  7. Giacco R, Costabile G, Della Pepa G, Anniballi G, Griffo E, Mangione A, Cipriano P, Viscovo D, Clemente G, Landberg R, Pacini G, Rivellese AA, Riccardi G (2014) A whole-grain cereal-based diet lowers postprandial plasma insulin and triglyceride levels in individuals with metabolic syndrome. Nutr Metab Cardiovasc Dis 24(8):837–844. doi:10.1016/j.numecd.2014.01.007

    Article  CAS  Google Scholar 

  8. Ye EQ, Chacko SA, Chou EL, Kugizaki M, Liu SM (2012) Greater Whole-Grain Intake Is Associated with Lower Risk of Type 2 Diabetes, Cardiovascular Disease, and Weight Gain. J Nutr 142(7):1304–1313. doi:10.3945/jn.111.155325

    Article  CAS  Google Scholar 

  9. Knudsen MD, Kyrø C, Olsen A, Dragsted LO, Skeie G, Lund E, Åman P, Nilsson LM, Bueno-de-Mesquita H, Tjønneland A (2014) Self-Reported Whole-Grain Intake and Plasma Alkylresorcinol Concentrations in Combination in Relation to the Incidence of Colorectal Cancer. Am J Epidemiol. doi:10.1093/aje/kwu031

    Google Scholar 

  10. Adom KK, Liu RH (2002) Antioxidant activity of grains. J Agric Food Chem 50(21):6182–6187. doi:10.1021/Jf0205099

    Article  CAS  Google Scholar 

  11. Aune D, Chan DSM, Lau R, Vieira R, Greenwood DC, Kampman E, Norat T (2011) Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose–response meta-analysis of prospective studies. Br Med J 343. doi:10.1136/Bmj.D6617

  12. Okarter N, Liu RH (2010) Health Benefits of Whole Grain Phytochemicals. Crit Rev Food Sci Nutr 50(3):193–208. doi:10.1080/10408390802248734

    Article  CAS  Google Scholar 

  13. Adom KK, Sorrells ME, Liu RH (2005) Phytochemicals and antioxidant activity of milled fractions of different wheat varieties. J Agric Food Chem 53(6):2297–2306. doi:10.1021/Jf048456d

    Article  CAS  Google Scholar 

  14. Zhao Y, Sun J, Yu LL, Chen P (2013) Chromatographic and mass spectrometric fingerprinting analyses of Angelica sinensis (Oliv.) Diels-derived dietary supplements. Anal Bioanal Chem 405(13):4477–4485. doi:10.1007/s00216-012-6668-1

    Article  CAS  Google Scholar 

  15. Sun JH, Chen P (2012) Chromatographic fingerprint analysis of yohimbe bark and related dietary supplements using UHPLC/UV/MS. J Pharm Biomed Anal 61:142–149. doi:10.1016/j.jpba.2011.11.013

    Article  CAS  Google Scholar 

  16. Sun J, Chen P (2011) Differentiation of Panax quinquefolius grown in the USA and China using LC/MS-based chromatographic fingerprinting and chemometric approaches. Anal Bioanal Chem 399(5):1877–1889. doi:10.1007/s00216-010-4586-7

    Article  CAS  Google Scholar 

  17. Chen P, Sun J, Ford P (2014) Differentiation of the Four Major Species of Cinnamons (C. burmannii, C. verum, C. cassia, and C. loureiroi) Using a Flow Injection Mass Spectrometric (FIMS) Fingerprinting Method. J Agric Food Chem 62(12):2516–2521. doi:10.1021/jf405580c

    Article  CAS  Google Scholar 

  18. Sun J, Chen P (2011) A flow-injection mass spectrometry fingerprinting method for authentication and quality assessment of Scutellaria lateriflora-based dietary supplements. Anal Bioanal Chem 401(5):1577–1584. doi:10.1007/s00216-011-5246-2

    Article  Google Scholar 

  19. Wold S (1976) Pattern recognition by means of disjoint principal components models. Pattern Recogn 8(3):127–139. doi:10.1016/0031-3203(76)90014-5

    Article  Google Scholar 

  20. Zhang M, Harrington PB (2015) Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression. Chemosphere 118:187–193. doi:10.1016/j.chemosphere.2014.08.018

    Article  CAS  Google Scholar 

  21. Dunn WJ, Stalling DL, Schwartz TR, Hogan JW, Petty JD, Johansson E, Wold S (1984) Pattern recognition for classification and determination of polychlorinated biphenyls in environmental samples. Anal Chem 56(8):1308–1313. doi:10.1021/ac00272a026

    Article  CAS  Google Scholar 

  22. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17. doi:10.1016/0003-2670(86)80028-9

    Article  CAS  Google Scholar 

  23. Moore J, Hao Z, Zhou K, Luther M, Costa J, Yu LL (2005) Carotenoid, tocopherol, phenolic acid, and antioxidant properties of Maryland-grown soft wheat. J Agric Food Chem 53(17):6649–6657. doi:10.1021/jf050481b

    Article  CAS  Google Scholar 

  24. Kessner D, Chambers M, Burke R, Agus D, Mallick P (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24(21):2534–2536. doi:10.1093/bioinformatics/btn323

    Article  CAS  Google Scholar 

  25. Diagram of a Kernel. Montana Wheat and Barley Committee. http://wbc.agr.mt.gov/wbc/Consumer/Diagram_kernel/. Accessed 14 July 2015

  26. Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828

    Article  CAS  Google Scholar 

  27. Becchi M, Fraisse D (1989) Fast Atom Bombardment and Fast Atom Bombardment Collision-Activated Dissociation Mass-Analyzed Ion Kinetic-Energy Analysis of C-Glycosidic Flavonoids. Biomed Environ Mass Spectrom 18(2):122–130. doi:10.1002/bms.1200180207

    Article  CAS  Google Scholar 

  28. Cuyckens F, Claeys M (2004) Mass spectrometry in the structural analysis of flavonoids. J Mass Spectrom 39(1):1–15. doi:10.1002/Jms.585

    Article  CAS  Google Scholar 

  29. Li QM, Vandenheuvel H, Dillen L, Claeys M (1992) Differentiation of 6-C-Glycosidic and 8-C-Glycosidic Flavonoids by Positive-Ion Fast-Atom-Bombardment and Tandem Mass-Spectrometry. Biol Mass Spectrom 21(4):213–221. doi:10.1002/bms.1200210406

    Article  CAS  Google Scholar 

  30. Dinelli G, Segura-Carretero A, Di Silvestro R, Marotti I, Arraez-Roman D, Benedettelli S, Ghiselli L, Fernadez-Gutierrez A (2011) Profiles of phenolic compounds in modern and old common wheat varieties determined by liquid chromatography coupled with time-of-flight mass spectrometry. J Chromatogr A 1218(42):7670–7681. doi:10.1016/j.chroma.2011.05.065

    Article  CAS  Google Scholar 

  31. Feng X, Jiang D, Shan Y, Dai T, Dong Y, Cao W (2008) New flavonoid-C-glycosides from Triticum aestivum. Chem Nat Compd 44(2):171–173

    Article  CAS  Google Scholar 

  32. Colborne AJ, Laidman DL (1975) The extraction and analysis of wheat phospholipids. Phytochemistry 14(12):2639–2645

    Article  CAS  Google Scholar 

  33. Taguchi R, Houjou T, Nakanishi H, Yamazaki T, Ishida M, Imagawa M, Shimizu T (2005) Focused lipidomics by tandem mass spectrometry. J Chromatogr B 823(1):26–36

    Article  CAS  Google Scholar 

  34. Harrison KA, Murphy RC (1995) Negative electrospray ionization of glycerophosphocholine lipids: formation of [M–15]− ions occurs via collisional decomposition of adduct anions. J Mass Spectrom 30(12):1772–1773

    Article  CAS  Google Scholar 

  35. Ekroos K, Ejsing CS, Bahr U, Karas M, Simons K, Shevchenko A (2003) Charting molecular composition of phosphatidylcholines by fatty acid scanning and ion trap MS3 fragmentation. J Lipid Res 44(11):2181–2192

    Article  CAS  Google Scholar 

  36. Zhang M, Harrington PB (2013) Automated pipeline for classifying Aroclors in soil by gas chromatography/mass spectrometry using modulo compressed two-way data objects. Talanta 117:483–491. doi:10.1016/j.talanta.2013.09.050

    Article  CAS  Google Scholar 

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Acknowledgments

The authors would like to thank Mr Reuben McClean from Pendleton Flour Mills for providing local flour samples.

Conflict of interest

The authors declare no competing financial interest.

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Authors and Affiliations

  1. Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture, Building 161, BARC-East, 10300 Baltimore Avenue, Beltsville, MD, 20705, USA

    Ping Geng, Mengliang Zhang, James M. Harnly, Devanand L. Luthria & Pei Chen

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  1. Ping Geng
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  2. Mengliang Zhang
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Correspondence to Pei Chen.

Electronic supplementary material

The variable line plot of the ion at m/z 769.20 (Fig. S1), MSn spectra of the ion at m/z 769.2 (Fig. S2), MS–MS spectra of the ion at m/z 564.33 (Fig. S3), MSn spectra of the ion at m/z 476.3 (Fig. S4), and PCA for all RF/WF mixtures prepared from three cultivars (Fig. S5) are presented in a supplemental file.

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Geng, P., Zhang, M., Harnly, J.M. et al. Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and chemometric analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour. Anal Bioanal Chem 407, 7875–7888 (2015). https://doi.org/10.1007/s00216-015-9007-5

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  • DOI: https://doi.org/10.1007/s00216-015-9007-5

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