Abstract
Introduction
LC–MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry.
Objectives
To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study.
Methods
IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets.
Results
IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%.
Conclusion
SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.
Similar content being viewed by others
Change history
30 July 2020
Following publication of the original article, the authors would like to correct the authors and author affiliations.
References
Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control,39, 172–184.
Bonner, R., & Hopfgartner, G. (2018). SWATH data independent acquisition mass spectrometry for metabolomics. TrAC Trends in Analytical Chemistry,120, 115278.
Broeckling, C. D., Hoyes, E., Richardson, K., Brown, J. M., & Prenni, J. E. (2018). Comprehensive tandem-mass-spectrometry coverage of complex samples enabled by data-set-dependent acquisition. Analytical Chemistry,90, 8020–8027.
Castro-Puyana, M., Pérez-Míguez, R., Montero, L., & Herrero, M. (2017). Reprint of: Application of mass spectrometry-based metabolomics approaches for food safety, quality and traceability. TrAC Trends in Analytical Chemistry,96, 62–78.
Chen, G., Walmsley, S., Cheung, G. C. M., Chen, L., Cheng, C. Y., Beuerman, R. W., et al. (2017). Customized consensus spectral library building for untargeted quantitative metabolomics analysis with data independent acquisition mass spectrometry and MetaboDIA workflow. Analytical Chemistry,89, 4897–4906.
Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., et al. (2018). MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Research,46, W486–W494.
Diaz, R., Pozo, O. J., Sancho, J. V., & Hernandez, F. (2014). Metabolomic approaches for orange origin discrimination by ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Food Chemistry,157, 84–93.
Dizy, M., Martín-Alvarez, P. J., Cabezudo, M. D., & Polo, M. C. (2010). Grape, apple and pineapple juice characterisation and detection of mixtures. Journal of the Science of Food & Agriculture,60, 47–53.
Gillet, L. C., Navarro, P., Tate, S., Rost, H., Selevsek, N., Reiter, L., et al. (2012). Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis. Molecular Cell Proteomics,11(O111), 016717.
Gómez-Ariza, J. L., Villegas-Portero, M. J., & Bernal-Daza, V. (2005). Characterization and analysis of amino acids in orange juice by HPLC–MS/MS for authenticity assessment. Analytica Chimica Acta,540, 221–230.
Hopfgartner, G., Tonoli, D., & Varesio, E. (2012). High-resolution mass spectrometry for integrated qualitative and quantitative analysis of pharmaceuticals in biological matrices. Analytical and Bioanalytical Chemistry,402, 2587–2596.
Ines, C., Parra-Lobato, M. C., Paredes, M. A., Labrador, J., Gallardo, M., Saucedo-Garcia, M., et al. (2018). Sphingolipid distribution, content and gene expression during olive-fruit development and ripening. Frontier in Plant Science,9, 28.
Jaiswal, D., Prasannan, C. B., Hendry, J. I., & Wangikar, P. P. (2018). SWATH tandem mass spectrometry workflow for quantification of mass isotopologue distribution of intracellular metabolites and fragments labeled with isotopic 13C carbon. Analytical Chemistry,90, 6486–6943.
Jandrić, Z., & Cannavan, A. (2017). An investigative study on differentiation of citrus fruit/fruit juices by UPLC-QToF MS and chemometrics. Food Control,72, 173–180.
Jandrić, Z., Islam, M., Singh, D. K., & Cannavan, A. (2017). Authentication of Indian citrus fruit/fruit juices by untargeted and targeted metabolomics. Food Control,72, 181–188.
Johnson, A. E., Sidwick, K. L., Pirgozliev, V. R., Edge, A., & Thompson, D. F. (2018). Metabonomic profiling of chicken eggs during storage using high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Analytical Chemistry,90, 7489–7494.
Koda, M., Furihata, K., Wei, F., Miyakawa, T., & Tanokura, M. (2012). Metabolic discrimination of mango juice from various cultivars by band-selective NMR spectroscopy. Journal of Agricultural and Food Chemistry,60, 1158–1166.
Lai, Z., Tsugawa, H., Wohlgemuth, G., Mehta, S., Mueller, M., Zheng, Y., et al. (2018). Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nature Methods,15, 53–56.
Louche, M. M., Gaydou, E. M., & Lesage, J. C. (1998). Determination of phlorin as peel marker in orange (Citrus sinensis) Fruits and Juices. Journal of Agricultural and Food Chemistry,46, 4193–4197.
Matthias, F., Sandra, B., Reinhold, C., & Kammerer, D. R. (2012). Characterization and quantitation of low and high molecular weight phenolic compounds in apple seeds. Journal of Agricultural and Food Chemistry,60, 1232–1242.
Moore, J. C., Spink, J., & Lipp, M. (2012). Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. Journal of Food Science,77, R118–R126.
Mullard, G., Allwood, J. W., Weber, R., Brown, M., Begley, P., Hollywood, K. A., et al. (2015). A new strategy for MS/MS data acquisition applying multiple data dependent experiments on Orbitrap mass spectrometers in non-targeted metabolomic applications. Metabolomics,11, 1068–1080.
Navarro, M., Nunez, O., Saurina, J., Hernandez-Cassou, S., & Puignou, L. (2014). Characterization of fruit products by capillary zone electrophoresis and liquid chromatography using the compositional profiles of polyphenols: application to authentication of natural extracts. Journal of Agricultural and Food Chemistry,62, 1038–1046.
Rodionova, O. Y., Oliveri, P., & Pomerantsev, A. L. (2016). Rigorous and compliant approaches to one-class classification. Chemometrics and Intelligent Laboratory Systems,159, 89–96.
Roemmelt, A. T., Steuer, A. E., & Kraemer, T. (2015). Liquid chromatography, in combination with a quadrupole time-of-flight instrument, with sequential window acquisition of all theoretical fragment-ion spectra acquisition: Validated quantification of 39 antidepressants in whole blood as part of a simultaneous screening and quantification procedure. Analytical Chemistry,87, 9294–9301.
Rozanska, A., Dymerski, T., & Namiesnik, J. (2018). Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography. Monatshefte für Chemie-Chemical Monthly,149, 1615–1621.
Sabina, B., Ivana, B., Verheij, E. R., Raymond, R., Sunil, K., Macdonald, I. A., et al. (2006). Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Analytical Chemistry,78, 567–574.
Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience,2, 13.
Sanzani, S. M., De Girolamo, A., Schena, L., Solfrizzo, M., Ippolito, A., & Visconti, A. (2008). Control of Penicillium expansum and patulin accumulation on apples by quercetin and umbelliferone. European Food Research and Technology,228, 381–389.
Shahidi, F., & Ambigaipalan, P. (2015). Phenolics and polyphenolics in foods, beverages and spices: Antioxidant activity and health effects—A review. Journal of Functional Foods,18, 820–897.
Shao, Y., Zhu, B., Zheng, R., Zhao, X., Yin, P., Lu, X., et al. (2015). Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery. Journal of Proteome Research,14, 906–916.
Sidwick, K. L., Johnson, A. E., Adam, C. D., Pereira, L., & Thompson, D. F. (2017). Use of liquid chromatography quadrupole time-of-flight mass spectrometry and metabonomic profiling to differentiate between normally slaughtered and dead on arrival poultry meat. Analytical Chemistry,89, 12131–12136.
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R. D., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics,3, 211–221.
Thomas, A., Déglon, J., Lenglet, S., Mach, F., Mangin, P., Wolfender, J.-L., et al. (2010). High-throughput phospholipidic fingerprinting by online desorption of dried spots and quadrupole-linear ion trap mass spectrometry: Evaluation of atherosclerosis biomarkers in mouse plasma. Analytical Chemistry,82, 6687–6694.
Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., et al. (2015). MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods,12, 523–526.
Tsugawa, H., Kind, T., Nakabayashi, R., Yukihira, D., Tanaka, W., Cajka, T., et al. (2016). Hydrogen rearrangement rules: Computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Analytical Chemistry,88, 7946–7958.
Vaclavik, L., Schreiber, A., Lacina, O., Cajka, T., & Hajslova, J. (2011). Liquid chromatography–mass spectrometry-based metabolomics for authenticity assessment of fruit juices. Metabolomics,8, 793–803.
Wu, J., Gao, H., Zhao, L., Liao, X., Chen, F., Wang, Z., et al. (2007). Chemical compositional characterization of some apple cultivars. Food Chemistry,103, 88–93.
Xu, L., Lao, F., Xu, Z., Wang, X., Chen, F., Liao, X., et al. (2019). Use of liquid chromatography quadrupole time-of-flight mass spectrometry and metabolomic approach to discriminate coffee brewed by different methods. Food Chemistry,286, 106–112.
Yang, Q., Li, X., Lin, X., Zhou, Y., Yuan, J., Wang, H., et al. (2013). Characterization of free endogenous sphingoid bases in the golden apple snailPomacea canaliculata: Involvement in snail development and nutrient limitation. Invertebrate Reproduction & Development,57, 287–292.
Zha, H., Cai, Y., Yin, Y., Wang, Z., Li, K., & Zhu, Z. J. (2018). SWATHtoMRM: Development of high-coverage targeted metabolomics method using SWATH technology for biomarker discovery. Analytical Chemistry,90, 4062–4070.
Zhang, Y., Bilbao, A., Bruderer, T., Luban, J., Strambio-De-Castillia, C., Lisacek, F., et al. (2015). The use of variable Q1 isolation windows improves selectivity in LC-SWATH-MS acquisition. Journal of Proteome Research,14, 4359–4371.
Zheng, Z., & Shetty, K. (1998). Solid-state production of beneficial fungi on apple processing wastes using glucosamine as the indicator of growth. Journal of Agricultural and Food Chemistry,46, 783–787.
Zhu, X., Chen, Y., & Subramanian, R. (2014). Comparison of information-dependent acquisition, SWATH, and MS(All) techniques in metabolite identification study employing ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Analytical Chemistry,86, 1202–1209.
Acknowledgements
We acknowledge the funding from National Key R&D Program of China (2017YFD0400705), and the data analysis training support from International Atomic Energy Agency (IAEA) project of Peaceful Uses Initiative TN-PUI-JPN-EVT1902783.
Author information
Authors and Affiliations
Contributions
LX and ZX conceived and designed research. LX conducted experiments. XL contributed reagents and analytical tools. LX and ZX analyzed data. LX wrote the manuscript. IS improved the language expression. All authors discussed the results from the experiments, read and approved the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Research involving animal and human rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Xu, L., Xu, Z., Strashnov, I. et al. Use of information dependent acquisition mass spectra and sequential window acquisition of all theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication. Metabolomics 16, 81 (2020). https://doi.org/10.1007/s11306-020-01701-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11306-020-01701-2