NMR-based metabolic profiling discriminates the geographical origin of raw sesame seeds

Journal Pre-proof NMR-based metabolic profiling discriminates the geographical origin of raw sesame seeds Seok-Young Kim, EunBi Kim, Byeung Kon Shin, Jeong-Ah Seo, Young-Suk Kim, Do Yup Lee, Hyung-Kyoon Choi PII:

S0956-7135(20)30029-3

DOI:

https://doi.org/10.1016/j.foodcont.2020.107113

Reference:

JFCO 107113

To appear in:

Food Control

Received Date: 20 August 2019 Revised Date:

9 January 2020

Accepted Date: 12 January 2020

Please cite this article as: Kim S.-Y., Kim E., Shin B.K., Seo J.-A., Kim Y.-S., Lee D.Y. & Choi H.-K., NMR-based metabolic profiling discriminates the geographical origin of raw sesame seeds, Food Control (2020), doi: https://doi.org/10.1016/j.foodcont.2020.107113. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Credit Author Statement Seok-Young Kim: Formal analysis, Investigation, Writing - Original Draft, Methodology, EunBi Kim: Investigation, Resources, Byeung Kon Shin: Resources, Validation, Jeong-Ah Seo: Resources, Validation, Young-Suk Kim: Resources, Validation, Do Yup Lee: Conceptualization, Supervision, Validation, Writing – review & editing, Hyung-Kyoon Choi: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

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[Original article]

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NMR-based metabolic profiling discriminates the geographical origin of raw sesame

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seeds

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Seok-Young Kima, EunBi Kima, Byeung Kon Shinb, Jeong-Ah Seoc, Young-Suk Kimd, Do

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Yup Leee, Hyung-Kyoon Choia,*

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a

College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea

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b

National Agricultural Products Quality Management Service, Gimcheon, Republic of Korea

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c

School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea

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d

Department of Food Science and Engineering, Ewha Woman’s University, Seoul, Republic

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of Korea

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e

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Institute for Agricultural and Life Sciences, Seoul National University, Seoul, Republic of

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Korea

Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Research

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* Corresponding author.

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College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea

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Phone: +82-2-820-5605

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Fax: +82-2-812-3921

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E-mail address: [email protected] (Hyung-Kyoon Choi)

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Abstract

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Sesame seeds are an oil crop mainly cultivated in Asian and African countries.

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Identification of the geographical origin of sesame seeds is an important issue for preventing

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adulteration and for quality assurance. This study was performed to establish a discrimination

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model and investigate potential biomarkers for differentiating the geographical origin of

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sesame seeds from Korea, China, and other countries (India, Nigeria, and Ethiopia) by

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nuclear magnetic resonance -spectroscopy based metabolic profiling. A total of 24 polar

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metabolites in sesame seeds from 10, 6, and 4 samples of Korea, China, and other countries,

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respectively, were identified, and an orthogonal partial least squares-discriminant analysis

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model was established applying a total normalization and unit variance scaling method.

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Leave-one-out cross validation showed an accuracy of 97.5, 90.0, and 100.0% in

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differentiating the sesame seed geographical origin. Acetate, phenylalanine, and tryptophan

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were suggested as potential biomarkers by variable influence on projection value (over 1.0)

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and area under the curve value (over 0.75). This study demonstrated that 1H-NMR analysis

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with multivariate and univariate statistical analyses of the polar metabolites in sesame seeds

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could be successfully applied to discriminate the geographical origin of sesame seeds. These

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results could be applied to develop a standard analytical process to verify seed origin and halt

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the global distribution of falsely labeled sesame seeds.

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Keywords: sesame seeds, biomarker, geographical origin, NMR, metabolic profiling

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1. Introduction

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Asia and Africa account for over 96% of sesame seed production in the world with India,

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China, and Sudan acting as the major sesame seed producing countries (Dossa et al., 2016).

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The identification of origin of sesame seeds is an important factor influencing its price

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(Horacek et al., 2015). The similar appearance of sesame seeds does not allow for visual

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differentiation of seed origin. Therefore, the development of a precise and objective

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standardized analytical process that can discriminate between imported and domestic sesame

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seeds is needed.

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Metabolomics can be used to distinguish food products with similar chemical composition

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characteristics that may not be identifiable by appearance and flavor as it enables the

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identification of food components for food adulteration and quality assessment (Oms-Oliu,

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Odriozola-Serrano, & Mart´ın-Belloso, 2013). Therefore, the profiling of certain food

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resources based on the identification and quantification of characteristic metabolites that

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differ based on geographical origin may be possible (Mazzei & Piccolo, 2012). Direct

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analysis by nuclear magnetic resonance (NMR) spectroscopy is ideal in high-throughput

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metabolomics applications as it can detect a wide range of metabolites in an inherently

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quantitative and unbiased manner (Mahrous & Farag, 2015). Multiple studies have been

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performed to identify the geographical origins of diverse agricultural food resources using

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NMR based metabolomics (Huo et al., 2017; Lamanna, Cattivelli, Miglietta, & Troccoli,

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2011; Ritota et al., 2012).

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Though sesame seeds are well known as a source of edible oil, they are also a good source

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of proteins high in sulfur-containing amino acids (3.8-5.5%), which are considered beneficial

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for supplementing soybean, peanut, and other vegetable proteins (Johnson, Suleiman, &

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Lucas, 1974; Bandyopadhyay & Ghosh, 2002). Previous studies have focused on 4

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discriminating the geographical origin of sesame oil using NMR spectroscopy (Jin et al.,

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2017), near infrared (NIR) spectroscopy (Kim, Scotter, Voyiagis, & Hall, 1998), isotope ratio

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mass spectrometry (Jeon et al., 2015), gas chromatography-mass spectrometry (GC-MS), and

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high performance liquid chromatography (HPLC) analyses of fatty acids and lignans (Jeon et

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al., 2013). However, few studies have been conducted to determine the geographical origin of

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sesame seeds (as opposed to sesame oil) with a focus on analyzing the polar metabolites in

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sesame seeds. Kwon and Cho (1998) reported that defatted sesame seeds mainly containing

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proteins instead of oil were more useful for discriminating the geographical origin of the

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seeds. Thus, it is anticipated that the polar metabolites in sesame seeds, including amino acids,

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sugars, and organic acids will play an important role in characterizing sesame seeds

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cultivated in diverse regions. To the best of our knowledge, no previous studies have focused

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on discriminating the geographical origin of sesame seeds by profiling polar metabolites

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using NMR-based metabolic profiling.

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The objective of this research was to develop a novel model for discriminating the

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geographical origin of sesame seeds by NMR-based metabolic profiling mainly focusing on

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polar metabolites. This differs from other studies as they have generally analyzed non-polar

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metabolites in sesame seed oil. This study hypothesized that the polar metabolites in sesame

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seeds play an important role in discriminating geographical origin. Therefore, polar

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metabolite profiling of sesame seeds originating from the Republic of Korea (Korea,

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hereafter), China, India, Nigeria, and Ethiopia was performed. A discrimination model was

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established to specify the different geographical origins of sesame seeds using multivariate

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statistical analysis. Further, potential biomarkers for discriminating the geographical origin of

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sesame seeds were also suggested. The results of the present study can be applied to the

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development of practical and trustworthy platform for preventing the deliberate mislabeling of the geographical origin of sesame seeds.

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2. Materials and Methods

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2.1. Sesame seed samples and reagents

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Raw sesame seed samples were obtained from Korea (ten samples), China (six samples),

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India (one sample), Nigeria (two samples), and Ethiopia (one sample) (Fig. S1). Ten samples

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of Korean sesame seeds harvested in 2017 were provided by the National Agricultural

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Products Quality Management Service of Korea. Samples (1.5-2 kg) were collected from

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each of the following provinces and cities: Gangwon (Chuncheon, Inje), Gyeonggi (Paju,

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Icheon,), Chungcheong (Choongju, Dangjin), Gyeongsang (Angong, Jinju), and Jeolla (Iksan,

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Muan). Raw sesame seeds from China (500-600 g) harvested in 2017 were purchased from

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online suppliers in 2018. Those samples were obtained from the following regions: east-north

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(Heilongjiang, Jilin), middle (Henan, Anhui), and south (Yunnan, Guangxi Zhuang

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Autonomous Region). Raw sesame seeds from India, Nigeria, and Ethiopia (500-600 g)

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harvested in 2017 were obtained from Ottogi Co., Ltd, CJ CheilJedang Co., Ltd, and Daesang

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Co., Ltd, which are the representative imported sesame suppliers in Korea designated by the

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Korea

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(http://www.at.or.kr/home/apen000000/index.action). Manufacturer and supplier information

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is listed in Supplementary Table S1. All samples were stored at -70°C in a deep freeze (Ilshin,

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Gyeonggi-do, Korea) until use.

Agro-Fisheries

Trade

Corporation

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HPLC grade chloroform, methanol, and water were obtained from Fisher Chem Alert (Fair

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Lawn, NJ, USA). Methanol-d4 (CD3OD, 99.8% atom D) including 0.05% 3-(trimethylsilyl)

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propionic acid- d4 sodium salt (TSP) was obtained from Cambridge Isotope Laboratories 6

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(Tewksbury, MA, USA). Phosphate buffer (90 mM) was prepared using potassium phosphate

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(KH2PO4, ≥99.0%, Sigma-Aldrich, St. Louis, MO, USA) and deuterium oxide (D2O, 99.9%

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atom D, Sigma-Aldrich). pH was adjusted to 6.0 using sodium deuteroxide solution (NaOD,

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99.5%, 40% in D2O, Cambridge Isotope Laboratories) (Kim, Choi, & Verpoorte, 2010).

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2.2. Pre-preparation and extraction of sesame seeds

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Pooled samples collected from each region were quick-frozen in liquid nitrogen,

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pulverized with blender, and freeze-dried for 48 h. Samples were again stored in a deep

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freezer until NMR analysis. A modified Bligh & Dyer method was applied to prepare the

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polar metabolite extracts from sesame seeds (Bligh & Dyer, 1959; Mannina et al., 2008).

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Lyophilized sesame seeds (0.1 g) in a 2 mL centrifugal tube (Eppendorf tube, Hamburg,

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Germany) were extracted with 1600 µL of cold chloroform and methanol mixture (1:1) that

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was vortexed for 1 min, shaken with ice for an hour (80 rpm), then centrifuged (10 000 × g,

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4 °C, 10 min). The supernatant was collected and 400 µL of 4 °C water was added. The

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suspension was centrifuged (10 000 × g, 4 °C, 10 min) and the upper hydro-soluble phase

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was filtered with a PTFE 0.45 µm syringe filter (Whatman, Maidstone, UK). The filtered

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extract (600 µL) was dried under a gentle nitrogen stream then resuspended in 600 µL of 7:3

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CD3OD and KH2PO4 buffer in D2O (pH 6.0). The resuspended solutions were transferred to 5

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mm NMR tubes (Norell Landisville, NJ, USA). Four replicates of sesame seed samples from

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each region were extracted and analyzed. Quality control (QC) samples (n = 13, pooled

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sample including equal portions of each sample) were also analyzed to assure instrumental

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conditions and data quality during the analyses.

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2.3. Peak NMR spectra assignment

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One- and two-dimensional NMR experiments were performed on a JEOL 600 MHz

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spectrometer (JNM-ECZ 600R, JEOL, Japan) and a Bruker 600 MHz spectrometer (Avance

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600, Bruker, Germany), respectively. For the one dimensional 1H-NMR experiment, samples

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were measured at 25 °C and the spectra were acquired at 16 K data points with 5 s of

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relaxation delay, and a spectral width of 9024.4 Hz. A scan number of 128 and an acquisition

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time of 1.45 s were used. Water suppression was conducted to exclude the region between δ

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= 4.7 to 5.0. For two-dimensional NMR spectra, 1H-1H correlation spectroscopy (COSY)

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spectra were acquired under following conditions: 32 scans, relaxation delay of 1.9 s, and a

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6068.0 Hz spectral width. 1H–13C heteronuclear single quantum correlation (HSQC) spectra

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were obtained with 32 scans, a 2.0 s relaxation delay, and 7183.9 and 36231.9 Hz spectral

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widths. Baseline correction and identification of all 1H NMR spectra was performed using

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Chenomx NMR suite software (version 8.1, Chenomx, Edmonton, AB, Canada). Peak

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identification was performed with non-overlapping peaks. MestReNova (version 6.0.4,

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Mestrelab Research, Santiago de Compostela, Spain) was used to calculate the J value of the

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peak, and to identify the peaks of the 1H-1H COSY and 1H-13C HSQC spectra.

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2.4. NMR data processing and statistical analyses

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Binning and normalization of 1H NMR spectral data were performed by Chenomx NMR

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suite software. NMR spectral data from 0.08 to 10.00 ppm were segmented into a series of

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bins (245 total) with 0.04 ppm widths, with the exception of the water suppression region at

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4.68-4.88 ppm. For establishing the optimal model, two normalization (total and standardized

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area) and two scaling (unit variance (UV) and Pareto) methods were applied and their

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performances were compared. Relative intensities of binned spectral data in total and 8

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standardized area normalization were obtained by dividing the spectral data by the total area

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of all bins and the area of reference peak, respectively. Binned datasets were converted to

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Microsoft Office Excel (version 2010, Microsoft, Redmond, WA, USA) to create a

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compatible format for measuring each metabolite by its loading value. Binning values of

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metabolites with multiple non-overlapping peaks were summed. Then, the data were

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imported into SIMCA-P+ (version 14.0, Umetrics, Umeå, Sweden) for principle component

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analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) of

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sesame seed samples (n = 80). Optimal OPLS-DA models were determined by good-fit, R2Y

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and predictability, Q2Y, R2Y-intercept values, and Q2Y-intercept values obtained by

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permutation tests. The number of components was determined using the autofit function in

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SIMCA P+ software to select the significant number of components. Leave-one-out cross

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validation was performed to detect and prevent model over-fitting. Sensitivity, specificity,

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and accuracy were calculated to evaluate the classification performance of the model based

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on the class prediction value of samples obtained from the leave-one-out cross validation (Y-

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predcv) using SIMCA-P+ software.

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The non-parametric Kruskal-Wallis test was performed to compare the relative intensities

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of sesame seed samples from Korea, China, and other countries (India, Nigeria, and Ethiopia)

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by SPSS statistical analysis (SPSS, Version 25.0 for Windows, SPSS Inc, Chicago, IL, USA).

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A p-value < 0.05 was considered statistically significant.

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To suggest the potential biomarkers for discriminating the geographical origin of sesame

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seed samples from Korea, China, and other countries (India, Nigeria, and Ethiopia), OPLS-

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biplots and variable influence on projection (VIP) values were investigated. Metabolites

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having VIP values over 1.0 were selected, which were generally accepted as the most

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significant variables contributing to the group separation. In addition, receiver operating 9

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characteristic (ROC) analysis was conducted to evaluate the predictive performance of

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potential biomarkers using MetaboAnalyst 4.0 (http://www.metaboanalyst.ca/).

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3. Results

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3.1. Identification of polar metabolites in sesame seeds by 1H NMR

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Assigned peaks are listed in Table 1, and the representative NMR spectrum for the extract

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of polar metabolites in sesame seeds is shown in Fig. 1. Twenty-four polar metabolites

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including 12 amino acids, 4 sugars, 3 organic acids, and 5 others were identified in sesame

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seeds by one dimensional NMR analysis. Of the 12 amino acids, isoleucine, leucine, valine,

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phenylalanine, and tryptophan were identified as essential amino acids. Arabinose, glucose,

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sucrose, and xylose were sugars identified in sesame seeds. Organic acids including acetate,

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malate, and succinate were identified. Further metabolite identification was confirmed by

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two-dimensional NMR spectroscopy. As shown in Supplementary Fig. S2 and Table 1,

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isoleucine, threonine, asparagine, betaine, xylose, sucrose, uridine, tyrosine, phenylalanine,

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and tryptophan were assigned in COSY experiments, while glucose, xylose, sucrose, and

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arabinose were assigned in HSQC experiments.

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3.2. PCA and OPLS-DA for discriminating the geographical origin of sesame seeds

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In the PCA score plot (Supplementary Fig. S3a), sesame seed samples from each of the

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three groups, Korea, China, and other countries (India, Nigeria, and Ethiopia), were well

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separated with partial overlapping. The 13 QC samples were also well clustered in the PCA

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score plot (Supplementary Fig. S3b), indicating the instrumental stability of NMR

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spectroscopy and the reliability of the data analyses.

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Further, a supervised classification method, OPLS-DA, established a discriminative and

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predictive model for determining the geographical origin of sesame seeds and identified

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potential biomarkers involved in the separation of sesame seeds by region. The optimal

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OPLS-DA model was established by applying total area normalization, UV scaling, and six

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components (2 predictive + 4 orthogonal) with the highest R2Y (0.835) and Q2Y (0.769)

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values as listed in Table 2. The separation of sesame seeds from Korea, China, and other

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countries (India, Nigeria, and Ethiopia) was clear in the two predictive components of the

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OPLS-DA score plot (Fig. 2a). With the first predictive component, Korean and Chinese

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sesame seeds were clustered and clearly separated from those from India, Nigeria, and

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Ethiopia. After performing 999 permutation tests to avoid the possibility of random group

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designations, satisfactory R2Y and Q2Y intercept values of 0.155 and -0.330, respectively,

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were obtained as shown in Fig. 2b.

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Leave-one-out cross validation evaluated the classification rates of sesame seeds from

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Korea, China, and other countries according to geographical origin. Three cases of leave-one-

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out cross validation for the three groups were performed and the results are shown in Fig. 2c-

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e. When Korean sesame seeds were used as a control group, only two samples (China and

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Korea) were misclassified with a threshold value of 0.5, which resulted in 97.5% of

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sensitivity, specificity, and accuracy (Fig. 2c). When Chinese sesame seeds were designated

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as a control group, eight misclassified samples (two from Korea and six from China) were

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identified with 96.4, 75.0, and 90.0% of sensitivity, specificity, and accuracy, respectively

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(Fig. 2d). Sesame seed samples from India, Nigeria, and Ethiopia were clearly separated from

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the other groups with 100% of sensitivity, specificity, and accuracy (Fig. 2e).

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3.3. Potential biomarkers for discriminating sesame seed geographical origin 11

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As shown in Fig. 3a, phenylalanine, tryptophan, and acetate were found to be the most

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relevant biomarkers as the plots of those were markedly positioned within the circles

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coordinated at radius of 1.0 and 0.75, and those plots were near the plots representing

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samples from India, Nigeria, and Ethiopia. In this biplot, the combination of X-variables

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(metabolites), Y-variables (group information), and observations were simultaneously

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displayed in a two-dimensional space to describe the grouping of observations and the

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correlation of the variables (Hesaka et al., 2019). Three ellipses graphed at the inner (0.5),

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middle (0.75), and outer (1.0) areas of the plot coordinates show the explained variance of 50,

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75, and 100%, respectively (Lutsiv, McGinley, Neil, & Thompson, 2019). Variables

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positioned near observations describe high level metabolites in the sample groups, while low

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level metabolites are in an opposite position to the sample groups. The closer the variables

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are to the outer circle of the plot, the better they are described by the model components

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(Thompson et al, 2016).

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Univariate statistical analysis showed that significantly higher levels of betaine, glycine,

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isoleucine, phenylalanine, tryptophan, acetate, malate, arabinose, glucose, sucrose, ascorbate,

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choline, gallate, and uridine were observed in sesame seeds from India, Nigeria, and Ethiopia.

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Of these, only four metabolites (phenylalanine, tryptophan, acetate, and uridine) showed

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significantly different levels among three groups (Table 3). Variables with a VIP cut off

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value over 1.0 were tyrosine (1.71), acetate (1.35), threonine (1.29), valerate (1.28),

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phenylalanine (1.27), tryptophan (1.23), glucose (1.20), glycine (1.13), proline (1.09), malate

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(1.05), and arabinose (1.02) as listed in Table 3. Of those metabolites, acetate, phenylalanine,

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and tryptophan were also suggested as potential biomarkers based on their higher VIP cut off

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values. Subsequently, ROC analysis showed that AUC values ranged from 0.845 to 0.894,

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indicating the acceptable accuracy of potential biomarkers when separating Korean and 12

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Chinese sesame seeds (Fig. 3b-d). When separating sesame seeds from Korea and other

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countries, AUC values over 0.9 (0.977-0.998) showed excellent accuracy (Fig. 3e-g), and

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AUC values ranging from 0.751-0.974 were obtained when separating sesame seeds from

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China and other countries (Fig. S4a-c).

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4. Discussion

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Published studies have analyzed the oil components in sesame seeds by NMR

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spectroscopy to discriminate different producing regions (Zhang, Zhao, Shen, Zhong, & Feng,

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2018), detect adulterated seed oil (Vigli, Philippidis, Spyros, & Dais, 2003; Nam et al., 2014;

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Kim et al., 2015), and investigate oxidative stability and ozone reactivity (Guillén & Ruiz,

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2004; Sega et al., 2010). However, no reports have analyzed the polar metabolites in sesame

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seeds by NMR-based metabolic profiling. This study used NMR-based metabolic profiling of

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the polar metabolites in sesame seeds to determine their use as potential biomarkers in

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discriminating the geographical origin of sesame seeds. Amino acids were the most identified

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metabolites in sesame seeds by 1H NMR. Betaine, proline, threonine, and glycine showed

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higher relative intensities in sesame seeds (Table 3). Sesame seeds have a high protein

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content, especially the essential amino acids leucine, isoleucine, valine, and threonine (Kanu,

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2011). Five essential amino acids in sesame seeds, leucine, isoleucine, threonine, tryptophan,

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and valine, were identified in our study (Table 1).

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Sesame seeds contain 18-20% (w/w) carbohydrates consisting mainly of glucose (3.2%),

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fructose (2.6%), and sucrose (0.2%) as free sugars (Namiki, 2007; Anilakumar, Pal, Khanum,

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& Bawa, 2010). Arabinose, xylose, galactose, and mannose were also found in sesame seeds

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(Ghosh et al, 2005). Arabinose and glucose were the main sesame seed metabolites identified

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in our study (Table 3). Interestingly, acetate, malate, and succinate were identified as organic

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acids in sesame seeds by NMR spectroscopy for the first time in this study.

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The quality and chemical composition of sesame seed oil have been reported to vary based

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on diverse factors such as cultivars, land, processing, and cultivation regions (Deng et al.,

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2012). In our study, the characteristics of polar metabolites in sesame seeds were very

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different depending on the cultivation conditions and growth region. Although the cultivar

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information of sesame seed samples could not be obtained in this study, it is thought that

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those differences might be derived from the different landrace cultivars, considering that

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unique landrace genetic information vary depending on local adaptations (Kang et al., 2006).

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In addition, since 1978 and 1950, Korea and China, respectively, have developed and

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cultivated diverse modern sesame cultivars, replacing historical landraces (Kang et al., 2006;

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Zhang, Sun, Zhang, Wang, & Che, 2011). African countries, including Ethiopia and Nigeria

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also initiated and performed sesame seed breeding research in the late 1960s and 1965-1973,

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respectively, for developing conventional sesame seed cultivars. These cultivars are high-

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yield genotypes with improved nutritional quality and resistance to environmental stresses

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such as drought, heat, pests and disease (Alegbejo, Iwo, Abo, & Idowu, 2003; Daniel, 2017).

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Owing to artificial selection, these modern sesame cultivars are assumed to have a lower

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environmental adaptation capability than landraces (Yu et al., 2019). In particular, modern

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sesame cultivars in China contain unique genes for energy metabolism (oxidative

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phosphorylation and photosynthesis), lipid metabolism (oil and fatty acid synthesis), and

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amino acid metabolism (cysteine and methionine metabolism), which are mainly relevant to

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seed quality-related traits rather than disease resistance and environmental adaptation (Yu et

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al., 2019). Accordingly, different metabolite characteristics in sesame seed samples from

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Korea, China, and other countries (India, Nigeria, and Ethiopia) might be affected by the 14

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inherent genetic characteristics of modern cultivars, which have been differently developed,

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selected, and cultivated in those countries.

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Some outliers in the PCA-derived score plots (Supplementary Fig. S3a) were from China-

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derived samples, which were all collected from Heilongjiang province, located in

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northeastern China. The extremely contrasting climate seen in Heilongjiang province

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compared to other regions might affect the metabolic profiles of sesame seed samples. In the

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middle and lower regions of the Yangtze River Valley, a heavy rainfall trend dominates in

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summer and winter seasons, whereas a trend of drying and drought has appeared in north and

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northeast China (Hu, Yang, & Wu, 2003). Further, the largest warming trend has appeared in

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northern China with an average temperature increase of 0.36 °C per decade, while southwest

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China has shown the smallest warming trend (0.15 °C increase per decade) (Hu et al., 2003;

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Piao et al., 2010). Based on those reports, the unique metabolite characteristics of sesame

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seeds cultivated in Heilongjiang province might result from the regional climate traits of a

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warming and drying trend with low precipitation in northeastern China.

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OPLS-DA was used in this study to discriminate and predict the geographical origins of

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sesame seeds sourced from Korea, China, and other countries (India, Nigeria, and Ethiopia).

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As shown in the OPLS-DA derived score plot (Fig. 2a), sesame seeds obtained from Korea,

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China, and other countries (India, Nigeria, and Ethiopia) were well separated by applying

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predetermined optimal data preprocessing methods. The optimal conditions for OPLS-DA

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model development selected the total area normalization and UV scaling method with the

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highest R2Y and Q2Y values (Table 2). Data preprocessing steps in metabolomics studies are

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very important to minimize the systematic intensity variations within all variables that could

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mask relevant biological differences. Thus, optimal normalization and scaling methods

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should be selected to reduce unwanted systematic errors in signal intensity measurements, 15

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retaining the meaningful biological information (Skov, Honore, Jensen, Næs, & Engelsen,

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2014).

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For further validation of the model, leave-one-out cross validation was performed (Fig. 2c-

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e). Sensitivity is the parameter that measures the ability of the model to correctly classify

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cases, whereas specificity measures the predictive ability of the model to correctly classify

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the controls (Szymanska, Saccenti, Smilde, & Westerhuis, 2012). As shown in Fig. 2c-e, high

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values of over 90.0% accuracy were obtained in three cases of validation testing to

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differentiate sesame seed samples from Korea, China, and other countries (India, Nigeria, and

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Ethiopia). This indicates that the OPLS-DA model established in this study could be used to

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determine the geographical origin of sesame seeds with high sensitivity, specificity, and

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accuracy.

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Phenylalanine, tryptophan, and acetate were the potential biomarkers identified by the

348

OPLS-biplot that discriminated the geographical origin of sesame seeds from Korea, China,

349

and other countries (India, Nigeria, and Ethiopia) in this study. These potential biomarkers

350

were further validated using a ROC curve analysis. Higher AUC values over 0.75 identified

351

in three potential biomarkers (phenylalanine, tryptophan, and acetate) indicated that these

352

metabolites contributed considerably to determining the geographical origin of sesame seeds

353

from Korea, China, and other countries (India, Nigeria, and Ethiopia).

354

This study suggests that the polar metabolites in sesame seeds can be used to discriminate

355

geographical origin, differentiating our study from others that focus on the non-polar lipid

356

metabolites in sesame seeds to determine geographical origin.

357 358

5. Conclusions

16

359

This is the first study to develop a discriminatory predictive model to determine the

360

geographic origin of sesame seeds from Korea, China, and other countries (India, Nigeria,

361

and Ethiopia) by NMR based metabolomics focusing on polar metabolites. Twenty-four polar

362

metabolites were identified, and amino acids had the highest proportion. Total area

363

normalization and UV scaling methods established the optimal OPLS-DA model with 97.5,

364

90.0, and 100.0% accuracy in leave-one-out cross validation testing, indicating a stable and

365

usable model. As potential biomarkers, the relative levels of phenylalanine, tryptophan, and

366

acetate differed significantly among the three groups. This study suggests an effective, novel

367

strategy for developing a practical and trustworthy platform that can be used to remedy the

368

emerging social problem of false sesame seed sourcing information. As this study was limited

369

by the restricted number of samples, regions, and countries that sesame seeds could be

370

obtained from, further studies should analyze additional samples cultivated in diverse

371

countries to further evaluate the effectiveness of this method for practical application.

372 373

Declarations of interest: none.

374 375

Acknowledgments

376

This work was supported by the National Research Foundation of Korea (NRF) grant

377

funded by the Korean government (MSIP) [NRF-2015R1A5A1008958], and the Korea

378

Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and

379

Fisheries (IPET) through Advanced Production Technology Development Program, funded

380

by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA) [316081-04].

17

381

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23

516

Figure captions

517

Fig. 1. Representative one dimensional 1H-NMR spectra of sesame seed samples.

518 519

Fig. 2. Development of a multivariate statistical model for discriminating the

520

geographical origin of sesame seeds from Korea, China, and other countries (India,

521

Nigeria, and Ethiopia). (a) OPLS-DA score plot of sesame seeds from Korea (blue), China

522

(red), and other countries (yellow) using six components (two predictive and four orthogonal

523

components). (b) A test plot using 999 permutations for the OPLS-DA model. R2Y (green

524

circle) and Q2Y (blue square) are shown in both original and permuted values with the R2Y

525

and Q2Y intercept values. Leave-one-out cross validation plots for differentiating sesame

526

seeds from Korea (c), China (d), and other countries (e) to test OPLS-DA model accuracy

527

showing calculated predicted Y values after cross validation. Misclassified samples are

528

marked as a circle with a threshold value of 0.5 on the Y-axis. Calculated values for

529

sensitivity, specificity, and accuracy are shown in the table.

530 531

Fig. 3. OPLS-biplot and receiver operating characteristic (ROC) curves for

532

discriminating the geographical origin of sesame seeds using three individual

533

metabolites. (a) OPLS biplot revealing the correlation of all metabolites (X-variables),

534

sample clusters (observations), and group information (Y-variables). Loading vectors of pq

535

(combination of p, X-variable and q, Y-variable vectors) and a score vector of t are displayed

536

as correlation scaled pq(corr) and t(corr) with the first and second predictive component.

537

ROC curves for acetate (b), phenylalanine (c), and tryptophan (d) on discriminating sesame

538

seeds from Korea and China. ROC curves for acetate (e), tryptophan (f), and phenylalanine (g)

539

on discriminating sesame seeds from Korea and other countries (India, Nigeria, and Ethiopia). 24

540

The area under curve (AUC) values for each metabolite are presented with the true positive

541

rate (sensitivity) against the false positive rate (specificity). The best cut off value is

542

determined as the nearest point to the upper left corner in the graph.

25

543

Table 1. Peak assignment of NMR spectra in sesame seeds. No.

Compounds

Chemical shift (multiplicity, J value)

1

Valerate

0.87 (t, J = 7.2), 1.28-1.32 (m), 2.14 (t, J = 7.2)

Assignment method 1D

2

Isoleucine

0.94 (t, J = 7.2), 0.99 (d, J = 7.2)

1D, COSY

3

Leucine

0.96 (t, J = 6.0)

1D

4

Valine

5

Threonine

1.33 (d, J = 6.6), 3.60 (d, J = 4.8)

1D, COSY

6

Alanine

1.48 (d, J = 7.2), 3.79 (q, J = 7.2)

1D

7

Acetate

1.90 (s)

1D

8

Proline

2.01-2.07 (m), 2.00-2.07 (m), 2.29-2.37 (m)

1D

9

Malate

2.38 (dd, J = 14.4, 10.8), 2.67 (dd, J = 15.6, 3)

1D

10

Succinate

2.39 (s)

1D

11

Asparagine

2.92-2.98 (m)

1D, COSY

12

Choline

3.22 (s), 3.48-3.51 (m)

1D

13

Betaine

3.24 (s), 3.89 (s),

1D, COSY

14

Glucose

15

Xylose

3.41(t, J = 9.0)

16

Glycine

3.55 (s)

1D

17

Ascorbate

3.70-3.76 (m)

1D

18

Sucrose

3.75 (t, J = 9.6), 3.79-3.84 (m), 3.81-3.85 (m),

1D, COSY,

3.82-3.87 (m), 4.02 (t, J = 8.4), 5.40 (d, J = 3.6)

HSQC

19

Arabinose

3.77 (dd, J = 11.4, 3.6), 3.80 (dd), 4.00-4.04 (m)

1D, HSQC

20

Uridine

21

Tyrosine

6.88-6.91 (m), 7.15-7.18 (m)

1D, COSY

22

Gallate

7.03 (s)

1D

23

Phenylalanine

7.33 (d, J = 7.2), 7.35-7.40 (m),

1D, COSY

0.96 (d, J = 6.6), 1.02 (d, J = 7.2), 3.60 (d, J = 4.2)

3.38-3.42 (m), 3.40-3.44 (m), 3.51-3.55 (dd, J = 10.2, 3.6), 3.82-3.86 (m)

5.88 (d, J = 9.0), 5.89 (d, J = 4.8), 7.84 (d, J = 7.8)

26

1D

1D, HSQC 1D, COSY, HSQC

1D, COSY

24

Tryptophan

7.15-7.19 (m), 7.32 (s), 7.53 (d, J = 7.8), 7.71 (d, J = 7.8)

s, singlet; d, doublet; dd, doublet of doublet; t, triplet; q, quartet; m, multiplet 544

27

1D, COSY

545

Table 2. Selection of the optimal OPLS-DA model based on various normalization and

546

scaling methods for discriminating the geographical origin of sesame seeds.

Scaling method

Component number

R2Y

Q2 Y

R2Y intercept

Q2 Y intercept

UV

2+1

0.647

0.589

0.040

-0.134

2

Par

2+2

0.690

0.575

0.068

-0.167

3

UV

2+4

0.835

0.769

0.155

-0.330

Par

2+4

0.810

0.753

0.114

-0.272

Group No.

Normalization method

1 Standardized area

Total area 4 UV, unit variance; Par, pareto 547

28

548

Table 3. Relative intensity of polar metabolites in sesame seed samples from Korea,

549

China, and other countries and variable influence on projection (VIP) values derived

550

from the OPLS-DA model for discriminating the geographical origin of sesame seeds.

551

Compounds Amino acids alanine asparagine betaine glycine isoleucine leucine phenylalanine proline threonine tyrosine tryptophan valine Organic acids acetate malate succinate Sugars arabinose glucose sucrose xylose Others ascorbate choline gallate uridine valerate Relative intensities in

552

obtained from peak intensity of NMR spectra with total area normalization multiplied by

553

1,000. *, #, and & indicate significant difference in the values between Korea and China,

554

Korea and other countries, and China and other countries, respectively determined by non-

555

parametric Kruskal-Wallis test (p< 0.05). NS means not significant.

Korea

China

Others

VIP value

0.56 ± 0.17 NS 0.86 ± 0.24 NS 10.18 ± 1.28 2.04 ± 0.84 0.37 ± 0.13 0.59 ± 0.27NS 0.37 ± 0.09* 7.27 ± 1.68 6.53 ± 1.22 0.88 ± 0.18* 0.59 ± 0.14* 0.59 ± 0.24 NS

0.62 ± 0.16 NS 0.84 ± 0.23 NS 10.66 ± 2.95& 2.07 ± 0.84& 0.36 ± 0.09& 0.52 ± 0.21 NS 0.53 ± 0.14& 6.54 ± 0.58& 6.02 ± 1.17& 1.65 ± 0.53 0.80 ± 0.18& 0.62 ± 0.18 NS

0.58 ± 0.10 NS 0.89 ± 0.09 NS 12.34 ± 0.91# 4.62 ± 1.17# 0.48 ± 0.12# 0.49 ± 0.09 NS 0.61 ± 0.08# 3.42 ± 0.47# 1.20 ± 0.18# 1.43 ± 0.16# 1.05 ± 0.10# 0.61 ± 0.12 NS

0.60 0.15 0.56 1.13 0.58 0.34 1.27 1.09 1.29 1.71 1.23 0.13

1.24 ± 0.38* 0.29 ± 0.08 3.60 ± 1.20

1.88 ± 0.31& 0.35 ± 0.09& 3.97 ± 1.90&

2.66 ± 0.28# 0.53 ± 0.08# 1.17 ± 0.10#

1.35 1.05 0.97

23.96 ± 5.26 25.20 ± 2.14 15.04 ± 2.94 11.68 ± 1.63

21.97 ± 6.15& 25.11 ± 5.90& 13.72 ± 3.94& 10.93 ± 1.87&

32.41 ± 2.62# 40.62 ± 6.98# 16.85 ± 1.25# 12.51 ± 1.22

1.02 1.20 0.83 0.67

13.84 ± 2.49 13.39 ± 3.74& 18.28 ± 2.31# 9.95 ± 1.15 9.94 ± 1.94& 12.60 ± 1.01# 0.31 ± 0.10 0.35 ± 0.08& 0.53 ± 0.08# 1.00 ± 0.15# 0.63 ± 0.18* 0.79 ± 0.19& & 21.42 ± 3.84 21.21 ± 6.73 2.80 ± 0.59# the table represent the mean ± standard deviation of

29

0.84 0.97 0.96 0.99 1.28 binning values

556 557

Fig. 1

30

558 559

Fig. 2

31

560 561

Fig. 3 32

Highlights •

Polar metabolites evaluated by NMR spectroscopy differentiated sesame seed origins

•

An OPLS-DA model successfully discriminated the geographic seed origin

•

Acetate, phenylalanine, and tryptophan differentiated seed origin

•

Seed origin determined with an accuracy of 97.5, 90.0, and 100.0%