Front. Pharmacol. Frontiers in Pharmacology Front. Pharmacol. 1663-9812 Frontiers Media S.A. 10.3389/fphar.2020.549181 Pharmacology Original Research Determination of Differentiating Markers in Coicis Semen From Multi-Sources Based on Structural Similarity Classification Coupled With UPCC-Xevo G2-XS QTOF ZhuRuyi † XuXiaofen † ShanQiyuan † WangKuilong CaoGang * WuXin * Research Center of TCM Processing Engineering, College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China

Edited by: Anthony Booker, University of Westminster, United Kingdom

Reviewed by: Jianxin Chan, Beijing University of Chinese Medicine, China; Francisc Dulf, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Romania

*Correspondence: Gang Cao, caogang33@163.com; Xin Wu, wuhsin@qq.com

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

†These authors have contributed equally to this work

 16 10 2020 2020 11 549181 05 04 2020 18 08 2020 Copyright © 2020 Zhu, Xu, Shan, Wang, Cao and Wu 2020 Zhu, Xu, Shan, Wang, Cao and Wu

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Coicis semen, a medicinal food, is derived from the dried and mature seeds of Coix lacryma-jobi L. var. ma-yuen (Rom.Caill.) Stapf, a member of the Gramineae family. Lipids are its main constituents. Previous literature reported that coicis semen contains twenty triglycerides and twelve diglycerides. However, we identified thirty-five triglycerides, sixteen diglycerides, four monoglycerides, and two sterols under the preoptimized conditions of UPCC-Xevo G2-XS QTOF combined with a personalized TCM database. Furthermore, we successfully determined glycerol trioleate content to evaluate quality differences. Finally, we identified the fatty acid compositions of seven out of nine differential markers via Progenesis QI using principal component analysis, orthogonal projection to latent structures–discriminant analysis, and the LipidMaps database. In addition, we applied a software-based classification, a method that was previously developed by our team, to verify and predict structurally similar compounds. Our findings confirmed that UPCC-Xevo G2-XS QTOF combined with software-based group classification could be used as an efficient method for exploring the potential lipid markers of seed medicine.

 markers coicis semen triglyceride qualitative and quantitative MATLAB 2017C33175 Zhejiang Province Public Welfare Technology Application Research Project10.13039/501100010248 National Natural Science Foundation of China10.13039/501100001809 Introduction

Common sense dictates that various natural ingredients exist in TCM. However, most reports on the active components of TCM have focused on polysaccharides, alkaloids, and flavonoids. Fatty oils are widely available ingredients of herbs, and the limited attention that they have received may restrict their further development and application. Fatty oils can be obtained as an active ingredient from animals and plants (Liu et al., 2015; Chen et al., 2016; Hong et al., 2016). A number of TCM contain fatty oils, which are mainly derived from the seeds and fruits of herbs. Diverse fatty oils comprise of glycerol and different types of saturated, monounsaturated, and polyunsaturated fatty acids that each exert therapeutic effects.

Coicis semen (Job’s tears seed or adlay), which has been documented in the 2015 edition of the Chinese pharmacopoeia, is the dry and mature seed of Coix lacryma-jobi L. var. ma-yuen (Rom.Caill.) Stapf. It is not only a commonly used TCM, but it is also a commonly consumed food. Coicis semen has been proven to have numerous functions, such as detoxification and dampness and arthralgia removal; it also reduces cancer risk (Yu et al., 2011; Bai et al., 2018; Duan, 2018; Xu et al., 2018; Choi et al., 2019; Huang Q. et al., 2019; Huang Y. L. et al., 2019; Li et al., 2019; Liu H. Q. et al., 2019; Liu Y. N. et al., 2019; Manosroi et al., 2019; Qian et al., 2019; Zhang et al., 2019). Moreover, it has a wide range of anti-inflammatory, antioxidation, analgesic, and sedative effects, as well as pharmacological effects against gastric cancer, hepatocellular cancer, Lewis lung cancer, non-small cell lung cancer, pancreatic cancer, and pulmonary cancer (“Fuzheng” category represented by coicis semen oil) (Manosroi et al., 2016a; Manosroi et al., 2016b; Qian et al., 2016; Xi et al., 2016; Wang et al., 2016; Han et al., 2017; Qu et al., 2017a; Schwartzberg et al., 2017; Jang et al., 2018; Kim et al., 2018; Zhang et al., 2018). It has a variety of clinical dosage forms, such as microporous microspheres, microemulsions, and intravenous emulsions (Qu et al., 2016; Qu et al., 2017b; Qu et al., 2017c; Trinh et al., 2017; Chen et al., 2018; Guo et al., 2019). Discrepancies in curative effects can be attributed to the various types, contents, and other nutrients of different fatty oils.

Recent research shows that the variety and content of the active components of different fatty oils remarkably influence human health and have their own specific advantages in treatment. Near-infrared spectroscopy analysis showed that the lipid contents of forty-one polished coicis semen samples range from 5.14% to 9.40%. Coicis semen oils contain seven types of triglycerides (trilinolein, 1,2-linolein-3-olein, 1-palmitin-2-linolein-3-olein, 1-palmitin-2,3-linolein, 1-palmitin-2, 3-olein, triolein, and 1,2-olein-3-linolein). Hou et al. identified twenty triglycerides and twelve diglycerides in the lipid profile of coicis semen and developed a green quantification strategy for simultaneously determining the content of 7 TGs (LLL, LLP, LLO, POL, OOL, OOP, and OOO) by combining core–shell column technology and SSDMCs. Lin et al. found β-sitosterol and stigmasterol in the ethyl acetate fraction of an adlay hull extract. Dong et al. established a rapid and reliable m-SPE approach using magnetic multiwalled carbon nanotubes as the adsorbent for the purification of type A trichothecenes, including T-2 toxins, HT-2 toxins, diacetoxyscirpenol, and neosolaniol, in coicis semen (Dong et al., 2016; Hou et al., 2018a; Hou et al., 2018b; Lin et al., 2019).

However, several components, especially glycerides, of coicis semen oil still require analysis, and the identification of the active ingredients of this material needs in-depth research. Therefore, our team used Acquity UPCC-Xevo G2-XS QTOF coupled with software-based group classification to further excavate, identify, and visually classify active ingredients in coicis semen oils. Coicis semen oils have boundless development prospects and need to be explored in-depth to lay a foundation for its new preparation, development, and extensive clinical application.

 Materials and Methods Materials and Reagents

Glycerol trioleate with the purity of more than 99.9% as determined via HPLC–ELSD was purchased from the Nature Standard Co. Ltd (Shanghai, China). Acetonitrile and methanol (HPLC–MS grade) were purchased from Merck (Darmstadt, Germany). Ammonium formate (HPLC grade) was purchased from Sigma-Aldrich. High-purity CO2 (99.999%) was purchased from the Shanghai Yizhi Industry Gases Co., Ltd. (Shanghai, China). All other reagents used in sample preparation were of analytical grade. Seven batches of dried coicis semen were purchased from different TCM enterprises in China. The manufacturers and batch numbers of the samples were as follows: batch number 180901 (Zhejiang Chinese Medical University Medical Pieces Co., Ltd., Hangzhou); batch number 190101 (Jirentang Pharmaceutical Co., Ltd., Guiyang); batch number 190105 (Zuoli Baicao Herbal Pieces Co., Ltd., Jiangxi); batch number 181201 (Huadong Herbal Pieces Co., Ltd., Hangzhou); batch number 181206 (Zuoli Baicao Herbal Pieces Co., Ltd., Zhejiang); batch number 190122 (Haiyuan Prepared Slices of Chinese Crude Drugs Co., Ltd., Nanjing); and batch number 190216 (Haichang Chinese Medicine Group Co., Ltd., Nanjing).

 Preparation of Reference and Sample Solutions

The appropriate amount of glyceryl trioleate, which was used as the reference substance, was weighed accurately and diluted with n-hexane to prepare a series of working solutions with concentrations of 0.0099, 0.0988, 0.9881, 4.9405, and 9.8810 μg/mL.

All samples were ground and passed through a No. 3 sieve (355 ± 13 μm). The passing rate of the particles was maintained at more than 80%. The sample preparation procedure was as follows:

A total of 50.0 mL of n-hexane was added to 0.6 g of powdered coicis semen. The seed powder was soaked for 2 h and then sonicated (50 kHz, 250 W, KQ-500DB) for 30 min. The supernatant was filtered to obtain the sample solution. The filtrate was diluted with n-hexane 5 times and 100-fold for the analysis of different components of different samples and the quantitative analysis of glyceride trioleate, respectively. A total of 200 μL solution of each batch was mixed together and used as a pooled QC sample solution for the analysis of free fatty acids.

 UPCC-Xevo G2-XS QTOF Parameters

On the basis of preliminary experiments, the final experimental conditions were determined as follows: Liquid phase system: ACQUITY UPCC; column: Torus 2-PIC, 3.0 × 100 mm, 1.7 μm; mobile phase A: CO2, mobile phase B: methanol acetonitrile = 9:1; column temperature: 55°C, ABPR: 2600 psi; compensation solution: methanol solution with 0.5 mM ammonium formate; flow rate: 0.5 mL/min; and injection volume: 0.3 μL. The gradient program was used with a flow rate of 1.0 mL/min and was as follows: 0–2 min (1% B), 2–7 min (1%–5% B), 7–10 min (5% B), and 10–13 min (5%–1% B).

The conditions for mass spectrometry are as follows: Mass spectrometry system: Xevo G2-XS QTOF; ionization method: ESI+/−; data acquisition mode: MSE; MSE impact energy: low, off, high, 20–50 eV; quantitative ion: m/z 902.8177; collection mass range: 100–1200 Da; capillary voltage: 2.0 kV; cone-hole voltage: 40 V; ion source temperature: 120°C; atomization temperature: 500°C; cone-hole gas flow rate: 50 L/h; and atomized gas flow rate: 1000 L/h. Data calibration was performed by using an external reference (LockSpray) with the constant infusion of leucine–enkephalin solution (200 pg/μL) at a flow rate of 5 μL/min.

The data processing software included UNIFI 1.9.4 and Masslynx V 4.1.

 Data Acquisition and Analysis

Xevo G2-XS QTOF uses the patented LockSpray technology to ensure the accuracy of the collected data in real time. High-accuracy mass numbers can be obtained, and the combination of high-accuracy mass numbers and isotope distribution and secondary fragment information accurately provides molecular formulas. Xevo G2-XS QTOF applies patented MSE technology to obtain the primary and secondary mass spectral information of the compounds for further structural confirmation with one injection at the same time.

The ESI+/− mode was used for the analysis of glycerides and free fatty acids. The first step involved understanding the fragmentation pattern of glycerides as a whole. In the second step, by combining the fragmentation patterns and consulting related literature, a UNIFI database for the glyceride analysis of coicis semen was established. In the third step, the self-built database was imported into UNIFI in addition to the ChemSpider online database, and the appropriate analysis method was set. Furthermore, the software automatically analyzed primary and secondary mass spectral information. Finally, it quickly screened out the target through a unique workflow.

Our team developed a classification program in the Visual Basic for Applications (VBA; Microsoft, USA) and MATLAB v7.1 (The Mathworks, Natick, USA) environments. The classification program consisted of three parts (Shan et al., 2012).

A total of 2,916 features were introduced into the SIMCA-P 13.5 software (Umetrics, Umeå, Sweden) for principal component analysis (PCA) and orthogonal projection to latent structures–discriminant analysis (OPLS–DA). The corresponding variable importance in the projection value (VIP value) was calculated in the OPLS–DA model. A potential differential marker was selected when its VIP value exceeded 2.00 and its S-Plot exceeded 0.95.

 Results and Discussion Qualitative Results of Glycerides and Free Fatty Acids in Coicis Semen Oils

In the ESI+ mode, fifty-six and fifty-seven compounds were identified in five (No. 180901, No. 181201, No. 181206, No. 190101, and No. 190122) and two (No. 190105 and No. 190216) batches of samples, respectively. These compounds were mainly composed of glycerides. The total ion chromatogram of all samples is provided in Figure 1A, and the corresponding identified glycerides are listed in Table 1. Among them, fifty-six common compounds, including thirty-five triglycerides, fifteen diglycerides, four glycerides, and two sterols, were identified in comparison with the corresponding results of twenty triglycerides and twelve diglycerides (Hou et al., 2018a). However, the OP of diglycerides was identified only in two batches of samples, namely 190105 and 190216, likely because of the different processing technologies of different medical enterprises. The QC sample mentioned in Figure 1B was overlaid in the following differential component analysis.

Total ion chromatogram of (A) (qualitative analysis of glyceride contents of seven batches of coicis semen oils in ESI+); (B) (overlay of the QC sample for the differential component analysis of different samples in ESI+); (C) (qualitative analysis of free fatty acids of QC sample in ESI−).

The qualitative results of glycerides in coicis semen oils in ESI+.

No. Component name Formula Neutral mass (Da) Observed m/z Mass error (mDa) Observed RT (min) Response Adducts
1 β-Sitosterol C29H50O 414.3862 397.3827 -0.20 2.65 175317 -H2O+H
2 PPP C51H98O6 806.7363 824.7724 2.20 2.87 76543 +NH4, +Na
3 Stigmasterol C29H48O 412.3705 413.3782 0.40 2.98 280236 +H, +Na, -H2O+H
4 PMO C51H96O6 804.7207 822.7572 2.70 3.01 40426 +NH4, +Na
5 PPS C53H102O6 834.7676 852.8045 3.10 3.11 19271 +NH4, +Na
6 PML C51H94O6 802.7050 820.7414 2.50 3.18 31125 +NH4, +Na
7 PPO C53H100O6 832.7520 850.7880 2.20 3.26 4297765 +NH4, +H, +Na, -H2O+H
8 PSS C55H106O6 862.7989 880.8372 4.40 3.38 5232 +NH4, +Na
9 PPL C53H98O6 830.7363 848.7720 1.90 3.46 4668112 +NH4, +H, +Na, -H2O+H
10 POS C55H104O6 860.7833 878.8203 3.20 3.54 1031215 +NH4, +H, +Na
11 PPOL C53H96O6 828.7207 846.7580 3.40 3.64 344524 +NH4, +H, +Na
12 POO C55H102O6 858.7676 876.8048 3.40 3.71 20321224 +NH4, +H, +Na, -H2O+H
13 OPC20:0 C57H108O6 888.8146 906.8520 3.60 3.80 323033 +NH4, +H, +Na
14 LLM C53H94O6 826.7050 844.7426 3.70 3.83 49997 +NH4, +H, +Na
15 PLO C55H100O6 856.7520 874.7887 2.90 3.89 21370394 +NH4, +H, +Na, -H2O+H
16 OOS C57H106O6 886.7989 904.8357 2.90 3.96 3378949 +NH4, +H, +Na, -H2O+H
17 OLC17:0 C56H102O6 870.7676 888.8046 3.20 4.01 273530 +NH4, +H, +Na
18 OPC22:0 C59H112O6 916.8459 934.8839 4.10 4.03 51651 +NH4, +Na
19 LLP C55H98O6 854.7363 872.7728 2.60 4.06 8367471 +NH4, +H, +Na, -H2O+H
20 OOO C57H104O6 884.7833 902.8202 3.10 4.11 21932960 +NH4, +H, +Na, -H2O+H
21 OOO20:0 C59H110O6 914.8302 932.8674 3.30 4.19 590110 +NH4, +H, +Na
22 OOC19:1 C58H106O6 898.7989 916.8371 4.30 4.21 28860 +NH4
23 LLPo C55H96O6 852.7207 870.7575 3.00 4.23 335763 +NH4, +H, +Na, -H2O+H
24 OOL C57H102O6 882.7676 900.8044 2.90 4.27 26267878 +NH4, +H, +Na, -H2O+H
25 OLC20:0 C59H108O6 912.8146 930.8517 3.30 4.34 795167 +NH4, +H, +Na, -H2O+H
26 OLC19:1 C58H104O6 896.7833 914.8206 3.40 4.37 39779 +NH4, +Na
27 LLO C57H100O6 880.7520 898.7884 2.60 4.43 22285984 +NH4, +H, +Na, -H2O+H
28 LOC20:1 C59H106O6 910.7989 928.8353 2.50 4.51 663937 +NH4, +H, +Na
29 LLC19:1 C58H102O6 894.7676 912.8009 -0.60 4.53 34245 +NH4
30 LOC22:0 C61H112O6 940.8459 958.8826 2.80 4.54 183383 +NH4, +Na
31 OOO24:0 C63H118O6 970.8928 988.9309 4.20 4.57 53885 +NH4
32 LLL C57H98O6 878.7363 896.7721 2.00 4.60 8926747 +NH4, +H, +Na, -H2O+H
33 LLC22:0 C61H110O6 938.8302 956.8666 2.60 4.69 163391 +NH4, +Na
34 LOO24:0 C63H116O6 968.8772 986.9141 3.10 4.73 113189 +NH4, +Na
35 LLLn C57H96O6 876.7207 894.7569 2.40 4.76 375488 +NH4, +H, +Na, -H2O+H
36 LLC24:0 C63H114O6 966.8615 984.8983 2.90 4.87 76278 +NH4, +Na
37 OLS C57H104O6 884.7833 885.7856 -4.90 5.21 3683 +H
38 PP C35H68O5 568.5067 551.5035 0.10 5.43 414186 -H2O+H, +Na
39 PS C37H72O5 596.5380 579.5348 0.10 5.63 579793 -H2O+H, +Na
40 PO C37H70O5 594.5223 577.5192 0.10 5.78 924986 -H2O+H, +H, +Na
41 PL C37H68O5 592.5067 575.5031 -0.30 5.95 728564 -H2O+H, +H, +NH4, +Na
42 OS C39H74O5 622.5536 605.5500 -0.30 5.98 150906 -H2O+H, +H, +Na
43 OO C39H72O5 620.5380 603.5344 -0.30 6.11 2014873 -H2O+H, +H, +NH4, +Na
44 PP C35H68O5 568.5067 551.5030 -0.40 6.19 17291 -H2O+H, +Na
45 OL C39H70O5 618.5223 601.5188 -0.20 6.28 2010772 -H2O+H, +H, +NH4, +Na
46 LL C39H68O5 616.5067 617.5130 -1.00 6.48 681857 +H, +NH4, +Na, -H2O+H
47 OP C37H70O5 594.5223 617.5130 1.40 6.48 681857 +Na, +H, +NH4, -H2O+H
48 LP C37H68O5 592.5067 575.5037 0.40 6.67 311434 -H2O+H, +H, +NH4, +Na
49 OS C39H74O5 622.5536 645.5432 0.30 6.70 44689 +Na, +H, +NH4, -H2O+H
50 OO C39H72O5 620.5380 603.5350 0.30 6.81 707855 -H2O+H, +H, +NH4, +Na
51 LO C39H70O5 618.5223 601.5193 0.20 6.98 697567 -H2O+H, +H, +NH4, +Na
52 LL C39H68O5 616.5067 639.4962 0.30 7.13 505275 +Na, +H, +NH4, -H2O+H
53 LLn C39H66O5 614.4910 637.4809 0.60 7.30 13264 +Na, +H, +NH4, -H2O+H
54 MG(16:0/0:0/0:0) C19H38O4 330.2770 353.2667 0.50 7.97 239151 +Na, -H2O+H
55 MG(17:0/0:0/0:0) C20H40O4 344.2927 367.2841 2.20 8.08 632 +Na, -H2O+H
56 MG(18:0/0:0/0:0) C21H42O4 358.3083 381.2977 0.20 8.19 259875 +Na, -H2O+H
57 MG(20:0/0:0/0:0) C23H46O4 386.3396 409.3288 0.00 8.40 2705 +Na, -H2O+H

A total of thirty free fatty acids were identified in the ESI− mode, and some unsaturated fatty acids could have had isomers, which needed to be confirmed further by using a reference substance. The total ion chromatogram of the QC sample is given in Figure 1C, and the corresponding identified free fatty acids are listed in Table 2.

The qualitative results free fatty acids in coicis semen oils in ESI-.

No. Component name Formula Neutral mass (Da) Observed m/z Mass error (mDa) Observed RT (min) Response Adducts
1 Oleic acid C18H34O2 282.2559 281.2486 0.0 4.74 7887221 -H, +HCOO
2 Linoleic acid C18H32O2 280.2402 279.2330 0.1 4.92 4629367 -H, +HCOO
3 Palmitic acid C16H32O2 256.2402 255.2329 0.0 4.32 2866760 -H, +HCOO
4 Stearic acid C18H36O2 284.2715 283.2642 -0.1 4.58 940078 -H, +HCOO
5 Arachidic acid C20H40O2 312.3028 311.2956 0.0 4.82 131638 -H, +HCOO
6 cis-Vaccenic acid C18H34O2 282.2559 281.2484 -0.2 6.02 126409 -H
7 9, 10- EPOXYOCTADECANOIC ACID C18H34O3 298.2508 297.2432 -0.3 5.79 88969 -H
8 9, 10- EPOXYOCTADECANOIC ACID C18H34O3 298.2508 297.2432 -0.3 5.92 79177 -H
9 11-Eicosenoic acid C20H38O2 310.2872 309.2798 -0.1 4.97 75288 -H
10 Linolenic acid C18H30O2 278.2246 277.2172 -0.1 5.09 61260 -H
11 Lignoceric acid C24H48O2 368.3654 367.3579 -0.2 5.23 58141 -H
12 Behenic acid C22H44O2 340.3341 339.3265 -0.3 5.03 55860 -H
13 Palmitoleic acid C16H30O2 254.2246 253.2173 0.0 4.50 38739 -H
14 Margaric acid C17H34O2 270.2559 269.2486 0.0 4.45 29580 -H
15 Arachidonic acid C20H32O2 304.2402 349.2359 -2.5 4.74 22657 +HCOO
16 TRICOSANOIC ACID C23H46O2 354.3498 353.3422 -0.3 5.13 17977 -H
17 Myristic acid C14H28O2 228.2089 227.2017 0.1 4.05 15915 -H
18 10-Heptadecenoic acid C17H32O2 268.2402 267.2327 -0.3 4.62 11423 -H
19 Eicosapentaenoic acid C20H30O2 302.2246 347.2200 -2.8 4.92 11013 +HCOO, -H
20 Hexacosanoic acid C26H52O2 396.3967 395.3887 -0.7 5.42 8914 -H
21 Pentacosanoic acid C25H50O2 382.3811 381.3733 -0.5 5.33 8708 -H
22 Pentadecylic acid C15H30O2 242.2246 241.2173 0.0 4.20 8677 -H
23 NONADECANOIC ACID C19H38O2 298.2872 297.2798 -0.1 4.69 6204 -H
24 HENEICOSANOIC ACID C21H42O2 326.3185 325.3111 -0.1 4.93 5972 -H
25 Octacosanoic acid C28H56O2 424.4280 423.4200 -0.7 5.60 5361 -H
26 Laurie acid C12H24O2 200.1776 199.1704 0.0 3.71 5056 -H
27 Erucic acid C22H42O2 338.3185 337.3115 0.3 5.19 4770 -H
28 Nervonic acid C24H46O2 366.3498 411.3476 -0.4 6.38 3848 +HCOO
29 11-14-Eicosadienoic acid C20H36O2 308.2715 307.2643 0.1 5.14 2012 -H
30 Homo gamma linolenic acid C20H34O2 306.2559 351.2511 -3.0 4.58 1984 +HCOO

As shown in Figure 2A, PLO (tR 3.90 min) provided a precursor ion ([M+NH4]+) at m/z 874.7874 with a double-bond equivalent. The MS/MS product ions at m/z 601.5184 ([M-P+H]+ palmitic acid), m/z 577.5175 ([M-L+H]+ linoleic acid), and m/z 575.5025 ([M-O+H]+ oleic acid) resulted from the sn-1, sn-2, and sn-3 cleavages of the ester groups, respectively. Figure 2B shows the secondary fragment matching diagram for OOO generated by the UNIFI software.

Fragmentation patterns of PLO (A) and OOO (B) with MS1 and MS2 spectra.

 Software-Based Group Classification of Glycerides

Our teams previously developed a program in VBA and MATLAB for the classification of multiple complex components. This program successfully grouped the constituents in the n-hexane extract of coicis semen. Through the comprehensive analysis of herbal samples, fifty-seven peaks were identified and divided into four groups as shown in Figures 3, 4. Three of these groups consisted of triglycerides and diglycerides. The remaining group was composed of four monoglycerides, two diglycerides, and two sterols. The chemical structures and special MS fragmentation pathways of these compounds indicated that the same group might have similar features, and unknown ingredients could be identified through the comprehensive software-based group classification of these compounds.

Affinity diagram of the mass spectra of glycerides with software-based group classification.

Network diagram of the mass spectra of glycerides with software-based group classification.

 Differential Component Analysis of Different Samples

The Progenesis QI omics analysis software was used for differential component research. Before exploring quality markers, the analytical system was first validated for repeatability upon the injection of six QC samples. A total of 2,916 features were extracted and then imported into EZinfo for multivariate statistical analysis. PCA was used to study the variations in the oils of seven batches of coicis semen (Figure 5A). The differences between the groups of samples, namely No. 181216 and No.190122, were large. Furthermore, No. 190105 and No. 190101, which showed the largest differences, were subjected to OPLS–DA analysis (Figure 5B). These two groups were clearly distinct. Furthermore, we selected compounds with S Plot ≥ 0.95 and VIP ≥ 2 as markers (Figures 5C, D), and transferred them back to the QI for identification. Finally, nine markers were found (Table 3). Then, the LipidBlast, LipidMaps, and Chemspider databases were searched in QI for further identification. Among the nine markers found, seven were identified (five diglycerides, one triglyceride, and one stigmasterol), and the molecular formulas of the remaining two unknown compounds were estimated using an elemental composition tool. The abundance distribution of the nine markers in all the samples is shown in Figure 6. The abundance of markers, except diglycerides (16:0/18:0/0:0), in No. 190105 were remarkably higher than that in No. 190101. This result could be related to the largest differences between No. 190105 and No. 190101 and indicated that massive differences in resources and processing technologies existed among medical enterprises.

Differential component analysis of different samples. (A): PCA classification of seven batches of samples; (B) OPLS–DA analysis of No. 190101 and No. 190105 with significant differences; (C) S-Plot of No. 190101 and No. 190105; (D) VIP diagram of No. 190101 and No. 190105.

The identified results of nine differential markers between No. 190101 and No. 190105.

No. m/z Retention time (min) Identity Formula Neutral mass (Da)
1 413.3781 2.99 Stigmasterol C29H48O 412.3708
2 579.5337 5.64 DG(16:0/18:0/0:0) C37H72O5 596.5370
3 963.7297 5.69 unknown C53H102O14 962.7270
4 962.7224 5.69 TG(18:4/20:5/22:6) C63H92O6 944.6894
5 961.7175 6.18 unknown C53H100O14 960.7113
6 577.5188 6.52 DG(18:1/16:0/0:0) C37H70O5 594.5220
7 603.5345 6.82 DG(18:1/18:1/0:0) C39H72O5 620.5378
8 641.5118 6.99 DG(18:2/18:1/0:0) C39H70O5 618.5222
9 639.4958 7.14 DG(18:2/18:2/0:0) C39H68O5 616.5069

Abundance distribution of nine markers in all samples.

 Quantitation of Glycerol Trioleate in Coicis Semen Oils Investigation of Linear Relations

Reference solutions with concentrations of 0.0099, 0.0988, 0.9881, 4.9405, and 9.8810 μg/mL were used to perform three consecutive injections. The results showed that glyceryl trioleate had a good linear relationship in the range of 0.0090–9.8810 μg/mL, r2> 0.9990.

 Quantitative Limit Investigation

The reference solution was diluted stepwise at certain multiples until glyceride trioleate presented S/N ≈ 10. The results showed that the quantitative limit was 4.94 ng/mL.

 Instrument Precision Inspection

The low, middle, and high concentrations (0.0099, 0.9881, and 9.8810 μg/mL) of the reference solution on the calibration curve were taken and used in six consecutive injections to check instrument precision. The RSD value was less than 3%, which indicated good precision.

 Repeatability Test

Six powder samples (0.6 g each) of the same batch (No. 180901) were weighed and prepared via the sample solution preparation method. The average content of glyceryl trioleate was determined and calculated as 0.91%. The results showed that the RSD value was 4.12% (n = 6).

 Recovery Rate Test

ine powder samples (0.3 g each) of the same batch of known content were weighed, and then low, medium, and high levels of the three different concentrations of the reference substance were added precisely. The reference substance/sample ratio was controlled at 0.5:1, 1:1, 1.5:1, and each concentration level was tested in triplicate. The results showed a high average recovery of 102.28%.

 Sample Measurement Results

The content of glyceryl trioleate in seven batches of the samples was determined using the established method above. The representative chromatogram is shown in Figure 7. Each batch was replicated in triplicate. The average content ranged from 0.84% to 1.05%. The RSD value was less than 5%.

(A) Chromatogram of reference (glycerol trioleate); (B) EIC diagram of representative sample (No. 180901).

 Conclusion

The established analytical method fully demonstrated that ACQUITY UPCC enabled the fast and efficient chromatographic separation of lipids in coicis semen. Xevo G2-XS QTOF combined with LockSpray real-time external standard mass calibration technology ensured mass accuracy. The data collection method based on MSE tandem mass spectrometry without content discrimination ensured the full collection of information, and one-shot collection could obtain precursor ion and fragment ion information simultaneously with convenient, fast, and high-throughput characteristics.

By using the ACQUITY UPCC/Xevo G2-XS QTOF system combined with the UNIFI software, fifty-seven compounds of glycerides were identified and divided into four groups on the basis of their similar features via software-based group classification in the ESI+ mode. Moreover, thirty free fatty acids were identified in ESI−. In addition, QI omics analysis software found nine differential compounds between No. 190101 and No. 190105, and seven of these compounds were identified. Finally, the quantitative analysis of glyceryl trioleate (quality control component in the 2015 Edition of the Chinese Pharmacopoeia) and methodological verification were performed, and the results showed that the linearity, precision, reproducibility, recovery, and other parameters of the method were good. The established quantitative method determined that the glyceryl trioleate contents of the seven batches of samples ranged from 0.84% to 1.05%.

In summary, we identified additional glycerides and free fatty acids in coicis semen oils. Our results could supplement corresponding component research. Furthermore, nine differential components were found to be potential markers of quality for differentiating coicis semen with different origins. Finally, glyceryl trioleate was determined to evaluate its pros and cons. This approach might be useful for assessing the quality of TCM.

 Data Availability Statement

All datasets presented in this study are included in the article/Supplementary Material.

 Author Contributions

Conceptualization: XW and GC. Data curation: RZ, XX, and QS. Formal analysis: KW. Funding acquisition: GC. Investigation: RZ, XX, and KW. Writing—original draft: RZ and XX. Writing—review and editing: XW and GC. All authors contributed to the article and approved the submitted version.

 Funding

This research was supported by the Zhejiang Public Welfare Technology Application Research Project (2017C33175) and the National Natural Science Foundation of China (No. 81703707).

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2020.549181/full#supplementary-material

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