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. 2022 Nov 30;13:989755. doi: 10.3389/fpls.2022.989755

Metabolomic profiling of developing perilla leaves reveals the best harvest time

Jiabao Chen 1,†, Long Guo 1,2,3,†, Guiya Yang 1, Aitong Yang 1, Yuguang Zheng 2,3,4,*, Lei Wang 1,2,3,*
PMCID: PMC9748349  PMID: 36531401

Abstract

Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) and gas chromatography-mass spectrometry (GC-MS) were applied to analyze metabolites in perilla leaves (PLs) during its developmental process. In total, 118 metabolites were identified, including volatile and non-volatile compounds, such as terpenoids, sugars, amino acids, organic acids, fatty acids, phenolic acids, flavonoids, and others. Principal component analysis (PCA) indicated great variations of metabolites during PLs development. Clustering analysis (CA) clarified the dynamic patterns of the metabolites. The heatmap of CA showed that most of the detected metabolites were significantly accumulated at stage 4 which is the pre anthesis period, and declined afterwards. The results of the present study provide a comprehensive overview of the metabolic dynamics of developing PLs which suggested that pre anthesis period is the best harvest time for PLs.

Keywords: Perilla leaf, mass spectrometry, metabolomic dynamics, harvest time, multivariate statistical analysis

1. Introduction

Perilla frutescens (L.) Britt. is an annual herbal plant that belongs to the family of Lamiaceae. It is widely cultivated in Asia counties, such as China, Japan, Korea, Vietnam and other regions (Yu et al., 2017; Zhang et al., 2021). Perilla leaves (PLs) are commonly consumed as kitchen herb in salads, sushi, soups, and as spice, garnish, or food colorant. PLs are also used as traditional Chinese medicine to relieve exterior, dispersing cold, ease stomach pain, reduce phlegm and relieve cough and asthma (Ha et al., 2012; Igarashi and Miyazaki, 2013). Phytochemical studies indicated PLs were rich in essential oils, flavonoids, fatty acids, phenolic compounds, etc (Ahmed, 2018). Compounds of PLs showed various biological activities such as antioxidant, antimicrobial, anti-allergic, antidepressant, anti-inflammatory, and anticancer effects (Banno et al., 2004; Ghimire et al., 2019; Wang et al., 2021; Yang et al., 2021). PLs has been used as a natural herbal medicine for treatment of depression-related disease, asthma, tumors, coughs, allergies, intoxication, fever, chills, headache, stuffy nose, and some intestinal disorders (Ito et al., 2011; Kim et al., 2012; Zhou et al., 2021). Owing to these health benefits, the food and pharmaceutical industries are increasingly interested in PLs.

The pharmacological activities of perilla are closely related to its chemical constituents. Some studies have revealed that great dynamic variation in the nutritional components and phytochemical substances might occur during plant development. Ghimire et al. (Ghimire et al., 2017) compared the total volatile contents of eighteen accessions of PLs and most of them were higher before the flowering time than at the flowering stage. Luo et al. (Luo et al., 2021) invested variation of two phenolic acids and six flavonoids during PLs development and suggested to harvest PLs at different times basing on the targeted metabolites. Peiretti et al. (Peiretti, 2011) evaluated perilla quality according to the content of fatty acid, fiber, crude protein, organic matter and gross energy during the growth cycle of perilla. According to their result, it is better to harvest perilla at around two months after sowing. Though these studies provided a general feature of perilla nutritional contents, a more comprehensive and detailed dynamic profile of developing PLs is still essential for providing more information to determine the harvest time according to different application.

In this study, mass spectrometry (MS) based high throughput metabolomic platforms were applied to ascertain the dynamic trajectory of complex ingredients of PLs during developmental process. In addition, multiple statistical analysis methods, including principal component analysis (PCA) and Clustering analysis (CA) were used to clarify the dynamic patterns of the detected metabolites. These data provide data support for determining the best harvest time of perilla leaves.

2. Materials and methods

2.1. Chemicals and reagents

HPLC grade methanol (MeOH), acetonitrile (ACN) and formic acid were purchased from Fisher Scientific (Pittsburgh, PA, United States) Ultrapure water was prepared by Synergy water purification system (Millipore, Billerica, United States). The reserpine standards (HPLC grade) and GC grade derivatizing regent MSTFA (N-methyl-N-(trimethylsilyl)trifluoroacetamide), methoxyamine hydrochloride were purchased from Sigma-Aldrich (St. Louis, MO, USA). Chemical reagent n-hexane (GC grade) and Anhydrous pyridine (GC grade) were obtained from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Salicylic acid, luteolin, apigenin and rosmarinic acid standards were provided by Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China). Reference standards of luteolin-7-O-glucoside, scutellarin, luteolin-7-O-glucuronide, apigenin-7-glucoside and apigenin-7-O-glucuronide were purchased from Shanghai Standard Technology Co., Ltd. (Shanghai, China). The purities of all standards were determined to be higher than 98%. Other chemicals and reagents were analytical grade.

2.2. Plant materials

The PLs were randomly collected from Perilla frutescens (L.) Britt. cultivated in the plant base of Hebei Academy of Agriculture and Forestry Sciences in Shijiazhuang (China 38°06′41.7′′ N, 114°45′35.8′′E) in mid May 2019, and the samples were collected semimonthly from July 2019 to October 2019. The mean annual temperature was 14.4°C, mean annual humidity was 62%, mean annual precipitation was 422.6 mm, mean annual sunshine hours was 2235.4 hours. Growth process of perilla was performed using manual fertilization, therefore, soil is rich in organic elements. Three biological replicates were collected for each developmental phase ( Table 1 ). The plant was identified by professor Yuguang Zheng (Hebei Chemical and Pharmaceutical College, China), and voucher specimens were deposited in Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine. The harvested leaves were air-dried in the dark at room temperature for 2 weeks to acquire consistently low water content.

Table 1.

Information of samples collected at different developmental times.

No. Collection date Growth phase (Wei et al., 2017) Sample number Specimen No.
1 July 15, 2019 nutritional phase stage 1-1 PF2019071501
2 July 15, 2019 nutritional phase stage 1-2 PF2019071502
3 July 15, 2019 nutritional phase stage 1-3 PF2019071503
4 July 30, 2019 nutritional phase stage 2-1 PF2019073001
5 July 30, 2019 nutritional phase stage 2-2 PF2019073002
6 July 30, 2019 nutritional phase stage 2-3 PF2019073003
7 August 15, 2019 nutritional phase stage 3-1 PF2019081501
8 August 15, 2019 nutritional phase stage 3-2 PF2019081502
9 August 15, 2019 nutritional phase stage 3-3 PF2019081503
10 August 30, 2019 nutritional phase stage 4-1 PF2019083001
11 August 30, 2019 nutritional phase stage 4-2 PF2019083002
12 August 30, 2019 nutritional phase stage 4-3 PF2019083003
13 September 15, 2019 flowering phase stage 5-1 PF2019091501
14 September 15, 2019 flowering phase stage 5-2 PF2019091502
15 September 15, 2019 flowering phase stage 5-3 PF2019091503
16 September 30, 2019 flowering phase stage 6-1 PF2019093001
17 September 30, 2019 flowering phase stage 6-2 PF2019093002
18 September 30, 2019 flowering phase stage 6-3 PF2019093003
19 October 15, 2019 fruiting phase stage 7-1 PF2019101501
20 October 15, 2019 fruiting phase stage 7-2 PF2019101502
21 October 15, 2019 fruiting phase stage 7-3 PF2019101503
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2.3. Analysis of the volatile metabolites by GC-MS

2.3.1. Sample pretreatment

The dried PF samples were pulverized with grinder (FW100, Taisite, Tianjin, China), and screened through 60 mesh sieves. 100 mg of each accurately weighted pulverized sample were thoroughly mixed with 1 mL of n-hexane then sonicated (300 W, 40 kHz) 15 min at room temperature. The extracted solution was centrifuged at 13000 rpm at room temperature for 10 min. The supernatant was injected into the GC-MS for analysis.

2.3.2. Instrument parameters

The GC-MS analysis was performed with an Agilent 7890B-5977B GC-MS (Agilent, Santa Clara, CA, USA) coupled with a HP-5MS capillary column (30 m × 0.25 mm, 0.25 μm film thickness, Agilent, Santa Clara, CA, USA). Helium (≥ 99.999%) was used as carrier gas at a constant flow rate of 1.0 mL·min-1. 1 μL of the prepared supernatant solution was injected in split-mode with the split ratio set to 2:1 at a temperature of 250°C. The oven temperature program was initially set at 45°C, then increased to 100°C at a rate of 10°C·min-1, and subsequently increased to 280°C at a rate of 4°C·min-1, finally held for 10 min. The electronic ionization voltage of electron-impact (EI) ion source was 70 eV. The mass spectrometer was operated in full scan mode with a scanning range of 50-550 m/z. n-Alkane standard solution (C8-C20, 40 mg·L-1, Sigma-Aldrich, Switzerland) was analyzed under the same condition for retention index (RI) calculation.

2.4. Analysis of non-volatile metabolites by GC-MS

2.4.1. Sample pretreatment

An integrative extraction of primary metabolites and secondary metabolites was performed according to a universal extraction protocol (Weckwerth et al., 2010; Mari et al., 2013; Wang et al., 2017) with some modifications. 100 mg of each pulverized samples were extracted with 1 mL of extraction solution (methanol: water: formic acid = 70:28:2) by sonication 15 min. The crude extract was centrifuged at 13,000 rpm at room temperature for 10 min. 50 μL of the supernatant together with 20 μL of salicylic acid (1 mg·mL-1, internal standard) was dried using a SpeedVac (Thermo Scientific, Inc., Bremen, Germany) at 5000 rpm and 40°C for 90 min. Methoxyamination of the carbonyl groups was performed by adding 20 μL of methoxyamine hydrochloride (40 mg·mL-1) in pyridine to each sample followed by incubation in metal bath at 30°C for 90 min. Subsequently, 80 μL of MSTFA (N-Methyl-N-(trimethylsilyl) trifluoroacetamide) was added and the mixtures were incubated at 37°C for 30 min. The derivatized samples were centrifuged at 13,000 rpm at room temperature for 10 min with the supernatants prepared for GC-MS analysis.

2.4.2. Instrument parameters

Aforementioned GC-MS instrument and column (see 2.3.2) was also applied for analysis of derivatized samples. 1 μL of the derivatized sample was injected using 5:1 split-mode at a temperature of 250°C. The temperature gradient program was as follows: Initial temperature was 80°C, increased to 200°C at a rate of 10°C·min-1; then increased to 250°C at a rate of 6°C·min-1; subsequently increased to 310°C at a rate of 6°C·min-1 and hold at 310°C for 5 min. EI ion source was adjusted to 230°C with electronic energy of 70 eV. The mass spectrometer was determined by the full-scan method ranging from 50 to 550 (m/z). n-Alkane standard solution (C8-C20, 40 mg·L-1, Sigma-Aldrich, Switzerland) was analyzed under the same condition for retention index (RI) calculation.

2.5. Analysis of the non-volatile metabolites by LC-MS

2.5.1. Sample pretreatment

100 μL of the above mentioned crude extract (see in 2.4.1) was mixed with 100 μL of 5 μg·mL-1 reserpine (internal standard) and diluted with 800 μL of extract solution then centrifuged at 13,000 rpm at room temperature for 10 min with the supernatants prepared for the LC-MS analysis.

2.5.2. Instrument parameters

The UHPLC-Q/TOF-MS analysis was performed on an Agilent 1290 UHPLC system coupled with an Agilent 6545 quadrupole time-of-flight mass spectrometer system (Agilent, Santa Clara, CA, United States). Chromatographic separation was performed on an Agilent ZORBAX SB C18 column (4.6 × 50 mm, 1.8 μm).

UHPLC chromatographic conditions: the 0.5 μL of prepared samples were loaded on an Agilent 1290 UHPLC system and eluted with 0.1% formic-water (mobile phase A) and acetonitrile (mobile phase B) in the following gradient: 0-2 min, 12% B; 2-26 min, 12%-24% B; 26-35 min, 24%-50% B; 35-38 min, 50%-100% B; 38-45 min, 100% B. The flow rate was maintained at 0.4 mL·min-1, the column temperature was set at 25°C.

The MS acquisition parameters were referred to Chang et al. (2021) with minor modifications. The capillary voltage was set to 4000 V; and the collision energy was 20 eV and 35 eV. The analysis was operated in positive mode with the mass range of m/z 50-1000 Da.

2.6. Data processing and multivariate statistical analysis

For qualitative analysis, the metabolites detected by GC-MS with a similarity more than 80% to the NIST17 standard library were identified using the Agilent MassHunter analysis program (Agilent, Santa Clara, CA, USA). The RI of all the identified compounds were calculated by comparing their corresponding peak retention time to that of n-alkanes (C8–C20) (Chaturvedula and Prakash, 2013; Ma et al., 2014). The identification of detected metabolites in the LC-MS analysis was based on their accurate precursor masses and fragment masses. For quantitative analysis, the integrated peak area was considered to be a variable for analysis and normalized to internal standard. The combined GC-MS and LC-MS dataset was transformed to -1~1 by Min-Max Normalization method. SIMCA P13 software (Umetrics, Umea, Sweden) was used for principal component analysis (PCA). Cluster analysis (CA) and heatmap was performed with Origin Pro 2020 (OriginLab Corporation, USA) software. Duncan’s test was performed with IBM SPSS Statistics 23.0 (IBM, USA) software.

3. Results and discussion

3.1. Identification of detected metabolites

The typical total ion chromatograms (TICs) of GC-MS, pre-column derivatized GC-MS and LC-MS showed metabolomic profiles of PLs ( Figures 1A-C ). With reference to the NIST17 database, 47 volatile metabolites including aldehydes, ketones, alcohols, fatty acids, steroids and others ( Table 2 ) were identified according to their retention times and mass spectrums. 51 peaks in Figure 1B were identified including sugars, amino acids, organic acids, fatty acids, and phenolic compounds ( Table 3 ). The identification of non-volatile metabolites form LC-MS data were based on their precursor ions and fragmentation patterns. 28 metabolites, mainly flavonoids and anthocyanidins, were identified with their detail information such as retention time, chemical formula, ppm errors and fragment ions were listed in Table 4 . Among the putatively identified compounds, eight metabolites (luteoloside (peak C11), scutellarin (peak C16), luteolin-7-O-glucuronide (peak C17), apigenin-7-O-glucoside (peak C18) apigenin-7-O-glucuronide (peak C23), rosmarinic acid (peak C24), luteolin (peak C26), apigenin (peak C27)) were confirmed with reference substances ( Figure 1D ). The chemical fingerprints showed distinct differences in the chemical composition of PLs at different harvesting ( Figure 2 ).

Figure 1.

Figure 1

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The typical total ion chromatograms of PLs by GC-MS and LC-MS. (A) TIC of volatile metabolites in pooled samples by GC-MS; (B) TIC of non-volatile metabolites in pooled samples by GC-MS after derivatization; (C) TIC of non-volatile metabolites in pooled samples by LC-MS; (D) TIC of reference substances by LC-MS.

Table 2.

Identification of volatile compounds analyzed by GC-MS.

No. RT (min) Compounds MF MW Class RI
A1 5.01 α-Pinene C10H16 136 Bicyclic monoterpenoids 918
A2 5.65 Pseudolimonene C10H16 136 Mononcyclic monoterpenoids 964
A3 6.44 D-Limonene C10H16 136 Mononcyclic monoterpenoids 1017
A4 7.51 α-Terpinene C10H16 136 Mononcyclic monoterpenoids 1082
A5 7.69 Linalool C10H18O 154 Acyclic monoterpenoids 1093
A6 9.71 α-Terpineol C10H18O 154 Mononcyclic monoterpenoids 1193
A7 10.53 Nerol C10H18O 154 Acyclic monoterpenoids 1228
A8 11.21 Perilla ketone C10H14O2 166 Acyclic monoterpenoids 1257
A9 11.71 Shisool C10H18O 154 Mononcyclic monoterpenoids 1277
A10 11.86 Perillaldehyde C10H14O 150 Mononcyclic monoterpenoids 1283
A11 13.43 γ-Elemene C15H24 204 Mononcyclic sesquiterpenoids 1344
A12 14.51 α-Copaene C15H24 204 Tricyclic sesquiterpenoids 1385
A13 14.76 β-Bourbonene C15H24 204 Tricyclic sesquiterpenoids 1394
A14 14.93 β-Elemene C15H24 204 Mononcyclic sesquiterpenoids 1401
A15 15.77 β-Caryophyllene C15H24 204 Bicyclic sesquiterpenoids 1431
A16 16.16 Perillic acid C10H14O2 166 Mononcyclic monoterpenoids 1446
A17 16.68 α-Humulene C15H24 204 Mononcyclic sesquiterpenoids 1465
A18 17.44 β-Copaene C15H24 204 Tricyclic sesquiterpenoids 1492
A19 17.73 Cis-α-Bergamotene C15H24 204 Bicyclic sesquiterpenoids 1503
A20 17.87 Bicyclogermacrene C15H24 204 Bicyclic sesquiterpenoids 1508
A21 18.09 α-Farnesene C15H24 204 Acyclic sesquiterpenoids 1516
A22 18.51 Myristicin C11H12O3 192 Aromatic compounds 1531
A23 18.59 δ-Cadinene C15H24 204 Bicyclic sesquiterpenoids 1534
A24 19.43 Elemicin C12H16O3 208 Aromatic compounds 1565
A25 19.63 Nerolidol C15H26O 222 Acyclic sesquiterpenoids 1572
A26 20.11 Espatulenol C15H24O 220 Tricyclic sesquiterpenoids 1590
A27 20.27 β-Caryophyllene oxide C15H24O 220 Bicyclic sesquiterpenoids 1595
A28 20.59 α-Patchoulene C15H24 204 Tricyclic sesquiterpenoids 1607
A29 22.16 Isoelemicin C12H16O3 208 Aromatic compounds 1666
A30 26.89 Phytyl acetate C22H42O2 338 Acyclic diterpenoids 1849
A31 27.23 Pentadecanone C18H36O 268 Acyclic sesquiterpenoids 1862
A32 27.52 Myristic acid C14H28O2 228 Aliphatic compounds 1874
A33 31.96 Palmitic acid C16H32O2 256 Aliphatic compounds 2059
A34 33.38 Phytol C20H40O 296 Acyclic diterpenoids 2118
A35 35.75 α-Linolenic acid C18H30O2 278 Aliphatic compounds 2217
A36 45.07 Heptacosane C27H56 380 Aliphatic compounds 2607
A37 47.41 Squalene C30H50 410 Acyclic triterpenoids 2705
A38 48.55 Nonacosane C29H60 408 Aliphatic compounds 2753
A39 49.16 1-Heptatriacotanol C37H76O 536 Aliphatic compounds 2779
A40 51.90 Hentriacontane C31H64 436 Aliphatic compounds 2893
A41 52.60 α-Tocopherol C29H50O2 430 Mononcyclic triterpenoids 2923
A42 54.44 Campesterol C28H48O 400 Steroids 2999
A43 55.19 β-Stigmasterol C29H48O 412 Steroids 3031
A44 56.32 Dotriacontane C32H66 450 Aliphatic compounds 3078
A45 56.67 γ-Sitosterol C29H50O 414 Steroids 3093
A46 58.29 β-Amyrone C30H48O 424 Tetracyclic triterpenoids 3161
A47 58.69 α-Amyrin C30H50O 426 Tetracyclic triterpenoids 3178
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RT, retention time.

MF, molecular formula.

MW, molecular weight.

RI, retention index.

Table 3.

Identification of non-volatile metabolites analyzed by pre-column derivatization combining with GC-MS.

No. RT Compounds MF MW RI
B1 4.03 Lactic acid (2TMS) C9H22O3Si2 234 1063
B2 4.49 L-Alanine (2TMS) C9H23NO2Si2 233 1102
B3 4.67 Glycine (TMS) C8H21NO2Si2 219 1118
B4 4.94 Oxalic acid (2TMS) C8H18O4Si2 234 1141
B5 5.71 Propanedioic acid (2TMS) C9H20O4Si2 248 1206
B6 5.88 L-Valine (2TMS) C11H27NO2Si2 261 1220
B7 6.38 L-Serine (2TMS) C9H23NO3Si2 249 1260
B8 6.57 L-Leucine (2TMS) C12H29NO2Si2 275 1275
B9 6.62 Glycerol (3TMS) C12H32O3Si3 308 1280
B10 6.86 L-Isoleucine (TMS) C12H29NO2Si2 275 1299
B11 6.91 L-Proline (2TMS) C11H25NO2Si2 259 1303
B12 7.03 Glycine (3TMS) C11H29NO2Si3 291 1312
B13 7.36 Glyceric acid (3TMS) C12H30O4Si3 322 1339
B14 7.73 L-Serine (3TMS) C12H31NO3Si3 321 1368
B15 8.08 L-Threonine (3TMS) C13H33NO3Si3 335 1396
B16 9.35 Malic acid (3TMS) C13H30O5Si3 350 1499
B17 9.61 Salicylic acid (2TMS) C13H22O3Si2 282 1522
B18 9.73 L-Aspartic acid (3TMS) C13H31NO4Si3 349 1532
B19 9.81 γ-Aminobutanoic acid (3TMS) C13H33NO2Si3 319 1539
B20 10.27 L-Glutamic acid (3TMS) C14H33NO4Si3 363 1578
B21 10.97 L-Phenylalanine (2TMS) C15H27NO2Si2 309 1639
B22 11.05 L-Asparagine (4TMS) C16H40N2O3Si4 420 1646
B23 11.19 Tartaric acid (4TMS) C16H38O6Si4 438 1659
B24 11.48 L-Asparagine (3TMS) C13H32N2O3Si3 348 1685
B25 12.14 Xylitol (5TMS) C20H52O5Si5 512 1745
B26 12.84 L-Glutamine (3TMS) C14H34N2O3Si3 362 1810
B27 13.21 Citric acid (4TMS) C18H40O7Si4 480 1843
B28 13.95 D-Fructose (5TMS) C21H52O6Si5 540 1909
B29 14.18 D-Galactose (5TMS) C21H52O6Si5 540 1930
B30 14.26 D-Glucose (5TMS) C22H55NO6Si5 569 1936
B31 14.32 L-Lysine (4TMS) C18H46N2O2Si4 434 1942
B32 14.54 L-Tyrosine (3TMS) C18H35NO3Si3 397 1961
B33 14.64 D-Glucitol (6TMS) C24H62O6Si6 614 1970
B34 14.72 D-Sorbitol (6TMS) C24H62O6Si6 614 1977
B35 15.24 D-Tagatose (6TMS) C24H61NO6Si6 627 2022
B36 15.53 D-Gluconic acid (6TMS) C24H60O7Si6 628 2047
B37 16.12 Palmitic acid (TMS) C19H40O2Si 328 2098
B38 16.62 Myo-Inositol (6TMS) C24H60O6Si6 612 2142
B39 16.91 Caffeic acid (3TMS) C18H32O4Si3 396 2167
B40 17.16 Oleic acid (TMS) C21H40O2Si 352 2189
B41 17.81 α-Linolenic acid (TMS) C21H38O2Si 350 2245
B42 18.09 Stearic acid (TMS) C21H44O2Si 356 2270
B43 20.23 D-Galacturonic acid (5TMS) C21H50O7Si5 554 2456
B44 22.57 Lactulose (8TMS) C36H86O11Si8 918 2660
B45 23.25 Sucrose (8TMS) C36H86O11Si8 918 2719
B46 23.93 D-Lactose (8TMS) C36H86O11Si8 918 2778
B47 24.19 Maltose (8TMS) C36H86O11Si8 918 2801
B48 25.25 D-Cellobiose (8TMS) C36H86O11Si8 918 2893
B49 26.65 Galactinol (9TMS) C38H92O11Si9 976 3015
B50 29.49 Rosmarinic acid (5TMS) C33H56O8Si5 720 3262
B51 30.95 D-Mannose (8TMS) C36H86O11Si8 918 3389
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RT, retention time.

MF, molecular formula.

MW, molecular weight.

RI, retention index.

Table 4.

Identification of non-volatile metabolites analyzed by UPLC-ESI-Q-TOF-MS/MS.

No. RT (min) Adduct ions (m/z) Molecular ions(m/z) Fragment ions in MS/MS (m/z) Molecular formula Molecular weight Error (ppm) Identification References
C1 4.06 [M+NH4]+ 256.0813 237.9925, 196.9654, 181.0494 C11H10O6 238.0415 -0.99 Acetyloxycaffeic acid (Ma et al., 2014)
C2 4.44 [M+NH4]+ 344.1340 165.0546, 147.0442, 119.0490 C15H18O8 326.1002 0.14 Coumaric acid-4-O-glucoside (Chaturvedula and Prakash, 2013; Ma et al., 2014)
C3 5.69 [M+H]+ 209.1535 191.1425, 167.1432, 109.0650 C11H12O4 208.1460 -0.89 Caffeic acid ethyl ester (Chaturvedula and Prakash, 2013; Ma et al., 2014)
C4 5.98 [M+NH4]+ 406.2073 227.1279, 209.1172, 191.1064, 167.1068, 149.0959, 131.0852 C18H28O9 388.1733 0.08 Tuberonic acid glucoside (Quirantes-Piné et al., 2010)
C5 6.26 [M+H]+ 227.1279 191.1071, 163.1112, 149.0964, 131.0855, 107.0857 C12H18O4 226.1205 0.63 Tuberonic acid (Quirantes-Piné et al., 2010)
C6 6.41 [M+H]+ 595.1661 577.1559, 457.1138, 379.0818, 325.0710, 295.0601 C27H30O15 594.1589 0.68 Apigenin-7-O-dilgucoside (Yamazaki et al., 2003; Zheng et al., 2020)
C7 8.03 [M+H]+ 639.1201 463.0880, 287.0554 C27H26O18 638.1129 1.52 Scutellarin-7-O-diglucuronide (Yamazaki et al., 2003; Kaufmann et al., 2016)
C8 9.11 [M+H]+ 639.1198 463.0876, 287.0553 C27H26O18 638.1126 1.14 Luteolin-7-O-diglucuronide (Meng et al., 2008; He et al., 2015)
C9 10.73 [M+H]+ 479.0822 303.0501 C21H18O13 478.0749 0.28 Quercetin-3-O-glucuronide (Kaufmann et al., 2016)
C10 10.90 [M+H]+ 757.1977 595.1453, 449.1088, 287.0558, C36H36O18 756.1907 0.68 Cis-shisonin (Yamazaki et al., 2003; He et al., 2015)
C11 11.59 [M+H]+ 449.1087 287.0555, 153.0181 C21H20O11 448.1013 1.74 Luteoloside* (Meng et al., 2008; Kaufmann et al., 2016)
C12 11.76 [M+NH4]+ 374.1449 231.0504, 159.0287, 145.0494, 127.0389 C15H16O10 356.1110 0.83 Caffeic acid-3-O-glucuronide (Zheng et al., 2020)
C13 12.44 [M+H]+ 623.1252 447.0927, 271.0607, 141.0182 C27H26O17 622.1178 1.36 Apigenin-7-O-diglucuronide (Meng et al., 2008; Kaufmann et al., 2016)
C14 13.15 [M+H]+ 465.1029 303.0505, 285.0399, 85.0254 C21H20O12 464.0956 0.24 Quercetin-3-O-glucoside (Pereira et al., 2012; Kaufmann et al., 2016)
C15 14.73 [M+H]+ 757.1975 595.1442, 449.1076, 287.0547 C36H36O18 756.1901 -0.04 Shisonin (Yamazaki et al., 2003; He et al., 2015)
C16 15.00 [M+H]+ 463.0877 287.0554 C21H18O12 462.0805 1.49 Scutellarin* (Yamazaki et al., 2003; Kaufmann et al., 2016)
C17 15.28 [M+H]+ 463.0876 287.0555 C21H18O12 462.0803 0.98 Luteolin-7-O-glucuronide* (Kaufmann et al., 2016)
C18 15.57 [M+H]+ 433.1132 271.0604, 153.0181, 85.0282 C21H20O10 432.1059 0.68 Apigenin-7-O-glucoside* (Yamazaki et al., 2003; Kaufmann et al., 2016)
C19 17.39 [M+H]+ 317.1021 197.0446, 182.0214, 147.0440 C13H16O9 316.0948 0.31 Protocatechuic acid-3-O-glucoside (Yamazaki et al., 2003; Zheng et al., 2020)
C20 18.51 [M+H]+ 843.1985 595.1451, 535.1078, 287.0547 C39H38O21 842.1912 0.73 Malonyl-shisonin (Yamazaki et al., 2003; He et al., 2015)
C21 18.88 [M+NH4]+ 392.2282 195.1380, 177.1271, 149.1328, 135.1169 C19H18O8 374.1944 0.89 Rosmarinic acid methyl ester (Kaufmann et al., 2016; Zheng et al., 2020)
C22 19.67 [M+NH4]+ 738.2030 523.1245, 343.0818, 181.0496, 163.0390 C36H32O16 720.1693 0.43 Caffeic acid tetramer (Zheng et al., 2020)
C23 20.11 [M+H]+ 447.0927 271.0597, 153.0176 C21H18O11 446.0854 1.12 Apigenin-7-O-glucuronide* (Yamazaki et al., 2003; Kaufmann et al., 2016)
C24 21.70 [2M+Na]+ 743.1578 383.0746, 221.0421, 203.0315, 185.0207 C18H16O8 360.3150 0.5 Rosmarinic acid* (Zhou et al., 2014; Kaufmann et al., 2016)
C25 29.25 [M+H]+ 287.0553 241.0497, 153.0183, 135.0439, C15H10O6 286.0480 0.87 Luteolin* (Lee et al., 2013; Zhou et al., 2014)
C26 29.40 [M+H]+ 301.1075 197.0446, 182.0211, 103.0540 C16H12O6 300.1002 1.31 Chrysoeriol (Lee et al., 2013; Guan et al., 2014)
C27 32.00 [M+H]+ 271.0601 243.0652, 153.0180, 119.0492 C15H10O5 270.2370 0.14 Apigenin* (Pereira et al., 2012; Lee et al., 2013)
C28 34.64 [M+H]+ 609.2814 577.2529, 448.1980, 397.2128, C33H40N2O9 608.2739 -1.9 Reserpine Internal standard
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RT, retention time.

“*”, confirmed with reference substances.

Figure 2.

Figure 2

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The chemical fingerprints of PLs at different harvesting by GC-MS and LC-MS. (A) Fingerprints of volatile metabolites by GC-MS; (B) Fingerprints of non-volatile metabolites by GC-MS after derivatization; (C) Fingerprints of non-volatile metabolites by LC-MS.

3.2. Principal component analysis (PCA) reveals metabolic variation of PLs at different harvest times

PCA was carried out for an overview of the dataset. In the PCA plot, three biological replicates of each stage were compactly gathered together ( Figure 3 ) while samples at different harvest time were clearly separated indicating metabolomic changes during PLs development. PC1 and PC2 explained 77.7% of the total variance. Samples collected at harvest time 4 were completely separated with samples harvested at other periods on PC1 indicating a special and significant meaning of this harvest period. The loading values of all the metabolites are listed in Table 5 .

Figure 3.

Figure 3

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The principal component analysis (PCA) score plots of of PLs samples at different harvesting times.

Table 5.

The PCA loading values and Duncan’s test result of metabolites identified in developing PLs.

No. Compounds PC1 PC2 Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7
A1 α-Pinene 0.10 -0.04 d bc cd a ab bc cd
A2 Pseudolimonene 0.10 0.00 c c b a b b b
A3 D-limonene 0.08 -0.09 c bc bc a a a b
A4 α-Terpinene 0.09 -0.03 c d d a bc b d
A5 Linalool 0.09 0.08 bc bcd d a b cd e
A6 α-Terpineol 0.11 0.08 de b bc a cd e f
A7 Nerol 0.10 0.07 bcd bc b a d cd e
A8 Perilla ketone 0.05 0.10 bc a a ab bc bc c
A9 Shisool 0.11 -0.09 e e c a bc b d
A10 Perillaldehyde 0.12 0.01 f b c a d d e
A11 γ-Elemene 0.06 -0.12 f f e c b a d
A12 α-Copaene 0.11 -0.08 e d d a b c d
A13 β-Bourbonene 0.11 -0.01 d d d a b c e
A14 β-Elemene 0.10 0.06 c c b a a d d
A15 β-Caryophyllene 0.11 0.06 c b bc a bc bc d
A16 Perillic acid 0.11 0.03 d d b a c d d
A17 α-Humulene 0.10 0.10 c c a a b d f
A18 β-Copaene 0.11 0.05 c c b a c b d
A19 Cis-α-Bergamotene 0.10 0.09 e b c a d f f
A20 Bicyclogermacrene 0.11 0.05 e c b a d d e
A21 α-Farnesene 0.10 -0.07 e de cd a c b cde
A22 Myristicin 0.12 0.00 f de b a c d e
A23 δ-Cadinene 0.10 0.06 c c ab a a bc d
A24 Elemicin 0.11 0.02 cd cd bc a b d d
A25 Nerolidol 0.11 -0.04 e e e a b c d
A26 Espatulenol 0.11 0.08 bc b b a b c d
A27 β-Caryophyllene oxide 0.07 -0.11 d bc c bc a ab bc
A28 α-Patchoulene 0.12 -0.02 f f c a b d e
A29 Isoelemicin 0.11 0.01 cd cd bc a b d cd
A30 Phytyl acetate 0.09 0.05 c b b a c c c
A31 Pentadecanone 0.11 0.01 d c d a b d d
A32 Myristic acid 0.12 0.00 d c c a b c d
A34 Phytol 0.10 0.04 c c b a d cd cd
A36 Heptacosane 0.03 -0.06 c c c c b a d
A37 Squalene 0.12 -0.04 c c b a b b c
A38 Nonacosane 0.09 -0.10 d c c a bc ab bc
A39 1-Heptatriacotanol 0.10 -0.06 d c b a a a cd
A40 Hentriacontane 0.06 -0.14 c b b a a a a
A41 α-Tocopherol 0.11 -0.05 f d c a a b e
A42 Campesterol 0.06 0.11 bc a a a b c c
A43 β-Stigmasterol 0.03 0.12 bc a b bc bc c d
A44 Dotriacontane 0.06 -0.14 d d c b c a bc
A45 γ-Sitosterol 0.07 0.07 d a c b c d d
A46 β-Amyrone 0.06 0.09 e a b c d e e
A47 α-Amyrin 0.02 0.10 cd a b c c d d
B1 Lactic acid 0.11 -0.02 c b b a b b bc
B2 L-Alanine 0.08 -0.13 e e d a b c c
B3&B12 L-Glycine -0.01 0.17 a b c d d e f
B4 Oxalic acid 0.11 -0.03 d c ab a bc c c
B5 Propanedioic acid 0.09 -0.11 d c bc a b b b
B6 L-Valine 0.11 -0.04 d c c a b b c
B8 L-Leucine 0.12 -0.01 e d b a b c de
B9 Glycerol 0.12 0.00 d de c a b d e
B10 L-Isoleucine 0.12 0.00 e e b a c d e
B11 L-Proline 0.10 -0.10 e d c a b bc c
B13 Glyceric acid 0.11 0.03 c ab a a a b c
B7&14 L-Serine 0.01 0.17 a ab b c c d e
B15 L-Threonine 0.12 -0.04 f e b a c d d
B16 Malic acid 0.11 -0.02 d b a a b bc c
B18 L-Aspartic acid 0.10 0.10 d b a a c d e
B19 γ-Aminobutanoic acid 0.11 -0.01 c b a a a b b
B20 L-Glutamic acid 0.11 -0.01 d cd ab a bc bcd cd
B21 L-Phenylalanine -0.02 0.17 a b c d e f f
B22&24 L-Asparagine 0.04 0.15 a a a a b b c
B23 Tartaric acid 0.11 0.06 d b a a c c d
B25 Xylitol 0.01 0.17 a b b b c d d
B26 L-Glutamine 0.11 -0.08 f e b a c c d
B27 Citric acid 0.11 0.04 d ab ab a b c cd
B28 D-Fructose 0.02 -0.16 f e d cd c b a
B29 D-Galactose -0.01 -0.17 e d c c b a a
B30 D-Glucose 0.04 -0.16 e d c b b a a
B31 L-Lysine -0.03 0.17 a b c d e f f
B32 L-Tyrosine 0.00 0.17 a a bc b c d e
B33 D-Glucitol -0.02 0.17 a a b bc c d d
B34 D-Sorbitol -0.07 -0.15 d d e e c b a
B35 D-Tagatose -0.02 -0.15 c c c b b b a
B36 D-Gluconic acid -0.04 -0.16 f e f d c b a
B37 Palmitic acid 0.10 0.07 c b b a c c c
B38 Myo-Inositol -0.04 0.15 a b bc cd cd d d
B39 Caffeic acid 0.11 -0.08 e d b a a b c
B40 Oleic acid 0.12 -0.03 f f b a c d e
B41 α-Linolenic acid 0.10 0.05 c cd b a c de e
B42 Stearic acid 0.12 0.01 d cd b a bc cd d
B43 D-Galacturonic acid -0.04 0.13 a b b b bc c d
B44 Lactulose -0.03 -0.17 f ef de d c b a
B45 Sucrose -0.03 -0.16 d cd cd c c b a
B47 Maltose -0.07 -0.13 c c c c c b a
B46 D-Lactose -0.04 -0.16 e d d d c b a
B48 D-Cellobiose -0.03 -0.13 c b b b b b a
B49 Galactinol -0.03 0.16 a b b c d d e
B51 D-Mannose -0.03 -0.14 e c cd d cd b a
C1 Acetyloxycaffeic acid 0.06 -0.11 e e c b d a c
C2 Coumaric acid-4- O- glucoside 0.11 -0.02 c b a a a a d
C3 Caffeic acid ethyl ester 0.00 -0.15 f g d c e a b
C4 Tuberonic acid glucoside 0.12 -0.01 f e c a b d g
C5 Tuberonic acid 0.12 0.00 f e b a c d f
C6 Apigenin-7-O-dilgucoside 0.12 0.06 e c b a c d f
C7 Scutellarin-7-O-diglucuronide 0.11 -0.01 d e a a b c e
C8 Luteolin-7-O-diglucuronide 0.11 0.02 bc b b a b c c
C9 Quercetin-3-O-glucuronide 0.12 0.02 f d b a c d e
C10 Cis-shisonin 0.08 -0.02 d d d b a c e
C11 Luteoloside 0.12 -0.05 d c c a b c c
C12 Caffeic acid-3-O-glucuronide 0.06 -0.13 f f d b e a c
C13 Apigenin-7-O-diglucuronide 0.13 0.00 d c c a b c d
C14 Quercetin-3-O-glucoside 0.11 0.05 d b a a a c e
C15 Shisonin 0.04 -0.07 e d cd c a b cd
C16 Scutellarin 0.12 -0.01 d c b a b b d
C17 Luteolin-7-O-glucuronide 0.12 -0.01 f d ab a bc cd e
C18 Apigenin-7-O-glucoside 0.12 -0.03 e c b a b c d
C19 Protocatechuic acid-3-O-glucoside 0.10 0.08 e a a a b c d
C20 Malonyl-shisonin 0.06 -0.07 e d cd b a b c
C21 Rosmarinic acid methyl ester 0.06 0.06 c d a b e c f
C22 Caffeic acid tetramer 0.11 -0.05 f d d a b c e
C23 Apigenin-7-O-glucuronide 0.12 -0.03 e c b a b b d
C24 Rosmarinic acid 0.11 -0.09 e d b a b b c
C25 Luteolin 0.11 0.06 d c b a a cd e
C29 Chrysoeriol 0.11 0.08 e c b a d e f
C27 Apigenin 0.12 0.05 d c c a b d e
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a, b, c, d, e, f indicated significant levels according to Duncan’s test (p < 0.05).

3.3. Clustering analysis reveals dynamic patterns of metabolites in PLs during developmental process

To observe the dynamic changes of metabolites in different harvest periods in a more intuitive manner, a heatmap of the 118 different metabolites was obtained ( Figure 4A ).

Figure 4.

Figure 4

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Metabolome dynamics of developing PLs. (A) Overview of the metabolite dynamics with clustering heat map. (B–G) Present the dynamics of volatile oils, sugars, phytosterols and fatty acids, amino acids, phenolic acids and organic acids, derivatives, flavonoids and anthocyanins.

3.3.1. Dynamic patterns of volatile compounds

Volatile oil is a very important and widely studied class of metabolites in perilla. They showed bioactivities such as antibacterial, antiviral, anti-inflammatory, anticarcinogenic, antioxidant, etc (Raut and Karuppayil, 2014). In most flowering plants, the production and emission of volatile metabolites are developmentally regulated and show similar developmental characteristics. Normally, volatile oil accumulates in the early developmental stage when fruits are not mature or before the flowers are ready for pollination. Then a release of volatile components to attract pollinators might cause a decrease of volatile compounds in the early stage of flowering (Dudareva et al., 2000; Dudareva et al., 2013). In the present study, most of volatile oil compounds showed highest level at stage 4 which was pre anthesis period ( Figure 4B ). Only heptacosane and γ-elemene showed the highest level at stage 6 which was a stage before fruiting period ( Figure 4B ). The dynamic patterns of volatile compounds indicated their crucial function in plant pollination and reproduction.

3.3.2. Sugars and derivatives

During photosynthesis, all kinds of carbon is fixed in the forms of sugars and sugar derivatives (Smeekens and Hellmann, 2014; Sakr et al., 2018) Sugars help plants store energy and play essential roles in signalling pathways of plant growth and development. In this study, the main sugars in PLs are D-fructose, D-glucose and sucrose. They accumulated constantly during the developmental process of PLs and were with highest levels in fruiting phase ( Figure 4C ). Most of the sugars and sugar derivatives showed similar dynamic patterns as them ( Figure 4C ). Only five sugar derivatives (xylitol, D-glucitol, myo-inositol, galactinol and galacturonic acid) changed differently, with higher content at early developmental stage and decreased throughout the development process ( Figure 4C ). Accumulation of sugar content during plant development was also observed in Cichorium spinosum (Petropoulos et al., 2018).

3.3.3. Phytosterols and fatty acids

The sterol composition of plants is complex and diverse. The main membrane sterols in higher plants are β-sitosterol, stigmasterol and campesterol (Ruan, 2014). Sterols are not only signal and regulatory molecules involved in plant growth and development, but also play key roles in cell proliferation and differentiation (Guo et al., 1995; Moreau et al., 2018). In this study, all phytosterols were showed the highest level at stage 2, and decreased gradually (γ-sitosterol, β-amyrone, α-amyrin, stigmasterol, campesterol) ( Figure 4D ). This trend may be due to the vigorous metabolism of cells in the nutritional stage.

Fatty acids and lipids provide structural integrity and energy for various metabolic processes (Lim et al., 2017). The predominant fatty acids detected in PLs were palmitic acid, oleic acid and α-linolenic acid which increased pre anthesis period and declined afterwards ( Figure 4D ). Oleic acid and α-linolenic acid are essential unsaturated fatty acids (UFAs) and recommended for consumption for their multiple health benefits, such as anti-obesity (Fan et al., 2020), cardioprotection (Russell et al., 2020), anti-diabetes (Canetti et al., 2014), anti-inflammation (Wang et al., 2020), anti-cancer (Schiessel et al., 2015), neuroprotection (Kumari et al., 2019) and so on. Intake of α-linolenic acid rich P. frutescens leaf powder in Japanese adults showed some cardiovascular protective effects (Hashimoto et al., 2020). Considering the health benefits of these unsaturated fatty acids, stage 4, the pre anthesis period would be suitable harvest time for ensuring high content of these UFAs in perilla leaves.

3.3.4. Amino acids

Amino acids are not only important components for plants to complete their life cycle activities (Paulusma et al., 2022), but also essential nutrients for humans and other animals. PLs are rich in amino acids. Amino acids in PLs showed two distinct dynamic patterns during PLs development. Some amino acids were with higher content at early stages and decreased throughout the developmental process, such as L-serine, L-lysine, L-phenylalanine, L-tyrosine, L-glycine ( Figure 4E ). Other amino acids were showed the highest level at stage 4, and decreased afterwards, such as L-aspartic acid, L-isoleucine, L-threonine, L-leucine, L-glutamine, L-proline, L-valine, L-alanine, etc ( Figure 4E ). Free amino acids could elicit complex gustatory sensation (Kawai et al., 2012), especially the taste of umami. They can bring fresh and brisk tastes to PLs and participate in the formation of aroma substances (Lee et al., 2019). With the maturity and senescence of leaves, there may be two reasons for the decrease of amino acids. First, amino acids might be involved in the synthesis of storage proteins. Second, the complete oxidation of amino acids produces the energy required to meet the special needs of certain organs, such as stressed leaves or roots. The molecular mechanism of regulation of amino acid catabolism in plants is complex and unclear so far (Hildebrandt et al., 2015). Considering the nutritional value and gustatory sensation of amino acids, it would be appropriate to harvest perilla leaves before the pre anthesis period.

3.3.4. Phenolic acids and organic acids

Phenolic acids have various pharmacological activities, such as anti-inflammatory, anti-anxiety, and anti-depressive activities (Tinikul et al., 2018; Deguchi and Ito, 2020). Some of them are connected to the polymer of the cell wall through covalent bonds, which is crucial to the process of plant immune mechanism (Stuper-Szablewska and Perkowski, 2019). The predominant phenolic acids detected in PLs were rosmarinic acid and caffeic acid, which showed highest level at stage 4 ( Figure 4F ).

Organic acids are the intermediate products of cell metabolic tricarboxylic acid (TCA) cycle (Xiao and Wu, 2014). Many environmental stresses stimulate the biosynthesis and release of organic acids. For example, plants secrete organic acids in root exudates to mobilize phosphorus in deficient soil (Panchal et al., 2021). The main organic acids in PLs are lactic acid, malic acid, tartaric acid and citric acid. They also increased at early stages, showed highest level at around pre anthesis period and decreased afterwards ( Figure 4F ). Organic acids contribute to the sourness and fruity taste of plants, while inhibit the bitterness taste (Wang et al., 2021). Therefore, considering the high content of these compounds in PLs at the stage 4, alternative uses for food or pharmaceutical can be proposed.

3.3.5. Flavonoids and natural pigments

Flavonoids play an important role in plant development and defense, have the ability to scavenge reactive oxygen species (ROS) and protect plants against damage from biotic and abiotic stresses (Iwashina, 2003; Pourcel et al., 2007). During perilla leaves development, the detected flavonoids presented an unanimous changing pattern. All the flavonoids accumulated pre anthesis period and showed the highest level at stage 4 ( Figure 4G ). Previous studies reported that flavonoids have many biological functions such as anti-inflammatory, anti-oxidative, anti-diabetic, and anti-hypertensive activities (Kawser Hossain et al., 2016; Jiang et al., 2020).

The color of fruits and flowers is crucial in plant ecology, can attract pollinators and seed-dispersal organisms (Grotewold, 2006). The molecular signals that induce pigment biosynthesis during pollination are unclear, but light plays a central role (Farzad et al., 2002). Natural pigments from PLs have exhibited a wide range of bioactive properties including antioxidant effects, anti-inflammatory effects, etc (Chang et al., 2005; Wang and Stoner, 2008; Lila et al., 2016). Natural pigments detected in PLs including shisonin and its derivatives. They showed the highest level at stage 5 (vigorous flowering period) ( Figure 4G ). According to this result, if the targeted metabolites are these pigments, it is better to harvest PLs during flowering period.

4. Conclusion

In this study, our results showed the advantages of applying an integrated LC-MS and GC-MS metabolomic platforms the evaluation of optimal harvesting period for plants. We employed metabolomic analysis to clarified the evolutionary trajectories and dynamic changes of volatile oil compounds, sugars, flavonoids, amino acids, organic acids, etc. The results of this study provide a theoretical basis for the development of PLs and offer data support for the optimal harvesting period of PLs. Considering the content of most of the nutrients and bioactive components, pre anthesis period is a suitable harvest time for PLs.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

LW and YZ conceived and designed the experiments. JC performed the experiments. JC, GY, and AY analyzed the data. JC wrote the manuscript. LW, YZ and LG revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Natural Science Foundation of Hebei Province (C2020423047); Research Foundation of Hebei Provincial Administration of Traditional Chinese Medicine (2019083); The Innovation Team of Hebei Province Modern Agricultural Industry Technology System (HBCT2018060205).

Acknowledgments

We would like to thank Prof. Chunxiu Wen and her team for providing us the plant materials. We would like to thank the gardeners for their great maintenance of the perilla. We would like to thank all the members in Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province for fruitful discussions.

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.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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