Three‐dimensional spectrochromatographic determination of chlorogenic acid in Melampyrum stenophyllum Boiss. extracts by parallel factor analysis

Zehra Ceren Ertekin; Ayşegül Köroğlu; Erdal Dinç

doi:10.1002/pca.3439

. 2024 Sep 2;36(1):279–288. doi: 10.1002/pca.3439

Three‐dimensional spectrochromatographic determination of chlorogenic acid in Melampyrum stenophyllum Boiss. extracts by parallel factor analysis

Zehra Ceren Ertekin ¹, Ayşegül Köroğlu ², Erdal Dinç ^1,^✉

PMCID: PMC11743061 PMID: 39221871

Abstract

Introduction

Objectives

This study aimed to develop a method to determine chlorogenic acid in Melampyrum stenophyllum Boiss. extracts without complete chromatographic separation, to validate the method, and to cross‐validate assay results against a classical ultra‐performance liquid chromatography (UPLC) method.

Methodology

Ultra‐performance liquid chromatography‐photodiode array (UPLC‐PDA) spectrochromatograms were arranged into a three‐way data cube with dimensions of time, wavelength, and sample and then decomposed using parallel factor analysis to reveal chromatographic, spectral, and concentration profiles. The chromatographic and spectral profiles were used to identify chlorogenic acid in overlapping signals. The relative concentration profile was used to quantify it in the plant extract. The assay results were statistically compared with those from an in‐house classical UPLC method.

Results

Chlorogenic acid was co‐eluted at 1.45 min and quantified as 16.11 mg per gram dry weight of Melampyrum stenophyllum extracts (SD = 0.28), despite significant interference in a 4‐min runtime. The analytical validity was confirmed by recovery calculations from standard solutions and standard addition samples (RSD < 2%), and the t‐test resulted in a p‐value of 0.09 (α = 0.05), indicating no significant difference between the results obtained from mathematical elution and chromatographic separation.

Conclusion

Chlorogenic acid was quantified from plant material accurately despite the co‐elution. Validation and cross‐validation results support the method's applicability.

Keywords: chemometrics, chlorogenic acid, co‐elution, natural products, parallel factor analysis, UPLC

Short abstract

Co‐elution is a common challenge in phytochemical chromatography. Full chromatographic separation often requires extensive optimization, long analysis times, and excessive solvent use. This work proposes using parallel factor analysis to decompose a three‐dimensional dataset obtained from UPLC‐PDA to identify and quantify chlorogenic acid in Melampyrum stenophyllum Boiss. extracts without complete chromatographic separation. The proposed analytical method was validated and cross‐validated with a classical UPLC method in which complete separation was achieved.

1. INTRODUCTION

Chlorogenic acid is a prevalent secondary metabolite in the human diet due to its widespread distribution in various plants. As an ester formed from caffeic acid and quinic acid, chlorogenic acid belongs to the class of polyphenols. ¹ It protects the plants against oxidative stress, pathogens, insect herbivores, and infections. ² , ³ The scientific community has extensively studied chlorogenic acid, revealing its diverse biological effects, such as antioxidant, anti‐inflammatory, antibacterial, antiviral, hypoglycemic, lipid‐lowering, cardioprotective, immunomodulatory, and antimutagenic activities. ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ , ⁹ It has been used in health care, food, and the chemical industry. ¹⁰ , ¹¹ It is commonly found in fruits, vegetables, medicinal plants, and dietary supplements, making it an important component of a healthy diet. It is the main active ingredient of many traditional Chinese herbal compound preparations for antibacterial and anti‐inflammatory purposes. ⁷ Because of its health‐promoting activities, chlorogenic acid has garnered significant attention from both the consumer market and society.

The Orobanchaceae family has about 35 taxa worldwide and 17 genera in Türkiye. ¹² Among them, the genus Melampyrum L. is widespread in Asia and Europe, and several species are distributed naturally in Türkiye (Melampyrum arvense L., M. cristatum L., M. pratense L., M. elatius Reut. ex Boiss., M. stenophyllum Boiss. [Syn.: M. arvense L. var. elatius Boiss.]). ¹² , ¹³ M. arvense L. var. elatius had been reported to be endemic to Türkiye, ¹² but recent data show that it is also distributed in North Caucasus as well. ¹³ The variety M. arvense L. var. elatius has been elevated to species rank, and its name has been changed as M. stenophyllum Boiss. ¹³

Melampyrum species are locally known as “inekbuğdayı, tilkibuğdayı, pişmezot” in Türkiye. ¹² Although no ethnobotanical use of Melampyrum stenophyllum was recorded in Türkiye, Melampyrum species are used in traditional Austrian medicine for the treatment of gout and rheumatism ¹⁴ and in traditional Romanian medicine for rheumatic disorders and skin infections. ¹⁴ , ¹⁵ The genus Melampyrum is rich in species containing iridoid glycosides, flavonoids, phenolcarboxylic acids, and pyrrolizidine alkaloids. ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ , ²¹ , ²² Several pharmacological effects of Melampyrum species have been reported, such as anti‐inflammatory, antimicrobial, antioxidant, soothing, and antiprotozoal activities. ¹⁴ , ¹⁵ , ¹⁹ , ²² , ²³

Kırmızıbekmez et al. previously isolated iridoid glucosides (aucubin, melampyroside, mussaenoside, mussaenosidic acid, 8‐epi‐loganin), flavonoids (apigenin, luteolin, luteolin 7‐O‐β‐glucopyranoside), fatty acids, β‐Sitosterol, dehydrodiconiferyl alcohol 9‐O‐β‐glucopyranoside, and benzoic acid from Melampyrum arvense var. elatius methanolic extract and then evaluated the antiprotozoal activity of the methanol extract, its subextracts, and the metabolites isolated from them. ¹⁹ The in vitro antimicrobial and antioxidant activities of the species were investigated by Karadağ and Tosun. Phytochemical analysis of ethyl acetate extract revealed phenolic compounds chlorogenic acid, caffeic acid, luteolin‐7‐O‐glycoside, coumaric acid, ferulic acid, and quercetin. ²⁴

Extraction and quantification of bioactive molecules from plants are crucial tasks. These processes are important for standardization, drug discovery, and further scientific investigation, as they play a significant role in the food, pharmaceutical, cosmetic, and chemical industries. Extraction of secondary metabolites from plants requires procedures based on the use of different solvent systems. Although extraction removes the vast majority of unwanted and inactive constituents of the plant sample, the obtained plant extract is still a complex sample containing several compounds and sample matrix. Therefore, the analysis of the plant extracts requires the use of powerful analytical techniques to avoid laborious sample preparation techniques and additional experimental efforts. High‐performance liquid chromatography (HPLC) is one of the most frequently used methods for analyzing natural products and plant extracts. ²⁵ , ²⁶ , ²⁷ , ²⁸ Ultra‐performance liquid chromatography (UPLC) analysis is a more advanced analytical technique than HPLC, providing better chromatographic resolution, decreasing runtime, and reducing solvent consumption. ²⁸ , ²⁹ , ³⁰ However, the complexity of plant extracts and the similar physicochemical properties of the compounds of interest give rise to co‐eluted peaks, a common problem encountered in both HPLC and UPLC. In this case, achieving desirable elution of components in the analyzed extract requires additional experimental studies to determine the optimal chromatographic parameters, resulting in consumption of more resources. An improved approach to resolve this problem is the mathematical elution of analytes based on a three‐way analysis of the data. ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶ Among the various three‐way analysis models, parallel factor analysis (PARAFAC) is one of the most popular ones. ³⁷ , ³⁸ , ³⁹ , ⁴⁰ It is robust and easy to interpret and provides a unique solution. ⁴¹ , ⁴² , ⁴³ , ⁴⁴ Indeed, the mathematical elution of the compounds of interest by PARAFAC offers a significant advantage for analyzing complex samples, such as plant extracts. This strategy provides shorter analysis times, lower costs, and reduced waste and is a shortcut for chromatographic method development because it does not require complete chromatographic separation.

Methanol extraction usually provides the highest total phenolic content ⁴⁵ , ⁴⁶ and is commonly used for total polar extraction for the phytochemical analysis of phenolic compounds. The limited literature on Melampyrum stenophyllum has not reported a common and expected secondary metabolite, chlorogenic acid, in methanol extracts. ¹⁹ , ²⁴ We hypothesized that chlorogenic was present in M. stenophyllum, and chromatographic analysis of its methanol extract could result in co‐elution. Therefore, we aimed to develop an analytical method for the determination of chlorogenic acid in M. stenophyllum methanol extracts by liquid chromatography despite its possible co‐elution with other constituents resulting from strong matrix effect. For this purpose, a new three‐way analysis method was developed for the mathematical elution and quantification of chlorogenic acid in Melampyrum stenophyllum extract. The method was based on implementing PARAFAC on the three‐dimensional UPLC‐PDA data array. A new classical in‐house UPLC method ensuring the complete chromatographic separation of chlorogenic acid was also developed to evaluate and compare the performance of the proposed PARAFAC‐UPLC method. Both chromatographic methods were validated by analyzing independent test samples and standard addition samples.

2. EXPERIMENTAL

2.1. Materials

A Waters H‐Class Acquity UPLC™ system (Waters, MA, USA), equipped with a quaternary pump, auto sampler, and photodiode array detector, was used for the chromatographic analyses. The analytical UPLC BEH C18 column (100 mm × 2.1 mm i.d., 1.7 μm) was procured from Waters (MA, USA), as well. Acrodisc polytetrafluoroethylene (PTFE) syringe filters (Pall Corporation, NY, USA) were used to filter the solutions prior to chromatographic injection. Sartorius cellulose nitrate membrane filters (pore size 0.2 μm) were used to filter the aqueous portion of the mobile phase.

Standard chlorogenic acid (CAS:327‐97‐9) (>98%) was purchased from Tokyo Chemical Industry Co. Ltd (Tokyo, Japan). Methanol (≥99.8%), gradient grade acetonitrile, and trichloroacetic acid (≥99.0%) were purchased from Sigma‐Aldrich (St. Louis, MO, USA). Ultra‐pure water was obtained from the Milli‐Q water filtration purification system (Millipore, MA, USA). A rotary evaporator from Buchi (Flawil, Switzerland) was used.

2.2. Plant material and plant extracts

The plant material was collected from Artvin, Türkiye in 2013. Plant species were identified by Prof. Dr. Ayşegül Köroğlu and a voucher specimen was deposited in the Ankara University Faculty of Pharmacy Herbarium (AEF 26637), Türkiye. The aerial parts of Melampyrum stenophyllum were dried at room temperature in shade. Dried aerial parts were grounded, and 10 g of this powder was macerated with 100‐mL methanol (≥99.8%) for 8 h × 3 at 50°C. The obtained extract was filtered, combined, and concentrated under reduced pressure at 40°C by a rotary evaporator. The extraction yield calculated for the dry weight of the plant material was 22.17%, (w/w). The extraction yield was calculated with the following formula: yield = (weight of dry extract/weight of powdered plant material)*100. A schematic presentation of the extraction procedure is given in Figure S1.

2.3. Software

The chromatographic system was controlled using Waters® Empower2 software. PARAFAC decomposition was performed using N‐way Toolbox ⁴⁷ in Matlab (Mathworks, MA, USA). Regression and quantitative predictions were performed by in‐house algorithms in Matlab.

2.4. Chromatographic parameters

For the PARAFAC‐UPLC analysis, overlapping chromatographic conditions were as follows: a mixture of water, acetonitrile, and aqueous trichloroacetic acid (0.025%) (25:20:55 v/v), as mobile phase, 0.2 mL/min for flow rate, 1.0 μL for injection volume, and 40°C as column temperature. The absorbance data were recorded between 210–400 nm (Δλ = 1.2 nm) with a sampling rate of 20 points per second.

The classical in‐house UPLC method was developed using the same analytical column but with a different ratio of the same mobile phase constituents (25:15:60 v/v). The flow rate, injection volume, sample temperature, and column temperature were kept the same as those of the PARAFAC‐UPLC method.

2.5. Preparation of standard solutions and plant extract samples

A stock solution containing 100‐μg/mL chlorogenic acid was prepared in methanol. The stock solution was sonicated for 5 min and filtered through a 0.20‐μm hydrophilic polytetrafluoroethylene (PTFE) syringe filter. A calibration set of five standard chlorogenic acid solutions at the concentrations of 10, 20, 30, 40, and 50 μg/mL was prepared by diluting the stock solutions with methanol.

In order to validate the model, a series of validation samples, consisting of an independent test set and a standard addition set were prepared. An independent test set was prepared by diluting the chlorogenic acid stock solution with methanol to obtain concentrations of 10, 20, 30, 40, and 50 μg/mL. To obtain the standard addition sample set, the standard chlorogenic acid solutions in the concentrations of 10, 20, and 30 μg/mL were added to a constant amount of plant extract solutions. To calculate the added amount and the recovery percentage of chlorogenic acid in the standard addition samples, the blank extract samples were prepared without the addition of standard chlorogenic acid and only with the constant amount of plant extract solution. For statistical calculations, each experiment of standard addition samples was repeated three times at each concentration level.

Ten milligrams of plant extract was dissolved in 10‐mL methanol. The sample was sonicated and filtered by 0.20‐μm PTFE syringe filters. Five aliquots were prepared without further dilution to obtain five plant extract samples.

3. RESULT AND DISCUSSION

In the chromatographic analysis of plant extracts, the main problem is the co‐elution or partial separation of the analyzed compounds in a chromatogram. This issue can also arise from interference of the compound of interest with the extract matrices or solvent system. This chromatographic challenge can be addressed in two ways: through chemometric resolution or by undertaking classical, lengthy, and tedious chromatographic studies. In this study, both chemometric analysis and the classical UPLC method were comparatively applied as an exemplar study for the determination of chlorogenic acid in methanol extract of Melampyrum stenophyllum.

In preliminary experiments, several mobile phase systems using a Waters UPLC BEH C18 column (100 mm × 2.1 mm i.d.,1.7 μm) were tested to develop a new UPLC method for the chlorogenic acid assay. The mobile phase consisting of water, acetonitrile, and 0.025% aqueous trichloroacetic acid (25:20:50, v/v) gave rise to co‐elution of chlorogenic acid with the unknown components and solvent system. Figure 1A,B represents the three‐dimensional plots of pure chlorogenic acid and plant extract sample containing chlorogenic acid, respectively. The retention time of chlorogenic acid was 1.45 min, and the quantification of chlorogenic acid in the plant sample was not possible by classical univariate analysis because of the inappropriate elution. To resolve this issue, additional chromatographic experiments are required to achieve complete chromatographic separation. However, the traditional chromatographic or HPLC approach to this problem may not be preferred by analysts because it usually involves tedious studies that require long period of experimentation.

(A) Three‐dimensional chromatogram of pure chlorogenic acid, (B) three‐dimensional chromatogram of plant extract sample.

A more efficient way to solve this chromatographic problem was to apply a three‐way analysis technique (PARAFAC model as in this case) to the UPLC‐PDA dataset. In spite of interference from the mobile phase's composition and the extract sample matrix, we hypothesized that the PARAFAC approach would not require any additional chromatographic experiments and would provide a short analysis time with low cost. In this context, PARAFAC modeling was applied to the UPLC‐PDA dataset to quantify chlorogenic acid in the analyzed plant without additional chromatographic experiments and with a short runtime and low cost.

3.1. Three‐way analysis model

For the three‐way analysis of UPLC data, the spectrochromatograms of the calibration, validation, and plant extract samples were recorded as a function of retention time and wavelength within the ranges of 0.0–4.0 min and 210–400 nm, respectively. Figure S2 illustrates the spectrochromatograms of the calibration samples (10‐, 20‐, 30‐, 40‐, 50‐μg/mL chlorogenic acid), to provide examples of the two‐dimensional data matrices obtained from each chromatographic run. In total, 27 spectrochromatograms were recorded, including five calibration solutions, five independent test samples, 12 standard addition samples, and five plant extract samples. Twenty‐seven spectrochromatograms were concatenated using the Matlab platform to form a three‐dimensional data cube, mentioned as UPLC‐PDA dataset in this paper. UPLC‐PDA dataset was subsequently decomposed by PARAFAC into trilinear components, describing the data in time, wavelength, and sample dimensions. See Figure S1 for a graphical representation of these steps.

In the initial model development step, different PARAFAC models using two, three, four, and five components with and without non‐negativity constraints in three modes were applied to obtain the chromatographic, spectral, and concentration profiles. The correct number of components and required constraints were determined by the evaluation of the core consistency diagnostic ⁴⁸ and visual comparison of the relative profiles obtained from the tested models with the experimental data of chlorogenic acid in the standard sample solutions. ⁴⁴ As a result, the three‐component PARAFAC model with nonnegative constraints in three modes was found to be the appropriate model to describe the data in time, wavelength, and sample dimensions. Figure 2IA,IB,IC shows the estimated chromatographic, spectral, and concentration profiles, respectively. The core consistency diagnostic of the developed three‐component model was reported to be 99.7198% as given in Figure S3, indicating the appropriateness of the model and the trilinearity of the original three‐way data. As seen in Figure S3, when the dataset is modeled with increasing number of components, ¹ , ² , ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ , ⁹ , ¹⁰ , ¹¹ , ¹² , ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ , ²¹ , ²² , ²³ , ²⁴ , ²⁵ , ²⁶ , ²⁷ the core size starts near 1, indicating a high core consistency; then decreases when the correct number of components, 3; and is exceeded because non‐trilinear variation increases with higher number of components. ⁴⁴

Estimated chromatographic (A), spectral (B), and concentration (C) profiles of chlorogenic acid, unknown components, and solvent system, obtained by PARAFAC decomposition. Panel (II) shows the extracted profile of chlorogenic acid from Panel (I). Black curve in Panel (II)B indicates the experimental spectrum of standard chlorogenic acid.

The contribution of chlorogenic acid in the estimated profiles is illustrated in color pink in Figure 2IA,IB,IC. The individual curves of the chlorogenic acid were extracted from these profiles to better visualize, compare, and interpret the data. Figure 2IIA,IIB,IIC illustrates the estimated chromatographic, spectral, and concentration profiles of chlorogenic acid. In Figure 2IIB, the pink line representing the estimated spectrum of chlorogenic acid coincides with the experimental spectrum of standard chlorogenic acid illustrated in black. In Figure 2IIC, the curve of the estimated concentration of chlorogenic acid in the calibration, independent test, standard addition, and plant extract samples is given. A priori knowledge of the concentration of the standard solutions, including calibration (10, 20, 30, 40, 50 μg/mL), independent test samples (10, 20, 30, 40, 50 μg/mL), and standard addition samples (extract + 10 μg/mL, extract + 20 μg/mL, extract + 30 μg/mL, three repetitions), is also in accordance with relative concentration trend of chlorogenic acid.

The quantification of chlorogenic acid in the validation and plant extract samples was performed using a calibration curve representing the mathematical relationship between the actual concentration and the estimated relative concentration of the calibration samples in concentration profile (see Figure 2IIC). In order to construct the linear calibration curve, the relative concentration values obtained by the PARAFAC model, represented by the first five points in Figure 2IIC, were regressed on the known values of chlorogenic acid concentration (10, 20, 30, 40, 50 μg/mL) in the calibration set using least squares fitting. The equation of the curve was computed as y = 0.0104x − 0.0189 as indicated in Table 1, along with statistical results of the linear regression.

TABLE 1.

Linear regression analysis and corresponding statistical results using PARAFAC‐UPLC and classical UPLC methods.

Parameter	PARAFAC‐UPLC	Classical UPLC
Slope	0.0104	0.7065
Intercept	−0.0189	−0.8574
Correlation coefficient	0.9996	0.9996
SD(m) ^a	1.74 × 10⁻⁴	1.15 × 10⁻²
SD(n) ^b	5.76 × 10⁻³	3.82 × 10⁻¹
SD(r) ^c	9.64 × 10⁻³	9.42 × 10⁻³
LOD ^d (μg/mL)	1.66	1.62
LOQ ^e (μg/mL)	5.54	5.41

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^{^a}

SD(m) = Standard deviation of the slope.

^{^b}

SD(n) = Standard deviation of the intercept.

^{^c}

SD(r) = Standard deviation of correlation.

^{^d}

LOD = Limit of detection.

^{^e}

LOQ = Limit of quantitation.

3.2. Analytical validation of the proposed method

The developed method was validated by considering the linearity, limit of detection, limit of quantification, accuracy, precision, and specificity parameters. The validation procedure was carried out by analyzing an independent test set and standard addition sample set.

For the linearity of the proposed PARAFAC‐UPLC method, the plot of the actual concentration of the analyte versus the relative concentration of chlorogenic acid in the concentration mode (see Figure 2IIC) was evaluated. The statistical parameters of the calibration curve obtained by the least squares regression method are given in Table 1, depicting a correlation coefficient of 0.9996. The limit of detection (LOD) and limit of quantitation (LOQ) with signal‐to‐noise ratios of 3:1 and 10:1 were considered, and the LOD and LOQ values of chlorogenic acid were found to be 1.70 and 5.67 μg/mL, respectively, as given in Table 1.

The accuracy and precision of the proposed PARAFAC‐UPLC method were estimated from the mean recovery data of chlorogenic acid in the independent test set at five different concentration levels (10, 20, 30, 40, and 50 μg/mL). The mean recovery results revealed a good accuracy of the PARAFAC model (see Table 2). The standard deviation and its relative standard deviation (RSD%) values were calculated from recovery studies to evaluate the precision of the method and were found to be satisfactory, as given in Table 2.

TABLE 2.

Recovery results obtained by analyzing the independent test samples and added recovery results obtained by analyzing the standard addition samples.

Sample		PARAFAC‐UPLC				Classical UPLC
Sample	Added (μg/mL)	Measured (μg/mL)	Recovery			Measured (μg/mL)	Recovery
Independent test	10	10.39	103.9			10.43	104.3
	20	19.89	99.4			19.98	99.9
	30	30.32	101.1			30.06	100.2
	40	40.86	102.2			41.96	104.9
	50	49.98	100.0			50.92	101.8
		Mean	101.1			Mean	102.3
		SD ^a	1.80			SD ^a	2.30
		RSD ^b	1.78			RSD ^b	2.24
Standard addition	Added (μg/mL)	Measured ^c (μg/mL)	Added recovery	SD ^a	RSD ^b	Measured ^c (μg/mL)	Added recovery	SD ^a	RSD ^b
	10	10.14	100.9	1.21	1.20	9.98	99.8	2.26	2.27
	20	19.92	99.6	1.41	1.42	20.56	102.8	2.32	2.26
	30	29.97	99.9	1.12	1.12	30.38	101.3	2.14	2.11

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^{^a}

SD = Standard deviation.

^{^b}

RSD = Relative standard deviation.

^{^c}

n = 3.

In the PARAFAC application to the UPLC‐PDA dataset in the presence of strong overlapping signals of the components in the plant extract sample, the specificity/selectivity of the method was estimated by analyzing the standard addition samples. For the standard addition experiments, the added and measured amounts, recovery, and relative standard deviations are listed in Table 2. The analysis results were the average of three replicate experiments for each concentration level, calculated by subtracting the assay result of the blank extract sample from the total content of chlorogenic acid in the standard addition samples. As indicated by the high recovery results, the PARAFAC model was found to be selective for the analysis of chlorogenic acid despite the co‐elution of components in the spectrochromatogram.

3.3. Analysis of chlorogenic acid in plant extract

After the method validation procedure, the proposed strategy based on the PARAFAC model of the spectrochromatographic measurements was applied to the quantitative determination of chlorogenic acid in the methanol extract of Melampyrum stenophyllum. The amount of chlorogenic acid in the plant extract sample solution was calculated by substituting the relative concentration values of the sample in the concentration profile into the calibration curve. The amount of chlorogenic acid was calculated as mg per gram dry weight of plant extract. The assay results with standard deviation and relative standard deviation for chlorogenic acid are presented in Table 3. The average result of five experiments was reported to be 16.1 mg/g with a relative standard deviation of 1.72 mg/g.

TABLE 3.

Assay results of chlorogenic acid in real samples using PARAFAC‐UPLC and classical UPLC.

Sample number	mg/g
Sample number	PARAFAC‐UPLC	Classical UPLC
1	16.49	16.71
2	16.09	16.84
3	16.03	16.01
4	16.21	16.30
5	15.73	16.55
Mean	16.11	16.48
SD ^a	0.28	0.33
RSD ^b	1.72	2.01
F‐test	1.44	F‐_crit = 6.39
t‐test	1.93	t‐_crit 2.31

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^{^a}

SD = Standard deviation.

^{^b}

RSD = Relative standard deviation.

3.4. Classical ultra‐performance liquid chromatography method

A reference analytical method was required to demonstrate the applicability of the proposed method and evaluate its analytical performance by comparing the assay results. A classical in‐house UPLC method, with effective and complete chromatographic separation of chlorogenic acid from other components in the plant extract, was developed by additional experimental efforts. The classical in‐house UPLC method was developed by changing the ratio of organic and inorganic modifiers in the mobile phase and was applied to the quantitative analysis of the methanolic extract containing chlorogenic acid. The chromatographic parameters of the classical UPLC method were detailed in Section 2.4. The retention time of chlorogenic acid was reported as 1.71 min within the run time of 9 min. The preparation of the standard and extract sample solutions were specified in Section 2.5. The chromatograms were recorded at the detection wavelength of 324 nm by injecting the samples into the UPLC system using the mentioned chromatographic parameters. Examples of the chromatograms recorded for calibration and extract sample solutions are presented in Figure 3A,B, respectively.

Chromatograms recorded at 324 nm of (A) standard chlorogenic acid and (B) plant extract sample obtained by the application of classical UPLC method (retention time of chlorogenic acid: 1.71 min).

In the classical UPLC method, the linear relationship between the actual concentration and the integrated peak areas corresponding to the calibration set was modeled by least squares regression. The integrated peak areas of chlorogenic acid, calculated by Empower2 software, were regressed on the known values of chlorogenic acid concentration (10, 20, 30, 40, 50 μg/mL) in the calibration set using least squares fitting. The equation of the curve was calculated as y = 0.7065x − 0.8574 as indicated in Table 1, along with statistical results of the linear least square regression. This equation was used to calculate chlorogenic acid concentrations in the validation and plant extract samples. The LOD and LOQ values of the method are also depicted in Table 1. The results of the validation procedure, given in Table 2, indicate the accuracy, precision, and specificity of the classical UPLC method. The extract sample solutions were then injected to the UPLC system, and chromatograms were recorded at 324 nm (see Figure 3B). The chlorogenic acid content of five plant extract solutions was calculated using the integrated peak areas of chlorogenic acid in the plant extract chromatograms and the calibration equation. The chlorogenic acid assay results are given in Table 3. The average result was reported to be 16.48‐mg chlorogenic acid per gram dry weight of plant extract, with a standard deviation of 2.01 mg/g.

3.5. Comparison of the methods

In order to evaluate the performance of the PARAFAC approach, the assay results of chlorogenic acid in methanolic extracts provided from PARAFAC and classical UPLC approaches were statistically compared using F‐test and Student's t‐test at a significance level of p = 0.05. First, the null hypothesis that the assay results from the two methods have the same variance was tested using the F‐test, with the assumption that the results were normally distributed. The formula F = $S_{X}^{2} / S_{Y}^{2}$ was used where $S_{X}^{2}$ denotes the variance of the assay results obtained by classical UPLC, and $S_{Y}^{2}$ corresponds to the variance of assay results obtained by PARAFAC. The F‐value (1.66) was smaller than the critical value 6.39, and the p‐value (0.37) was greater than the significance level. Therefore, the null hypothesis was accepted.

Student's t‐test with equal variances was used to evaluate whether the mean values obtained by the two methods were the same (null hypothesis). The t‐value was calculated using the formula $t = \frac{(\bar{x} - \bar{y})}{\sqrt{S_{X}^{2} / n_{x} + S_{Y}^{2} / n_{y}}}$ , where $\bar{x}$ and $\bar{y}$ correspond to the mean values of the assay results obtained by classical UPLC and PARAFAC approach, respectively. $S_{X}^{2}$ and $S_{Y}^{2}$ denote to the variance of the groups, $n_{x}$ and $n_{y}$ are the number of observations in each group, which are both 5 in our case. The t‐statistic was calculated as 1.93, smaller than the critical value of 2.31, and the p‐value was computed as 0.09, greater than the significance level of p = 0.05. The null hypothesis was accepted, indicating that the difference in the mean assay results was not statistically significant. Consequently, the statistical tests indicated that the assay results obtained using both methods were comparable. The statistical test results are summarized in Table 3. Although no statistical difference was observed between the assay results, the PARAFAC method provided slightly lower relative standard deviation values (see Table 3), indicating a better precision.

In the mathematical elution approach, the use of spectrochromatographic data of overlapping peaks provided an obvious advantage of short analysis time. The retention time of chlorogenic acid was shorter in the UPLC‐PDA method than in the classical UPLC method (1.45 min vs. 1.71 min). Although the difference in retention time of the compound of interest was quite small, the analysis time differed significantly. As seen in Figure 3B, the interferent compounds in the plant extract were retained in the column for 9 min. Although the classical UPLC method required a runtime of 9 min, a 4‐min runtime was enough for the PARAFAC‐UPLC method to elute all compounds in plant extract samples. The acetonitrile ratio in the mobile phase for the PARAFAC‐UPLC method was slightly higher than that of the classical method, but the reduction in analysis time resulted in less solvent use per analysis when compared with the classical method. Consequently, acetonitrile use was reduced by almost 60% per chromatographic run. The PARAFAC‐UPLC method provided a faster, cheaper, and greener option compared with the classical method. Moreover, as the PARAFAC‐UPLC method did not require a complete chromatographic separation, the development and optimization of the chromatographic method in this approach was straightforward and easy. However, more time, experimental effort, and chemical resources were needed to develop the classical UPLC method to ensure the complete separation of chlorogenic acid.

In this work, a common and expected secondary metabolite, chlorogenic acid, was identified and quantified in Melampyrum stenophyllum extracts despite challenges such as co‐elution with sample matrix constituents and interference from solvent peaks in chromatographic data. We hypothesized that mathematical elution using the PARAFAC model could be useful to overcome the co‐elution problem and offer an alternative to the classical approach. The UPLC‐PDA dataset in co‐elution conditions was arranged as a three‐way array and decomposed using a three‐component PARAFAC model to obtain estimated profiles in the time, wavelength, and sample domains. The estimated chromatographic and spectral profiles were used for qualitative determination, and quantitation of chlorogenic acid was performed using the estimated concentration profile in the sample domain. After analytical validation, the proposed PARAFAC‐UPLC method was applied to quantitatively determine the chlorogenic acid content in methanolic extracts of Melampyrum stenophyllum. Our experiments demonstrated that chlorogenic acid could be effectively resolved, identified, and quantified using the PARAFAC‐UPLC method without the need for complete chromatographic separation. To further validate our findings, we developed an in‐house classical UPLC method for comparison. This method required longer analysis times and additional efforts during chromatographic optimization to achieve complete separation of chlorogenic acid from matrix constituents. The statistical comparison of the assay results showed no significant differences, but the classical UPLC method required twice the analysis time and greater solvent use per chromatographic run.

This work demonstrated the utility of the PARAFAC model in overcoming challenges such as co‐elution and matrix interference in phytochemical chromatography. The PARAFAC approach offers significant advantages, including shorter analysis times, reduced costs, decreased waste, and elimination of tedious chromatographic optimization steps. Future research should explore the application of three‐way analysis methods for multicomponent analysis, fingerprinting, and classification of natural products to better understand their performance, advantages, and limitations. This strategy has significant potential for achieving complex chromatographic analyses of plant samples when the classical approach is either not preferred or simply not feasible.

Supporting information

Figure S1. Graphical abstract of the study.

Figure S2. Spectrochromatographic data matrices of the calibration samples containing 10, 20, 30, 40, 50 μg/ml chlorogenic acid.

Figure S3. Core consistency diagnostics calculated for different number of components (Core consistency = 99.7198% for 3 components).

PCA-36-279-s001.docx^{(8.1MB, docx)}

ACKNOWLEDGMENTS

Chromatographic and chemometric studies of this work were carried out in the Chemometrics Laboratory of the Faculty of Pharmacy, which was established with the scientific research project number 10A3336001 supported by Ankara University, Scientific Research Projects Coordination Unit. The authors are grateful to Assoc. Prof. Dr. Muhammed Mesud HÜRKÜL (Ankara University, Faculty of Pharmacy, Department of Pharmaceutical Botany) and pharmacist Betül Sena SOYAL for their assistance in handling the plant material.

Ertekin ZC, Köroğlu A, Dinç E. Three‐dimensional spectrochromatographic determination of chlorogenic acid in Melampyrum stenophyllum Boiss. extracts by parallel factor analysis. Phytochemical Analysis. 2025;36(1):279‐288. doi: 10.1002/pca.3439

DATA AVAILABILITY STATEMENT

Data available on request from the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Graphical abstract of the study.

Figure S2. Spectrochromatographic data matrices of the calibration samples containing 10, 20, 30, 40, 50 μg/ml chlorogenic acid.

Figure S3. Core consistency diagnostics calculated for different number of components (Core consistency = 99.7198% for 3 components).

PCA-36-279-s001.docx^{(8.1MB, docx)}

Data Availability Statement

Data available on request from the corresponding author.

[pca3439-bib-0001] 1. Upadhyay R, Mohan Rao LJ. An outlook on chlorogenic acids—occurrence, chemistry, technology, and biological activities. Crit Rev Food Sci Nutr. 2013;53(9):968‐984. doi: 10.1080/10408398.2011.576319 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0002] 2. Kundu A, Vadassery J. Chlorogenic acid‐mediated chemical defence of plants against insect herbivores. Plant Biol (Stuttg). 2019;21(2):185‐189. doi: 10.1111/plb.12947 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0003] 3. Amin M, Sharif S, Akram S, et al. A dispersive liquid–liquid microextraction followed by reverse‐phase high‐performance liquid chromatography for QuEChERS determination of chlorogenic acid. Phytochem Anal. 2023;34(1):30‐39. doi: 10.1002/pca.3174 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0004] 4. Kim S‐M, Shang YF, Um B‐H. Preparative separation of chlorogenic acid by centrifugal partition chromatography from highbush blueberry leaves (Vaccinium corymbosum L.). Phytochem Anal. 2010;21(5):457‐462. doi: 10.1002/pca.1218 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0005] 5. Naveed M, Hejazi V, Abbas M, et al. Chlorogenic acid (CGA): a pharmacological review and call for further research. Biomed Pharmacother. 2018;97:67‐74. doi: 10.1016/j.biopha.2017.10.064 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0006] 6. Tajik N, Tajik M, Mack I, Enck P. The potential effects of chlorogenic acid, the main phenolic components in coffee, on health: a comprehensive review of the literature. Eur J Nutr. 2017;56(7):2215‐2244. doi: 10.1007/s00394-017-1379-1 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0007] 7. Miao M, Xiang L. Pharmacological action and potential targets of chlorogenic acid. Adv Pharmacol. 2020;87:71‐88. doi: 10.1016/bs.apha.2019.12.002 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0008] 8. Liang N, Kitts DD. Role of chlorogenic acids in controlling oxidative and inflammatory stress conditions. Nutrients. 2016;8(1):16. doi: 10.3390/nu8010016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pca3439-bib-0009] 9. Yan Y, Zhou X, Guo K, Zhou F, Yang H. Use of chlorogenic acid against diabetes mellitus and its complications. J Immunol Res. 2020;2020:9680508. doi: 10.1155/2020/9680508 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pca3439-bib-0010] 10. Santana‐Gálvez J, Cisneros‐Zevallos L, Jacobo‐Velázquez DA. Chlorogenic acid: recent advances on its dual role as a food additive and a nutraceutical against metabolic syndrome. Molecules. 2017;22(3):358. doi: 10.3390/molecules22030358 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pca3439-bib-0011] 11. Singh Kumar A, Singla Kumar R, Pandey KA. Chlorogenic acid: a dietary phenolic acid with promising pharmacotherapeutic potential. Curr Med Chem. 2023;30(34):3905‐3926. doi: 10.2174/0929867329666220816154634 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0012] 12. Güner A, Aslan S, Ekim T, Vural M, Babaç MT. Türkiye Bitkileri Listesi (Damarlı Bitkiler). Nezahat Gökyiğit Botanik Bahçesi ve Flora Araştırmaları Derneği Yayını; 2012:654. [Google Scholar]

[pca3439-bib-0013] 13. WFO . World Flora Online. http://www.worldfloraonline.org/

[pca3439-bib-0014] 14. Vogl S, Atanasov AG, Binder M, et al. The herbal drug Melampyrum pratense L. (Koch): isolation and identification of its bioactive compounds targeting mediators of inflammation. Evid Based Complement Alternat Med. 2013;2013:395316. doi: 10.1155/2013/395316 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pca3439-bib-0015] 15. Mogoşan C, Munteanu MF. A comparative study on antiinflammatory effect of the tinctures from Melampyrum bihariense Kern and Melapyrum cristatum L. (Scrophulariaceae) Farmacia. 2008;56:389‐392. [Google Scholar]

[pca3439-bib-0016] 16. Damtoft S, Hansen SB, Jacobsen B, Jensen SR, Nielsen BJ. Iridoid glucosides from Melampyrum . Phytochemistry. 1984;23(10):2387‐2389. doi: 10.1016/S0031-9422(00)80564-6 [DOI] [Google Scholar]

[pca3439-bib-0017] 17. Roeder E, Bourauel T. Pyrrolizidine alkaloids from Melampyrum pratense . Nat Toxins. 1992;1(1):35‐37. doi: 10.1002/nt.2620010108 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0018] 18. Galishevskaya EE, Petrichenko VM. Phenolic compounds from two Melampyrum species. Pharm Chem J. 2010;44(9):497‐500. doi: 10.1007/s11094-010-0501-y [DOI] [Google Scholar]

[pca3439-bib-0019] 19. Kirmizibekmez H, Atay I, Kaiser M, et al. Antiprotozoal activity of Melampyrum arvense and its metabolites. Phytother Res. 2011;25(1):142‐146. doi: 10.1002/ptr.3233 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0020] 20. Munteanu MF, Vlase L. The determination of the iridoids from the Melampyrum species by modern chromatographic methods. Not Bot Horti Agrobot Cluj‐Napoca. 2011;39(1):79‐83. doi: 10.15835/nbha3915803 [DOI] [Google Scholar]

[pca3439-bib-0021] 21. Háznagy‐Radnai E, Wéber E, Czigle S, Berkecz R, Csedő K, Hohmann J. Identification of iridoids, flavonoids and triterpenes from the methanolic extract of Melampyrum bihariense A. Kern and the antioxidant activity of the extract. Chromatographia. 2014;77(17):1153‐1159. [Google Scholar]

[pca3439-bib-0022] 22. Korkotian E, Botalova A, Odegova T, Galishevskaya E, Skryabina E, Segal M. Complex effects of aqueous extract of Melampyrum pratense and of its flavonoids on activity of primary cultured hippocampal neurons. J Ethnopharmacol. 2015;163:220‐228. doi: 10.1016/j.jep.2015.01.036 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0023] 23. Háznagy‐Radnai E, Fási L, Wéber E, et al. Anti‐inflammatory activity of Melampyrum barbatum and isolation of iridoid and flavonoid compounds. Nat Prod Commun. 2018;13(3):235‐236. doi: 10.1177/1934578X1801300301 [DOI] [Google Scholar]

[pca3439-bib-0024] 24. Karadağ AE, Tosun F. In vitro antimicrobial and antioxidant activity evaluation of Melampyrum arvense L. var. elatius Boiss. and Sedum spurium M. Bieb extracts. Acta Pharm Sci. 2019;57:193‐201. [Google Scholar]

[pca3439-bib-0025] 25. Govindarajan R, Tejas V, Pushpangadan P. High‐performance liquid chromatography (HPLC) as a tool for standardization of complex herbal drugs. J AOAC Int. 2019;102(4):986‐992. doi: 10.5740/jaoacint.18-0378 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0026] 26. Gupta P, Sreelakshmi Y, Sharma R. A rapid and sensitive method for determination of carotenoids in plant tissues by high performance liquid chromatography. Plant Methods. 2015;11(1):5. doi: 10.1186/s13007-015-0051-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pca3439-bib-0027] 27. Sik B, Székelyhidi R, Lakatos E, Kapcsándi V, Ajtony Z. Analytical procedures for determination of phenolics active herbal ingredients in fortified functional foods: an overview. Eur Food Res Technol. 2022;24(2):329‐344. doi: 10.1007/s00217-021-03908-6 [DOI] [Google Scholar]

[pca3439-bib-0028] 28. Nahar L, Onder A, Sarker SD. A review on the recent advances in HPLC, UHPLC and UPLC analyses of naturally occurring cannabinoids (2010‐2019). Phytochem Anal. 2020;31(4):413‐457. doi: 10.1002/pca.2906 [DOI] [PubMed] [Google Scholar]

[pca3439-bib-0029] 29. Gumułka P, Żandarek J, Dąbrowska M, Starek M. UPLC technique in pharmacy—an important tool of the modern analyst. Processes. 2022;10(12):2498. doi: 10.3390/pr10122498 [DOI] [Google Scholar]

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PERMALINK

Three‐dimensional spectrochromatographic determination of chlorogenic acid in Melampyrum stenophyllum Boiss. extracts by parallel factor analysis

Zehra Ceren Ertekin

Ayşegül Köroğlu

Erdal Dinç

Abstract

Introduction

Objectives

Methodology

Results

Conclusion

Short abstract

1. INTRODUCTION

2. EXPERIMENTAL

2.1. Materials

2.2. Plant material and plant extracts

2.3. Software

2.4. Chromatographic parameters

2.5. Preparation of standard solutions and plant extract samples

3. RESULT AND DISCUSSION

FIGURE 1.

3.1. Three‐way analysis model

FIGURE 2.

TABLE 1.

3.2. Analytical validation of the proposed method

TABLE 2.

3.3. Analysis of chlorogenic acid in plant extract

TABLE 3.

3.4. Classical ultra‐performance liquid chromatography method

FIGURE 3.

3.5. Comparison of the methods

Supporting information

ACKNOWLEDGMENTS

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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