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Ahmad A. Deeb Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman 11733, Jordan

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Mohammad Abuothman Automated Chemical Laboratories, Royal Scientific Society, Amman 11941, Jordan

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Mohammad Hailat Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman 11733, Jordan

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Omar Abuyaman Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa 13133, Jordan

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Ruba K. Albizreh Automated Chemical Laboratories, Royal Scientific Society, Amman 11941, Jordan

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Abstract

Coloring agents in foods and drinks have been popular for centuries. This study aims to analyze the presence of ten synthetic colors (namely, (allura red (E129), amaranth (E123), sunset yellow (E110), tetrazine (E102), fast green (E143), ponceau 4R (New Coccine) (E124), erythrosin B (E127), brilliant blue FCF (E133), brilliant black (E151) and carmoisine (E122))) in food and drink samples using ultra-high-performance liquid chromatography diode array detection (UHPLC-DAD). The present analytical method was carried out using Agilent Poroshell 120 HPH-C18 column, 3 × 100 mm, 2.7 µm, and a mobile phase consisting of 10 mM Na2HPO4, pH 7, mixed with methanol as a time-increment gradient solution until the time was 20 min, then decreased with time until the time was 26 min. The pH was set by orthophosphoric acid at 7 and 5 μL injection volume, 0.50 mL flow rate, and the elution systems were monitored at 428 nm for E102, 518 nm for E124, E110, E129, E122, 530 nm for E151, E127, 622 nm for E143, and E133, respectively. The limit of detection and quantification for all colors ranged from 0.017 to 0.025 and 0.057 and 0.082 mg L−1, respectively. The correlation coefficient values ranged between 0.9991 and 1.0. The selectivity of the assay revealed no interference from other components in the analyzed samples. The percent recovery and precision (intra- and inter-day) of the spiked samples were within the acceptable limits of the ICH guidelines. Five analytical parameters were employed, and the results showed a new, novel, and robust method according to ICH guidelines for analyzing these colors. While most of the investigated food and drinks fell within the accepted range, some fell outside. The current sample preparation and analytical methods are comprehensive and universal for extracting and measuring synthetic colors in various food and drink samples.

Abstract

Coloring agents in foods and drinks have been popular for centuries. This study aims to analyze the presence of ten synthetic colors (namely, (allura red (E129), amaranth (E123), sunset yellow (E110), tetrazine (E102), fast green (E143), ponceau 4R (New Coccine) (E124), erythrosin B (E127), brilliant blue FCF (E133), brilliant black (E151) and carmoisine (E122))) in food and drink samples using ultra-high-performance liquid chromatography diode array detection (UHPLC-DAD). The present analytical method was carried out using Agilent Poroshell 120 HPH-C18 column, 3 × 100 mm, 2.7 µm, and a mobile phase consisting of 10 mM Na2HPO4, pH 7, mixed with methanol as a time-increment gradient solution until the time was 20 min, then decreased with time until the time was 26 min. The pH was set by orthophosphoric acid at 7 and 5 μL injection volume, 0.50 mL flow rate, and the elution systems were monitored at 428 nm for E102, 518 nm for E124, E110, E129, E122, 530 nm for E151, E127, 622 nm for E143, and E133, respectively. The limit of detection and quantification for all colors ranged from 0.017 to 0.025 and 0.057 and 0.082 mg L−1, respectively. The correlation coefficient values ranged between 0.9991 and 1.0. The selectivity of the assay revealed no interference from other components in the analyzed samples. The percent recovery and precision (intra- and inter-day) of the spiked samples were within the acceptable limits of the ICH guidelines. Five analytical parameters were employed, and the results showed a new, novel, and robust method according to ICH guidelines for analyzing these colors. While most of the investigated food and drinks fell within the accepted range, some fell outside. The current sample preparation and analytical methods are comprehensive and universal for extracting and measuring synthetic colors in various food and drink samples.

1 Introduction

Food colorants are ingredients added to a food or beverage to change color [1]. Food and drink coloring has been widely used for centuries. The color was often added to signal quality, prompt instantaneous consumer awareness of the flavor, and ensure conformity with their expectations [2]. Products such as biscuits, pastries, cakes, processed meats, cheese, margarine, confectionery, ice cream, cordials, and soft beverages still have artificial coloring. Adding color to food and drink serves several technical purposes, including compensating for color loss during processing (which can occur due to factors like heat and time), enhancing natural color, maintaining consistent color from batch to batch (which is essential for brand recognition), and shielding flavor compounds and vitamins from light damage during storage [1].

Moreover, there is a correlation between a food's colorant and its freshness, safety, and flavor. For example, the air-oxidized extracts of green, medium-roasted, and dark-roasted coffee beans appeared different because of the variable degrees of roasting. NaIO4-oxidized extracts had dark-brown tones, indicating that the iodine complexes remained after purification, but CuSO4-oxidized extracts had the usual green color of Cu (II) complexes [3]. Although natural food colors have gained popularity in recent years, they are more costly and volatile than synthetic dyes, which are widely used in manufacturing. Manufacturers prefer using artificial food colorings because of this [4]. The natural colors of food are regularly replaced with synthetic food dyes to boost sales. These colors are typically acidic, water-soluble sodium salts, and a wide range of reasonably priced synthetic food dyes with high stability are available. However, there have been reports of detrimental consequences induced by excessive intake. Thus, synthetic food colors are prohibited [5, 6].

Some commercially available meals and drinks may include excessive amounts of permissible synthetic colors or contain synthetic colors prohibited by law. Extreme health issues may result in mutations, malignancies, anemia, and allergic responses. In addition, sixty percent of the drinks lacked the required colorant components on their labels [7]. As a result, many methods have been reported for the analysis of food dyes in a variety of food matrices, including in soft drinks and chewing gum [8], using HPLC-DAD [9], fluorescence detection (FLD) [10], high-performance liquid chromatography with a photodiode array detector (HPLC-PDA) [11], capillary electrophoresis [12], thin-layer chromatography [13], unified chromatography with mass spectrometric detection [14], tetraalkylammonium based liquid-liquid extraction [15], modified carbon paste electrodes [16], and many others. However, no previous studies were reported in Jordan to analyze synthetic colors in food and drink samples.

The regulation defines the foodstuffs to which the additive may be added and the degree of addition, considering the Acceptable Daily Intakes (ADIs). ADI estimates the quantity of a chemical in food or drinking water consumed daily without causing significant harm, given in milligrams per kilogram of body weight [17]. The majority of synthetic colors are acidic water-soluble compounds that are often utilized as sodium salts. They are classified as azo, triarylmethane, chinophthalon (quinoline yellow, E104), xanthene (erythrosine E127), and indigo (E132). Two Azo colorants are distinguished by at least one or two azo groups in the molecule, which connects two or three aromatic ring systems. Tartrazine (E102), sunset yellow (E110), azorubine (E122), amaranth (E123), cochineal red (E124), red 2G (E128), allura red (E129), brilliant black BN (E151), brown FK (E154), and brown HT are all permitted azo dyes (E155). Three substituted aryl residues are covalently bonded to the central carbon atom in triarylmethane colorants. Patent blue V (E131), brilliant blue FCF (E133), and green S (E142) are the resultant food colorants [18]. The novelty of this work is that no previous studies in Jordan have been reported to analyze synthetic colors in food and drink samples.

As a result, the purpose of this research is to create and test a new, novel, simple, and universal extraction and analysis technique based on Ultra High-Performance Liquid Chromatographic coupled with a Diode Array Detector (UHPLC-DAD) for the detection and quantification of ten synthetic color contents (namely (allura red (E129), amaranth (E123), sunset yellow (E110), tetrazine (E102), fast green (E143), ponceau 4R (new coccine) (E124), erythrosin B (E127), brilliant blue FCF (E133), brilliant black (E151) and carmoisine (E122)), and similar to these compounds in several types of food and drink samples, including sugars, sesame tahini, chicken broth cubes, mushroom, tomato paste, hookah honey, spices, liquid milk, candies, jams, ice cream, hummus, and juice.

2 Materials and methods

2.1 Chemicals, reagents, and equipment

Highly purified water and super gradient grade methanol were purchased from Lab-Scan (Bangkok, Thailand). Dimethyl sulphoxide (DMSO) was purchased from Sigma-Aldrich (MO, USA). Disodium hydrogen phosphate was purchased from Fluka (Germany). Allura red (E129) standard, amaranth (E123) standard, sunset yellow (E110) standard, tetrazine (E102) standard, fast green (E143) standard, ponceau 4R (New Coccine) (E124) standard, erythrosin B (E127) standard, brilliant blue FCF (E133) standard, brilliant black (E151) standard, and carmoisine (E122) standard were purchased from Sigma-Aldrich (MO, USA). A total number of 390 samples (30 for each of the sugars, sesame tahini, chicken broth cubes, mushroom, tomato paste, hookah honey, spices, liquid milk, candies, jams, ice cream, hummus, and juice) for recovery and quantification analysis were purchased from the local market. The equipment was UHPLC-DAD associated with Chromeleon software version 7.3. The column was Agilent Poroshell 120 HPH-C18 or equivalent ultrasonic bath, vortex, and centrifuge. FAPAS® proficiency tests (Food Analysis Performance Assessment Scheme) were purchased from BioFront Technologies, Inc. (Florida, USA). Food and drink samples, including sugars, sesame tahini, chicken broth cubes, mushroom, tomato paste, hookah honey, spices, liquid milk, candies, jams, ice cream, hummus, and juice, were purchased from the local market.

2.2 UHPLC system

The UHPLC system worked at ambient temperature and has the parameters shown in Table 1.

Table 1.

Chromatographic parameters used for Thermo Fisher Scientific UltiMate 3000 UHPLC System

ParameterThermo Fisher Scientific UltiMate 3000 UHPLC System
ColumnAgilent Poroshell 120 HPH-C18, 3 × 100 mm, 2.7 µm.
Column oven45 °C
Injection volume5 µL
Sample thermostat5 °C
Mobile phase A10 mM Na2HPO4, pH 7
Mobile phase BMethanol
GradientAt 0 min: 2% B
At 1 min: 2% B
At 4 min: 15% B
At 10 min: 30% B
At 14 min: 40% B
At 18 min: 95% B
At 20 min: 95% B
At 22 min: 40% B
Run timeAt 23 min: 2% B
At 26 min: 2% B
26 min.
Flow rate0.50 ml min−1
Data acquisition428 nm: Tartrazine (E102)
518 nm: Amaranth (E123)
Ponceau 4R (New Coccine) (E124)
Sunset Yellow (E110)
Allura Red (E129)
Carmoisine (E122)
530 nm: Brilliant Black (E151)
Erythrosin B (E127)
622 nm: Fast Green (E143)
Brilliant Blue FCF (E133)

2.3 Validation of analytical procedure and method development

2.3.1 Preparation of calibration standards

Allura red (E129), amaranth (E123), sunset yellow (E110), tetrazine (E102), fast green (E143), ponceau 4R (new coccine) (E124), erythrosin B (E127), brilliant blue FCF (E133), brilliant black (E151), and carmoisine (E122) standard stock solutions should be prepared individually by weighing approximately 20 mg of the standard and transferring it to a 10-mL.

Each flask received 300 µL of DMSO, and mobile phases A and B were diluted with the diluent mentioned above at a ratio of 80:20. The use of sonication is optional.

2.3.2 Linearity and working range

The initial stock solution concentration mixture was 200 mg L−1. The used concentrations for the calibration curve were 0.1, 0.2, 0.5, 1, 5, 10, 20, and 40 mg L−1.

2.4 Authentic sample treatment and batch processing

2.4.1 Sampling and sample preparation of synthetic colored samples

Colorants were easily extracted from food and drink samples using the sequential addition of 400 µL DMSO and 20 mL diluent to 2 g of material, followed by sonication for 2 min and centrifugation at 8,000 rpm for 10 min in an Eppendorf centrifuge 5804 R. The solution was decanted and filtered through a 0.45 µm PTFE syringe filter membrane. Recovery tests were conducted using spiked samples that had been purposely adulterated with a ten mg L−1 standard solution. The extraction method followed the same general outline used for reference solutions.

2.4.2 Limit of detection (LOD) and limit of quantification (LOQ)

LOD was defined as the analyte concentration that yields a signal-to-noise ratio (S/N) of more than three, while LOQ was defined as the analyte concentration that yields an S/N of higher than ten. Table 2 displays the measured LOD and LOQ for each colorant.

Table 2.

LOD, LOQ, and linearity results of all ten colors

Peak No.Compound nameLOD (mg L−1)LOQ (mg L−1)Linearity range (mg L−1)Retention timeAbsolute λmax (nm)Linearity equationR2 value
1Tartrazine (E102)0.0200.0670.1–203.55428Y = 0.3225X−0.39870.9991
2Amaranth (E123)0.0200.0670.1–205.24518Y = 0.2370X+0.03531.0000
3Ponceau 4R (New Coccine) (E124)0.0180.0610.1–207.52506Y = 0.3153X−0.01421.0000
4Sunset Yellow (E110)0.0200.0680.1–208.78518Y = 0.1909X+0.01861.0000
5Allura Red (E129)0.0180.0590.1–2011.29507Y = 0.3625X−0.05411.0000
6Carmoisine (E122)0.0200.0670.1–2015.32608Y = 0.4024X−0.00601.0000
7Brillant Black (E151)0.0200.0660.1–209.71575Y = 0.1643X−0.06631.0000
8Erythrosin B (E127)0.0180.0600.1–2018.37530Y = 0.6060X+0.02691.0000
9Fast Green (E143)0.0170.0570.1–2016.35622Y = 0.1917X+0.13270.9998
10Brillant Blue FCF (E133)0.0250.0820.1–2016.78630Y = 0.1470X+0.07061.0000

2.4.3 Robustness

The approach's robustness was assessed by purposefully altering five crucial method parameters. The resultant area and retention time deviations were computed and compared to the original technique. A standard spike mixing solution of colorant standards was administered in six replicates. The following variables were adjusted to test the method's robustness: flow rate (±2%), Injection volume (±2%), Wavelength (±3 nm), pH (±0.15), and Column temperature (±2 °C). Table 3 lists the robustness test conditions utilized in this investigation, and Figs 2 and 3 describe the findings of the robustness study.

Table 3.

The experimental setup for the robustness tests performed in this study

Parameter (Actual value)Measured deviationModified value
Flow rate (0.5)2%0.51 mL min−1
0.49 mL min−1
Injection volume (5 µL)2%5.1 µL
4.9 µL
Wavelength (428, 518, 530, 622 nm)(±) 3 nmWavelength (431, 521, 533, 625 nm)
Wavelength (425, 515, 527, 619 nm)
pH (7.0)(±) 0.1510 mm Buffer pH 7.15
10 mm Buffer pH 6.85
Column temperature (45 °C)(±) 2 °C47 °C
43 °C

3 Results and discussion

3.1 Separation and detection

An Agilent Poroshell 120 HPH-C18 (100 mm × 3.0 mm, 2.7 m) column provided excellent separation of 10 colorants in 20 min. The maximum absorption was discovered to be variable for each colorant. Figure 1 depicts the chromatographic elution patterns of ten different colorants. We utilized the ChemStation software's peak purity tool to analyze the purity of each peak, and therefore, the specificity of the procedure was assessed. Precision, linear range, accuracy, specificity, recovery, and robustness experiments were performed to verify the approach.

Fig. 1.
Fig. 1.

Separation of 10 dyes on an Agilent Poroshell 120 HPH-C18 column (or a suitable alternative). Seven spectral traces are superimposed on one another

Citation: Acta Chromatographica 36, 4; 10.1556/1326.2023.01175

HPLC dramatically saves the analysis time when numerous are evaluated simultaneously [19–21]. The current study describes a new UHPLC analytical approach for simultaneously assessing ten colorants using an Agilent Poroshell 120 HPH-C18 column. This novel method was created and tested using ICH guidelines for linearity, accuracy, precision, LOQ, and recovery. However, little analytical research on assessing several colorants has been published [1, 4, 5, 8, 9]. Figure 1 shows the separation of ten colorants using an Agilent Poroshell 120 HPH-C18 column or equivalent, Fig. 1.

3.2 FAPAS® quality control sample analysis

A FAPAS® quality control sample containing Allura Red (E129), Green S (E124), and Tartrazine (E102) was assessed using this technique to determine the method's accuracy. The average level of Allura Red (E129), Green S (E124), and Tartrazine (E102) was 28.36, 22.6, and 15.1 mg L−1, respectively, which was within the specified limits of 22.4–33.1, 18.0–27.1, and 11.9–18.4 mg L−1. These findings proved the correctness of the existing approach.

3.3 Linearity and range

The linear range is the valid interval of the signal's functional dependency on concentration or mass, commonly estimated using the least squares technique, which presupposes homeostatic observations throughout the linear range [22]. To show an appropriate linear range, it is usually recommended to create five different standard solutions ranging from 50 to 150% of the desired analytical concentration. However, it is vital to realize that the provided ranges often fluctuate depending on the intended use of the operation. For example, the ICH suggests preparing five standard solutions (plus a blank) ranging from 80 to 120% to analyze medicine or completed products [23]. In our research, every ten dyes have a linearity between 1 and 100 mg L−1. The R2 values ranged between 0.9991 and 1.0, Table 2.

3.4 Recovery and precision of colorants from samples

Standard addition was used to analyze recovery and precision (intra-day and inter-day) for many colorants in multiple actual samples. Twenty-five mgilligrams per liter (mg L−1) of a standard mixed solution, including all ten colorants, was employed in this study. Each colorant's peak area in the spiked sample, the control sample, and the reference chromatogram was determined independently. Recovery was calculated as a percentage based on the difference between the detector's response to the spiked and unspiked samples and the response shown in the standard chromatogram. The sample color recovery rate was above 90% for every colorant. All samples have a relative standard deviation (RSD) of colorants below 2%. The results are presented in Supplementary Tables S1–S13.

3.5 LOD and LOQ

Table 2 displays the method's LODs and LOQs for this investigation. LODs and LOQs ranged from 0.17–0.25 and 0.57–0.82 mg L−1, depending on the dye used. The dyes provided high absorbance at 622 nm even when used at very dilute concentrations (0.17 mg L−1). LOD was defined as an analyte concentration with a signal-to-noise ratio (S/N) higher than three, while LOQ was defined as an analyte concentration with an S/N larger than ten. Table 2 displays the observed LOD and LOQ values for each colorant.

3.6 Robustness

Five crucial technique parameters were manipulated to assess the approach's robustness. Differences in area and retention time were measured and compared to the standard procedure. We administered a conventional spike mix solution of colorant standards in six separate experiments. The ±2% tolerance was chosen for retention time and area (ICH reference). The robustness test conditions used in this study are noted in Table 1, and results from the robustness study are summarized in Figs 2 and 3.

Fig. 2.
Fig. 2.

Robustness test results summary for the area

Citation: Acta Chromatographica 36, 4; 10.1556/1326.2023.01175

Fig. 3.
Fig. 3.

Robustness test results in summary for retention time

Citation: Acta Chromatographica 36, 4; 10.1556/1326.2023.01175

All ten colorant's area deviations were within the range of acceptability across all different measures. In addition, the variance in retention time across all ten colorants was determined to be well within the acceptable range for this robustness analysis. The robustness findings show that the technique may be trusted for practical applications since the performance does not degrade noticeably when specific parameters are manipulated.

All ten colorant's area deviations were determined to be within the acceptable limit for all different criteria. Furthermore, all ten colors' retention time variation was determined to be within the acceptable limit for this robustness analysis. The robustness findings show that the approach is dependable for typical use and that purposeful parameter changes have little effect on performance.

3.7 Quantification and application of synthetic colors in food and drink samples

The area response was used to identify the presence of color additives in a wide range of food and drink samples. The computation was based on linearity equations that were derived using linearity curves. Additionally, spectral matching was employed to identify the compounds using the in-house-made UV spectral library. Table 4 shows the colorant concentrations found in various samples. All synthetic colors were tested in mushroom, tomato paste, hookah honey, spices, candies, jams, and ice cream samples. Furthermore, liquid milk has the fewest positive samples, while hookah honey has all positive samples.

Table 4.

Synthetic color concentrations (mg kg−1) in real food and drink samples

Sample typeNumber of positive samplesE102E110E122E123E124E127E129E133E143E151
Sugars182.1–7.21.3–2.40.19–0.60.8–4.21.8–3.70.2–2.8
Sesame tahini151.5–3.70.9–1.61.5–3.41.9–5.42.2–4.12.5–9.31.8–3.5
Chicken broth cubes190.6–2.80.9–4.50.5–3.81.5–4.03.3–7.5
Mushroom172.6–7.10.7–1.80.12–0.190.7–3.83.7–7.51.9–9.20.5–2.9
Tomato paste141.7–6.10.9–2.30.8–2.20.8–3.10.13–0.25.5–9.11.7–6.81.6–2.2
Hookah honey180.9–5.60.2–1.20.13–0.170.3–2.50.3–3.10.8–2.71.4–3.50.7–1.4
Spices202.5–5.81.1–3.80.14–0.210.4–3.30.8–2.11.3–7.20.8–2.41.9–5.7
Liquid milk81.7–3.20.3–2.40.7–3.11.5–3.4
Candies240.8–3.20.5–4.11.5–4.70.2–1.51.1–3.22.7–4.8
Jams211.5–8.70.2–1.51.8–3.20.7–1.31.7–5.12.9–4.60.6–3.12.1–4.5
Ice cream262.2–6.91.5–3.70.11–0.271.5–2.20.12–0.170.9–5.44.7–6.21.5–6.80.7–2.3
Hummus163.5–4.70.4–2.31.4–3.22.7–8.6
Juices111.7–3.40.9–2.71.6–4.21.4–3.91.8–6.20.5–1.4

This method was used to analyze thirteen different commercial food and drink products (including sugars, sesame tahini, chicken broth cubes, mushroom, tomato paste, hookah honey, spices, liquid milk, candies, jams, ice cream, hummus, and juice) that were colored with synthetic colors. They were obtained from Jordanian markets. Calibration curves were used to quantify the samples, and the results were confirmed by comparing their absorption spectra with food and drink colorant standards. In addition, Jordan generally adheres to EU regulations. Table 5 summarizes the allowed concentrations of the previously indicated colorants.

Table 5.

Acceptable Daily Intake (ADI), mg kg−1 body weight (BW)/day for several synthetic colors

Synthetic ColorAcceptable Daily Intake (ADI), mg kg−1 body weight (BW)/day
Tartrazine (E102)0–7.5 [24]
Amaranth (E123)0–0.15 [25]
Ponceau 4R (New Coccine) (E124)0–4 [26]
Sunset Yellow (E110)0–2.5 [27]
Allura Red (E129)0–7 [28]
Carmoisine (E122)0–4 [29]
Brillant Black (E151)0–5 [30]
Erythrosin B (E127)0–0.1 [31]
Fast Green (E143)0–25 [32]
Brillant Blue FCF (E133)0–12.5 [33]

Looking at the results and comparing them with the corresponding Acceptable Daily Intakes (ADIs), we find that E102 is nearly in the acceptable range for all foods and drinks except for the upper limit for jams (3 samples), where it greatly exceeded the upper limit, even if it did not exceed the upper limit for others. According to the E123 analysis, some of the foods and drinks under investigation exceeded the upper limit in mushrooms (three samples), hookah honey (five samples), spices (two samples), and ice cream (six samples). For E124, all foods and drinks tested were within the acceptable limit. The results of E110 exceeded the upper limit in four samples of chicken broth cubes and seven samples of candies. According to the E129 results, all tested foods and drinks were within the acceptable limit. The E122 test results exceeded the limit for chicken broth cubes (three samples), candies (five samples), and juices (two samples). Except for four samples of spices, the E151 results showed that all tested foods and drinks were within the acceptable range. Even for E127, the investigation foods and drinks are limited, but tomato paste (five samples) and ice cream (three samples) exceeded the limit. On the contrary, all foods and drinks tested are within the acceptable ranges for E143 and E133.

4 Conclusions

The extraction procedure and ultra-high-performance liquid chromatography coupled with diode array detection (UHPLC-DAD) have allowed the accurate quantification of ten synthetic colors in various food and drink samples. These dyes were evaluated in 18.5 min using a short analytical column. This approach provided consistent and repeatable outcomes for regular examination of synthetic colors with appropriate detection limits and fast analysis time.

Funding

None.

Conflict of interest

The authors declare that there is no conflict of interest.

Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1556/1326.2023.01175.

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    Solfrizzo, M.; Piemontese, L.; Gambacorta, L.; Zivoli, R.; Longobardi, F. Food coloring agents and plant food supplements derived from vitis Vinifera: a new source of human exposure to Ochratoxin A. J. Agric. Food Chem. 2015, 63, 36093614. https://doi.org/10.1021/acs.jafc.5b00326.

    • Search Google Scholar
    • Export Citation
  • 11.

    Maria de Souza Santos Cheibub, A.; Silva Bahiense de Lyra, E.; Jardim Alves, B.; Andrade Donagemma, R.; Duarte Pereira Netto, A. Development and validation of a multipurpose and multicomponent method for the simultaneous determination of six synthetic dyes in different foodstuffs by HPLC-UV-DAD. Food Chem. 2020, 323, 126811. https://doi.org/10.1016/j.foodchem.2020.126811.

    • Search Google Scholar
    • Export Citation
  • 12.

    Cifuentes, A. Recent advances in the application of capillary electromigration methods for food analysis. Electrophoresis 2006, 27, 283303.

    • Search Google Scholar
    • Export Citation
  • 13.

    de Andrade, F. I.; Florindo Guedes, M. I.; Pinto Vieira, Í.G.; Pereira Mendes, F. N.; Salmito Rodrigues, P. A.; Costa Maia, C. S.; Marques Ávila, M. M.; de Matos Ribeiro, L. Determination of synthetic food dyes in commercial soft drinks by TLC and ion-pair HPLC. Food Chem. 2014, 157, 193198. https://doi.org/10.1016/j.foodchem.2014.01.100.

    • Search Google Scholar
    • Export Citation
  • 14.

    Pauk, V.; Krejčí, M.; Lemr, K. Unified chromatography – mass spectrometry as a versatile tool for determination of food dyes. Anal Chim. Acta 2021, 1157, 338401. https://doi.org/10.1016/j.aca.2021.338401.

    • Search Google Scholar
    • Export Citation
  • 15.

    Smirnova, S. v.; Lyskovtseva, K. A.; Pletnev, I. v. Extraction and determination of synthetic food dyes using tetraalkylammonium based liquid-liquid extraction. Microchemical J. 2021, 162, 105833. https://doi.org/10.1016/j.microc.2020.105833.

    • Search Google Scholar
    • Export Citation
  • 16.

    Mahale, R. S.; Shashanka, R.; Vasanth, S.; Vinaykumar, R. Voltammetric determination of various food azo dyes using different modified carbon paste electrodes. Biointerface Res. Appl. Chem. 2022, 12, 45574566.

    • Search Google Scholar
    • Export Citation
  • 17.

    Mbabazi, J. Principles and methods for the risk assessment of chemicals in food. Int. J. Environ. Stud. 2011, 68, 251252. https://doi.org/10.1080/00207233.2010.549617.

    • Search Google Scholar
    • Export Citation
  • 18.

    Vlase, L.; Muntean, D.; Cobzac, S. C.; Filip, L. Development and validation of an HPLC-UV method for determination of synthetic food colorants. Revue Roumaine de Chim. 2014, 59, 719725.

    • Search Google Scholar
    • Export Citation
  • 19.

    Alkather, Z.; Hailat, M.; Al-Shdefat, R.; Abu Dayyih, W. Development and validation of HPLC method for five gliptins in pharmaceutical dosage forms in finished marketed products. Curr. Pharm. Anal 2020, 17, 12631271. https://doi.org/10.2174/1573412917999201102212635.

    • Search Google Scholar
    • Export Citation
  • 20.

    Hailat, M.; Al-Ani, I.; Hamad, M.; Zakareia, Z.; Abu Dayyih, W. Development and validation of a method for quantification of favipiravir as COVID-19 management in spiked human plasma. Molecules 2021, 26, Page 3789 2021, 26, 3789 https://doi.org/10.3390/MOLECULES26133789.

    • Search Google Scholar
    • Export Citation
  • 21.

    Deeb, A. A.; Hailat, M.; Aldoqum, H. M.; Abuothman, M.; Abuyaman, O.; Abu Dayyih, W. An analytical method for determining N -nitrosodimethylamine and N -nitrosodiethylamine contamination in Irbesartan, Olmesartan and metformin by UPLC-APCI-MS/MS in tablet dosage form. J. Chromatogr. Sci. 2023. https://doi.org/10.1093/chromsci/bmad068.

    • Search Google Scholar
    • Export Citation
  • 22.

    Lavine, B. Chemometrics: statistics and Computer Application in Analytical Chemistry By Matthias Otto (Freiberg University of Mining and Technology). Wiley-VCH: Weinheim. 1999. $69.95. 307 Pp. ISBN 3-527-29628-X. J. Am. Chem. Soc. 2000, 122, 59035904. https://doi.org/10.1021/ja995724t.

    • Search Google Scholar
    • Export Citation
  • 23.

    Araujo, P. Key Aspects of Analytical Method Validation and Linearity Evaluation. J. Chromatogr. B Analyt Technol. Biomed. Life Sci. 2009, 877, 22242234. https://doi.org/10.1016/j.jchromb.2008.09.030.

    • Search Google Scholar
    • Export Citation
  • 24.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation Tartrazine (E 102). EFSA J. 2009, 7, 1331. https://doi.org/10.2903/j.efsa.2009.1331.

    • Search Google Scholar
    • Export Citation
  • 25.

    European Food Safety Authority Refined Exposure Assessment for Amaranth (E 123). EFSA J. 2013, 11, 3442. https://doi.org/10.2903/j.efsa.2013.3442.

    • Search Google Scholar
    • Export Citation
  • 26.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Ponceau 4R (E 124) as a Food Additive. EFSA J. 2009, 7, 1328. https://doi.org/10.2903/j.efsa.2009.1328.

    • Search Google Scholar
    • Export Citation
  • 27.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Sunset Yellow FCF (E 110) as a Food Additive. EFSA J. 2009, 7, 1330. https://doi.org/10.2903/j.efsa.2009.1330.

    • Search Google Scholar
    • Export Citation
  • 28.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Allura Red AC (E 129) as a Food Additive. EFSA J. 2009, 7, 1327. https://doi.org/10.2903/j.efsa.2009.1327.

    • Search Google Scholar
    • Export Citation
  • 29.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Azorubine/Carmoisine (E 122) as a Food Additive. EFSA J. 2009, 7, 1332. https://doi.org/10.2903/j.efsa.2009.1332.

    • Search Google Scholar
    • Export Citation
  • 30.

    European Food Safety Authority Refined Exposure Assessment for Brilliant Black BN (E 151). EFSA J. 2015, 13, 3960. https://doi.org/10.2903/j.efsa.2015.3960.

    • Search Google Scholar
    • Export Citation
  • 31.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Erythrosine (E 127) as a Food Additive. EFSA J. 2011, 9, 1854. https://doi.org/10.2903/j.efsa.2011.1854.

    • Search Google Scholar
    • Export Citation
  • 32.

    World health Organization (WHO) Fast Green , FCF . Stain. Available online: https://apps.who.int/food-additives-contaminants-jecfa-database/Home/Chemical/1356 (accessed on October 5, 2022).

    • Search Google Scholar
    • Export Citation
  • 33.

    Kumar, N.; Raghavendra, M.; Tokas, J.; Singal, H. R. Flavor Addition in Dairy Products: Health Benefits and Risks. In Nutrients in Dairy and Their Implications for Health and Disease; Academic Press, 2017; pp 123135, ISBN 9780128097632.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

  • 1.

    Asif Ahmed, M.; Al-Khalifa, A. S.; Al-Nouri, D. M.; El-din, M. F. S. Dietary intake of artificial food color additives containing food products by school-going children. Saudi J. Biol. Sci. 2021, 28, 2734. https://doi.org/10.1016/j.sjbs.2020.08.025.

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  • 2.

    Becker, L.; van Rompay, T. J. L.; Schifferstein, H. N. J.; Galetzka, M. Tough package, strong taste: the influence of packaging design on taste impressions and product evaluations. Food Qual. Prefer 2011, 22, 1723. https://doi.org/10.1016/j.foodqual.2010.06.007.

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    • Export Citation
  • 3.

    Sunoqrot, S.; Al-Shalabi, E.; Al-Bakri, A. G.; Zalloum, H.; Abu-Irmaileh, B.; Ibrahim, L. H.; Zeno, H. Coffee bean polyphenols can form biocompatible template-free antioxidant nanoparticles with various sizes and distinct colors. ACS Omega 2021, 6, 27672776. https://doi.org/10.1021/acsomega.0c05061.

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  • 4.

    Stich, E. Food color and coloring food: quality, differentiation and regulatory requirements in the European Union and the United States. Quality, differentiation and regulatory requirements in the European Union and the United States. In Handbook on Natural Pigments in Food and Beverages: Industrial Applications for Improving Food Color; Woodhead Publishing, 2016; pp 327, ISBN 9780081003923.

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    • Export Citation
  • 5.

    Henry, B. S. Natural food colours. In Natural Food Colorants; Springer: Boston, MA, 1996; pp 4079.

  • 6.

    Campos-Montiel, R. G.; Pimentel-González, D. J.; Figueira, A. C. Chemical and functional properties of food components. In Advances in Food Science and Technology;, 2013; pp 151183, ISBN 9781118121023.

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    • Export Citation
  • 7.

    Dey, S.; Nagababu, B. H. Applications of food color and bio-preservatives in the food and its effect on the human health. Food Chem. Adv. 2022, 1, 100019. https://doi.org/10.1016/J.FOCHA.2022.100019.

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    • Export Citation
  • 8.

    Ntrallou, K.; Gika, H.; Tsochatzis, E. Analytical and sample preparation techniques for the determination of food colorants in food matrices. Foods 2020, 9, 58.

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    • Export Citation
  • 9.

    Palianskikh, A. I.; Sychik, S. I.; Leschev, S. M.; Pliashak, Y. M.; Fiodarava, T. A.; Belyshava, L. L. Development and validation of the HPLC-DAD method for the quantification of 16 synthetic dyes in various foods and the use of liquid anion exchange extraction for qualitative expression determination. Food Chem. 2022, 369, 130947. https://doi.org/10.1016/j.foodchem.2021.130947.

    • Search Google Scholar
    • Export Citation
  • 10.

    Solfrizzo, M.; Piemontese, L.; Gambacorta, L.; Zivoli, R.; Longobardi, F. Food coloring agents and plant food supplements derived from vitis Vinifera: a new source of human exposure to Ochratoxin A. J. Agric. Food Chem. 2015, 63, 36093614. https://doi.org/10.1021/acs.jafc.5b00326.

    • Search Google Scholar
    • Export Citation
  • 11.

    Maria de Souza Santos Cheibub, A.; Silva Bahiense de Lyra, E.; Jardim Alves, B.; Andrade Donagemma, R.; Duarte Pereira Netto, A. Development and validation of a multipurpose and multicomponent method for the simultaneous determination of six synthetic dyes in different foodstuffs by HPLC-UV-DAD. Food Chem. 2020, 323, 126811. https://doi.org/10.1016/j.foodchem.2020.126811.

    • Search Google Scholar
    • Export Citation
  • 12.

    Cifuentes, A. Recent advances in the application of capillary electromigration methods for food analysis. Electrophoresis 2006, 27, 283303.

    • Search Google Scholar
    • Export Citation
  • 13.

    de Andrade, F. I.; Florindo Guedes, M. I.; Pinto Vieira, Í.G.; Pereira Mendes, F. N.; Salmito Rodrigues, P. A.; Costa Maia, C. S.; Marques Ávila, M. M.; de Matos Ribeiro, L. Determination of synthetic food dyes in commercial soft drinks by TLC and ion-pair HPLC. Food Chem. 2014, 157, 193198. https://doi.org/10.1016/j.foodchem.2014.01.100.

    • Search Google Scholar
    • Export Citation
  • 14.

    Pauk, V.; Krejčí, M.; Lemr, K. Unified chromatography – mass spectrometry as a versatile tool for determination of food dyes. Anal Chim. Acta 2021, 1157, 338401. https://doi.org/10.1016/j.aca.2021.338401.

    • Search Google Scholar
    • Export Citation
  • 15.

    Smirnova, S. v.; Lyskovtseva, K. A.; Pletnev, I. v. Extraction and determination of synthetic food dyes using tetraalkylammonium based liquid-liquid extraction. Microchemical J. 2021, 162, 105833. https://doi.org/10.1016/j.microc.2020.105833.

    • Search Google Scholar
    • Export Citation
  • 16.

    Mahale, R. S.; Shashanka, R.; Vasanth, S.; Vinaykumar, R. Voltammetric determination of various food azo dyes using different modified carbon paste electrodes. Biointerface Res. Appl. Chem. 2022, 12, 45574566.

    • Search Google Scholar
    • Export Citation
  • 17.

    Mbabazi, J. Principles and methods for the risk assessment of chemicals in food. Int. J. Environ. Stud. 2011, 68, 251252. https://doi.org/10.1080/00207233.2010.549617.

    • Search Google Scholar
    • Export Citation
  • 18.

    Vlase, L.; Muntean, D.; Cobzac, S. C.; Filip, L. Development and validation of an HPLC-UV method for determination of synthetic food colorants. Revue Roumaine de Chim. 2014, 59, 719725.

    • Search Google Scholar
    • Export Citation
  • 19.

    Alkather, Z.; Hailat, M.; Al-Shdefat, R.; Abu Dayyih, W. Development and validation of HPLC method for five gliptins in pharmaceutical dosage forms in finished marketed products. Curr. Pharm. Anal 2020, 17, 12631271. https://doi.org/10.2174/1573412917999201102212635.

    • Search Google Scholar
    • Export Citation
  • 20.

    Hailat, M.; Al-Ani, I.; Hamad, M.; Zakareia, Z.; Abu Dayyih, W. Development and validation of a method for quantification of favipiravir as COVID-19 management in spiked human plasma. Molecules 2021, 26, Page 3789 2021, 26, 3789 https://doi.org/10.3390/MOLECULES26133789.

    • Search Google Scholar
    • Export Citation
  • 21.

    Deeb, A. A.; Hailat, M.; Aldoqum, H. M.; Abuothman, M.; Abuyaman, O.; Abu Dayyih, W. An analytical method for determining N -nitrosodimethylamine and N -nitrosodiethylamine contamination in Irbesartan, Olmesartan and metformin by UPLC-APCI-MS/MS in tablet dosage form. J. Chromatogr. Sci. 2023. https://doi.org/10.1093/chromsci/bmad068.

    • Search Google Scholar
    • Export Citation
  • 22.

    Lavine, B. Chemometrics: statistics and Computer Application in Analytical Chemistry By Matthias Otto (Freiberg University of Mining and Technology). Wiley-VCH: Weinheim. 1999. $69.95. 307 Pp. ISBN 3-527-29628-X. J. Am. Chem. Soc. 2000, 122, 59035904. https://doi.org/10.1021/ja995724t.

    • Search Google Scholar
    • Export Citation
  • 23.

    Araujo, P. Key Aspects of Analytical Method Validation and Linearity Evaluation. J. Chromatogr. B Analyt Technol. Biomed. Life Sci. 2009, 877, 22242234. https://doi.org/10.1016/j.jchromb.2008.09.030.

    • Search Google Scholar
    • Export Citation
  • 24.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation Tartrazine (E 102). EFSA J. 2009, 7, 1331. https://doi.org/10.2903/j.efsa.2009.1331.

    • Search Google Scholar
    • Export Citation
  • 25.

    European Food Safety Authority Refined Exposure Assessment for Amaranth (E 123). EFSA J. 2013, 11, 3442. https://doi.org/10.2903/j.efsa.2013.3442.

    • Search Google Scholar
    • Export Citation
  • 26.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Ponceau 4R (E 124) as a Food Additive. EFSA J. 2009, 7, 1328. https://doi.org/10.2903/j.efsa.2009.1328.

    • Search Google Scholar
    • Export Citation
  • 27.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Sunset Yellow FCF (E 110) as a Food Additive. EFSA J. 2009, 7, 1330. https://doi.org/10.2903/j.efsa.2009.1330.

    • Search Google Scholar
    • Export Citation
  • 28.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Allura Red AC (E 129) as a Food Additive. EFSA J. 2009, 7, 1327. https://doi.org/10.2903/j.efsa.2009.1327.

    • Search Google Scholar
    • Export Citation
  • 29.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Azorubine/Carmoisine (E 122) as a Food Additive. EFSA J. 2009, 7, 1332. https://doi.org/10.2903/j.efsa.2009.1332.

    • Search Google Scholar
    • Export Citation
  • 30.

    European Food Safety Authority Refined Exposure Assessment for Brilliant Black BN (E 151). EFSA J. 2015, 13, 3960. https://doi.org/10.2903/j.efsa.2015.3960.

    • Search Google Scholar
    • Export Citation
  • 31.

    EFSA Panel on Food Additives and Nutrient Sources added to Food Scientific Opinion on the Re-Evaluation of Erythrosine (E 127) as a Food Additive. EFSA J. 2011, 9, 1854. https://doi.org/10.2903/j.efsa.2011.1854.

    • Search Google Scholar
    • Export Citation
  • 32.

    World health Organization (WHO) Fast Green , FCF . Stain. Available online: https://apps.who.int/food-additives-contaminants-jecfa-database/Home/Chemical/1356 (accessed on October 5, 2022).

    • Search Google Scholar
    • Export Citation
  • 33.

    Kumar, N.; Raghavendra, M.; Tokas, J.; Singal, H. R. Flavor Addition in Dairy Products: Health Benefits and Risks. In Nutrients in Dairy and Their Implications for Health and Disease; Academic Press, 2017; pp 123135, ISBN 9780128097632.

    • Search Google Scholar
    • Export Citation
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Senior editors

Editor(s)-in-Chief: Sajewicz, Mieczyslaw, University of Silesia, Katowice, Poland

Editors(s)

  • Danica Agbaba, University of Belgrade, Belgrade, Serbia (1953-2024)
  • Łukasz Komsta, Medical University of Lublin, Lublin, Poland
  • Ivana Stanimirova-Daszykowska, University of Silesia, Katowice, Poland
  • Monika Waksmundzka-Hajnos, Medical University of Lublin, Lublin, Poland

Editorial Board

  • Ravi Bhushan, The Indian Institute of Technology, Roorkee, India
  • Jacek Bojarski, Jagiellonian University, Kraków, Poland
  • Bezhan Chankvetadze, State University of Tbilisi, Tbilisi, Georgia
  • Michał Daszykowski, University of Silesia, Katowice, Poland
  • Tadeusz H. Dzido, Medical University of Lublin, Lublin, Poland
  • Attila Felinger, University of Pécs, Pécs, Hungary
  • Kazimierz Glowniak, Medical University of Lublin, Lublin, Poland
  • Bronisław Glód, Siedlce University of Natural Sciences and Humanities, Siedlce, Poland
  • Anna Gumieniczek, Medical University of Lublin, Lublin, Poland
  • Urszula Hubicka, Jagiellonian University, Kraków, Poland
  • Krzysztof Kaczmarski, Rzeszow University of Technology, Rzeszów, Poland
  • Huba Kalász, Semmelweis University, Budapest, Hungary
  • Katarina Karljiković Rajić, University of Belgrade, Belgrade, Serbia
  • Imre Klebovich, Semmelweis University, Budapest, Hungary
  • Angelika Koch, Private Pharmacy, Hamburg, Germany
  • Piotr Kus, Univerity of Silesia, Katowice, Poland
  • Debby Mangelings, Free University of Brussels, Brussels, Belgium
  • Emil Mincsovics, Corvinus University of Budapest, Budapest, Hungary
  • Ágnes M. Móricz, Centre for Agricultural Research, Budapest, Hungary
  • Gertrud Morlock, Giessen University, Giessen, Germany
  • Anna Petruczynik, Medical University of Lublin, Lublin, Poland
  • Robert Skibiński, Medical University of Lublin, Lublin, Poland
  • Bernd Spangenberg, Offenburg University of Applied Sciences, Germany
  • Tomasz Tuzimski, Medical University of Lublin, Lublin, Poland
  • Adam Voelkel, Poznań University of Technology, Poznań, Poland
  • Beata Walczak, University of Silesia, Katowice, Poland
  • Wiesław Wasiak, Adam Mickiewicz University, Poznań, Poland
  • Igor G. Zenkevich, St. Petersburg State University, St. Petersburg, Russian Federation

 

SAJEWICZ, MIECZYSLAW
E-mail:mieczyslaw.sajewicz@us.edu.pl

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2023  
Web of Science  
Journal Impact Factor 1.7
Rank by Impact Factor Q3 (Chemistry, Analytical)
Journal Citation Indicator 0.43
Scopus  
CiteScore 4.0
CiteScore rank Q2 (General Chemistry)
SNIP 0.706
Scimago  
SJR index 0.344
SJR Q rank Q3

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Acta Chromatographica
Language English
Size A4
Year of
Foundation
1988
Volumes
per Year
1
Issues
per Year
4
Founder Institute of Chemistry, University of Silesia
Founder's
Address
PL-40-007 Katowice, Poland, Bankowa 12
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2083-5736 (Online)

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