Folin-Ciocâlteu, RP-HPLC (reverse phase-high performance liquid chromatography), and LC-MS (liquid chromatography-mass spectrometry) provide complementary information for describing cider (Malus spp.) apple juice

This study aims to analyze the polyphenol profiles of 14 apple (Malus spp.) genotypes used for hard cider production. Using the Folin-Ciocâlteu assay, RP-HPLC, and untargeted LC-MS, the research quantifies key polyphenols and identifies novel compounds with potential health benefits.

The findings highlight apple genotypes with distinctive polyphenol compositions that could enhance cider flavor, aroma, and health value, while also informing breeding programs and further physiological studies.

The original article

Folin-Ciocâlteu, RP-HPLC (reverse phase-high performance liquid chromatography), and LC-MS (liquid chromatography-mass spectrometry) provide complementary information for describing cider (Malus spp.) apple juice

Kamal Tyagi, Andy C.W. Lui, Sheng Zhang, Gregory Michael Peck 

Journal of Food Composition and Analysis, Volume 137, Part A, 2025, 106844

https://doi.org/10.1016/j.jfca.2024.106844

licensed under CC-BY 4.0

Selected sections from the article follow. Formats and hyperlinks were adapted from the original.

Apple (Malus spp.) polyphenols are important in the human diet for their antioxidant, anti-carcinogenic, anti-inflammatory, among other nutraceutical properties (Guyot et al., 2003, Karikas, 2010, Bondonno et al., 2017, Septembre-Malaterre et al., 2018). Additionally, apple polyphenols confer important organoleptic attributes to hard (fermented) cider, such as color, bitterness, astringency, and both colloidal and microbial stability (Maragò et al., 2016; Karl et al., 2023; Septembre-Malaterre et al., 2018; Zuriarrain-Ocio et al., 2021). Juice and cider color are predominantly influenced by chlorogenic acid, with smaller contributions from flavan-3-ol monomers, procyanidins, and dihydrochalcones (Verdu et al., 2014, Cilliers et al., 1990). Flavan-3-ol monomers and oligomeric procyanidins [particularly those with a mean degree of polymerization (mDP) less than 6] confer bitter taste, while higher molecular-weight procyanidins (mDP greater than 6) impart astringent “mouthfeel” (Robichaud and Noble, 1990). Moreover, hydroxycinnamates such as p-coumaroyl quinic and chlorogenic acids are precursors that impart the characteristic “bittersweet” flavors found in cider (Lea et al., 2003).

Apple polyphenols show a huge diversity of structure and function, and can be divided into five major classes. In apple flesh, the primary classes of polyphenols include flavan-3-ols (monomers and procyanidins), hydroxycinnamic acids, and dihydrochalcones, while flavonols and anthocyanins are predominantly found in peel tissue (Renard et al., 2007). Flavan-3-ol monomers are largely represented by (–)-epicatechin, along with lower levels of (+)-catechin. Procyanidins are oligomers and polymers of flavan-3-ol monomers, of which epicatechin is the major constituent. Apple procyanidins, classified as B-type, are composed of flavan-3-ol monomers connected at C4-C6 or C4-C8. Hydroxycinnamic acids mainly comprise chlorogenic acid, and dihydrochalcones mainly comprise phlorizin and phloretin glucoside (Guyot et al., 2003).

Due to the importance of polyphenols in foods and beverages, many analytical methods have been developed for determining total polyphenolic concentration, such as the Folin-Ciocâlteu assay, Löwenthal permanganate assay, 4-dimethylaminocinnamaldehyde (DMAC) assay, bovine serum albumin assay, and ultraviolet and near-infrared spectroscopy (Ma et al., 2019, Girschik et al., 2017, Pissard et al., 2018). Among these, the Folin-Ciocâlteu assay is frequently used for total polyphenol estimations in apple juice and cider due to its cost-effectiveness, relatively quick setup, and ease of use (Singleton and Rossi, 1965, Ainsworth and Gillespie, 2007, Pérez et al., 2023, Raposo et al., 2024).

Similarly, many analytical techniques have been developed to quantify and/or identify individual polyphenols, such as liquid and gas chromatography, nuclear magnetic resonance, and capillary electrophoresis. RP-HPLC is one of the most commonly used techniques, even though it is incapable of measuring polyphenols present in low concentration, and co-eluting peaks that can pose challenges for the accurate analysis of polyphenols (Tyagi et al., 2020, Khanizadeh et al., 2008, Sawikowska et al., 2021). In recent years, liquid chromatography coupled with mass spectrometry (LC-MS) has gained greater use as a “metabolomic” analytical technique due to its high sensitivity, specificity, and accuracy (Myrtsi et al., 2021, Li et al., 2022, Nowicka et al., 2019, Thompson-Witrick et al., 2014, Yadav et al., 2021). Untargeted metabolomics enables compound identification, facilitating the greater potential for discovery, while partial- to fully-targeted metabolomics utilizes standards, offering precision and sensitivity for quantifying known metabolites (Schrimpe-Rutledge et al., 2016). Untargeted LC-MS has been widely employed for comprehensive profiling and identification of a wide range of polyphenols (De Rosso et al., 2015; Flamini et al., 2013; Yadav et al., 2021).

Because of its widespread use, it is useful to compare Folin-Ciocâlteu estimations of total polyphenol levels with chromatographic techniques. In our current study, we profiled 14 apple genotypes, representing varying levels of total polyphenols based on the Folin-Ciocâlteu assay, using RP-HPLC and untargeted LC-MS. Our findings highlight the suitability of each method and identify a few genotypes with high polyphenol content, which can potentially be used in breeding or physiological studies.

2. Material and methods

2.3. RP-HPLC analysis

The juice samples were diluted in a 1:1 ratio with 100 % methanol (containing 0.1 % ascorbic acid and 0.1 % hydrochloric acid), vortexed, and centrifuged at 20,000 g for 10 min at 4 °C. The supernatant was centrifuged at 20,000 g for 15 min at 4 °C before injection. Polyphenols were chromatographically separated by RP-HPLC using a Poroshell HPH-C18 column (4.6 ×100 mm, 2.7 μm particle size) on an Agilent Infinity series 1260 HPLC system (Agilent Technologies, Santa Clara, CA, USA). The system was equipped with a diode array detector (DAD) and operated using a binary solvent gradient with mobile phase A [1.5 % (v/v) formic acid in ultrapure Milli-Q water] and mobile phase B [1.5 % (v/v) formic acid and 1.4 % (v/v) water in acetonitrile] with a flow rate on 1 mL min⁻¹. A 10 µL sample was injected into the column for analysis and the column temperature was maintained at 35 °C. The starting condition of the gradient was 95 % of solvent A and 5 % of solvent B. Subsequently, solvent B was linearly increased to 15 % in 25 min, then to 27 % in 10 min, and kept at 27 % for 3 min. Thereafter, the mobile phase was reverted to the initial condition in 2 min and held for 3 min for re-equilibration of the column before the next injection, for a total run time of 43 min.

All data processing and analysis were completed using Agilent CDS ChemStation software.

2.4. Untargeted LC-MS/MS analysis

For untargeted metabolomics, 100 µL of apple juice was first dried down in a Speedvac at room temperature, and then reconstituted in 90 µL of a 75 % methanolic/aqueous solution containing 1 % acetic acid and a 5 ppm standard mixture of sulfadimethoxine, ^13 Cpyruvic acid, and ^13 Cvaline for quality assurance to allow monitoring of LC-MS instrument performance over time. Simultaneously, an equal amount of each of the 42 samples was pooled to serve as global quality control (QC) samples for normalization in relative quantitation by LC-MS analysis and for metabolomics identifications by LC-MS/MS analysis. All samples, including quality controls, were centrifuged at 17,000 g for 5 min at 4 °C before transfer to an LC-MS sampler vial.

Chromatographic separation of polyphenols was carried out on a Vanquish UHPLC System with an Accucore Vanquish C18+ column (particle size 1.5 µm, 2.1 mm ID x 100 mm) coupled to a Q Exactive™ Hybrid Quadrupole-Orbitrap High-Resolution Mass Spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) (Ledet et al., 2020, Bhawal et al., 2021). The gradient comprised a binary solvent system consisting of solvent A [0.1 % (v/v) formic acid in ultrapure Milli-Q water] and solvent B [0.1 % (v/v) formic acid in acetonitrile] at a flow rate of 320 μL min⁻¹. The injection volume was 2 μL with a column temperature of 45 °C and an autosampler temperature of 4 °C. The gradient starts at 99 % solvent A and 1 % solvent B for 2 min. Solvent B was then increased to 30 % for 18 min and then to 95 % for 2 min. The mobile phase was then reverted to 1 % solvent B in 0.5 min and held for 2.5 min for re-equilibration of the column before the next injection with a total run time of 25 min. To avoid possible bias, the sequence of injections was randomized. Additionally, a total of 12 QC samples were run, with 3 at the beginning followed by 1 QC at an interval of every 5 samples and at the end of the sequence.

Data acquisition was performed on Xcalibur 4.0 operation software (Thermo-Fisher Scientific).

2.5. Untargeted metabolite data processing for polyphenol identification

The acquired datasets, which comprised full MS and data-dependent MS-MS raw files, were processed using Compound Discoverer 3.2 (Thermo-Fisher Scientific) involving several steps including normalization, peak alignment, compound identification, and related statistical analyses. An untargeted metabolomics workflow with putative identification through a public mzCloud database and an in-house spectral library (containing 942 compounds including some polyphenols) was used to identify compounds on MS/MS level with a mass tolerance of 10 ppm. The identification of compounds relies on raw data processing with specific algorithms that generate molecular formulas based on m/z and relevant isotope patterns which measure relative abundances, and the fragment patterns for each specific molecule. Successful metabolite identification and correct annotation rely on available, searchable metabolomic databases. Additional databases including, ChemSpider, BioCyc, Human Metabolome Database, and KEGG, were used to annotate features based on exact mass with a mass tolerance of 5 ppm as well as the Compound Discoverer internal database (an endogenous metabolites database of 4400 compounds). The software parameters for alignment were 5 ppm mass tolerance for the adaptive curve model and 0.5 min maximum shift for alignment. The software parameters for detecting unknown compounds were 5 ppm mass tolerance for detection, 30 % intensity tolerance, 3 for the sensitivity and noise threshold, and 2 × 10⁶ minimum peak height. The initially identified molecules in apple juice samples were filtered out using Compound Discoverer 3.2 through background subtraction and exclusion of false positive or repetitive features without MS2 spectra, and removal of compounds not found in QC samples.

3. Results

3.1. Folin-Ciocâlteu total polyphenols

Total juice polyphenol concentration, as measured the Folin-Ciocâlteu assay, showed wide variations, ranging from 422 to 4860 mg L⁻¹, with a mean value of 1400 mg L⁻¹ (Wojtyna, 2018). Based on this analysis, 14 genotypes representing varying levels of total polyphenols (low, medium, and high) were selected for further analysis (Fig. 1A). Among them, 11 were M. × domestica, 1 was a M. coronaria hybrid (‘Zapta’), 1 was a M. ioensis hybrid (‘Kola’), and 1 was a M. sieversii (‘Kaz 95 18–06’). Of those 14, M. × domestica ‘Liberty’ exhibited the lowest total polyphenol content (560 mg L⁻¹), while the M. sieversii seedling (‘Kaz 95 18–06’) had the greatest concentration (4860 mg L⁻¹) (Fig. 1A).

Journal of Food Composition and Analysis, Volume 137, Part A, 2025, 106844: Fig. 1. Total juice polyphenol concentration of 14 apple genotypes harvested from the USDA-PGRU (Geneva, NY) in 2017 (n=3 samples per genotype). (A) Bar chart showing mean total polyphenols content measured by RP-HPLC (sum of individual polyphenols) and Folin-Ciocâlteu Assay, with standard deviation. (B) Regression analysis of total polyphenols measured using RP-HPLC and Folin-Ciocâlteu Assay. Each dot represents one genotype.

3.2. Polyphenol profiling using RP-HPLC

A total of 19 polyphenols were identified in the juice of 14 genotypes using RP-HPLC, belonging to the following classes: flavan-3-ols, hydroxycinnamates, flavonols, and dihydrochalcones. A representative chromatogram for polyphenol separation has been provided in the supplementary materials (Fig. S1). ‘Golden Delicious’ had the least total polyphenol concentration by RP-HPLC (4 mg L⁻¹) whereas ‘Kola’ had the greatest (1429 mg L⁻¹) (Fig. 1A). A moderate, linear, and positive relationship (R²=0.619, p=0.0008) between Folin-Ciocâlteu and RP-HPLC was found using a Pearson correlation analysis (Fig. 1B).