RP-HPLC Analysis of Polyphenols and Antioxidants in Dark Chocolate
A recent study set out to assess the significance of geographical and varietal factors in the content of alkaloids, phenolic compounds, and the antioxidant capacity of chocolate samples. Filtered extracts were analyzed by reversed-phase high-performance liquid chromatography (RP-HPLC) with ultraviolet (UV) and spectrophotometric methods to determine individual phenolics and overall indexes of antioxidant and flavonoid content.
Dark chocolates were characterized according to geographical origin, cocoa variety, and cocoa content in a recent study published in the Journal of Food Sciences (1), using the methylxanthine and polyphenolic composition and antioxidant activity as the data. The researchers were attempting to uncover sample patterns and identify possible markers of quality, variety, or origin to deal with authentication or fraud detection issues. Filtered extracts were analyzed by reversed-phase high-performance liquid chromatography (RP-HPLC) with ultraviolet (UV) and spectrophotometric methods to determine individual phenolics and overall indexes of antioxidant and flavonoid content.
Dark chocolate’s popularity has increased considerably in recent years, both because of its taste and the health-related properties it offers, including antioxidant, anti-inflammatory, antimicrobial, anticancer, and cardioprotective activities (2-6). These benefits are primarily related to the high polyphenol content of dark chocolate due to its main ingredient being cocoa (7). The authentication and detection of possible adulterations can be done through a profiling approach based on various natural components as a source of information (8,9) including phenolic compounds (10,11) together with methylxanthines (12-14). Amino acids and biogenic amines have also been used to characterize cocoa or chocolate (15).
A set of 26 dark chocolates available in supermarkets and specialized shops was analyzed in this work. The cocoa used to produce the chocolates was from Africa (Ghana, Madagascar, Tanzania, Cameroun, or Ivory Coast), South America (Ecuador, Peru, or Venezuela), Central America (Mexico, Nicaragua, the Dominican Republic, Haiti, or Cuba), and Asia (Papua New Guinea and the Philippines). Criollo (and Nacional), Forastero, and Trinitario cocoa samples were available. Although the study design covered a small number of samples, the analyte concentrations depend on geographical and varietal factors, in addition to the content of cocoa. The research determined that chocolates made with South American cocoa were richer in bioactive compounds; similarly, Criollo cocoa also appears to contain higher levels of phenolics. Despite the multifactorial nature of the analyte composition, the authors said that their exploratory study can be the basis for addressing further cocoa or chocolate authentication studies. Furthermore, combining data from individual compounds and generic phenolic indices or antioxidant capacity for multivariate analysis may provide more comprehensive information on the sample behavior, both at the level of descriptors and to infer potential beneficial qualities for health (1).
Dark chocolate pieces crushed and cocoa beans, culinary background.
© Sea Wave - stock.adobe.com
References
1. Parada, T.; Pardo, P.; Saurina, J.; Sentellas, S. Characterization of Dark Chocolates Based on Polyphenolic Profiles and Antioxidant Activity. J. Food Sci. 2024. DOI: 10.1111/1750-3841.17451
2. Febrianto, N. A.; Wang, S.; Zhu, F. Chemical and Biological Properties of Cocoa Beans Affected by Processing: A Review. Crit. Rev. Food Sci. Nutr. 2022, 62 (30), 8403–8434. DOI: 10.1080/10408398.2021.1928597
3. Gil, M.; Uribe, D.; Gallego, V.; Bedoya, C.; Arango-Varela, S. Traceability of Polyphenols in Cocoa During the Postharvest and Industrialization Processes and Their Biological Antioxidant Potential. Heliyon 2021, 7 (8), e07738. DOI: 10.1016/j.heliyon.2021.e07738
4. Ludovici, V.; Barthelmes, J.; Nägele, M. P.; Enseleit, F.; Ferri, C.; Flammer, A. J.; Ruschitzka, F.; Sudano, I. Cocoa, Blood Pressure, and Vascular Function. Front Nutr. 2017, 4, 36. DOI: 10.3389/fnut.2017.00036
5. Samanta, S.; Sarkar, T.; Chakraborty, R.; Rebezov, M.; Shariati, M. A.; Thiruvengadam, M.; Rengasamy, K. R. R. Dark Chocolate: An Overview of its Biological Activity, Processing, and Fortification Approaches. Curr. Res. Food Sci. 2022, 5, 1916–1943. DOI: 10.1016/j.crfs.2022.10.017
6. Tan, T. Y. C.; Lim, X. Y.; Yeo, J. H. H.; Lee, S. W. H.; Lai, N. M. The Health Effects of Chocolate and Cocoa: A Systematic Review. Nutrients 2021, 13 (9), 2909. DOI: 10.3390/nu13092909
7. Cinar, Z. Ö.; Atanassova, M.; Tumer, T. B.; Caruso, G.; Antika, G.; Sharma, S.; Sharifi-Rad, J.; Pezzani, R. Cocoa and Cocoa Bean Shells Role in Human Health: An Updated Review. J. Food Compos. Anal. 2021, 103, 104115. DOI: 10.1016/j.jfca.2021.104115
8. Hernandez, C. E.; Granados, L. Quality Differentiation of Cocoa Beans: Implications for Geographical Indications. J. Sci. Food Agric. 2021, 101 (10), 3993-4002. DOI: 10.1002/jsfa.11077
9. Quelal-Vasconez, M. A.; Lerma-Garcia, M. J.; Perez-Esteve, E.; Arnau-Bonachera, A.; Barat, J. M.; Talens, P. Changes in Methylxanthines and Flavanols During Cocoa Powder Processing and Their Quantification by Near-Infrared Spectroscopy. LWT – Food Sci. Technol. 2020, 117, 108598. DOI: 10.1016/j.lwt.2019.108598
10. Agudelo, C.: Acevedo, S.; Carrillo-Hormaza, L.; Galeano, E.; Osorio E. Chemometric Classification of Colombian Cacao Crops: Effects of Different Genotypes and Origins in Different Years of Harvest on Levels of Flavonoid and Methylxanthine Metabolites in Raw Cacao Beans. Molecules 2022, 27 (7), 2068. DOI: 10.3390/molecules27072068
11. Cambrai, A.; Marchioni, E.; Julien-david, D.; Marcic, C. Discrimination of Cocoa Bean Origin by Chocolate Polyphenol Chromatographic Analysis and Chemometrics. Food Anal. Meth. 2017, 10, 1991–2000. DOI: 10.1007/s12161-016-0744-7
12. Nascimento, M. M.; Santos, H. M.; Coutinho, J. P.; Lobo, I. P.; da Silva Junior, A. L. S.; Santos, A. G.; de Jesus, R. M. Optimization of Chromatographic Separation and Classification of Artisanal and Fine Chocolate Based on its Bioactive Compound Content Through Multivariate Statistical Techniques. Microchem. J. 2020, 152, 104342. DOI: 10.1016/j.microc.2019.104342
13. Samaniego, I.; Espín, S.; Quiroz, J.; Ortiz, B.; Carrillo, W.; García-Viguera, C.; Mena, P. Effect of the Growing Area on the Methylxanthines and Flavan-3-ols Content in Cocoa Beans from Ecuador. J. Food Compos. Anal. 2020, 88, 103448. DOI: 10.1016/j.jfca.2020.103448
14. Febrianto, N. A.; Zhu, F. Diversity in Composition of Bioactive Compounds Among 26 Cocoa Genotypes. J. Agric. Food Chem. 2019, 67 (34), 9501–9509. DOI: 10.1021/acs.jafc.9b03448
15. Tran, P. D.; Van de Walle, D.; De Clercq, N.; De Winne, A.; Kadow, D.; Lieberei, R.; Messens, K.; Tran, D. N.; Dewettinck, K.; Van Durme, J. Assessing Cocoa Aroma Quality by Multiple Analytical Approaches. Food Res. Int. 2015, 77, 657–669. DOI: 10.1016/j.foodres.2015.09.019
SPME GC-MS–Based Metabolomics to Determine Metabolite Profiles of Coffee
November 14th 2024Using a solid phase microextraction gas chromatography-mass spectrometry (SPME GC-MS)-based metabolomics approach, a recent study by the School of Life Sciences and Technology at Institut Teknologi Bandung (Indonesia) investigated the impact of environmental factors (including temperature, rainfall, and altitude) on volatile metabolite profiles of Robusta green coffee beans from West Java.
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.
