Optimization of QuEChERS Extraction for Determination of Carotenoids, Polyphenols, and Sterols in Orange Juice Using Design of Experiments and Response Surface Methodology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Chemical and Reagents
2.2. Design of Experiments (DoE)
2.3. LC/PDA and LC/MS/MS Instrumentation and Conditions
2.4. Preparation of Extracts from Citrus Juice
2.5. Statistical Analysis
3. Results and Discussion
3.1. Examination of LC/PDA and LC/MS/MS Conditions to Measure the Polyphenols, Phytosterols, and Carotenoids
3.2. Optimization of QuEChERS Extraction with Response Surface Methodology
3.3. Assessment of Recovery Rate with LC/PDA and LC/MS/MS Measurement Using Optimized QuEChERS Extraction Method
3.4. Determination of Polyphenols, Sterols, and Carotenoids in Citrus Juice with QuEChERS Extraction and LC/MS/MS Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Buscemi, S.; Rosafio, G.; Arcoleo, G.; Mattina, A.; Canino, B.; Montana, M.; Verga, S.; Rini, G. Effects of red orange juice intake on endothelial function and inflammatory markers in adult subjects with increased cardiovascular risk. Am. J. Clin. Nutr. 2012, 95, 1089–1095. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cardile, V.; Graziano, A.C.; Venditti, A. Clinical evaluation of Moro (Citrus sinensis (L.) Osbeck) orange juice supplementation for the weight management. Nat. Prod. Res. 2015, 29, 2256–2260. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Escudero-López, B.; Ortega, Á.; Cerrillo, I.; Rodríguez-Griñolo, M.R.; Muñoz-Hernández, R.; Macher, H.C.; Martín, F.; Hornero-Méndez, D.; Mena, P.; Del Rio, D.; et al. Consumption of orange fermented beverage improves antioxidant status and reduces peroxidation lipid and inflammatory markers in healthy humans. J. Sci. Food. Agric. 2018, 98, 2777–2786. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Iwasaki, Y.; Hirasawa, T.; Maruyama, Y.; Ishii, Y.; Ito, R.; Saito, K.; Umemura, T.; Nishikawa, A.; Nakazawa, H. Effect of interaction between phenolic compounds and copper ion on antioxidant and pro-oxidant activities. Toxicol. In Vitro 2011, 25, 1320–1327. [Google Scholar] [CrossRef]
- Iwasaki, Y.; Okumura, M.; Matsumoto, H.; Ando, C.; Kamei, J. Generation of Reactive Oxygen and Nitrogen Species by Interaction of Food Ingredients. Bunseki Kagaku 2021, 70, 573–581. [Google Scholar] [CrossRef]
- García-Nicolás, M.; Ledesma-Escobar, C.A.; Priego-Capote, F. Spatial Distribution and Antioxidant Activity of Extracts from Citrus Fruits. Antioxidants 2023, 12, 781. [Google Scholar] [CrossRef] [PubMed]
- Sharma, A.; Kaur, M.; Katnoria, J.K.; Nagpal, A.K. Polyphenols in Food: Cancer Prevention and Apoptosis Induction. Curr. Med. Chem. 2018, 25, 4740–4757. [Google Scholar] [CrossRef] [PubMed]
- Martínez-Noguera, F.J.; Marín-Pagán, C.; Carlos-Vivas, J.; Alcaraz, P.E. 8-Week Supplementation of 2S-Hesperidin Modulates Antioxidant and Inflammatory Status after Exercise until Exhaustion in Amateur Cyclists. Antioxidants 2021, 10, 432. [Google Scholar] [CrossRef]
- Iwasaki, Y.; Manabe, R.; Kimoto, M.; Fukuda, M.; Mase, N.; Miyazawa, M.; Hosokawa, K.; Kamei, J. Copper-Induced Interactions of Caffeic Acid and Sinapic Acid to Generate New Compounds in Artificial Biological Fluid Conditions. Antioxidants 2022, 11, 1307. [Google Scholar] [CrossRef]
- Formisano, C.; Rigano, D.; Lopatriello, A.; Sirignano, C.; Ramaschi, G.; Arnoldi, L.; Riva, A.; Sardone, N.; Taglialatela-Scafati, O. Detailed Phytochemical Characterization of Bergamot Polyphenolic Fraction (BPF) by UPLC-DAD-MS and LC-NMR. J. Agric. Food Chem. 2019, 67, 3159–3167. [Google Scholar] [CrossRef]
- Reynoso-Camacho, R.; Rodríguez-Villanueva, L.D.; Sotelo-González, A.M.; Ramos-Gómez, M.; Pérez-Ramírez, I.F. Citrus decoction by-product represents a rich source of carotenoid, phytosterol, extractable and non-extractable polyphenols. Food Chem. 2021, 350, 129239. [Google Scholar] [CrossRef]
- Cerón-García, M.C.; González-López, C.V.; Camacho-Rodríguez, J.; López-Rosales, L.; García-Camacho, F.; Molina-Grima, E. Maximizing carotenoid extraction from microalgae used as food additives and determined by liquid chromatography (HPLC). Food Chem. 2018, 257, 316–324. [Google Scholar] [CrossRef] [PubMed]
- Montoya-Arroyo, A.; Toro-González, C.; Sus, N.; Warner, J.; Esquivel, P.; Jiménez, V.M.; Frank, J. Vitamin E and carotenoid profiles in leaves, stems, petioles and flowers of stinging nettle (Urtica leptophylla Kunth) from Costa Rica. J. Sci. Food Agric. 2022, 102, 6340–6348. [Google Scholar] [CrossRef] [PubMed]
- Barfi, B.; Asghari, A.; Rajabi, M.; Barfi, A.; Saeidi, I. Simplified miniaturized ultrasound-assisted matrix solid phase dispersion extraction and high performance liquid chromatographic determination of seven flavonoids in citrus fruit juice and human fluid samples: Hesperetin and naringenin as biomarkers. J. Chromatogr. A 2013, 1311, 30–40. [Google Scholar] [CrossRef] [PubMed]
- Gómez-Mejía, E.; Rosales-Conrado, N.; León-González, M.E.; Madrid, Y. Citrus peels waste as a source of value-added compounds: Extraction and quantification of bioactive polyphenols. Food Chem. 2019, 295, 289–299. [Google Scholar] [CrossRef] [PubMed]
- Casado, N.; Morante-Zarcero, S.; Pérez-Quintanilla, D.; Câmara, J.S.; Sierra, I. Dispersive Solid-Phase Extraction of Polyphenols from Juice and Smoothie Samples Using Hybrid Mesostructured Silica Followed by Ultra-high-Performance Liquid Chromatography-Ion-Trap Tandem Mass Spectrometry. J. Agric. Food Chem. 2019, 67, 955–967. [Google Scholar] [CrossRef] [PubMed]
- Aguiar, J.; Gonçalves, J.L.; Alves, V.L.; Câmara, J.S. Chemical Fingerprint of Free Polyphenols and Antioxidant Activity in Dietary Fruits and Vegetables Using a Non-Targeted Approach Based on QuEChERS Ultrasound-Assisted Extraction Combined with UHPLC-PDA. Antioxidants 2020, 9, 305. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Iwasaki, Y.; Matsumoto, H.; Okumura, M.; Inoue, H.; Kaji, Y.; Ando, C.; Kamei, J. Determination of neurotransmitters in mouse brain using miniaturized and tableted QuEChERS for the sample preparation. J. Pharm. Biomed. Anal. 2022, 217, 114809. [Google Scholar] [CrossRef]
- Aslantas, S.; Golge, O.; González-Curbelo, M.Á.; Kabak, B. Determination of 355 Pesticides in Lemon and Lemon Juice by LC-MS/MS and GC-MS/MS. Foods 2023, 12, 1812. [Google Scholar] [CrossRef]
- Prado-Cabrero, A.; Beatty, S.; Stack, J.; Howard, A.; Nolan, J.M. Quantification of zeaxanthin stereoisomers and lutein in trout flesh using chiral high-performance liquid chromatography-diode array detection. J. Food Compost. Anal. 2016, 50, 19–22. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Y.; Yu, J.; Jiao, C.; Tong, J.; Zhang, L.; Chang, Y.; Sun, W.; Jin, Q.; Cai, Y. Optimization of quercetin extraction method in Dendrobium officinale by response surface methodology. Heliyon 2019, 5, e02374. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Weremfo, A.; Abassah-Oppong, S.; Adulley, F.; Dabie, K.; Seidu-Larry, S. Response surface methodology as a tool to optimize the extraction of bioactive compounds from plant sources. J. Sci. Food Agric. 2023, 103, 26–36. [Google Scholar] [CrossRef]
- Fernández-García, R.; Lalatsa, A.; Statts, L.; Bolás-Fernández, F.; Ballesteros, M.P.; Serrano, D.R. Transferosomes as nanocarriers for drugs across the skin: Quality by design from lab to industrial scale. Int. J. Pharm. 2016, 498, 153–169. [Google Scholar] [CrossRef] [PubMed]
- Soni, P.K.; Saini, T.R. Formulation design and optimization of cationic-charged liposomes of brimonidine tartrate for effective ocular drug delivery by design of experiment (DoE) approach. Drug Dev. Ind. Pharm. 2021, 47, 1847–1866. [Google Scholar] [CrossRef]
- Chauhan, B.; Gupta, R. Application of statistical experimental design for optimization of alkaline protease production from Bacillus sp. RGR-14. Process Biochem. 2004, 39, 2115–2122. [Google Scholar] [CrossRef]
- Le Man, H.; Behera, S.K.; Park, H.S. Optimization of operational parameters for ethanol production from Korean food waste leachate. Int. J. Environ. Sci. Technol. 2010, 7, 157–164. [Google Scholar] [CrossRef] [Green Version]
- Danbaba, N.; Nkama, I.; Badau, M.H. Application of Response Surface Methodology (RSM) and Central Composite Design (CCD) to Optimize Minerals Composition of Rice-Cowpea Composite Blends during Extrusion Cooking. Int. J. Food Sci. Nutr. Eng. 2015, 5, 40–52. [Google Scholar]
- Niu, Y.; Hu, W.; Shen, T.; Dong, K.; Wang, D. Response Surface Methodology for the Optimization of Zn-Contaminated Soil Remediation by Soil Washing with Water-Soluble Chitosan. ACS Omega 2022, 7, 41929–41936. [Google Scholar] [CrossRef]
- Ingle, R.G.; Zeng, S.; Jiang, H.; Fang, W.J. Current developments of bioanalytical sample preparation techniques in pharmaceuticals. J. Pharm. Anal. 2022, 12, 517–529. [Google Scholar] [CrossRef]
- Câmara, J.S.; Perestrelo, R.; Berenguer, C.V.; Andrade, C.F.P.; Gomes, T.M.; Olayanju, B.; Kabir, A.; Rocha, C.M.R.; Teixeira, J.A.; Pereira, J.A.M. Green Extraction Techniques as Advanced Sample Preparation Approaches in Biological, Food, and Environmental Matrices: A Review. Molecules 2022, 27, 2953. [Google Scholar] [CrossRef]
- Mesquita, E.; Monteiro, M. Simultaneous HPLC determination of flavonoids and phenolic acids profile in Pêra-Rio orange juice. Food Res. Int. 2018, 106, 54–63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Andrade Barreto, S.M.; Martins da Silva, A.B.; Prudêncio Dutra, M.D.C.; Costa Bastos, D.; de Brito Araújo Carvalho, A.J.; Cardoso Viana, A.; Narain, N.; Dos Santos Lima, M. Effect of commercial yeasts (Saccharomyces cerevisiae) on fermentation metabolites, phenolic compounds, and bioaccessibility of Brazilian fermented oranges. Food Chem. 2023, 408, 135121. [Google Scholar] [CrossRef] [PubMed]
(A) | |||||||||
---|---|---|---|---|---|---|---|---|---|
Analytes | Concentration (μM) | Accuracy (%) | Precision (%) | Concentration (μM) | Accuracy (%) | Precision (%) | |||
p-Coumaric acid | 10 | 94.3 | 3.92 | 25 | 110 | 8.36 | |||
Ferulic acid | 10 | 87.8 | 8.36 | 25 | 93.8 | 6.17 | |||
Rutin | 10 | 88.5 | 9.71 | 25 | 94.0 | 11.4 | |||
Narirutin | 200 | 90.1 | 2.32 | 500 | 95.5 | 4.03 | |||
Naringin | 10 | 97.5 | 10.9 | 25 | 96.6 | 2.44 | |||
Hesperidin | 1000 | 86.8 | 4.29 | 2500 | 91.9 | 3.93 | |||
Diosmin | 10 | 92.5 | 3.81 | 25 | 92.9 | 6.01 | |||
β-Cryptoxanthin | 10 | 107 | 4.14 | 25 | 99.1 | 2.80 | |||
Campesterol | 100 | 111 | 7.97 | 250 | 105 | 4.10 | |||
Stigmasterol | 100 | 107 | 3.41 | 250 | 103 | 2.37 | |||
Sitosterol | 1000 | 82.7 | 4.95 | 2000 | 87.3 | 8.17 | |||
β-Carotene | 100 | 97.2 | 11.5 | 250 | 106 | 6.78 | |||
(B) | |||||||||
Analytes | Concentration (μM) | Accuracy (%) | Precision (%) | Concentration (μM) | Accuracy (%) | Precision (%) | Concentration (μM) | Accuracy (%) | Precision (%) |
p-Coumaric acid | 5 | 92.6 | 16.0 | 10 | 90.3 | 9.71 | 20 | 103 | 11.4 |
Ferulic acid | 50 | 111 | 17.5 | 100 | 110 | 4.57 | 200 | 105 | 9.82 |
Rutin | 5 | 86.9 | 18.9 | 10 | 98.3 | 9.74 | 20 | 95.7 | 8.83 |
Narirutin | 100 | 99.8 | 17.9 | 200 | 91.8 | 12.2 | 400 | 98.8 | 11.5 |
Naringin | 0.5 | 108 | 15.3 | 1 | 96.7 | 7.85 | 2 | 103 | 8.92 |
Hesperidin | 500 | 92.2 | 16.3 | 1000 | 87.5 | 16.4 | 2000 | 94.1 | 11.9 |
Diosmin | 0.5 | 97.4 | 3.76 | 1 | 92.2 | 9.67 | 2 | 96.8 | 3.85 |
β-Cryptoxanthin | 0.5 | 84.2 | 11.6 | 1 | 86.1 | 10.5 | 2 | 98.1 | 12.6 |
Campesterol | 25 | 65.0 | 11.2 | 50 | 85.9 | 3.73 | 100 | 73.6 | 4.02 |
Stigmasterol | 25 | 73.6 | 11.6 | 50 | 87.9 | 10.5 | 100 | 102 | 8.48 |
Sitosterol | 100 | 54.3 | 10.9 | 200 | 80.7 | 8.08 | 400 | 86.0 | 7.15 |
β-Carotene | 5 | 96.8 | 5.68 | 10 | 104 | 14.8 | 20 | 96.0 | 2.83 |
(C) | |||||||||
Analytes | Concentration (μM) | Accuracy (%) | Precision (%) | Concentration (μM) | Accuracy (%) | Precision (%) | Concentration (μM) | Accuracy (%) | Precision (%) |
p-Coumaric acid | 5 | 96.4 | 3.43 | 10 | 102 | 6.97 | 20 | 107 | 8.49 |
Ferulic acid | 50 | 109 | 12.7 | 100 | 102 | 8.65 | 200 | 104 | 3.47 |
Rutin | 5 | 93.0 | 9.19 | 10 | 95.1 | 5.23 | 20 | 97.8 | 3.53 |
Narirutin | 100 | 99.5 | 10.4 | 200 | 95.8 | 6.72 | 400 | 99.6 | 4.94 |
Naringin | 0.5 | 98.4 | 8.19 | 1 | 94.8 | 5.50 | 2 | 104 | 3.13 |
Hesperidin | 500 | 98.0 | 8.88 | 1000 | 90.7 | 13.3 | 2000 | 98.7 | 4.36 |
Diosmin | 0.5 | 98.5 | 4.67 | 1 | 104 | 6.14 | 2 | 98.0 | 1.31 |
β-Cryptoxanthin | 0.5 | 89.8 | 6.20 | 1 | 92.2 | 1.99 | 2 | 95.9 | 1.60 |
Campesterol | 25 | 75.6 | 9.28 | 50 | 81.4 | 3.32 | 100 | 81.9 | 6.81 |
Stigmasterol | 25 | 85.2 | 8.44 | 50 | 91.8 | 1.08 | 100 | 99.5 | 4.55 |
Sitosterol | 100 | 69.7 | 13.3 | 200 | 84.4 | 12.0 | 400 | 91.0 | 9.37 |
β-Carotene | 5 | 96.5 | 5.74 | 10 | 96.5 | 7.32 | 20 | 99.1 | 4.72 |
Analytes | LOD (μM) | LOQ (μM) | Calibration Range | Linearity |
---|---|---|---|---|
p-Coumaric acid | 1 | 2.5 | 2.5–50 | 0.999 |
Ferulic acid | 10 | 25 | 25–500 | 0.999 |
Rutin | 0.5 | 2.5 | 2.5–50 | 0.999 |
Narirutin | 1 | 50 | 50–1000 | 0.999 |
Naringin | 0.1 | 0.5 | 0.5–10 | 0.999 |
Hesperidin | 1 | 250 | 250–2500 | 0.999 |
Diosmin | 0.1 | 0.25 | 0.25–5 | 0.999 |
β-Cryptoxanthin | 0.1 | 0.25 | 0.25–10 | 0.999 |
Campesterol | 10 | 50 | 50–250 | 0.999 |
Stigmasterol | 10 | 50 | 50–500 | 0.999 |
Sitosterol | 10 | 100 | 100–500 | 0.999 |
β-Carotene | 0.5 | 5 | 5–100 | 0.999 |
Sample No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Manufacturer | USA | USA | USA | USA | USA | Japan | Japan | Japan |
Fruit juice ratio | 30 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Analytes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
p-Coumaric acid | N.D. | Trace (0.84 ± 0.07) | Trace (1.17 ± 0.26) | Trace (1.18 ± 0.32) | Trace (2.37 ± 0.27) | N.D. | Trace (1.16 ± 0.30) | Trace (1.64 ± 0.10) |
Ferulic acid | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. |
Rutin | Trace (0.65 ± 0.04) | 4.95 ± 0.07 | 5.62 ± 0.26 | 5.12 ± 0.09 | 5.80 ± 0.21 | 5.83 ± 0.18 | 5.57 ± 0.08 | 20.04 ± 0.79 |
Narirutin | Trace (17.7 ± 0.5) | 100.4 ± 2.2 | 120.2 ± 5.8 | 101.0 ± 2.8 | 110.7 ± 3.8 | 109.4 ± 5.1 | 103.8 ± 2.9 | 347.0 ± 17.6 |
Naringin | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. | 0.44 ± 0.06 | N.D. |
Hesperidin | Trace (93 ± 3) | 916 ± 8 | 1054 ± 29 | 883 ± 20 | 862 ± 25 | 837 ± 27 | 680 ± 13 | 1096 ± 34 |
Diosmin | Trace (0.11 ± 0.01) | 0.65 ± 0.01 | 0.72 ± 0.01 | 0.64 ± 0.02 | 0.62 ± 0.03 | 0.61 ± 0.01 | 0.42 ± 0.01 | 0.38 ± 0.01 |
β-Cryptoxanthin | Trace (0.10 ± 0.01) | 0.68 ± 0.04 | 0.70 ± 0.03 | 0.55 ± 0.03 | 0.68 ± 0.04 | 1.10 ± 0.05 | 0.92 ± 0.04 | 8.62 ± 0.06 |
Stigmasterol | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. | N.D. |
Sitosterol | N.D. | 112.6 ± 7.3 | 124.0 ± 9.8 | 110.5 ± 10.0 | 97.7 ± 1.8 | 111.7 ± 0.8 | 109.3 ± 7.4 | 148.2 ± 5.4 |
β-Carotene | Trace (0.62 ± 0.03) | 6.04 ± 0.12 | 6.50 ± 0.07 | 5.55 ± 0.03 | 8.08 ± 0.23 | 8.23 ± 0.13 | 7.27 ± 0.29 | 57.11 ± 0.79 |
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Iwasaki, Y.; Yamada, S.; Sakuma, S.; Kanba, S.; Youda, C.; Ono, M.; Ito, R.; Kamei, J.; Akiyama, H. Optimization of QuEChERS Extraction for Determination of Carotenoids, Polyphenols, and Sterols in Orange Juice Using Design of Experiments and Response Surface Methodology. Foods 2023, 12, 3064. https://doi.org/10.3390/foods12163064
Iwasaki Y, Yamada S, Sakuma S, Kanba S, Youda C, Ono M, Ito R, Kamei J, Akiyama H. Optimization of QuEChERS Extraction for Determination of Carotenoids, Polyphenols, and Sterols in Orange Juice Using Design of Experiments and Response Surface Methodology. Foods. 2023; 12(16):3064. https://doi.org/10.3390/foods12163064
Chicago/Turabian StyleIwasaki, Yusuke, Saki Yamada, Shinya Sakuma, Shunpei Kanba, Chinatsu Youda, Mizuki Ono, Rie Ito, Junzo Kamei, and Hiroshi Akiyama. 2023. "Optimization of QuEChERS Extraction for Determination of Carotenoids, Polyphenols, and Sterols in Orange Juice Using Design of Experiments and Response Surface Methodology" Foods 12, no. 16: 3064. https://doi.org/10.3390/foods12163064
APA StyleIwasaki, Y., Yamada, S., Sakuma, S., Kanba, S., Youda, C., Ono, M., Ito, R., Kamei, J., & Akiyama, H. (2023). Optimization of QuEChERS Extraction for Determination of Carotenoids, Polyphenols, and Sterols in Orange Juice Using Design of Experiments and Response Surface Methodology. Foods, 12(16), 3064. https://doi.org/10.3390/foods12163064