Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Sweet Cherry Samples
2.2. Sweet Cherry Biometric and Chemical and Color Characterization
2.3. E-Tongue Equipment and Analysis
2.3.1. Apparatus
2.3.2. E-Tongue Analysis: Sweet Cherry Sample Preparation and Potentiometric Assays
2.4. Statistical Analysis
3. Results and Discussion
3.1. Biometric and Physicochemical Characterization of Sweet Cherries and Cultivar Effect Evaluation
3.2. Discrimination of Cherry Cultivar Using Biometric or Physicochemical Data or a Low Level Data Fusion
3.3. Discrimination of Cherry Cultivar Using E-Tongue Potentiometric Profiles
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Ganopoulos, I.; Tsaballa, A.; Xanthopoulou, A.; Madesis, P.; Tsaftaris, A. Sweet Cherry Cultivar Identification by High-Resolution-Melting (HRM) Analysis Using Gene-Based SNP Markers. Plant Mol. Biol. Report. 2013, 31, 763–768. [Google Scholar] [CrossRef]
- Faienza, M.F.; Corbo, F.; Carocci, A.; Catalano, A.; Clodoveo, M.L.; Grano, M.; Wang, D.Q.-H.; D’Amato, G.; Muraglia, M.; Franchini, C.; et al. Novel insights in health-promoting properties of sweet cherries. J. Funct. Foods 2020, 69, 103945. [Google Scholar] [CrossRef]
- Li, X.; Wei, Y.; Xu, J.; Feng, X.; Wu, F.; Zhou, R.; Jin, J.; Xu, K.; Yu, X.; He, Y. SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology. Postharvest Biol. Technol. 2018, 143, 112–118. [Google Scholar] [CrossRef]
- Ceccarelli, D.; Talento, C.; Favale, S.; Caboni, E.; Cecchini, F. Phenolic compound profile characterization by Q-TOF LC/MS in 12 Italian ancient sweet cherry cultivars. Plant Biosyst. 2018, 152, 1346–1353. [Google Scholar] [CrossRef]
- Jia, C.; Waterhouse, G.I.N.; Sun-Waterhouse, D.; Sun, Y.G.; Wu, P. Variety–compound–quality relationship of 12 sweet cherry varieties by HPLC-chemometric analysis. Int. J. Food Sci. Technol. 2019, 54, 2897–2914. [Google Scholar] [CrossRef]
- Papapetros, S.; Louppis, A.; Kosma, I.; Kontakos, S.; Badeka, A.; Kontominas, M.G. Characterization and differentiation of botanical and geographical origin of selected popular sweet cherry cultivars grown in Greece. J. Food Compos. Anal. 2018, 72, 48–56. [Google Scholar] [CrossRef]
- Safarzadeh, Z.; Sharifani, M.; Shakeri, A.; Hemmati, K.; Dinary, M. Discrimination of two early and late ripening cherry cultivars, using chemical indices and gel permeation chromatography (GPC). Acta Hortic. 2020, 1275, 69–74. [Google Scholar] [CrossRef]
- Martini, S.; Conte, A.; Tagliazucchi, D. Phenolic compounds profile and antioxidant properties of six sweet cherry (Prunus avium) cultivars. Food Res. Int. 2017, 97, 15–26. [Google Scholar] [CrossRef]
- Papapetros, S.; Louppis, A.; Kosma, I.; Kontakos, S.; Badeka, A.; Papastephanou, C.; Kontominas, M.G. Physicochemical, spectroscopic and chromatographic analyses in combination with chemometrics for the discrimination of four sweet cherry cultivars grown in northern Greece. Foods 2019, 8, 442. [Google Scholar] [CrossRef] [Green Version]
- Pérez-Sánchez, R.; Gómez-Sánchez, M.Á.; Morales-Corts, M.R. Description and quality evaluation of sweet cherries cultured in Spain. J. Food Qual. 2010, 33, 490–506. [Google Scholar] [CrossRef]
- Matos-Reyes, M.N.; Simonot, J.; Lopez-Salazar, O.; Cervera, M.L.; De La Guardia, M. Authentication of Alicante’s Mountain cherries protected designation of origin by their mineral profile. Food Chem. 2013, 141, 2191–2197. [Google Scholar] [CrossRef] [PubMed]
- Lafuente, V.; Val, J.; Negueruela, A.I. Non-destructive determination of the optimum harvest time of the sweet cherry cultivar ‘Cashmere’ through CIELab colour coordinates and the principal component analysis (PCA). Acta Hortic. 2018, 1194, 1099–1102. [Google Scholar] [CrossRef]
- Shao, Y.; Xuan, G.; Hu, Z.; Gao, Z.; Liu, L. Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis. PLoS ONE 2019, 14, e0222633. [Google Scholar] [CrossRef] [PubMed]
- Hayaloglu, A.A.; Demir, N. Phenolic Compounds, Volatiles, and Sensory Characteristics of Twelve Sweet Cherry (Prunus avium L.) Cultivars Grown in Turkey. J. Food Sci. 2016, 81, C7–C18. [Google Scholar] [CrossRef] [PubMed]
- Di Matteo, A.; Russo, R.; Graziani, G.; Ritieni, A.; Di Vaio, C. Characterization of autochthonous sweet cherry cultivars (Prunus avium L.) of southern Italy for fruit quality, bioactive compounds and antioxidant activity. J. Sci. Food Agric. 2017, 97, 2782–2794. [Google Scholar] [CrossRef]
- Wiersma, P.A.; Erogul, D.; Ali, S. DNA fingerprinting of closely related cultivars of sweet cherry. J. Am. Soc. Hortic. Sci. 2018, 143, 282–288. [Google Scholar] [CrossRef] [Green Version]
- Ivanovych, Y.; Volkov, R. Genetic relatedness of sweet cherry (Prunus avium L.) cultivars from Ukraine determined by microsatellite markers. J. Hortic. Sci. Biotechnol. 2018, 93, 64–72. [Google Scholar] [CrossRef]
- Ünsal, S.G.; Çiftçi, Y.Ö.; Eken, B.U.; Velioğlu, E.; Di Marco, G.; Gismondi, A.; Canini, A. Intraspecific discrimination study of wild cherry populations from North-Western Turkey by DNA barcoding approach. Tree Genet. Genomes 2019, 15, 16. [Google Scholar] [CrossRef]
- Lvova, L.; Jahatspanian, I.; Mattoso, L.H.C.; Correa, D.S.; Oleneva, E.; Legin, A.; Natale, C.D.; Paolesse, R. Potentiometric E-tongue system for geosmin/isoborneol presence monitoring in drinkable water. Sensors 2020, 20, 821. [Google Scholar] [CrossRef] [Green Version]
- Ouyang, Q.; Yang, Y.; Wu, J.; Chen, Q.; Guo, Z.; Li, H. Measurement of total free amino acids content in black tea using electronic tongue technology coupled with chemometrics. LWT-Food Sci. Technol. 2020, 118, 108768. [Google Scholar] [CrossRef]
- Patel, H.K.; Patel, P.H.; Patel, H. Innovative application electronic nose and electronic tongue techniques for food quality estimation. Int. J. Recent Technol. Eng. 2019, 8, 318–323. [Google Scholar]
- Ciosek, P.; Wróblewski, W. Sensor arrays for liquid sensing—Electronic tongue systems. Analyst 2007, 132, 963–978. [Google Scholar] [CrossRef]
- Marx, Í.; Rodrigues, N.; Dias, L.G.; Veloso, A.C.A.; Pereira, J.A.; Drunkler, D.A.; Peres, A.M. Sensory classification of table olives using an electronic tongue: Analysis of aqueous pastes and brines. Talanta 2017, 162, 98–106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marx, Í.M.; Rodrigues, N.; Dias, L.G.; Veloso, A.C.A.; Pereira, J.A.; Drunkler, D.A.; Peres, A.M. Quantification of table olives’ acid, bitter and salty tastes using potentiometric electronic tongue fingerprints. LWT Food Sci. Technol. 2017, 79, 394–401. [Google Scholar] [CrossRef] [Green Version]
- Rodrigues, N.; Marx, Í.M.G.; Dias, L.G.; Veloso, A.C.A.; Pereira, J.A.; Peres, A.M. Monitoring the debittering of traditional stoned green table olives during the aqueous washing process using an electronic tongue. LWT Food Sci. Technol. 2019, 109, 327–335. [Google Scholar] [CrossRef] [Green Version]
- Guilherme, R.; Rodrigues, N.; Marx, Í.M.G.; Dias, L.G.; Veloso, A.C.A.; Ramos, A.C.; Peres, A.M.; Pereira, J.A. Sweet peppers discrimination according to agronomic production mode and maturation stage using a chemical-sensory approach and an electronic tongue. Microchem. J. 2020, 157, 105034. [Google Scholar] [CrossRef]
- Apetrei, C.; Apetrei, I.M.; Villanueva, S.; de Saja, J.A.; Gutierrez-Rosales, F.; Rodriguez-Mendez, M.L. Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness. Anal. Chim. Acta 2010, 663, 91–97. [Google Scholar] [CrossRef]
- Apetrei, C.; Gutierez, F.; Rodriguez-Mendez, M.L.; de Saja, J.A. Novel method based on carbon paste electrodes for the evaluation of bitterness in extra virgin olive oils. Sens. Actuators B Chem. 2007, 121, 567–575. [Google Scholar] [CrossRef]
- Apetrei, I.M.; Apetrei, C. Detection of virgin olive oil adulteration using a voltammetric e-tongue. Comput. Electron. Agric. 2014, 108, 148–154. [Google Scholar] [CrossRef]
- Rodriguez-Mendez, M.L.; Apetrei, C.; de Saja, J.A. Evaluation of the polyphenolic content of extra virgin olive oils using an array of voltammetric sensors. Electrochim. Acta 2008, 53, 5867–5872. [Google Scholar] [CrossRef]
- Borges, T.H.; Peres, A.M.; Dias, L.G.; Seiquer, I.; Pereira, J.A. Application of a potentiometric electronic tongue for assessing phenolic and volatile profiles of Arbequina extra virgin olive oils. LWT Food Sci. Technol. 2018, 93, 150–157. [Google Scholar] [CrossRef]
- Harzalli, U.; Rodrigues, N.; Veloso, A.C.A.; Dias, L.G.; Pereira, J.A.; Oueslati, S.; Peres, A.M. A taste sensor device for unmasking admixing of rancid or winey-vinegary olive oil to extra virgin olive oil. Comput. Electron. Agric. 2018, 144, 222–231. [Google Scholar] [CrossRef] [Green Version]
- Veloso, A.C.A.; Dias, L.G.; Rodrigues, N.; Pereira, J.A.; Peres, A.M. Sensory intensity assessment of olive oils using an electronic tongue. Talanta 2016, 146, 585–593. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Veloso, A.C.A.; Silva, L.M.; Rodrigues, N.; Rebello, L.P.; Dias, L.G.; Pereira, J.A.; Peres, A.M. Perception of olive oils sensory defects using a potentiometric taste device. Talanta 2018, 176, 610–618. [Google Scholar] [CrossRef] [Green Version]
- Bobiano, M.; Rodrigues, N.; Madureira, M.; Dias, L.G.; Veloso, A.C.A.; Pereira, J.A.; Peres, A.M. Unmasking sensory defects of olive oils flavored with basil and oregano using an electronic tongue-chemometric tool. J. Am. Oil Chem. Soc. 2019, 96, 751–760. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Wang, X.; Xing, S.; Ma, Y.; Wang, X. Multi-sensors enabled dynamic monitoring and quality assessment system (DMQAS) of sweet cherry in express logistics. Foods 2020, 9, 602. [Google Scholar] [CrossRef]
- NP-1421, 1977. Géneros Alimentícios Derivados De Frutos e Produtos Hortícolas. Determinação Da Acidez. Portuguese Regulation NP-1421. Available online: https://lojanormas.ipq.pt/product/np-1421-1977 (accessed on 20 May 2020).
- Vlasov, Y.; Legin, A.; Rudnitskaya, A.; Di Natale, C.; D’Amico, C. Nonspecific sensor arrays (“electronic tongue”) for chemical analysis of liquids: (IUPAC technical report). Pure Appl. Chem. 2005, 77, 1965–1983. [Google Scholar] [CrossRef]
- Kobayashi, Y.; Habara, M.; Ikezazki, H.; Chen, R.; Naito, Y.; Toko, K. Advanced taste sensors based on artificial lipids with global selectivity to basic taste qualities and high correlation to sensory scores. Sensors 2010, 10, 3411–3443. [Google Scholar] [CrossRef] [Green Version]
- Dias, L.G.; Peres, A.M.; Veloso, A.C.A.; Reis, F.S.; Vilas Boas, M.; Machado, A.A.S.C. An electronic tongue taste evaluation: Identification goat milk adulterations with bovine milk. Sens. Actuators B Chem. 2009, 136, 209–217. [Google Scholar] [CrossRef]
- Dias, L.G.; Peres, A.M.; Barcelos, T.P.; Sá Morais, J.; Machado, A.A.S.C. Semi-quantitative and quantitative analysis of soft drinks using an electronic tongue. Sens. Actuators B Chem. 2011, 154, 111–118. [Google Scholar] [CrossRef]
- Rodrigues, N.; Dias, L.G.; Veloso, A.C.A.; Pereira, J.A.; Peres, A.M. Monitoring olive oils quality and oxidative resistance during storage using an electronic tongue. LWT Food Sci. Technol. 2016, 73, 683–692. [Google Scholar] [CrossRef] [Green Version]
- Bertsimas, D.; Tsitsiklis, J. Simulated annealing. Stat. Sci. 1993, 8, 10–15. [Google Scholar] [CrossRef]
- Cadima, J.; Cerdeira, J.O.; Minhoto, M. Computational aspects of algorithms for variable selection in the context of principal components. Comput. Stat. Data Anal. 2004, 47, 225–236. [Google Scholar] [CrossRef]
- Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P. Optimization by simulated annealing. Science 1983, 220, 671–680. [Google Scholar] [CrossRef] [PubMed]
- Bishop, C.M. Pattern Recognition and Machine Learning, 1st ed.; Springer: New York, NY, USA, 2006. [Google Scholar]
- Venables, W.N.; Ripley, B.D. Modern Applied Statistics with S (Statistics and Computing), 4th ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
- Ballistreri, G.; Continella, A.; Gentile, A.; Amenta, M.; Fabroni, S.; Rapisarda, P. Fruit quality and bioactive compounds relevant to human health of sweet cherry (Prunus avium L.) cultivars grown in Italy. Food Chem. 2013, 140, 630–638. [Google Scholar] [CrossRef] [PubMed]
- Radunić, M.; Jukić Špika, M.; Strikić, F.; Ugarković, J.; Čmelik, Z. Pomological and chemical characteristics of sweet cherry cultivars grown in Dalmatia, Croatia. Acta Hortic. 2014, 1020, 385–388. [Google Scholar] [CrossRef]
- Santos, A.C.A.; Palma, V.; Rato, A.E.; Machado, G.; Lozano, M.; González-Gómez, D. Quality of ‘Sweetheart’ cherry under different storage conditions. Acta Hortic. 2014, 1020, 101–110. [Google Scholar] [CrossRef] [Green Version]
- Hayaloglu, A.A.; Demir, N. Physicochemical characteristics, antioxidant activity, organic acid and sugar contents of 12 sweet cherry (Prunus avium L.) cultivars grown in Turkey. J. Food Sci. 2015, 80, C564–C570. [Google Scholar] [CrossRef]
- Skrzyński, J.; Leja, M.; Gonkiewicz, A.; Banach, P. Cultivar effect on the sweet cherry antioxidant and some chemical attributes. Folia Hortic. 2016, 28, 95–102. [Google Scholar] [CrossRef] [Green Version]
- Velardo-Micharet, B.; Peñas Díaz, L.; Tapia García, I.M.; Nieto Serrano, E.; Campillo Torres, C. Effect of irrigation on postharvest quality of two sweet cherry cultivars (Prunus avium L). Acta Hortic. 2017, 1161, 667–672. [Google Scholar] [CrossRef]
- Chiabrando, V.; Giacalone, G. Factors affecting the quality of fresh-cut sweet cherry. Acta Hortic. 2018, 1209, 103–107. [Google Scholar] [CrossRef]
- Correia, S.; Queirós, F.; Ribeiro, C.; Vilela, A.; Aires, A.; Barros, A.I.; Schouten, R.; Silva, A.P.; Gonçalves, B. Effects of calcium and growth regulators on sweet cherry (Prunus avium L.) quality and sensory attributes at harvest. Sci. Hortic. 2019, 248, 231–240. [Google Scholar] [CrossRef]
- Iurea, E.; Corneanu, M.; Militaru, M.; Sîrbu, S. Assessment of new sweet cherry cultivars released at RSFG Iaşi, Romania. Not. Bot. Horti Agrobot. Cluj Napoca 2019, 47, 729–733. [Google Scholar] [CrossRef] [Green Version]
- Iurea, E.; Sîrbu, S.; Corneanu, M.; Butac, M.; Titirică, I.; Militaru, M. Assessment of some sweet cherry cultivars in comparison with their genitors under the conditions of the north-eastern area of Romania. Not. Bot. Horti Agrobot. Cluj Napoca 2019, 47, 207–212. [Google Scholar] [CrossRef] [Green Version]
- Martínez-Hernández, G.B.; Blanco, V.; Blaya-Ros, P.J.; Torres-Sánchez, R.; Domingo, R.; Artés-Hernández, F. Effects of UV–C on bioactive compounds and quality changes during shelf life of sweet cherry grown under conventional or regulated deficit irrigation. Sci. Hortic. 2020, 269, 109398. [Google Scholar] [CrossRef]
- De Juan, A.; Tauler, R. Data fusion by multivariate curve resolution. In Data Handling Science Technology; Elsevier: Amsterdam, The Netherlands, 2019; pp. 205–233. [Google Scholar]
- Dias, L.G.; Sequeira, C.; Veloso, A.C.A.; Sousa, M.E.B.C.; Peres, A.M. Evaluation of Healthy and Sensory Indexes of Sweetened Beverages using an Electronic Tongue. Anal. Chim. Acta 2014, 848, 32–42. [Google Scholar] [CrossRef] [Green Version]
- Dias, L.G.; Alberto, Z.; Veloso, A.C.A.; Peres, A.M. Electronic tongue: A versatile tool for mineral and fruit-flavored waters recognition. J. Food Meas. Charact. 2016, 10, 264–273. [Google Scholar] [CrossRef] [Green Version]
Parameters | Cherry Cultivar | ||||
---|---|---|---|---|---|
Durona | Lapins | Summit | Van | p-Value 1 | |
Biometric | |||||
Fruit mass (g) | 6.25 ± 0.96 b | 6.78 ± 1.38 a | 7.15 ± 1.06 a | 6.79 ± 1.08 a | <0.0001 |
Length (mm) | 19.1 ± 1.2 d | 20.6 ± 1.7 b | 21.4 ± 1.4 a | 19.7 ± 1.6 c | <0.0001 |
Maximum diameter (mm) | 22.6 ± 1.6 d | 23.2 ± 1.2 c | 24.8 ± 1.4 a | 24.2 ± 1.7 b | <0.0001 |
Minimum diameter (mm) | 20.4 ± 1.3 | 20.5 ± 1.5 | 20.7 ± 1.2 | 20.6 ± 1.4 | 0.2000 |
Stone mass (g) | 0.36 ± 0.06 a,b | 0.37 ± 0.06 a | 0.35 ± 0.04 b | 0.31 ± 0.04 c | <0.0001 |
Pulp/stone ratio | 16.8 ± 3.8 b | 17.8 ± 4.2 b | 19.7 ± 3.0 a | 20.8 ± 3.4 a | <0.0001 |
Physicochemical | |||||
Force (N) | 2.58 ± 0.46 b | 2.91 ± 0.60 a | 2.50 ± 0.34 b | 3.01 ± 0.51 a | <0.0001 |
L* (CIELAB scale) | 32.3 ± 3.1 c | 35.6 ± 4.7 a,b | 37.2 ± 4.1 a | 35.0 ± 4.6 b | <0.0001 |
a* (CIELAB scale) | 27.8 ± 5.6 c | 33.8 ± 6.8 a,b | 34.7 ± 4.6 a | 31.6 ± 6.2 b | <0.0001 |
b* (CIELAB scale) | 10.4 ± 3.6 c | 14.8 ± 5.4 a,b | 16.3 ± 4.0 a | 13.5 ± 4.6 b | <0.0001 |
Titratable acidity (TA, g gallic acid/100 g fw) | 0.17 ± 0.01 | 0.17 ± 0.04 | 0.21 ± 0.01 | 0.21 ± 0.04 | 0.0312 |
Total soluble solids (TSSs, °Brix) | 14.1 ± 1.5 b | 14.1 ± 2.3 b | 12.6 ± 1.5 c | 15.5 ± 3.0 a | <0.0001 |
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Rodrigues, I.; Rodrigues, N.; Marx, Í.M.G.; Veloso, A.C.A.; Ramos, A.C.; Pereira, J.A.; Peres, A.M. Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints. Appl. Sci. 2020, 10, 7053. https://doi.org/10.3390/app10207053
Rodrigues I, Rodrigues N, Marx ÍMG, Veloso ACA, Ramos AC, Pereira JA, Peres AM. Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints. Applied Sciences. 2020; 10(20):7053. https://doi.org/10.3390/app10207053
Chicago/Turabian StyleRodrigues, Isabel, Nuno Rodrigues, Ítala M. G. Marx, Ana C. A. Veloso, Ana Cristina Ramos, José Alberto Pereira, and António M. Peres. 2020. "Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints" Applied Sciences 10, no. 20: 7053. https://doi.org/10.3390/app10207053
APA StyleRodrigues, I., Rodrigues, N., Marx, Í. M. G., Veloso, A. C. A., Ramos, A. C., Pereira, J. A., & Peres, A. M. (2020). Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints. Applied Sciences, 10(20), 7053. https://doi.org/10.3390/app10207053