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Food and Bioprocess Technology

, Volume 10, Issue 10, pp 1755–1766 | Cite as

Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality

  • V. Cortés
  • J. Blasco
  • N. Aleixos
  • S. Cubero
  • P. Talens
Original Paper

Abstract

Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. ‘Big Top’ and cv. ‘Magique’, were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (R 2 P of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.

Keywords

Fruit quality Spectroscopy Nectarine Chemometrics Prediction Discrimination 

Notes

Acknowledgements

This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by the INIA and FEDER funds through projects RTA2012-00062-C04-01 and 03, and RTA2015-00078-00-00. Victoria López Cortés thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202). The authors are also grateful to Fruits de Ponent (Lérida) for providing the fruit.

References

  1. Bachion de Santana, F., Caixeta Gontijo, L., Mitsutake, H., Júnior Mazivilla, S., Maria de Souza, L., & Borges Neto, W. (2016). Non-destructive fraud detection in rosehip oil by MIR spectroscopy and chemometrics. Food Chemistry, 209, 228–233.CrossRefGoogle Scholar
  2. Bakeev, K. A. (2010). Process analytical technology. United Kingdom: Wiley.CrossRefGoogle Scholar
  3. Bonany, J., Buehler, A., Carbó, J., Codarin, S., Donati, F., Echeverria, G., Egger, S., Guerra, W., Hilaire, C., Höller, I., Iglesias, I., Jesionkowska, K., Konopacka, D., Kruczynska, D., Martinelli, A., Pitiot, C., Sansavini, S., Stehr, R., & Schoorl, F. (2013). Consumer eating quality acceptance of new apple varieties in different European countries. Food Quality and Preference, 30, 250–259.CrossRefGoogle Scholar
  4. Bruun, S. W., Sondergaard, I., & Jacobsen, S. (2007). Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 1. Gluten powder. Journal of Agricultural and Food Chemistry, 55, 7234–7243.CrossRefGoogle Scholar
  5. Carlomagno, G., Capozzo, L., Attolico, G., & Distante, A. (2004). Non-destructive grading of peaches by near-infrared spectrometry. Infrared Physics & Technology, 46, 23–29.CrossRefGoogle Scholar
  6. Carr, G. L, Chubar, O., Dumas, P. (2005). Multichannel detection with a synchrotron light source: Design and potential. Spectrochemical Analysis Using Multichannel Detectors Analytical Chemistry Series, edited by Bhargava P, Levin I. Chapter 3, (pp. 56–84). Oxford: Wiley-BlackwellGoogle Scholar
  7. Cayuela, J. A., & Weiland, C. (2010). Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biology and Technology, 58(2), 113–120.CrossRefGoogle Scholar
  8. Clareton, M. (2000). Peach and nectarine production in France: trends, consumption and perspectives. Summaries Prunus Breeders Meeting. EMPRABA, Clima Temperado. Pelotas (RS) Brazil, November 29 to December 2000, pp. 83–91Google Scholar
  9. Cortés, V., Ortiz, C., Aleixos, N., Blasco, J., Cubero, S., & Talens, P. (2016). A new internal quality index for mango and its prediction by external visible and near infrared reflection spectroscopy. Postharvest Biology and Technology, 118, 148–158.CrossRefGoogle Scholar
  10. Crisosto, C. H. (2002). How do we increase peach consumption? Proceedings of 5th International Symposium on Peach, ISHS. Acta Horticulturae, 592, 601–605.CrossRefGoogle Scholar
  11. Crisosto, C., & Crisosto, G. (2005). Relationship between ripe soluble solids concentration (RSSC) and consumer acceptance of high and low acid meeting flesh peach and nectarine (Prunus persica (L.) Batsch) cultivars. Postharvest Biology and Technology, 38, 239–246.CrossRefGoogle Scholar
  12. Crisosto, C. H., Garner, D., Crisosto, G. M., Wiley, P., & Southwick, S. (1997). Evaluation of the minimum maturity index for new cherry cultivars growing in the San Joaquin Valley. Visalia: California Cherry Growers Association.Google Scholar
  13. Crisosto, C. H., Crisosto, G. M., & Ritenour, M. A. (2002). Testing the reliability of skin color as an indicator of quality for early season ‘Brooks’ (Prunus avium L.) cherry. Postharvest Biology and Technology, 24, 147–154.CrossRefGoogle Scholar
  14. Crisosto, C. H., Crisosto, G. M., & Metheney, P. (2003). Consumer acceptance of ‘Brooks’ and ‘Bing’ cherries is mainly dependent on fruit SSC and visual skin color. Postharvest Biology and Technology, 28, 159–167.CrossRefGoogle Scholar
  15. Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4, 487–504.CrossRefGoogle Scholar
  16. Cunha, L. C., Teixeira, G. H. A., Nardini, V., & Walsh, K. (2016). Quality evaluation of intact açaí and juçara fruit by means of near infrared spectroscopy. Postharvest Biology and Technology, 112, 64–74.CrossRefGoogle Scholar
  17. Della Cara, R. (2005). In calo i consumi e l’export de pesche e nettarine italiane. Rivista di Frutticoltura, 7–8, 19–20.Google Scholar
  18. Downey, G. (1997). Authentication of food and food ingredients by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 4, 47–61.CrossRefGoogle Scholar
  19. Eskin, N.A.M. & Hoehn, E. (2013). Fruits and vegetables. Eskin, N.A.M., Shahidi, F. (Eds.), Biochemistry of foods, 3rd edn. Amsterdam, The Netherlands: Elsevier Inc. pp. 49–126.Google Scholar
  20. Faber, N. M. (1999). Multivariate sensitivity for the interpretation of the effect of spectral pretreatment methods on near-infrared calibration model predictions. Analytical Chemistry, (71), 557–565.Google Scholar
  21. Fang, L., Li, H., Liu, Z., & Xian, X. (2013). Online evaluation of yellow peach quality by visible and near-infrared spectroscopy. Advance Journal of Food Science and Technology, 5(5), 606–612.Google Scholar
  22. Ferrer, P., Montesinos, J. L., Valero, F., & Solá, C. (2001). Production of native and recombinant lipases by Candida rugosa. Applied Biochemistry and Biotechnology, 95(3), 221–255.CrossRefGoogle Scholar
  23. Font, D., Tresanchez, M., Pallejà, T., Teixidó, M., Martinez, D., Moreno, J., & Palacín, J. (2014). An image processing method for in-line nectarine variety verification based on the comparison of skin feature histogram vectors. Computers and Electronics in Agriculture, 102, 112–119.CrossRefGoogle Scholar
  24. Fu, X., Yibin, Y., Lu, H., Xu, H., & Yu, H. (2007). FT-NIR diffuse reflectance spectroscopy for kiwifruit firmness detection. Sensing and Instrumentation for Food Quality and Safety, 1, 29–35.CrossRefGoogle Scholar
  25. GenCat: Generalitat de Cataluña. 2013.Technical report 1/2011 and Technical Indicator A2. <http://www20.gencat.cat> (accessed 13.05.13).Google Scholar
  26. Ghiani, A., Negrini, N., Morgutti, S., Baldin, F., Nocito, F. F., Spinardi, A., Mignani, I., Bassi, D., & Cocucci, M. (2011). Melting of ‘Big Top’ nectarine fruit: some physiological, biochemical, and molecular aspects. Journal of the American Society for Horticultural Science, 136, 61–68.Google Scholar
  27. Golic, M., & Walsh, K. B. (2006). Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Analytica Chimica Acta, 555(2), 286–291.CrossRefGoogle Scholar
  28. Gorry, P. A. (1990). General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Analytical Chemistry, 62, 570–573.CrossRefGoogle Scholar
  29. He, Y., Li, X. L., & Shao, Y. N. (2006). Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model. Spectroscopy and Spectral Analysis, 26, 850–853.Google Scholar
  30. Hernández, A., He, Y., & García, A. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 77, 313–319.CrossRefGoogle Scholar
  31. Huang, H., Yu, H., Xu, H., & Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food Engineering, 87(3), 303–313.CrossRefGoogle Scholar
  32. Huang, L., Wu, D., Jin, H., Zhang, J., He, Y., & Lou, C. (2011). Internal quality determination of fruit with bumpy surface using visible and near infrared spectroscopy and chemometrics: a case study with mulberry fruit. Biosystems Engineering, 109(4), 377–384.CrossRefGoogle Scholar
  33. Iglesias, I. (2013). Peach production in Spain: current situation and trends, from production to consumption. Proceedings of the 4th Conference, Innovation in Fruit Growing, 75–96. D. Milatovic (Ed), Serbia (Belgrad)Google Scholar
  34. Iglesias, I., & Echeverría, G. (2009). Differential effect of cultivar and harvest date on nectarine colour, quality and consumer acceptance. Scientia Horticulturae, 120, 41–50.CrossRefGoogle Scholar
  35. Jaiswal, P., Jha, S. N., & Bharadwaj, R. (2012). Non-destructive prediction of quality of intact banana using spectroscopy. Scientia Horticulturae, 135, 14–22.CrossRefGoogle Scholar
  36. Kamruzzaman, M., ElMasry, G., Sun, D., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovate Food Science and Emerging Technologies, 16, 218–226.CrossRefGoogle Scholar
  37. Kozak, M., & Scaman, C. H. (2008). Unsupervised classification methods in food sciences: discussion and outlook. Journal of the Science of Food and Agriculture, 88, 1115–1127.CrossRefGoogle Scholar
  38. Lichtenthaler, H.K. & Buschmann, C. (2001). Chlorophylls and carotenoids: measurement and characterization by UV-VIS spectroscopy. Current Protocols in Food Analytical Chemistry, pp. F.4.3.1–F.4.3.8. Wiley, New York.Google Scholar
  39. Liu, Y., Chen, X., & Ouyang, A. (2008). Nondestructive determination of pear internal quality indices by visible and near infrared spectrometry. LWT - Food Science and Technology, 41, 1720–1725.CrossRefGoogle Scholar
  40. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technology, 5, 1121–1142.CrossRefGoogle Scholar
  41. Lorente, D., Escandell-Montero, P., Cubero, S., Gómez-Sanchis, J., & Blasco, J. (2015). Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit. Journal of Food Engineering, 163, 17–21.CrossRefGoogle Scholar
  42. Lu, R. (2004). Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biology and Technology, 31(2), 147–157.CrossRefGoogle Scholar
  43. Ma, G., Fu, X. P., Zhou, Y., Ying, Y. B., Xu, H. R., Xie, L. J., & Lin, T. (2007). Nondestructive sugar content determination of peaches by using near infrared spectroscopy technique. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, 27(5), 907–910.Google Scholar
  44. Magwaza, L. S., Opara, L. U., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5, 425–444.CrossRefGoogle Scholar
  45. Martens, H., Nielsen, J. P., & Engelsen, S. B. (2003). Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry, 75, 394–404.CrossRefGoogle Scholar
  46. Martins, P. A., Cirino de Carvalho, L., Cunha, L. C., Manhas, F., & Teixeira, G. H. (2016). Robust PLS models for soluble solids content and firmness determination in low chilling peach using near infrared spectroscopy (NIR). Postharvest Biology and Technology, 111, 345–351.CrossRefGoogle Scholar
  47. McGlone, V. A., & Kawano, S. (1998). Firmness, dry-matter and soluble-solids assessment of post-harvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology, 13, 131–141.CrossRefGoogle Scholar
  48. Merzlyak, M. N., Solo, A. E., & Gitelson, A. A. (2003). Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biology and Technology, 27, 197–211.CrossRefGoogle Scholar
  49. Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, I. K., & Lammertyn, J. (2007). Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology, 46, 99–118.CrossRefGoogle Scholar
  50. Osborne, B. G., Fearn, T., & Hindle, P. H. (1993). Practical NIR spectroscopy with applications in food and beverage analysis (2nd ed.pp. 123–132). Burnt Mill, Harlow, Essex, England: Longman Group.Google Scholar
  51. Padilla-Zakour, O. I. (2009). Good manufacturing practices. In N. Heredia, I. Wesley, & S. Garcia (Eds.), Microbiologically safe foods (pp. 395–415). New York: John Wiley and Sons Inc..CrossRefGoogle Scholar
  52. Peiris, K. H. S., Dull, G. G., Leffler, R. G., & Kays, S. J. (1998). Near-infrared spectrometric method for nondestructive determination of soluble solids content of peaches. Journal of the American Society for Horticultural Science, 123(5), 898–905.Google Scholar
  53. Pérez-Marín, D., Sánchez, M. T., Paz, P., González-Dugo, V., & Soriano, M. A. (2011). Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy. Food Science and Technology, 44, 1405–1414.Google Scholar
  54. Ravaglia, G., Sansavini, S., Ventura, M., & Tabanelli, D. (1996). Indici di maturazione e miglioramento cualitativo delle pesche. Revista di Frutticoltura, 3, 61–66.Google Scholar
  55. Reita, G., Peano, C., Saranwong, S., & Kawano, S. (2008). An evaluating technique for variety compatibility of fruit applied to a near infrared Brix calibration system: a case study using Brix calibration for nectarines. Journal of Near Infrared Spectroscopy, 16(2), 83–89.CrossRefGoogle Scholar
  56. Rodriguez-Campos, J., Escalona-Buendía, H. B., Orozco-Avila, I., Lugo-Cervantes, E., & Jaramillo-Flores, M. E. (2011). Dynamics of volatile and non-volatile compounds in cocoa (Theobroma cacao L.) during fermentation and drying processes using principal components analysis. Food Research International, 44, 250–258.CrossRefGoogle Scholar
  57. Sádecká, J., Jakubíková, M., Májek, P., & Kleinová, A. (2016). Classification of plum spirit drinks by synchronous fluorescence spectroscopy. Food Chemistry, 196, 783–790.CrossRefGoogle Scholar
  58. Sánchez, M. T., De la Haba, M. J., Guerrero, J. E., Garrido-Varo, A., & Pérez-Marín, D. (2011). Testing of a local approach for the prediction of quality parameters in intact nectarines using a portable NIRS instrument. Postharvest Biology and Technology, 60(2), 130–135.CrossRefGoogle Scholar
  59. Santos, P., Santos, F., Santos, J., & Bezerra, H. (2013). Application of extended multiplicative signal correction to short-wavelength near infrared spectra of moisture in marzipan. Journal of Data Analysis and Information Processing, 1, 30–34.CrossRefGoogle Scholar
  60. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified squares procedures. Analytical Chemistry, 36, 1627–1639.CrossRefGoogle Scholar
  61. Shao, Y., He, Y., Gómez, A. H., Pereir, A. G., Qiu, Z., & Zhang, Y. (2007). Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’ (Lycopersicum esculentum) quality characteristics. Journal of Food Engineering, 81(4), 672–678.CrossRefGoogle Scholar
  62. Singh, Z., Singh, R. K., Sane, V. A., & Nath, P. (2013). Mango—postharvest biology and biotechnology. Critical Reviews in Plant Sciences, 32(4), 217–236.CrossRefGoogle Scholar
  63. Soares, S. F. C., Gomes, A. A., Galvão Filho, A. R., Araújo, M. C. U., & Galvão, R. K. H. (2013). The successive projections algorithm. Trends in Analytical Chemistry, 42, 84–98.CrossRefGoogle Scholar
  64. Tijskens, L. M. M., Zerbini, P. E., Schouten, R. E., Vanoli, M., Jacob, S., Grassi, M., & Torricelli, A. (2007). Assessing harvest maturity in nectarines. Postharvest Biology and Technology, 45, 204–213.CrossRefGoogle Scholar
  65. Valero, A., Marín, S., Ramos, A. J., & Sanchis, V. (2007). Effect of preharvest fungicides and interacting fungi on Aspergillus carbonarius growth and ochratoxin A synthesis in dehydrating grapes. Letters in Applied Microbiology, 45, 194–199.CrossRefGoogle Scholar
  66. Walsh, K. B., Golic, M., & Greensill, C. V. (2004). Sorting of fruit and vegetables using near infrared spectroscopy: application to soluble solids and dry matter content. Journal of Near Infrared Spectroscopy, 12, 141–148.CrossRefGoogle Scholar
  67. Williams, P. C. & Norris, K. H. (1987). Qualitative applications of near infrared reflectance spectroscopy. P. C. Williams & K. H. Norris (Eds.), Near infrared technology in the agricultural and food industries, pp. 241–246. St. Paul, MN: American Association of Cereal Chemist.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • V. Cortés
    • 1
    • 2
  • J. Blasco
    • 2
  • N. Aleixos
    • 3
  • S. Cubero
    • 2
  • P. Talens
    • 1
  1. 1.Departamento de Tecnología de AlimentosUniversitat Politècnica de ValènciaMoncadaSpain
  2. 2.Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias (IVIA)MoncadaSpain
  3. 3.Departamento de Ingeniería GráficaUniversitat Politècnica de ValènciaMoncadaSpain

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