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In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties

  • V. Cortés
  • S. Cubero
  • J. Blasco
  • N. Aleixos
  • P. TalensEmail author
Original Paper
  • 61 Downloads

Abstract

One of the most studied techniques for the non-destructive determination of the internal quality of fruits has been visible and near-infrared (VIS-NIR) reflectance spectroscopy. This work evaluates a new non-destructive in-line VIS-NIR spectroscopy prototype for in-line identification of five apple varieties, with the advantage that it allows the spectra to be captured with the probe at the same distance from all the fruits regardless of their size. The prototype was tested using varieties with a similar appearance by acquiring the diffuse reflectance spectrum of the fruits travelling on the conveyor belt at a speed of 0.81 m/s which is nearly 1 fruit/s. Principal component analysis (PCA) was used to determine the variables that explain the most variance in the spectra. Seven principal components were then used to perform linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). QDA was found to be the best in-line classification method, achieving 98% and 85% success rates for red and yellow apple varieties, respectively. The results indicated that the in-line application of VIS-NIR spectroscopy that was developed is potentially feasible for the detection of apple varieties with an accuracy that is similar to or better than a laboratory system.

Keywords

Apple In-line Varietal discrimination Visible-near-infrared spectroscopy Non-destructive 

Notes

Funding Information

This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by INIA and FEDER funds through project RTA2015-00078-00-00. Victoria Cortés López thanks the Spanish Ministry of Education, Culture and Sports for FPU grant (FPU13/04202).

References

  1. Aleixandre-Tudo, J. L., Nieuwoudt, H., & du Toit, W. (2019). Towards on-line monitoring of phenolic content in red wine grapes: a feasibility study. Food Chemistry, 270, 322–331.Google Scholar
  2. Alonso, J., Artigas, J., & Jimenez, C. (2003). Analysis and identification of several apple varieties using ISFETs sensors. Talanta, 59(6), 1245–1252.Google Scholar
  3. Beebe, K. R., Pell, R. J., & Seasholtz, M. B. (1998). In: Chemometrics: a practical guide, New York. USA: John Wiley and Sons.Google Scholar
  4. Beghi, R., Giovenzana, V., Brancadoro, L., & Guidetti, R. (2017). Rapid evaluation of grape phytosanitary status directly at the check point station entering the winery by using visible/near infrared spectroscopy. Journal of Food Engineering, 204, 46–54.Google Scholar
  5. Brunt, K., Smits, B., & Holthuis, H. (2010). Design, construction, and testing of an automated NIR in-line analysis system for potatoes. Part II. Development and testin of the automated semi-industrial system with in-line NIR for the characterization of potatoes. Potato Research, 53(1), 41–60.Google Scholar
  6. 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(18), 7234–7243.Google Scholar
  7. Carr, G. L., Chubar, O., & Dumas, P. (2005). Spectrochemical analysis using infrared multichannel detectors. In R. Bhargava & I. W. Levin (Eds.), 1st ed (pp. 56–84). Oxford: Wiley-Blackwell.Google Scholar
  8. Casale, M., Casolino, C., Ferrari, G., & Forina, M. (2008). Near infrared spectroscopy and class modelling techniques for geographical authentication of Ligurian extra virgin olive oil. Journal of Near Infrared Spectroscopy, 16(1), 39–47.Google 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.Google Scholar
  10. Fernández-Ahumada, E., Garrido-Varo, A., Guerrero-Ginel, A. E., Wubbels, A., van der Sluis, C., & van der Meer, J. M. (2006). Understanding factors affecting near infrared analysis of potato constituents. Journal of Near Infrared Spectroscopy, 14(1), 27–35.Google Scholar
  11. He, Y., Li, X., & Shao, Y. (2007). Fast discrimination of apple varieties using Vis/NIR spectroscopy. International Journal of Food Properties, 10(1), 9–18.Google Scholar
  12. 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.Google Scholar
  13. 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.Google Scholar
  14. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: with applications in R. New York: springer.Google Scholar
  15. Jie, D., Xie, L., Rao, X., & Ying, Y. (2014). Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biology and Technology, 90, 1–6.Google Scholar
  16. Kader, A. A., Kasmire, R. F., Mitchell, F. G., Reid, M. S., Sommer, N. F., & Thompson, J. F. (1985). Postharvest technology of horticultural crops (Special publication, mum. 3311, p. 192). Davis: Cooperative Extension, University of California.Google Scholar
  17. Kozak, M., & Scaman, C. H. (2008). Unsupervised classification methods in food sciences: discussion and outlook. Journal of the Science of Food and Agriculture, 88(7), 1115–1127.Google Scholar
  18. Lammertyn, J., De Baerdemaeker, J., & Nicolaï, B. (2000). Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology, 18(2), 121–132.Google Scholar
  19. Liu, F., Jiang, Y., & He, Y. (2009). Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer. Analytica Chimica Acta, 635(1), 45–52.Google Scholar
  20. López, A. F. (2003). ‘Manual para la preparación y venta de frutas y hortalizas, del campo al mercado’. PDF File: Boletín de servicios agrícolas de la FAO, 151. http://www.fao.org/tempref/docrep/fao/006/y4893S/y4893S00.pdf. Accessed 20 Aug 2018.
  21. 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.Google Scholar
  22. Luo, W., Huan, S., Fu, H., Wen, G., Cheng, H., Zhou, J., Wu, H., Shen, G., & Yu, R. (2011). Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apples. Food Chemistry, 128(2), 555–561.Google Scholar
  23. Marrazzo, W. N., Heinemann, P. H., Crassweller, R. E., & LeBlanc, E. (2005). Electronic nose chemical sensor feasibility study for the differentiation of apple cultivars. American Society of Agricultural Engineers, 48(5), 1995–2002.Google Scholar
  24. 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(3), 394–404.Google Scholar
  25. Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2002). A user-friendly guide to multivariate calibration and classification. Chichester: NIR Publications.Google Scholar
  26. Rodríguez-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(1), 250–258.Google Scholar
  27. Ronald, M., & Evans, M. (2016). Classification of selected apple fruit varieties using Naive Bayes. Indian Journal of Computer Science and Engineering, 7(1), 13–19.Google Scholar
  28. Sabanci, K., & Ünlersen, M. F. (2016). Different apple varieties classification using kNN and MLP algorithms. International Journal of Intelligent Systems and Applications in Engineering, 4(1), 166–169.Google Scholar
  29. Sádecká, J., Jakubíková, M., Májek, P., & Kleinová, A. (2016). Classification of plum spirit drinks by synchronous fluorescence spectroscopy. Food Chemistry, 196, 783–790.Google Scholar
  30. Salguero-Chaparro, L., Baeten, V., Abbas, O., & Peña-Rodríguez, F. (2012). On-line analysis of intact olive fruits by vis-NIR spectroscopy: optimisation of the acquisition parameters. Journal of Food Engineering, 112(3), 152–157.Google Scholar
  31. 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(03), 30–34.Google Scholar
  32. Shang, L., Guo, W., & Nelson, S. O. (2015). Apple variety identification based on dielectric spectra and chemometric methods. Food Anal. Methods, 8(4), 1042–1052.Google Scholar
  33. 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’ (Lycopersicumesculentum) quality characteristics. Journal of Food Engineering, 81(4), 672–678.Google Scholar
  34. Shenderey, C., Shmulevich, I., Alchanatis, V., Egozi, H., Hoffman, A., Ostrovsky, V., Lurie, S., Arie, R. B., & Schmilovitch, Z. (2010). NIRS detection of moldy core in apples. Food Bioprocess Technology, 3(1), 79–86.Google Scholar
  35. 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.Google Scholar
  36. Song, W., Wang, H., Maguire, P., & Nibouche, O. (2017). Differentiation of organic and non-organic apples using near infrared reflectance apectroscopy – a pattern recognition approach. In Unknown host publication (pp. 1–3).  https://doi.org/10.1109/ICSENS.2016.7808530.
  37. Sun, X., Liu, Y., Li, Y., Wu, M., & Zhu, D. (2016). Simultaneous measurements of Brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy. Postharvest Biology and Technology, 116, 80–87.Google Scholar
  38. Wojdyło, A., Oszmiański, J., & Laskowski, P. (2008). Polyphenolic compounds and antioxidant activity of new and old apple varieties. Journal of Agricultural and Food Chemistry, 56(15), 6520–6530.Google Scholar
  39. Wu, X., Wu, B., Sun, J., Li, M., & Du, H. (2016). Discrimination of apples using near infrared spectroscopy and sorting discriminant analysis. International Journal of Food Properties, 19(5), 1016–1028.Google Scholar
  40. Wu, X., Wu, B., Sun, J., & Yang, N. (2017). Classification of Apple varieties using near infrared reflectance spectroscopy and fuzzy discriminant C-Means clustering model. Journal of Food Process Engineering, 40, 1–7.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Departamento de Tecnología de AlimentosUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias (IVIA)ValenciaSpain
  3. 3.Departamento de Ingeniería GráficaUniversitat Politècnica de ValènciaValenciaSpain

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