Abstract
The objective of this research is to improve soluble solids content (SSC) prediction in navel oranges by visible/near infrared (Vis/NIR) semi-transmission spectra and uninformative variable elimination-genetic algorithm-least squares support vector machine (UVE-GA-LSSVM). Spectra of navel oranges were acquired using a QualitySpec spectrometer in the wavelength range of 350 ~ 1,000 nm. After applying spectral pretreatment methods, UVE-GA was used to select variables, then LSSVM with three kernel functions (RBF kernel, linear kernel, polynomial kernel) was used to develop calibration models. The results indicate that Vis/NIR semi-transmission spectra combined with UVE-GA-LSSVM has good performance on assessing SSC of navel oranges, and SSC is improved. The R 2s and RMSEPs of SSC for RBF kernel, linear kernel, and polynomial kernel in prediction set are 0.850, 0.848, 0.849 and 0.419, 0.421, 0.420 %, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Flores K, Sanchez MT, Perez-Marin DC et al (2008) Prediction of total soluble solid content in intact and cut melons and watermelons using near infrared spectroscopy. J Near Infrared Spec 16:91–98
Sun T, Lin HJ, Xu HR et al (2009) Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (pomaceae pyrifolia nakai cv. cuiguan) using PLS and LS-SVM regression. Postharvest Biol Technol 51:86–90
Moghimi A, Aghkhani MH, Sazgarnia A et al (2011) Improvement of NIR transmission mode for internal quality assessment of fruit using different orientations. J Food Process Eng 34:1759–1774
Antonucci F, Pallottino F, Paglia G et al (2011) Non-destructive estimation of mandarin maturity status through portable Vis-NIR spectrophotometer. Food Bioprocess Technol 4:809–813
Bertone E, Venturello A, Leardi R et al (2012) Prediction of the optimum harvest time of ‘scarlet’ apples using Dr-UV-Vis and NIR spectroscopy. Postharvest Biol Technol 69:15–23
Jha SN, Jaiswal P, Narsaiah K et al (2012) Non-destructive prediction of sweetness of intact mango using near infrared spectroscopy. Sci Hortic 138:171–175
Tian HQ, Wang CG, Zhang HJ et al (2012) Measurement of soluble solids content in melon by transmittance spectroscopy. Sensor Lett 10:570–573
Liu YD, Sun XD, Ouyang AG (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci Technol 43:602–607
Liu YD, Sun XD, Zhou JM et al (2010) Linear and nonlinear multivariate regressions for determination sugar content of intact gannan navel orange by Vis-NIR diffuse reflectance spectroscopy. Math Comput Model 51:1438–1443
Xue L, Li J, Liu M et al (2010) Nondestructive detection of soluble solids content on navel orange with Vis/NIR based on genetic algorithm. Laser Optoelectron Prog 47:123001
Liu YD, Gao RJ, Hao Y et al (2012) Improvement of near-infrared spectral calibration models for Brix prediction in ‘Gannan’ navel oranges by a portable near-infrared device. Food Bioprocess Technol 5:1106–1112
Isaksson T, Naes T (1988) The effect of multiplicative scatter correction (msc) and linearity improvement in nir spectroscopy. Appl Spectrosc 42:1273–1284
Barnes R, Dhanoa M, Lister J (1989) Standard normal variable transformation and detrending of near infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777
Jamshidi B, Minaei S, Mohajerani E et al (2008) Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of valencia oranges. Comput Electron Agric 85:64–69
Centner V, Massart DL, deNoord OE et al (1996) Elimination of uninformative variables for multivariate calibration. Anal Chem 68:3851–3858
Leardi R (2000) Application of genetic algorithm-pls for feature selection in spectral data sets. J Chemometr 14:643–655
Leardi R, Gonzalez AL (1998) Genetic algorithms applied to feature selection in pls regression: how and when to use them. Chemometr Intell Lab 41:195–207
Suykens JAK, Van Gestel T, De Brabanter J et al (2002) Least squares support vector machines. World Scientific, Singapore
Vapnik V (1995) The nature of statistical learning theory, 2nd edn. Springer, New York
Shao YN, Bao YD, He Y (2011) Visible/near-infrared spectra for linear and nonlinear calibrations: a case to predict soluble solids contents and pH value in peach. Postharvest Biol Technol 4:1376–1383
Wu D, He Y, Nie PC et al (2010) Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Anal Chim Acta 659:229–237
Xu HR, Qi B, Sun T et al (2012) Variable selection in visible and near-infrared spectra: application to on-line determination of sugar content in pears. J Food Eng 109:142–147
Williams PC, Sobering DC (1993) Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J Near Infrared Spec 1:25–32
Acknowledgment
The authors gratefully acknowledge the financial support provided by the National Nature Science Foundation of China (No. 30972052), New Century Excellent Talents in Support of Ministry of Education Project (No. NCET090168) and Technology Foundation for Selected Overseas Chinese (2012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, T., Xu, W., Wang, X., Liu, M. (2014). Improvement of Soluble Solids Content Prediction in Navel Oranges by Vis/NIR Semi-Transmission Spectra and UVE-GA-LSSVM. In: Wen, Z., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54930-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-54930-4_37
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54929-8
Online ISBN: 978-3-642-54930-4
eBook Packages: EngineeringEngineering (R0)