Similar articles

Abstract: Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400–1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.

[1]Abdi, H., Williams, L.J., 2010. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat., 2(4):433-459.

[2]Alamar, M.C., Bobelyn, E., Lammertyn, J., Nicolaï, B.M., Moltó, E., 2007. Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple. Postharv. Biol. Technol., 45(1):38-45.

[3]Beyer, J., Heesche, K., Hauptmann, W., Otte, C., Kruse, R., 2009. Ensemble Learning for Multi-source Information Fusion. Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Springer, Berlin Heidelberg, p.748-756.

[4]Bosco, G.L., 2010. James L. Waters Symposium 2009 on near-infrared spectroscopy. TrAC Trends Anal. Chem., 29(3):197-208.

[5]Cen, H., He, Y., 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Technol., 18(2):72-83.

[6]Cen, H., He, Y., Huang, M., 2007. Combination and comparison of multivariate analysis for the identification of orange varieties using visible and near infrared reflectance spectroscopy. Eur. Food Res. Technol., 225(5-6):699-705.

[7]de Maesschalck, R., Estienne, F., Verdú-Andrés, J., Candolfi, A., Centner, V., Despagne, F., Jouan-Rimbaud, D., Walczak, B., Massart, D.L., Jong, S.D., et al., 1999. The Development of Calibration Models for Spectroscopic Data Using Principal Component Regression. Available from http://www.vub.ac.be/fabi/tutorial/PCR.pdf [Accessed on Dec. 9, 2011].

[8]Despagne, F., Luc Massart, D., 1998. Neural networks in multivariate calibration. Analyst, 123(11):157R-178R.

[9]Fan, G., Zha, J., Du, R., Gao, L., 2009. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. J. Food Eng., 93(4):416-420.

[10]Fu, X.P., Li, J.P., Zhou, Y., Ying, Y.B., Xie, L.J., Niu, X.Y., Yan, Z.K., Yu, H.Y., 2009. Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy. J. Zhejiang Univ.-Sci. B, 10(2):120-125.

[11]Gemperline, P.J., 2006. Principal Component Analysis. Practical Guide to Chemometrics, 2nd Ed. CRC Press, p.69-104.

[12]Hastie, T., Tibshirani, R., Friedman, J., 2009. Ensemble Learning. The Elements of Statistical Learning. Springer, New York, p.1-20.

[13]Herold, B., Kawano, S., Sumpf, B., Tillmann, P., Walsh, K., 2008. VIS/NIR Spectroscopy. In: Zude, M. (Ed.), Optical Monitoring of Fresh and Processed Agricultural Crops: Contemporary Food Engineering. CRC Press, p.143-248.

[14]Jha, S., Garg, R., 2010. Non-destructive prediction of quality of intact apple using near infrared spectroscopy. J. Food Sci. Technol., 47(2):207-213.

[15]Lin, H., Ying, Y., 2009. Theory and application of near infrared spectroscopy in assessment of fruit quality: a review. Sens. Instrum. Food Qual. Saf., 3(2):130-141.

[16]Liu, Y., Gao, R., Hao, Y., Sun, X., Ouyang, A., 2010. Improvement of near-infrared spectral calibration models for brix prediction in ‘Gannan’ navel oranges by a portable near-infrared device. Food Bioprocess Technol., in press.

[17]Liu, Y.D., Ying, Y.B., 2004. Measurement of sugar content in Fuji apples by FT-NIR spectroscopy. J. Zhejiang Univ.-Sci., 5(6):651-655.

[18]Long, R.L., 2005. Improving Fruit Soluble Solids Content in Melon (Cucumis Melo L.) (Reticulatus Group) in the Australian Production System. PhD Thesis, Central Queensland University, Rockhampton, Australia.

[19]Lu, H.S., Xu, H.R., Ying, Y.B., Fu, X.P., Yu, H.Y., Tian, H.Q., 2006. Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits. J. Zhejiang Univ.-Sci. B, 7(10):794-799.

[20]Mouazen, A.M., Kuang, B., de Baerdemaeker, J., Ramon, H., 2010. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158(1-2):23-31.

[21]Næs, T., Mevik, B.H., 2001. Understanding the collinearity problem in regression and discriminant analysis. J. Chemom., 15(4):413-426.

[22]Næs, T., Isaksson, T., Fearn, T., Davies, T., 2002. A User-Friendly Guide to Multivariate Calibration and Classification. NIR Publications, p.24-25.

[23]Osborne, B.G., 2006. Near-Infrared Spectroscopy in Food Analysis. In: Meyers, R.A. (Ed.), Encyclopedia of Analytical Chemistry. John Wiley & Sons, Ltd., Chichester, p.1-14.

[24]Pathaveerat, S., Terdwongworakul, A., Phaungsombut, A., 2008. Multivariate data analysis for classification of pineapple maturity. J. Food Eng., 89(2):112-118.

[25]Peirs, A., Scheerlinck, N., Nicolaï, B.M., 2003. Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharv. Biol. Technol., 30(3):233-248.

[26]Peng, Y., Lu, R., 2007. Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images. J. Food Eng., 82(2):142-152.

[27]Rinnan, A., van der Berg, F., Engelsen, S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal. Chem., 28(10):1201-1222.

[28]Serneels, S., Verdonck, T., 2009. Principal component regression for data containing outliers and missing elements. Comput. Stat. Data Anal., 53(11):3855-3863.

[29]Shao, Y., He, Y., 2009. Measurement of soluble solids content and pH of yogurt using visible/near infrared spectroscopy and chemometrics. Food Bioprocess Technol., 2(2):229-233.

[30]Shao, Y., Bao, Y., He, Y., 2009. Visible/near-infrared spectra for linear and nonlinear calibrations: a case to predict soluble solids contents and pH value in peach. Food Bioprocess Technol., 4(8):1376-1383.

[31]Sundaram, J., Kandala, C., Butts, C., 2009. Application of near infrared spectroscopy to peanut grading and quality analysis: overview. Sens. Instrum. Food Qual. Saf., 3(3):156-164.

[32]Teerachaichayut, S., Kil, K.Y., Terdwongworakul, A., Thanapase, W., Nakanishi, Y., 2007. Non-destructive prediction of translucent flesh disorder in intact mangosteen by short wavelength near infrared spectroscopy. Postharv. Biol. Technol., 43(2):202-206.

[33]Tian, H.Q., Ying, Y.B., Lu, H.S., Fu, X.P., Yu, H.Y., 2007. Measurement of soluble solids content in watermelon by Vis/NIR diffuse transmittance technique. J. Zhejiang Univ.-Sci. B, 8(2):105-110.

[34]Timotheou, S., 2009. A novel weight initialization method for the random neural network. Neurocomputing, 73(1-3):160-168.

[35]Ventura, M., de Jager, A., de Putter, H., Roelofs, F.P.M.M., 1998. Non-destructive determination of soluble solids in apple fruit by near infrared spectroscopy (NIRS). Postharv. Biol. Technol., 14(1):21-27.

[36]Xing, J., van Linden, V., Vanzeebroeck, M., de Baerdemaeker, J., 2005. Bruise detection on Jonagold apples by visible and near-infrared spectroscopy. Food Control, 16(4):357-361.

[37]Yu, J., He, Y., 2009. Fast Measurement of Soluble Solid Content in Mango Based on Visible and Infrared Spectroscopy Technique. In: Li, D., Zhao, C. (Eds.), Computer and Computing Technologies in Agriculture II, Volume 1. IFIP Advances in Information and Communication Technology, Springer, Boston, Volume 293, p.89-95.

[38]Zhao, J., Vittayapadung, S., Chen, Q., Chaitep, S., Chuaviroj, R., 2009. Nondestructive measurement of sugar content of apple using hyperspectral imaging technique. Maejo Int. J. Sci. Technol., 3(1):130-142.

[39]Zhu, D., Ji, B., Meng, C., Shi, B., Tu, Z., Qing, Z., 2009. A Comparison of Linear Regression Methods for the Detection of Apple Internal Quality by Near Infrared Spectroscopy. In: Li, D., Zhao, C. (Eds.), Computer and Computing Technologies in Agriculture II, Volume 3. IFIP Advances in Information and Communication Technology, Springer, Boston, Volume 295, p.1671-1680.

[40]Zou, X.B., Zhao, J.W., Huang, X.Y., Li, Y.X., 2007. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models. Chemom. Intell. Lab. Syst., 87(1):43-51.

[41]Zude, M., Pflanz, M., Kaprielian, C., Aivazian, B.L., 2008. NIRS as a tool for precision horticulture in the citrus industry. Biosyst. Eng., 99(3):455-459.