A Hybrid Model for Nondestructive Measurement of Internal Quality of Peach
A nondestructive optical method for determining the sugar and acidity contents of peach was investigated. Two types of preprocessing were used before the data were analyzed with multivariate calibration methods of principle component analysis (PCA) and partial least squares (PLS). A hybrid model combined PLS with PCA was put forwarded. Spectral data set as the logarithms of the reflectance reciprocal was analyzed to build a best model for predicting the sugar and acidity contents of peach. A model with a correlation coefficient of 0.94/0.92, a standard error of prediction (SEP) of 0.50/0.07 and a bias of 0.02/−0.01 showed an excellent prediction performance to sugar/acidity. At the same time, the sensitive wavelengths corresponding to the sugar content and acidity of peaches or some element at a certain band were proposed on the basis of regression coefficients by PLS.
KeywordsSugar Content Principle Component Analysis Partial Little Square Model Near Infrared Spectroscopy Multivariate Calibration
Unable to display preview. Download preview PDF.
- 1.Marco, E., Maria, C.M., Fiorella, S., Antonino, N., Luigi, C., Ennio, L.N., Giuseppe, P.: Quality Evaluation of Peaches and Nectarines by Electrochemical and Multivariate Analyses: Relationships between Analytical Measurements and Sensory Attributes. Food Chemistry, 60(4) (1997) 659–666CrossRefGoogle Scholar
- 7.Downey, G., Fouratier, V., Kelly, J.D.: Detection of Honey Adulteration by Addition of Fructose and Glucose Using Near Infrared Spectroscopy. Journal of Near Infrared Spectroscopy, 11(6) (2004) 447–456Google Scholar
- 8.He, Y., Li, X. L., Shao, Y. N.: Quantitative Analysis of the Varieties of Apple Using Near Infrared Spectroscopy by Principle Component Analysis and BP Model. Lecture Notes in Artificial Intelligence, 3809 (2005) 1053–1056Google Scholar
- 9.Lammertyn, J., Nicolay, B., Ooms, K., Semedt, V.De, Baerdemaeker, J.De.: Non-destructive Measurement of Acidity, Soluble Solids and Firmness of Jonagold Apples Using NIR-spectroscopy. Transactions of the ASAE, 41(4) (1998) 1089–1094Google Scholar
- 11.Lu. R.: Predicting Firmness and Sugar Content of Sweet Cherries Using Near-infrared Diffuse Reflectance Spectroscopy. Transactions of the ASAE, 44(5) (2001) 1265–1271Google Scholar
- 12.McGlone, V.A., Fraser, D.G., Jordan, R.B., Kunnemeyer, R.: Internal Quality Assessment of Mandarin Fruit by Vis/NIR Spectroscopy. Journal of Near Infrared Spectroscopy, 11(5) (2003) 323–332Google Scholar
- 14.He, Y., Zhang, Y., Xiang, L. G.: Study of Application Model on BP Neural Network Optimized by Fuzzy Clustering. Lecture Notes in Artificial Intelligence, 3789 (2005) 712–720Google Scholar
- 15.Zhang, Y. D., Dong, K., Ren, L. F.: Patternre Cognition of Laser-induced Auto Fluorescence Spectrum from Colorectal Cancer Tissues Using Partial Least Square and Neural Network. China Medical Engineering, 12(4) (2004) 52–59Google Scholar
- 18.Slaughter, D.C.: Non-Destructive Determination of Internal Quality in Peaches and Nectarines. Transactions of the ASAE, 38(2) (1995) 617–623Google Scholar
- 19.Pieris, K.-H.S., Dull, G.G., Leffler, R.G., Kays, S.J.: Spatial Variability of Soluble Solids or Dry-matter Content within Individual Fruits, Bulbs, or Tubers: Implications for the Development and Use of NIR Spectrometric Techniques. Hortscience, 34(1) (1999) 114–118Google Scholar
- 22.Naes, T., Isaksson, T., Fearn, T., Davies, A.M.: A User-friendly Guide to Multivariate Calibration and Classification, NIR Publications, UK (2002)Google Scholar
- 25.He, Y.D.F.: 1998. The Method for Near Infrared Spectral Anlysis. In Yan, Y. L., Zhao, L. L., Han, D. H., Yang, S. M. (Eds.), The Analysis Basic and Application of Near Infrared Spectroscopy 354. Light Industry of China, Bei JingGoogle Scholar