苹果品质检测的可见-近红外激光漫反射光谱图像法的方法研究

doi:10.3964/j.issn.1000-0593.2008.06.005

Abstract
References
Related Citation (15)

Download: PDF (1263 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the present study, improved laser-induced light backscattering imaging was studied regarding its potential for analyzing apple SSC and fruit flesh firmness. Images of the diffuse reflection of light on the fruit surface were obtained from Fuji apples using laser diodes emitting at five wavelength bands (680, 780, 880, 940 and 980 nm). Image processing algorithms were tested to correct for dissimilar equator and shape of fruit, and partial least squares (PLS) regression analysis was applied to calibrate on the fruit quality parameter. In comparison to the calibration based on corrected frequency with the models built by raw data, the former improved r from 0.78 to 0.80 and from 0.87 to 0.89 for predicting SSC and firmness, respectively. Comparing models based on mean value of intensities with results obtained by frequency of intensities, the latter gave higher performance for predicting Fuji SSC and firmness. Comparing calibration for predicting SSC based on the corrected frequency of intensities and the results obtained from raw data set, the former improved root mean of standard error of prediction (RMSEP) from 1.28° to 0.84°Brix. On the other hand, in comparison to models for analyzing flesh firmness built by means of corrected frequency of intensities with the calibrations based on raw data, the former gave the improvement in RMSEP from 8.23 to 6.17 N·cm^－2.

Key words：Vis-NIR;Laser diffuse reflecting images;Frequency of intensities;SSC;Firmness;Fuji apple;Corrected algorithm

Received: 2007-01-19 Accepted: 2007-04-26

ZTFLH:	O657.3
	S37
	TP391

Corresponding Authors: JI Bao-ping E-mail: jbp@cau.edu.cn

Cite this article:

QING Zhao-shen,JI Bao-ping,SHI Bo-lin, et al. Improving Apple Fruit Quality Predictions by Effective Correction of Vis-NIR Laser Diffuse Reflecting Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(06): 1273-1277.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593.2008.06.005 OR https://www.gpxygpfx.com/EN/Y2008/V28/I06/1273

[1] YAN Yan-lu, ZHAO Long-lian, LI Jun-hui, et al(严衍禄，赵龙莲，李军会，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(6): 777.
[2] McGlone V A, Abe H, Kawano S. Journal of Near Infrared Spectroscopy, 1997, 5: 83.
[3] Lu R. Postharvest Biology and Technology, 2004, 31: 147.
[4] LIU Yan-de, YING Yi-bin, FU Xia-ping(刘燕德，应义斌，傅霞萍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(11): 1793.
[5] FU Xia-ping, YING Yi-bin, LIU Yan-de, et al(傅霞萍，应义斌，刘燕德，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(6): 1038.
[6] Shmulevich I, Galili N, Howarth M S. Postharvest Biology and Technology, 2003, 29: 287.
[7] Qing Z S, Ji B P, Zude M. Journal of Food Engineering, 2007, 82: 58.
[8] Qing Z S, Zude M, Ji B P. Wavelengths Selection for Predicting Physico-Chemical Apple Fruit Properties by Means of NIRS for Automation. In Proceeding of CIGR/EURAGENG/VDI/FAO Workshop on “Processing & Post Harvest Technology and Logistics”, Bonn, Germany, 2006.
[9] HE Bin, MA Tian-yu, WANG Yun-jian, et al(何斌，马天予，王运坚，等). Visual C++ Digital Image Processing, Second ed(Visual C++数字图像处理, 第2版). Beijing: Posts & Telecom Press(北京：人民邮电出版社), 2002.
[10] Kortuem G. Reflectance Spectroscopy: Principles, Methods, Applications. New York: Springer Verlag, 1969.
[11] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄，赵龙莲，韩东海，等). Foundation and Application of Near-Infrared Spectroscopy Analysis(近红外光谱分析基础与应用). Beijng: China Light Industry Press(北京：中国轻工业出版社), 2005.
[12] Peng Y K, Lu R F. New Approaches of Analyzing Multispectral Scattering Profiles for Predicting Apple Fruit Firmness and Solubles Solids Content. In Meeting Presentation of ASABE, Oregon, U.S.A., 2006.