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苹果糖度的光谱图像无损检测技术研究
引用本文:刘木华,陈全胜,林怀蔚.苹果糖度的光谱图像无损检测技术研究[J].光学学报,2007,27(11):2042-2046.
作者姓名:刘木华  陈全胜  林怀蔚
作者单位:1. 江西农业大学工学院,南昌,330045
2. 江苏大学食品与生物工程学院,镇江,212013
基金项目:国家自然科学基金(30460059),江苏省自然科学基金重点项目(BK2006707-1)资助课题
摘    要:应用光谱图像技术进行了苹果内部品质无损检测技术的研究。通过采集不同波长(分别为632 nm,650 nm,670 nm,780 nm,850 nm和900 nm)的光谱图像,对所采集的光谱图像灰度分布进行洛伦兹分布(LD)、高斯分布(GD)、指数分布(ED)函数的拟合,通过比较发现洛伦兹分布为最优灰度分布拟合函数。将苹果的糖度与洛伦兹分布函数拟合所得参量分别进行多元线性回归,建立最佳单波长、最佳双波长组合、最佳三波长组合和最佳四波长组合的校正方程,相关系数R分别为0.622、0.776、0.831、0.813。实验表明,利用光谱图像技术无损检测苹果糖度是可行性的,为计算机图像对水果进行内部品质的无损检测提供技术依据。

关 键 词:医用光学与生物技术  光谱图像  无损检测  多元线性回归  苹果糖度
文章编号:0253-2239(2007)11-2042-5
收稿时间:2007/1/19
修稿时间:2007-01-19

The Study of Non-Destructive Measurement of Fruit Internal Qualities Using Spectral Imaging
Liu Muhua,Chen Quansheng,Lin Huaiwei.The Study of Non-Destructive Measurement of Fruit Internal Qualities Using Spectral Imaging[J].Acta Optica Sinica,2007,27(11):2042-2046.
Authors:Liu Muhua  Chen Quansheng  Lin Huaiwei
Institution:1 College of Engineering, Jiangxi Agricultural University, Nanchang 330045 ;2 School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013
Abstract:The internal qualities of apple were detected using spectral imaging.The spectral imaging in wavelength of 632 nm,650 nm,670 nm,780 nm,850 nm and 900 nm were captured.The Lorentzian distribution(LD),Gaussian distribution(GD) and Exponential distribution(ED) with three parameters were used to fit scattering profiles for all wavelengths.LD was found to be the best function for fitting gray distribution of imaging.The multi-linear regression model relating Lorentzian parameters to fruit sugar content were development using best single wavelength,double wavelengths,three wavelengths and four wavelengths.The best model with four wavelengths was able to predict apple sugar content with r=0.831.Results show that the multi-spectral scattering imaging is nondestructive,fast and easy to implement,and it can provide a nondestructive means for measuring fruit internal quality.
Keywords:medical optics and biotechnology  non-destructive measurement  spectral imaging  multi-linear regression  apple sugar content
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