摘要
为了能够快速实时地识别温室中的黄瓜，研究了黄瓜和其茎叶的近红外反射光谱特性。利用近红外光谱仪在室内共采集138个样本（黄瓜46个，茎46个，叶46个）的反射光谱，进行Savitzky-Golay平滑后，抽取光谱中的108个样本作为校正集，采用偏差权重法选择信息量较大的光谱波段690-950nm进行研究。在主成分分析（PCA）的基础上，结合马氏距离建立识别模型，剔除了7个异常样本。用剩余的101个样本进行偏最小二乘法建模，对校正集之外的30个样本进行预测。结果显示预测值和实际值的相关性达0．9941，正确识别率达100％。说明黄瓜、茎和叶的近红外反射光谱特性之间有一定差异，可以用近红外光谱技术进行鉴别，为黄瓜识别提供了一种新的方法和思路。
To be able to quickly identify the cucumber real time, the present paper studied the near infrared reflectance characteristics of cucumber, stem and leaf. Spectral reflectance of 138 samples （46 cucumbers, 46 stems and 46 leaves） was collected using near infrared spectroscopy in the band range of 600- 1 099 nm indoor. After Savitzky-Golay smoothing preprocessing, random 108 spectral samples were put forward as calibration set. The weighted deviation method was used for choosing the spectral bands 690- 950 nm that include much more information. The samples were analyzed by PCA method to extract the principal component scores, combining the Mahalanobis distance method the recognition model was established, and seven abnormal sam- ples were excluded. The partial least squares （PLS） model was established by remaining 101 samples spectra of calibration set, which was used for predicting the validation set （30 samples except of the calibration set）. The result shows that the correlation of the predicted value and the actual value reaches up to 0. 994 1, and the correct recognition rate is 100%. This significantly illustrates that the near infrared spectral reflectance characteristics are different among the cucumbers, stems and leaves, which can be successfully applied to recognition of cucumber by the method. The developed technique can provide a new method for cucumber identification.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2011年第10期2834-2838,共5页
Spectroscopy and Spectral Analysis
基金
国家(863计划)项目(2006AA10Z259)资助
关键词
光谱分析
黄瓜识别
主成分分析
偏最小二乘法
马氏距离法
Spectral analysis
Cucumber recognition
Principal component analysis
Partial least squares
Mahalanobis distance
