光纤光谱结合模式识别无损检测苹果表面疤痕

为了实现基于光纤光谱技术结合模式识别无损检测苹果表面疤痕, 利用光纤光谱采集系统采集了完好无损和表面有疤痕苹果的光谱数据, 采用标准正态变换(SNV)和1阶导数对原始光谱数据进行预处理; 利用主成分分析方法对预处理后的光谱数据进行降维, 以提取能反映苹果表面疤痕的特征光谱; 利用k最近邻(KNN)模式识别方法和偏最小二乘判别分析方法, 建立了苹果表面疤痕的识别模型。结果表明, 采用主成分分析法选择了累计贡献率超过99%的前8个主成分作为样本集特征光谱数据, 很好地实现了光谱数据的降维; 利用1阶导数+KNN识别模型对校正集以及SNV+KNN识别模型对预测集中正常果和疤痕果的正确率识别均高达96.0%。验证了基于光纤光谱技术结合模式识别方法无损检测苹果表面疤痕的可行性。

Abstract

In order to prove that the non-destructive detection of apple surface defect combining optical fiber spectroscopy with pattern recognition was effective, an optical fiber spectrum acquisition system was used to collect spectral data of apples with and without surface defect. Standard normal variation (SNV) and first derivative were used to preprocess the original spectral data. Principal component analysis (PCA) was used to reduce the dimension of the pre-processed spectral data to extract the characteristic spectra of apples with surface defect. By using k nearest neighbor (KNN) pattern recognition method and partial least squares discriminant analysis method, recognition model of apple defect was established. The results show that, the first eight principal components with cumulative contribution over 99% are selected as the characteristic spectral data of the sample set by using principal component analysis and the dimensionality reduction of spectral data is well realized. By using first order derivative+KNN recognition model for correction set and SNV+KNN recognition model for prediction concentration, the recognition rate of normal apples and defected apples is 96.0%. The feasibility of non-destructive detection of apple surface defect based on optical fiber spectroscopy and pattern recognition is verified.

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孟庆龙, 张艳, 尚静. 光纤光谱结合模式识别无损检测苹果表面疤痕[J]. 激光技术, 2019, 43(5): 676. MENG Qinglong, ZHANG Yan, SHANG Jing. Nondestructive detection of apple defect combining optical fiber spectra with pattern recognition[J]. Laser Technology, 2019, 43(5): 676.

光纤光谱结合模式识别无损检测苹果表面疤痕

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