College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China.
Molecules. 2018 Jun 4;23(6):1352. doi: 10.3390/molecules23061352.
Hyperspectral images in the spectral range of 874⁻1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.
分别采集了三个品种的 14015、14300 和 15042 粒葡萄种子在 874-1734nm 光谱范围内的高光谱图像。通过小波变换对像素级光谱进行预处理，然后提取每个单粒葡萄种子的光谱。对高光谱图像进行主成分分析（PCA）。使用前六个主成分（PCs）的图像得分定性识别不同品种之间的模式。使用前六个 PCs 的加载值识别有效波长（EWs）。使用基于 EWs 的光谱，支持向量机（SVM）建立判别模型。结果表明，每个单粒葡萄种子的品种均可准确识别，校准准确率为 94.3%，预测准确率为 88.7%。使用每个品种的外部验证图像来评估所提出的模型，并形成分类图，其中每个单粒葡萄种子都明确地识别为属于不同的品种。总体结果表明，高光谱成像（HSI）技术与多元分析相结合可作为一种用于无损、快速品种鉴别和葡萄种子可视化的有效工具。该方法为未来实际应用开发多光谱成像系统具有很大的潜力。
