College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Institute of Horticulture, Zhejiang Academy of Agriculture Science, Hangzhou 310021, China.
Molecules. 2019 Jul 9;24(13):2506. doi: 10.3390/molecules24132506.
The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000-550 cm were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.
使用傅里叶变换红外（FTIR）光谱技术结合堆叠稀疏自编码器（SSAE）来识别兰花品种的可行性进行了研究。采集了涵盖 4000-550 cm 光谱范围的 13 种兰花品种的光谱数据，以建立判别模型并选择最佳光谱变量。使用全光谱建立了 K 最近邻（KNN）、支持向量机（SVM）和 SSAE 模型。SSAE 模型的性能优于 KNN 和 SVM 模型，在校准集和预测集中的分类准确率分别为 99.4%和 97.9%。然后，使用三种算法，即主成分分析载荷（PCA-loading）、竞争自适应重加权采样（CARS）和堆叠稀疏自编码器引导反向（SSAE-GB），分别选择了 39、300 和 38 个最佳波数。基于最佳波数建立了 KNN 和 SVM 模型。大多数基于最佳波数的模型的性能略优于基于所有波数的模型。从判别模型的准确性和最佳波数的数量来看，SSAE-GB 的性能优于其他两种算法。本研究结果表明，FTIR 光谱技术结合 SSAE 算法可用于兰花品种的鉴定。
