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Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

机译:电子鼻结合智能手机和云存储平台识别不同标记年龄的米酒

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摘要

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR.
机译:在这项研究中,便携式电子鼻(E-nose)是自行开发的,用于识别具有不同标记年龄的米酒-电子鼻的所有操作均由特殊的智能手机应用程序控制。 E型鼻子的传感器阵列由12个MOS传感器组成,并将获得的响应值通过无线通信模块传输到Smartphone。然后,阿里云作为云存储平台,用于存储响应和识别模型。电子鼻的测量由获得味道信息的阶段(TIOP)和获得回味信息的阶段(AIOP)组成。通过模式识别方法,将从TIOP获得的区域特征数据和从TIOP-AIOP获得的特征数据应用于识别黄酒。主成分分析(PCA),局部线性嵌入(LLE)和线性判别分析(LDA)用于这些葡萄酒样品的分类。基于从TIOP-AIOP获得的区域特征数据的LDA被证明是功能强大的工具,并显示出最佳的分类结果。应用偏最小二乘回归(PLSR)和支持向量机(SVM)预测明显的年龄,SVM(R 2 = 0.9942)的效果要好于PLSR。

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