Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks
المؤلفون المشاركون
Zhang, Lei
Ding, Xiangqian
Hou, Ruichun
المصدر
Journal of Analytical Methods in Chemistry
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-12
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco.
There are many applications for the identification of tobacco origin by near-infrared spectroscopy.
In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN.
Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification.
Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method.
The classification is performed by the combination of global and local training features.
Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared.
The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries.
The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method.
The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Lei& Ding, Xiangqian& Hou, Ruichun. 2020. Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks. Journal of Analytical Methods in Chemistry،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175047
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Lei…[et al.]. Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks. Journal of Analytical Methods in Chemistry No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1175047
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Lei& Ding, Xiangqian& Hou, Ruichun. Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks. Journal of Analytical Methods in Chemistry. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175047
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175047
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر