Qualitative and Quantitative Analysis of Caffeine in Medicines by Terahertz Spectroscopy Using Machine Learning Method

Yang, Qingrou; Wu, Lei; Shi, Chenjun; Wu, Xu; Chen, Xiaohong; Wu, Weinan; Yang, Huinan; Wang, Zijie; Zeng, Linggao; Peng, Yan

doi:10.1109/access.2021.3116980

Cited by 6 publications

(1 citation statement)

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“…Terahertz (THz) radiations [7], located between radio waves and infrared light, are electromagnetic waves ranging from 0.1 to 10 THz. The most important use of THz radiation was in the development of THz spectroscopy which has evolved into a tool of tremendous applications due to the development of modern ultrafast optics [8,9]. Terahertz imaging technology has unique advantages in both spectrum and imaging, containing a large amount of image and spectral information.…”

Section: Introductionmentioning

confidence: 99%

Identification of Rice Freshness Using Terahertz Imaging and Deep Learning

Wang,

Zhang,

et al. 2023

Photonics

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Retention of rice freshness is highly dependent on storage temperature. Timely and accurate identification of rice freshness is critical to ensure food security. Here, we characterize the freshness of rice in reference to storage temperature. Terahertz reflection imaging is a non-destructive and deeply penetrating technique that can be used for detecting rice freshness. Due to the shortcomings of traditional machine learning, such as limited processing of nonlinear problems and insufficient computing power. Deep learning has the advantages of strong learning ability and high portability. Therefore, for rice freshness identification, the VGG19 network and the Inception-ResNet-v2 network were used in this paper. Moreover, we propose an improved 1D-VGG19-Inception-ResNet-A network. This network possesses the advantages of low time consumption from the 1D-VGG19 network and high classification accuracy from the 1D-Inception-ResNet-V2 network. Compared with the traditional algorithms, the accuracy of the proposed network is significantly improved, with the rice freshness recognition accuracy of 99.80%. The experimental results indicate that terahertz spectral imaging and deep learning algorithms are viable tools for monitoring rice freshness.

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Section: Introductionmentioning

confidence: 99%