Determining moisture content of Traditional Chinese Medicines using a near-infrared LED-based moisture content sensor with spectrum analysis

Zang, Zhenzhong; Qiu, Xuanbing; Guan, Yongmei; Zhang, Enhua; He, Xiaohu; Guo, Guqing; Li, Chuanliang; Wei, Jilin; Yang, Ming

doi:10.1007/s11082-019-1848-2

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Tanh function is a commonly used activation function of the bidirectional long short-term memory neural network. e expression is given in equation (14). Mobile Information Systems e tanh activation function and its derivative curve are shown in Figure 4.…”

Section: Improved Activation Function Of the Parameter Self-mentioning

confidence: 99%

“…At the same time, it can also provide power for improving the economic benefits of the coal-fired power plant and the energy utilization rate of the coal-fired industry. So far, the coal quality measurement technologies include neutron activation analysis, laser-induced breakdown spectroscopy, and near-infrared spectroscopy [13,14]. e above technologies realize the real-time measurement of the coal quality in coal-fired power plants, but all rely on expensive hardware facilities.…”

Section: Introductionmentioning

confidence: 99%

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The Bidirectional Information Fusion Using an Improved LSTM Model

Zheng

Wang

Guo

et al. 2021

Mobile Information Systems

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The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained. Therefore, the information fusion technology needs to be introduced to obtain the implied information of the power plant. As a practical application, the soft measurement of coal quality is taken as the research object. This paper proposes an improved LSTM model combined with the bidirectional deep fusion, alertness mechanism, and parameter self-learning (DFAS-LSTM) to realize online soft computing for the coal quality analyses of industries and elements. First, a latent structure model is established to preprocess the noisy and redundant sensor network data. Second, an alertness mechanism is proposed and the self-learning method of the activation function parameters is used for the data feature extraction. Third, a deeply bidirectional fusion layer is added to the long short-term memory neural network model to solve the problem of the insufficient accuracy and the weak generalization. Using the historical data of the sensor network, the DFAS-LSTM model is established. Then, the online data of the sensor network is input to the DFAS-LSTM model to implement the online coal quality analyses. Experiment shows that the accuracy of the coal quality analyses is increased by 1%–2.42% compared to the traditionally bidirectional LSTM.

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Section: Improved Activation Function Of the Parameter Self-mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Bidirectional Information Fusion Using an Improved LSTM Model

Zheng

Wang

Guo

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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“…Since it is very difficult to assign characteristic features to specific chemical components, multivariate analysis (MVA) techniques, e.g., regression (PLSR), principal component regression (PCR), or multiple linear regression (MLR), are often employed to extract the desired chemical information and to bring out hidden data structures. ese days, an increasing number of research is focused on the chemical assembly of the medicinal plants to quantify the relevant compounds in the samples by NIRS [13,14], for example, Cortex phellodendri [15], Magnolia officianlis [16], Piper methysticum Forst. f. [17,18], and American ginseng [19].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Quantification of 14 Compounds in Achillea millefolium by GC-MS Analysis and Near-Infrared Spectroscopy Combined with Multivariate Techniques

Guo

Yang

Zhou

et al. 2021

Journal of Analytical Methods in Chemistry

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The proposed work is focused on the simultaneous quantification of 14 compounds in the medicinal plant Achillea millefolium based on Near-Infrared Spectroscopy (NIR). The regression model of single-compound models (SCMs) and multicompound model (MCM) were created by partial least-squares regression (PLSR). Also, these models were calibrated by gas chromatographic mass spectroscopy (GC-MS). The results showed that the averaged standard errors of prediction (SEP) for the SCMs and MCM were 0.49 and 0.62, respectively, and most of the 14 compounds were significantly correlated. 43 correlations were significant at the 0.01 level (47.25% of the total), and 11 correlations were significant at the 0.05 level (12.09% of the total). The first three principal components (PCs) of principal component analysis (PCA) can explain >78% of the total variance. According to the component matrix and the communality table, octadecanoic acid has the largest influence on PC 1 (extraction squared = 46.72%), whose extraction was 0.932. The communality of neophytadiene, Z,Z,Z-9,12,15-octadecatrienoic acid, and oleic acid was also found to be large, whose extractions were 0.955, 0.937, and 0.859, respectively. These results indicate that if one compound shows a linear relationship with the NIR absorbance signal (SCM) also, an MCM can be created due to the close interrelations of these compounds. In this context, the present work highlights a suitable sample preparation technique to perform NIR analysis of raw plant material to benefit from robust and precise calibrations. To sum up, this NIR spectroscopic approach offers a precise, rapid, and cost-effective high-throughput analytical technique to simultaneously and noninvasively perform quantitative analysis of raw plant materials.

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