Spectral fluctuation dividing for efficient wavenumber selection: Application to estimation of water and drug content in granules using near infrared spectroscopy

Miyano, Takuya; Kano, Manabu; Tanabe, Hideaki; Watanabe, Tetsu; Minami, Hidemi

doi:10.1016/j.ijpharm.2014.09.007

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…So, it is of great value to identify these differences in pharmaceutical supervision. Near infrared spectroscopy (NIRS) analysis technology can be used for quick and nondestructive detection of the drugs and has been widely used in the pharmaceutical industry, [1][2][3] for it has abundant frequency doubling and frequency vibration information in molecular groups.…”

Section: Introductionmentioning

confidence: 99%

Deep belief network-based drug identification using near infrared spectroscopy

Yang

Pan

et al. 2017

J. Innov. Opt. Health Sci.

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Near infrared spectroscopy (NIRS) analysis technology, combined with chemometrics, can be e®ectively used in quick and nondestructive analysis of quality and category. In this paper, an e®ective drug identi¯cation method by using deep belief network (DBN) with dropout mechanism (dropout-DBN) to model NIRS is introduced, in which dropout is employed to overcome the over¯tting problem coming from the small sample. This paper tests proposed method under datasets of di®erent sizes with the example of near infrared di®use re°ectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs, aluminum and nonaluminum packaged. Meanwhile, it gives experiments to compare the proposed method's performance with back propagation (BP) neural network, support vector machines (SVMs) and sparse denoising autoencoder (SDAE). The results show that for both binary classi¯cation and multi-classi¯cation, dropout mechanism can improve the classi¯cation accuracy, and dropout-DBN can achieve best classi¯cation accuracy in almost all cases. SDAE is similar to dropout-DBN in the aspects of classi¯cation accuracy and algorithm stability, which are higher than that of BP neural network and SVM methods. In terms of training time, dropout-DBN model is superior to SDAE model, but inferior to BP neural network and SVM methods. Therefore, dropout-DBN can be used as a §

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Section: Introductionmentioning

confidence: 99%

Deep belief network-based drug identification using near infrared spectroscopy

Yang

Pan

et al. 2017

J. Innov. Opt. Health Sci.

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“…Although soft-sensors are well-fitted to modeling data when numerous variables are used as the input, their performance may deteriorate when unimportant variables are used for estimation. In particular, input variable selection is a key when a calibration model is constructed from Near-infrared spectroscopy (NIRS) which is a powerful online measurement technology due to its short measuring time and non-invasiveness (Roggo et al, 2007 ; Miyano et al, 2014 ). The number of measured wavelengths of an NIR spectrum is usually more than 100.…”

Section: Introductionmentioning

confidence: 99%

Nearest Correlation-Based Input Variable Weighting for Soft-Sensor Design

Fujiwara

Kano

2018

Front. Chem.

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In recent years, soft-sensors have been widely used for estimating product quality or other important variables when online analyzers are not available. In order to construct a highly accurate soft-sensor, appropriate data preprocessing is required. In particular, the selection of input variables or input features is one of the most important techniques for improving estimation performance. Fujiwara et al. proposed a variable selection method, in which variables are clustered into variable groups based on the correlation between variables by nearest correlation spectral clustering (NCSC), and each variable group is examined as to whether or not it should be used as input variables. This method is called NCSC-based variable selection (NCSC-VS). However, these NCSC-based methods have a lot of parameters to be tuned, and their joint optimization is burdensome. The present work proposes an effective input variable weighting method to be used instead of variable selection to conserve labor required for parameter tuning. The proposed method, referred to herein as NC-based variable weighting (NCVW), searches input variables that have the correlation with the output variable by using the NC method and calculates the correlation similarity between the input variables and output variable. The input variables are weighted based on the calculated correlation similarities, and the weighted input variables are used for model construction. There is only one parameter in the proposed NCVW since the NC method has one tuning parameter. Thus, it is easy for NCVW to develop a soft-sensor. The usefulness of the proposed NCVW is demonstrated through an application to calibration model design in a pharmaceutical process.

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“…In the past decades, NIR has gained wide acceptance in the quality analysis in manufacturing process of traditional Chinese medicine including discrimination of herbal materials [13][14][15][16], intermediates [11,17], in the procedures of pharmaceutical technique including end point assessment of extraction process [1], elution process [18], purification process [19], alcohol precipitation [20,21] and also in the pharmaceutical products including granules [22], sustained release formulations [23], tablets [24,25], solid dispersions [26]. However, on line monitoring and quality control of oral solutions, used as a convenient dosage especially for children, by NIR were rarely reported.…”

Section: Introductionmentioning

confidence: 99%