Detection of Counterfeit Medicines Using Hyperspectral Sensing

Shinde, Swati; Bhavsar, Karan; Kimbahune, Sanjay; Khandelwal, Sundeep; Ghose, Avik; Pal, Arpan

doi:10.1109/embc44109.2020.9176419

Cited by 7 publications

(4 citation statements)

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“…In addition, Sanhueza et al, [73] introduced the application of a confocal laser scanning microscopy hyperspectral imaging technique to identify, locate and quantify the APIs in synthetic tablets. And they used the multivariate curve resolution alternating least squares method (MCR-ALS) to Raman spectroscopy with PCA and HCA [94] antiretroviral drug NIR chemical imaging and PCA [95] drugs for high levels of cholesterol Raman and NIR spectroscopy with PLS-DA and PCA [96] drugs for hypertension disease NIR chemical imaging and PCA [97] drugs for anti-diabetes NIR spectroscopy with SIMCA and PLS-DA [98] Augmentin multilayer perceptron method with NIR imaging [109] Viagra gray level co-occurrence matrix analysis and PCA [112] Drovativarine SIMCA with NIR spectroscopy [99] Detecting the content uniformity of tablets tablets of different batches…”

Section: Detection Of Active Pharmaceutical Ingredientsmentioning

confidence: 99%

“…However, most of the current detection methods are invasive and destructive detection methods, such as chemical detection methods and chromatograph detection methods. Not only are the detection speeds slow to achieve mass detection, but they also destroy the appearance of drugs and affect the quality of the products which is harmful to the second sale [92,[109][110][111]. For the detection of counterfeit drugs, Shinde et al, [109] combined visible and near-infrared hyperspectral imaging equipment and proposed a multi-layer perceptron method.…”

Section: Detection Of Counterfeit Drugs With Hyperspectral Imagingmentioning

confidence: 99%

“…Not only are the detection speeds slow to achieve mass detection, but they also destroy the appearance of drugs and affect the quality of the products which is harmful to the second sale [92,[109][110][111]. For the detection of counterfeit drugs, Shinde et al, [109] combined visible and near-infrared hyperspectral imaging equipment and proposed a multi-layer perceptron method. The author distinguishes between genuine and counterfeit drugs by adding calcium carbonate powder to standard drugs and achieved a classification accuracy of more than 90% in the final experimental results.…”

Section: Detection Of Counterfeit Drugs With Hyperspectral Imagingmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Wang

Mao

et al. 2022

J Intell Robot Syst

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The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

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Section: Detection Of Active Pharmaceutical Ingredientsmentioning

confidence: 99%

Section: Detection Of Counterfeit Drugs With Hyperspectral Imagingmentioning

confidence: 99%

Section: Detection Of Counterfeit Drugs With Hyperspectral Imagingmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Wang

Mao

et al. 2022

J Intell Robot Syst

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“…In recent years, hyperspectral imaging, as a fast, reliable, and non-destructive analysis technique, has been widely concerned and applied in various fields. It can be used to detect the internal properties of food products [2,3,4], analyze the chemical composition of pharmaceuticals [5,6], do medical research [7,8], and do some non-destructive testing [9,10], etc. Hyperspectral imaging is a technique that combines image information with spectral information to record spectral information in different wavelength ranges.…”

Section: Introductionmentioning

confidence: 99%

Bloodstain Identification Based on Visible/Near-Infrared Hyperspectral Imaging With Convolutional Neural Network

Yang

Jiang

et al. 2022

IEEE Access

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Blood samples are easily damaged in traditional bloodstain detection and identification. In complex scenes with interfering objects, bloodstain identification may be inaccurate, with low detection rates and false-positive results. In order to meet these challenges, we propose a bloodstain detection and identification method based on hyperspectral imaging and mixed convolutional neural networks, which enables fast and efficient non-destructive identification of bloodstains. In this study, we apply visible/nearinfrared reflectance hyperspectral imaging in the 380-1000 nm spectral region to analyze the shape, structure, and biochemical characteristics of bloodstains. Hyperspectral images of bloodstains on different substrates and six bloodstain analogs are experimentally obtained. The acquired spectral pixels are pre-processed by Principal Component Analysis (PCA). For bloodstains and different bloodstain analogs, regions of interest are selected from each substance to obtain pixels, which are further used in convolutional neural network (CNN) modeling. After the mixed CNN modeling is completed, pixels are selected from the hyperspectral images as a test set for bloodstains and bloodstain analogs. Finally, the bloodstain recognition ability of the mixed 2D-3D CNN model is evaluated by analyzing the kappa coefficient and classification accuracy. The experimental results show that the accuracy of the constructed CNN bloodstain identification model reaches 95.4%. Compared with other methods, the bloodstain identification method proposed in this study has higher efficiency and accuracy in complex scenes. The results of this study will provide a reference for the future development of the bloodstain online detection system.

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Automated Classification of Hyper Spectral Image Using Supervised Machine Learning Approach

Gadhave

Sedamkar²

2022

Lecture Notes in Electrical Engineering

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Detection of Counterfeit Medicines Using Hyperspectral Sensing

Cited by 7 publications

References 0 publications

A Review of Pharmaceutical Robot based on Hyperspectral Technology

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Bloodstain Identification Based on Visible/Near-Infrared Hyperspectral Imaging With Convolutional Neural Network

Automated Classification of Hyper Spectral Image Using Supervised Machine Learning Approach

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