Monitoring of thickness and conversion of thick pigmented UV-cured coatings by NIR hyperspectral imaging

Daikos, Olesya; Heymann, Katja; Scherzer, Tom

doi:10.1016/j.porgcoat.2018.08.022

Cited by 16 publications

(13 citation statements)

References 27 publications

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“…[13], [14]. Hyperspectral technique is mainly used in food detection, agriculture monitoring, resource exploration, archaeological investigation and so on [15]- [19]. S. Shrestha captured hyperspectral images of four tomato varieties and analyzed the data by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), and the results showed the application prospects of using hyperspectral imaging in varietal identification studies of tomato seeds [16].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al presented an automatic soil texture classification system using hyperspectral soil signatures and wavelet-based statistical models, and the results showed that the methods are both reliable and robust [18]. Daikos et al made use of hyperspectral imaging for in-line monitoring of thickness and conversion of white pigmented UV-cured acrylate coatings, proving that hyperspectral imaging has considerable potential for in-line process and quality control [19]. The mature application of hyperspectral technique in other fields and its great potential in on-line detection have attracted our wide attention.…”

Section: Introductionmentioning

confidence: 99%

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An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree

Qiu

Zhang

et al. 2019

IEEE Access

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The on-line detection of insulator pollution degree of transmission lines is important to the prevention and control of flashover. This paper proposed a non-contact detection method of insulator pollution degree based on hyperspectral technique. Firstly, hyperspectral images of the samples with different pollution degrees were obtained by hyper-spectrometer. Secondly, after original hyperspectral images were corrected by black-and-white correction and multiplicative scatter correction, hyperspectral curves from the region of interest (ROI) of corrected images were obtained. Finally, a multiclassification model of extreme learning machine (ELM) was built to realize the pollution degree classification of test samples. The results show that the absorption peak, the position of reflection peak, amplitude and the change trend of the hyperspectral curve obviously change with different kinds of pollution on the surface of silicone rubber, whereas only the amplitude obviously changes with same kind of pollution on the surface of silicone rubber; and the ELM-classification model can accurately and rapidly classify the pollution degree, with the pollution degree classification accuracy of NaCl, CaSO 4 and mixed NaCl-CaSO 4 respectively reaching 95%, 97.5% and 97.5%; and finally The ELM model based on hyperspectral curves data of the artificial pollution samples can classify the surface of insulator umbrellas with different pollution degrees, and the classification accuracy of CaSO 4 and mixed NaCl-CaSO 4 samples respectively are 87.5% and 90%. Consequently, the results of this study prove that hyperspectral technique has considerable potential for the non-contact detection of insulator pollution degree.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree

Qiu

Zhang

et al. 2019

IEEE Access

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“…Raman spectroscopy and PLS [108] Detection of APIs APIs in finished pharmaceutical products PLSR and PCR method with visible and NIR imaging [70] APIs under contamination surface PCA with hyperspectral imaging [71] rapid characterization of APIs PCA with hyperspectral imaging [72] Detection of drug coating thickness and hardness transdermal drug delivery systems PLS model with NIR hyperspectral imaging [118] acrylic coating PLS method combined with NIR hyperspectral imaging [119] hardness of pharmaceutical tablets PLSR and PCA method with NIR hyperspectral imaging [120] Detection of herbal medicine raw materials and tea bags PCA and PLS-DA with NIR hyperspectral imaging [125] the content of propylene glycol CARS method based on ELM model [126] pectin content in citrus peels multivariate statistical analysis with NIR hyperspectral imaging [127] NIR: near-infrared; PLS-DA: partial least-squares to discriminant analysis; PCA: principal component analysis; PLSR: partial least squares regression; SIMCA: soft independent modeling of class analogy; CLS: classical least-squares; UV: Ultraviolent; MCR-ALS: multivariate curve resolution with alternating least-squares; CARS: competitive adaptive reweighted sampling; ELM: extreme learning machine The results showed that the inhomogeneity of the drug is prominent, which is of great help to the evaluation of the solid-state stability of the drug. Nishii et al, [76] aimed at the detection of API content and surface coating content in pharmaceutical tablets, and proposed a multivariate data analysis method combined with a near-infrared hyperspectral imaging system.…”

Section: Detection Of Active Pharmaceutical Ingredientsmentioning

confidence: 99%

“…The results show that PLS model could detect changes in coating thickness and identify abnormalities such as coating uneven areas and bubbles. Daikos et al, [119] proposed a PLS method combined with near-infrared hyperspectral imaging technology for the detection of the thickness of the acrylic coating on the surface of drugs, and established a calibration model for the drug coating and measured the reference value of thickness conversion under the reflection imaging of infrared spectrum. This method also had an excellent effect in visualizing the spatial distribution of the coating surface and the unevenness of the coating.…”

Section: Detection Of Drug Coating Thickness and Hardnessmentioning

confidence: 99%

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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“…The research published with respect to the application of this technology in the field of organic coatings is scarce, but a few examples exist. Recently, Daikos et al 27 demonstrated the use of NIR-HSI camera in monitoring the dry film thickness (DFT) and the conversion of titanium dioxide pigmented urethanemodified acrylic coatings cured with UV light. The authors showed that NIR-HSI camera combined with suitable data processing techniques and calibration models (based upon partial least squares regression) can provide quantitative information regarding thickness and the degree of cure of the applied model coatings.…”

Section: Introductionmentioning

confidence: 99%

Detection of different chemical binders in coatings using hyperspectral imaging

et al. 2021

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Organic coatings protect metallic structures of significant commercial value. Regular inspections of coatings are required to ensure their integrity and, therefore, to verify their stated performance. However, for metallic structures located in harsh places, coating inspection can pose significant safety and logistical challenges. Near-infrared (NIR) spectroscopy is a rapid, nondestructive and relatively inexpensive analytical technique. It is currently employed to analyze different chemicals in fields like agriculture, food, and pharmaceuticals. Similarly, hyperspectral imaging (HSI) creates a spatial map of spectral information by measuring light reflected from a material. In this work, hyperspectral imaging in the NIR portion of the electromagnetic spectrum (NIR-HSI) is used to accurately distinguish between the chemically different binders employed in commercial organic coatings. In addition, k-means clustering is explored as a tool to provide diagnostic information about the spatial inhomogeneities in the chemical structure of an applied coating, which, if undetected, can lead to coating defects during service life. The results of this work suggest that the NIR-HSI could be used for remote inspections of organic coatings.

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Monitoring of thickness and conversion of thick pigmented UV-cured coatings by NIR hyperspectral imaging

Cited by 16 publications

References 27 publications

An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree

An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Detection of different chemical binders in coatings using hyperspectral imaging

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