Relevant bibliographies by topics / Colorimetric Sensor Array

Academic literature on the topic 'Colorimetric Sensor Array'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Colorimetric Sensor Array"

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Kangas, Michael James, Christina L. Wilson, Raychelle M. Burks, Jordyn Atwater, Rachel M. Lukowicz, Billy Garver, Miles Mayer, Shana Havenridge, and Andrea E. Holmes. "An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays." International Journal of Chemistry 10, no. 2 (April 25, 2018): 36. http://dx.doi.org/10.5539/ijc.v10n2p36.

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Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares – discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.
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Ameen, Abid, Manas Ranjan Gartia, Austin Hsiao, Te-Wei Chang, Zhida Xu, and Gang Logan Liu. "Ultra-Sensitive Colorimetric Plasmonic Sensing and Microfluidics for Biofluid Diagnostics Using Nanohole Array." Journal of Nanomaterials 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/460895.

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Colorimetric techniques provide a useful approach for sensing application because of their low cost, use of inexpensive equipment, requirement of fewer signal transduction hardware, and, above all, their simple-to-understand results. Colorimetric sensor can be used for both qualitative analyte identification as well as quantitative analysis for many application areas such as clinical diagnosis, food quality control, and environmental monitoring. A gap exists between high-end, accurate, and expensive laboratory equipment and low-cost qualitative point-of-care testing tools. Here, we present a label-free plasmonic-based colorimetric sensor fabricated on a transparent plastic substrate consisting of about one billion nanocups in an array with a subwavelength opening and decorated with metal nanoparticles on the side walls, to bridge that gap. The fabrication techniques of the plasmonic sensor, integration to portable microfluidic devices for lab on chip applications, demonstration of highly sensitive refractive-index sensing, DNA hybridization detection, and protein-protein interaction will be reviewed. Further, we anticipate that the colorimetric sensor can be applied to point-of-care diagnostics by utilizing proper surface functionalization techniques, which seems to be one of the current limiting factors. Finally, the future outlook for the colorimetric plasmonic sensors is discussed.
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Kim, Chuntae, Hansong Lee, Vasanthan Devaraj, Won-Geun Kim, Yujin Lee, Yeji Kim, Na-Na Jeong, et al. "Hierarchical Cluster Analysis of Medical Chemicals Detected by a Bacteriophage-Based Colorimetric Sensor Array." Nanomaterials 10, no. 1 (January 9, 2020): 121. http://dx.doi.org/10.3390/nano10010121.

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M13 bacteriophage-based colorimetric sensors, especially multi-array sensors, have been successfully demonstrated to be a powerful platform for detecting extremely small amounts of target molecules. Colorimetric sensors can be fabricated easily using self-assembly of genetically engineered M13 bacteriophage which incorporates peptide libraries on its surface. However, the ability to discriminate many types of target molecules is still required. In this work, we introduce a statistical method to efficiently analyze a huge amount of numerical results in order to classify various types of target molecules. To enhance the selectivity of M13 bacteriophage-based colorimetric sensors, a multi-array sensor system can be an appropriate platform. On this basis, a pattern-recognizing multi-array biosensor platform was fabricated by integrating three types of sensors in which genetically engineered M13 bacteriophages (wild-, RGD-, and EEEE-type) were utilized as a primary building block. This sensor system was used to analyze a pattern of color change caused by a reaction between the sensor array and external substances, followed by separating the specific target substances by means of hierarchical cluster analysis. The biosensor platform could detect drug contaminants such as hormone drugs (estrogen) and antibiotics. We expect that the proposed biosensor system could be used for the development of a first-analysis kit, which would be inexpensive and easy to supply and could be applied in monitoring the environment and health care.
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Guan, Binbin, Fuyun Wang, Hao Jiang, Mi Zhou, and Hao Lin. "Preparation of Mesoporous Silica Nanosphere-Doped Color-Sensitive Materials and Application in Monitoring the TVB-N of Oysters." Foods 11, no. 6 (March 12, 2022): 817. http://dx.doi.org/10.3390/foods11060817.

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In this work, a new colorimetric sensor based on mesoporous silica nanosphere-modified color-sensitive materials was established for application in monitoring the total volatile basic nitrogen (TVB-N) of oysters. Firstly, mesoporous silica nanospheres (MSNs) were synthesized based on the improved Stober method, then the color-sensitive materials were doped with MSNs. The “before image” and the “after image” of the colorimetric senor array, which was composed of nanocolorimetric-sensitive materials by a charge-coupled device (CCD) camera were then collected. The different values of the before and after image were analyzed by principal component analysis (PCA). Moreover, the error back-propagation artificial neural network (BP-ANN) was used to quantitatively predict the TVB-N values of the oysters. The correlation coefficient of the colorimetric sensor array after being doped with MSNs was greatly improved; the Rc and Rp of BP-ANN were 0.9971 and 0.9628, respectively when the principal components (PCs) were 10. Finally, a paired sample t-test was used to verify the accuracy and applicability of the BP-ANN model. The result shows that the colorimetric-sensitive materials doped with MSNs could improve the sensitivity of the colorimetric sensor array, and this research provides a fast and accurate method to detect the TVB-N values in oysters.
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Bo, Yu, Peng Shi, Can Wang, Fang Qin, and Huimei Wei. "Image Segmentation Algorithm of Colorimetric Sensor Array Based on Fuzzy C-Means Clustering." Mobile Information Systems 2022 (August 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/8333054.

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In the real world, the boundaries between many objective things are often fuzzy. When classifying things, they are accompanied by ambiguity, which leads to fuzzy cluster analysis. The most typical in fuzzy clustering analysis is the fuzzy C-means clustering algorithm. The fuzzy C-means clustering algorithm obtains the membership degree of each sample point to all the class centers by optimizing the objective function, so as to determine the category of the sample point to achieve the purpose of automatically classifying the sample data. Based on fuzzy C-means clustering, this paper analyzes the image segmentation algorithm of the chroma sensor array. The fuzzy C-means (FCM) algorithm for colorimetric sensor array image segmentation is an unsupervised fuzzy clustering and recalibration process, which is suitable for the existence of blur and uncertainty in colorimetric sensor array images. However, this algorithm has inherent defects; that is, it does not combine the characteristics of the current colorimetric sensor array diversity and instability, does not consider the spatial information of the pixels, and only uses the grayscale information of the image, making it effective for noise. The image segmentation effect is not ideal. Therefore, this paper proposes a new colorimetric sensor array image segmentation algorithm based on fuzzy C-means clustering. Through the image segmentation effect test, the image segmentation algorithm proposed in this paper demonstrates an overall optimal segmentation accuracy of 96.62% in all array point image segmentation, which can effectively and accurately achieve the target extraction of colorimetric sensor array images.
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Chung, Soo, Tu Park, Soo Park, Joon Kim, Seongmin Park, Daesik Son, Young Bae, and Seong Cho. "Colorimetric Sensor Array for White Wine Tasting." Sensors 15, no. 8 (July 24, 2015): 18197–208. http://dx.doi.org/10.3390/s150818197.

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Lim, Sung H., Jonathan W. Kemling, Liang Feng, and Kenneth S. Suslick. "A colorimetric sensor array of porous pigments." Analyst 134, no. 12 (2009): 2453. http://dx.doi.org/10.1039/b916571a.

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Zhang, Chen, and Kenneth S. Suslick. "Colorimetric Sensor Array for Soft Drink Analysis." Journal of Agricultural and Food Chemistry 55, no. 2 (January 2007): 237–42. http://dx.doi.org/10.1021/jf0624695.

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JIA, Ming-Yan, and Liang FENG. "Progress in Optical Colorimetric/Fluorometric Sensor Array." CHINESE JOURNAL OF ANALYTICAL CHEMISTRY (CHINESE VERSION) 41, no. 5 (2013): 795. http://dx.doi.org/10.3724/sp.j.1096.2013.21011.

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LI, Yan-Qi, and Liang FENG. "Progress in Paper-based Colorimetric Sensor Array." Chinese Journal of Analytical Chemistry 48, no. 11 (November 2020): 1448–57. http://dx.doi.org/10.1016/s1872-2040(20)60057-3.

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Dissertations / Theses on the topic "Colorimetric Sensor Array"

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Zhang, Chen. "A colorimetric sensor array for aqueous analyses /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3250354.

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Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 2006.
Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 0944. Adviser: Kenneth S. Suslick. Includes bibliographical references. Available on microfilm from Pro Quest Information and Learning.
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Ponder, Jennifer B. "Colorimetric sensor array : do I see what you smell? /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3242962.

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Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 2006.
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6368. Adviser: Kenneth S. Suslick. Includes bibliographical references. Available on microfilm from Pro Quest Information and Learning.
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Book chapters on the topic "Colorimetric Sensor Array"

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Şener, Gülsu, and Adil Denizli. "Identification of Several Toxic Metal Ions Using a Colorimetric Sensor Array." In Biomimetic Sensing, 81–86. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9616-2_7.

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Şener, Gülsu, and Adil Denizli. "Colorimetric Sensor Array Based on Amino Acid-Modified Gold Nanoparticles for Toxic Metal Ion Detection in Water." In Biomimetic Sensing, 75–80. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9616-2_6.

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Wang, Xingbo, Philip J. Green, Jean-Baptiste Thomas, Jon Y. Hardeberg, and Pierre Gouton. "Evaluation of the Colorimetric Performance of Single-Sensor Image Acquisition Systems Employing Colour and Multispectral Filter Array." In Lecture Notes in Computer Science, 181–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15979-9_18.

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Shibata, Hiroyuki, Yuma Ikeda, and Daniel Citterio. "Inkjet-Printed Colorimetric Paper-Based Gas Sensor Arrays for the Discrimination of Volatile Primary Amines with Amine-Responsive Dye-Encapsulating Polymer Nanoparticles." In Biomimetic Sensing, 101–14. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9616-2_9.

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"Colorimetric and Fluorometric Sensor Arrays." In Materials Research Foundations, 220–44. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901175-8.

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The design and construction of colorimetric/fluorometric sensor arrays with high selectivity and sensitivity has been of considerable attention as an emerging technology for mobile chemical detection. Nowadays, many approaches have been made to design sensors, fabricate arrays and generalize their usage areas especially in daily life applications and industrial sectors. This chapter introduces the fundamentals, fabrication methods, and applications of colorimetric and fluorometric sensor arrays. The readers will find detailed information about mechanism of chemical sensing and optical sensor arrays; solid state sensor fabrication methodologies; and specific applications environmental, pharmaceutical, medical, and food sectors.
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Ebralidze, Iraklii I., Nadia O. Laschuk, Jade Poisson, and Olena V. Zenkina. "Colorimetric Sensors and Sensor Arrays." In Nanomaterials Design for Sensing Applications, 1–39. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-814505-0.00001-1.

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Askim, J. R., and K. S. Suslick. "Colorimetric and Fluorometric Sensor Arrays for Molecular Recognition." In Comprehensive Supramolecular Chemistry II, 37–88. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-409547-2.12616-2.

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Conference papers on the topic "Colorimetric Sensor Array"

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Alstrom, Tommy S., Bjorn S. Jensen, Mikkel N. Schmidt, Natalie V. Kostesha, and Jan Larsen. "Haussdorff and hellinger for colorimetric sensor array classification." In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349724.

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Kostesha, N. V., A. Boisen, M. H. Jakobsen, T. S. Alstrom, and J. Larsen. "Multi-colorimetric sensor array for detection of illegal materials." In 2012 IEEE Sensors. IEEE, 2012. http://dx.doi.org/10.1109/icsens.2012.6411474.

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Palinski, Timothy J., Bin Guan, Bronwyn H. Bradshaw-Hajek, Michael A. Lienhard, Craig Priest, and Felix A. Miranda. "Fast Vapor Detection by a Micropillar Array-integrated Colorimetric Sensor." In 2022 IEEE Sensors. IEEE, 2022. http://dx.doi.org/10.1109/sensors52175.2022.10015718.

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Xingyi Huang, Junwei Xin, and Jiewen Zhao. "Rapid determination of fish freshness using colorimetric sensor array." In 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.29636.

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Kostesha, N., T. S. Alstrøm, C. Johnsen, K. A. Nielsen, J. O. Jeppesen, J. Larsen, A. Boisen, and M. H. Jakobsen. "Multi-colorimetric sensor array for detection of explosives in gas and liquid phase." In SPIE Defense, Security, and Sensing. SPIE, 2011. http://dx.doi.org/10.1117/12.883895.

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Kostesha, N. V., T. S. Alstrøm, C. Johnsen, K. A. Nilesen, J. O. Jeppesen, J. Larsen, M. H. Jakobsen, and A. Boisen. "Development of the colorimetric sensor array for detection of explosives and volatile organic compounds in air." In SPIE Defense, Security, and Sensing, edited by Tuan Vo-Dinh, Robert A. Lieberman, and Günter Gauglitz. SPIE, 2010. http://dx.doi.org/10.1117/12.850310.

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Alstrom, Tommy S., Raviv Raich, Natalie V. Kostesha, and Jan Larsen. "Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288331.

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Alstrom, Tommy S., Jan Larsen, Natalie V. Kostesha, Mogens H. Jakobsen, and Anja Boisen. "Data representation and feature selection for colorimetric sensor arrays used as explosives detectors." In 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2011. http://dx.doi.org/10.1109/mlsp.2011.6064615.

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