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Journal articles on the topic 'Colorimetric Sensor Array'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Lee, Jun-Seok, Jae Wook Lee, and Young-Tae Chang. "Counterion Free Colorimetric Metal Cation Sensor Array." Journal of Combinatorial Chemistry 9, no. 6 (November 2007): 926–28. http://dx.doi.org/10.1021/cc070065z.

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12

Chan, Shu Ann, Jun-Seok Lee, and Young-Tae Chang. "Colorimetric Sensor Array for Qualitative Water Analysis." Australian Journal of Chemistry 62, no. 9 (2009): 1040. http://dx.doi.org/10.1071/ch09289.

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A chemosensor array comprising 45 off-the-shelf colorimetric dyes, dubbed the Singapore Tongue (SGT), that is capable of discriminating different brands of bottled water and waters of different geographical attribute is described. Twelve kinds of bottled waters were tested by the SGT, and changes of absorbance spectra were analyzed by unsupervised classification methods to validate the SGT system for water analysis. All 12 bottled waters were discriminated at 1 × concentration, and SGT could distinguish the identity of samples of the waters diluted up to 100 times, except distilled waters. Following the study of 63 tap waters in different mass rapid transit stations in Singapore, two distinct clusters were observed from a principal component analysis plot, which correspond to the origin of the tap water. The successful discrimination and identification of in this study demonstrates the practical application of the SGT as a simple tool for water analysis.

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13

Rakow, Neal A., and Kenneth S. Suslick. "A colorimetric sensor array for odour visualization." Nature 406, no. 6797 (August 2000): 710–13. http://dx.doi.org/10.1038/35021028.

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14

Rankin, Jacqueline M., Qifan Zhang, Maria K. LaGasse, Yinan Zhang, Jon R. Askim, and Kenneth S. Suslick. "Solvatochromic sensor array for the identification of common organic solvents." Analyst 140, no. 8 (2015): 2613–17. http://dx.doi.org/10.1039/c4an02253j.

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15

Suslick, Kenneth S. "An Optoelectronic Nose:“Seeing” Smells by Means of Colorimetric Sensor Arrays." MRS Bulletin 29, no. 10 (October 2004): 720–25. http://dx.doi.org/10.1557/mrs2004.209.

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AbstractA new approach to general sensors for odors and volatile organic compounds (VOCs) using thin films of chemically responsive dyes as a colorimetric sensor array is described. This optoelectronic “nose,” by using an array of multiple dyes whose colors change based on the full range of intermolecular interactions, provides enormous discriminatory power among odorants in a simple device that can be easily digitally imaged. High sensitivities (ppb) have been demonstrated for the detection of biologically important analytes such as amines, carboxylic acids, and thiols. By the proper choice of dyes and substrate, the array can be made essentially nonresponsive to changes in humidity.

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16

Duffy, Emer, Kati Huttunen, Roosa Lahnavik, Alan F. Smeaton, and Aoife Morrin. "Visualising household air pollution: Colorimetric sensor arrays for monitoring volatile organic compounds indoors." PLOS ONE 16, no. 10 (October 6, 2021): e0258281. http://dx.doi.org/10.1371/journal.pone.0258281.

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Indoor air quality monitoring as it relates to the domestic setting is an integral part of human exposure monitoring and health risk assessment. Hence there is a great need for easy to use, fast and economical indoor air quality sensors to monitor the volatile organic compound composition of the air which is known to be significantly perturbed by the various source emissions from activities in the home. To meet this need, paper-based colorimetric sensor arrays were deployed as volatile organic compound detectors in a field study aiming to understand which activities elicit responses from these sensor arrays in household settings. The sensor array itself is composed of pH indicators and aniline dyes that enable molecular recognition of carboxylic acids, amines and carbonyl-containing compounds. The sensor arrays were initially deployed in different rooms in a single household having different occupant activity types and levels. Sensor responses were shown to differ for different room settings on the basis of occupancy levels and the nature of the room emission sources. Sensor responses relating to specific activities such as cooking, cleaning, office work, etc were noted in the temporal response. Subsequently, the colorimetric sensor arrays were deployed in a broader study across 9 different households and, using multivariate analysis, the sensor responses were shown to correlate strongly with household occupant activity and year of house build. Overall, this study demonstrates the significant potential for this type of simple approach to indoor air pollution monitoring in residential environments.

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17

Thaler, Erica R., Daniel D. Lee, and C. William Hanson. "Diagnosis of rhinosinusitis with a colorimetric sensor array." Journal of Breath Research 2, no. 3 (September 1, 2008): 037016. http://dx.doi.org/10.1088/1752-7155/2/3/037016.

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18

Yoon, Sumi, Kook-Nyung Lee, Dong-ki Hong, Hye-Lim Kang, Sunga Song, Won-Hyo Kim, Woo Kyeong Seong, Dong-Sik Shin, Hana Cho, and Hyewon Kim. "Colorimetric Sensor Array Reader System Using Smartphone Camera." ECS Meeting Abstracts MA2020-01, no. 34 (May 1, 2020): 2399. http://dx.doi.org/10.1149/ma2020-01342399mtgabs.

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19

JIA, Ming-Yan, and Liang FENG. "Recent Progresses in Optical Colorimetric/Fluorometric Sensor Array." Chinese Journal of Analytical Chemistry 41, no. 5 (May 2013): 795–802. http://dx.doi.org/10.1016/s1872-2040(13)60658-1.

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20

Kim, Su-Yeon, and Bo-Sik Kang. "A colorimetric sensor array-based classification of coffees." Sensors and Actuators B: Chemical 275 (December 2018): 277–83. http://dx.doi.org/10.1016/j.snb.2018.08.058.

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21

Mahmoudi, Morteza, Samuel E. Lohse, Catherine J. Murphy, and Kenneth S. Suslick. "Identification of Nanoparticles with a Colorimetric Sensor Array." ACS Sensors 1, no. 1 (November 9, 2015): 17–21. http://dx.doi.org/10.1021/acssensors.5b00014.

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22

Li, Zheng, and Kenneth S. Suslick. "Colorimetric Sensor Array for Monitoring CO and Ethylene." Analytical Chemistry 91, no. 1 (December 14, 2018): 797–802. http://dx.doi.org/10.1021/acs.analchem.8b04321.

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23

Lin, Hengwei, Minseok Jang, and Kenneth S. Suslick. "Preoxidation for Colorimetric Sensor Array Detection of VOCs." Journal of the American Chemical Society 133, no. 42 (October 26, 2011): 16786–89. http://dx.doi.org/10.1021/ja207718t.

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24

Zhang, Chen, and Kenneth S. Suslick. "A Colorimetric Sensor Array for Organics in Water." Journal of the American Chemical Society 127, no. 33 (August 2005): 11548–49. http://dx.doi.org/10.1021/ja052606z.

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25

Mao, Jinpeng, Yuexiang Lu, Ning Chang, Jiaoe Yang, Sichun Zhang, and Yueying Liu. "Multidimensional colorimetric sensor array for discrimination of proteins." Biosensors and Bioelectronics 86 (December 2016): 56–61. http://dx.doi.org/10.1016/j.bios.2016.06.040.

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26

Zhong, Haotian, Yuting Xue, Bin Liu, Zhengbo Chen, Kai Li, and Xia Zuo. "Construction of a colorimetric sensor array based on the coupling reaction to identify phenols." Analytical Methods 14, no. 9 (2022): 892–99. http://dx.doi.org/10.1039/d1ay02076e.

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We report a colorimetric sensor array, which uses two nanozymes as electronic tongues for fingerprint identification of six phenols. The six phenols at 50 nM have their own response patterns. The sensor array had distinguished the six phenols in actual samples successfully.

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27

Zhang, Chi, Juan Huang, Wei Wei, and Zhengbo Chen. "Colorimetric identification of lanthanide ions based on two carboxylic acids as an artificial tongue." Analyst 145, no. 9 (2020): 3359–63. http://dx.doi.org/10.1039/d0an00357c.

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28

Wang, Feiyang, Yuexiang Lu, Jiacheng Yang, Ying Chen, Wenjie Jing, Liuying He, and Yueying Liu. "A smartphone readable colorimetric sensing platform for rapid multiple protein detection." Analyst 142, no. 17 (2017): 3177–82. http://dx.doi.org/10.1039/c7an00990a.

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We have developed a very simple colorimetric sensor array by using only unmodified gold nanoparticles and NaCl salt for discrimination of multiple proteins. The inexpensive and convenient sensor array and the ubiquitous smartphone are coupled to achieve an immediate point-of-care diagnosis without additional devices.

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29

Kangas, Michael, Adreanna Ernest, Rachel Lukowicz, Andres Mora, Anais Quossi, Marco Perez, Nathan Kyes, and Andrea Holmes. "The Identification of Seven Chemical Warfare Mimics Using a Colorimetric Array." Sensors 18, no. 12 (December 6, 2018): 4291. http://dx.doi.org/10.3390/s18124291.

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Chemical warfare agents pose significant threats in the 21st century, especially for armed forces. A colorimetric detection array was developed to identify warfare mimics, including mustard gas and nerve agents. In total, 188 sensors were screened to determine the best sensor performance, in order to identify warfare mimics 2-chloro ethyl ethylsulfide, 2-2′-thiodiethanol, trifluoroacetic acid, methylphosphonic acid, dimethylphosphite, diethylcyanophosphonate, and diethyl (methylthiomethyl)phosphonate. The highest loadings in the principle component analysis (PCA) plots were used to identify the sensors that were most effective in analyzing the RGB data to classify the warfare mimics. The dataset was reduced to only twelve sensors, and PCA results gave comparable results as the large data did, demonstrating that only twelve sensors are needed to classify the warfare mimics.

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30

Zhang, Tiantian, Xiuzhi Zhuo, Guoyue Shi, and Min Zhang. "Colorimetric recognition of lanthanide ions with a complexometric indicator array." Analyst 146, no. 14 (2021): 4441–45. http://dx.doi.org/10.1039/d1an00710f.

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31

Qin, Honglian, Chengli Liao, Yaohui Zhang, Yumin Leng, Mingjiong Zhou, Jiao Wu, Man Dang, Xing Li, and Fang Hu. "Colorimetric Sensor Array for Detection of Iron(II) Ion." Current Organic Chemistry 22, no. 8 (May 23, 2018): 831–34. http://dx.doi.org/10.2174/1385272821666171002122854.

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32

Greene, Nathaniel T., and Ken D. Shimizu. "Colorimetric Molecularly Imprinted Polymer Sensor Array using Dye Displacement." Journal of the American Chemical Society 127, no. 15 (April 2005): 5695–700. http://dx.doi.org/10.1021/ja0468022.

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33

Chen, Quansheng, Aiping Liu, Jiewen Zhao, Qin Ouyang, Zongbao Sun, and Lin Huang. "Monitoring vinegar acetic fermentation using a colorimetric sensor array." Sensors and Actuators B: Chemical 183 (July 2013): 608–16. http://dx.doi.org/10.1016/j.snb.2013.04.033.

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34

Guan, Binbin, Jiewen Zhao, Hongjuan Jin, and Hao Lin. "Determination of Rice Storage Time with Colorimetric Sensor Array." Food Analytical Methods 10, no. 4 (October 4, 2016): 1054–62. http://dx.doi.org/10.1007/s12161-016-0664-6.

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35

Kim, Su-Yeon, Hye-Young Seo, and Ji-Hyoung Ha. "A colorimetric sensor array for the discrimination of glucosinolates." Food Chemistry 328 (October 2020): 127149. http://dx.doi.org/10.1016/j.foodchem.2020.127149.

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36

Shui, Zhengfan, Jiawei Li, Ping Yang, Danqun Huo, Changjun Hou, and Caihong Shen. "Amino acid-modulating gold nanoparticle sensor array: an express metal ion recognition system." Analytical Methods 11, no. 44 (2019): 5691–98. http://dx.doi.org/10.1039/c9ay01791g.

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We report a novel colorimetric sensor array for MIs discrimination based on soft and weak bond, which increase the array sensitivity by reducing cross-response specificity to achieve high-throughput detection at low-concentration.

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37

Sun, Shan, Sihua Qian, Jianping Zheng, Zhongjun Li, and Hengwei Lin. "A colorimetric sensor array for the discrimination of Chinese liquors." Analyst 145, no. 21 (2020): 6968–73. http://dx.doi.org/10.1039/d0an01496f.

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38

Chen, Mei Yung, and Hsi Che Chen. "Monitoring Volatile Organic Compound by Visual Recognition Technology of Application." Applied Mechanics and Materials 190-191 (July 2012): 1016–19. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.1016.

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To recognize volatile organic compound for colorimetric sensor array which constitute with chemical dyes. The rapid and low cost system can identify dilute compound. Every different compound is presented specified color on the colorimetric sensor array. In this research, we use CCD (charge couple device) to clearly classify the change of color on base, and make sure what the compound would be by Neural Network model. Based on the experimental result, we totally test for 33 group data, and precisely classify all type of volatile organic compound. It’s successfully to achieve the target in this research.

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39

Shariati-Rad, Masoud, and Zahra Ghorbani. "Carbon dot-based colorimetric sensor array for the discrimination of different water samples." Analytical Methods 11, no. 43 (2019): 5584–90. http://dx.doi.org/10.1039/c9ay01439j.

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40

Lim, Sung H., Samantha Mix, Victoria Anikst, Indre Budvytiene, Michael Eiden, Yair Churi, Nuria Queralto, et al. "Bacterial culture detection and identification in blood agar plates with an optoelectronic nose." Analyst 141, no. 3 (2016): 918–25. http://dx.doi.org/10.1039/c5an01990g.

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41

Lin, Hao, Zhong-xiu Man, Bin-bin Guan, Quan-sheng Chen, Hong-juan Jin, and Zhao-li Xue. "In situ quantification of volatile ethanol in complex components based on colorimetric sensor array." Anal. Methods 9, no. 40 (2017): 5873–79. http://dx.doi.org/10.1039/c7ay01639e.

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42

Abedalwafa, Mohammed Awad, Yan Li, Chunfang Ni, and Lu Wang. "Colorimetric sensor arrays for the detection and identification of antibiotics." Analytical Methods 11, no. 22 (2019): 2836–54. http://dx.doi.org/10.1039/c9ay00371a.

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43

Zhang, Miao, Jiangfan Shi, Chenglong Liao, Qingyun Tian, Chuanyi Wang, Shuai Chen, and Ling Zang. "Perylene Imide-Based Optical Chemosensors for Vapor Detection." Chemosensors 9, no. 1 (December 22, 2020): 1. http://dx.doi.org/10.3390/chemosensors9010001.

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Perylene imide (PI) molecules and materials have been extensively studied for optical chemical sensors, particularly those based on fluorescence and colorimetric mode, taking advantage of the unique features of PIs such as structure tunability, good thermal, optical and chemical stability, strong electron affinity, strong visible light absorption and high fluorescence quantum yield. PI-based optical chemosensors have now found broad applications in gas phase detection of chemicals, including explosives, biomarkers of some food and diseases (such as organic amines (alkylamines and aromatic amines)), benzene homologs, organic peroxides, phenols and nitroaromatics, etc. In this review, the recent research on PI-based fluorometric and colorimetric sensors, as well as array technology incorporating multiple sensors, is reviewed along with the discussion of potential applications in environment, health and public safety areas. Specifically, we discuss the molecular design and aggregate architecture of PIs in correlation with the corresponding sensor performances (including sensitivity, selectivity, response time, recovery time, reversibility, etc.). We also provide a perspective summary highlighting the great potential for future development of PIs optical chemosensors, especially in the sensor array format that will largely enhance the detection specificity in complexed environments.

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44

Huang, Yuanfang, Peiwen Cheng, and Chunyan Tan. "Visual artificial tongue for identification of various metal ions in mixtures and real water samples: a colorimetric sensor array using off-the-shelf dyes." RSC Advances 9, no. 47 (2019): 27583–87. http://dx.doi.org/10.1039/c9ra05983k.

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45

Askim, Jon R., Zheng Li, Maria K. LaGasse, Jaqueline M. Rankin, and Kenneth S. Suslick. "An optoelectronic nose for identification of explosives." Chemical Science 7, no. 1 (2016): 199–206. http://dx.doi.org/10.1039/c5sc02632f.

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46

Xi, Hongyan, Xin Li, Qingyun Liu, and Zhengbo Chen. "Cationic polymer-based plasmonic sensor array that discriminates proteins." Analyst 143, no. 22 (2018): 5578–82. http://dx.doi.org/10.1039/c8an01360h.

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Breaking the restrictions of the lock-and-key sensing strategy which relies only on the most dominant interactions between the sensing element and target, here, we develop a colorimetric sensor array with three kinds of cationic polymers (polydiallyl dimethylammonium chloride (PDDA), chitosan (CTS), and cetyltrimethylammonium bromide (CTAB)) as nonspecific receptors.

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47

Šídlo, Michal, Přemysl Lubal, and Pavel Anzenbacher. "Colorimetric Chemosensor Array for Determination of Halides." Chemosensors 9, no. 2 (February 18, 2021): 39. http://dx.doi.org/10.3390/chemosensors9020039.

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The halide anions are essential for supporting life. Therefore, halide anion analyses are of paramount importance. For this reason, we have performed both qualitative and quantitative ana- lyses of halides (chloride, bromide, iodide) using the Tl(III) complex of azodye, 4-(2-pyridylazo)re- sorcinol (PAR), a potential new chemical reagent/sensor that utilizes the substitution reaction whereas the Tl(III)PAR complex reacts with a halide to yield a more stable thallium(III)-halide while releasing the PAR ligand in a process accompanied by color change of the solution. The experimental conditions (e.g., pH, ratio metal ion-to-ligand ratio, etc.) for the substitution reaction between the metal complex and a halide were optimized to achieve increased sensitivity and a lower limit of detection (chloride 7 mM, bromide 0.15 mM, iodide 0.05 mM). It is demonstrated that this single chemosensor can, due to release of colored PAR ligand and the associated analyte-specific changes in the UV/VIS spectra, be employed for a multicomponent analysis of mixtures of anions (chloride + bromide, chloride + iodide, bromide + iodide). The spectrophotometric data evaluated by artificial neural networks (ANNs) enable distinguishing among the halides and to determine halide species concentrations in a mixture. The Tl(III)-PAR complex was also used to construct sensor arrays utilizing a standard 96-well plate format where the output was recorded at several wavelengths (up to 7) using a conventional plate reader. It is shown that the data obtained using a digital scanner employing only three different input channels may also be successfully used for a subsequent ANN analysis. The results of all approaches utilized for data evaluation were similar. To increase the practical utility of the chemosensor, we have developed a test paper strip indicator useful for routine naked-eye visual determination of halides. This test can also be used for halide anion determination in solutions using densitometer. The methodology described in this paper can be used for a simple, inexpensive, and fast routine analysis both in a laboratory as well as in a field setting.

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48

Qian, Sihua, Yumin Leng, and Hengwei Lin. "Strong base pre-treatment for colorimetric sensor array detection and identification of N-methyl carbamate pesticides." RSC Advances 6, no. 10 (2016): 7902–7. http://dx.doi.org/10.1039/c5ra25805g.

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Zheng, Jia, Ping Yang, Jiawei Li, Pan Li, Jingzhou Hou, Danqun Huo, Changjun Hou, and Suyi Zhang. "A nanophase material and organic dye modified colorimetric sensor array for the discrimination of baijiu." Analytical Methods 10, no. 47 (2018): 5679–86. http://dx.doi.org/10.1039/c8ay02108b.

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Ghasemi, Forough, M. Reza Hormozi-Nezhad, and Morteza Mahmoudi. "Label-free detection of β-amyloid peptides (Aβ40 and Aβ42): a colorimetric sensor array for plasma monitoring of Alzheimer's disease." Nanoscale 10, no. 14 (2018): 6361–68. http://dx.doi.org/10.1039/c8nr00195b.

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