Abstract
This study explores innovative ways to enhance chemistry education by leveraging smartphone applications and advanced techniques like digital imaging and X-Ray Fluorescence (XRF) spectroscopy. Students analyze coins of varying colors to link visible differences to their chemical compositions. Images are captured, analyzed, and converted into ten-color scale matrices, enabling students to explore color variations beyond the naked eye's perception. XRF spectroscopy, a non-destructive and rapid method, identifies elemental composition, ensuring safe and practical analysis. The activity emphasizes critical skills such as data organization, modern analysis methods, and elemental identification, which are essential in today’s digital age. Designed for senior students, it fosters curiosity about chemistry by demonstrating its everyday relevance. Students learn how Digital Imaging (DI) and chemometric techniques reveal chemical distinctions, with XRF showcasing how specific elements drive color diversity. This hands-on, creative approach highlights the role of technology in education, inspiring deeper engagement and appreciation for chemistry.
References
Bro, R.; Smilde, A. G. Principal component analysis. Anal. Methods. 2014, 6, 2812–2831. https://doi.org/10.1039/C3AY41907J
Camargo, V. R.; Santos, L. J.; Pereira, F. M. V. A proof of concept study for the parameters of corn grains using digital images and a multivariate regression model. Food Anal. Methods. 2018, 11, 1852–1856. https://doi.org/10.1007/s12161-017-1028-6
Costa, V. Neiva, A.; Pereira-Filho, E. Chromium speciation in leather samples: an experiment using digital images, mobile phone, and environmental concepts. Eclét. Quím. 2019, 44 (1), 62–74. https://doi.org/10.26850/1678-4618eqj.v44.1.2019.p62-74
Diniz, P. H. G. D. Chemometrics-assisted color histogram-based analytical systems. J. Chemom. 2020, 34 (12), e3242. https://doi.org/10.1002/cem.3242
Ellis, A. T. Energy-Dispersive X-ray Fluorescence Analysis Using X-ray Tube Excitation. In Handbook of X-ray Spectrometry; Van Grieken, R.; Markowicz, A. A., Eds.; Marcel Dekker: New York, 2002; Vol. 29; pp 199–236.
Falomo Bernarduzzi, L.; Bernardi, E. M.; Ferrari, A.; Garbarino, M. C.; Vai, A. Augmented Reality Application for Handheld Devices. Sci. Educ. 2021, 30, 755–773. https://doi.org/10.1007/s11191-021-00197-z
Festa, G.; Saladino, M. L.; Mollica Nardo, V.; Armetta, F.; Renda, V.; Nasillo, G.; Pitonzo, R.; Spinella, A.; Borla, M.; Ferraris, E.; Turina, V.; Ponterio, R. C. Identifying the unknown content of an ancient Egyptian sealed alabaster vase from kha and merit’s tomb using multiple techniques and multicomponent sample analysis in an interdisciplinary applied chemistry course. J. Chem. Educ. 2021, 98 (2), 461–468. https://doi.org/10.1021/acs.jchemed.0c00386
Guedes, W. N.; Pereira, F. M. V. Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system. Comput. Electron. Agric. 2019, 156, 307–311. https://doi.org/10.1016/j.compag.2018.11.039
James, H.; Honeychurch, K. C. Digital Image Colorimetry Smartphone Determination of Acetaminophen. J. Chem. Educ. 2024, 101 (1), 187–196. https://doi.org/10.1021/acs.jchemed.3c00659
Jenkins, R. X-ray Fluorescence Spectrometry, 2nd ed.; Wiley-Interscience: New York, 1999. https://doi.org/10.1002/9781118521014
Nalhiati, G.; Borges, G. G.; Sperança, M. A.; Pereira, F. M. V. Color classification for red alcohol vinegar to control the quality of the end-product. Food Anal. Methods. 2023, 16, 1283–1290. https://doi.org/10.1007/s12161-023-02509-1
Oliveira, L. F.; Canevari, N. T.; Guerra, M. B. B.; Pereira, F. M. V.; Schaefer, C. E. G. R.; Pereira-Filho, E. R. Proposition of a simple method for chromium (VI) determination in soils from remote places applying digital images: A case study from Brazilian Antarctic Station. Microchem. J. 2013, 109, 165–169. https://doi.org/10.1016/j.microc.2012.03.007
Patricio, M. A.; Maravall, D. A. A novel generalization of the gray-scale histogram and its application to the automated visual measurement and inspection of wooden pallets. Image Vis. Comput. 2007, 25 (6), 805–816. https://doi.org/10.1016/j.imavis.2006.05.02
Pereira, F. M. V.; Bueno, M. I. M. S. Image evaluation with chemometric strategies for quality control of paints. Anal. Chim. Acta. 2007, 588, 184–191. https://doi.org/10.1016/j.aca.2007.02.009
Pereira-Filho, E.; Pereira, F. Relevant Topics in the Interpretation of Chemometric Data. In Chemometrics Data Treatment and Applications; Fernandes, F. A. N.; Rodrigues, S.; Alves Filho, E. G., Eds.; Elsevier: Amsterdam, 2024; pp 9–38. https://doi.org/10.1016/B978-0-443-21493-6.00002-2
Qiu, G.; Feng, X.; Fang, J. Compressing Histogram Representations for Automatic Color Photo Categorization. Pattern Recognit. 2004, 37, 2177–2193. https://doi.org/10.1016/j.patcog.2004.03.006
Santos, P. M.; Wentzell, P. D.; Pereira-Filho, E. R. Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk. Food Anal. Methods. 2012, 5, 89–95. https://doi.org/10.1007/s12161-011-9216-2
Santos, M. C.; Nascimento, P. A. M.; Guedes, W. N.; Pereira-Filho, E. R.; Filletti, E. R.; Pereira, F. M. V. Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018. Eclét. Quím. 2019, 44 (2), 11–25. https://doi.org/10.26850/1678-4618eqj.v44.2.11-25
Sequeira, C. A.; Borges, E. M. Enhancing Statistical Education in Chemistry and STEAM Using JAMOVI. Part 2. Comparing Dependent Groups and Principal Component Analysis (PCA). J. Chem. Educ. 2024, 101 (11). 5040–5049. https://doi.org/10.1021/acs.jchemed.4c00342
Tsuji, K.; Injuk, J.; Van Grieken, R. X-Ray Spectrometry: Recent Technological Advances, 1st ed.; John Wiley & Sons: Chichester, 2004. https://doi.org/10.1002/0470020431

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