Institute for Advanced Studies in Basic Sciences Chemistry Department Robust and predictive quantitative analysis for mixtures of four dyes through smartphone spectrometer and neural networks Fereshteh Matinrad Mohsen Kompany-Zareh*, Saeed Bagheri, M.Taghi Baharifard 24th Iranian Seminar of Analytical Chemistry Azarbaijan Shahid Madani University, Tabriz
Introduction Since the introduction of smartphones, hundreds of millions of the devices have been sold. Cellphones in general and smartphones in particular, are equipped with good megapixel cameras, wifi, Bluetooth, temperature sensor, light source or LED flashlight etc. Many research groups across the globe are currently actively engaged in developing or converting the smartphone into an optical sensing tool for different applications. Smartphones are clearly ubiquitous in the hands of students. These devices have many valuable capabilities that have tremendous potential for use in chemical education. Smartphone-based pH sensor, AIP ADVANCES, 2015, 5, 057151. Smartphone and Molecular Modeling Fe, CHEMICAL EDUCATION, 2016, 93, 1760. 1
Introduction Optical imaging and sensing techniques based on smartphones have drawn huge attention as they can eliminate the need for bulky and costly optical instrumentation while retaining high sensitivity and image resolution. Low-cost, portable sensing systems are crucial in resource limited and remote regions of the world for medical diagnosis, environmental monitoring and nutrition examination. Smartphone Spectrometer for colorimetric bio sensing, Analyst, 2016, 141, 3233. 2
Smartphone Spectrometer Source: A common desk lamp with incandescent light bulb Case : Black paper box Dispersing element: a piece of Compact disc or DVD Detector and readout: Smart phone 3
Materials Crystal violet Amaranth Tartrazine Indigo carmine (0.5,1, 2,5,10,20,50 ppm) (0.1, 0.2, 0.5,1,2,5,10,20 ppm) Tartrazine Indigo carmine (1,2,5,10,20,40,50,100,200 ppm) (0.5,1,2,5,10,20,50 ppm) 4
Images obtained through smartphone spectrometer Amaranth 1 ppm 10 ppm 20 ppm Crystal violet 0.2 ppm 5 ppm 10 ppm Indigo carmine 1 ppm 10 ppm 20 ppm Tartrazine 2 ppm 100 ppm 200 ppm 5
Transformation of image to spectrum Mean 6
Transmittance spectra of different Dyes Amaranth Crystal violet Indigo carmine Tartrazine 7
Experimental design Violet Tar Indig Amrt Violet Tar Indig Amrt 1 2 3 2 3 0.2 0.5 5 4 10 6 20 7 8 9 11 40 12 13 14 Violet Tar Indig Amrt 15 10 16 0.1 17 2 20 18 1 0.5 19 21 5 22 23 24 25 0.2 26 40 27 28
Transmittance spectra of dye mixtures 9
PLS Calibration for Smartphone Spectrometer data Poor performance in prediction of test set due to the non-linear relation of transmittance to concentration 10
PLS calibration for Commercial Spectrometer data High prediction performance in test set through PLS calibration of spectra of commercial spectrometer as a reference data and method Results show that dye solutions made properly 11
RBFN (Radial Basis Function network) ℎ 𝑗 𝑥 =𝑒𝑥𝑝 − 𝑥− 𝑐 𝑗 2 𝑟 𝑗 2 𝑄 2 =1− 𝑖=1 𝑛 𝐸𝑋𝑇 𝑦 𝑖 − 𝑦 𝑖 2 𝑛 𝐸𝑋𝑇 𝑗=1 𝑛 𝑇𝑅 𝑦 𝑗 − 𝑦 𝑇𝑅 2 𝑛 𝑇𝑅 Local modeling radial basis function networks. Chemometrics and Intelligent Laboratory Systems, 2000, 50, 179. 12
Calibration with RBFN Q2 Sigma Center High performance in prediction of test set Dye Q2 Sigma Center Crystal violet 0.97 625 18 Tartrazine 660 14 Indigo carmine 0.99 670 Amaranth 0.94 685 13 13
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Diffraction Grating we considered to use digital versatile disks (DVDs) as monochromator. Spacing between the recording tracks in DVDs are 0.74 µm, making a grating of about 1350 lines/mm, which it is comparable to the gratings in spectrophotometers routinely used in laboratories ( 300 – 2400 lines/mm ) . A DVD Spectroscope, Journal of Chemical Education, 2006, 83, 56.
Absorbance spectra obtained by the smartphone spectrometer Indigo carmine Zoom
Measurement of solution’s pH Tartrazine mg/l pH 200 7.79 100 7.73 50 7.76 10 7.85 5 7.88 Crystal violet mg/l pH 20 6.89 10 6.84 5 6.75 2 6.69 1 6.63 Indigo carmine pH 50 7.51 20 7.47 10 7.56 5 7.42 Amaranth mg/l pH 50 7.12 20 7.23 10 7.19 5 7.14