Quantitative Analysis Using a Flatbed Scanner - ACS Publications

Communication Cite This: J. Chem. Educ. 2019, 96, 1519−1526

pubs.acs.org/jchemeduc

Quantitative Analysis Using a Flatbed Scanner: Aspirin Quantification in Pharmaceutical Tablets Rodrigo Sens da Silva† and Endler Marcel Borges*,†,‡ †

Downloaded via VOLUNTEER STATE COMMUNITY COLG on July 17, 2019 at 08:43:36 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

Departamento de Química, Universidade Regional de Blumenau, FURB, Campus 1, Rua Antônio da Veiga, 140, Victor Konder, 89012-900 Blumenau, SC, Brazil ‡ Núcleo Biotecnológico, Universidade do Oeste de Santa Catarina, UNOESC, Rua Paese, 198, Bairro Universitário-Bloco K, 89560-000 Videira, SC, Brazil S Supporting Information *

ABSTRACT: Here, students determine aspirin (acetylsalicylic acid) mass in pharmaceutical tablets using a colorimetric method. Aspirin, salicylate, and salicylic acid do not absorb visible light. Thus, in alkaline medium, acetylsalicylic acid was hydrolyzed to salicylate; then, it was reacted with an acidic Fe(III) solution, and a violet complex was formed. Quantitative analysis was carried out using absorbance measured at 535 nm (standard method) and digital images obtained with a flatbed scanner (proposed method). Results obtained with both methods were compared using an F-test and a t-test; both methods had shown equivalent accuracy and precision at the 95% confidence level. In addition, one-way ANOVA showed that aspirin masses found by five student groups using both methods are equivalent at the 95% confidence level. In the proposed method, samples were placed in a 96 microwell plate, and RGB values were extracted automatically from all wells in less than 5 min using ImageJ’s plugin “ReadPlate”; data obtained were organized using a spreadsheet to determine aspirin mass in pharmaceutical tablets, recovery, and percent error. Our goal was to design a portable, cost-effective, and user-friendly platform and to develop an experimental methodology that can easily be applied to any research and education laboratory using just a flatbed scanner. KEYWORDS: First-Year Undergraduate/General, Second-Year Undergraduate, Analytical Chemistry, Chemoinformatics, Hands-On Learning/Manipulatives, UV−Vis Spectroscopy

■

INTRODUCTION Colorimetric determinations are commonly selected as undergraduate chemistry experiments to quantify the amount of analyte found in a sample; bridging the gap between basic knowledge and applied chemistry, this method also provides reliable results.1−3 In recent years, digital images, which were captured using charge-coupled devices, such as smartphones, scanners, and digital cameras, have been widely used in chemistry research4 and education5−22 to replace conventional photometers. These methods provide an effective instrumental alternative to spectrophotometric methods, which can be especially beneficial in those cases where purchasing and maintaining a spectrophotometer is a challenge.23 When charge-coupled devices are used as detectors, students can observe the color of light absorbed in the digital image directly and compare it to the color of the solution. These experiments allow students to explore the process of light absorption by a sample, which is the basic principle in absorption photometry.24 In 2006, Kohl et al.20 showed that the principle of absorbance could be easily demonstrated using colored © 2019 American Chemical Society and Division of Chemical Education, Inc.

solutions and digital images obtained with charge-coupled devices. Since 2006, there are several laboratory practices, published in this Journal, in which smartphones, scanners, and digital cameras were used as photometers, for example, in the detection of the end point in wine titration,5 determination of phosphate in water,6 pH determination,7 quantification of colored substances (food dye, sports drink, and iron chloride),8 measurement of the binary diffusion coefficients of liquid substances,9 chemical kinetic experiments,10 quantification of sodium in coconut water and seawater,11 evaluation of iron corrosion rates in simulated seawater,12 binding constant and stoichiometry ratio in metal complexes,13 fluorescence observations,14 quantification of gold nanoparticle in a dietary supplement,15 protein quantification,16 determination of copper(II)17,18 and iron(II) concentrations in solution,18 and measurements of amylase activity.21 Recently, Oskolok et al.25 described the design and operations principles of a prototype of an optical device Received: August 1, 2018 Revised: May 2, 2019 Published: May 22, 2019 1519

DOI: 10.1021/acs.jchemed.8b00620 J. Chem. Educ. 2019, 96, 1519−1526

Journal of Chemical Education

Communication

based on an office flatbed scanner with a slide adapter. They designed an apparatus to place cuvettes in a flatbed scanner, and they used it to determinate aspirin concentration in Cardiomagnyl drug using the same method described in this paper. In here, quantitative analysis was carried out using a 96 microwell plate placed directly in the flatbed scanner, where 96 samples may be analyzed at the same time.26

determine aspirin mass and apply statistical methods to compare results.5 To illustrate the use of digital images for quantitative applications, we choose the quantitative analysis of aspirin in pharmaceutical tablets. Initially, aspirin was hydrolyzed in alkaline medium according to Scheme 1a; the hydrolyzed

■

Scheme 1. Conversion of Aspirin to a Spectrophotometrically Active Iron Complex

STUDENTS’ LEARNING GOALS Regarding the traineeship discussed in this paper, students were introduced to molecular spectrophotometry as a conventional methodology for the determination of aspirin in pharmaceutical tablets using colorimetry. Here, the proposed method, which uses digital images, was compared with the standard method, which uses absorption measured at 535 nm using a photometer. There are many papers published in this Journal dealing with quantitative analysis using charge-coupled devices. However, these papers described few statistical tests comparing the proposed method with the standard method. We believe that comparison of the proposed method with a standard method is a valuable tool to teach statistical analysis. Thus, accuracy and precision of the proposed method were evaluated against the standard method using the F-test and ttest, respectively. Therefore, beginning the quantitative analysis laboratory with an experiment that emphasizes the statistical analysis of data is one way to help students appreciate the difference between precision and accuracy.27 Results obtained by groups using the proposed method and the standard method may also be compared using one-way ANOVA. In addition, students also learn how a spike recovery and percent error are used to validate an analytical method.29 As a result of performing these experiments, it is expected that students • Understand the basic principles of molecular spectroscopy and use it in quantitative analyses • Understand how a quantitative analysis can be carried out using digital images • Compare the proposed method and the standard method using F-test and t-test • Understand the basic principle of a recovery test • Understand the basic principle of a percent error test

products are salicylate acid and acetate. Aspirin, salicylate, and salicylic acid do not absorb visible light. However, salicylate forms violet complexes with Fe(III) ions (Scheme 1b), which were quantified using absorbance measured at 535 nm and digital images obtained with a flatbed scanner.30 Digital images obtained with charge-coupled devices use the additive color model RGB rules, the generation of a wide pattern of secondary colors mixing the three additive primary colors of light: red (R), green (G), and blue (B).2 Generally, RGB extraction of digital images is hard work, and there are two options to do it. The first option is to crop figures as squares with the same size and save each crop with individual names, and then extract the RGB with software like R-project, Octave, and MATLAB.7,11−13 The second option is manual extraction using software like Photoshop, Colo Piker, and ImageJ.9,14−16 We used both approaches,5,31 where the first option is time-consuming, and students were unfamiliar with the software. Diawati et al.32 claimed that experiments carried out using charge-coupled devices allow students to explore the basic principle in absorption photometry. However, because these experiments require a quite sophisticated analysis of digital images, these experiments involve drawbacks for those looking for a simplified approach and make the quantitative analysis more complicated. Here, to overcome these drawbacks, we used automated digital image analysis: solutions were placed in a 96 microwell plate, RGB values were automatically extracted from all wells, and then, data was exported to a Microsoft Excel table that organizes it to build calibration plots, determine solutions concentrations, and calculate recovery and percent error. We used a scanner instead of a smartphone’s camera because images acquired with scanners have a low influence from ambient light.24. In our previous paper,31 we determined manganese in batteries using digital images, when digital images were acquired with a smartphone’s camera, calibration plots had correlation coefficients lower than 0.7, and manganese concentrations had relative standard deviations, RSDs, higher than 35%. These drawbacks were overcome by acquiring images with a flatbed scanner. Here, we observed similar drawbacks using digital images acquired with at ambient light with a smartphone’s camera.

■

EXPERIMENTAL OVERVIEW The goal of this project was to develop a simple colorimetric method based on images obtained with a regular flatbed scanner. These results would then be compared to measurements from a conventional spectrometric method using an Ftest and a t-test.5 This lab provided students with the opportunity to work with modern imaging techniques for data collection and then used Microsoft Excel to obtain calibration plots and determine concentrations, recovery, and percent error. In line with previous studies, the automated digital image analysis,5,28 described by Soldat et al.,6 was used to extract RGB values from digital images of 96 microwell plates. Students would find this lab interesting because they could extract RGB values from images of 96 microwell plate digital images using ReadPlate, and then use a spreadsheet to transform RGB values into analytical signal and use it to 1520



■

Communication

Statistical Tests

MATERIALS AND METHODS

F-test, t-test, and one-way ANOVA were carried out using Analysis ToolPak in Excel.34−37 The F-test compares the variances of two independent groups (n = 2). When Fcalculated < Fcritical, the null hypothesis is retained: both methods have equivalent precision. Fcalculated may be calculated using eq 2. Fcritical may be found in statistical tables or by using the Microsoft Excel equation FINV(confidence interval; numerator degrees of freedom; denominator degrees of freedom s2) = FINV(0.05;3;3). In eq 2, s1 > s2; thus, F is always higher than 1.6,34

Equipment

Deionized water was obtained using a Permutation deionization system from E. J. Kringer & Cia LTDA (Curitiba, Paraná, Brazil). All solutions were prepared using deionized water. A Shimadzu diode array (model 1800) UV−vis spectrophotometer was used for absorbance measurements. Acquiring Images

The 96 microwell plate images were acquired using a Canon Lide 120 scanner; images were recorded in JPEG (joint photographic experts group) format with 300 dpi (dots per inch).

F=

Automated Digital Image Analysis

s22

(2)

The F-test compares the means of two independent groups (n = 2). When tcalculated < tcritical, the null hypothesis is retained: both methods have equivalent accuracy. The t-test, presuming equivalent variances, may also be calculated using eq 3. m is the average, and n is the number of measures. spooled is calculated with eq 4. The tcritical value may be taken in statistical tables or by using the Microsoft Excel equation TINV(probability; degree of freedom) = TINV(0.05;6), where the degree of freedom is n − 2.5,27,29,34

Scanned images were opened with public domain image analysis software, ImageJ, that splits all pixels within an image into their red (R), green (G), and blue (B) components. To automate the image analysis process, we used the plugin ReadPlate. The plugin exports the median values of red, green, and blue color channels from a circle at the center of each well into an ImageJ spreadsheet. Then, all data collected were imported to a Microsoft Excel spreadsheet, and the analytical signal (S) was obtained as indicated in eq 1, where I refers to the R, G, or B normalized values of the color in each well and I0 refers to the blank value (deionized water plus Fe(III)).33 S = −log10(I /I0)

s12

t=

|m1 − m2| spooled

(1)

spooled =

In the proposed method, solutions used to generate the standard calibration curve were placed in lines A, B, G, and H; samples solutions were placed in lines C, D, E, and F as shown in Figure 1. The ReadPlate plugin extracts RGB values from all wells at the same time.

n1n2 n1 + n2

(3)

s12(n1 − 1) + s22(n2 − 1) n1 + n2 − 2

(4)

One-way ANOVA (analysis of variance) compares the means of two or more independent groups (n ≥ 2) in order to determine whether there is statistical evidence that the associated population means are significantly different.34 Like in the F-test, when Fcalculated < Fcritical, the null hypothesis is retained: all groups have equivalent means. Fcritical may be found in statistical tables or by using the Microsoft Excel equation FINV. The F-test and ANOVA are different tests, but Fcritical was calculated in the same manner.5,27,29,34 Recovery

Recovery was also studied; it was calculated using eq 5. %R =

(Cspiked − Cunspiked) Cadded

100

(5)

To determine the percent recovery, %R, of a spike, analyte concentration is determined in the spiked, Cspiked, and unspiked samples, Cunspiked; Cadded is the concentration of analyte added to the spiked portion.38 Recovery test using digital images may be calculated using Table S2 in the Supporting Information.

Figure 1. 96 microwell plate used to generate calibration plots and determinate the aspirin mass in a pharmaceutical tablet. This figure was generated using a Canon LIDE 120 flatbed scanner.

Percent Error

Aspirin mass determination in pharmaceutical tablets and calibration plot using the standard method and the proposed method may be calculated using Table S1 and Table UV, respectively, in the Supporting Information.

Another important parameter is percent error; it was calculated as shown in eq 6, where Cexp is the experimental concentration found and CT is the theoretical concentration.39

Reactants

%E =

Na(OH) was purchased from Dinâmica (Diadema, São Paulo). Salicylic acid (99.8%), (NH4 ) 2Fe(SO 4 )2 ·6H2O (98%), and H2SO4 (97%) were purchased from Vetec (Duque de Caxias, Rio de Janeiro).

(Cexp − C T) CT

100

(6)

Percent error test was carried out using salicylic acid solutions ranging from 3 to 13 mmol L−1; see the laboratory 1521



Communication

documentation. Percent error test using digital images may be calculated using Table S3 in the Supporting Information. Percent error test and recovery using the standard method may be calculated using Table UV in the Supporting Information.

■

HAZARDS AND SAFETY Care must be taken in handling all chemicals. NaOH and (NH4)2Fe(SO4)2·6H2O are corrosive to skin and eyes and are an irritant to the respiratory tract if inhaled. Safety glasses and rubber gloves should be worn when manipulating the chemicals, and proper clothing and footwear should be worn always.30,40

■

Figure 2. Calibration plot obtained using the reference method. This data was obtained by group 4.

RESULTS AND DISCUSSION In the first semester of 2018, this laboratory experiment was carried out with third-year chemical engineering students. It was typically finished in approximately 200 min with 15 students working in groups of three. During the first 150 min of the class, students hydrolyze acetylsalicylic acid (aspirin) to salicylate, prepare calibrations standards, fortify samples, measure solution’s absorbance, place samples in the 96 microwell plate, and then place it in a scanner and get a digital image. After the measurement process, the data treatment and interpretation required approximately 50 min in a separate class where computers with a spreadsheet and ImageJ were provided. During the separate class, each member of the group extracted RGB values using ImageJ’s plugin ReadPlate, and copied and pasted it in spreadsheets, which were provided in the Supporting Information. Then, salicylate concentrations, recovery, relative standard deviations, and percent error were calculated using spreadsheets. Here, the method carried out using results of absorbance measured at 535 nm using a UV−vis spectrophotometer is named the standard method, and the method carried out using G values extracted from digital images obtained with a flatbed scanner is named the proposed method.

Figure 3. Calibration plots obtained using the proposed method (eq 1). RGB values were extracted from Figure 1. This data was obtained by group 4. Standards used to plot the standard calibration curve shown in Figure 2 were also used for this figure.

the most quantitative results.8 The green channel, G, corresponds to the light at 535 nm, and it provided the most quantitative results as claimed be Kehoe and Penn; hence, G was chosen for further investigation.43 Sample and standard solutions were prepared; then, absorbances of each solution were measured at 525 nm. Then, solutions were placed in a 96 microwell plate (Figure 1), and RGB values were extracted simultaneously from all 96 wells with ImageJ’s plugin ReadPlate. The data was exported to Table S1 that provides standard calibration plots and aspirin mass in each sample. Figure 3 was plotted using RGB values extracted from Figure 1. Aspirin mass was determined in pharmaceutical tablets using the standard method and the proposed method. Five students’ groups measured aspirin mass in pharmaceutical tablets of the same lot as shown in Table 1.

Calibration Plots

Standard calibration plots are used to understand the instrumental response to an analyte and predict the concentration in an unknown sample.41,42 Generally, a set of standard samples are made at various concentrations with a range than includes the unknown of interest, and the instrumental response at each concentration is recorded.41,42 Figure 2 shows a standard calibration plot obtained by students for the standard method. Figure 3 show standard calibration plots obtained for the proposed method. Both methods provided standard calibration plots with high linearity, in the 3−14 mmol L−1 range, with R2 values higher than 0.99. According to Kuntzleman and Jacobson,17 it is useful to use a color wheel to estimate the color of light that is absorbed by a compound in solution. This approximation is done by noting the color on the wheel opposite the observed color of the compound. For example, if a compound appears violet in solution, it probably absorbs green light. Thus, in Figure 3, the calibration plot using G values has an angular coefficient higher than those obtained using R and B values. Kehoe and Penn claimed that if the wavelength of the absorption maximum for a particular solution is known, then R, G, and B values corresponding to that wavelength provide

Statistical Test Results

Statistics is a powerful tool with applications in chemistry, the health sciences, and the social sciences.44 Comparing the proposed method with the standard method may be used to teach statistical analysis. The standard deviation comparison (precision) between the standard and proposed methods was carried out using the Ftest; the F value was calculated using eq 2, where the larger variance was put in the numerator (s1) to obtain an F > 1. Applying eq 2 to the data obtained by group 1, we get F = (15.9)2/(6.5)2 = 5.2. For classes 2−4, F values are shown in Table 1. 1522



Communication

Table 1. Quantification of Aspirin Mass in Pharmaceutical Tablets Mass of Aspirin Determined, mg, by Student Groups Group 1 Results c

Run

UV

1 2 3 4 Mean, N = 4 SD, N = 4 F Valuea t Valueb

519 527 511 519 519 6.5

Group 2 Results

d

c

G

UV

G

528 492 510 522 513 15.9

519 473 510 541 511 28.3

582 506 509 525 531 35.3

5.9 0.7

Group 3 Results

d

c

UV

G

562 510 503 519 523 26.5

528 492 510 522 513 15.9

1.6 0.9

Group 4 Results

d

UV

c

484 543 484 507 505 27.9

2.7 0.7

Group 5 Results

d

G

UVc

Gd

528 511 499 506 511 12.4

545 528 519 538 532 11.4

540 503 516 527 522 6.5

6.9 0.4

1.9 1.1

a

Calculated using eq 2. bCalculated using eq 3. cResults obtained with the standard method. dResults obtained with the proposed method.

Table 2. Comparative Experimental Results Measuring Salicylate in Unspiked and Spiked Samples Salicylate Concentration, mmol L−1 a

Sample Status by Group

Added

Found UV

Unspiked Spike 1 Spike 2 Spike 3 Spike 4

0.0 1.2 2.4 3.6 4.7

3.4 4.4 5.6 6.8 8.4


0.0 1.2 2.4 3.6 4.7

2.9 4.2 5.0 6.1 7.2


0.0 1.2 2.4 3.6 4.7

3.4 4.4 5.6 6.8 8.4


0.0 1.2 2.4 3.6 4.7

3.0 4.5 5.7 7.1 8.0


0.0 1.2 2.4 3.6 4.7

3.3 4.7 5.7 6.9 8.2

Found Gb

Recovery UVc

Recovery Gc

3.8 4.3 6.6 8.2 8.7

82 93 96 106

39 117 124 104

3.9 5.0 6.6 8.3 9.7

112 89 89 91

24 53 72 80

3.1 4.3 4.8 6.9 8.2

82 93 96 106

101 72 108 108

3.9 5.0 6.6 8.3 9.7

121 110 116 105

87 113 122 123

3.6 4.7 6.3 7.6 9.1

113 101 102 103

92 112 112 117

Group 1

Group 2

Group 3

Group 4

Group 5

a

Results obtained with the standard method. bResults obtained with the proposed method. cCalculated using eq 5.

Within the confidence level of 95%, we found Fcalculated < Fcritical (F3,3critical = 9.27; at 95% confidence interval). Thus, it was concluded that the differences between the standard deviations were not significant and both methods have the same precision. The t-test compares the means of two independent groups to determine whether there is statistical evidence that the associated population means are significantly different, within the confidence level of 95%.5,27,29,34,39,44 In Table 1, using data obtained by group 1, from eq 4, the pooled value of the standard deviation is given by

spooled = =

s12(n1 − 1) + s22(n2 − 1) n1 + n2 − 2

72(4 − 1) + 162(4 − 1) = 12.1 4+4−2

From eq 3 t= 1523

|m1 − m2| spooled

n1n2 |519 − 513| = n1 + n2 12.1

4×4 = 0.7 4+4



Communication

There are 6 degrees of freedom, so the critical value is t6critical = 2.4 (p = 0.05). The observed value is less than the critical value, so the null hypothesis is retained: both methods provide equivalent results. Using the t-test, assuming equivalent variances (eq 3), in data obtained by groups 1−5, it was found that tcalculated < tcritical = 1.94 (at 95% confidence interval and 6 degrees of freedom) as shown in Table 1. Thus, it was concluded that both methods have the same accuracy. One-way ANOVA calculation were carried out using just the Analysis ToolPak in Excel because students had found calculations complicated and tedious. Within the confidence level of 95%, we found Fcalculated = 0.71 < Fcritical = 2.21 (F9,30critical = 2.21; FINV(0.05;9;30)) showing that results obtained for both methods for five groups were equivalent at a 95% confidence interval.5 The F-test and one-way ANOVA are different tests, but the Fcritical value was calculated using the same function. For example, in Table 1 for one-way ANOVA, Fcritical = FINV(confidence interval; groups degrees of freedom; samples degrees of freedom).

to build calibration plots and do calculations using spreadsheets. Comparing prelab and final-lab questionnaires. It was observed that students get a better understanding of the Ftest and the t-test

■

CONCLUSIONS This paper reports on the use of digital images acquired with a flatbed scanner in a quantitative analysis; it is portable, costeffective, and user-friendly.45 Thus, this method can be applied to a wide range of laboratory settings, both in research and in education.8 Students can compare the proposed method with the reference methods using the t-test for comparing mean values (accuracy) and the F-test for comparing precisions.27 This approach has a direct relationship to improving students’ critical thinking, and problem-solving skills.39 Using aspirin as an everyday thing, students were introduced to spectroscopy; they learned analytical chemistry and associated it with real problems using a simple and applicable method.

■

Recovery Difficulties

During the laboratory class, each group spiked four sample at one level; results obtained using the proposed method and the standard method were shown in Table 2. Obtained %R values were acceptable. However, in group 1 and group 2, %R values obtained for the first spiking level using the proposed method were unacceptable.

* Supporting Information The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.8b00620. Student laboratory handout for the experiment and notes for instructors including materials and hazards (PDF, DOCX) Table S1 for conversion of G values extracted from digital images, in calibration curves, and for determination of aspirin mass in pharmaceutical tablets from digital images (XLSX) Table S2 for calculation of recovery from digital images (XLSX) Table S3 for calculation of the percent error from digital images (XLSX) Table UV (using absorbances measured at 535 nm) for obtaining calibration curves, determining aspirin mass in pharmaceutical tablets, and calculating recovery and percent error (XLSX) Student evaluation data of the work (ZIP) Images used in the prelaboratory section (ZIP)

Percentage Error Found by Students

Table 3 shows percent error found by students for some salicylate solutions of known concentrations using digital Table 3. Percent Error Obtained for Salicylate Solutions Using Digital Images Concentration, mmol L−1 Theoretical

Experimental

RSD (N = 4)

Error, %

3.2 4.7 6.3 7.9 9.5 11.0 12.6

3.1 4.1 6.9 8.5 10.4 12.3 13.2

2.1 2.2 1.2 4.1 7.1 2.5 4.8

−2.9 −14.1 8.6 8.0 9.9 11.6 4.4

ASSOCIATED CONTENT

S

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] and [email protected].

images, where percent errors were lower than 15% and RSD lower than 7.1%.

ORCID

Description of Assessment of Learning Outcomes

Endler Marcel Borges: 0000-0002-9260-3639

Students found that digital image analysis can add extra value to the learning experience, especially during the conversions of red, green, and blue values into absorbance, which enables them to understand the principles behind absorbance and color science.6 They found the laboratory experiment exciting and enjoyed working in teams. They also gained experience with analyses of data, calibration plots, measurements of concentration for both known and unknown samples, spike recovery, critical judgment, and hands-on experience. Students found the proposed method simple and fast, since RGB extraction was carried out using automated digital image analysis. They liked the ReadPlate visual interface. They liked

Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors acknowledge financial support and fellowships from the Brazilian agencies FAPESC (Fundaçaõ de Amparo a Pesquisa do Estado de Santa Catarina), CNPq (Conselho ́ Nacional de Desenvolvimento Cientifico e Tecnológico) Project 402226/2016-0, and CAPES (Coordenação de ́ Superior). The authors Aperfeiçoamento de Pessoal de Nivel also would like to thank the editor and anonymous reviewers 1524



Communication

(18) Montangero, M. Determining the Amount of Copper(II) Ions in a Solution Using a Smartphone. J. Chem. Educ. 2015, 92, 1759− 1762. (19) Koesdjojo, M. T.; Pengpumkiat, S.; Wu, Y.; Boonloed, A.; Huynh, D.; Remcho, T. P.; Remcho, V. T. Cost effective paper-based colorimetric microfluidic devices and mobile phone camera readers for the classroom. J. Chem. Educ. 2015, 92, 737−741. (20) Kohl, S. K.; Landmark, J. D.; Stickle, D. F. Demonstration of absorbance using digital color image analysis and colored solutions. J. Chem. Educ. 2006, 83, 644. (21) Dangkulwanich, M.; Kongnithigarn, K.; Aurnoppakhun, N. Colorimetric Measurements of Amylase Activity: Improved Accuracy and Efficiency with a Smartphone. J. Chem. Educ. 2018, 95, 141−145. (22) Hosker, B. S. Demonstrating Principles of Spectrophotometry by Constructing a Simple, Low-Cost, Functional Spectrophotometer Utilizing the Light Sensor on a Smartphone. J. Chem. Educ. 2018, 95, 178−181. (23) Solana-Altabella, A.; Sánchez-Iranzo, M. H.; Bueso-Bordils, J. I.; Lahuerta-Zamora, L.; Mellado-Romero, A. M. Computer VisionBased Analytical Chemistry Applied to Determining Iron in Commercial Pharmaceutical Formulations. Talanta 2018, 188, 349−355. (24) Christodouleas, D. C.; Nemiroski, A.; Kumar, A. A.; Whitesides, G. M. Broadly Available Imaging Devices Enable HighQuality Low-Cost Photometry. Anal. Chem. 2015, 87, 9170−9178. (25) Oskolok, K. V.; Shults, E. V.; Monogarova, O. V.; Chaplenko, A. A. Optical molecular analysis using office flatbed photo scanner: New approaches and solutions. Talanta 2018, 178, 377−383. (26) Botasini, S.; Luzuriaga, L.; Cerdá, M. F.; Méndez, E.; FerrerSueta, G.; Denicola, A. Multiple experiments and a single measurement: Introducing microplate readers in the laboratory. J. Chem. Educ. 2010, 87, 1011−1014. (27) Harvey, D. T. Statistical evaluation of acid/base indicators: A first experiment for the quantitative analysis laboratory. J. Chem. Educ. 1991, 68, 329. (28) Colzani, H.; Rodrigues, Q. E. A. G.; Fogaça, C.; Gelinski, J. M. L. N.; Pereira-Filho, E. R.; Borges, E. M.; et al. Determinaçaõ de Fosfato Em Refrigerantes Utilizando Um Scanner de Mesa e Análise Automatizada de Dados: Um Exemplo Didático Para Ensino de ́ Quimica. Quim. Nova 2017, 40 (7), 833−839. (29) Harvey, D. T. External standards or standard addition? Selecting and validating a method of standardization. J. Chem. Educ. 2002, 79, 613. (30) Mitchell-Koch, J. T.; Reid, K. R.; Meyerhoff, M. E. Salicylate Detection by Complexation with Iron (III) and Optical Absorbance Spectroscopy: An Undergraduate Quantitative Analysis Experiment. J. Chem. Educ. 2008, 85, 1658. (31) Colzani, H.; Scolaro, A. M. T.; Gelinski, J. M. L. N.; Borges, E. M. Determinaçaõ de Manganês em Pilhas Utilizando um Scanner. Rev. Virtual Quim. 2017, 9, 1672−1685. (32) Diawati, C.; Liliasari; Setiabudi, A.; Buchari. Using ProjectBased Learning To Design, Build, and Test Student-Made Photometer by Measuring the Unknown Concentration of Colored Substances. J. Chem. Educ. 2018, 95, 468−475. (33) Ravazzi, C. G.; Franco, M. D. O. K.; Vieira, M. C. R.; Suarez, W. T. Smartphone application for captopril determination in dosage forms and synthetic urine employing digital imaging. Talanta 2018, 189, 339−344. (34) Miller, J. N.; Miller, J. C. Statistics and Chemometrics for Analytical Chemistry, 6th ed.; Pearson Education Limited: Edinburgh Gate, U.K., 2010. (35) Harris, D. C. Quantitative Chemical Analysis, 8th ed.; W. H. Freeman and Company: New York, NY, 2010. (36) Larsen, R. D. Lessons learned from Lord Rayleigh on the importance of data analysis. J. Chem. Educ. 1990, 67, 925−928. (37) Vitha, M. F.; Carr, P. W. A laboratory exercise in statistical analysis of data. J. Chem. Educ. 1997, 74, 998−1000.

whose valuable comments and feedback helped us to improve this paper.

■

REFERENCES

(1) Gonzalez-Jimenez, M.; Arenas-Valgañon, J.; Cespedes-Camacho, I. F.; García-Prieto, J. C.; Calle, E.; Casado, J. Detection of Nitrite in Water Using Minoxidil as a Reagent. J. Chem. Educ. 2013, 90, 1053− 1056. (2) Garrido-Gonzalez, J. J.; Trillo-Alcalá, M.; Sanchez-Arroyo, A. J. Lights, Camera, Spectroscope! The Basics of Spectroscopy Disclosed Using a Computer Screen. J. Chem. Educ. 2018, 95, 903−907. (3) Soares, C.; Correia, M.; Delerue-Matos, C.; Barroso, M. F Investigating the Antioxidant Capacity of Fruits and Fruit Byproducts through an Introductory Food Chemistry Experiment for High School. J. Chem. Educ. 2017, 94, 1291−1295. (4) Capitán-Vallvey, L. F.; López-Ruiz, N.; Martínez-Olmos, A.; Erenas, M. M.; Palma, A. J. Recent Developments in Computer Vision-based Analytical Chemistry: A Tutorial Review. Anal. Chim. Acta 2015, 899, 23−56. (5) Volmer, D. A.; Curbani, L.; Parker, T. A.; Garcia, J.; Schultz, L. D.; Borges, E. M. Determination of Titratable Acidity in Wine Using Potentiometric, Conductometric, and Photometric Methods. J. Chem. Educ. 2017, 94, 1296−1302. (6) Soldat, D. J.; Barak, P.; Lepore, B. J. Microscale Colorimetric Analysis Using a Desktop Scanner and Automated Digital Image Analysis. J. Chem. Educ. 2009, 86, 617−621. (7) Chang, B.-Y. Smartphone-Based Chemistry Instrumentation: Digitization of Colorimetric Measurements. Bull. Korean Chem. Soc. 2012, 33, 549−552. (8) Kehoe, E.; Penn, R. L. Introducing Colorimetric Analysis with Camera Phones and Digital Cameras: An Activity for High School or General Chemistry. J. Chem. Educ. 2013, 90, 1191−1195. (9) Rice, N. P.; de Beer, M. P.; Williamson, M. E. A Simple Educational Method for the Measurement of Liquid Binary Diffusivities. J. Chem. Educ. 2014, 91, 1185−1190. (10) Knutson, T. R.; Knutson, C. M.; Mozzetti, A. R.; Campos, A. R.; Haynes, C. L.; Penn, R. L. A Fresh Look at the Crystal Violet Lab with Handheld Camera Colorimetry. J. Chem. Educ. 2015, 92, 1692− 1695. (11) Moraes, E. P.; da Silva, N. S.; de Morais, C. D. L.; Neves, L. S. D.; Lima, K. M. D. Low-Cost Method for Quantifying Sodium in Coconut Water and Seawater for the Undergraduate Analytical Chemistry Laboratory: Flame Test, a Mobile Phone Camera, and Image Processing. J. Chem. Educ. 2014, 91, 1958−1960. (12) Moraes, E. P.; Confessor, M. R.; Gasparotto, L. H. S. Integrating Mobile Phones into Science Teaching To Help Students Develop a Procedure To Evaluate the Corrosion Rate of Iron in Simulated Seawater. J. Chem. Educ. 2015, 92, 1696−1699. (13) de Morais, C. D. L.; Silva, S. R.; Vieira, D. S.; Lima, K. M. Integrating a Smartphone and Molecular Modeling for Determining the Binding Constant and Stoichiometry Ratio of the Iron (II)− Phenanthroline Complex: An Activity for Analytical and Physical Chemistry Laboratories. J. Chem. Educ. 2016, 93, 1760−1765. (14) Koenig, M. H.; Yi, E. P.; Sandridge, M. J.; Mathew, A. S.; Demas, J. N. Open-Box” Approach to Measuring Fluorescence Quenching Using an iPad Screen and Digital SLR Camera. J. Chem. Educ. 2015, 92, 310−316. (15) Campos, A. R.; Knutson, C. M.; Knutson, T. R.; Mozzetti, A. R.; Haynes, C. L.; Penn, R. L. Quantifying Gold Nanoparticle Concentration in a Dietary Supplement Using Smartphone Colorimetry and Google Applications. J. Chem. Educ. 2016, 93, 318−321. (16) Gee, C. T.; Kehoe, E.; Pomerantz, W. C.; Penn, R. L. Quantifying Protein Concentrations Using Smartphone Colorimetry: A New Method for an Established Test. J. Chem. Educ. 2017, 94, 941−945. (17) Kuntzleman, T. S.; Jacobson, E. C. Teaching Beer’s Law and Absorption Spectrophotometry with a Smart Phone: A Substantially Simplified Protocol. J. Chem. Educ. 2016, 93, 1249−1252. 1525



Communication

(38) Khalafi, L.; Doolittle, P.; Wright, J. Speciation and Determination of Low Concentration of Iron in Beer Samples by Cloud Point Extraction. J. Chem. Educ. 2018, 95, 463−467. (39) Sedwick, V.; Leal, A.; Turner, D.; Kanu, A. B. Quantitative Determination of Aluminum in Deodorant Brands: A Guided Inquiry Learning Experience in Quantitative Analysis Laboratory. J. Chem. Educ. 2018, 95, 451−455. (40) van Opstal, M. T.; Nahlik, P.; Daubenmire, P. L.; Fitch, A. Physicians as the First Analytical Chemists: Gall Nut Extract Determination of Iron Ion (Fe2+) Concentration. J. Chem. Educ. 2018, 95, 456−462. (41) JoVE Science Education Database. Analytical Chemistry. Calibration Curves; JoVE: Cambridge, MA, 2018. (42) Adem, S. M.; Leung, S. H.; Sharpe Elles, L. M.; Shaver, L. A. A Laboratory Experiment for Rapid Determination of the Stability of Vitamin C. J. Chem. Educ. 2016, 93, 1766. (43) Phadungcharoen, N.; Patrojanasophon, P.; Opanasopit, P.; Ngawhirunpat, T.; Chinsriwongkul, A.; Rojanarata, T. SmartphoneBased Ellman’s Colourimetric Methods for the Analysis of dPenicillamine Formulation and Thiolated Polymer. Int. J. Pharm. 2019, 558, 120−127. (44) Matsumoto, P. S. The importance and efficacy of using statistics in the high school chemistry laboratory. J. Chem. Educ. 2006, 83, 1649. (45) Kılıç, V.; Alankus, G.; Horzum, N.; Mutlu, A. Y.; Bayram, A.; Solmaz, M. E. Single-Image-Referenced Colorimetric Water Quality Detection Using a Smartphone. ACS Omega 2018, 3, 5531−5536.

1526


Quantitative Analysis Using a Flatbed Scanner - ACS Publications

Recommend Documents