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. 2019 Oct 22;4(19):18361–18369. doi: 10.1021/acsomega.9b02612

Colorimetric Detection of Aliphatic Alcohols in β-Cyclodextrin Solutions

Anna Haynes †, Priva Halpert ‡, Mindy Levine †,*
PMCID: PMC6844157  PMID: 31720538

Abstract

graphic file with name ao9b02612_0007.jpg

The sensitive, selective, and practical detection of aliphatic alcohols is a continuing technical challenge with significant impact in public health research and environmental remediation efforts. Reported herein is the use of a β-cyclodextrin derivative to promote proximity-induced interactions between aliphatic alcohol analytes and a brightly colored organic dye, which resulted in highly analyte-specific color changes that enabled accurate alcohol identification. Linear discriminant analysis of the color changes enabled 100% differentiation of the colorimetric signals obtained from methanol, ethanol, and isopropanol in combination with BODIPY and Rhodamine dyes. The resulting solution-state detection system has significant broad-based applicability because it uses only easily available materials to achieve such detection with moderate limits of detection obtained. Future research with this sensor system will focus on decreasing limits of detection as well as on optimizing the system for quantitative detection applications.

Introduction

Increased interest in using non-mass spectrometry-based techniques for the detection of small organic compounds has arisen due to practical challenges associated with the use of mass spectrometry that limit broad-based applicability.1 Such challenges include the fact that expensive, bulky instrumentation is often required to accomplish mass spectrometry-based detection combined with significant user training to operate the instrumentation effectively, which prevents detection by relatively untrained citizen scientists.2 Many newly developed chemosensors have focused on systems that allow for portable, on-site testing of the target analytes without requiring high-end, costly laboratory instrumentation.3 A challenging aspect of designing portable chemosensors is the need to maintain high selectivity, sensitivity, and broad-based applicability in the efficient detection of various analytes, especially among analytes that are similar in structure and size.

By utilizing the ability of cyclodextrin to act as a supramolecular scaffold that facilitates proximity-induced, highly analyte-specific interactions between an analyte of interest and a high-quantum yield fluorophore, the Levine group has developed sensitive and selective fluorescence-based systems for analyte detection.4,5 The systems utilize cyclodextrin-promoted fluorescence energy transfer from an analyte to a high quantum yield fluorophore for photophysically active analytes6 or cyclodextrin-promoted, analyte-specific fluorescence modulation for nonphotophysically active analytes.7 In addition to monitoring the analyte-specific fluorescence changes, there are often analyte-specific color changes in the fluorophore, promoted through the cyclodextrin-assisted interaction of the brightly colored fluorophore and the target analyte.8 Advantages of colorimetric detection include the fact that the color changes can be easily detected using naked eye detection9 or RGB analysis.10 Significant literature precedent indicates that colorimetric analysis can be optimized for the detection of very small concentrations of toxicants both in solution-state and in solid-state detection devices.11−13

Colorimetric detection has potential utility in the detection of aliphatic alcohols, a class of analytes commonly found in commercial products that can cause health concerns, especially at elevated concentrations.14 These alcohols, including isopropanol, ethanol, and methanol, are found in household cleaners,15 paints,16 self-care and beauty products,17 and beverages.18 Moreover, the need for aliphatic alcohol detection is rising with the increasing prevalence of at-home beer and alcohol production.19 With almost no regulation of this process currently in place, there is significant potential for poorly controlled ethanol concentrations20 as well as the potential for methanol contamination and associated methanol toxicity.21 Furthermore, the brewing process can sometimes lead to the formation of other byproducts, including n-propanol, isobutanol, and isoamyl alcohol.22 Because methods to detect these byproducts are not widely available, significant public health risks from their ingestion remain.23 Additional potential applications of colorimetric aliphatic alcohol detection include the use of a colorimetric device to detect alcohol intoxication in both medical24 and law-enforcement settings.25 Finally, forensic analyses would benefit from the detection of a range of aliphatic alcohols that are known bacterial byproducts and could provide important forensic information.26

Previous reports on the detection of aliphatic alcohols by colorimetric methods include the use of a single ionic liquid, containing a modified pH indicator, to distinguish between eight aliphatic alcohols27 as well as copper-containing metal–organic frameworks28 and iron complexes29 to accomplish effective detection. Cyclodextrins have also been reported in isolated instances to act as sensors for aliphatic alcohols through the use of cyclodextrin-based stationary phases in chiral chromatography30 as well as through the use of a quartz crystal microbalance coated with cyclodextrin-derived compounds.31 To the best of our knowledge, the combination of cyclodextrin-based complexation and colorimetric detection has not been reported to date.

While the fluorescence modulation method used previously in the Levine group provided good sensitivity and high selectivity among structurally similar analytes, it required laboratory-grade instrumentation, which severely limits widespread usage. Although portable fluorimetry has been accomplished using smartphone-based systems,32 these systems can be challenging for the user to implement, which means that portable colorimetric systems can have notable advantages. Reported herein is the development of an extremely practical colorimetric detection system for isopropanol, ethanol, and methanol, based on color changes in a dye–cyclodextrin association complex upon addition of the aliphatic alcohol, with such color changes intimately dependent on the structure of each of the alcohols (structures 1–3, Figure 1) and its association with both the cyclodextrin scaffold and the colorimetric dye (BODIPY (4) or Rhodamine 6G (5), Figure 1). This system is highly robust with alcohol-induced color changes detectable even by a high school student working with unpurified tap water solutions and, even in its optimized formulation, uses no laboratory-grade instrumentation. Rather, the system uses a spray-painted plastic box equipped with LED lights to facilitate consistent coloration and enable reproducible results.

Figure 1.

Figure 1

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Structure of alcohol analytes (1–3) and highly colored dyes (4 and 5).

Results and Discussion

Selection of Data Processing and Analytical Methods

Separation of the signals obtained from the photographs was performed using linear discriminant analysis (LDA) as such an analysis enables the user to find the axes of maximum separation, which in turn facilitates highly accurate identification of unknown analytes.33 LDA has been used for analysis of a variety of related systems,34 including colorimetric detection schemes,35 and is used herein to enable high differentiation between signals that correspond to the different aliphatic alcohol analytes. The input data for LDA was the red, green, and blue (RGB) values of the photographs, which has strong precedence in the colorimetric sensing literature.36−38 In general, high clustering among points from the same analyte (represented by the same color and shape in Figures 2, 3, 5, and 6) and large amounts of space between the analyte clusters lead to high proportions of total dispersion (values close to 1) and indicates a high-performing system. Less effective clustering within the same group and/or an overlap between the clusters of two different groups leads to lower values for proportion of total dispersion, which in turn leads to a higher ratio of misclassification of unknown analytes.

Figure 2.

Figure 2

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Generated arrays for the detection of ethanol, isopropanol, and methanol with each cyclodextrin supramolecular host using BODIPY (4): (A) β-cyclodextrin, (B) methyl-β-cyclodextrin, and (C) 2-hydroxypropyl-β-cyclodextrin.

Figure 3.

Figure 3

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Generated arrays for the detection of ethanol, isopropanol, and methanol with each cyclodextrin supramolecular host using Rhodamine (5): (A) β-cyclodextrin, (B) methyl-β-cyclodextrin, and (C) 2-hydroxypropyl-β-cyclodextrin.

Figure 5.

Figure 5

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Linear discriminant analysis results obtained at 3.0 M analyte concentration for (A) BODIPY (4) and (B) Rhodamine (5). All results were obtained using Systat version 13 and following the procedures detailed in the Experimental Section.

Figure 6.

Figure 6

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Linear discriminant analysis results generated at lower analyte concentrations. (A) BODIPY (4) at 0.5 M concentration of the analyte; (B) BODIPY (4) at 1.0 M concentration of the analyte; (C) BODIPY (4) at 2.0 M concentration of the analyte; (D) Rhodamine (5) at 0.5 M concentration of the analyte; (E) Rhodamine (5) at 1.0 M concentration of the analyte; and (F) Rhodamine (5) at 2.0 M concentration of the analyte. All results were obtained using Systat version 13 and following the procedures detailed in the Experimental Section.

Optimization of Cyclodextrin

A variety of cyclodextrin hosts was screened with the goal of determining which supramolecular host would provide maximum separation between the analyte-induced color changes, with such separation quantified as the “cumulative proportion of total dispersion.” An example of significant dispersion of analyte clusters is shown for 2-HP-β-CD (Figure 2C), and a contrasting example with overlapping areas between clusters is shown for Me-β-CD (Figure 2B). Using linear discriminant analysis of the RGB data collected from the sample photos, it was determined that the 2-HP-β-CD had the highest dispersion using both BODIPY and Rhodamine (dyes 4 and 5) as color-changing elements with cumulative proportions of total dispersion values of 1.000 and 0.998, respectively (Table 1). Similar trends in the cyclodextrin host were seen with Rhodamine (5) (Figure 3) with 2-HP-β-CD showing the greatest dispersion (Figure 3C) compared to that of β-CD and Me-β-CD (Figure 3A,B, respectively).

Table 1. Cumulative Proportions of Total Dispersion for each Cyclodextrin with Dyes 4 and 5a.

dye β-CD Me-β-CD 2-HP-β-CD
4 0.915 0.986 1.000
5 0.778 0.749 0.998
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a

Values were generated after analysis using SYSTAT 13 LDA software.

The higher signal dispersion that was observed when using 2-HP-β-CD is likely due to strong binding between the dyes and 2-HP-β-CD as well as the high flexibility and water solubility of 2-HP-β-CD compared to the other hosts investigated.39 High binding constants between the alcohol analytes and cyclodextrin hosts increase the strength of interactions between the analyte, host, and dye, resulting in more sensitive analyte-induced signal changes, whereas greater flexibility and water solubility increase the availability of this host to participate in the desired interactions.40−42 Of note, control experiments conducted in the absence of cyclodextrin resulted in markedly lower dispersion of the signals with significant misclassification of the analytes observed. This was especially true when using Rhodamine as the signal transducing element (75% correct identification of analytes using jackknife classification analysis) but was also decreased using BODIPY (92% correct identification).

Dye Selection

The dyes selected for analysis include highly colored BODIPY (compound 4) and Rhodamine (compound 5) analogues. These dyes were selected due to their known strong coloration (aligned with their high molar absorptivity coefficients)43 as well as good aqueous stability44 and solubility.45 Moreover, because this system is expected to be used in a wide variety of environments, the toxicity of all components is an important consideration. BODIPY has relatively low toxicity and has been used in a variety of biologically relevant applications;46,47 while Rhodamine 6G has some toxicity,48 this toxicity is tunable via judicious choice of counterions,49 and related dyes have still been used for intracellular applications.50 As a result, both reported dyes have significant potential in the development of practical detection schemes.

Selection of the Aqueous Environment

All experiments reported herein were conducted in deionized water, although a demonstration of the efficacy of this method in tap water samples would strengthen the applicability of this method. High concentrations of ions in tap water have been reported51 and include calcium and magnesium ions,52 which both have been shown to interact with cyclodextrin.53,54 To measure the general applicability of this method to tap water samples, we conducted experiments using dye 4 in tap water. Results of this experiment indicated somewhat decreased selectivity compared to deionized water, which shows that the tap water components have a deleterious effect on the system performance, although overall 75% correct identification using dye 4 was still observed.

Further insight into the selectivity observed between the alcohol analytes was obtained from computational investigations. Electrostatic potential maps of analytes 1–3, generated using Spartan ‘18 software, showed significant similarities in the analyte structures with areas of high polarity around the hydroxyl group (Figure 4). Differences between the analytes include noticeable size differences as well as a more concentrated region of positive electron density in methanol compared to the other analytes as shown by the dark blue color. Such differences, combined with steric matching of the analytes with the cyclodextrin cavity and aqueous miscibility of the analytes, contribute to differing binding affinities with the cyclodextrin,55 resulting in turn in high specificity in the analyte-induced color changes.

Figure 4.

Figure 4

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Electrostatic potential maps of (A) methanol, (B) ethanol, and (C) isopropanol. Red areas indicate regions of negative electron density, and blue areas indicate regions of positive electron density. These computations were done using Spartan version 18 computational software.

Determining the Optimal Concentrations for Testing

In order to determine the optimal analyte concentration to achieve high degrees of analyte-induced separation in the colorimetric responses, the dependence of the LDA plots on analyte concentration was explicitly investigated, and the results are summarized in Table 2. These results indicate that BODIPY was more effective in characterizing data at high analyte concentrations (3.0 M, Figure 5), whereas Rhodamine provided more dispersed and accurate results between 0.5 M and 2.0 M concentrations (Figure 6). A plausible explanation for these observed results relates to the higher binding constant between BODIPY and 2-HP-β-CD of 3.32 × 105 M–1 compared to 1.59 × 105 M–1 between Rhodamine and the cyclodextrin. The lower binding constant of Rhodamine allows for better detection at lower alcohol concentrations because the dye is being displaced easier. Because BODIPY binds with a slightly higher binding energy, a higher concentration of alcohol needs to be added to the system in order to displace the dye, causing an overall decrease in system performance. Of note, the fact that the dye is dissolved in isopropanol means that all systems contain a low percentage of this analyte; nonetheless, the markedly higher amounts of analytes added to the solution mean that the isopropanol concentration from the dye solution has a relatively minor effect on the overall signal observed.

Table 2. Percent Correct Classification Values Obtained from Jackknife Classification Analysis of the Arraysa.

dye 0.5 M 1.0 M 2.0 M 3.0 M
4 56 22 67 89
5 67 100 100 78
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a

Values taken after linear discriminant analysis were obtained using SYSTAT version 13 software.

Additional computational studies conducted using MOE 2018 software provided important information about the lowest energy docking conformation of each dye with 2-HP-β-CD in a pure water solvent system, and the results are shown in Figure 7. Of note, BODIPY (4) exhibited markedly more inclusion in the cyclodextrin host compared to Rhodamine (5) with a substantial portion of the Rhodamine remaining exposed to the solvent. This solvent-exposed area of Rhodamine has a greater ability to interact with the analyte in solution. This result implies that dye displacement by the alcohol may not be necessary to effect a color change if the alcohol and dye interact via the solvent-accessible portion. Such interactions are not dependent on the binding constant of the dyes in cyclodextrin and provide an additional mechanism by which the system can lead to analyte-specific color changes. Efforts to investigate the extent to which either or both mechanisms (i.e., dye displacement from the cavity and/or interactions between the dye and analyte through solvent-exposed areas) are operative in this system are currently underway in our laboratory.

Figure 7.

Figure 7

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Lowest energy conformations of BODIPY (4) and Rhodamine (5) in 2-HP-β-CD. (A) Side view of the complex with BODIPY (4). (B) Aerial view of the complex with BODIPY (4). (C) Side view of the complex with Rhodamine (5). (D) Aerial view of the complex with Rhodamine (5). Color coding: for the cyclodextrin host, the dark blue color represents the carbon atoms, the red color represents the oxygen atoms, and the gray color represents hydrogen atoms. For BODIPY, the purple color represents carbon atoms, gray represents hydrogen, blue represents nitrogen, orange represents boron, and green represents fluorine. For Rhodamine, the teal color represents carbon, gray represents hydrogen, red represents oxygen, and dark blue represents nitrogen.

Moreover, the addition of the alcohol analyte to the solution of dye in cyclodextrin had measurable changes on the supramolecular complex. In particular, computational results indicated that adding methanol to a solution of BODIPY in 2-HPCD resulted in the weakening of the association between BODIPY and 2-HPCD and strengthening of the affinity of the BODIPY for the solvent (Figure 8). This result supports that colorimetric changes induced by the addition of the alcohol analyte are a result of decreased affinity of the dye for the hydrophobic cyclodextrin cavity.

Figure 8.

Figure 8

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Lowest energy conformations of BODIPY (4) in 2-HP-β-CD following the introduction of methanol. The methanol is modeled as teal stick figures, and the colors of the BODIPY and cyclodextrin are identical to the colors used in Figure 7.

Determining the LOD and LOQ for the Analytes with Dyes 4 and 5

In addition to measuring the ability of the system to differentiate between structurally similar analytes, the sensitivity of the system to low analyte concentrations was also investigated. For these experiments, the green value of the photographs was taken to represent the signal output as our results indicate that this is the signal that changes most significantly in response to the changing concentration of the analyte. These results are summarized in Tables 3 (for 0.127 mM BODIPY and 0.093 mM Rhodamine) and 4 (for 0.382 mM BODIPY and 0.280 mM Rhodamine) and indicate that analyte concentrations as low as 0.2 mM were detectable via this method (for isopropanol, using relatively high concentrations of Rhodamine). Compared to the LODs reported in Table 3, there was no significant decrease in the LODs observed with BODIPY at higher concentrations. In contrast, the Rhodamine trial showed much better improvement at higher dye concentrations with a 48% decrease in the LOD and a decrease of 68% in the LOQ value. These marked changes are in line with the higher solvent and analyte accessibility displayed by Rhodamine (vide supra) and indicate substantial promise in the further optimization of sensitive alcohol sensors. An example of a color array that illustrates the visible color change of BODIPY in the presence of isopropanol is shown in Figure 9.

Table 3. LODs and LOQs of Each Alcohol with Dyes 4 and 5 in the Presence of 2-HP-β-CD.

dye alcohol LOD (M)a LOQ (M)b
4 isopropanol 0.319 0.805
ethanol 0.491 1.74
methanol 0.249 0.823
5 isopropanol 0.386 1.05
ethanol 0.216 0.442
methanol 0.331 0.730
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a

Values calculated according to eq 1 and the equation of the line of best fit for each dye–alcohol complex.

b

Values calculated according to eq 2 and the equation of the line of best fit for each dye–alcohol complex.

Table 4. LODs and LOQs of Isopropanol with Increased Concentrations of Dyes 4 and 5 and 2-HP-β-CD.

dye LOD (M) LOQ (M)
4 0.3170 (0.50%)a 0.7812 (3.0%)
5 0.2004 (48%) 0.3341(68%)
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a

Number in parentheses represents the percent change, which is, in all cases, a decrease from the LOD values obtained in Table 3 to the ones calculated using a higher concentration of the dye in solution.

Figure 9.

Figure 9

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Colorimetric array of the 60 samples from the trial using dye 4 and analyte 3. The concentration of the analyte increases from left to right.

Of note, visible color changes were present in both dye 4 and dye 5 trials. When using dye 4, the visible color change is seen going from a dull orange when no alcohol is present to a bright yellow after all additions, as can be seen in Figure 9. The stock solution of this dye, prepared in isopropanol, is bright fluorescent green. In the dye 5 trials, the initial color is a bright orange that also becomes a bright yellow after the analyte is added.

Conclusions

Harnessing the highly specific complexation of small-molecule guests inside supramolecular cyclodextrin hosts provides a fundamentally unique system for the detection of those guests. Reported herein is the application of such host–guest complexes for the colorimetric detection of alcohols using highly practical, easily available materials to achieve excellent selectivity (100% differentiation) and moderate sensitivity (as low as 0.2 M). Computational experiments involving the cyclodextrin, analytes, and highly colored dyes are invoked to explain the underlying basis of this strong analyte specificity as remarkably structurally similar analytes leading to noticeably different colorimetric read-out signals. Efforts to improve the sensitivity, broaden the scope of such detection, and develop sensors for mixtures of alcohols without requiring additional separation procedures (in accordance with literature reports of analogous systems)56 are currently underway in our laboratory, and results of these and other investigations will be reported in due course.

Experimental Section

Materials and Methods

The alcohol analytes 1–3 and dyes 4 and 5 shown in Figure 1 were obtained from the Millipore-Sigma chemical company, and the cyclodextrins were obtained from the Tokyo Chemical Industry (TCI) chemical company. All chemicals were used as received. All aqueous solutions were made in glass jars and transferred to 50 mL white, polypropylene cups that had previously been used in a Keurig machine and were washed thoroughly prior to usage. A plastic container (with dimensions 21 cm × 15 cm × 7 cm) was spray-painted using Krylon Fusion Satin Black spray paint to limit ambient light, and a 1.5 cm × 1.5 cm hole was cut in the center of the lid to enable photography of the solution. An additional polypropylene cup previously used for a Keurig machine was positioned under the opening and secured to the bottom of the container with electrical tape. Two strips of LED white light tape (purchased from The Home Depot, with a voltage of 12, a watt equivalence of 8.5, and an actual color temperature of 4000 K) were placed on the interior of the container, on all sides of the container, to provide uniform sample illumination. An annotated figure of the lightbox can be found in the Supporting Information. The cup that contained the sample was placed into the secured cup prior to imaging. Photographs of the solutions were taken from 2.0 cm above the top of the sample cup with a Samsung Galaxy S8+ (model number: G950U) in the manual mode with the following settings: ISO set to 100, aperture set to 1/350, macro-focused (close-up focus), and the white balance set at 5500 K. These settings were kept constant for all trials to avoid variation in color capture. Preliminary work that varied the smartphone camera settings led to changes in the RGB values obtained with automatic settings, leading to reduced consistency between trials and even within the same trial. Similar challenges in consistency were observed without using LED lights for consistent sample illumination. Using the manual mode in both the smartphone and LED lights around the sample solved the consistency challenges and enabled accurate and reproducible results to be obtained. Images were processed with ImageJ software to measure the red, green, and blue values (RGB) of the solutions following the procedures detailed below.

General Procedure for Making Stock Solutions

Three 250 mL solutions of β-cyclodextrin (β-CD), methyl-β-cyclodextrin (Me-β-CD), and 2-hydroxypropyl-β-cyclodextrin (2-HP-β-CD) cyclodextrin were made at relatively high concentrations (Table 5). The stock solutions for BODIPY and Rhodamine dyes were made in isopropanol at concentrations of 3.80 mM and 2.08 mM, respectively (1 mg/mL for each dye). Diluted dye solutions were prepared by adding 5.0 mL of the concentrated stock solutions to a 150 mL volumetric flask and diluting to the mark with water.

Table 5. Concentration of Cyclodextrins and Dyes in Solutiona.

solute solvent concentration (mM)
β-CD DI H2O 16.5
Me-β-CD DI H2O 9.69
2-HP-β-CD DI H2O 2.39
BODIPY (4) isopropanol 3.82
Rhodamine (5) isopropanol 2.08
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a

The final solution concentrations were calculated based on the amounts of solute and solvent added. See text for more information.

General Procedure for the Optimization of the Supramolecular Cyclodextrin Host

In a glass sample jar, 10.00 mL of a β-CD stock solution was combined with 10.00 mL of one of the diluted dye solutions. This mixture was manually shaken for 1 min to ensure thorough mixing. After mixing, 5.00 mL of the alcohol was added. This mixture was transferred to the sample cup and placed in the lightbox. The cover was placed on, and a photo was taken using the smartphone with the settings listed above. This procedure was repeated for Me-β-CD and 2-HP-β-CD with both dyes and each of the three alcohols (18 total samples). Four trials of each sample were completed with a calculated average standard deviation in red, green, and blue values of 0.08, 0.14, and 1.99%, respectively.

General Procedure for the Optimization of Analyte Concentration

Preparation of the cyclodextrin–dye solution was performed following the procedures detailed above with 2-HP-β-CD used as the host. A 0.5 M solution of the alcohol was made by adding alcohol to the cyclodextrin–dye solution in the glass jar with additional samples tested for each alcohol at a variety of concentrations (0.5, 1.0, 2.0, and 3.0 M) using the BODIPY and Rhodamine dyes (8 samples, 3 trials each). These solutions were transferred to a sample cup and placed in the lightbox, and a photo was taken of every sample.

General Procedure for Calculating the Limits of Detection (LOD) and Limits of Quantification (LOQ)

The limit of detection (LOD), defined as the lowest concentration of the analyte that can be detected, was obtained using the calibration curve method, following procedures reported by Loock and co-workers.57 The limit of detection of the blank (LODblank) is defined according to eq 1 below

graphic file with name ao9b02612_m001.jpg 1

where m is the average of the values obtained from the blank sample and SD is the standard deviation of those measurements. The limit of quantification (LOQ) is the lowest concentration of the analyte that can be quantified.58 The limit of quantification of the blank is defined according to eq 2 below

graphic file with name ao9b02612_m002.jpg 2

where m is the average of the values obtained from the blank sample and SD is the standard deviation of those measurements.

Preparation of the cyclodextrin–dye solution was performed as mentioned above using 2-HP-β-CD as the optimized supramolecular host. This solution was transferred to the sample cup and placed in the spray-painted box. A photograph was taken in order to obtain the blank measurement of the solution in the absence of any alcohol. Using a 20–200 μL Fisherbrand Elite micropipette, 100 μL of the alcohol was added, and a picture was taken. These 100 μL additions continued until 6.0 mL of the alcohol had been added to the solution. This process was repeated three times for each alcohol and in the presence of each dye. The RGB values of the solution were used to determine the level of detection of each alcohol in both dyes.

General Procedure for Obtaining RGB Values

Photos were cropped to be the same 500 × 500 pixel ratio focused on the center of the sample (using https://www.birme.net) to ensure that the area of the picture that was being measured was consistent across all samples. These images were processed using the RGB measurement tool plug-in that is available for ImageJ software. More details of these procedures can be found in the Supporting Information of this manuscript.

General Procedure for Conducting Linear Discriminant Analyses

SYSTAT 13 statistical computing software was used to quantify the degree of separation of color change in the solutions using the following settings for linear discriminant analysis (LDA): (a) Classical Discriminant Analysis; (b) Grouping Variable: Analytes (alcohols); (c) Predictors: Red, Green, and Blue; and (d) Long-Range Statistics: Mahal.

General Procedure for Computational Modeling

Spartan version ‘18 was used to calculate the equilibrium values for the analytes in their ground-state electric potential surfaces using a semi-empirical PM3 model for each analyte. Molecular Operating Environment 2018 (MOE) was used to do the docking studies for each dye, alcohol analyte, and 2-HP-β-CD host. A general energy minimization was performed using the “quick prep” function on the software. For the docking studies, the set of atoms defined as the receptor was both 2-HP-β-CD and the solvent so that the dye could move freely in the system. Placement was done using the Triangle Matches method with the London Dispersion dG score in 30 poses. Refinement was done using the Rigid Receptor method with the GBVI/WSA dG score in 5 poses. This generated the docking of the dye–cyclodextrin complex with the lowest energy conformation. Summary figures generated from these procedures can be found in the Supporting Information of this manuscript.

Acknowledgments

The authors wish to thank Mr. Benjamin Cromwell for his support in setting up and analyzing the computational components and the MAGIC (More Active Girls in Computing) mentoring program for connecting P.H. with Levine and co-workers and the associated research.

Supporting Information Available

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.9b02612.

  • Experimental details of all analytes, dyes, and hosts; details of lightbox construction; optimization of analyte concentrations and supramolecular host; determination of array-based selectivity, computational modeling, and limits of detection and quantification; and all summary tables and figures (PDF)

Author Present Address

§ Present address: Ari’el University, 65 Ramat HaGolan Street, Ari’el, Israel.

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

The authors declare no competing financial interest.

Supplementary Material

ao9b02612_si_001.pdf (3.3MB, pdf)

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