Disposable microfluidic sensor arrays for discrimination of antioxidants

Talanta 153 (2016) 163–169

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Talanta journal homepage: www.elsevier.com/locate/talanta

Disposable microfluidic sensor arrays for discrimination of antioxidants Seong H. Park a,b, Autumn Maruniak a, Jisun Kim a,b, Gi-Ra Yi b,n, Sung H. Lim a,nn a b

iSense, LLC 855 Maude Ave., Mountain View, CA 94040, USA Sungkyunkwan University, School of Chemical Engineering, Suwon 440746, South Korea

art ic l e i nf o

a b s t r a c t

Article history: Received 20 January 2016 Received in revised form 3 March 2016 Accepted 4 March 2016 Available online 5 March 2016

A microfluidic colorimetric sensor array was developed for detection and identification of various antioxidants. The sensor was fabricated by a photolithographic method, and consists of an array of printed cross-responsive indicators. The microfluidic design also incorporates pre-activation spots to allow printing of chemically incompatible components separately. Separately printed oxidizer allowed an oxidation of adjacent redox indicators only when aqueous sample was added to the sensor cartridge. Antioxidants were primarily detected by measuring the extent of inhibition of this oxidation reaction. Using this flow-based technique, a clear differentiation of 8 different antioxidants and 4 different teas has been demonstrated with 98.5% sensitivity. & 2016 Elsevier B.V. All rights reserved.

Keywords: Colorimetric sensors Paper-based devices Redox Antioxidants

1. Introduction Oxidative stress is a biochemical imbalance in which production of reactive oxygen species (ROS) or reactive nitrogen species (RNS) exceeds the cellular antioxidant capacity. While ROS are produced during normal cellular metabolism, excess ROS can cause oxidative stress, resulting in tissue damage and dysfunction through the chemical modification of structural or functional molecules (e.g., lipids, proteins, and nucleic acids) and alteration of redox-sensitive signaling pathways [1]. Consumption of antioxidants from fruits and vegetables has been suggested as a method to mitigate such oxidative damage, leading to increased popularity of antioxidant-rich foods, beverages, and supplements and a clear demand for identification and quantification of the antioxidant capacity in food and beverages. Several direct and indirect methods have been developed to evaluate antioxidant activity of foods and beverages [2]. While there is no standardized method for measuring antioxidant capacity, common in vitro methods, such as ABTS (2,2′-azino-bis(3ethylbenzothiazoline-6-sulfonic acid)) [3–5], DPPH (2,2-diphenyl1-picrylhydrazyl) [3–5], and ORAC (oxygen radical absorbance capacity) [6–9] assays are often used. Other approaches include CUPRAC (cupric ion reducing antioxidant capacity) [10,11], FRAP n

Corresponding author. Corresponding author. E-mail addresses: [email protected] (G.-R. Yi), [email protected] (S.H. Lim). nn

http://dx.doi.org/10.1016/j.talanta.2016.03.017 0039-9140/& 2016 Elsevier B.V. All rights reserved.

(ferric reducing-antioxidant power) [9,12], electrochemical [13], and ESR (electron spin resonance) assays [6], along with Folin– Ciocalteu method [8,14]. All of these methods, however, are solution-based tests, requiring cumbersome sample preparation and handling. Colorimetric sensor arrays (CSAs) present a new opportunity to measure antioxidant capacity, requiring little to no sample preparation while also allowing for discrimination of chemical components. Our CSA has been shown to discriminate a wide variety of chemical species based on their interaction with cross-responsive chromogenic dyes [15,16]. These CSAs have successfully differentiated various volatile analytes, including volatile organic compounds [17], toxic industrial chemicals [18–20], and microbial VOCs [21–23]. Zhang et al. first investigated liquid sensing capabilities of the CSA, and were able to discriminate 18 different aqueous solutions of organic compounds [24] and complex mixtures (represented by 14 commercial soft drinks and 18 commercial beers) [25,26]. Key to their success was employing a hydrophobic membrane and water-insoluble dyes, which minimized cross-reactivity to water and indicator leaching. By default, this approach is limited to the use of hydrophobic dyes and is not compatible with non-aqueous carrier solvents. A later generation array converted soluble dyes into insoluble nanoporous pigments by immobilizing the dyes in sol-gel matrices [27,28]. This improved sensor durability and performance for detection and identification of sugars. However, this method required pretreating sugars in the solution with boronic acid prior to any sensing experiments.

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Fig. 1. Images of the disposable microfluidic sensor array used in study. (a) Exploded view of the sensor cartridge. (b) The CSA with 24 chromogenic indicators and 10 activators. (c) Fully assembled cartridge. (d) The CSA sensors on a flatbed scanner and a computer.

Recently, microfluidic paper-based analytical devices (μPADs) have attracted a lot of interest due to their simplicity, low cost and rapid analysis [29]. A variety of fabrication techniques, including photolithography [30–33], wax printing [34–37], inkjet printing [38–42], flexographic printing [43,44], and paper cutting-shaping [45–47], have been applied for the colorimetric determination of various analytes, such as metal ions [36,41,48], biomarkers [49], DNA [50], and foodborne pathogens [51]. Colorimetric μPADs are well suited to the consumer market, because they can be manufactured cheaply and only require a simple imaging device (e.g., a scanner or a digital camera) for sensor response measurement. Sharpe et al. have reported a colorimetric metal oxide paper-based sensing array for the analysis of antioxidant-containing samples [52]. Several classes of antioxidants, including flavonoids, phenolic acids and amides, and polyphenols, were used to generate a colorimetric database for antioxidant identification and quantification in the mM range. Their sensor has recently been used to study the effect of brewing conditions on the antioxidant capacity of commercial green teas [53]. However, this sensor has been developed for spot test analysis and not a multichannel microfluidic device. Integrating paper-based microfluidics and colorimetric array technology will expand liquid sensing applications to the identification of a variety of chemical compounds and complex mixtures in solution. Herein, we report the development of such a device for the detection and quantification of antioxidants in solution. We enhanced the discriminatory power of the CSA for antioxidants by colorimetrically characterizing redox response. The sensor array primarily relies on the ability of antioxidants to prevent an activator-mediated oxidation of chromogenic redox indicators, which is reflected by a reduction in the intensity of color change. By printing an oxidizer adjacent to the redox indicators, we obviate the need to pre-treat the liquid analyte and analysis is accomplished in a single step in under 10 min. This sensor device can provide rapid, facile detection and discrimination of liquid solutions based on their antioxidant capacity. It also introduces the possibility of adding other types of solid state pre-activation agents to future generations of the CSA.

2. Experimental 2.1. Photolithographic patterning All reagents were used as received without further purification. Photocurable monomers EBECRYL-8602 (aliphatic urethane acrylate) and EBECRYL-130 (cyclic aliphatic diacrylate) were acquired from Allnex. Photoinitiator Darocure 1173 (2-hydroxy-2-

methylpropiophenone) was purchased from BASF. The two monomers were mixed in a 1:1 ratio, along with 1.0 wt% of photoinitiator. Cellulose paper (Whatman filter paper, Grade 4) was soaked in the resulting solution until air pockets were completely removed from the paper membrane. Photoresist soaked paper was then sandwiched between a photomask and a black plastic board. The photomask was prepared using an inkjet printer and a transparency film. Excess monomers were removed by flattening the photomask against the black board. The monomers were selectively polymerized with 70 mW/cm2 of UV light (IntelliRay 600, UVitron) for 2 s. Patterned substrate was washed with acetone several times to remove any unreacted monomer. The patterned cellulose paper was re-exposed to the UV light for 1 min for complete curing before washing again in acetone and drying in air on a 50 °C hot plate. 2.2. The colorimetric sensor array printing The pattern designed for the colorimetric liquid sensor array is shown in Fig. 1. The CSAs manufacturing procedures have been described previously, and the list of indicators used in this study is shown in Table 1 [33]. Each microfluidic channel has a unique indicator immobilized in plasticized polystyrene formulation (MW¼35,000 g/mole, Sigma-Aldrich, ratio of polystyrene, Triton X-100, 1,2-dichlorobenzene and 2-methoxyethanol was 1:1:9:9 by weight; for redox indicators (indicators 1–10) 2-methoxyethanol was replaced with 1,2-dichlorobenzene to improve the dye solubility). Aqueous Fe(NO3)3 solutions (25 mM or 100 mM) were used to print activation spots for channels 1–10. Each indicator and activation solution was sonicated for 5 min before printing on the patterned cellulose paper with 50 nL slotted pins (V&P scientific). Printed arrays were dried under dry nitrogen at room temperature for at least 3 days prior to any sensing experiment. The printed sensor was mounted inside a small plastic screwtop cartridge. Teflon spacer rings were placed on each side of the sensor to prevent the microfluidic channels from directly contacting the cartridge lid (Fig. 1). An additional Teflon ring was placed behind the sensor spots for uniform light reflection. A hole drilled in the cartridge lid allowed liquid sample to be injected through the septum in the center, so that the liquid sample traveled uniformly through 24 fluidic channels. To limit evaporation of the liquid analyte and ensure uniform analyte response, the lid hole was plugged with a rubber septum and the cartridge joint was wrapped with parafilm. 2.3. Sensing experiments Ascorbic acid, caffeic acid, chlorogenic acid, citric acid, p-coumaric acid, gallic acid, glutathione, and salicylic acid were

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Table 1 List of indicators used in microfluidic sensor array. Spot

Activator

Indicator

Indicator class

Reference

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 Fe(NO3)3 None None None None None None None None None None None None None None

N,N′-diphenyl-1,4-phenylenediamine N,N′-diphenyl-1,4-phenylenediamine N-phenyl-p-phenylenediamine N-phenyl-p-phenylenediamine N,N′-diethyl-1,4-phenylenediamine N,N′-diethyl-1,4-phenylenediamine N,N,N′,N′-tetramethyl-p-phenylenediamine N,N,N′,N′-tetramethyl-p-phenylenediamine o-phenylenediamine N-phenyl-o-phenylenediamine Methylene blue 2,6-dichlorophenolindophenol Tris-(4,7-diphenyl-1,10-phenanthroline) ruthenium(II) dichloride HgCl2 and Bromocresol Green Xylenol orange 1,5-diphenylcarbazide Tetracyanoethylene 2,4-dihydroxybenzaldehyde Tetraiodophenolsulfonephthalein Nitrazine yellow Bromophenol blue 2,3,7,8,12,13,17,18-Octaethyl-21H,23H-porphine iron(III) chloride 5,10,15,20-Tetraphenyl-21H,23H-porphine cobalt(II) and Bromocresol purple 5,10,15,20-Tetraphenyl-21H,23H-porphine zinc(II) and Bromophenol blue

Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator (phenylenediamine derivative) Redox indicator Redox indicator O2 indicator Ligand displacement and pH indicator Cation chelator Cation chelator Electron transfer acceptor Electrophilic indicator pH indicator pH indicator pH indicator Metalloporphyrin Metalloporphyrin and pH indicator Metalloporphyrin and pH indicator

[54,63] [54,63] [54,64] [54,64] [54,65] [54,65] [54,66,67] [54,66,67] [54,68] [54,69] [70] [71] [72] [73] [74] [48] [75,76] [77] [21] [21,73] [21,73] [21] [21] [21,73]

100 mM 25 mM 100 mM 25 mM 100 mM 25 mM 100 mM 25 mM 100 mM 100 mM

prepared as 10 mM solutions in ethanol/deionized water (1:1, v/v). Black tea (Earl Gray), green tea (China green tea), herbal tea (Passion) were purchased from TAZO. Solomon's tea was purchased from Dongsuh Foods. Teas were brewed per each company's instructions. Black tea and herbal tea were brewed in 8 fl oz of boiling water for 5 min, green tea in 8 fl oz of boiling water for 3 min, Solomon's tea in 3.4 fl oz of boiling water for 2 min. After brewing, the tea was mixed with an equal volume of ethanol to avoid spurious discrimination due to difference in solvents, and 35 mL of the resulting solution was added by pipette to the center of the array. To measure sensor response, the sensor cartridge was placed on a commercial flatbed scanner (EPSON Perfection V600), and imaged through the transparent lid in 30 s intervals for 45 min. The scanner was configured to collect images with a resolution of 800 dpi and 16-bit color depth. The sensor was scanned with default scanner settings with no image adjustments (e.g., brightness, contrast, and saturation all set to 0). A 72-dimensional vector in RGB space (24 indicators  RGB values) was extracted for each image by taking the median of all pixels within a 10-pixel-radius

disk centered in each indicator. The color vector of a reference image collected before any analyte exposure was then subtracted, creating a time series of color differences. Pixel values were extracted using custom automated image-processing software written in C#/C þ þ.

3. Results and discussion 3.1. Patterned CSA fabrication We have previously reported a photolithographic method for porous glass fiber membrane to enhance printability of the colorimetric sensor array [33]. While this photoresist formulation worked well for a glass fiber membrane, the patterning was not compatible with cellulose paper. Since it was more difficult to cure the monomers on cellulose-based membranes, we sought to optimize our photolithographic formulation. As shown in Fig. 2, patterned substrates from the original formulation (9:1 mixture of TPGDA:TMPTA) were chemically susceptible to common organic

Fig. 2. Images of the microfluidic channels on cellulose membranes before and after 10 min exposure to selected organic solvents. (a) Microfluidic channels developed using previously reported 9:1 mixture of TPGDA:TMPTA. (b) Microfluidic channels developed using 1:1 mixture of EBECRYL-8602 and EBECRYL-130. The new photoresist formulation is completely resistant to common organic solvents.

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Scheme 1. Schematic illustration of antioxidant detection principal using the oxidant and redox indicators (spots 1–10). (For interpretation of the references to color in this scheme, the reader is referred to the web version of this article.)

solvents required for subsequent washing steps. These solvents caused the pattern to crack, and the cured polymer dissolved in more aggressive solvents like CHCl3. We believe this reduced chemical resistance on cellulose was due to low cross-linking density of the polymer due to incomplete UV light penetration. Hence, we evaluated different photoresists with higher crosslinking functionalities. Among different combination of photoresists investigated, EBECRYL-8602 was most effective in yielding a highly chemical resistant patterned substrate. EBECRYL-8602 is undiluted aliphatic 9-functional urethane acrylate that can provide high cross-linking density for enhanced mechanical and chemical resistance. This photoresist was then mixed with EBECRYL-130, a low viscosity aliphatic diacrylate. This mixture of highly crosslinkable photoresist with low viscosity diluent allowed facile diffusion of the monomers into the membrane without any solvent, and yielded highly cross-linked polymer resistant to organic solvents (Fig. 2b). Furthermore, use of diacrylate as a diluent prevented cracking of the substrate during photopolymerization. This improved chemical resistance allowed rigorous washing of the freshly patterned substrates with organic solvent to completely remove unreacted monomers and initiators, which was critical in order to minimize radical reactions with the radicalsensitive indicators and analytes. The improved chemical resistance also allowed application of test samples in non-aqueous solvents without degradation of the microfluidic device. 3.2. Operational principle of the colorimetric sensor array Colorimetric sensor are cross-reactive sensors that mimic the mammalian olfactory or gustatory system [15]. A single receptor does not identify individual molecules; rather, the analyte is identified as a result of the collective pattern of response of many receptors. Similarly, the CSA technology consists of cross-reactive indicators coupled with a pattern recognition algorithm to detect and identify analyte response signature. The CSA used in this study contained 24 different indicators chosen for specific dye-analyte chemical interactions: (i) metal-ion-containing dyes that respond to Lewis basic compounds (e.g., metalloporphyrins, such as Zn(II) TPP, that can accept electron pair donation), (ii) pH indicators that respond to Brønsted acidity/basicity (e.g, halochromic dyes, such as bromophenol blue, that respond to proton concentration), (iii) dyes with large permanent dipoles that respond to local polarity, (iv) metal chelators that interact with cations (e.g., xylenol orange and 1,5-diphenylcarbazide) and (v) redox indicators (e.g., N,N ′-diphenyl-1,4-phenylenediamine, methylene blue and 2,6-dichlorophenol-indophenol). However, many of these indicators respond to more than one type of chemical interactions. For instance, pH indicators respond to more than pH changes, and can also indicate changes in local polarity. While the primary mechanism of the CSA is the chromogenic

response of indicators with the analyte, the sensor performance is highly influenced by the host material. For optical sensing applications, polymers are often used to immobilize and solvate the indicators the indicators, while also allowing analyte to diffuse through the matrix. For liquid sensing application, the host material must also minimize dye leaching during an experiment. Among, different polymers we investigated, including ethyl cellulose, polyvinyl chloride, polystyrene, and poly(vinylidene fluoride-hexafluoropropylene), plasticized polystyrene is most effective at both immobilizing and solvating indicators. As described in Section 2, plasticized polystyrene formulations containing these indicators were printed onto designated spots at the end of each microfluidic channel. Ten of the microfluidic channels included an “activation spot” printed midway down the channel, containing Fe(NO3)3 to oxidize the redox indicators when a test solution was added to the sensor cartridge. Quantification of antioxidant capacity is not standardized, because there are several mechanisms by which antioxidants operate. Aromatic or olefinic antioxidants may directly react with free radicals, accepting or donating unpaired electrons and stabilizing them through delocalization. Other antioxidants may chemically reduce reactive oxygen species (e.g., H2O2). Still others may chelate transition metals responsible for radical generation. Fe(III) is a common oxidant used to evaulate antioxidant capacity, although the chromogenic reporter is usually the reduced Fe(II)-bipyridyl ligand charge transfer complex, as in the FRAP assay. In the present application, various phenylenediamines served as reporters via Fe(III)-catalyzed oxidation to the corresponding diimines [54]. Test solutions containing antioxidants reduced the Fe(III) activator to Fe(II), preventing oxidation (color change) of phenylenediamines at the channel terminus. As illustrated in Scheme 1, solutions with no antioxidants present would not react with Fe(NO3)3, allowing it to travel down the channel to react with the redox indicator at the end. Since oxidized indicators (i.e., redox indicators pre-mixed with Fe(NO3)3) are not stable over time, this flow-based technique allowed us to store incompatible components separately using a microfluidic pattern. Dried oxidizers were combined with the indicators only during the sensing experiment. 3.3. Sensor response and measurement of antioxidant activity The CSA was imaged with a commercially available flatbed scanner, using custom image acquisition software to automatically collect the sensor images at set intervals. A pre-analyte-exposed image was collected as the reference image. This allowed calibration of the individual sensor, which may contain small variations arising from the sensor manufacturing process. Average color difference maps of 8 individual antioxidants at 10 mM and four types of tea samples are shown in Fig. 3. For visual clarity only, we also subtracted the sensor response to the solvent. These color difference maps illustrate a broad range of color difference patterns, which can be used to easily discriminate different antioxidants and teas (Fig. 3). For redox indicators printed alongside the Fe(NO3)3 activator, response magnitude was highly correlated to the Fe(III)-reducing capacity and concentration of the analyte. In the absence of antioxidant activity, Fe(NO3)3 oxidized the phenylenediamine redox indicators, resulting in a large color changes. As the reducing capacity of the test solution increased, however, this oxidation reaction was inhibited, resulting in lower color response, represented by Euclidean distance between initial and final spot values in RGB space (Fig. 4). Euclidean distance is a common RGB color quantization approach, where “distance” between two RGB values is determined by taking square root of the sum of squares [17,19]:

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Fig. 3. (a) Raw color images of liquid CSA before and after exposure to ascorbic acid at 10 mM and the solvent mixture, along with a color difference image. (b) Color difference maps (ΔR, ΔG, ΔB) for 8 antioxidants at 10 mM and 4 teas. For each analyte, 35 μL was added to the sensor array. The color difference maps were generated by subtracting the solvent response. For display purposes only, the color range has been expanded from 4–15 to 0–255. Microfluidic channels with the activator spots are shown in a red box. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Colorimetric response of spot 1 (100 mM Fe(NO3)3/N,N′-diphenyl-1,4-phenylenediamine) to different antioxidants (10 mM for single analytes). The relative Fe(III)-reducing capacity can be measured using this liquid sensor array.

(ΔR)2 +(ΔG)2 +(ΔB)2 Results from Spot 1 are illustrated as a representative example. In general, there was greater sensor response with 100 mM Fe(NO3)3 activator. Of the individual antioxidants we tested, ascorbic acid and glutathione had the strongest antioxidant activity. Indeed, the affinity for and redox mechanisms of ascorbic acid and glutathione with Fe(III) have been well characterized in the literature [55,56]. Phenolic compounds are known to reduce transition metal ions by formation of stabilized phenoxy radical intermediates. In this array antioxidant activity was correlated to the number and location of hydroxyl groups. Gallic acid, the phenolic

compound with highest antioxidant activity, has three ortho-hydroxy groups which can form bidentate complexes with Fe(III) to better facilitate electron transfer. Compounds with also two orthohydroxy groups (caffeic and chlorogenic acid) had slightly lower activity but were similar to each other in their ability to reduce Fe (III). Though there is a large Euclidean distance observed for the interaction with salicylic acid similar to the control of water/ ethanol, the test might be confounded by the ability of Fe(III) to form brightly colored coordination complexes with salicylic acid. While concentrations of single antioxidants were controlled, tea solutions were not normalized by leaf mass or volume and reducing capacity is characteristic of a standard brewed volume. Total reducing capacity per cup of brewed liquid was more qualitative and followed the expected trend that green tea has greater antioxidant capacity than black tea due to a higher concentration of polyphenols. The herbal tea was an infusion of hibiscus flowers and rose hips, which contain high concentrations of ascorbic acid. This may explain why the overall reducing capacity of the herbal tea was greater than the other teas tested. In addition to evaluating the discriminating power of the microfluidic CSA, ascorbic acid was selected as a representative example of semi-quantitative measurement of antioxidant concentration. The CSAs were exposed to different concentrations of ascorbic acid ranging from 10 mM to 10 mM. While each concentration of ascorbic acid has a unique color difference pattern, the overall sensor response decreased as the concentration of ascorbic acid increased. This is best visualized by subtracting the pure solvent response (control) from analyte response at different concentrations, as in Fig. 5a. Some of the indicators, such as Nphenyl-p-phenylenediamine, had a very linear response to

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Fig. 5. (a) Color difference maps (ΔR, ΔG, ΔB) for ascorbic acid at different concentrations. The color difference maps were generated by subtracting the solvent response. For display purposes, the color range has been expanded from 4–15 to 0–255. Microfluidic channels with the Fe(NO3)3 spot are shown in a red box. (b) Colorimetric response of spot 3 (Fe(NO3)3 and N-phenyl-p-phenylenediamine) to various concentrations of ascorbic acid. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

logarithmic change in analyte concentrations (Fig. 5b). Fig. 5 indicates that ascorbic acid can be detected at 10 mM even by visual inspection of the color difference maps. Concentrations of ascorbic acid in common fruit juices range from 0.5 to 2.0 mM [57], so this method may be suitable for portable measurement of antioxidant capacity of food and beverage. Of note, one of the strongest discriminating channels for ascorbic acid included o-phenylenediamine, (spot 9), known to condense with dehydroascorbic acid to form a chromogenic pyrazine [58]. The primary goal of this work was to demonstrate that the discriminatory power of the CSA can be significantly enhanced by the inclusion of a procedure to measure redox response (aside from common redox indicators like methylene blue). To our knowledge, the addition of activators to a mPAD to exploit redox properties of analytes has not been applied to colorimetric sensor arrays. One caveat in this work is that we did not test a wide range of antioxidants, interferents, and complex mixtures. Some antioxidants were screened out because of insolubility in our ethanol/ water solution, and many complex mixtures containing antioxidants were highly colored, which affected the colorimetric readout. One way to handle colored solutions in future studies would be to filter solutions through charcoal, though that may also lower the concentration of antioxidants in solution. Additives in commercial products like metal chelators (e.g., EDTA) may coordinate metal activators while preservatives with redox properties could reduce the oxidant, so additional pre-activators might be necessary to test complex mixtures.

The random forests method is common machine learning algorithm that operates by constructing a multitude of decision trees with a training set of data [59,60]. The classification was performed in R using the stats and e1071 packages [61], and we used 10 repetitions of 4-fold stratified cross-validation to evaluate classifier performance on a test set that was not included in the training set used to build the classifier. We also report one-vs.-all classification performance for individual classes using sensitivity and specificity [62]. Cross-validation results are shown in Table 2. For 12 tested analytes, the overall sensitivity and specificity are 98.5% and 99.9%, respectively. While citric acid and herbal tea were detected with low 90% sensitivity, the rest of the analytes were discriminated with very high sensitivity. Sensitivity drops to 77.1% overall (and in the 30–50% for some single analytes) when activator/redox indicators are excluded from the analysis, indicating that this unique mode of differentiation expands the power of the CSA to differentiate closely related redox-active small molecules. To validate the accuracy of our predictive model, we repeated the cross-validation using a label permutation test. This test is useful in identifying false positives or coincidental correlations in the data. First, the analyte labels associated with the sensor data were randomly shuffled, then the same random forest statistical method was applied to the data. This process was repeated 10 times to generate average sensitivity and specificity for the permuted labels. The permutation test resulted in sensitivity of just 10.4%, yielding a chance-level accuracy similar to the prevalence of that class.

3.4. Statistical analysis 4. Conclusions For quantitative analysis, we trained a random forest (RF) method using pairwise comparisons to separate multiple classes.

The integration of our improved patterning method for

Table 2 RF classification results of 8 antioxidants and 4 teas. 10 repeats of 4-fold cross validation method was used in the analysis. Analyte

n

True labels

Scrambled labels

All spots

Ascorbic acid Black tea Caffeic acid Chlorogenic acid Citric acid Control Gallic acid Glutathione Green tea Herbal tea p-Coumaric acid Salicylic acid Solomon tea Total

5 4 5 5 5 5 5 5 5 7 5 5 5 66

Spots 11–24 only

All spots

Spots 11–24 only

Sens. (%)

Spec. (%)

Sens. (%)

Spec. (%)

Sens. (%)

Spec. (%)

Sens. (%)

Spec. (%)

96.2 100 100 100 100 93.8 97.5 100 100 100 92.5 100 100 98.5%

100 100 100 99.8 99.9 99.5 100 99.8 99.8 100 99.6 100 100 99.9%

95 95 95 37.5 92.5 78.8 70 95 42.5 100 51.2 67.5 82.5 77.1%

99.6 99.8 99.8 95.3 99.6 96.2 98.3 98.8 96.1 99.8 95.6 97.3 99.6 98.1%

15 2.5 2.5 30 0 0 2.5 10 33.8 5 0 7.5 26.2 10.4%

91 95.5 92.8 88.5 95.9 93.7 89.5 89.5 94.4 84.7 94.9 96.8 95.5 92.5%

18.8 0 0 3.8 5 22.5 0 0 31.2 26.2 0 1.2 0 8.4%

94.7 95.2 96.5 92.3 95.3 90 94.7 93.7 85.8 81.9 92.4 93.3 95.3 92.4%

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microfluidic channels on a cellulose membrane with colorimetric sensor array technology allowed discrimination and quantification of several common antioxidants found in nature. The microfluidic channel design incorporates a pre-activation spot to allow printing of chemically incompatible components, eliminating the need to manipulate liquid samples before the antioxidant measurement. Using a chemically diverse set of cross-responsive indicators, these antioxidants could not only be detected, but also easily discriminated. Because antioxidants operate through several different mechanisms, additional oxidative “activation agents” (e.g., peroxide-generators, radical initiators, and other transition metal salts) would improve the ability of the CSA to identify, measure, and quantify antioxidant capacity. This two-step mechanism can be applied beyond oxidants to other “activators” with different mechanisms of reactivity with analytes and reporters. Because our CSA technology is simple, rapid, and easily imaged with commercial hardware, this new technique holds promise as a simple analytical tool for consumers and the food and beverage quality control industry.

Acknowledgments We thank Richard Huang for assistance in designing the microfluidic pattern and the sensor cartridge. This study was supported by iSense, LLC, Mountain View, CA and the National Research Foundation of Korea (NRF) under award no. 2010-0029409 and NRF-2014M3A9B8023471.

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