Design and Test of a Biosensor-Based Multisensorial System: A Proof of Concept Study

2.1. Anthocyanin Extraction and Quantification

A red rose (Rosa europeana), a blue hortensia flower (Hydrangea macrophylla) and a red cabbage (Brassica oleracea var. capitata f. rubra) were used as source of the natural pigments investigated in the present study. For extraction, fresh plant tissues (10 g) were harvested and immediately incubated for 2 h at room temperature in an acidic methanolic solution (0.1% HCl v/v). Crude extracts were clarified by centrifugation at 10.000 rcf and 4 °C for 15 min to remove any traces of particulates. The recovered supernatant was then stored at −20 °C until use. Total phenolic content of purified extracts was spectrophotometrically determined by the Folin-Ciocalteu method [40] and results were expressed as gallic acid equivalent (GAE) in g/100 g of material. The concentration of polyphenols in samples was derived from a standard curve of gallic acid (Sigma-Aldrich, Milan, Italy) ranging from 1 to 7.5 μg/mL (Pearson's correlation coefficient: r² = 0.99). The total anthocyanin concentration was estimated by the pH differential method [41]. The absorbance values were related to anthocyanin content using the molar extinction coefficient calculated for cyanidin-3-O-glucoside.

2.2. Absorbance Spectra Determination

Optical properties of anthocyanins extracts were investigated recording the absorbance in the ultraviolet-visible (UV-Vis) spectral region ranging from 230 nm to 800 nm. To evaluate the color transition four different buffer mixtures were prepared: solution A (25 mM potassium chloride, pH 1.0), solution B (0.4 M sodium acetate, pH 4.5), solution C (0.4 M sodium acetate, pH 6), solution D (0.1 M disodium hydrogen phosphate, pH 7.5). Samples were diluted in the proper solution before the analysis so as not to exceed the buffer capacity of the reagents. Once analyzed each sample was recovered, placed in polypropylene vials and challenged for different storage conditions. Anthocyanins extracts diluted in aqueous buffered solutions were kept independently for 14 days at room temperature and 4 °C or for 2 h at 40 °C. All the spectrophotometric analyses were performed on a Shimadzu UV-1800 instrument (Shimadzu, Milan, Italy).

2.3. HPLC UV/Vis Analysis

Anthocyanins composition of the extracts was determined on a Shimadzu Prominence HPLC-20AD XR system equipped with a SPD-20 AD UV detector. A sample (20 μL) of the acidic methanol extract was injected onto an analytical reverse phase C18 Discovery HS column (250 × 2.1 mm, 5 μm particle size; Supelco/Sigma-Aldrich, Bellefonte, PA, USA) and the separation was carried out eluting with a high-pressure binary gradient. The mobile phase was composed of 5% formic acid in water (Solvent A) and methanol (Solvent B). The gradient conditions were as follows: from 10% to 20% B in 30 min, from 20% to 25% B in 5 min, from 25% to 60% B in 20 min, from 60% to 100% B in 1 min, then held for 10 min before returning to the initial conditions. Chromatograms were recorded at absorbance of 520 nm and 280 nm. Data acquisition and processing were performed with the Shimadzu LC Solution software. Three separate analyses were carried out for each sample.

2.4. Transducers

The transducers used for the gas sensor array are quartz crystals with a resonance frequency of 20 MHz in the thickness shear mode (fabricated by CNR IMM, Rome, Italy) [42]. The sensing material covering each crystal was deposited by drop casting and was a combination of anthocyanins extracted from three different plant tissues: red rose, red cabbage, blue hortensia. The list of the six sensors is: sensor 1, RR (red rose extract 65 mM); sensor 2, BH (blue hortensia extract 65 mM); sensor, 3 RC (red cabbage extract 65 mM); sensor 4, RRS (red rose extract 65 mM + sucrose 10 mM); sensor 5, RCS (red cabbage extract 65 mM + sucrose 10 mM); sensor 6, BHS (blue hortensia extract 65 mM + sucrose 10 mM). To test system reproducibility, each sensor was prepared in triplicate depositing 10 μL of the specific sensing material and then letting it air dry.

The liquid sensor array is composed of screen-printed gold electrodes (Aux.: Pt; Ref.: Ag) fabricated by DropSens S.L. (Llanera (Asturias), Spain); the working (4 mm diameter) electrode is made of gold, the counter electrode is made of platinum, the reference electrode is made of Ag and electric contacts are made of silver. Its dimensions (3.4 × 1.0 × 0.05 cm—Length × Width × Height) are ideal for working with 50 μL volume. The sensing material used to functionalize the electrode surface was a combination of anthocyanins extracted from red cabbage and polymerized with 4.5% (w/v) agarose. Liquid sensors were freshly prepared before each analysis by casting 10 μL of the sensing material solution onto the working electrode surface and then letting it air drying.

The electronic interface for voltage input and for the reading of the out current is shown in Figure 1. Opamp 1 allows the reference electrode to keep a selected voltage (also time dependent) without having current through it. Opamp 2 transforms the working electrode current in an output voltage representing the overall circuit response. The electronic board controlling the whole system is based on a STM 32 F303VC, fabricated by ST Microelectronics (Geneva, Switzerland).

2.5. Calibration Set-Up

In order to perform gas and vapor measurements in the concentration range below 1 ppm, a strategy based on permeation tubes has been utilized. A similar setup was already used with the electronic nose of the University of Tor Vergata and it is fully described in a work by the same authors of the present paper [43]. The selected compounds were ethanol, hexane and isopropyl alcohol. The concentration levels tested in these experiments for ethanol were: 0.5, 1.5, 3.2, 4.5 and 6.1 ppm (five measurements each). The concentration levels tested in these experiments for hexane were: 0.7, 2.1, 4.1, 6.3 and 7.2 ppm (five measurements each). The concentration levels tested in these experiments for isopropyl alcohol were: 0.6, 1.8, 3.9, 5.4 and 6.3 ppm (five measurements each). For the test of the liquid sensor array cyclic voltammetry in the range from −1 to 1 Volt has been employed using a triangular function at 1 mHz and at 10 mHz. Liquid samples were enclosed in 3 mL polyethylene vials normally used for photospectrometry.

2.6. Data Analysis

The parameters (calculated for each measured compound) provide the elements needed to assess the performance of the sensors, and they could be used to fill-in a sort of data sheet of the instrument. These technical notes could orient the device for specific applications. This information can be also used for the system re-calibration and standardization using one of the tabled compounds, depending on the specific application. The sensors' behavior has been analyzed taking into account the following different aspects:

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calibration curve (QMB frequency shift versus VOC concentration)

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sensitivity (slope of the linearized calibration curve)

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experimental Limit of Detection (LOD) determined around the left end part of the calibration curve

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theoretical LOD (estimated considering a signal to noise ratio S/N equal to 1)

Sensitivity and LOD role consists of supporting the e-nose pertinence to many different applications.

For the multivariate data analysis approach, Principal Component Analysis (PCA) has been used as an unsupervised method; as a supervised method Partial Least Square Discriminant Analysis (PLS-DA) has been applied, with Leave-One-Out as Cross-validation criterion. PCA and PLS-DA have been performed using the PLS-Toolbox SW (Eigenvector, Wenatchee, WA, USA) in the Matlab environment ( The Mathworks, Natick, MA, USA).

3.1. Gas Sensor Array

Calibration curves of the two best-performing sensors (S1 and S2) are reported in Figure 2.

3.2. Response to Ethanol

Reproducibility can be observed by the error bars and the fact that discrimination among the measured concentration levels is not disturbed by overlapping standard deviation, being it smaller than the difference calculated between contiguous concentration levels.

Calibration curves are not linear and they can be fitted prima facie by a Langmuir Isotherm curve. Sensor 3 does not show a good performance, being its sensitivity very low and its calibration curve very similar to a saturation behavior. Sensitivity goes from a minimum value of about 9 Hz/ppm to a maximum value of about 93 Hz/ppm.

The experimentally measured LOD is 0.5 ppm, with a S/N ratio still far from 1. In fact, this is the lowest measured level of ethanol concentration, and the relative response values range from a minimum of 19 Hz to a maximum of 278 Hz. Given the noise level of 3 Hz, the theoretical LOD goes from 333 ppb to 32 ppb.

3.3. Response to Hexane

Reproducibility can be observed by the error bars as in the previous case (ethanol). Calibration curves appear to be linear. Further, in this case, three different behaviors can be observed: sensor 3 does not show a good performance, being its sensitivity very low and its calibration curve similar to a saturation behavior; sensors 1, 2 and 6 show a rather good sensitivity: from a minimum value of about 17 Hz/ppm to a maximum value of about 72 Hz/ppm; sensors 4 and 5 show a good sensitivity: from a minimum value of about 5.5 Hz/ppm to a maximum value of about 7.7 Hz/ppm.

The experimentally measured LOD is 0.7 ppm, with an S/N ratio still far from 1. In fact, this is the lowest measured level of hexane concentration, and the relative response values range from a minimum of 26 Hz to a maximum of 112 Hz. Given the noise level of 3 Hz, the theoretical LOD goes from 540 ppb to 42 ppb.

3.4. Response to Isopropyl Alcohol

Reproducibility can be observed by the error bars as before (ethanol and hexane). Calibration curves appear to be linear. Furthermore, in this case, three different behaviors can be observed: sensor 3 does not show a good performance, being its sensitivity low and its calibration curve similar to a saturation behavior; sensitivity goes from a minimum value of about 10 Hz/ppm to a maximum value of about 110 Hz/ppm. The experimentally measured LOD is 0.6 ppm, with an S/N ratio, still far from 1. In fact, this is the lowest measured level of isopropyl aclohol concentration, and the relative response values range from a minimum of 32 Hz to a maximum of 454 Hz. Given the noise level of 3 Hz, the theoretical LOD goes from 300 ppb to 27 ppb.

3.5. Liquid Sensor Array

The same compounds tested with gas sensors have been measured in the liquid phase: ethanol, hexane and isopropyl alcohol diluted in water at 98 μM, 73 μM and 89 μM, respectively. Figure 3 shows the obtained results. In Figure 3, voltammetry cycles show different curve shapes, evidencing current peaks for each compound, which appears to be different in terms of voltage values (horizontal axis) at which the peaks occur and in terms of the intensity (vertical axis) of the peaks themselves. Each curve has been treated as a single measure composed by 100 input voltage values and the 100 corresponding output current values. The array of the 12 (raw) measurements (four for each compound) and of the 100 columns has been analyzed by Principal Component Analysis (PCA). The scores plot of the first two Principal Components (PCs), accounting for 77.5 of the explained variance, is reported in Figure 3. Four distinct clusters can be observed for the four compounds: water and the three water-based solutions. Polar and non polar compounds are distinguished along the Principal Component 1 (PC1), while along the PC2 it is possible to discriminate the three polar compounds. The dimension of the liquid sensor patterns can be increased by mean of three strategies: (a) using the same electrode with the same sensing material at different frequencies of the input voltage function; (b) using different electrodes with same sensing materials; (c) using the same electrodes with different sensing materials. The set-up used for the measurements reported in this work is: Pt electrodes functionalized with the red cabbage anthocyanins extract and with an input signal frequency of 10 mHz.

3.6. Optical Properties

Plant materials from red cabbage, rose and hortensia flowers were incubated in an acidified alcoholic mixture at room temperature and in the dark to extract anthocyanins without disrupting the general architecture of the tissues. At the end of the incubation, the petals of the flowers and the leaves of the vegetable matter were completely white, whereas the recovered extraction solutions appeared intensely colored.

Each extract has been subsequently characterized by three physico-chemical parameters: the total polyphenolic content, the total monomeric anthocyanin content and the stability under different storage conditions. According to the well-known antioxidant properties of anthocyanins, the Folin-Ciocalteu assay was applied as preliminary analysis to determine the total phenolic content. The highest values were observed in the rose extract with 5.75 g/L (GAE). The amount of phenolic compounds measured in the red cabbage and the hortensia flower extracts was almost the same, 1.46 g/L and 1.68 g/L (GAE) respectively, with values significantly lower than the rose one. As a major drawback of the general method used for preparing anthocyanin-rich extracts from plant materials, all the molecules with polar character and phenolic groups were co-purified and quantified by the Folin-Ciocalteu reagent. To obtain a better estimation of the amount of anthocyanin pigments within the extracts, the pH differential method was performed. Samples were diluted with aqueous pH 1.0 and 4.5 buffers and absorbance measurements were taken at 520 nm, the wavelength at which the anthocyanin chromophore had the maximum absorbance in the pH 1.0 solution (Figure 4).

Turbidity has been also taken into account by measuring the absorbance at 700 nm and subtracting this from the values recorded at the wavelength of maximum adsorption. In contrast with the previously reported results, the red cabbage extract showed the highest concentration of anthocyanins with values comparable to rose extract. On the other hand, the amount of anthocyanin pigments measured in the hortensia extract was significantly lower than expected. To investigate further the observed discrepancy, anthocyanins in each extract were characterized by performing HPLC analysis (Figure 5).

Identification of the anthocyanins was attempted by comparison of the retention times of the observed peaks with those of standard molecules and well-known anthocyanin-rich matrices. Results obtained from analytical separation confirmed a general lower complexity of the latter in terms of total amount as well as number of different anthocyanin molecules (Figure 5). In the hortensia chromatogram four peaks were detected, with only one peak being the absolutely dominant component (Figure 5A). Conversely, rose and cabbage chromatograms showed a more complex anthocyanins composition. In particular, the rose chromatogram highlighted the presence of at least six different anthocyanins with two of them representing 92% of the total amount of molecules (Figure 5B, peaks 1 and 2), whereas the red cabbage extract showed an even more complex and heterogeneous anthocyanin composition (Figure 5C). Additionally, when the samples were analyzed by HPLC and detected by UV absorption a manifold presence of co-pigments in all the extracts was highlighted (data not shown). Exploiting further the good correlation between chemical characteristics and colorimetric information, the anthocyanin extracts were exposed to different storage conditions and their structural stability evaluated by measuring the absorbance behavior along an UV-Vis spectrum (Figure 4b). At very acidic pH (pH 1.0) the anthocyanins rich extracts resulted stable along a period of 14 days in all the storage conditions tested (room temperature, 4 °C; Figure 4). Nevertheless, anthocyanins stability in slightly acidic (pH 4.5 and pH 6.0) and alkaline (pH 7.5) environments was affected in an extract specific fashion. The anthocyanins deriving from cabbage were protected from degradation even after 14 days whereas the rose and hortensia extracts resulted unstable, especially in alkaline conditions (data not shown). Hortensia extract loss the distinctive absorption peak after one day of storage while rose anthocyanins resulted more stable undergoing degradation around at 7 days. In both cases, the degradation was observed with the same occurrence modality independently from the storage condition. Furthermore, incubating the three extracts for 2 h at 40 °C did not alter the absorbance spectra in any of the pH (from pH 1.0 to pH 7.5) tested (data not shown).

3.7. Gas-Liquid Sensor Synergy

Using the setup reported in Figure 6, the water-based solutions analyzed by the liquid sensors (see the section on gas sensor array calibration) have been also measured simultaneously with the liquid and the gas-vapor sensor arrays. This setup has been tested as a proof of concept. Each compound has been measured at three concentration levels: ethanol (10.9, 32.6, 98 μM); hexane (8.1, 24.3, 73 μM); isopropanol (10, 30, 89 μM). The multidimensional approach already described has been here used calculating a Partial Least Square Discriminant Analysis (PLS-DA) model on the entire data set array (90 measurements, 10 for each concentration level × 100 potentials, from −1 V to 1 V; steps of 20 mV). The calculated PLS-DA models show a rather high degree of efficiency in the concentration discrimination, as shown in Figure 7, left panel. The Root Mean Square Error in Cross Validation (Leave One Out criterion has been used), 0.066, 0.303, 0.182 μM for ethanol, hexane and isopropanol respectively, are promising and relatively low. The score plot of the first three Latent Variables (LVs) reported in the right panel of Figure 7 shows the ability of the system to sharply discriminate between different solutions at different concentration levels. Vapor released by each solution has been simultaneously measured, and the demonstration of the liquid-gas sensor system functionality is given by the fact that the three gas mixtures have been correctly discriminated. The identification of the different gas concentration levels is not possible in this configuration, because the vapor headspace rapidly reaches the saturation vapor pressure. Anyway this is not the purpose of the liquid-gas sensor synergy, which is instead oriented to the analysis of complex biological samples (urine, sputum, sweat, beverage) in order to acquire the overall information represented by each specimen. In Figure 6 the arrangement to be realized for the optical reading of the sample, exploiting the peculiarity of the anthocyanins is also preconized.