- Email: [email protected]
A new electrochemical sensor for extra-virgin olive oils classification
Food Control 109 (2020) 106903
Contents lists available at ScienceDirect
Food Control journal homepage: www.elsevier.com/locate/foodcont
A new electrochemical sensor for extra-virgin olive oils classification a,∗
a
b
a
D. Zappi , C. Sadun , L. Gontrani , D. Dini , M.L. Antonelli a b
T
a
La Sapienza University of Rome – Chemistry Department, Piazzale Aldo Moro, 5, 00185, Rome, Italy Department of Chemistry “Giacomo Ciamician”, University of Bologna, v. F. Selmi 2, 40126, Bologna, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Olive oils Cultivar Sensor Bio-based RTIL Nanomaterials Screen-printed electrode
A screen-printed electrode was modified using multi-walled carbon nanotubes (MWCNT) and titanium oxide nanoparticles (TiO2), with a bio-based ionic liquid (RTIL) as a drop-casting medium. The RTILs here used have been proved to be useful for immobilizing nanomaterials on the working electrode surface. The proposed platform has been used to analyze extra-virgin olive oils (EVOOs), produced with olives of known cultivar and geographic origin. The EVOOs were produced with the olives that have been collected in the Italian region of Lazio during 2016, 2017 and 2018 harvesting seasons. The analyses of the oils were done by means of cyclic voltammetry measures. The results here obtained encourage the establishment of criteria for the classification of oils in terms of the corresponding cultivar. Both the electrode modification and the olive oil analysis do not involve any organic solvents. Therefore, the proposed approach represents an environmentally friendly method for field analyses of EVOOs.
1. Introduction An essential part of the so-called “Mediterranean diet” is the extravirgin olive oil (EVOO) (Bullo, Lamuela-Raventos, & Salas-Salvado, 2011). Due to the huge market involved (2,186 million tons of olive oil produced in Europe) and the large request (1,584 million tons consumed only in Europe) (Zolichová, 2018) the risk of frauds concerning the adulteration of olive oils is high. Typical frauds range from the replacement of all or part of the olive oil with different seed oils (sunflower, hazelnut, etc.), to the mixing of EVOO with olive oils of lower quality, e.g. virgin or lampante virgin olive oils, as well as the replacement of the PDO oils (Protected Denomination of Origin) with oils of unknown origin. The analytical techniques proposed by the European Union to ascertain olive oil quality and to certify the commercial category (Commission delegated regulation (EU) 2015/1830, 2015; Commission Regulation (EEC) No 2568/91, 1991) do not classify EVOOs according to their composition or geographical origin (Baiano, Terracone, Viggiani, & Nobile, 2013). Therefore, the development of suitable analytical methodologies is needed to know the exact geographical origin of EVOO. The consumers that are aware of the possible types of fraud in progress have turned to the purchase of EVOO from mono-cultivar having a certified origin. As a consequence of this market choice, the scammers have also changed their goal. The most common frauds now concern the replacement of declared quality oils with others of low
∗
quality and/or unknown origin. This kind of frauds is even more difficult to identify. The current analytical techniques (Bosque-Sendra, Cuadros-Rodríguez, Ruiz-Samblás, & de la Mata, 2012; Merchak et al., 2018; Van Durme & Vandamme, 2016) allow identifying only a small percentage of adulterations and frauds. Moreover, these methods are expensive and time-consuming. A new opportunity to detect such frauds comes from the development of innovative sensing and biosensing platforms. Several researchers tried to develop so-called “electronic tongues” and “electronic noses” (Dias et al., 2014; Dias, Rodrigues, Veloso, Pereira, & Peres, 2016; Slim et al., 2017) that constitute useful devices for the determination of the chemical composition of different olive oils, and the successive correlation with the geographical origin of olives. Such analytical tools are extremely sensible and accurate but need calibration and rigorous pretreatment of the sample to remove possible interfering substances. A previous work (Daniele Zappi et al., 2019), concerning the modification of a screen-printed electrode (SPE) with nanomaterials and bio-based ionic liquids for the analysis of EVOOs, suggested that correlations between the modified SPE electrochemical response and the olives cultivar and/or their geographical origin could be established. Thus, the SPE platform has been tested to obtain a screening analytical tool, quick and easy to use, able to identify the cultivar of the plants from which the oils are produced. The work was carried out on olives hand-picked from specific plants
Corresponding author. E-mail address: [email protected] (D. Zappi).
https://doi.org/10.1016/j.foodcont.2019.106903 Received 16 July 2019; Received in revised form 27 August 2019; Accepted 19 September 2019 Available online 21 September 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
Food Control 109 (2020) 106903
D. Zappi, et al.
utilized for the electrochemical measures.
of known cultivar and fully laboratory-processed over three years of harvesting: 2016, 2017 and 2018. All the oil processing method and the analytical methodology here described have been conceived with the aim of being as environmentally friendly as possible.
2.4. Instrumentation All electrochemical experiments have been performed by means of a PalmSens Electrochemical Sensor Interface (PalmSens BV Utrecht, The Netherlands), using Screen Printed disposable electrodes (SPEs) printed on plastic support, with glassy carbon working electrode (WE), silver reference electrode (RE), and counter carbon electrode (CE) (GSI Technologies Burr Ridge, IL, USA).
2. Materials and methods 2.1. Chemical and reagents Sodium dihydrogen phosphate (NaH2PO4) and sodium hydrogen phosphate (Na2HPO4) were obtained from Merck (Kenilworth, NJ, USA). Potassium chloride and nitric acid were purchased from Carlo Erba (Cornaredo, MI, Italy). Sodium hydroxide and multi-walled carbon nanotubes (MWCNT) (O.D. = (10 ± 1) nm; I.D. = (4.5 ± 0.5) nm; L = 3–6 μm) were supplied from Sigma-Aldrich (Buchs, Switzerland). Anatase titanium dioxide (99% purity; 5 nm) was purchased from NanoAmor Nanostructured and Amorphous Materials Inc. (Houston, TX, USA). 2-Hydroxy-N, N, N trimethylethan-1-aminium hydroxide (Choline hydroxide) and the two amino acids (serine, phenylalanine) were purchased from Alfa Aesar (Kander, Germany). All choline-amino acid room temperature ionic liquids (RTILs): choline-serine [Ch][Ser], choline-phenylalanine [Ch][Phe] were synthesized following the titrimetric methodology reported by De Santis et al. (Santis et al., 2015) Fungal lipase from Candida rugosa (EC 3.1.1.3, activity: 4320 U mg−1), stored at 4 °C was purchased from Sigma-Aldrich (Buchs, Switzerland). All the solutions have been prepared by means of high purity deionized water (Resistance = 18.2 MΩ × cm at 25 °C; TOC < 10 μg L−1), obtained from Millipore Direct-Q UV3 device (Molsheim, France).
2.5. Electrode modification SPE modification has been described in detail in previous works (D. Zappi et al., 2017; Daniele Zappi et al., 2019). In the present work, the method employing the external incubation has been applied, since it gave results with a smaller standard deviation when an additional step of sonication was included. The appropriate amount of multiwalled carbon nanotubes blended with TiO2 nanoparticles was dispersed in RTIL aqueous solution and the resulting mixture was put in an ultrasonic bath for 15 min in order to minimize the formation of aggregates in the deposition. A volume of 40 μL of the sonicated suspension was dropped on the working electrode and the thus modified SPE was successively dried in a desiccator overnight. The effect of the sonication step on the procedure of electrode modification was analyzed by measuring the electroactive area of the modified electrode and by monitoring its variation until a significant degradation was observed. The data were compared with those reported in previous works (Fig. 2). The addition of the sonication step in the electrode modification procedure increased considerably the time stability of the resulting systems: with the previous modification methodology we could attain electrodes with maximum storage time of seven days, whereas the new modification procedure allows the electrodes to be stored up to 30 days before significant degradation of the electroactive areas occurs. It has been supposed that this increase of stability is due to a more intimate mixing of the nanomaterials with the ionic liquid obtained thanks to the sonication step. When comparing these electrodes with those modified with the previous methodology, the formers appear to have a more smooth and uniform working electrode surface. Furthermore, when washed with distilled water after use, the electrodes modified with the new methodology lost only small amounts of modification material while there was a complete loss of the nanomaterial/ ionic liquid mixture in the ones modified with the old methodology. This suggests that the sonication step allows the nanomaterials/ionic liquid mixture to create stronger bonds with the electrode surface. This results in a more stable modification over time and offers the possibility of reusing these modified electrodes. Since stability over time is a highly appreciated property for this type of sensors, this new modification process has been adopted for the preparation of all the electrodes here reported.
2.2. Olive oil samples Six Italian olives cultivar were studied: Frantoio (FRA), Leccino (LEC), Moraiolo (MOR), Nostrale (NOS), Canino (CAN), Itrana (ITR), produced in the center of Italy, Lazio region. Their detailed geographical origin is shown in Fig. 1. During three harvest seasons (2016, 2017 and 2018), 42 olive samples from specific trees of known cultivar and geographical origin were hand-picked and used to produce olive oils (Table 1). The monovarietal oils were laboratory-processed using a standardized method described in the previous work (Zappi et al., 2019). Shortly, the olive samples were hand-picked and processed within 48 h of collection. The olives were crushed at a controlled speed and temperature lower than 25 °C. The obtained olive pomace was centrifuged at fixed speed and temperature, the resulting supernatant oil was then stored in brown bottles at 4 °C. Within a few days from production, each oil sample was tested for free fatty acid content, peroxide value and polyphenols content using a CDR-FoodLab (CDR s.r.l - Ginestra Fiorentina - Firenze - ITALIA) instrument. The obtained values for all the produced oils fall inside the limits proposed by the EU for extra-virgin olive oils (acidity value < 0.8%, peroxides value < 20 meq O2/Kg oil).
2.6. Electrochemical measurements Treated samples were analyzed using cyclic voltammetry techniques (CV). A scanning range from 0.0 to 0.8 V (vs Ag/AgCl) has been used at 0.01 V/s scan rate and a 0.001 V potential step. Measurements were performed in a closed box to better control temperature and humidity. Triplicate measures were done for each sample using three different electrodes.
2.3. Sample treatment The olive oil samples were stored at 4 °C, when necessary melted and brought back to room temperature before analysis. A volume of 20 μL of oil sample was mixed in a vial with 20 μL of [Ch][Ser] ionic liquid and 10 μL of pH 7.4 phosphate buffer containing the lipase enzyme. The vial was vortex mixed until an emulsion was obtained, then it was incubated at 37 °C for 15 min to allow the release of antioxidants (Pauliukaite, Doherty, Murnaghan, & Brett, 2011). After incubation, the vial was once more mixed, and the content immediately
3. Results and discussion The acidity and the content of peroxides were measured for the different oil samples. Results are shown in Fig. 3. The acidity values (in terms of free fatty acids percentage) are similar for all the examined oils, except for an outsider: a Moraiolo 2
Food Control 109 (2020) 106903
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Fig. 1. Map of the EVOO samples territorial origin.
cultivar oil (which is still an EVOO). As far as the peroxide content is concerned the values seem to be quite dispersed. Peroxide content can vary even for oils of the same cultivar and for oils of different cultivar. Therefore, the classification and/or discrimination of the oil variety based on these parameters remains not possible. As previously stated, the proposed sensing enzymatic method allows wide-spectrum oxidation of the antioxidant compounds that are naturally present in EVOOs and are released by the oil matrix upon hydrolytic action of lipase enzyme. For each oil sample three independent treatments were performed (see 2.3) and each resulting emulsion was analyzed with a different single-use modified electrode. For each triplet of measurements, the standard deviation values of both potential and current intensity were calculated. For potential measurements, the standard deviation was found to be between 0.0006 and 0.001; for current intensity measurements, it was between 0.181 and 0.982. The average values of the results obtained for each sample are expressed in terms of potential and current intensity of the anodic peak and have been reported in Fig. 4. As can be seen, a categorization of the oils based on cultivar can be easily performed. The potential values of the anodic peak appear to be the most correlated variable with the olive's cultivar. This suggests that the differences in the relative abundance of antioxidants in the oils differing for the cultivar constitute the basis of the classification process. Furthermore, the potential values seem to undergo only small variations for oils of different production years and obtained from the same cultivar. This finding strengthens the validity of the present approach for the definition of criteria that categorize olive oils. In Table 2 the average potential found for each cultivar has been reported. Moreover, for each cultivar, a “potential window” has been determined. This includes all the samples belonging to that cultivar.
Table 1 Monocultivar olive oil samples collected during 2016, 2017 and 2018 harvesting seasons. Samples bearing the same number have been collected from the same plant in different years. Harvest 2016 FRA_1/16 LEC_2/16 LEC_3/16 LEC_4/16 MOR_5/16 MOR_6/16 NOS_7/16
2017 FRA_1/17 LEC_2/17 LEC_3/17 MOR_5/17 MOR_6/17 NOS_7/17 CAN-8/17 FRA_9/17 FRA_10/17 FRA_11/17 FRA_12/17 FRA_13/17 FRA_14/17 FRA_15/17 ITR_16/17 LEC_17/17 LEC_18/17 MOR_19/17 NOS_20/17 NOS_21/17
2018 LEC_2/18 LEC_3/18 MOR_5/18 NOS_7/18 CAN_8/18
LEC_17/18 LEC_18/18
NOS_22/18 FRA_23/18 FRA_24/18 LEC_25/18 LEC_26/18 LEC_27/18 MOR_28/18 MOR_28/18 FRA_30/18
3
Food Control 109 (2020) 106903
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Fig. 2. Comparison of electroactive areas of electrodes modified with the old (red) and new (blue) methodologies. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
On the other hand, the parameter of the current intensity of the anodic peak shows no correlation with the oil cultivar. It is supposed that the measured oxidation potential depends mostly on the nature of the antioxidant species that vary from one cultivar to another while the corresponding current intensity is determined by their total content. 4. Conclusions The proposed platform has been tested for the analysis of 42 monovarietal olive oils. A laboratory milling procedure has been used to prepare the oils from hand-picked olives of six different cultivars. Olives were collected in different zones of the Lazio region in Italy (Fig. 1, Table 1). It has been considered that numerous external factors such as climate, meteorological events, and territory orography affect the chemical composition of oils, in particular the antioxidant compounds. All the oils were tested for acidity and peroxide content, in order to verify their quality. The relative values confirmed that all the prepared oils were of extra-virgin quality, according to the EU regulations. The results obtained show that the platform allows the classification of EVOOs from different mono-cultivar on the basis of the oxidation potential of the antioxidant compounds, made available by the enzymatic hydrolysis. Since no enzyme is immobilized on the electrode, there is no risk of performance degradation after long storage periods. Furthermore, thanks to the improved modification methodology, the platform has been demonstrated to be stable up to 30 days after the deposition of the nanomaterials/ionic liquid mixture. The disposable and single-use nature of the platform assures that the fouling phenomena occurring at the electrode surface do not represent a concern. It is important to underline that the method does not involve organic solvents during the pretreatment of the sample. The materials used to modify the electrode are easy to dispose of and the proposed platform affords results quickly and easily. As such, the platform is suitable for field screening analyses. A classification system for single-cultivar extra-virgin oils has been proposed. No concentration of specific analytes has been measured, resulting in no need to calibrate the platform response with respect to different antioxidants. In particular, a costeffective sensor useable by untrained personnel has been developed. The sensor here described is able to identify the olive cultivar in a quick and reliable way. Finally, the antioxidant class recognized by the proposed platform is not exclusive to olive oils but can be found in numerous other food matrixes (Acero, Gradillas, Beltran, García, & Muñoz Mingarro, 2019). Therefore, it is possible to envisage that this methodology can be applied to perform screening analyses on other food categories.
Fig. 3. Acidity determination (A) and Peroxides quantification (B) on olive oil samples. Different colors refer to different olive oil samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
The various cultivars produce oils presenting a potentiometric response quite specific for each. Indeed, the ranges of potential values for each cultivar are well separated and do not present overlap. This feature minimizes the risks of assigning the incorrect category to an oil sample with unknown provenience. The cyclic voltammetries of the oils obtained from each of the four identified categories are reported in Fig. 5. 4
Food Control 109 (2020) 106903
D. Zappi, et al.
Fig. 4. Results obtained for each oil sample produced from olives harvested in 2016 (red symbols), 2017 (blue symbols) and 2018 (green symbols). Different symbol shapes indicate different olives cultivar. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Declaration of interests
Table 2 Average potential and the potential window for each examined cultivar. Cultivar
Potential average ± potential window (V)
CAN FRA ITR LEC MOR NOS
0.193 0.238 0.182 0.250 0.210 0.225
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
± 0.003 ± 0.003 ± 0.003 ± 0.002
Acknowledgement This work was supported by the Italian Education Ministry Ministero dell'Istruzione, dell'Universita e della Ricerca, Italy (MIUR) Protocol number: RM11715C53850E54 titled: “Sviluppo di metodi di
Fig. 5. Cyclic voltammetries of the oils obtained from each of the four identified categories: Leccino (black); Moraiolo (blue); Frantoio (red); Nostrale (green). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 5
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screening basati su elettrodi enzimatici modificati da liquidi ionici come biosensori amperometrici e analisi di dati di raggi X in fluorescenza e diffrazione, rivolti alla classificazione ed al controllo della qualità degli oli di oliva”.
attributes and an electronic tongue. European Food Research and Technology, 242(2), 259–270. https://doi.org/10.1007/s00217-015-2537-4. Merchak, N., Rizk, T., Silvestre, V., Remaud, G. S., Bejjani, J., & Akoka, S. (2018). Olive oil characterization and classification by 13C NMR with a polarization transfer technique: A comparison with gas chromatography and 1H NMR. Food Chemistry, 245, 717–723. https://doi.org/10.1016/j.foodchem.2017.12.005. Pauliukaite, R., Doherty, A. P., Murnaghan, K. D., & Brett, C. M. A. (2011). Application of room temperature ionic liquids to the development of electrochemical lipase biosensing systems for water-insoluble analytes. Journal of Electroanalytical Chemistry, 656(1), 96–101. https://doi.org/10.1016/j.jelechem.2010.12.033. Santis, S. D., Masci, G., Casciotta, F., Caminiti, R., Scarpellini, E., Campetella, M., et al. (2015). Cholinium-amino acid based ionic liquids: A new method of synthesis and physico-chemical characterization. Physical Chemistry Chemical Physics, 17(32), 20687–20698. https://doi.org/10.1039/c5cp01612f. Slim, S., Rodrigues, N., Dias, L. G., Veloso, A. C. A., Pereira, J. A., Oueslati, S., et al. (2017). Application of an electronic tongue for Tunisian olive oils' classification according to olive cultivar or physicochemical parameters. European Food Research and Technology, 243(8), 1459–1470. https://doi.org/10.1007/s00217-017-2856-8. Van Durme, J., & Vandamme, J. (2016). Non-thermal plasma as preparative technique to evaluate olive oil adulteration. Food Chemistry, 208, 185–191. https://doi.org/10. 1016/j.foodchem.2016.04.007. Zappi, D., Caminiti, R., Ingo, G. M., Sadun, C., Tortolini, C., & Antonelli, M. L. (2017). Biologically friendly room temperature ionic liquids and nanomaterials for the development of innovative enzymatic biosensors. Talanta, 175, 566–572. https://doi. org/10.1016/j.talanta.2017.07.081. Zappi, D., Gabriele, S., Gontrani, L., Dini, D., Sadun, C., Marini, F., et al. (2019). Biologically friendly room temperature ionic liquids and nanomaterials for the development of innovative enzymatic biosensors: Part II. Talanta, 194, 26–31. https:// doi.org/10.1016/j.talanta.2017.07.081. Zolichová, L. (2018). Committee for the common organisation of the agricultural markets – arable crops and olive oil. 26. Recovered from European Commission for Agriculture and rural development website https://ec.europa.eu/agriculture/olive-oil_en.
References Acero, N., Gradillas, A., Beltran, M., García, A., & Muñoz Mingarro, D. (2019). Comparison of phenolic compounds profile and antioxidant properties of different sweet cherry (Prunus avium L.) varieties. Food Chemistry, 279, 260–271. https://doi. org/10.1016/j.foodchem.2018.12.008. Baiano, A., Terracone, C., Viggiani, I., & Nobile, M. A. D. (2013). Effects of cultivars and location on quality, phenolic content and antioxidant activity of extra-virgin olive oils. Journal of the American Oil Chemists’ Society, 90(1), 103–111. https://doi.org/10. 1007/s11746-012-2141-8. Bosque-Sendra, J. M., Cuadros-Rodríguez, L., Ruiz-Samblás, C., & de la Mata, A. P. (2012). Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data—a review. Analytica Chimica Acta, 724, 1–11. https://doi.org/10.1016/j.aca.2012.02.041. Bullo, M., Lamuela-Raventos, R., & Salas-Salvado, J. (2011). Mediterranean diet and oxidation: Nuts and olive oil as important sources of fat and antioxidants. [Text]https://doi. org/10.2174/156802611796235062. Commission regulation (EEC) No 2568/91. , Pub. L. No. 2568/91 (1991). Commission delegated regulation (EU) 2015/1830. , Pub. L. No. 2015/1830 (2015). Dias, L. G., Fernandes, A., Veloso, A. C. A., Machado, A. A. S. C., Pereira, J. A., & Peres, A. M. (2014). Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue. Food Chemistry, 160, 321–329. https://doi.org/10.1016/j. foodchem.2014.03.072. Dias, L. G., Rodrigues, N., Veloso, A. C. A., Pereira, J. A., & Peres, A. M. (2016). Monovarietal extra-virgin olive oil classification: A fusion of human sensory
6
Contents lists available at ScienceDirect
Food Control journal homepage: www.elsevier.com/locate/foodcont
A new electrochemical sensor for extra-virgin olive oils classification a,∗
a
b
a
D. Zappi , C. Sadun , L. Gontrani , D. Dini , M.L. Antonelli a b
T
a
La Sapienza University of Rome – Chemistry Department, Piazzale Aldo Moro, 5, 00185, Rome, Italy Department of Chemistry “Giacomo Ciamician”, University of Bologna, v. F. Selmi 2, 40126, Bologna, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Olive oils Cultivar Sensor Bio-based RTIL Nanomaterials Screen-printed electrode
A screen-printed electrode was modified using multi-walled carbon nanotubes (MWCNT) and titanium oxide nanoparticles (TiO2), with a bio-based ionic liquid (RTIL) as a drop-casting medium. The RTILs here used have been proved to be useful for immobilizing nanomaterials on the working electrode surface. The proposed platform has been used to analyze extra-virgin olive oils (EVOOs), produced with olives of known cultivar and geographic origin. The EVOOs were produced with the olives that have been collected in the Italian region of Lazio during 2016, 2017 and 2018 harvesting seasons. The analyses of the oils were done by means of cyclic voltammetry measures. The results here obtained encourage the establishment of criteria for the classification of oils in terms of the corresponding cultivar. Both the electrode modification and the olive oil analysis do not involve any organic solvents. Therefore, the proposed approach represents an environmentally friendly method for field analyses of EVOOs.
1. Introduction An essential part of the so-called “Mediterranean diet” is the extravirgin olive oil (EVOO) (Bullo, Lamuela-Raventos, & Salas-Salvado, 2011). Due to the huge market involved (2,186 million tons of olive oil produced in Europe) and the large request (1,584 million tons consumed only in Europe) (Zolichová, 2018) the risk of frauds concerning the adulteration of olive oils is high. Typical frauds range from the replacement of all or part of the olive oil with different seed oils (sunflower, hazelnut, etc.), to the mixing of EVOO with olive oils of lower quality, e.g. virgin or lampante virgin olive oils, as well as the replacement of the PDO oils (Protected Denomination of Origin) with oils of unknown origin. The analytical techniques proposed by the European Union to ascertain olive oil quality and to certify the commercial category (Commission delegated regulation (EU) 2015/1830, 2015; Commission Regulation (EEC) No 2568/91, 1991) do not classify EVOOs according to their composition or geographical origin (Baiano, Terracone, Viggiani, & Nobile, 2013). Therefore, the development of suitable analytical methodologies is needed to know the exact geographical origin of EVOO. The consumers that are aware of the possible types of fraud in progress have turned to the purchase of EVOO from mono-cultivar having a certified origin. As a consequence of this market choice, the scammers have also changed their goal. The most common frauds now concern the replacement of declared quality oils with others of low
∗
quality and/or unknown origin. This kind of frauds is even more difficult to identify. The current analytical techniques (Bosque-Sendra, Cuadros-Rodríguez, Ruiz-Samblás, & de la Mata, 2012; Merchak et al., 2018; Van Durme & Vandamme, 2016) allow identifying only a small percentage of adulterations and frauds. Moreover, these methods are expensive and time-consuming. A new opportunity to detect such frauds comes from the development of innovative sensing and biosensing platforms. Several researchers tried to develop so-called “electronic tongues” and “electronic noses” (Dias et al., 2014; Dias, Rodrigues, Veloso, Pereira, & Peres, 2016; Slim et al., 2017) that constitute useful devices for the determination of the chemical composition of different olive oils, and the successive correlation with the geographical origin of olives. Such analytical tools are extremely sensible and accurate but need calibration and rigorous pretreatment of the sample to remove possible interfering substances. A previous work (Daniele Zappi et al., 2019), concerning the modification of a screen-printed electrode (SPE) with nanomaterials and bio-based ionic liquids for the analysis of EVOOs, suggested that correlations between the modified SPE electrochemical response and the olives cultivar and/or their geographical origin could be established. Thus, the SPE platform has been tested to obtain a screening analytical tool, quick and easy to use, able to identify the cultivar of the plants from which the oils are produced. The work was carried out on olives hand-picked from specific plants
Corresponding author. E-mail address: [email protected] (D. Zappi).
https://doi.org/10.1016/j.foodcont.2019.106903 Received 16 July 2019; Received in revised form 27 August 2019; Accepted 19 September 2019 Available online 21 September 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
Food Control 109 (2020) 106903
D. Zappi, et al.
utilized for the electrochemical measures.
of known cultivar and fully laboratory-processed over three years of harvesting: 2016, 2017 and 2018. All the oil processing method and the analytical methodology here described have been conceived with the aim of being as environmentally friendly as possible.
2.4. Instrumentation All electrochemical experiments have been performed by means of a PalmSens Electrochemical Sensor Interface (PalmSens BV Utrecht, The Netherlands), using Screen Printed disposable electrodes (SPEs) printed on plastic support, with glassy carbon working electrode (WE), silver reference electrode (RE), and counter carbon electrode (CE) (GSI Technologies Burr Ridge, IL, USA).
2. Materials and methods 2.1. Chemical and reagents Sodium dihydrogen phosphate (NaH2PO4) and sodium hydrogen phosphate (Na2HPO4) were obtained from Merck (Kenilworth, NJ, USA). Potassium chloride and nitric acid were purchased from Carlo Erba (Cornaredo, MI, Italy). Sodium hydroxide and multi-walled carbon nanotubes (MWCNT) (O.D. = (10 ± 1) nm; I.D. = (4.5 ± 0.5) nm; L = 3–6 μm) were supplied from Sigma-Aldrich (Buchs, Switzerland). Anatase titanium dioxide (99% purity; 5 nm) was purchased from NanoAmor Nanostructured and Amorphous Materials Inc. (Houston, TX, USA). 2-Hydroxy-N, N, N trimethylethan-1-aminium hydroxide (Choline hydroxide) and the two amino acids (serine, phenylalanine) were purchased from Alfa Aesar (Kander, Germany). All choline-amino acid room temperature ionic liquids (RTILs): choline-serine [Ch][Ser], choline-phenylalanine [Ch][Phe] were synthesized following the titrimetric methodology reported by De Santis et al. (Santis et al., 2015) Fungal lipase from Candida rugosa (EC 3.1.1.3, activity: 4320 U mg−1), stored at 4 °C was purchased from Sigma-Aldrich (Buchs, Switzerland). All the solutions have been prepared by means of high purity deionized water (Resistance = 18.2 MΩ × cm at 25 °C; TOC < 10 μg L−1), obtained from Millipore Direct-Q UV3 device (Molsheim, France).
2.5. Electrode modification SPE modification has been described in detail in previous works (D. Zappi et al., 2017; Daniele Zappi et al., 2019). In the present work, the method employing the external incubation has been applied, since it gave results with a smaller standard deviation when an additional step of sonication was included. The appropriate amount of multiwalled carbon nanotubes blended with TiO2 nanoparticles was dispersed in RTIL aqueous solution and the resulting mixture was put in an ultrasonic bath for 15 min in order to minimize the formation of aggregates in the deposition. A volume of 40 μL of the sonicated suspension was dropped on the working electrode and the thus modified SPE was successively dried in a desiccator overnight. The effect of the sonication step on the procedure of electrode modification was analyzed by measuring the electroactive area of the modified electrode and by monitoring its variation until a significant degradation was observed. The data were compared with those reported in previous works (Fig. 2). The addition of the sonication step in the electrode modification procedure increased considerably the time stability of the resulting systems: with the previous modification methodology we could attain electrodes with maximum storage time of seven days, whereas the new modification procedure allows the electrodes to be stored up to 30 days before significant degradation of the electroactive areas occurs. It has been supposed that this increase of stability is due to a more intimate mixing of the nanomaterials with the ionic liquid obtained thanks to the sonication step. When comparing these electrodes with those modified with the previous methodology, the formers appear to have a more smooth and uniform working electrode surface. Furthermore, when washed with distilled water after use, the electrodes modified with the new methodology lost only small amounts of modification material while there was a complete loss of the nanomaterial/ ionic liquid mixture in the ones modified with the old methodology. This suggests that the sonication step allows the nanomaterials/ionic liquid mixture to create stronger bonds with the electrode surface. This results in a more stable modification over time and offers the possibility of reusing these modified electrodes. Since stability over time is a highly appreciated property for this type of sensors, this new modification process has been adopted for the preparation of all the electrodes here reported.
2.2. Olive oil samples Six Italian olives cultivar were studied: Frantoio (FRA), Leccino (LEC), Moraiolo (MOR), Nostrale (NOS), Canino (CAN), Itrana (ITR), produced in the center of Italy, Lazio region. Their detailed geographical origin is shown in Fig. 1. During three harvest seasons (2016, 2017 and 2018), 42 olive samples from specific trees of known cultivar and geographical origin were hand-picked and used to produce olive oils (Table 1). The monovarietal oils were laboratory-processed using a standardized method described in the previous work (Zappi et al., 2019). Shortly, the olive samples were hand-picked and processed within 48 h of collection. The olives were crushed at a controlled speed and temperature lower than 25 °C. The obtained olive pomace was centrifuged at fixed speed and temperature, the resulting supernatant oil was then stored in brown bottles at 4 °C. Within a few days from production, each oil sample was tested for free fatty acid content, peroxide value and polyphenols content using a CDR-FoodLab (CDR s.r.l - Ginestra Fiorentina - Firenze - ITALIA) instrument. The obtained values for all the produced oils fall inside the limits proposed by the EU for extra-virgin olive oils (acidity value < 0.8%, peroxides value < 20 meq O2/Kg oil).
2.6. Electrochemical measurements Treated samples were analyzed using cyclic voltammetry techniques (CV). A scanning range from 0.0 to 0.8 V (vs Ag/AgCl) has been used at 0.01 V/s scan rate and a 0.001 V potential step. Measurements were performed in a closed box to better control temperature and humidity. Triplicate measures were done for each sample using three different electrodes.
2.3. Sample treatment The olive oil samples were stored at 4 °C, when necessary melted and brought back to room temperature before analysis. A volume of 20 μL of oil sample was mixed in a vial with 20 μL of [Ch][Ser] ionic liquid and 10 μL of pH 7.4 phosphate buffer containing the lipase enzyme. The vial was vortex mixed until an emulsion was obtained, then it was incubated at 37 °C for 15 min to allow the release of antioxidants (Pauliukaite, Doherty, Murnaghan, & Brett, 2011). After incubation, the vial was once more mixed, and the content immediately
3. Results and discussion The acidity and the content of peroxides were measured for the different oil samples. Results are shown in Fig. 3. The acidity values (in terms of free fatty acids percentage) are similar for all the examined oils, except for an outsider: a Moraiolo 2
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Fig. 1. Map of the EVOO samples territorial origin.
cultivar oil (which is still an EVOO). As far as the peroxide content is concerned the values seem to be quite dispersed. Peroxide content can vary even for oils of the same cultivar and for oils of different cultivar. Therefore, the classification and/or discrimination of the oil variety based on these parameters remains not possible. As previously stated, the proposed sensing enzymatic method allows wide-spectrum oxidation of the antioxidant compounds that are naturally present in EVOOs and are released by the oil matrix upon hydrolytic action of lipase enzyme. For each oil sample three independent treatments were performed (see 2.3) and each resulting emulsion was analyzed with a different single-use modified electrode. For each triplet of measurements, the standard deviation values of both potential and current intensity were calculated. For potential measurements, the standard deviation was found to be between 0.0006 and 0.001; for current intensity measurements, it was between 0.181 and 0.982. The average values of the results obtained for each sample are expressed in terms of potential and current intensity of the anodic peak and have been reported in Fig. 4. As can be seen, a categorization of the oils based on cultivar can be easily performed. The potential values of the anodic peak appear to be the most correlated variable with the olive's cultivar. This suggests that the differences in the relative abundance of antioxidants in the oils differing for the cultivar constitute the basis of the classification process. Furthermore, the potential values seem to undergo only small variations for oils of different production years and obtained from the same cultivar. This finding strengthens the validity of the present approach for the definition of criteria that categorize olive oils. In Table 2 the average potential found for each cultivar has been reported. Moreover, for each cultivar, a “potential window” has been determined. This includes all the samples belonging to that cultivar.
Table 1 Monocultivar olive oil samples collected during 2016, 2017 and 2018 harvesting seasons. Samples bearing the same number have been collected from the same plant in different years. Harvest 2016 FRA_1/16 LEC_2/16 LEC_3/16 LEC_4/16 MOR_5/16 MOR_6/16 NOS_7/16
2017 FRA_1/17 LEC_2/17 LEC_3/17 MOR_5/17 MOR_6/17 NOS_7/17 CAN-8/17 FRA_9/17 FRA_10/17 FRA_11/17 FRA_12/17 FRA_13/17 FRA_14/17 FRA_15/17 ITR_16/17 LEC_17/17 LEC_18/17 MOR_19/17 NOS_20/17 NOS_21/17
2018 LEC_2/18 LEC_3/18 MOR_5/18 NOS_7/18 CAN_8/18
LEC_17/18 LEC_18/18
NOS_22/18 FRA_23/18 FRA_24/18 LEC_25/18 LEC_26/18 LEC_27/18 MOR_28/18 MOR_28/18 FRA_30/18
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Fig. 2. Comparison of electroactive areas of electrodes modified with the old (red) and new (blue) methodologies. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
On the other hand, the parameter of the current intensity of the anodic peak shows no correlation with the oil cultivar. It is supposed that the measured oxidation potential depends mostly on the nature of the antioxidant species that vary from one cultivar to another while the corresponding current intensity is determined by their total content. 4. Conclusions The proposed platform has been tested for the analysis of 42 monovarietal olive oils. A laboratory milling procedure has been used to prepare the oils from hand-picked olives of six different cultivars. Olives were collected in different zones of the Lazio region in Italy (Fig. 1, Table 1). It has been considered that numerous external factors such as climate, meteorological events, and territory orography affect the chemical composition of oils, in particular the antioxidant compounds. All the oils were tested for acidity and peroxide content, in order to verify their quality. The relative values confirmed that all the prepared oils were of extra-virgin quality, according to the EU regulations. The results obtained show that the platform allows the classification of EVOOs from different mono-cultivar on the basis of the oxidation potential of the antioxidant compounds, made available by the enzymatic hydrolysis. Since no enzyme is immobilized on the electrode, there is no risk of performance degradation after long storage periods. Furthermore, thanks to the improved modification methodology, the platform has been demonstrated to be stable up to 30 days after the deposition of the nanomaterials/ionic liquid mixture. The disposable and single-use nature of the platform assures that the fouling phenomena occurring at the electrode surface do not represent a concern. It is important to underline that the method does not involve organic solvents during the pretreatment of the sample. The materials used to modify the electrode are easy to dispose of and the proposed platform affords results quickly and easily. As such, the platform is suitable for field screening analyses. A classification system for single-cultivar extra-virgin oils has been proposed. No concentration of specific analytes has been measured, resulting in no need to calibrate the platform response with respect to different antioxidants. In particular, a costeffective sensor useable by untrained personnel has been developed. The sensor here described is able to identify the olive cultivar in a quick and reliable way. Finally, the antioxidant class recognized by the proposed platform is not exclusive to olive oils but can be found in numerous other food matrixes (Acero, Gradillas, Beltran, García, & Muñoz Mingarro, 2019). Therefore, it is possible to envisage that this methodology can be applied to perform screening analyses on other food categories.
Fig. 3. Acidity determination (A) and Peroxides quantification (B) on olive oil samples. Different colors refer to different olive oil samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
The various cultivars produce oils presenting a potentiometric response quite specific for each. Indeed, the ranges of potential values for each cultivar are well separated and do not present overlap. This feature minimizes the risks of assigning the incorrect category to an oil sample with unknown provenience. The cyclic voltammetries of the oils obtained from each of the four identified categories are reported in Fig. 5. 4
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Fig. 4. Results obtained for each oil sample produced from olives harvested in 2016 (red symbols), 2017 (blue symbols) and 2018 (green symbols). Different symbol shapes indicate different olives cultivar. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Declaration of interests
Table 2 Average potential and the potential window for each examined cultivar. Cultivar
Potential average ± potential window (V)
CAN FRA ITR LEC MOR NOS
0.193 0.238 0.182 0.250 0.210 0.225
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
± 0.003 ± 0.003 ± 0.003 ± 0.002
Acknowledgement This work was supported by the Italian Education Ministry Ministero dell'Istruzione, dell'Universita e della Ricerca, Italy (MIUR) Protocol number: RM11715C53850E54 titled: “Sviluppo di metodi di
Fig. 5. Cyclic voltammetries of the oils obtained from each of the four identified categories: Leccino (black); Moraiolo (blue); Frantoio (red); Nostrale (green). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 5
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screening basati su elettrodi enzimatici modificati da liquidi ionici come biosensori amperometrici e analisi di dati di raggi X in fluorescenza e diffrazione, rivolti alla classificazione ed al controllo della qualità degli oli di oliva”.
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