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Hue parameter fluorescence identification of edible oils with a smartphone Aron Hakonen, and Jonathon E Beves ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.8b00409 • Publication Date (Web): 05 Oct 2018 Downloaded from http://pubs.acs.org on October 8, 2018
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Hue parameter fluorescence identification of edible oils with a smartphone Aron Hakonen*† and Jonathon E. Beves‡ †. ‡.
Sensor Visions AB, Legendgatan 116, 422 55 Hisings Backa, Sweden, e-mail:
[email protected] School of Chemistry, UNSW Australia, Sydney, Australia
Supporting Information Available: The following files are available free of charge. Supporting info.pdf. Method and results ABSTRACT: Food-fraud can be highly lucrative and high accuracy authentication of various foodstuffs is becoming essential. Olive oil is one of the most investigated food matrices, due to its high price and low production globally, with recent food-fraud examples showing little or no high quality olive oil in the tested oils. Here a simple method using a 405 nm-LED flashlight and a smartphone is developed for edible oil authentication. Identification is fingerprinted by intrinsic fluorescent compounds in the oils, such as chlorophylls and poly-phenols. This study uses the hue parameter of HSV-colorspace to authenticate 24 different edible oils of nine different types and 15 different brands. For extra virgin olive oil all the nine samples are well separated from the other oil samples. The rest of the samples were also well typedistinguished by the hue parameter, which is complemented by hue-histogram analysis. This opens up opportunities for low-cost and high-throughput smartphone field-testing of edible oils on all levels of the production and supply chain. Keywords: Hue parameter fluorescence, Olive oil, Edible oil, Authentication method, Histogram analysis
Food fraud and counterfeiting within the multi-billion dollar food industry are a major problem with significant examples concerning dairy products, wine, fruits, meats, cocoa, vita1 mins and fats. In part the problem originated from the 1 globalization of the food market. In extreme situations food fraud can be of serious danger for consumers, leading to death in cases such as in the melamine in milk powder scan2 3 dal in 2008. Edible/dietary oils, and in particular olive oil which is one of the three mostly investigated food matrices 4 worldwide. Commonly complex measurement techniques are used for authentication of edible oils, for example 2D5 6 3b 7 NMR, qPCR, GC-MS , MALDI and terahertz spectrosco8 9 py, often in conjunction with chemometrics. Fluorescence assessment of edible oils, including olive oil authentication, 10 has been extensively explored. However, in all reported examples spectrometers are required often together with multi-variate chemometrics. Different oils naturally contain different intrinsic fluorescent molecules, for example in fish oils alfatocoferol (vitamin E) and small amounts of polyaro11 matic hydrocarbons (PAHs) have been found, while some
Fig. 1. Principles of smartphone measurements and analysis. 405 nm LED excitation wavelength and sample vial volume of 3.5 ml was used. Images were sent to a computer and processed using the open software ImageJ. of the major fluorescent species in olive oils are chloro12 phylls, poly-phenols and alfatocopherol. In today’s modern world more or less everybody carries a smartphone, which in many cases can act as a powerful 13 and convenient analytical tool/accessory. Camera based optical chemical sensors have been demonstrated for vari14 15 ous analytes, including for pH, ammonium and ammo16 nia. The hue parameter of HSV digital color-space was introduced as a quantitative tool for optical chemical sen17 sors in 2009. HSV (Hue, Saturation, Value) is a cylindrical color-space commonly used in digital imaging. Where hue (H) corresponds to the CCD/CMOS color, saturation (S) represents (max – min) RGB channel and value (V) is the maximum channel value. Hue parameter fluorescence measurements was introduced by pH measurements using a fluorescent probe, LED light-source and a simple digital camera in a system that demonstrated hue quantification was superior to ratiometric and intensity-based fluorescence
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case the hue value), and by inspection of a histogram the viewer can quickly evaluate the entire tonal distribution at a glance. Some quantitative data easily extracted from the histogram data are mean, standard deviation, min, max and mode (including counts). Most are self-explanatory, but to be clear the min and max are the lowest and highest hue values in the image, and mode is the peak position value (i.e. the most popular value). The hue histogram data can be used to set quantitative intervals for authentication.
Fig. 2. Hue spectrum images and histograms of a) Extra virgin olive oil; b) Cold pressed rapeseed oil; c) Sunflower oil, o processed; d) Corn oil, processed. 90 fluorescence measured with a smartphone in the Lego prototype. 405 nm LED excitation
A range of common edible oils (24 in total) were selected for analysis: ten olive oil samples from eight different brands; nine extra virgin olive oils (EVOO); one mixed of processed olive oil (70 %) and EVOO (30 %); four rapeseed oils, of which three were cold pressed (Extra virgin, EV) and one was processed; two extra virgin avocado oils of the same brand; two sunflower oils of different brands; two fish oils of the same brand; and one of each of sesame, corn, walnut and linseed oils were included in the analysis. Detailed information about the samples can be found in the supporting information.
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measurements. Other recent examples of the use of the hue parameter as an analytical tool include nanoplasmon19 20 21 ics, DNA detection, polymer stretch measurements and 22 as a titration tool for metal ions. The HSV, RGB and other digital image parameters can also be applied in multi-variate 23 analysis. Significantly, optical chemical sensors typically rely on a probe, sensor film or reagent as the signal transducer. This study will instead exploit the intrinsic fluorescence of the natural compounds in the samples. This approach makes this measurement method highly attractive, simple, noninvasive and non-destructive. This simple method was developed using common smartphones and cheap 405 nm-LED flashlights. No optical filters, gratings or prisms were used. Also, no chemical additives such as solvents, reagents or probes were needed or used. Devices and prototypes were built with Lego™ bricks or similar, and could also be easily 3D printed. Figure 1 demonstrates the measurement principles used in this study on edible/dietary oils. A Lego prototype was built with a plastic sample bottle (3.5 ml) mounted close to a smartphone camera lens and a 405 nm LED flashlight mounted at 90 degrees to the camera hole. Full zoom (4x) was used to minimize edge scattering effects. Images were transferred to a computer for image transformations and analysis was performed with the open software ImageJ from 24 National Institutes of health (NIH). As illustrated in Figure 1 the RGB image from the smartphone is transformed to a hue image according to the HSV colorspace equations (Supporting Information). A circular colorspace “spectrum” is chosen among look-up-tables (LUTs) within ImageJ. Hue histograms are calculated from these hue spectrum images. An image histogram typically acts as a graphical representation of the tonal distribution in a digital image. The histogram plots the number of pixels for each tonal value (in our
Large fluorescence differences are found between extra virgin olive oil (EVOO) and most common edible/cooking oils. Figure 2 displays hue (spectrum) images and histograms of one of each of: EVOO, cold pressed rapeseed oil, sunflower oil and corn oil. With such major differences hue histogram analysis is more or less redundant. However, to illustrate the principles used here the corresponding histograms are also shown in Figure 2. A larger scale reproducibility and proof of authenticity concept was performed using 24 different oils, of which ten were olive oils, six non-consecutive replicates were performed on each sample. These data are displayed in Figure 3. Average hue values and standard deviations calculated from the six replicative determinations in Figure 3 are tabulated in Table S2 in decreasing hue order. A key finding is the almost perfect organization of the different oil types, indicating that even without further analysis this method is a strong proof-of-concept as a food-stuff oil authentication tool. All the EVOO samples had mean hue values (238.9– 242.1), significantly (several standard deviations) higher than the olive mix (233), avocado EV (232), sunflower oils (161-172), rapeseed (152), corn (143), walnut (136), sesame (106) and fish oils (99). This importantly allows simple discrimination between the most valuable EVOOs and cheaper oils. Sample RGB images (Fig. S1), hue spectrum images (Fig. S2) and hue spectrum histograms (Fig. S3) are presented for all 24 samples (one replicate each) in the supporting information. While the hue parameter itself provides a direct quantitative means to identify the oils, hue histogram analysis provide a qualitative and supporting mode. However, hue analysis has the potential to become fully quantitative by multivariate analysis, although this was considered outside the scope of this study. Some of the oil samples could not simply be discriminated by the hue parameter alone, and hue histogram analysis provided valuable support (Fig. S3). Note, however, that the tonal distribution
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Fig. 3 A bar chart showing hue values for the 24 edible oil samples (each bar represents one sample image replicate averaged over 6 Mpixels). Six replicate images were made on each sample. Samples were acquired in the sample order defined in table S1, first replicate on all sample oils before the next replicate and so on.
demonstrated here in the experimental fluorescence images, and consequent hue histograms, is not the same as the visible homogeneous edible oil tone seen in normal ambient o light. Within this work the fluorescence detected at 90 from the excitation light, which is the standard mode of fluorescence measurement for liquids. This can result in inner-filter effects caused by particles, the optical density and fluorophore/molecule concentration of the liquid sample. This was initially thought to be a possible drawback giving inhomogeneous sample images. However, it turns out that one of the main findings of this study is that this actually provides additional information about the samples which can be used to fingerprint different oils. Fish, sesame and linseed oils have similar hue values but are quite well identified by their hue- histogram fingerprints (Supporting Fig. S3). Where the two fish oil hue histograms are very much alike, the sesame oil is somewhat similar but with a general shift toward higher hue values and the linseed oil histogram are completely different showing almost inverse hue values.
The more challenging discriminations come with the EVOOs, which almost seem impossible to distinguish between brands. However, for the EVOOs it may be interesting to notice that the two cheapest EVOOs are in the end points of the total EVOO hue range tested here, and the most expensive ones, are in the middle of the range. So maybe there is something to be found in more detailed hue histogram analysis. Figure 4 shows the histograms of EVOOs 1, 2 and 7. All six replicates (for each) are displayed (4a) and Fig. 4b is showing the average histograms for the three oils. Some potential for hue histogram identification of different EVOOs seem plausible, and EVOO 7 could possible even be an example of fraud EVOO. This cheap EVOO has been in the spotlight previously for food authenticity issues. For reference some testing was performed on the sam25 ples using a practical handheld Raman instrument, with resulting 80% authentication accuracy between oil types, using the intrinsic (multivariate) algorithms. Olive oil mix was readily mistaken for EVOO and vice versa, while EVOO 7 were overall not identified as an EVOO.
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study – to authenticate edible oils using a reliable, cheap and versatile method.
ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. The following file is available: Supporting info.pdf. The file content is describing methods and results.
b
Notes The authors declare no competing financial interests.
REFERENCES
Fig. 4 Detailed hue histogram analysis of EVOOs 1, 2 and 7. Histograms of the six replicates of the three oils (a) and their average hue histograms (b). A major implication of this work is that hue-based fluorescence of edible oils is ideal for simple app-based analysis. Cold pressed and processed oils are easy to differentiate, most vegetable oils can be readily differentiated from fishoils. Libraries will be built with more tested oils and an app will be developed feasibly with cloud-connected analytical and developer tools. Fluorescent species in the oils will be assessed with surface-enhanced Raman scattering on ultra-sensitive substrates previously used for common 26 25, 27 dyes and difficult threat molecules. Raman and SERS measurements on the oils may also be a powerful complement to the hue-fluorescence method for authentication. For in situ flow measurements a system has been developed previously with commercial parts for ratiometric pH meas28 urement with a probe. That system could be used, in conjunction with hue analysis, for on-line quality control in edible oil production. These measurement principles may be universal and may be applied on multiple sample matrixes. Some examples in the food industry could be: wine, beer, honey, whiskey, proteins, amino acids, vitamins and other nutritional supplements. In conclusion, vast potential of hue based fluorescence identification of many common edible oils with simple instrumentation has been recognized here. In particular, this study focused on “fraud” recurrent extra virgin olive oils. Hue values alone demonstrated easy access discrimination of EVOOs from most other edible oils, and in conjunction with hue histogram analysis even the EVOOs can be selected for different brands. These bulk measurements allow the mixture of fluorescent components to be authenticated as being from a particular type of oil, leading to the goal of this
1. Danezis, G. P.; Tsagkaris, A. S.; Camin, F.; Brusic, V.; Georgiou, C. A., Food authentication: Techniques, trends & emerging approaches. TrAC Trends in Analytical Chemistry 2016, 85, 123-132. 2. Pei, X.; Tandon, A.; Alldrick, A.; Giorgi, L.; Huang, W.; Yang, R., The China melamine milk scandal and its implications for food safety regulation. Food Policy 2011, 36 (3), 412-420. 3. (a) Zhang, L.; Li, P.; Sun, X.; Mao, J.; Ma, F.; Ding, X.; Zhang, Q., One-class classification based authentication of peanut oils by fatty acid profiles. RSC Advances 2015, 5 (103), 85046-85051; (b) Xu, B.; Zhang, L.; Wang, H.; Luo, D.; Li, P., Characterization and authentication of four important edible oils using free phytosterol profiles established by GCGC-TOF/MS. Analytical Methods 2014, 6 (17), 6860-6870; (c) Tan, J.; Li, R.; Jiang, Z.-T.; Tang, S.-H.; Wang, Y.; Shi, M.; Xiao, Y.-Q.; Jia, B.; Lu, T.-X.; Wang, H., Synchronous front-face fluorescence spectroscopy for authentication of the adulteration of edible vegetable oil with refined used frying oil. Food Chemistry 2017, 217, 274-280; (d) FerreiroGonzález, M.; Barbero, G. F.; Álvarez, J. A.; Ruiz, A.; Palma, M.; Ayuso, J., Authentication of virgin olive oil by a novel curve resolution approach combined with visible spectroscopy. Food Chemistry 2017, 220, 331-336; (e) Jiménez-Sanchidrián, C.; Ruiz, J. R., Use of Raman spectroscopy for analyzing edible vegetable oils. Applied Spectroscopy Reviews 2016, 51 (5), 417-430. 4. Esslinger, S.; Riedl, J.; Fauhl-Hassek, C., Potential and limitations of non-targeted fingerprinting for authentication of food in official control. Food Research International 2014, 60, 189-204. 5. Gouilleux, B.; Marchand, J.; Charrier, B.; Remaud, G. S.; Giraudeau, P., High-throughput authentication of edible oils with benchtop Ultrafast 2D NMR. Food Chemistry 2018, 244, 153-158. 6. Alonso-Rebollo, A.; Ramos-Gómez, S.; Busto, M. D.; Ortega, N., Development and optimization of an efficient qPCR system for olive authentication in edible oils. Food Chemistry 2017, 232, 827-835. 7. Ng, T.-T.; So, P.-K.; Zheng, B.; Yao, Z.-P., Rapid screening of mixed edible oils and gutter oils by matrixassisted laser desorption/ionization mass spectrometry. Anal. Chim. Acta 2015, 884, 70-76. 8. Zhan, H.; Xi, J.; Zhao, K.; Bao, R.; Xiao, L., A spectralmathematical strategy for the identification of edible and
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ACS Sensors swill-cooked dirty oils using terahertz spectroscopy. Food Control 2016, 67, 114-118. 9. (a) Rohman, A., The use of infrared spectroscopy in combination with chemometrics for quality control and authentication of edible fats and oils: A review. Applied Spectroscopy Reviews 2017, 52 (7), 589-604; (b) Rohman, A.; Che Man, Y. b.; Ismail, A.; Hashim, P., FTIR spectroscopy coupled with chemometrics of multivariate calibration and discriminant analysis for authentication of extra virgin olive oil. International Journal of Food Properties 2017, 20 (sup1), S1173-S1181. 10. (a) Poulli, K. I.; Mousdis, G. A.; Georgiou, C. A., Rapid synchronous fluorescence method for virgin olive oil adulteration assessment. Food Chemistry 2007, 105 (1), 369375; (b) Guzmán, E.; Baeten, V.; Pierna, J. A. F.; García-Mesa, J. A., Evaluation of the overall quality of olive oil using fluorescence spectroscopy. Food Chemistry 2015, 173, 927934; (c) Mbesse Kongbonga, Y.; Ghalila, H.; Majdi, Y.; Mbogning Feudjio, W.; Ben Lakhdar, Z., Investigation of Heat-Induced Degradation of Virgin Olive Oil Using Front Face Fluorescence Spectroscopy and Chemometric Analysis. Journal of the American Oil Chemists' Society 2015, 92 (10), 1399-1404. 11. (a) Pena, E. A.; Ridley, L. M.; Murphy, W. R.; Sowa, J. R.; Bentivegna, C. S., Detection of polycyclic aromatic hydrocarbons (PAHs) in raw menhaden fish oil using fluorescence spectroscopy: Method development. Environmental Toxicology and Chemistry 2015, 34 (9), 19461958; (b) Bentivegna, C. S.; DeFelice, C. R.; Murphy, W. R., Excitation–emission matrix scan analysis of raw fish oil from coastal New Jersey menhaden collected before and after Hurricane Sandy. Marine Pollution Bulletin 2016, 107 (2), 442-452. 12. Kyriakidis, N. B.; Skarkalis, P., Fluorescence Spectra Measurement of Olive Oil and Other Vegetable Oils. Journal of AOAC International 2000, 83 (6), 1435-1439. 13. (a) Dutta, S.; Saikia, G. P.; Sarma, D. J.; Gupta, K.; Das, P.; Nath, P., Protein, enzyme and carbohydrate quantification using smartphone through colorimetric digitization technique. Journal of Biophotonics 2017, 10 (5), 623-633; (b) Nie, H.; Wang, W.; Li, W.; Nie, Z.; Yao, S., A colorimetric and smartphone readable method for uracilDNA glycosylase detection based on the target-triggered formation of G-quadruplex. Analyst 2015, 140 (8), 27712777; (c) Kanakasabapathy, M. K.; Sadasivam, M.; Singh, A.; Preston, C.; Thirumalaraju, P.; Venkataraman, M.; Bormann, C. L.; Draz, M. S.; Petrozza, J. C.; Shafiee, H., An automated smartphone-based diagnostic assay for point-of-care semen analysis. Science Translational Medicine 2017, 9 (382); (d) Hong, J. I.; Chang, B.-Y., Development of the smartphonebased colorimetry for multi-analyte sensing arrays. Lab on a Chip 2014, 14 (10), 1725-1732; (e) Dong, C.; Wang, Z.; Zhang, Y.; Ma, X.; Iqbal, M. Z.; Miao, L.; Zhou, Z.; Shen, Z.; Wu, A., High-Performance Colorimetric Detection of Thiosulfate by Using Silver Nanoparticles for SmartphoneBased Analysis. ACS Sens. 2017, 2 (8), 1152-1159. 14. Hakonen, A.; Hulth, S.; Dufour, S., Analytical performance during ratiometric long-term imaging of pH in bioturbated sediments. Talanta 2010, 81 (4-5), 1393-1401. 15. Hakonen, A.; Stromberg, N., Plasmonic nanoparticle interactions for high-performance imaging fluorosensors. Chem. Commun. 2011, 47 (12), 3433-3435.
16. Stromberg, N.; Hakonen, A., Plasmophore sensitized imaging of ammonia release from biological tissues using optodes. Anal. Chim. Acta 2011, 704 (1-2), 139145. 17. antrell . renas . . de rbe- ay I. apit n-Vallvey, L. F., Use of the Hue Parameter of the Hue, Saturation, Value Color Space As a Quantitative Analytical Parameter for Bitonal Optical Sensors. Analytical Chemistry 2009, 82 (2), 531-542. 18. Hakonen, A.; Beves, J. E.; Stromberg, N., Digital colour tone for fluorescence sensing: a direct comparison of intensity, ratiometric and hue based quantification. Analyst 2014, 139 (14), 3524-3527. 19. King, N. S.; Liu, L.; Yang, X.; Cerjan, B.; Everitt, H. O.; Nordlander, P.; Halas, N. J., Fano Resonant Aluminum Nanoclusters for Plasmonic Colorimetric Sensing. ACS Nano 2015, 9 (11), 10628-10636. 20. Dziuba, D.; Pospisil, P.; Matyasovsky, J.; Brynda, J.; Nachtigallova, D.; Rulisek, L.; Pohl, R.; Hof, M.; Hocek, M., Solvatochromic fluorene-linked nucleoside and DNA as color-changing fluorescent probes for sensing interactions. Chemical Science 2016, 7 (9), 5775-5785. 21. Battisti, A.; Minei, P.; Pucci, A.; Bizzarri, R., Huebased quantification of mechanochromism towards a costeffective detection of mechanical strain in polymer systems. Chem. Commun. 2017, 53 (1), 248-251. 22. Zhai, J.; Xie, X.; Cherubini, T.; Bakker, E., IonophoreBased Titrimetric Detection of Alkali Metal Ions in Serum. ACS Sens. 2017, 2 (4), 606-612. 23. Macedo dos Santos, P.; Pereira-Filho, E. R., Digital image analysis - an alternative tool for monitoring milk authenticity. Analytical Methods 2013, 5 (15), 3669-3674. 24. Schneider, C. A.; Rasband, W. S.; Eliceiri, K. W., NIH Image to ImageJ: 25 years of image analysis. Nat Meth 2012, 9 (7), 671-675. 25. Hakonen, A.; Wang, F. C.; Andersson, P. O.; Wingfors, H.; Rindzevicius, T.; Schmidt, M. S.; Soma, V. R.; Xu, S. C.; Li, Y. Q.; Boisen, A.; Wu, H. A., Hand-Held Femtogram Detection of Hazardous Picric Acid with Hydrophobic Ag Nanopillar SERS Substrates and Mechanism of Elasto-Capillarity. ACS Sens. 2017, 2 (2), 198-202. 26. (a) Hakonen, A.; Svedendahl, M.; Ogier, R.; Yang, Z. J.; Lodewijks, K.; Verre, R.; Shegai, T.; Andersson, P. O.; Kall, M., Dimer-on-mirror SERS substrates with attogram sensitivity fabricated by colloidal lithography. Nanoscale 2015, 7 (21), 9405-9410; (b) Wu, K.; Rindzevicius, T.; Schmidt, M. S.; Mogensen, K. B.; Hakonen, A.; Boisen, A., Wafer-Scale Leaning Silver Nanopillars for Molecular Detection at Ultra-Low Concentrations. J. Phys. Chem. C 2015, 119 (4), 2053-2062. 27. Hakonen, A.; Rindzevicius, T.; Schmidt, M. S.; Andersson, P. O.; Juhlin, L.; Svedendahl, M.; Boisen, A.; Kall, M., Detection of nerve gases using surface-enhanced Raman scattering substrates with high droplet adhesion. Nanoscale 2016, 8 (3), 1305-1308. 28. Hakonen, A.; Anderson, L. G.; Engelbrektsson, J.; Hulth, S.; Karlson, B., A potential tool for high-resolution monitoring of ocean acidification. Anal. Chim. Acta 2013, 786, 1-7.
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