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Use of Electronic Nose and Tongue to Track Freshness of Cherry Tomatoes Squeezed for Juice Consumption: Comparison of Different Sensor Fusion Approaches

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Abstract

Fruits freshness is relatively easy to authenticate from their morphological characteristics while the act of processing fruits into juices makes it difficult to track/identify their freshness. Eight datasets, extracted from an e-nose and an e-tongue, and six sensor fusion approaches using both instruments, were applied to detect 100 % juices squeezed from cherry tomatoes with different post-harvest storage times (ST). Discrimination of the juices was mainly performed by canonical discriminant analysis (CDA) and library support vector machines (Lib-SVM). Tracking and prediction of physicochemical qualities (pH, soluble solids content (SSC), Vitamin C (VC), and firmness) of the fruit were performed using principle components regression (PCR). All eight datasets presented good classification results with classifiers trained by e-tongue dataset and fusion dataset 2 (stepwise selection) presented the best classification performances. Though quality regression models trained by either e-nose or e-tongue dataset were not robustness enough, sensor fusion approaches make it possible to build more robust prediction models that can correctly predict quality indices for a totally new juice sample. This study indicates the potential for tracking quality/freshness of fruit squeezed for juice consumption using the e-nose and e-tongue, and that sensor fusion approach would be better than individual utilization only if proper fusion approaches are used.

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References

  • Baldwin, E., Scott, J., Einstein, M., Malundo, T., Carr, B., Shewfelt, R., & Tandon, K. (1998). Relationship between sensory and instrumental analysis for tomato flavor. Journal of the American Society for Horticultural Science, 123(5), 906–915.

    CAS  Google Scholar 

  • Beghi, R., Spinardi, A., Bodria, L., Mignani, I., & Guidetti, R. (2013). Apples nutraceutic properties evaluation through a visible and near-infrared portable system. Food and Bioprocess Technology, 6(9), 2547–2554.

    Article  CAS  Google Scholar 

  • Berna, A. Z., Lammertyn, J., Saevels, S., Natale, C. D., & Nicolaı̈, B. M. (2004). Electronic nose systems to study shelf life and cultivar effect on tomato aroma profile. Sensors and Actuators B: Chemical, 97(2), 324–333.

    Article  CAS  Google Scholar 

  • Beullens, K., Kirsanov, D., Irudayaraj, J., Rudnitskaya, A., Legin, A., Nicolaï, B. M., & Lammertyn, J. (2006). The electronic tongue and ATR–FTIR for rapid detection of sugars and acids in tomatoes. Sensors and Actuators B: Chemical, 116(1), 107–115.

    Article  CAS  Google Scholar 

  • Bleibaum, R. N., Stone, H., Tan, T., Labreche, S., Saint-Martin, E., & Isz, S. (2002). Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Quality and Preference, 13(6), 409–422.

    Article  Google Scholar 

  • Brudzewski, K., Osowski, S., & Markiewicz, T. (2004). Classification of milk by means of an electronic nose and SVM neural network. Sensors and Actuators B: Chemical, 98(2), 291–298.

    Article  CAS  Google Scholar 

  • Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.

    Google Scholar 

  • Ciosek, P., Brzózka, Z., & Wróblewski, W. (2004). Classification of beverages using a reduced sensor array. Sensors and Actuators B: Chemical, 103(1), 76–83.

    Article  CAS  Google Scholar 

  • Cole, M., Covington, J. A., & Gardner, J. W. (2011). Combined electronic nose and tongue for a flavour sensing system. Sensors and Actuators B: Chemical, 156(2), 832–839.

    Article  CAS  Google Scholar 

  • Cosio, M. S., Ballabio, D., Benedetti, S., & Gigliotti, C. (2007). Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue. Food Chemistry, 101(2), 485–491.

    Article  CAS  Google Scholar 

  • Di Natale, C., Paolesse, R., Macagnano, A., Mantini, A., D’Amico, A., Legin, A., Lvova, L., Rudnitskaya, A., & Vlasov, Y. (2000). Electronic nose and electronic tongue integration for improved classification of clinical and food samples. Sensors and Actuators B: Chemical, 64(1), 15–21.

    Article  Google Scholar 

  • Di Natale, C., Macagnano, A., Martinelli, E., Paolesse, R., Proietti, E., & D’Amico, A. (2001). The evaluation of quality of post-harvest oranges and apples by means of an electronic nose. Sensors and Actuators B: Chemical, 78(1), 26–31.

    Article  Google Scholar 

  • Escuder-Gilabert, L., & Peris, M. (2010). Review: Highlights in recent applications of electronic tongues in food analysis. Analytica Chimica Acta, 665(1), 15–25.

    Article  CAS  Google Scholar 

  • Fallik, E., Alkali-Tuvia, S., Horev, B., Copel, A., Rodov, V., Aharoni, Y., Ulrich, D., & Schulz, H. (2001). Characterisation of ‘Galia’melon aroma by GC and mass spectrometric sensor measurements after prolonged storage. Postharvest Biology and Technology, 22(1), 85–91.

    Article  CAS  Google Scholar 

  • Faria, M., Magalhães, A., Nunes, M., & Oliveira, M. (2013). High resolution melting of trnL amplicons in fruit juices authentication. Food Control, 33(1), 136–141.

    Article  CAS  Google Scholar 

  • Gallardo, J., Alegret, S., & del Valle, M. (2005). Application of a potentiometric electronic tongue as a classification tool in food analysis. Talanta, 66(5), 1303–1309.

    Article  CAS  Google Scholar 

  • Gardner, J. W., & Bartlett, P. N. (1994). A brief history of electronic noses. Sensors and Actuators B: Chemical, 18(1), 210–211.

    Article  Google Scholar 

  • GB/T 6195–1986 (1986). Determination of vitamin C in vegetables and fruits (2,6-dichloro-indophenol titration method). National Standard of the People’s Republic of China

  • Gobbi, E., Falasconi, M., Concina, I., Mantero, G., Bianchi, F., Mattarozzi, M., Musci, M., & Sberveglieri, G. (2010). Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: an emerging diagnostic tool. Food Control, 21(10), 1374–1382.

    Article  Google Scholar 

  • Gomez, A. H., Wang, J., Hu, G., & Pereira, A. G. (2008). Monitoring storage shelf life of tomato using electronic nose technique. Journal of Food Engineering, 85(4), 625–631.

    Article  Google Scholar 

  • Gómez-Sanchis, J., Blasco, J., Soria-Olivas, E., Lorente, D., Escandell-Montero, P., Martínez-Martínez, J., Martínez-Sober, M., & Aleixos, N. (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology, 82, 76–86.

    Article  Google Scholar 

  • Hong, X., Wang, J., & Hai, Z. (2012). Discrimination and prediction of multiple beef freshness indexes based on electronic nose. Sensors and Actuators B: Chemical, 161(1), 381–389.

    Article  CAS  Google Scholar 

  • Jodas, D. S., Marranghello, N., Pereira, A. S., & Guido, R. C. (2013). Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations. Procedia Computer Science, 18, 240–249.

    Article  Google Scholar 

  • Kantor, D. B., Hitka, G., Fekete, A., & Balla, C. (2008). Electronic tongue for sensing taste changes with apricots during storage. Sensors and Actuators B: Chemical, 131(1), 43–47.

    Article  CAS  Google Scholar 

  • Legin, A., Rudnitskaya, A., Vlasov, Y., Di Natale, C., Davide, F., & D’Amico, A. (1997). Tasting of beverages using an electronic tongue. Sensors and Actuators B: Chemical, 44(1), 291–296.

    Article  CAS  Google Scholar 

  • Pan, S., Iplikci, S., Warwick, K., & Aziz, T. Z. (2012). Parkinson’s disease tremor classification—a comparison between support vector machines and neural networks. Expert Systems with Applications, 39(12), 10764–10771.

    Article  Google Scholar 

  • Ping, W., Yi, T., Haibao, X., & Farong, S. (1997). A novel method for diabetes diagnosis based on electronic nose. Biosensors and Bioelectronics, 12(9), 1031–1036.

    Article  Google Scholar 

  • Raffo, A., Leonardi, C., Fogliano, V., Ambrosino, P., Salucci, M., Gennaro, L., Bugianesi, R., Giuffrida, F., & Quaglia, G. (2002). Nutritional value of cherry tomatoes (Lycopersicon esculentum cv. Naomi F1) harvested at different ripening stages. Journal of Agricultural and Food Chemistry, 50(22), 6550–6556.

    Article  CAS  Google Scholar 

  • Reinhard, H., Sager, F., & Zoller, O. (2008). Citrus juice classification by SPME-GC-MS and electronic nose measurements. LWT--Food Science and Technology, 41(10), 1906–1912.

    Article  CAS  Google Scholar 

  • Roussel, S., Bellon-Maurel, V., Roger, J.-M., & Grenier, P. (2003). Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry. Journal of Food Engineering, 60(4), 407–419.

    Article  Google Scholar 

  • Rudnitskaya, A., Kirsanov, D., Legin, A., Beullens, K., Lammertyn, J., Nicolaï, B. M., & Irudayaraj, J. (2006). Analysis of apples varieties—Comparison of electronic tongue with different analytical techniques. Sensors and Actuators B: Chemical, 116(1), 23–28.

    Article  CAS  Google Scholar 

  • Schaller, E., Bosset, J. O., & Escher, F. (1998). ‘Electronic noses’ and their application to food. LWT--Food Science and Technology, 31(4), 305–316.

    Article  CAS  Google Scholar 

  • Shaw, P. E., Rouseff, R. L., Goodner, K. L., Bazemore, R., Nordby, H. E., & Widmer, W. W. (2000). Comparison of headspace GC and electronic sensor techniques for classification of processed orange juices. LWT--Food Science and Technology, 33(5), 331–334.

    Article  CAS  Google Scholar 

  • Szöllősi, D., Dénes, D. L., Firtha, F., Kovács, Z., & Fekete, A. (2012). Comparison of six multiclass classifiers by the use of different classification performance indicators. Journal of Chemometrics, 26(3–4), 76–84.

    Article  Google Scholar 

  • Torri, L., Sinelli, N., & Limbo, S. (2010). Shelf life evaluation of fresh-cut pineapple by using an electronic nose. Postharvest Biology and Technology, 56(3), 239–245.

    Article  CAS  Google Scholar 

  • Tudu, B., Shaw, L., Jana, A., Bhattacharyya, N., & Bandyopadhyay, R. (2012). Instrumental testing of tea by combining the responses of electronic nose and tongue. Journal of Food Engineering, 110(3), 356–363.

    Article  Google Scholar 

  • Unay, D., & Gosselin, B. (2006). Automatic defect segmentation of ‘Jonagold’apples on multi-spectral images: a comparative study. Postharvest Biology and Technology, 42(3), 271–279.

    Article  Google Scholar 

  • USDA. (1997). United States standards for grades of fresh tomatoes. United States Department of Agriculture

  • Wei, Z., & Wang, J. (2011). Detection of antibiotic residues in bovine milk by a voltammetric electronic tongue system. Analytica Chimica Acta, 694(1), 46–56.

    Article  CAS  Google Scholar 

  • Wei, Z., Wang, J., & Liao, W. (2009). Technique potential for classification of honey by electronic tongue. Journal of Food Engineering, 94(3), 260–266.

    Article  Google Scholar 

  • Winquist, F., Wide, P., & Lundström, I. (1997). An electronic tongue based on voltammetry. Analytica Chimica Acta, 357(1–2), 21–31.

    Article  CAS  Google Scholar 

  • Zhang, H., Wang, J., & Ye, S. (2008). Prediction of soluble solids content, firmness and pH of pear by signals of electronic nose sensors. Analytica Chimica Acta, 606(1), 112–118.

    Article  CAS  Google Scholar 

  • Zhang, H., Wang, J., Ye, S., & Chang, M. (2012). Application of electronic nose and statistical analysis to predict quality indices of peach. Food and Bioprocess Technology, 5(1), 65–72.

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge the financial support of the National Key Technology R&D Program 2012BAD29B02-4 and the Chinese National Foundation of Nature and Science Projects 31071548 and 31201368.

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Authors and Affiliations

  1. Department of Biosystems Engineering, Zhejiang University, 688 Yuhangtang Road, Hangzhou, 310058, People’s Republic of China

    Xuezhen Hong & Jun Wang

  2. College of Quality & Safety Engineering, China Jiliang University, Hangzhou, 310018, People’s Republic of China

    Xuezhen Hong

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  1. Xuezhen Hong
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  2. Jun Wang
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Correspondence to Jun Wang.

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Hong, X., Wang, J. Use of Electronic Nose and Tongue to Track Freshness of Cherry Tomatoes Squeezed for Juice Consumption: Comparison of Different Sensor Fusion Approaches. Food Bioprocess Technol 8, 158–170 (2015). https://doi.org/10.1007/s11947-014-1390-y

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  • DOI: https://doi.org/10.1007/s11947-014-1390-y

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