https://doi.org/10.4081/jae.2022.1389
E-nose: a low-cost fruit ripeness monitoring system
Published: 3 November 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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All fruits emit some specific volatile organic compounds (VOCs) during their life cycle. These VOCs have specific characteristics; by using these characteristics fruit ripening stage can be identified with-out destroying the fruit. In this study, an application-specific electron-ic nose device was designed for monitoring fruit ripeness. The proposed electronic nose is cost-efficient and does not require any modern or costly laboratory instruments. Metal oxide semiconductor (MOS) sensors were used for designing the pro-posed electronic nose. These MOS sensors were integrated with a microcontroller board to detect and extract the meaningful fea-tures of VOCs, and an artificial neural network (ANN) algorithm was used for pattern recognition. Measurements were done with apples, bananas, oranges, grapes, and pomegranates. The designed electronic nose proved reliable in classifying fruit samples into three different fruit ripening stages (unripe, ripe, and over-ripe) with high precision and recall. Furthermore, the proposed elec-tronic nose performed uniformly on all three fruit ripening stages with an average accuracy of ≥95%.
Adak M.F., Yumusak N. 2016. Classification of E-nose aroma data of four fruit types by ABC-based neural network. Sensors 16(3):304.
DOI: https://doi.org/10.3390/s16030304
Baietto M., Wilson A.D. 2015. Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 15:899-931.
DOI: https://doi.org/10.3390/s150100899
Beghi R., Buratti S., Giovenzana V., Benedetti S., Guidetti R. 2017. Electronic nose and visible-near infrared spectroscopy in fruit and vegetable monitoring. Rev Anal Chem 36:4.
DOI: https://doi.org/10.1515/revac-2016-0016
Brezmes J., Fructuoso M.L., Llobet E., Vilanova X., Recasens I., Orts J., Saiz G., Correig X. 2005. Evaluation of an electronic nose to assess fruit ripeness. IEEE Sens. J. 5(1):97-108.
DOI: https://doi.org/10.1109/JSEN.2004.837495
Chen L., Wu C., Chou T., Chiu S., Tang K. 2018. Development of a dual MOS Electronic nose/camera system for improving fruit ripeness classification. Sensors 18(10):3256.
DOI: https://doi.org/10.3390/s18103256
El Hadi MA., Zhang FJ., Wu FF., Zhou CH., Tao J. 2013. Advances in fruit aroma volatile research. Molecules 18(7):8200-8229.
DOI: https://doi.org/10.3390/molecules18078200
Fine G., Cavanagh L., Afonja A., Binions R. 2010. Metal oxide semi-conductor gas sensors in environmental monitoring. Sensors 10(6):5469-5502.
DOI: https://doi.org/10.3390/s100605469
Ghasemi M., Mohtasebi S., Rodriguez M., Lozano J., Razavi S., Ahmadi H. 2011. Potential application of electronic nose technology in brewery. TRENDS FOOD SCI TECH. 22(4):165-174.
DOI: https://doi.org/10.1016/j.tifs.2010.12.005
Giovenzana V., Beghi R., Buratti S., Civelli R. Guidetti R. 2014. Monitoring of fresh-cut Valerianella locusta Laterr. shelf life by electronic nose and VIS–NIR spectroscopy. Talanta 120: 368-375.
DOI: https://doi.org/10.1016/j.talanta.2013.12.014
Haugen J.E., Kvaal K. 1998. Electronic nose and artificial neural network. Meat sci. 49:273-286.
DOI: https://doi.org/10.1016/S0309-1740(98)90054-7
Karami H., Rasekh M., Mirzaee G.E. 2020. Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. J. Food Process Pres. 44(9):14696.
DOI: https://doi.org/10.1111/jfpp.14696
Khodabakhshian R., Emadi B., Khojastehpour M., Golzarian M.R., Sazgarnia A. 2017. Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy. Int. J. Food Prop. 20:41-52.
DOI: https://doi.org/10.1080/10942912.2015.1126725
Kodogiannis V.S. 2017 Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage. Food Bioprocess Tech. 10(4):730-749.
DOI: https://doi.org/10.1007/s11947-016-1851-6
Mamat M., Samad S.A, Hannan M.A. 2011. An electronic nose for reliable measurement and correct classification of beverages. Sensors 11(6):6435-6453.
DOI: https://doi.org/10.3390/s110606435
Mavani N., Ali J., Othman S., Hussain M., Hashim H. Rahman N. 2021 Application of artificial intelligence in food industry—A guideline. Food Eng. Rev. 9:1-42.
DOI: https://doi.org/10.1007/s12393-021-09290-z
Mirzaee E., Taheri A., Ayari F., Lozano J. 2020. Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an E-nose machine coupled fuzzy KNN. Food Anal Method 13(3):678-689.
DOI: https://doi.org/10.1007/s12161-019-01682-6
Pearce TC. 1997. Computational parallels between the biological olfactory pathway and its analogue The Electronic Nose': Part II. Sensor-based machine olfaction. BioSystems 41(2):69-90.
DOI: https://doi.org/10.1016/S0303-2647(96)01660-7
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V. 2011. Scikit-learn: Machine learning in Python. J. Mach. Lear. Res. 12:2825-2830.
Poghossian A., Geissler H., Schöning M. 2019. Rapid methods and sensors for milk quality monitoring and spoilage detection. Biosens. Bioelectron. 140:111272.
DOI: https://doi.org/10.1016/j.bios.2019.04.040
Sanaeifar A., Mohtasebi S., Ghasemi M., Ahmadi H., Lozano J. 2014. Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM). 32(6):538-548.
DOI: https://doi.org/10.17221/113/2014-CJFS
Schaller E., Bosset J., Escher F. 1998. Electronic noses and their application to food. Food Sci. Tech-brazil 31(4):305-316.
DOI: https://doi.org/10.1006/fstl.1998.0376
Semwal R., Aier I., Tyagi P., Varadwaj P. 2021. DeEPn: a deep neural network based tool for enzyme functional annotation. J. Biomol. Struct. Dyn. 39(8):2733-2743.
DOI: https://doi.org/10.1080/07391102.2020.1754292
Singh B., Chadha K., Sahai S. 2010. Performance of litchi cultivar for yield and physico-chemical quality of fruits. Indian J. Hortic. 67(4):96-98.
Singh D., Singh B. 2019. Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97:105524.
DOI: https://doi.org/10.1016/j.asoc.2019.105524
Singh P., Singh I. 1994. Physico-chemical changes during storage of litchi (Litchi chinensis) beverages. Indian J. Agr. Sci.
Tan J., Xu J. 2020. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture. 4:104-15.
DOI: https://doi.org/10.1016/j.aiia.2020.06.003
Tang K., Chiu S., Pan C., Hsieh H., Liang Y., Liu S. 2010. Development of a portable electronic nose system for the detection and classification of fruity odors. Sensors 10:9179-9193.
DOI: https://doi.org/10.3390/s101009179
Voss H., Stevan J., Ayub R. 2019. Peach growth cycle monitoring using an electronic nose. Comput. Electron. Agr. 163:104858.
DOI: https://doi.org/10.1016/j.compag.2019.104858
Wang L., Brown S. 2006. Prediction of DNA-binding residues from sequence features. J. Bioinf. Comput Biol. 4(06):1141-1158.
DOI: https://doi.org/10.1142/S0219720006002387
Wijaya D., Sarno R., Zulaika E., Sabila S. 2017. Development of mobile electronic nose for beef quality monitoring. Procedia Comput. Sci. 124:728-735.
DOI: https://doi.org/10.1016/j.procs.2017.12.211
Xu S., Lü E., Lu H., Zhou Z., Wang Y., Yang J., Wang Y. 2016. Quality detection of litchi stored in different environments using an electronic nose. Sensors 16(6):852.
DOI: https://doi.org/10.3390/s16060852
Yang X., Chen J., Jia L., Yu W., Wang D., Wei W., Li S., Tian S., Wu D. 2020. Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics. Sensors 20(7):1866.
DOI: https://doi.org/10.3390/s20071866
Yang X., Yu Q., He L., Guo T. 2013. The one-against-all partition based binary tree support vector machine algorithms for multi-class classification. Neurocomputing 113:1-7.
DOI: https://doi.org/10.1016/j.neucom.2012.12.048
Yoshida K., Ishikawa E., Joshi M., Lechat H., Ayouni F., Bonnefille M. 2012. Quality control and rancidity tendency of nut mix using an electronic nose. In: Indo-Japanese Conference on Perception and Machine Intelligence. Indo-Japanese Conference on Perception and Machine Intelligence. Springer, Berlin, Heidelberg. 163-170.
DOI: https://doi.org/10.1007/978-3-642-27387-2_21
Yu H., Wang J., Yao C., Zhang H., Yu Y. 2008. Quality grade identification of green tea using E-nose by CA and ANN. Food Sci. Tech-brazil 41(7):1268-1273.
DOI: https://doi.org/10.1016/j.lwt.2007.08.018
Zhaoqi Z., Xuequn P., Xuewu D., Zuoliang J. 2002. Change of anthocyanin content and its determination during lychee pericarp browning. J. South China Agric. 23(1):16-19.
How to Cite
Tyagi, P. (2022) “E-nose: a low-cost fruit ripeness monitoring system”, Journal of Agricultural Engineering, 54(1). doi: 10.4081/jae.2022.1389.
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