Abstract
Cardamom is an export-oriented commodity of India, used for cooking and has medicinal values. This calls for the need of rapid and effective method for quality assessment of cardamom. The major parameters for quality determination of cardamom are flavor and aroma. The present practice of quality estimation involves assessment by human experts based on physical characteristics, such as freshness, shape, size, color, and aroma which is subject to biasness and error. The alternate evaluation technique comprises various qualitative and quantitative chemical tests of the samples (using gas chromatography and mass spectrometer), which is an expensive, laborious, time-consuming, and skilled man-power demanding process. This paper presents a novel approach to predict the oil yield and constituent chemicals 1,8-cineole and alpha-terpinyl acetate, which are responsible for the flavor of cardamom using a handheld electronic nose (HEN) developed by Centre for Development of Advanced Computing (C-DAC), Kolkata. We worked with thirteen different cardamom samples with varying oil yield percentages and different measures of the constituent chemicals of the extracted essential oil. We considered cardamom samples, with and without husk separately in our study. Initially, we have applied clustering algorithms like Principal Component Analysis (PCA) and Density-Based Spatial Clustering on Applications with Noise (DB-SCAN) on the data values collected by five different metal oxide semiconductor (MOS) sensors of HEN and the device was able to group the samples into distinct clusters with considerable accuracy. Then, Partial Least Square (PLS) Regression was applied on the dataset to train the system and eventually predict the quality of unknown cardamom samples. The model has given around 98% and 95% accuracy for oil yield and 1,8-cineole prediction, respectively, for the samples with husk.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Sample 3 (GG1), 4 (GG2), and 5 (WONDE) were recorded multiple times for experimental purpose.
References
Tambe E, Gotmare S (2019) Chemical characterization of three cardamom oils (Elettaria cardamomum) By GCMS. World J Pharm Pharmaceut Sci 08:876–885. https://doi.org/10.20959/wjpps201911-14879
Ashokkumar K, Murugan M, Dhanya M, Raj S, Kamaraj D (2019) Phytochemical variations among four distinct varieties of Indian cardamom Elettaria cardamomum (L.) Maton. Nat Prod Res 34:1919–1922. https://doi.org/10.1080/14786419.2018.1561687
Chempakam B, Sindhu S (2008) Cardamom. In: Parthasarathy VA, Chempakam B, John Zachariah T (eds) Chemistry of spices. CABI.com, pp 41–58
Karakaya D, Ulucan O, Turkan M (2020) Electronic nose and its applications: a survey. Int J Autom Comput 17:179–209. https://doi.org/10.1007/s11633-019-1212-9
Vestergaard JS, Martens M, Turkki P (2007) Application of an electronic nose system for prediction of sensory quality changes of a meat product (pizza topping) during storage. LWT-Food Sci Technol 40(6):1095–1101. https://doi.org/10.1016/j.lwt.2006.06.008
Das A, Ghosh A, Ghosh TK, Ray H (2011) Portable electronic nose for tea aroma detection. Int J Comput Appl 6(1):39–43
Majchrzak T, Wojnowski W, Dymerski T, Gebicki J, Namiesnik J (2018) Electronic noses in classification and quality control of edible oils: a review. Food Chem 246:192–201. https://doi.org/10.1016/j.foodchem.2017.11.013
Dragonieri S, Schot R, Mertens BJ, Le Cessie S, Gauw SA, Spanevello A, Resta O, Willard NP, Vink TJ, Rabe KF, Bel EH, Sterk PJ (2007) An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol 120(4):856–862. https://doi.org/10.1016/j.jaci.2007.05.043
Robroeks CMHHT, van Berkel JJBN, Dallinga JW, Jobsis Q, Zimmermann LJI, Hendriks HJE, Wouters MFM, van der Grinten CPM, van de Kant KDG, van Schooten FJ, Dompeling E (2010) Metabolomics of volatile organic compounds in cystic fibrosis patients and controls. Pediatr Res 68(1):75–80. https://doi.org/10.1203/00006450-201011001-00143
Leela NK, Muneeb AM, Mukherjee S, Ghosh D, Bhattacharya N (2017) Essential oil content of cardamom (Elettaria cardamomum Maton) by hand-held electronic nose. J Spices Aromatic Crops 131. https://doi.org/10.25081/josac.2017.v26.i2.882
Ghosh D et al (2012) Exploratory study on aroma profile of cardamom by GC-MS and electronic nose. In: 2012 sixth international conference on sensing technology (ICST), pp 399–403. https://doi.org/10.1109/ICSensT.2012.6461708
Bhattacharyya N, Bandyopadhyay R, Bhuyan M, Tudu B, Ghosh D, Jana A (2008) Electronic nose for Black Tea Classification and correlation of measurement with “Tea Taster” Marks. IEEE Trans Instrum Measur 57(7)
Ghosh A, Ray H, Ghosh T, Das A, Bhattacharyya N (2014) Generic handheld e-nose platform for quality assessment of agricultural produces and biomedical applications. Chem Eng Trans 40:259–264. https://doi.org/10.3303/CET1440044
Nabarun B, Bipan T, Arun J, Devdulal G, Rajib B, Manabendra B (2008) Preemptive identification of optimum fermentation time for black tea using electronic nose. Sens Actuators B 131(1):110–116
Sehgal S, Singh H, Agarwal M, Bhasker V, Shantanu (2014) Data analysis using principal component analysis. In: 2014 international conference on medical imaging, m-health and emerging communication systems (MedCom), pp 45–48
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining (KDD'96). AAAI Press, pp 226–231
Larbi M, Trinchero R, Canavero FG, Besnier P, Swaminathan M (2020) Analysis of parameter variability in integrated devices by partial least squares regression. In: 2020 IEEE 24th workshop on signal and power integrity (SPI), pp 1–4. https://doi.org/10.1109/SPI48784.2020.9218175
Acknowledgements
The authors are thankful to the scientists of ICAR—Indian Institute of Spices Research (IISR), Kozhikode, for providing labeled samples and chemical analysis of the samples.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ghosh, M., Ghosh, D., Bhattacharyya, N. (2022). Cardamom Quality Evaluation Employing Electronic Nose. In: Mandal, L., Tavares, J.M.R.S., Balas, V.E. (eds) Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1657-1_4
Download citation
DOI: https://doi.org/10.1007/978-981-19-1657-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1656-4
Online ISBN: 978-981-19-1657-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)