Electronic Detection of Garlic Density in Various Kinds of Yogurts Using Statistical Features

Abstract

Accurate detection of food components plays a critical role in developing modern culinary technologies and food safety practices. This study uses electronic nose technology to determine garlic concentration in garlic yogurts. An electronic nose system consisting of 11 different MQ brand gas sensors was used in the study. Five different yogurt types were prepared with three different garlic concentrations: plain, low, and high. A total of 225 odor records were taken from 15 yogurt samples, and various features were extracted from these data, which were analyzed using four different classification algorithms. The Extra Trees algorithm was the most successful method, with 89.14% classification accuracy, 89.80% sensitivity, and 94.57% specificity rates. The results of the study show that electronic nose technology can be used in many application areas, especially in smart kitchen devices analyzing food ingredients to provide information about freshness and composition, in the food industry to ensure standardization of product quality in production processes and to ensure that intense aromatic ingredients such as garlic are used in the right amount, and in the development of food products suitable for consumers’ special diets or personal tastes.

Keywords

• Garlic yogurt
• odor classification
• electronic nose
• extra trees algorithm.

References

1. 1. N. Altawell, Introduction to Machine Olfaction Devices. Elsevier, 2021.
2. 2. J. W. Gardner and P. N. Bartlett, “A brief history of electronic noses,” Sens. Actuators B Chem., vol. 18, no. 1, pp. 210–211, Mar. 1994, doi: 10.1016/0925-4005(94)87085-3.
3. 3. N. Husni, A. Handayani, S. Nurmaini, and I. Yani, Odor classification using Support Vector Machine. 2017, p. 76. doi: 10.1109/ICECOS.2017.8167170.
4. 4. M. Cao and X. Ling, “Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose,” Appl. Sci., vol. 12, no. 19, Art. no. 19, Jan. 2022, doi: 10.3390/app12199716.
5. 5. A. Khorramifar et al., “Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors,” Sensors, vol. 23, no. 12, Art. no. 12, Jan. 2023, doi: 10.3390/s23125716.
6. 6. A. D’Amico et al., “An investigation on electronic nose diagnosis of lung cancer,” Lung Cancer Amst. Neth., vol. 68, no. 2, pp. 170–176, May 2010, doi: 10.1016/j.lungcan.2009.11.003.
7. 7. B. Ibrahim et al., “Non-invasive phenotyping using exhaled volatile organic compounds in asthma,” Thorax, vol. 66, no. 9, pp. 804–809, Sep. 2011, doi: 10.1136/thx.2010.156695.
8. 8. B. H. Tozlu, C. Şimşek, O. Aydemir, and Y. Karavelioglu, “A High performance electronic nose system for the recognition of myocardial infarction and coronary artery diseases,” Biomed. Signal Process. Control, vol. 64, p. 102247, Feb. 2021, doi: 10.1016/j.bspc.2020.102247.

9. 9. A. Bermak and M. Hassan, “Noninvasive Diabetes Monitoring with Electronic Nose,” presented at the Qatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1, Hamad bin Khalifa University Press (HBKU Press), Mar. 2016, p. HBPP2776. doi: 10.5339/qfarc.2016.HBPP2776.
10. 10. O. Zaim, T. Saidi, N. El Bari, and B. Bouchikhi, “Assessment Of ‘Breath Print’ In Patients With Chronic Kidney Disease During Dialysis By Non-Invasive Breath Screening Of Exhaled Volatile Compounds Using An Electronic Nose,” in 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Fukuoka, Japan: IEEE, May 2019, pp. 1–4. doi: 10.1109/ISOEN.2019.8823338.
11. 11. A. de la Rica-Martinez et al., “Low-Cost Electronic Nose for the Determination of Urinary Infections,” Sensors, vol. 24, no. 1, Art. no. 1, Jan. 2024, doi: 10.3390/s24010157.
12. 12. M. Xu, J. Wang, and L. Zhu, “Tea quality evaluation by applying E-nose combined with chemometrics methods,” J. Food Sci. Technol., vol. 58, no. 4, pp. 1549–1561, Apr. 2021, doi: 10.1007/s13197-020-04667-0.
13. 13. E. Osmólska, M. Stoma, and A. Starek-Wójcicka, “Juice Quality Evaluation with Multisensor Systems—A Review,” Sensors, vol. 23, no. 10, Art. no. 10, Jan. 2023, doi: 10.3390/s23104824.
14. 14. S. Güney and A. Atasoy, “Study of fish species discrimination via electronic nose,” Comput. Electron. Agric., vol. 119, pp. 83–91, Nov. 2015, doi: 10.1016/j.compag.2015.10.005.
15. 15. K. Fujioka, “Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study,” Sensors, vol. 21, no. 24, Art. no. 24, Jan. 2021, doi: 10.3390/s21248368.
16. 16. A. N. Damdam, L. O. Ozay, C. K. Ozcan, A. Alzahrani, R. Helabi, and K. N. Salama, “IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection,” Foods Basel Switz., vol. 12, no. 11, p. 2227, May 2023, doi: 10.3390/foods12112227.
17. 17. A. Poghossian, H. Geissler, and M. J. Schöning, “Rapid methods and sensors for milk quality monitoring and spoilage detection,” Biosens. Bioelectron., vol. 140, p. 111272, Sep. 2019, doi: 10.1016/j.bios.2019.04.040.
18. 18. C. Gonzalez Viejo, E. Tongson, and S. Fuentes, “Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity,” Sensors, vol. 21, no. 6, Art. no. 6, Jan. 2021, doi: 10.3390/s21062016.
19. 19. H. Zeng, H. Han, Y. Huang, and B. Wang, “Rapid prediction of the aroma type of plain yogurts via electronic nose combined with machine learning approaches,” Food Biosci., vol. 56, p. 103269, Dec. 2023, doi: 10.1016/j.fbio.2023.103269.
20. 20. S. Grassi, S. Benedetti, L. Magnani, A. Pianezzola, and S. Buratti, “Seafood freshness: e-nose data for classification purposes,” Food Control, vol. 138, p. 108994, Aug. 2022, doi: 10.1016/j. foodcont.2022.108994.
21. 21. Y. Xiong et al., “Non-Destructive Detection of Chicken Freshness Based on Electronic Nose Technology and Transfer Learning,” Agriculture, vol. 13, no. 2, Art. no. 2, Feb. 2023, doi: 10.3390/agriculture13020496.
22. 22. L. Qiu, M. Zhang, A. S. Mujumdar, and L. Chang, “Effect of edible rose (Rosa rugosa cv. Plena) flower extract addition on the physicochemical, rheological, functional and sensory properties of set-type yogurt,” Food Biosci., vol. 43, p. 101249, Oct. 2021, doi: 10.1016/j.fbio.2021.101249.
23. 23. M. Kaur and S. Barringer, “Effect of Yogurt on the Deodorization of Raw Garlic (Allium sativum L.) Sulfur Volatiles in Breath and the Roles of Its Components,” Dairy, vol. 5, no. 2, Art. no. 2, Jun. 2024, doi: 10.3390/dairy5020026.
24. 24. M. Kaur and S. Barringer, “Effect of Yogurt and Its Components on the Deodorization of Raw and Fried Garlic Volatiles,” Mol. Basel Switz., vol. 28, no. 15, p. 5714, Jul. 2023, doi: 10.3390/molecules28155714.
25. 25. K. Tamaki, S. Sonoki, T. Tamaki, and K. Ehara, “Measurement of odor after in vitro or in vivo ingestion of raw or heated garlic, using electronic nose, gas chromatography, and sensory analysis,” Int. J. Food Sci. Technol., vol. 43, no. 1, pp. 130–139, 2008, doi: 10.1111/j.1365-2621.2006.01403.x.
26. 26. F. Suarez, J. Springfield, J. Furne, and M. Levitt, “Differentiation of mouth versus gut as site of origin of odoriferous breath gases after garlic ingestion,” Am. J. Physiol., vol. 276, no. 2, pp. G425-430, Feb. 1999, doi: 10.1152/ajpgi.1999.276.2.G425.
27. 27. A. Makarichian, R. Amiri Chayjan, E. Ahmadi, and S. S. Mohtasebi, “Assessment the influence of different drying methods and pre-storage periods on garlic (Allium Sativum L.) aroma using electronic nose,” Food Bioprod. Process., vol. 127, pp. 198–211, May 2021, doi: 10.1016/j.fbp.2021.02.016.
28. 28. J. Liu, Y. Liu, X. Li, J. Zhu, X. Wang, and L. Ma, “Drying characteristics, quality changes, parameters optimization and flavor analysis for microwave vacuum drying of garlic (Allium sativum L.) slices,” LWT, vol. 173, p. 114372, Jan. 2023, doi: 10.1016/j.lwt.2022.114372.
29. 29. E. Ozturk Kiyak, B. Ghasemkhani, and D. Birant, “High-Level K- Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis,” Electronics, vol. 12, no. 18, Art. no. 18, Jan. 2023, doi: 10.3390/electronics12183828.
30. 30. T. Kavzoglu and F. Bilucan, “Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm,” Earth Sci. Inform., vol. 16, no. 1, pp. 415–435, Mar. 2023, doi: 10.1007/s12145-022-00874-9.
31. 31. A. K. V, A. A, B. Jose, K. Anilkumar, and O. T. Lee, “Phishing Detection using Extra Trees Classifier,” in 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Oct. 2021, pp. 1–6. doi: 10.1109/ISCON52037.2021.9702372.
32. 32. Y. Chen, Z. Jia, D. Mercola, and X. Xie, “A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index,” Comput. Math. Methods Med., vol. 2013, no. 1, p. 873595, 2013, doi: 10.1155/2013/873595.
33. 33. Ö. Aydemir, “Common spatial pattern-based feature extraction from the best time segment of BCI data,” Turk. J. Electr. Eng. Comput. Sci., vol. 24, no. 5, pp. 3976–3986, Jan. 2016, doi: 10.3906/elk-1502-162.
34. 34. S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Sci. Rep., vol. 12, p. 5979, Apr. 2022, doi: 10.1038/s41598-022-09954-8.