Avokado meyve kalitesinin izlenmesi için destek vektör makinelerine dayalı bir sınıflandırma

Öz

Bilimsel olarak, bir yöntemin etkinliği, mevcut senaryo, parametre ve/veya veriler içinde belirli bir süreci takip eden bir değerlendirmeye dayalı olarak en iyi tahmin/hesaplama gücünü ifade eder. İyi bir tahmin için probleme en uygun yaklaşım(lar)ın göz önünde bulundurulması ve ilgili testlerin güvenilir bir şekilde yapılması gerekmektedir. Gıda güvenliği ve meyve kalitesi alanında yapılan uygulamalı çalışmalar, kullanılan yöntemlerin doğruluğu, hızı ve ekonomik parametrelerinin özellikle önemli olması ile birlikte kritik öneme sahiptir. Bu çalışmada, literatürde ilk kez, Arduino tabanlı bir sıcaklık ve gaz izleme sistemi (e-burun olarak isimlendirilir) ile kontrollü bir deney ortamında avokado meyvesinin çürümesi izlenerek verileri alınmakta ve çürümeyi tespit etmek (sınıflandırmak) için bir makine öğrenmesi yöntemi olan destek vektör makineleri kullanılmaktadır. Bu çalışmada, sınıflandırma için çok az eğitim verisi ile %99'un üzerinde test ve doğrulama başarısı elde edilmiştir. Elde edilen sonuçlar, geliştirilen e-burun tespit sonuçları ve soğuk hava deposunda diğer meyvelerdeki çürüme seviyesinin belirlenmesinde kullanılan yöntem açısından cesaret vericidir.

Anahtar Kelimeler

• Gıda güvenliği
• Makine öğrenmesi
• Destek vektör makineleri
• E-burun
• Meyve kalitesi
• Avokado

A classification based on support vector machines for monitoring avocado fruit quality

Abstract

Scientifically, the efficiency of a method refers to its power to best predict/calculate based on an evaluation following a certain process within the current scenario, parameter and/or data. For a good prediction, the most appropriate approach(es) to a problem should be considered and the related tests should be done reliably. Practical studies in the field of food safety and fruit quality are critical, with the accuracy, speed and economic parameters of the methods used being of particular importance. In this study, for the first time in literature an Arduino-based temperature and gas monitoring system (called e-nose) is used to monitor the decay of avocado fruit in a controlled experimental environment and support vector machines, a machine learning method, are used to detect (classification) the decay. In this study, test and validation success of over 99% was achieved with very few training-data for classification. The obtained results are encouraging in terms of the detection results of the developed e-nose and the method used to determine the level of decay in other fruit in cold storage.

Keywords

• Food safety
• Machine learning
• Support vector machines
• E-nose
• Fruit quality
• Avocado

References

1. [1] Maksimović M, Omanović-Mikličanin E, Badnjević A. Nanofood and internet of nano things. 1st ed. Switzerland AG, Springer International Publishing, 2019.
2. [2] Jiang X, Chen Y. “The Potential of Absorbing Foreign Agricultural Investment to Improve Food Security in Developing Countries”. Sustainability, 12(6), 1-19, 2020.
3. [3] Vågsholm I, Arzoomand NS, Boqvist S. “Food security, safety, and sustainability-getting the trade-offs right”. Frontiers in Sustainable Food System, 4(16), 1-14, 2020.
4. [4] Aday S, Aday MS. “Impact of COVID-19 on the food supply chain”. Food Quality and Safety, 4(4), 167-180, 2020.
5. [5] Chowdhury M, Sarkar A, Paul SK, Moktadir M. “A case study on strategies to deal with the impacts of COVID-19 pandemic in the food and beverage industry”. Operations Management Research, 15, 166-178, 2022.
6. [6] Singh S, Kumar R, Panchal R, Tiwari MK. “Impact of COVID-19 on logistics systems and disruptions in food supply chain”. International Journal of Production Research, 59(7), 1993-2008, 2021.
7. [7] Chowdhury P, Paul SK, Kaisar S, Moktadir MA. “COVID-19 pandemic related supply chain studies: A systematic review”. Transportation Research Part E: Logistics and Transportation Review, 148(1), 1-26, 2021.
8. [8] Saberi S, Kouhizadeh M, Sarkis J, Shen L. “Blockchain technology and its relationships to sustainable supply chain management”. International Journal of Production Research, 57(7), 2117-2135, 2019.

9. [9] Cole R, Stevenson M, Aitken J. “Blockchain technology: implications for operations and supply chain management”. Supply Chain Management: An International Journal, 24(4), 469-483, 2019.
10. [10] Li J, Zhu S, Zhang W, Yu L. “Blockchain-driven supply chain finance solution for small and medium enterprises”. Frontiers of Engineering Management, 7(1), 500-511, 2020.
11. [11] Masarirambi MT, Mavuso V, Songwe VD., Nkambule TP. “Indigenous postharvest handling and processing of traditional vegetables in Swaziland: A review”. African Journal of Agricultural Research, 5(24), 3333-3341, 2010.
12. [12] Caleb OJ, Opara UL, Witthuhn CR. “Modified Atmosphere Packaging of Pomegranate Fruit and Arils: A Review”. Food Bioprocess Technology, 5(1), 15-30, 2012.
13. [13] Wani SH, Herath V. Cold tolerance in plants. 1st ed. Switzerland AG, Springer International Publishing, 2018.
14. [14] Farber JM. “Microbiological aspects of modified-atmosphere packaging technology-a review”. Journal of Food protection, 54(1), 58-70, 1991.
15. [15] Halonen N, Pálvölgyi PS, Bassani A, Fiorentini C, Nair R, Spigno G, Kordas K. “Bio-based smart materials for food packaging and sensors–a review”. Frontiers in Materials, 7(1), 82-96, 2020.
16. [16] Jhuria M, Kumar A, Borse R. “Image processing for smart farming: Detection of disease and fruit grading”. IEEE second international conference on image information processing, Shimla, India, 9-11 December 2013.
17. [17] Munera S, Gómez-Sanchís J, Aleixos N, Vila-Francés J, Colelli G, Cubero S, Blasco J. “Discrimination of common defects in loquat fruit cv.‘Algerie’using hyperspectral imaging and machine learning techniques”. Postharvest Biology and Technology, 171(1), 1-8, 2021.
18. [18] Caya MVC, Cruz FRG, Fernando CMN, Lafuente RMM, Malonzo MB, Chung WY. “Monitoring and Detection of Fruits and Vegetables Spoilage in the Refrigerator using Electronic Nose Based on Principal Component Analysis”. IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, Laoag, Philippines, 29 November 2019.
19. [19] Sarno R, Wijaya DR. “Recent development in electronic nose data processing for beef quality assessment”. Telkomnika, 17(1), 337-348, 2019.
20. [20] Ahmed I, Lin H, Zou L, Li Z, Brody AL, Qazi IM, Sun L. “An overview of smart packaging technologies for monitoring safety and quality of meat and meat products”. Packaging Technology and Science, 31(7), 449-471, 2018.
21. [21] Balasubramanian S, Panigrahi S, Logue CM, Gu H, Marchello M. “Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification”. Journal of Food Engineering, 91(1), 91-98, 2009.
22. [22] Zhang S, Wang Q, Guo Y, Kang L, Yu Y. “Carbon monoxide enhances the resistance of jujube fruit against postharvest Alternaria rot”. Postharvest Biology and Technology, 168, 1-6, 2020.
23. [23] Halim ZA, Sidek O. “A FPGA-Based Smell Sensing System for Micromachined Gas Sensor Application”. 31st IEEE/CPMT International Electronics Manufacturing Technology Symposium, Petaling Jaya, Malaysia, 8-10 November 2006.
24. [24] Benrekia F, Attari M, Bermak A, Belhout K. “FPGA implementation of a neural network classifier for gas sensor array applications”. 6th International Multi-Conference on Systems, Signals and Devices, Djerba, Tunisia, 23-26 March 2009.
25. [25] Powder POOR. “Artificial neural network for mix proportioning optimization of reactive powder concrete”. Journal of Theoretical and Applied Information Technology, 96(23), 3861-3872, 2018.
26. [26] Enériz D, Medrano N, Calvo B. “An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using Sensor Fusion”. Biosensors, 11(10), 366-382, 2021.
27. [27] Atzori L, Iera A, Morabito G. “The internet of things: A survey”. Computer networks, 54(15), 2787-2805, 2010.
28. [28] Sundmaeker H, Guillemin P, Friess P, Woelfflé S. Vision and Challenges for Realising the Internet of Things. 1st ed. European Commision, Brussels, 2010.
29. [29] Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. “Internet of things: A survey on enabling technologies, protocols, and applications”. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376, 2015.
30. [30] Gubbi J, Buyya R, Marusic S, Palaniswami M. “Internet of Things (IoT): A vision, architectural elements, and future directions”. Future Generation Computer Systems, 29(7), 1645-1660, 2013.
31. [31] Sankar S, Srinivasan P. “Internet of things (iot): A survey on empowering technologies, research opportunities and applications”. International Journal of Pharmacy and Technology, 8(4), 26117-26141, 2016.
32. [32] Misra G, Kumar V, Agarwal A, Agarwal K. “Internet of things (iot)–a technological analysis and survey on vision, concepts, challenges, innovation directions, technologies, and applications (an upcoming or future generation computer communication system technology)”. American Journal of Electrical and Electronic Engineering, 4(1), 23-32, 2016.
33. [33] Panigrahi S, Kizil U, Balasubramanian S, Doetkott C, Logue C, Marchello M, Wiens R. “Electronic nose system for meat quality evaluation”. In International Meeting of the American Society of Agricultural Engineers at Chicago, Illinois, 28-31 July 2002.
34. [34] Scott S, James D, Ali Z. “Data analysis for electronic nose systems”. Microchim Acta, 156(1), 183–207, 2006.
35. [35] Shi H, Zhang M, Adhikari B. “Advances of electronic nose and its application in fresh foods: A review”. Critical reviews in food science and nutrition, 58(16), 2700-2710, 2018.
36. [36] Gardner JW, Shin HW, Hines EL. “An electronic nose system to diagnose illness”. Sensors and Actuators B: Chemical, 70(1-3), 19-24, 2000.
37. [37] Bonah E, Huang X, Yi R, Aheto JH, Osae R, Golly M. “Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm”. Journal of Food Process Engineering, 42(6), 1-12, 2019.
38. [38] Weng X, Luan X, Kong C, Chang Z, Li Y, Zhang S, Al-Majeed S, Xiao Y. “A comprehensive method for assessing meat freshness using fusing electronic nose, computer vision, and artificial tactile technologies”. Journal of Sensors, 2020, 1-14, 2020.
39. [39] Longobardi F, Casiello G, Centonze V, Catucci L, Agostiano A. “Electronic Nose in Combination with Chemometrics for Characterization of Geographical Origin and Agronomic Practices of Table Grape”. Food Analytical Methods, 12(5), 1229–1237, 2019.
40. [40] Araújo RG, Rodriguez-Jasso RM, Ruiz HA, Pintado MME, Aguilar CN. “Avocado by-products: Nutritional and functional properties”. Trends in Food Science & Technology, 80, 51-60, 2018.
41. [41] Dantas D, Pasquali MA, Cavalcanti-Mata M, Duarte ME, Lisboa HM. “Influence of spray drying conditions on the properties of avocado powder drink”. Food Chemistry, 266, 284-291, 2018.
42. [42] Ortiz-Viedma J, Rodriguez A, Vega C, Osorio F, Defillipi B, Ferreira R, Saavedra J. “Textural, flow and viscoelastic properties of Hass avocado (Persea americana Mill.) during ripening under refrigeration conditions”. Journal of Food Engineering, 219, 62-70, 2018.
43. [43] Yahia EM, Woolf AB. Avocado (Persea americana Mill.). 1st ed., Massachusetts, USA, Cambridge Woodhead, 2011.
44. [44] Tzatzani TT, Kavroulakis N, Doupis G, Psarras G, Papadakis IE. “Nutritional status of ‘Hass’ and ‘Fuerte’avocado (Persea americana Mill.) plants subjected to high soil moisture”. Journal of Plant Nutrition, 43(3), 327-334, 2020.
45. [45] Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. 1st ed., Cambridge, United Kingdom, 2000.
46. [46] El Barbri N, Llobet E, El Bari N, Correig X, Bouchikhi B. “Electronic nose based on metal oxide semiconductor sensors as an alternative technique for the spoilage classification of red meat”. Sensors, 8(1), 142-156, 2008.
47. [47] Hasan NU, Ejaz N, Ejaz W, Kim HS. “Meat and fish freshness inspection system based on odor sensing”. Sensors, 12(11), 15542-15557, 2012.
48. [48] Papadopoulou OS, Panagou EZ, Mohareb FR, Nychas GJE. “Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis”. Food Research International, 50(1), 241-249, 2013.
49. [49] Alpaydin E. Introduction to Machine Learning. 4th ed. MIT Press, London, England, 2020.
50. [50] Carrillo-Amado YR, Califa-Urquiza MA, Ramón-Valencia JA. “Calibration and standardization of air quality measurements using MQ sensors”. Respuestas, 25(1), 70-77, 2020.
51. [51] Saini J, Dutta M, Marques G. “Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review”. Environmental Monitoring and Assessment, 193(66), 1-32, 2021.
52. [52] Vapnik VN. The Nature of Statistical Learning Theory, Springer Verlag, New York, USA, 1995.
53. [53] Vapnik VN, Cortes C, “Support vector networks”. Machine Learning, 20, 273-297, 1995.
54. [54] Tan J, Xu J. “Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review”. Artificial Intelligence in Agriculture, 4, 104-115, 2020.
55. [55] Pulluri KK, Kumar VN. “Development of an Integrated Soft E-Nose for Food Quality Assessment”. IEEE Sensors Journal, 22(15), 15111-15122, 2022.
56. [56] Roy M, Yadav BK. “Electronic nose for detection of food adulteration: A review”. Journal of Food Science and Technology, 59(3), 846-858, 2022.
57. [57] Sanaeifar A, ZakiDizaji H, Jafari A, de la Guardia M. “Early detection of contamination and defect in foodstuffs by electronic nose: A review”. TrAC Trends in Analytical Chemistry, 97, 257-271, 2017.
58. [58] Rasekh M, Karami H. “E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices”. International Journal of Food Properties, 24(1), 592-602, 2021.
59. [59] Huang C, Gu Y. “A machine learning method for the quantitative detection of adulterated meat using a MOS-based E-nose”. Foods, 11(4), 1-17, 2022.
60. [60] Tatli S, Mirzaee-Ghaleh E, Rabbani H, Karami H, Wilson A D. “Rapid detection of urea fertilizer effects on VOC emissions from cucumber fruits using a MOS E-Nose Sensor Array”. Agronomy, 12(1), 1-20, 2022.
61. [61] Zarezadeh MR, Aboonajmi M, Varnamkhasti MG, Azarikia F. “Olive oil classification and fraud detection using E-nose and ultrasonic system”. Food Analytical Methods, 14, 2199-2210, 2021.