ارزیابی غیرمخرب کیفیت داخلی میوه سیب با استفاده از توموگرافی پرتو ایکس

ماشین های کشاورزی

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 تیر 1404   

نوع مقاله: مقاله پژوهشی انگلیسی

شناسه دیجیتال (DOI): 10.22067/jam.2025.90983.1317

نویسندگان

رسول خدابخشیان^{* 1}؛ رضا باغبانی²

¹گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه فردوسی مشهد، ایران

²گروه مهندسی کشاورزی، دانشگاه ملی مهارت، تهران، ایران

چکیده

در این مطالعه، از توموگرافی کامپیوتری پرتو ایکس به‌عنوان یک روش غیرمخرب برای ارزیابی کیفیت داخلی میوه سیب استفاده شد. برای این منظور، سه واریته سیب محلی شامل: رد دلیشز، گلدن دلیشز و گلاب انتخاب شدند. عدد CT تصاویر، که میزان جذب پرتو ایکس را نشان می‌دهد، با استفاده از نرم‌افزار K-PACS استخراج شد. پارامترهای کیفیتی مانند میزان محتوای مواد جامد محلول، اسیدیته قابل تیترات، شاخص طعم و pH واریته‌های مورد مطالعه اندازه‌گیری شد. رابطه بین پارامترهای کیفیت و عدد CT به‌دست‌آمده از تصاویر توموگرافی میوه‌ها در قالب مدل‌های رگرسیون خطی مورد بررسی قرار گرفت. بر اساس نتایج، همبستگی بین عدد CT و پارامترهای کیفیت در تمامی مدل‌ها بیشتر از 0.900 بود. برای واریته‌های مختلف، عدد CT همبستگی مثبتی با میزان اسیدیته قابل تیترات، شاخص طعم، pH و مواد جامد محلول داشت. روابط ارزیابی پارامترهای کیفیتی مربوط به واریته رد دلیشز بیشترین دقت را داشت (با ضرایب تبیین 0.952، 0.964، 0.941 و 0.969 به‌ترتیب برای میزان شاخص طعم، مواد جامد محلول، اسیدیته و pH). برای تمامی واریته‌ها، بیشترین همبستگی بین میزان pH و عدد CT مشاهده شد (با ضرایب تبیین 0.969، 0.972 و 0.996 به‌ترتیب برای واریته‌های رد دلیشز، گلدن دلیشز و گلاب). این نشان می‌دهد که توموگرافی پرتو ایکس می‌تواند به‌طور قابل‌اعتمادی ویژگی‌های کیفیت داخلی را بدون آسیب رساندن به میوه‌ها ارزیابی کند. مدل‌های رگرسیون خطی ایجادشده، روش معتبری و قابل بازتولید برای ارزیابی غیرمخرب کیفیت میوه سیب ارائه می‌دهند.

کلیدواژه‌ها

ارزیابی کیفیت؛ توموگرافی کامپیوتری اشعه ایکس؛ شماره سی‌تی؛ غیرمخرب؛ میوه سیب

مراجع

1. Abasi, S., Minaei, S., Jamshidi, B., & Fathi, D. (2018). Dedicated non-destructive devices for food quality measurement: A review. Trends in Food Science & Technology, 78, 197-205. https://doi.org/10.1016/j.tifs.2018.05.009
2. Akter, T., Bhattacharya, T., Kim, J.-H., Kim, M. S., Baek, I., Chan, D. E., & Cho, B.-K. (2024). A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies. Journal of Agriculture and Food Research, 15, 101068. https://doi.org/10.1016/j.jafr.2024.101068
3. Al-Maiman, S. A., & Ahmad, D. (2002). Changes in physical and chemical properties during pomegranate (Punica granatum) fruit maturation. Food Chemistry, 76(4), 437-441. https://doi.org/10.1016/S0308-8146(01)00301-6
4. Angeli, L., Populin, F., Morozova, K., Ding, Y., Asma, U., Bolchini, S., & Scampicchio, M. (2024). Comparative analysis of antioxidant activity and capacity in apple varieties: Insights from stopped flow DPPH kinetics, mass spectrometry and electrochemistry. Food Bioscience, 58, 103729. https://doi.org/10.1016/j.fbio.2024.103729
5. Arendse, E., Fawole, O. A., Magwaza, L. S., & Opara, U. L. (2018). Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review. Journal of Food Engineering, 217, 11-23. https://doi.org/10.1016/j.jfoodeng.2017.08.009
6. Argenta, L. C., do Amarante, C. V. T., de Freitas, S. T., Brancher, T. L., Nesi, C. N., & Mattheis, J. P. (2022). Fruit quality of ‘Gala’ and ‘Fuji’ apples cultivated under different environmental conditions. Scientia Horticulturae, 303, 111195. https://doi.org/10.1016/j.scienta.2022.111195
7. Asma, U., Morozova, K., Ferrentino, G., & Scampicchio, M. (2023). Apples and Apple By-Products: Antioxidant Properties and Food Applications. Antioxidants, 12(7). https://doi.org/10.3390/antiox12071456
8. Atamian, H. S., Davila, F. E. L., & Prakash, A. (2023). A transcriptomic study of ‘Granny Smith’ apple fruit response to x-ray irradiation using RNA-Seq. Scientia Horticulturae, 311, 111777. https://doi.org/10.1016/j.scienta.2022.111777
9. Bajramova, A., & Spégel, P. (2022). A comparative study of the fatty acid profile of common fruits and fruits claimed to confer health benefits. Journal of Food Composition and Analysis, 112, 104657. https://doi.org/10.1016/j.jfca.2022.104657
10. Barreiro, P., Zheng, C., Sun, D.-W., Hernández-Sánchez, N., Pérez-Sánchez, J. M., & Ruiz-Cabello, J. (2008). Non-destructive seed detection in mandarins: Comparison of automatic threshold methods in FLASH and COMSPIRA MRIs. Postharvest Biology and Technology, 47(2), 189-198. https://doi.org/10.1016/j.postharvbio.2007.07.008
11. Cakmak, H. (2019). Assessment of fresh fruit and vegetable quality with non-destructive methods. In C. M. Galanakis (Ed.), Food Quality and Shelf Life (pp. 303-331). Academic Press. https://doi.org/10.1016/B978-0-12-817190-5.00010-0
12. Cam, M., Hisil, Y., & Durmaz, G. (2009). Characterisation of pomegranate juices from ten cultivars grown in Turkey. International Journal of Food Properties, 12(2), 388-395. https://doi.org/10.1080/10942910701813917
13. Caporaso, N., Whitworth, M. B., Fowler, M. S., & Fisk, I. D. (2018). Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans. Food Chemistry, 258, 343-351. https://doi.org/10.1016/j.foodchem.2018.03.039
14. Chen, Q., Zhang, C., Zhao, J., & Ouyang, Q. (2013). Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety. TrAC Trends in Analytical Chemistry, 52, 261-274. https://doi.org/10.1016/j.trac.2013.09.007
15. Chen, C., Fu, Y., Wan, C., Liu, S., & Chen, J. (2019). Effects of hot water dipping on postharvest storage quality of Xinyu tangerines during cold storage. Food and Fermentation Industries, 45(5), 140-144. https://doi.org/ 13995/j.cnki.11-1802/ts.017835
16. Chigwaya, K., Karuppanapandian, T., Schoeman, L., Viljoen, D. W., Crouch, I. J., Nugraha, B., ..., & Crouch, E. M. (2021). X-ray CT and porosity mapping to determine the effect of ‘Fuji’ apple morphological and microstructural properties on the incidence of CO₂ induced internal browning. Postharvest Biology and Technology, 174, 111464. https://doi.org/10.1016/j.postharvbio.2021.111464
17. Doğan, D. E., Rashid, H. H. R., Lizalo, A., Soysal, D., & Demirsoy, H. (2024). Growth, fruit bearing behavior, yield and quality of some apple cultivars. Scientia Horticulturae, 327, 112762. https://doi.org/10.1016/j.scienta.2023.112762
18. Gao, Y., Liu, Y., Kan, C. N., Chen, M., & Chen, J. Y. (2019). Changes of peel color and fruit quality in navel orange fruits under different storage methods. Scientia Horticulturae, 256, 108522. https://doi.org/10.1016/j.scienta.2019.05.049
19. Ghasemi-Varnamkhasti, M., Apetrei, C., Lozano, J., & Anyogu, A. (2018). Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods. Trends in Food Science & Technology, 80, 71-92. https://doi.org/10.1016/j.tifs.2018.07.018
20. Guo, M., Li, C., Huang, R., Qu, L., Liu, J., Zhang, C., & Ge, Y. (2023). Ferulic acid enhanced resistance against blue mold of Malus domestica by regulating reactive oxygen species and phenylpropanoid metabolism. Postharvest Biology and Technology, 202, 112378. https://doi.org/10.1016/j.postharvbio.2023.112378
21. Groß, D., Zick, K., & Guthausen, G. (2017). Chapter four: Recent MRI and diffusion studies of food structures. In G. A. Webb (Ed.), Annual reports on NMR spectroscopy (Vol. 90, pp. 145–197). Academic Press.
22. Haff, R. P., Slaughter, D. C., Sarig, Y., & Kader, A. (2006). X-Ray Assessment of Translucency in Pineapple. Journal of Food Processing and Preservation, 30(5), 527-533. https://doi.org/10.1111/j.1745-4549.2006.00086.x
23. Han, Y., Su, Z., & Du, J. (2023). Effects of apple storage period on the organic acids and volatiles in apple wine. LWT, 173, 114389. https://doi.org/10.1016/j.lwt.2022.114389
24. Hasanzadeh, B., Abbaspour-Gilandeh, Y., Soltani-Nazarloo, A., Cruz-Gámez, E. D., Hernández-Hernández, J. L., & Martínez-Arroyo, M. (2022). Non-Destructive Measurement of Quality Parameters of Apple Fruit by Using Visible/Near-Infrared Spectroscopy and Multivariate Regression Analysis. Sustainability, 14(22). https://doi.org/10.3390/su142214918
25. Horvat, M., Guthausen, G., Tepper, P., Falco, L., & Schuchmann, H. P. (2014). Non-destructive, quantitative characterization of extruded starch-based products by magnetic resonance imaging and X-ray microtomography. Journal of Food Engineering, 124, 122-127. https://doi.org/10.1016/j.jfoodeng.2013.10.006
26. Jiang, Q., Zhang, M., Mujumdar, A. S., & Wang, D. (2023). Non-destructive quality determination of frozen food using NIR spectroscopy-based machine learning and predictive modelling. Journal of Food Engineering, 343, 111374. https://doi.org/10.1016/j.jfoodeng.2022.111374
27. Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016). Fusion of artificial senses as a robust approach to food quality assessment. Journal of Food Engineering, 171, 230-239. https://doi.org/10.1016/j.jfoodeng.2015.10.007
28. Khodabakhshian, R., & Emadi, B. (2016). Determination of ripeness stages of Mazafati variety of date fruit by Raman spectroscopy. Journal of Agricultural Machinery, 6(1), 201-213. (in Persian). https://doi.org/10.22067/jam.v6i1.34501
29. Khodabakhshian, R., Bayati, M. R., & Emadi, B. (2022). Adulteration detection of Sudan Red and metanil yellow in turmeric powder by NIR spectroscopy and chemometrics: The role of preprocessing methods in analysis. Vibrational Spectroscopy, 120, 103372. https://doi.org/10.1016/j.vibspec.2022.103372
30. Khodabakhshian, R., Seyedalibeyk Lavasani, H., & Weller, P. (2023). A methodological approach to preprocessing FTIR spectra of adulterated sesame oil. Food Chemistry, 419, 136055. https://doi.org/10.1016/j.foodchem.2023.136055
31. Khodabakhshian, R., & Baghbani, R. (2025). Qualitative Analysis of Apple Fruit during Storage using Magnetic Resonance Imaging. Journal of Agricultural Machinery, 15(1), 115-127. https://doi.org/10.22067/jam.2024.87861.1243
32. Kokalj, D., Zlatić, E., Cigić, B., Kobav, M. B., & Vidrih, R. (2019). Postharvest flavonol and anthocyanin accumulation in three apple cultivars in response to blue-light-emitting diode light. Scientia Horticulturae, 257, 108711. https://doi.org/10.1016/j.scienta.2019.108711
33. Kotwaliwale, N., Singh, K., Kalne, A., Jha, S. N., Seth, N., & Kar, A. (2014). X-ray imaging methods for internal quality evaluation of agricultural produce. Journal of Food Science and Technology, 51(1), 1-15. https://doi.org/10.1007/s13197-011-0485-y
34. Kumar, M., Barbhai, M. D., Esatbeyoglu, T., Zhang, B., Sheri, V., Dhumal, S., & Lorenzo, J. M. (2022). Apple (Malus domestica) seed: A review on health promoting bioactivities and its application as functional food ingredient. Food Bioscience, 50, 102155. https://doi.org/10.1016/j.fbio.2022.102155
35. Li, X., Zhang, L., Zhang, Y., Wang, D., Wang, X., Yu, L., ..., & Li, P. (2020). Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils. Trends in Food Science & Technology, 101, 172-181. https://doi.org/10.1016/j.tifs.2020.05.002
36. Lei, T., & Sun, D.-W. (2019). Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review. Trends in Food Science & Technology, 88, 527-542. https://doi.org/10.1016/j.tifs.2019.04.013
37. Lu, R., & Cen, H. (2013). Non-destructive methods for food texture assessment. In D. Kilcast (Ed.), Instrumental assessment of food sensory quality (pp. 230–255). Cambridge, UK: Woodhead Publishing. https://doi.org/10.1533/9780857098856.2.230
38. Lu, L., Hu, Z., Hu, X., Li, D., & Tian, S. (2022). Electronic tongue and electronic nose for food quality and safety. Food Research International, 162, 112214. https://doi.org/10.1016/j.foodres.2022.112214
39. Luna, J. A., Wijesinghe, R. E., Lee, S.-Y., Ravichandran, N. K., Saleah, S. A., Seong, D., & Kim, J. (2024). Non-destructive morphological screening for the assessment of postharvest storage effect on pears stored with apples using optical coherence tomography. Optik, 301, 171678. https://doi.org/10.1016/j.ijleo.2024.171678
40. Magwaza, L. S., & Opara, U. L. (2014). Investigating non-destructive quantification and characterization of pomegranate fruit internal structure using X-ray computed tomography. Postharvest Biology and Technology, 95, 1-6. https://doi.org/10.1016/j.postharvbio.2014.03.014
41. Martinez, J. J., Melgarejo, P., Hernández, F., Salazar, D. M., & Martinez, R. (2006). Seed characterisation of five new pomegranate (Punica granatum) varieties. Scientia Horticulturae, 110(3), 241-246. https://doi.org/10.1016/j.scienta.2006.07.018
42. Mazhar, M., Joyce, D., Cowin, G., Brereton, I., Hofman, P., Collins, R., & Gupta, M. (2015). Non-destructive 1H-MRI assessment of flesh bruising in avocado (Persea americana) cv. Hass. Postharvest Biology and Technology, 100, 33-40. https://doi.org/10.1016/j.postharvbio.2014.09.006
43. Modupalli, N., Naik, M., Sunil, C. K., & Natarajan, V. (2021). Emerging non-destructive methods for quality and safety monitoring of spices. Trends in Food Science & Technology, 108, 133-147. https://doi.org/10.1016/j.tifs.2020.12.021
44. Mohd Ali, M., & Hashim, N. (2022). Chapter 37: Non-destructive methods for detection of food quality. In R. Bhat (Ed.), Future foods (pp. 645-667). Academic Press.
45. Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2), 99-118. https://doi.org/10.1016/j.postharvbio.2007.06.024
46. Pires, T. C. S. P., Dias, M. I., Barros, L., Alves, M. J., Oliveira, M. B. P. P., Santos-Buelga, C., & Ferreira, I. C. F. R. (2018). Antioxidant and antimicrobial properties of dried Portuguese apple variety (Malus domestica cv Bravo de Esmolfe). Food Chemistry, 240, 701-706. https://doi.org/10.1016/j.foodchem.2017.08.010
47. Rana, S., Gupta, S., Rana, A., & Bhushan, S. (2015). Functional properties, phenolic constituents and antioxidant potential of industrial apple pomace for utilization as active food ingredient. Food Science and Human Wellness, 4(4), 180-187. https://doi.org/10.1016/j.fshw.2015.10.001
48. Rodríguez Madrera, R., Pando Bedriñana, R., & Suárez Valles, B. (2017). Enhancement of the nutritional properties of apple pomace by fermentation with autochthonous yeasts. LWT- Food Science and Technology, 79, 27-33. https://doi.org/10.1016/j.lwt.2017.01.021
49. Sethi, S., Joshi, A., Arora, B., Bhowmik, A., Sharma, R. R., & Kumar, P. (2020). Significance of FRAP, DPPH, and CUPRAC assays for antioxidant activity determination in apple fruit extracts. European Food Research and Technology, 246(3), 591-598. https://doi.org/10.1007/s00217-020-03432-z
50. Schoeman, L., Williams, P., du Plessis, A., & Manley, M. (2016). X-ray micro-computed tomography (μCT) for non-destructive characterisation of food microstructure. Trends in Food Science & Technology, 47, 10-24. https://doi.org/10.1016/j.tifs.2015.10.016
51. Tang, Y., Wang, F., Zhao, X., Yang, G., Xu, B., Zhang, Y., & Li, L. (2023). A nondestructive method for determination of green tea quality by hyperspectral imaging. Journal of Food Composition and Analysis, 123, 105621. https://doi.org/10.1016/j.jfca.2023.105621
52. Tempelaere, A., Minh Phan, H., Van De Looverbosch, T., Verboven, P., & Nicolai, B. (2023). Non-destructive internal disorder segmentation in pear fruit by X-ray radiography and AI. Computers and Electronics in Agriculture, 212, 108142. https://doi.org/10.1016/j.compag.2023.108142
53. Tempelaere, A., Van Doorselaer, L., He, J., Verboven, P., & Nicolai, B. M. (2024). BraeNet: Internal disorder detection in ‘Braeburn’ apple using X-ray imaging data. Food Control, 155, 110092. https://doi.org/10.1016/j.foodcont.2023.110092
54. Uzhel, A. S., Zatirakha, A. V., Smolenkov, A. D., & Shpigun, O. A. (2018). Quantification of inorganic anions and organic acids in apple and orange juices using novel covalently-bonded hyperbranched anion exchanger with improved selectivity. Journal of Chromatography A, 1567, 130-135. https://doi.org/10.1016/j.chroma.2018.06.065
55. Van Dael, M., Lebotsa, S., Herremans, E., Verboven, P., Sijbers, J., Opara, U. L., & Nicolaï, B. M. (2016). A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs. Postharvest Biology and Technology, 112, 205-214. https://doi.org/10.1016/j.postharvbio.2015.09.020
56. Van Dael, M., Verboven, P., Zanella, A., Sijbers, J., & Nicolai, B. (2019). Combination of shape and X-ray inspection for apple internal quality control: in silico analysis of the methodology based on X-ray computed tomography. Postharvest Biology and Technology, 148, 218-227. https://doi.org/10.1016/j.postharvbio.2018.05.020
57. Van De Looverbosch, T., Rahman Bhuiyan, M. H., Verboven, P., Dierick, M., Van Loo, D., De Beenbouwer, J., & Nicolaï, B. (2020). Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning. Food Control, 113, 107170. https://doi.org/10.1016/j.foodcont.2020.107170
58. Wang, Y., Zhang, J., Wang, D., Wang, X., Zhang, F., Chang, D., & Wang, X. (2023). Effects of cellulose nanofibrils treatment on antioxidant properties and aroma of fresh-cut apples. Food Chemistry, 415, 135797. https://doi.org/10.1016/j.foodchem.2023.135797
59. Zhang, S., Liu, S., Shen, L., Chen, S., He, L., & Liu, A. (2022). Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review. Current Research in Food Science, 5, 1305-1312. https://doi.org/10.1016/j.crfs.2022.08.006
60. Zhang, Y., Lin, Y., Tian, H., Tian, S., & Xu, H. (2023). Non-destructive evaluation of the edible rate for pomelo using X-ray imaging method. Food Control, 144, 109358. https://doi.org/10.1016/j.foodcont.2022.109358
61. Zoran, I. S., Nikolaos, K., & Ljubomir, S. (2014). Tomato fruit quality from organic and conventional production. In I. S. Zoran, K. Nikolaos, & S. Ljubomir (Eds.), Organic agriculture towards sustainability (pp. 147-169). Rijeka, Croatia: In Tech Europe. https://doi.org/10.5772/58239