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پایش و مدل سازی چروکیدگی ورقه های سیب هنگام خشک شدن در هوای گرم با استفاده از ویژگیهای بافت تصاویر پس پراکنش لیزر | ||
| تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی | ||
| مقاله 11، دوره 18، شماره 68، شهریور 1396، صفحه 133-150 اصل مقاله (1014.09 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/erams.2017.107124.1121 | ||
| نویسندگان | ||
| منصوره مظفری* 1؛ اصغر محمودی2؛ بهاره جمشیدی3 | ||
| 1دانشجوی دکتری گروه مهندسی بیوسیستم دانشکده کشاورزی دانشگاه تبریز و عضوهیئت علمی مرکز تحقیقات کشاورزی و منابع طبیعی استان آذربایجان‌شرقی | ||
| 2دانشیار گروه مهندسی بیوسیستم دانشکده کشاورزی دانشگاه تبریز | ||
| 3استادیار بخش تحقیقات ماشین‌های کشاورزی مؤسسه فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| چکیده | ||
در این مقاله، امکان استفاده از ویژگیهای بافت تصویرهای پسپراکنش نور لیزر در پایش و مدلسازی چروکیدگی بههنگام خشک شدن ورقههای سیب با هوای گرم در سه طول موج (650، 780 و 880 نانومتر) در محدودۀ مرئی و مادون قرمز نزدیک بررسی شد. ویژگیهای مرتبط با چهار روش آنالیز بافت تصویر، شامل آمارههای درجه اول هیستوگرام، ماتریس هموقوعی، ماتریس طول گام سطح خاکستری، و تبدیل موجک استخراج و بهصورت جداگانه برای هر روش و در ترکیب با هم در تدوین مدلهای واسنجی بهکار گرفته شد. مدل رگرسیون چندمتغیرۀ خطی گامبهگام برای مدلسازی و تعیین ویژگیهاییکه مؤثرترین نقش را در تدوین مدل داشتند، بهکار رفت. نتایج بهدست آمده قابلیت ویژگیهای مستخرج از بافت تصویرهای پسپراکنش نور لیزر را در ناحیۀ طولموجهای مادونقرمز نزدیک در پیشبینی چروکیدگی محصول نشان میدهد، بهطوریکه استفاده از ویژگی همگنی ماتریس هموقوعی در راستای 90 درجه در 880 نانومتر (با 95/0R2p= و 15/5RMSEp=) و ترکیب ویژگیهای مستخرج از چهار روش آنالیز بافت در 780 نانومتر (با 94/0 R2p = و 61/5RMSEp=) توانستند مدلهایی با دقت بالا حاصل کنند. این پژوهش نشان میدهد که در فرآیند خشککردن با هوایگرم، تصویربرداری پسپراکنش نور لیزر میتواند بهعنوان روشی غیرمخرب، سریع، و ارزان در پیشبینی چروکیدگی بهکار گرفته شود. | ||
| کلیدواژه‌ها | ||
| آنالیز بافت تصویر؛ تصویربرداری پس‌پراکنش‌لیزر؛ چروکیدگی؛ خشککردن | ||
| عنوان مقاله [English] | ||
| Monitoring and modeling of apple slices shrinkage during drying in hot air using texture features of laser backscattering images | ||
| نویسندگان [English] | ||
| Mansoureh Mozaffari1؛ Asghar Mahmoudi2؛ Bahareh Jamshidi3 | ||
| 1PHD student of Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran. | ||
| 2Associate professor of Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| 3Assistant professor of Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran. | ||
| چکیده [English] | ||
| The present paper investigated the feasibility of using texture-based features of laser backscattering images in monitoring and modeling of apple slices shrinkage during hot air drying. The backscattering imaging was performed at three wavelengths (650, 780 and 880 nm) in the visible and near-infrared regions. The acquired images were subjected to four texture analysis methods including first-order statistics of image histogram, co-occurrence matrix, gray level run-length matrix and wavelet transform. Stepwise multiple linear regressions was used to develop models and determine the most effective features by using individual types of feature sets and their combinations as inputs of calibration models. The results showed the capability of the texture features extracted from the laser backscattering images in the near-infrared wavelength range for prediction of apple slices shrinkage; by using homogeneity feature of co-occurrence matrix-90˚ at 880 nm (with Rp2=0.95, RMSEp=5.15) and fusion of the four feature sets extracted from different texture analysis methods at 780nm (with Rp2=0.94, RMSEp=5.61), could make models with high accuracy. This study showed that Laser backscattering imaging technique can be used as a non-destructive, rapid and low-cost method for prediction of the shrinkage in the process of hot air drying of apple slices. | ||
| کلیدواژه‌ها [English] | ||
| Hot Air Drying, Image Texture Analysis, Laser Backscattering Imaging, shrinkage | ||
| مراجع | ||
|
Aguilera, J. M. 2003. Drying and dried products under the microscope. Int. J. Food Sci. Tech.
Bevk, M. and Kononenko, I. 2002. A statistical approach to texture description of medical images: a preliminary study. Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems. June 4-7. Maribor, Slovenia
Bobelyn, E., Serban, A., Nicu, M., Lammertyn, J., Nicolai, B. and Saeys, W. 2010. Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol. Tec. 55(3): 133-143.
Contreras, C., Martín-Esparza, M. E., Chiralt, A. and Martínez-Navarrete, N. 2008. Influence of microwave application on convective drying: Effects on drying kinetics, and optical and mechanical properties of apple and strawberry. J. Food Eng. 88(1): 55-64.
Fernández, L., Castillero, C. and Aguilera, J. M. 2005. An application of image analysis to dehydration of apple discs. J. Food Eng. 67(1-2): 185-193.
Gao, X. and Tan, J. 1996. Analysis of expended-food texture by image processing part II: Mechanical properties. J. Food Process. Eng. 19, 445-456.
Haralick, R. M., Shanmugam, K. and Dinstein, I. 1973. Textural features for image classification. IEEE T. SYST. MAN CYB. 6, 610-621.
Hashim, N., Pflanz, M., Regen, C., Janius, R. B., Abdul Rahman, R., Osman, A., Shitan, M. and Zude, M. 2013. An approach for monitoring the chilling injury appearance in bananas by means of backscattering imaging. J. Food Eng. 116, 28-36.
Karimi, M., Fathi, M., Sheykholeslam, Z., Sahraiyan, B. and Naghipoor, F. 2012. Effect of different processing parameters on quality factors and image texture features of bread. J. Bioprocess. Biotech. 2, 127. doi: 10.4172/2155-9821.1000127.
Kavdir, I. and Guyer, D. E. 2002. Apple sorting using artificial neural networks and spectral imaging.
Kondo, N., Ahmad, U., Monta, M. and Murasc, H. 2000. Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Comput. Electron. Agric. 29, 135-147.
Krokida, M. K., Karathanos, V. T. and Maroulis, Z. B. 2000. Compression analysis of dehydrated agricultural products. Dry. Technol. 18(1-2): 395-408. doi: 10.1080/07373930008917711.
Lewicki, P. P. and Lukaszuk, A. 2000. Changes of rheological properties of apple tissue undergoing convective drying. Dry. Technol. 18(3): 707-722. doi: 10.1080/07373930008917733.
Lorente, D., Zude, M., Idler, C., Gomez-Sanchis, J. and Blasco, J. 2015. Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. J. Food Eng. 154, 76-85.
Mallat, S. G. 1989. A theory for multi resolution signal decomposition: the wavelet representation. IEEE T. PATTERN. ANAL. (7): 674-693.
Mayor, L. and Sereno, A. M. 2004. Modelling shrinkage during convective drying of food materials: A review. J. Food Eng. 61, 373-386.
Mollazade, K., Omid, M. Akhlaghian-Tab, F. and Mohtasebi, S. S. 2012. Principles and applications of light backscattering imaging in quality evaluation of agro-food products: a review. Food Bioprocess Tech. 5(5): 1465-1485. doi: 10.1007/s11947-012-0821-x.
Mollazade, K., Omid, M., Akhlaghian-Tab, F., Rezaei-Kalaj, Y. and Mohtasebi, S. S. 2013. Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging. Comput. Electron. Agr. 98, 34-45.
Moreira, R., Figueiredo, A. and Sereno, A. 2000. Shrinkage of apple disks during drying by warm air convectional freeze drying. Dry. Technol. 18(1-2): 279-294.
Mulet, A., Garcı´a-Reverter, J., Bon, J. and Berna, A. 2000. Effect of shape on potato and cauliflower shrinkage during drying. Dry. Technol. 18(6): 1201-1219.
Otsu, N, 1979. A threshold selection method from gray-level histograms. IEEE T. SYST. MAN CYB.
Qing, Z., Ji, B. and Zude, M. 2007. Predicting soluble solid content and firmness in apple fruit by means of laser light backscattering image analysis. J. Food Eng. 82, 58-67.
Ramos, I. N., Silva, C. L. M., Sereno, A. M. and Aguilera, J. M. 2004. Quantification of microstructural changes during first stage air drying of grape tissue. J. Food Eng. 62(2): 159-164.
Romano, G., Nagle, M. and Müller, J. 2016. Two-parameter Lorentzian distribution for monitoring physical parameters of golden colored fruits during drying by application of laser light in the Vis/NIR spectrum. Innov. Food Sci. Emerg. Technol. 33, 498-505.
Romano, G., Baranyai, L., Gottschalk, K. and Zude, M. 2008. An approach for monitoring the moisture content changes of drying banana slices with laser light backscattering imaging. Food Bioprocess. Technol. 1(4): 410-414.
Romano, G., Nagle, M., Argyropoulos, D. and Muller, J. 2011. Laser light backscattering to monitor moisture content, soluble solid content and hardness of apple tissue during drying. J. Food Eng. 104, 657-662.
Schultz, E. L., Mazzuco, M. M., Machado, R. A. F., Bolzan, A., Quadri, M. B. and Quadri, M. G. N. 2007. Effect of pre-treatment on drying, density and shrinkage of apple slices. J. Food Eng. 78, 1103–1110.
Seifert, B., Zude, M., Spinelli, L. and Torricelli, A. 2014. Optical properties of developing pip and stone fruit reveal underlying structural changes. Physiol. Plantarum. doi:10.1111/ppl.12232.
Seiiedlou, S., Ghasemzadeh, H. R., Hamdami, N., Talati, F. and Moghaddam, M. 2010. Convective drying of apple: Mathematical modeling and determination of some quality parameters. Int. J. Agric. Biol. 12, 171-178.
Seiiedlou, S. S., Nalbandi, H., Ghasemzadeh, H. R. and Hamdami, N. 2014. Modeling of apple slices shrinkage during the convectional drying to use in simulation of heat and moisture transfer. J. Agric. Mech. 1(2): 25-35. (in Persian)
Talla, A., Puiggali, J. R., Jomaa, W. and Jannot, Y. 2004. Shrinkage and density evolution during drying of tropical fruits: application to banana. J. Food Eng. 64, 103-109.
Tang, X. 1998. Texture information in run-length matrices. IEEE T. Image Process. 7, 1602-1609.
Udomkun, P., Nagle, M., Mahayothee, B. and Müller, J. 2014. Laser-based imaging system for
Udomkun, P., Nagle, M., Argyropoulos, M., Mahayothee, B. and Müller, J. 2016. Multi-sensor
Yadollahinia, A., Latifi, A. and Mahdavi, R. 2009. New method for determination of potato slice shrinkage during drying. Comput. Electron. Agric. 65(2): 268-274.
Zheng, C., Sun, D. W. and Zheng, L. 2006. Recent applications of image texture for evaluation of food qualities - a review. Trends Food Sci. Tech. 17, 113-128. | ||
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