| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,006 |
| تعداد مقالات | 33,642 |
| تعداد مشاهده مقاله | 44,578,476 |
| تعداد دریافت فایل اصل مقاله | 31,655,240 |
پیش‌بینی و تصویرسازی توزیع عیار چغندرقند با استفاده از روش تصویربرداری فراطیفی | ||
| چغندرقند | ||
| مقاله 8، دوره 40، شماره 2، مهر 1403، صفحه 237-250 اصل مقاله (797.26 K) | ||
| نوع مقاله: کامل علمی - پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/jsb.2025.368340.1381 | ||
| نویسندگان | ||
| محسن مهدیانی1؛ مهدی خجسته پور* 1؛ محمودرضا گلزاریان2 | ||
| 1گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| 2گروه فناوری اطلاعات و مهندسی، دانشکده مرداک، پرث، استرالیا | ||
| چکیده | ||
| تعیین ساکارز چغندرقند (عیار)، به‌عنوان شاخص مهم، هم از نظر اقتصادی برای کشاورزان، هم از جهت کیفیت فرآیند تولید برای کارخانه‌های قند و هم برای مراکز تحقیقاتی در ارزیابی عملکرد بذرهای مختلف چغندرقند از اهمیت بسزایی برخوردار است. پلاریمتری روشی متداول برای اندازه‌گیری عیار چغندرقند است که علاوه بر مصرف مواد شیمیایی و نیاز به نیروی متخصص، زمان‌بر نیز است. برای مقابله با این معایب، ضرورت جایگزینی این روش با روش‌های سریع‌تر و مقرون‌به‌صرفه و با قابلیت اطمینان بالا، اجتناب‌ناپذیر است، با این هدف از 150 محموله چغندرقند تحویلی به شرکت قند تربت‌حیدریه نمونه‌برداری شد. هر نمونه شامل 40 الی 50 ریشه چغندرقند بود که به‌صورت تصادفی انتخاب، شستشو و خمیرگیری شدند. از هر نمونه حداقل 500 گرم خمیر یکنواخت تهیه گردید. بخشی از خمیر به‌دست‌آمده عیارسنجی و بخشی دیگر مورد تصویربرداری فراطیفی در دامنه‌ی طول موجی 400 تا 950 نانومتری با وضوح 2 نانومتری قرار گرفت. در این مطالعه از دو روش پیش‌پردازش طیفی، دو روش انتخاب طول موج مؤثر و سه روش رگرسیونی استفاده شد. از 18 مدل رگرسیونی توسعه‌یافته، مدل حداقل مربعات جزئی با پیش‌پردازش نرمال متغیر استاندارد و الگوریتم طرح‌ریزی متوالی بهترین عملکرد را با ضریب تعیین و ریشه میانگین مربعات خطای کالیبراسیون به ترتیب برابر با 89/0 و 28/0 و برای اعتبارسنجی، به ترتیب برابر با 91/0 و 24/0 ارائه داد. این مطالعه برای نخستین‌بار امکان تصویرسازی توزیع مکانی عیار قند را در سطح پیکسل خمیر چغندرقند با مقدار ضریب تعیین و ریشه میانگین مربعات خطای به ترتیب برابر 71/0 و 89/2 فراهم آورد. این توانایی نوآورانه، امکان شناسایی ناهمگنی عیار در سطح نمونه‌ها را فراهم می‌کند. | ||
| کلیدواژه‌ها | ||
| پیش‌پردازش طیفی؛ تصویربرداری فراطیفی؛ عیار چغندرقند | ||
| عنوان مقاله [English] | ||
| Prediction and visualization of sugar content distribution in sugar beet using hyperspectral imaging | ||
| نویسندگان [English] | ||
| Mohsen Mahdiani1؛ Mehdi Khojastehpour1؛ Mahmood Reza Golzarian2 | ||
| 1Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 2School of IT & Engineering, Murdoch University, WA 6150, Australia | ||
| چکیده [English] | ||
| Extended Abstract Introduction Sugar beet (Beta vulgaris) is the second-largest source of sugar globally and plays a critical role in food security. The sucrose content (sugar content) of sugar beet is a key indicator that determines its economic value for growers, the quality of sugar production in factories, and the performance of cultivars in research. Traditionally, polarimetry has been used to measure sugar content; however, it is time-consuming, relies on hazardous chemicals like lead acetate, and requires skilled personnel. These limitations necessitate faster, cost-effective, and reliable alternatives. This study explores hyperspectral imaging to predict and visualize sugar content in sugar beet paste, addressing the research question: can hyperspectral imaging accurately estimate and map sugar content at the pixel level?. The hypothesis is that hyperspectral imaging, combined with advanced preprocessing and regression models, can provide a robust alternative to polarimetry. Materials and Methods A total of 150 sugar beet samples, each containing 40–50 roots, were randomly collected from consignments delivered to the Torbat-Heydarieh Sugar Company. The samples were washed, pulped using an industrial 8-blade pulper, and homogenized for approximately 3 minutes to produce 400–500 grams of uniform pulp per sample. Each pulp was then divided into two portions: one for polarimetric sugar content measurement at the Sugar Beet Seed Institute (Karaj, Iran), using a Betalyzer device at a wavelength of 589 nm (ICUMSA method), and the other for hyperspectral imaging at the Automation and Computer Vision Laboratory, Ferdowsi University of Mashhad. A desktop hyperspectral imaging system (Parto Afzar Sanat, Zanjan, Iran) operating in the 400–950 nm range with a spectral resolution of 2 nm was used. The system featured a 200 mm scanning length, a spatial resolution of 0.05 mm, and four halogen light sources. Images were periodically calibrated using both white and dark reference panels. Two preprocessing methods-Standard Normal Variate (SNV) and Savitzky-Golay (SG) with a 5-point window and third-degree polynomial-were applied. Two wavelength selection techniques were evaluated: Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). Three regression models were used: Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Support Vector Regression (SVR). Outliers were removed using Monte Carlo Partial Least Squares (MCPLS) with 5000 iterations. Data analysis was performed using MATLAB (2022b) on an Acer Aspire system equipped with 20 GB RAM and an Intel Core i7 processor. Results and Discussion Among the 18 regression models developed, the PLS model with SNV preprocessing and SPA wavelength selection outperformed the others, achieving a coefficient of determination (R²) of 0.89 and a root mean square error (RMSE) of 0.28 for calibration, and an R² of 0.91 and RMSE of 0.24 for validation. This model effectively identified key wavelengths associated with sucrose absorption in the 400–950 nm range, with regression coefficients indic ating strong correlations (e.g., peaks at 620 nm and 750 nm, linked to chlorophyll and a water overtone, respectively). For the first time, this study visualized the spatial distribution of sugar content at the pixel level in sugar beet pulp, yielding an R² of 0.71 and RMSE of 2.89. The lower pixel-level accuracy compared with mean predictions may stem from localized sucrose variations, spectral noise, or model limitations, as the model was trained on average spectra. This finding aligns with previous studies, which reported superior PLS performance in sugar content prediction. The SNV preprocessing significantly reduced the effects of light scattering, enhancing model accuracy. The SPA algorithm efficiently selected optimal wavelengths, reducing model complexity. This visualization capability enables heterogeneity analysis, offering applications in quality control and process optimization in sugar factories. However, pixel-level accuracy requires further improvement through advanced algorithms and noise reduction techniques. Future studies should explore environmental factors (e.g., humidity, temperature) and integrate hyperspectral imaging with polarimetry to enhance precision.. Conclusion Hyperspectral imaging, combined with SNV preprocessing, SPA wavelength selection, and PLS regression, provides a rapid and reliable approach for predicting sugar content in sugar beet pulp, achieving high accuracy (R² = 0.91, RMSE = 0.24). The novel visualization of sugar content distribution at the pixel level offers valuable insights into sample heterogeneity, with promising applications in quality monitoring within the sugar industry. Further research is required to improve pixel-level accuracy and address susceptibility to environmental factors. | ||
| کلیدواژه‌ها [English] | ||
| Hyperspectral imaging, Spectral preprocessing, Sugar beet, Sugar content | ||
| مراجع | ||
|
Ames R, Camp S, Cox R, Mathurin G. The Automated laboratory for sugar processing. Journal of Sugar Beet Research. 2021; 58: 5-39. Doi: http://doi.org/10.5274/JSBR.58.1.5 Anonymous. FAO. OECD-FAO Agricultural Outlook 2024-2033, OECD Publishing, Paris/ FAO, Rome. 2024 Doi:https://doi.org/10.1787/4c5d2cfb-en Babaee B, Abdollahian Noghabi M, Jahadakabr M R, Uosefabadi V. Introduction of appropriate method for determining of sugar content in sugar beet produced under drought, salinity and normal conditions. Journal of Sugar Beet. (In Persion with English abstract). 2013; 29 (1): 111-199. Doi: https://doi.org/10.22092/jsb.2013.1298 Babaee B, Khanmohammadi M, Garmarudi AB,Abdollahin Noghabi M. Effect of peeling and point of spectral recording on sucrose determination in sugar beet root using near infrared spectroscopy. Infrared Physics and Technology. 2019; 103: 103065. Doi: https://doi.org/10.1016/j.infrared.2019.103065 Barreto A, Paulus S, Varrelmann M, Mahlein AK. Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms. Journal of Plant Diseases and Protection. 2020;127: 441-451. Doi: https://doi.org/10.1007/s41348-020-00344-8 Fasahat P, Rezaei J, Sharifi M, Azizi H, Fatuhi K, Mahdikhani P, Pedram A, Jalilian, A, Babaei B. Assessment of root and white sugar yield stability of sugar beet genotypes. Seed and Plant Journal. 2022; 38 (2): 223-237. Doi: https://doi.org/10.22092/spj.2023.361320.1297 Feng S, Shang J, Tan T, Wen Q and Meng Q. Nondestructive quality assessment and maturity classification of loquats based on hyperspectral imaging. Scientific Reports. 2023; 13 (1): 13189.Doi: https://doi.org/10.1038/s41598-023-40553-3 Governici JL, Faria RM, dos Reis Tinini RC, Mederos BJT. Tomatoes maturation analysis with reflectance spectral images. Journal of Agricultural Science and Technology B. 2017;Doi: http://dx.doi.org/10.17265/2161-6264/2017.06.007 Guo W-L, Du Y-P, Zhou Y-C, Yang S, Lu J-H, Zhao H-Y, Wang Y and Teng L-R. At-line monitoring of keyparameters of nisin fermentation by near infrared spectroscopy, chemometric modeling and model improvement. World Journal of Microbiology and Biotechnology. 2012; 28: 993-1002.Doi: https://doi.org/10.1007/s11274-011-0897-x Ji H, Wang W, Chong D, ZhangB. CARS algorithm-based detection of wheat moisture content before harvest. Symmetry. 2020; 12 (1): 115. Doi: https://doi.org/10.3390/sym12010115 Jo K, Lee S, Jeong S-K-C, Lee D-H, Jeon H, Jung S. Hyperspectral imaging–based assessment of fresh meat quality: Progress and applications. Microchemical Journal. 2024; 197.Doi: https://doi.org/10.1016/j.microc.2023.109785 Jong LS, Post AL, Geldof F, Dashtbozorg B, Ruers TJM, Sterenborg H. Separating surface reflectance from volume reflectance in medical hyperspectral imaging. Diagnostics (Basel). 2024; 14 (16)Doi: https://doi.org/10.3390/diagnostics14161812 Kunz M. Sugar analysis beet. The international commission for uniform methods of sugar analysis (ICUMSA). 2004. General Subject 6: 110-117 Leucker M, Mahlein A-K, Steiner U, Oerke E-C. Improvement of lesion phenotyping in Cercospora beticola–sugar beet interaction by hyperspectral imaging. Phytopathology. 2016; 106 (2): 177-184. Doi: https://doi.org/10.1094/PHYTO-04-15-0100-R Mahlein A-K, Steiner U, Hillnhütter C, Dehne H-W, Oerke E-C. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods. 2012; 8: 1-13. Doi: https://doi.org/10.1186/1746-4811-8-3 Minaei S, Bagherpour H, Abdollahian Noghabi M,horasani Fardvani M, Forughimanesh F. A comparative study concerning linear and nonlinear models to determine sugar content in sugar beet by near infrared spectroscopy (NIR). Journal of Food Biosciences and Technology. 2016; 6 (1): 13-22 Pan L, Lu R, Tu K, Cen H. Detection of moisture, soluble solids, and sucrose content and mechanical properties of sugar beet by hyperspectral scattering imaging. Transactions of the ASABE. 2014; Paper number 141912563. Doi: https://doi.org/10.13031/aim.20141912563 Pan L, Lu R, Zhu Q, McGrath J M, Tu K. Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy. Postharvest Biology and Technology. 2015a; 102: 42-50. Doi: https://doi.org/10.1016/j.postharvbio.2015.02.005 Pan L, Lu R, Zhu Q, Tu K, Cen H. Predict compositions and mechanical properties of sugar beet using hyperspectral scattering. Food and Bioprocess Technology. 2016; 9: 1177-1186.Doi: https://doi.org/10.1007/s11947-016-1710-5 Pan L, Zhu Q, Lu R, McGrath JM. Detection of sucrose content of sugar beet by visible/near infrared spectroscopy. ASABE Meeting Presentation; 2013 July 21- 24; Kansas City, Missouri, USA. 2013 p: 9. Doi: https://doi.org/10.13031/aim.20131619051 Pan L, Zhu Q, Lu R, McGrath JM. Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy. Food Chemistry. 2015b; 167: 264-271.Doi: https://doi.org/10.1016/j.foodchem.2014.06.117 Pirzad A, Mazlomi M, Zardashti MR. Effect of nano-iron foliar application on mineral elements and root α-aminose and its relation with yield in sugar beet (Beta vulgaris L.). Research in Field Crop Journal. 2013; 1 (1): 54-63 Roggo Y, Duponchel L. Huvenne JP. Quality evaluation of sugar beet (Beta vulgaris) by near-infrared spectroscopy. Journal of agricultural and food chemistry. 2004; 52 (5): 1055-1061.Doi: https://doi.org/10.1021/jf0347214 Roggo Y, Duponchel L, Huvenne JP. Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy. Analytica Chimica Acta. 2003; 477 (2): 187-200. Doi: https://doi.org/10.1016/S0003-2670(02)01422-8 Shao Y, Liu Y, Xuan G, Wang Y, Gao Z, Hu Z, Han X, Gao C and Wang K. Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato. RSC advances. 2020; 10 (55): 33148-33154.Doi: https://doi.org/10.1039/C9RA10630H Thien Pham Q, Liou NS. The development of on-line surface defect detection system for jujubes based on hyperspectral images. Computers and Electronics in Agriculture. 2022; 194: 106743. Doi: https://doi.org/10.1016/j.compag.2022.106743 Tian X, Li J, Wang Q, Fan S, Huang W and Zhao C. A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging. Biosystems Engineering. 2019; 183: 110-120. Doi: https://doi.org/10.1016/j.biosystemseng.2019.04.012 Viscarra Rossel RA. ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems. 2008; 90(1): 72-83. Doi: https://doi.org/10.1016/j.chemolab.2007.06.006 Wang Q, Che Y, Shao K, Zhu J, Wang R, Sui Y, Guo Y, Li B, Meng L, Ma Y. Estimation of sugar content in sugar beet root based on UAV multi-sensor data. Computers and Electronics in Agriculture. 2022; 203: 107433. Doi: https://doi.org/10.1016/j.compag.2022.107433 Xu S, Guo Y, Liang X, Lu H. Intelligent rapid detection techniques for low-content components in fruits and vegetables: a comprehensive review. Foods. 2024; 13(7): 1116. Doi: https://doi.org/10.3390/foods13071116 Xuan G, Gao C, Shao Y. Spectral and image analysis of hyperspectral data for internal and external quality assessment of peach fruit. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2022; 272: 121016. Doi: https://doi.org/10.1016/j.saa. 2022.121016 Zhang C, Liu F, Kong W , He Y. Application of visible and near-infrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves. Sensors. 2015; 15(7): 16576-16588. Doi: https://doi.org/10.3390/s150716576 | ||
|
آمار تعداد مشاهده مقاله: 36 تعداد دریافت فایل اصل مقاله: 33 |
||