شناسایی تقلب در پودر دارچین با استفاده از تصویربرداری فراطیفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران.

2 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران

3 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه ایلام، ایلام ، ایران.

چکیده

دارچین یکی از ادویه‌های مهم است که دارای خواص دارویی نیز می‌باشد. تشخیص تقلب در پودر دارچین با استفاده از روش‏های آزمایشگاهی پرهزینه، زمان‌بر و نیازمند متخصص است. هدف از تحقیق حاضر تشخیص تقلب در پودر دارچین با استفاده از تصویربرداری فراطیفی است. تصویربرداری فراطیفی به طور گسترده‌ای در ارزیابی کیفیت مواد غذایی استفاده شده است. در پژوهش حاضر تعداد 15 نمونه دارچین با سطوح تقلب 0، 5، 15، 30 و 50 درصد تهیه گردید. مواد تقلبی شامل آرد نخود، آرد گندم و کف دریا بوده که به طور جداگانه مورد استفاده قرار گرفتند. سامانه تصویربرداری فراطیفی نور ساتع شده از نمونه‏ها در محدوده مرئی و فروسرخ نزدیک از طول موج 400 تا 950 نانومتر را دریافت و به صورت تصویر فراطیفی در رایانه ذخیره نمود. پس از انتخاب طول موج‏های موثر و استخراج ویژگی از تصاویر، ویژگی‏های کارا انتخاب و با استفاده از روش ماشین بردار پشتیبان طبقه‏بندی شدند. نرخ طبقه‏بندی صحیح مدل طبقه‌بند با راهبرد یکی در برابر یکی در طبقه‏بندی ویژگی‏های کارای انتخاب شده از تصاویر فراطیفی مرتبط با نور ساتع شده از نمونه‏ها در محدوده مرئی و فروسرخ نزدیک به منظور تشخیص تقلب آرد گندم، نخود، و کف دریا در دارچین به ترتیب برابر 55/95، 56/85، و 66/96 درصد و نرخ طبقه‏بندی صحیح آن با راهبرد  یکی در برابر همه به ترتیب برابر 88/78، 77/77، و 44/94 درصد بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Detection of Adulteration in cinnamon powder using hyperspectral imaging

نویسندگان [English]

  • mohamadhossein nargesi 1
  • Jafar Amiri Parian 2
  • hossein bagherpour 1
  • Kamran Kheiralipour 3
1 Biosystem Mechanical Engineering Department, Faculty of Agriculture, Ilam University, Ilam, Iran.
2 Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
3 Department of Biosystem Engineering, Faculty of Agriculture, Ilam University, Ilam, Iran.
چکیده [English]

Cinnamon is one of the most important spices that has medicinal properties. Detecting adulteration in cinnamon powder using laboratory methods is expensive, time-consuming, and requires expert. Hyperspectral imaging is specifically used in the assessment of food safety and quality. The purpose of the present research is to detect adulteration in cinnamon powder using hyperspectral imaging. In the present study, 15 samples of cinnamon were prepared with 0, 5, 15, 30 and 50% adulteration levels. The adulterants were chickpea flour, wheat flour, and sea foam that were used separately. The hyperspectral imaging system received the light emitted from the samples in the visible and near-infrared ranges from 400 to 950 nm wavelength and recorded their hyperspectral images in the computer. After selecting the effective wavelengths and extracting the features from the images, the efficient features were selected and then classified using the support vector machine method. The correct classification rates of the classifier with one-against-one strategy in classification of the efficient features selected from the hyperspectral images related to the light emitted from the visible and infrared ranges to detect adulteration of wheat flour, chickpea flour, and sea foam powder in cinnamon were 95.55, 85.56, and 96.66%, respectively. Its correct classification rates with one-against-all strategy were equal to 78.88, 77.77, and 94.44%, respectively.

کلیدواژه‌ها [English]

  • Cinnamon
  • Adulteration
  • Hyperspectral imaging
  • Image processing
  • Machine learning

Determining the purity of cinnamon powder using hyperspectral imaging

EXTENDED ABSTRACT

 

Introduction

Cinnamon, an evergreen plant, is one of the most important food seasonings and spices that has been used for thousands of years as medicinal plants in the treatment of diseases. It has antioxidant and medical properties that regulates immune system and is an anti-inflammatory against various diseases. Hyperspectral imaging as a new technique to assess the quality and purity of agricultural and food products. The purpose of this study is to determine the purity of cinnamon powder using hyperspectral image processing technique.

 Materials and Methods

The present research was done in the image processing laboratory of Ilam University, Ilam, Iran. Five levels of impurities including 0, 5, 15, 30 and 50% were considered to be determined by the system. Impurity materials were wheat flour, chickpea flour, and sea foam powder. For each impurity level, three samples were provided and kept in bags. The image was acquired using a line scanning hyperspectral imager. Six hyperspectral images were acquired from each sample so that 18 hyperspectral images were acquired from each impurity level so that 270 hyperspectral images were obtained for each impurity material. MATLAB software was used to analyze hyperspectral images. Image processing step included wavelength selection, feature extraction, and feature selection. The efficient features were classified using the support vector machine method.

 Results and Discussion

The confusion matrixes of the classifier model based on support vector machine method with one-against-one and one-for-all strategies were obtained to calculate the correct classification rates of the models. The correct classification rates of the classifier with one-to-one strategy for detecting chickpea flour, wheat flour, and sea foam powder impurity in cinnamon powder were 95.55, 96.66, and 85.56%, respectively. The correct classification rates of the model with one-against-all strategy were 78.88, 94.44, and 77.77%, respectively.

Conclusion

The results of the present study showed the high ability of hyperspectral imaging technology combined with support vector machine classifier method with one-to-one strategy in detecting wheat flour, chickpea flour, and sea foam impurities in cinnamon powder. The proposed methodology in the present research has different advantages over laboratory-based methods, including non-destructiveness, high speed, and low cost. It is suggested to use other methods to classify hyperspectral images in order to detect impurity in cinnamon. The proposed method in the present research can be used in the future to detect other types of fraud in cinnamon.

Arjomandi, H. R., Kheiralipour, K., Amarloei, A. (2022). Estimation of dust concentration by a novel machine vision system. Scientific Reports, 12(1), 1-8.
Azadnia, R., & Kheiralipour, K. (2022). Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm. Ecological Informatics, 71, 101804. https://doi.org/10.1016/j.ecoinf.2022.101804.
Azadnia, R., Kheiralipour, K. (2021). Recognition of leaves of different medicinal plant species using a robust image processing algorithm and artificial neural networks classifier. Journal of Applied Research on Medicinal and Aromatic Plants, 100327.
Azarmdel, H., Jahanbakhshi, A., Mohtasebi, S. S., Mu˜noz, A.R. (2020). Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM), Postharvest Biol. Technol. 166, 111201.
Ciftci, M., Simsek, U.G., Yuce, A., Yilmaz, O., Dalkilic, B. (2010). Effects of dietary
antibiotic and cinnamon oil supplementation on antioxidant enzyme activities,
cholesterol levels and fatty acid compositions of serum and meat in broiler
chickens. Acta Veterinaria Brno, 79(1), 33-40.
Dashti-Rahmatabadi, M., Vahidi Merjardi, A., Pilavaran, A., Farzan, F. (2009).
Antinociceptive effect of cinnamon extract on formalin induced pain in rat.
SSU_Journals, 17(2), 190-199.
Dhanya, K., Kizhakkayil, J., Syamkumar, S., Sasikumar, B. (2007). Isolation and amplification of genomic DNA from recalcitrant dried berries of black pepper (Piper nigrum L.). A medicinal spice. Mol Biotechnol.7: 165-168.
Farokhzad, S., Modares Motlagh, A., Ahmadi Moghadam, P., Jalali Honarmand, S., Kheiralipour, K. (2020). Application of infrared thermal imaging technique and discriminant analysis methods for non-destructive identification of fungal infection of potato tubers. Journal of Food Measurement and Characterization. 14(1): 88-94.
Farokhzad, S., Modaress Motlagh, A., Ahmadimoghadam, P., Jalali Honarmand, S., & Khaieralipour, K. (2017). Fungal infection in potato tuber using thermal imaging. Iranian Journal of Biosystems Engineering. 48(3):243-253.
Ghandehari Yazdi, A.P., Nikooie, A., Sedaghat Boroujeni, L. (2014). A review of pharmacological properties and functional of Cinnamon. Journal of Medicinal Herbs. 5(3):127-135.
Gomez-Sanchis, J., Gomez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Molto, E., & Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89, 80-86.
Hajimonfarednejad, M., Ostovar, M., Raee, M.J., Hashempur, M.H., Mayer, J.G., Heydari, M. (2019). Cinnamon: A systematic review of adverse events. Clinical nutrition, 38, 2.602-594.
Hamidpour, R., Hamidpour, S., Hamidpour, M., Shahlari, M., Sohraby, M., Shahlari, N. (2017). Russian olive (Elaeagnus angustifolia L.): From a variety of traditional medicinal applications to its novel roles as active antioxidant, anti-inflammatory, anti-mutagenic and analgesic agent. eJTCM; 7 (1): 24-9.
Han, Z., Wan, J., Deng, L., Liu, K. (2016). Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA. PLOS ONE. doi: 10.1371/journal.pone.
Hosainpour, A., Kheiralipour, K., Nadimi, M., Paliwal, J. (2022). Quality Assessment of Dried White Mulberry (Morus alba L.) Using Machine Vision. Horticulturae, 8(11), 1011.
Jahanbakhshi, A., Kheiralipour, K. (2020). Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit. Food Science & Nutrition 8 (7), 3346-335.
Jayas, D. S. (2023). Image Processing: Advances in Applications and Research. Nova Science Publishers, New York, US.
Jiang, H., Wang, W., Zhuang, H., Yoon, S., Yang, Y., Zhao, X. (2019). Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef. Food Analytical Methods https://doi.org/10.1007/s12161-019-01577-6.
Kandahari Yazdi, A., Nikoyi, A., Sadafat Borojni, L. (2014). An overview of the medicinal and practical properties of cinnamon. Journal of Medicinal Plants. Herbal Medicines, Year 5, Number 3, Page 127-135. [in persian].
Khan, M., Saleem, Z., Ahmadm, M., Sohaib, M., Ayaz, H., Mazzara, M. (2020). Hyperspectral imaging for color adulteration detection in red Chili. Appl. Sci, 10, 5955.
Khazaee, Y., Kheiralipour, K., Hosainpour, A., Javadikia, H., Paliwal, J. (2022). Development of a novel image analysis and classification algorithms to separate tubers from clods and stones. Potato Research, 65(1): 1-22.
Kheiralipour, K. (2012). Implementation and construction of a system for detecting fungal infection in pistachio kernel based on thermal imaging (TI) and image processing technology. Ph.D. Dissertation, University of Tehran, Karaj, Iran.
Kheiralipour, K. (2020). Sustainable Production: Definitions, Aspects, and Elements. New York, USA: Nova Science Publishers.
Kheiralipour, K., & Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering. 40(6), e12558.
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S. (2018). Thermal Imaging, Principles, Methods and Applications. 1st Ed. Ilam University Publication, Ilam, Iran.
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D.S. and Siliveru K. (2015b). Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique. Quality Assurance and Safety of Crops & Foods., 8(1): 129-135.
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, DS., Siliveru, K., Malihipour, A. (2021). Processing the hyperspectral images for detecting infection of pistachio kernel by R5 and KK11 isolates of Aspergillus flavus fungus. Iranian Journal of Biosystems Engineering, 52(1): 13-25.
Kheiralipour, K., Chelladurai, V., Jayas, D.S. (2023a). Imaging Systems and Image Processing Techniques. In Image Processing: Advances in Applications and Research. Edited by Jayas, D.S. New York, USA: Nova Science Publishers.
Kheiralipour, K., Jayas D.S. (2023b). Applications of near infrared hyperspectral imaging in agriculture, natural resources, and food in Iran. 15th National and 1st International Congress of Mechanics of Biosystems Engineering and Agricultural Mechanization. Karaj, Iran.
Kheiralipour, K., Jayas D.S. (2024). Current and future applications of hyperspectral imaging in agriculture, nature and food. Trends in Technical & Scientific Research. 7(2), 1-9. DOI: 10.19080/TTSR.2024.07.555708.
Kheiralipour, K., Jayas, D.S. (2023a). Advances in image processing applications for assessing leafy materials. International Journal of Tropical Agriculture. 41(1-2), 31-47.
Kheiralipour, K., Jayas, D.S. (2023c). Image Processing for the Quality Assessment of Flour and Flour-Based Baked Products. In Image Processing: Advances in Applications and Research. Edited by Jayas, D.S. New York, USA: Nova Science Publishers.
Kheiralipour, K., Nadimi, M., Paliwal, J. (2022). Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios. Sensors. 22(19), 7134.
Kheiralipour, K., Singh, C. B., Jayas, D. S. (2023b). Applications of Visible, Thermal, and Hyperspectral Imaging Techniques in the Assessment of Fruits and Vegetables. In Image Processing: Advances in Applications and Research. Edited by Jayas, D.S.  New York, USA: Nova Science Publishers.
Koochaksaraie, R. R., Irani, M., Valizadeh, M. R., Rahmani, Z., Gharahveysi, S. (2010). A study on the effect of cinnamon powder in diet on serum glucose level in broiler chicks. Global Veterinaria, 4(6), 562-565.
Kumar, A., Bharti, V., Kumar, V., Kumar, U., & Meena, P. D. (2016). Hyperspectral imaging: A potential tool for monitoring crop infestation, crop yield and macronutrient analysis, with special emphasis to Oilseed Brassica. Journal of Oilseed Brassica, 7(2), 113-12.
Li, J., Huang, W., Tian, X., Wang, C., Fan, S., & Zhao, C. (2016). Fast detection and visualization of early decay in citrus using vis-NIR hyperspectral imaging. Computers and Electronics in Agriculture. 127, 582-592.
Lu, B., Dao, P. D., Liu, J., He, Y., Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12, 2659.
Mehrpouri, M., Hamidpour, R., Hamidpour, M. (2020). Cinnamon inhibits platelet function and improves cardiovascular system. J. Med. Plants; 19(73):1-11.
Mohammadi, M., Mirabzadeh, S., Shahvalizadeh, R., Hamishehkar, H. (2020). Development of novel active packaging films based on whey protein isolate incorporated with chitosan nanofiber and nano-formulated cinnamon oil. Int. J. Biol. Macromol, 149, 11–20.
Mohammadi, V., Kheiralipour, K., Ghasemi-Varnamkhasti, M. (2015). Detecting maturity of persimmon fruit based on image processing technique. Scientia Horticulturae. 184, 123-128.
Moosavian, A. (2012). Fault Diagnosis and Classification of Journal Bearings by Using Support Vector Machine, M. Sc. dissertation, University of Tehran, Karaj.
Rady, A., Adedeji, A. (2020). Application of hyperspectral imaging and machine learning methods to detect and quantify adulterants in minced meats. Food Analytical Methods https://doi.org/10.1007/s12161-020-01719-1.
Safari Amiri, Z., Ghasemi Varnamkhasadi, M., Tawhidi, M., Mohtasbi, S. S., Davalit, M. (2017). Using the olfactory system to detect caraway fraud. Scientific quarterly, new technologies in the food industry. Volume 5, Number 3, Pages 527-541. [in persian].
Salam, S., Kheiralipour, K., Jian, F. (2022). Detection of unripe kernels and foreign materials in chickpea mixtures using image processing. Agriculture, 12(7), 995.
Sang-Oh, P., Chae-Min, R., Byung-Sung, P., Jong, H. (2013). The meat quality and growth performance in broiler chickens fed diet with cinnamon powder. Journal of Environmental biology, 3.127 (1).
Shafiee, S., Polder, G., Minaei, S., Moghadam-Charkari, N. (2016). Detection of honey adulteration using hyperspectral imaging. IFAC. 311-314.
Singh, C. B. (2009). Detection of insect and fungal damage and incidence of sprouting in stored wheat using near-infrared hyperspectral and digital color imaging. Ph.D. Dissertation. University of Manitoba, Winnipeg, Canada.
Singh, C. B., Jayas, D. S., Paliwal, J., White, N. D. G. (2007). Fungal detection in wheat using near infrared hyperspectral imaging. Transactions of the ASAE, 50, 2171-2176.
Siripatrawan, U., Makino, Y. (2015). Monitoring fungal growth on brown rice grains using rapid and nondestructive hyperspectral imaging. International Journal of Food Microbiology, 199, 93-100.
Temiz, H., Ulas, B. (2021). A Review of recent studies employing hyperspectral imaging for the determination of food adulteration. Photochem. 1, 125-146.
Usefi, S., Farsi, H., Kheiralipour, K. (2016). Drop test of pear fruit: experimental measurement and finite element modelling. Biosystems Engineering. 147, 17-25.
Vasanthi, R. H., Parameswari, R. P. (2010). Indian spices for healthy heart-an overview. Current Cardiology Reviews; 6 (4): 274-9.
Vejarano, R., Siche, R., Tesfaye, W. (2017). Evaluation of biological contaminants in foods by hyperspectral imaging: A review. International Journal of Food Properties. 20(2), 1264-1297.