Determining the purity of black pepper powder by hyperspectral imaging method

Document Type : Research Article

Authors

1 Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

2 Biosystems Mechanical Engineering Department, Faculty of Agriculture, Ilam University, Ilam, Iran

Abstract

Introduction: Plants and spices are the source of many biologically active substances that can enhance the taste, color, and aroma of food, as well as influence the body's digestion and metabolic processes. A spice is a dried seed, fruit, root, bark, or vegetable substance primarily used for seasoning, coloring, or preserving food. Sometimes, spices are also used to mask other flavors. On the one hand, many spices have antimicrobial, anti-diabetic, anti-inflammatory, and anti-hypertensive properties. Black pepper, in particular, has been used as a pain reliever in traditional medicine for centuries. This plant has been cultivated since ancient times both as a spice and as a medicinal herb, and it has also been a significant commercial product. Emerging scientific techniques, such as hyperspectral imaging, are now used to evaluate the quality and purity of agricultural and food products. The purpose of this study is to determine the purity of black pepper powder using hyperspectral image processing techniques.
Materials and Methods: The line scan camera from the university's image processing workshop was used to conduct this research. Adulterants such as wheat flour, peas, and sea foam were mixed with black pepper powder at impurity levels of 0%, 5%, 15%, 30%, and 50%. Three samples were prepared for each level of impurity and stored in zip bags. Six images were recorded from each sample. A total of 270 hyperspectral images were recorded. MATLAB software was used to analyze these images. The samples underwent pre-processing, which included the selection of length, features, and characteristics. Efficient features were then classified using the support vector machine method.
Results and discussion: The confusion matrices of the support vector machine classifier model were calculated using one-for-one and one-for-all strategies to determine the correct classification rate for black pepper fraud detection. The accuracy of the support vector machine classification model with the one-against-one strategy in detecting adulteration with wheat flour, sea foam, and chickpea flour in black pepper was 98.88%, 98.88%, and 95.55%, respectively. Using the one-against-all strategy, the accuracy was 100%, 91.11%, and 93.33%, respectively.
Conclusions: In the present study, the classification of different levels of adulteration in black pepper was performed using the hyperspectral image processing method and support vector machine. Due to the varying levels of adulteration, two strategies were employed: one-against-one and one-against-all, with the one-against-one strategy yielding better performance. Besides, this research method offers several advantages over traditional laboratory methods, including non-destructiveness, high speed, and low cost. It is suggested to explore other classification methods for hyperspectral images to further improve the detection of impurities in black pepper.

Graphical Abstract

Determining the purity of black pepper powder by hyperspectral imaging method

Highlights

  • Wheat flour, sea foam, and chickpea flour were detected in black pepper powder at different levels from zero to 50%.
  • Hyperspectral images were prepared and after processing, effective wavelengths and effective features were extracted.
  • The accuracy of the classification model based on the support vector machine method with one-to-one strategy was 95 to 98.

Keywords

Main Subjects

References

[1] Peter, K.V., (Ed.). (2012). Handbook of herbs and spices, Elsevier.
[2] Aggarwal, B.B., & Kunnumakkara, A.B. (2009). Molecular Targets and Therapeutic Uses of Spices: Modern Uses for Ancient Medicine; World Scientific: Singapore.
[3] Roman, S., Sanchez-Siles, L.M., & Siegrist, M. (2017). The importance of food naturalness for consumers. Results of a systematic review. Trends In F Sci & Techno, 67, 44-57. https://doi.org/10.1016/j.tifs.2017.06.010.
[4] Omidbeigi, R., (2014). production and processing of medicinal plants, Astan Quds Razavi Publications] In Persian]
[5] Granata, G., Stracquadanio, S., Leonardi, M., Napoli, E., & Consoli, G.M.L. (2018). Essential oils encapsulated in polymer-based nanocapsules as potential candidates for application in food preservation. F. Chemi. 269: 286-292. http://doi.org/10.1016/j.foodchem.2018.06.140.
[6] Rawat, S. (2015). Food Spoilage: Microorganisms and their prevention. Asian J of Plant Sci & Research 5: 47-56.
[7] Gliszczyńska-Świgło, A. & Chmielewski, J. (2017). Electronic nose as a tool for monitoring the authenticity of food, Food Analytical Methods, 10(6), 1800-1816. DOI 10.1007/s12161-016-0739-4.
[8] Banerjee, D., Chowdhary, S., Chakraborty, S. & Bhattacharyya, R. (2017). Recent Advances in Detection of Food Adulteration. Academic Press, 145-160. https://doi.org/10.1016/B978-0-12-801773-9.00011-X.
[9] Li, Ch., Xu, F., Cao, Ch., Shang, M.Y., Zhang, C.Y., Yu, J., Liu, G.X., Wang, X. & Cai, SH.C. (2013). Comparative analysis of two species of Asari Radix et Rhizoma by electronic nose, headspace GC–MS and chemometrics, J of Pharma &Bio Analysis, 85, 231-238. https://doi.org/10.1016/j.jpba.2013.07.034
[10] Shafiqul Islam, A.K.M., Ismail, Z., Saad, B., Othman, A.R., Ahmad, M.N. & Shakaff, A.Y.Md. (2006). Correlation studies between electronic nose response and headspace volatiles of Eurycoma longifolia extracts. Sensors and Actuators B, 120, 245-251. https://doi.org/10.1016/j.snb.2006.02.020
[11] Temiz, H., & Ulas, B. (2021). A Review of recent studies employing hyperspectral imaging for the determination of food adulteration. Photochem. 1, 125-146. https://doi.org/10.3390/photochem1020008.
 [12] 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. https://doi.org/10.2754/avb201079010033.
 [13] 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. 7: 165-168. https://doi.org/10.1007/s12033-007-0044-y
 [14] 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. https://doi.org/10.1016/j.postharvbio.2020.111201
[15] Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., & Frias, J.M. (2017). Hyperspectral imaging–an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18, 590–598. https://doi.org/10.1016/j.tifs.2007.06.001
 [16] Soni, A., Dixit, Y., Reis, M.M., & Brightwell, G. (2022). Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants. Compr. Rev. Food Sci. Food Saf. 21, 3717–3745. https://doi.org/10.1111/1541-4337.12983
[17] Wu, X.Y., Zhu, S.P., Huang, H., & Xu, D. (2017). Quantitative identification of adulterated sichuan pepper powder by near-infrared spectroscopy coupled with chemometrics. J. Food Qual. 5019816. https://doi.org/10.1155/2017/5019816
 [18] Khan, M.H., Saleem, Z., Ahmad, M., Sohaib, A., Ayaz, H., & Mazzara, M. (2020). Hyperspectral imaging for color adulteration detection in red chili. Appl. Sci. 10, 5955. https://doi.org/10.3390/app10175955
 [19] 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.
[20] 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. https://doi.org/10.1080/10942912.2017.1338729
[21] 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. https://doi.org/10.3390/rs12162659
[22] Kheiralipour, K., Singh, C. B., & Jayas, D. S. (2023). 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.
[23] 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.
[24] 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. J of Food Eng, 89, 80-86. https://doi.org/10.1016/j.jfoodeng.2008.04.009
[25] Siripatrawan, U., & Makino, Y. (2015). Monitoring fungal growth on brown rice grains using rapid and nondestructive hyperspectral imaging. Inter J of Food Micro, 199, 93-100. https://doi.org/10.1016/j.ijfoodmicro.2015.01.001.
[26] Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D. S. and Siliveru K. (2015). 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. https://doi.org/10.3920/QAS2015.0606.
[27] 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. [In persion]
[28] Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., & Javan-Nikkhah. M. (2014). Classifying healthy and fungal infected-pistachio kernel by thermal imaging technology. International Journal of Food Properties. 18 (1), 93-99. https://doi.org/10.1080/10942912.2012.717155.
[29] 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. https://doi.org/10.1016/j.jarmap.2021.100327.
[30] 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. https://doi.org/10.3390/horticulturae8111011.
[31] 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. https://doi.org/10.1007/s11540-021-09528-7.
[32] Kheiralipour, K., & Marzbani. F. (2016). Pomegranate quality sorting by image processing and artificial neural network. 10th Iranian National Congress on AGR Machi Eng (Biosystems) and Mechanizasion. 29-31 August, Mashhad, Iran.
[33] Farokhzad, S., Modaress Motlagh, A., Ahmadimoghadam, P., Jalali Honarmand, S., & Khaieralipour, K. (2017). Fungal infection in potato tuber using thermal imaging. Iranian J of Bio Eng. 48(3):243-253. 10.22059/ijbse.2017.212753.664821
 [34] 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. https://doi.org/10.1016/j.ecoinf.2022.101804.
[35] Moosavian, A. (2012). Fault Diagnosis and Classification of Journal Bearings by Using Support Vector Machine, M. Sc. dissertation, University of Tehran, Karaj.
[36] Kheiralipour, K., & Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. J. of Food. Pro. Eng. 40(6), e12558. https://doi.org/10.1111/jfpe.12558.
[37] Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D.S., Siliveru, K., & Malihipour, A. (2021). Processing the hyperspectral images for detecting infection of pistachio kernel by R5 and KK11 isolates of Aspergillus flavus fungus. Iran. J. Biosyst. Eng 52 (1), 13-25. https://dx.doi.org/10.22059/ijbse.2020.299712.665293.
[38] Mousavi, S., A. Abbaszadeh Tehrani, N. & Jan Alipour, M. (2019). Estimation of the area under dryland wheat cultivation using Sentinel-2 satellite images. Envir Rese & Tech, 2019, 5 (7), 77-90. https://doi.org/10.3390/rs12061024.
[39] Nargesi, M. H. (2024). Detection of fraud in black pepper, red pepper, and cinnamon powder using hyperspectral imaging and artificial neural network. Ph.D. Dissertation, University of Bu-Ali Sina University, Hamedan, Iran.
[40] Nobari Moghaddam, H., Tamiji, Z., Akbari Lakeh, M., Khoshayand, M. R., & Haji Mahmoodi, M. (2022). Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics. J. of F. Compo. & Anal, 107 (December 2021). https://doi.org/10.1016/j.jfca.2021.104343.
[41] Malavi, D., Nikkhah, A., Alighaleh, P., Einafshar, S., Raes, K., & Haute, S. V. (2024). Detection of saffron adulteration with Crocus sativus style using NIR-hyperspectral imaging and chemometrics. Food Control. 157, 110189. https://doi.org/10.1016/j.foodcont.2023.110189.
[42] Deng, S., Xu, Y., Li, L., Li, X., & He, Y. (2013). A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis. J. of Food Eng, 119(1), 159–166. https://doi.org/10.1016/j. jfoodeng.2013.05.024.
[43] Chen, Q., Zhao, J., Fang, C. H., & Wang, D. (2007). Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 66(3), 568–574. https://doi.org/10.1016/j. saa.2006.03.038.
[44] Zeng, T., Matsunaga, T., & Shirai, T. (2019). Generalization of parameter selection of SVM and LS-SVM for regression. Machine Learning and Knowledge Extraction, 1(2), 745–755. https://doi.org/10.3390/make1020043.
[45] Park, J. J., Cho, J. S., Lee, G., Yun, D. Y., Park, S. K., Park, K. J., & Lim, J. H. (2023). Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging. Foods, 12, 3471. https://doi.org/10.3390/foods12183471.
[46] 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.
[47] Kheiralipour, K., Ahmadi, H., Rajabipour, A., & Rafiee, S. (2018). Thermal Imaging, Principles, Methods and Applications. 1st Ed. Ilam University Publication, Ilam, Iran.
[48] 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.
[49] 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.
[50] 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. [In persion]
[51] Salam, S., & Kheiralipour, K. (2022). Development and evaluation of chickpea classification system based on visible image processing technology and artificial neural network. Innovative Food Technologies. 9(2), 181-163. 10.22104/jift.2021.5173.2063.
[52] 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.
[53] Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios. Sensors. 22(19), 7134. https://doi.org/10.3390/s22197134.
 [54] Salam, S., Kheiralipour, K., & Jian, F. (2022). Detection of unripe kernels and foreign materials in chickpea mixtures using image processing. Agriculture, 12(7), 995. https://doi.org/10.3390/agriculture12070995.
[55] Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., & Jayas, D.S. (2013). Development of a new threshold-based classification model for analyzing thermal imaging data to detect fungal infection of pistachio kernel. Agricultural Research, 2, 127-131. https://doi.org/10.1007/s40003-013-0057-7.
[56] 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. https://doi.org/10.1007/s11694-019-00270-w.
 [57] Arjomandi, H.R., Kheiralipour, K., & Amarloei, A. (2022). Estimation of dust concentration by a novel machine vision system. Scientific Reports, 12(1), 1-8. https://doi.org/10.1038/s41598-022-18036-8.
Innovative Food Technologies
Volume 11, Issue 4
August 2024
Pages 295-312

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History

  • Receive Date: 15 June 2024
  • Revise Date: 22 July 2024
  • Accept Date: 13 August 2024

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