Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study
Abstract
This multidisciplinary research presents a comprehensive method to tackle the widespread problem of spice adulteration, which represents substantial risks to both public health and spices authenticity. A comprehensive approach is developed to authenticate spices with high accuracy and efficiency by combining old methods with contemporary approaches such as machine learning and artificial intelligence. This paper presents a specific case study where machine learning models, specifically using transfer learning with proven frameworks like MobileNetV2, were effectively employed. The models achieved an impressive accuracy of 98.67% in identifying Capsicum annum, a spice that is usually adulterated in the market. In addition, a wide range of traditional and advanced techniques, including qualitative testing, microscopy, colorimetry, density measurement, and spectroscopy, are reviewed closely. In addition, this article provides a detailed explanation of high-performance liquid chromatography based quantitation of capsaicin, which is the main active constituent for ascertaining the quality of C. annum. The present work defines a new interdisciplinary approach and also provides valuable information on evaluating the quality of spices and identifying adulterants using artificial intelligence. The outcomes presented here have the potential to completely transform the methods used to verify the authenticity of spices and herbal drugs, therefore ensuring the safety and health of consumers by confirming the quality.
Keywords:
Adulteration, Analytical techniques, Artificial Intelligence, Capsicum annum, Capsaicin, HPLC, Transfer learning, Machine learningDOI
https://doi.org/10.25004/IJPSDR.2024.160307References
Osman AG, Raman V, Haider S, Ali Z, Chittiboyina AG, Khan IA. Overview of analytical tools for the identification of adulterants in commonly traded herbs and spices. Journal of AOAC International.2019;102(2):376–385. Available from:https://doi.org/10.5740/jaoacint18-0389
de Lima ABS, Batista AS, de Jesus JC, de Jesus Silva J, de Araújo ACM, Santos LS. Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling. Food Control. 2020;107:106802. Available from:https://doi.org/10.1016/j.foodcont.2019.106802
Beniwal A, Khetarpaul N. Knowledge of consumers regarding the nature and extent of adulteration of Indian foods. Nutrition and Health. 1999;13(3):153–160.Available from:https://doi.org/10.1177/026010609901300303
Jay B, Sagar B, Pankaj N, Savalia V, Devang P. Isolation and characterization of marker compound for detecting adulteration of chlorophytum species in Asparagus racemosus. Research Journal of Pharmacy and Technology. 2021;14(2):800–802. Available from: http://dx.doi.org/10.5958/0974-360X.2021.00140.2
Santhosh Kumar JU, Krishna V, Seethapathy GS, Ganesan R, Ravikanth G, Shaanker RU. Assessment of adulteration in raw herbal trade of important medicinal plants of India using DNA barcoding. 3 Biotech. 2018;8(3):1–8. Available from: https://doi.org/10.1007/s13205-018-1169-3
Xue CY, Li DZ, Lu JM, Yang JB, Liu JQ. Molecular authentication of the traditional Tibetan medicinal plant Swertia mussotii. Planta Med. 2006;72(13):1223–1226. Available from: 10.1055/s-2006-951695
Singh PA, Bajwa N, Naman S, Baldi A. A review on robust computational approaches based identification and authentication of herbal raw drugs. Letterin Drug Design and Discovery. 2020;17(9):1066–1083.Available from: https://doi.org/10.2174/1570180817666200304125520
Sasikumar B, Swetha VP, Parvathy VA, Sheeja TE. Advances in adulteration and authenticity testing of herbs and spices. In: Advances in food authenticity testing. Elsevier; 2016. pp 585–624. Available from: https://doi.org/10.1016/B978-0-08-100220-9.00022-9.
Oliveira MM, Cruz‐Tirado JP, Barbin DF. Nontargeted analytical methods as a powerful tool for the authentication of spices and herbs: A review. Comprehensive Reviews in Food Science and Food Safety. 2019;18(3):670–689. Available from: https://doi.org/10.1111/1541-4337.12436.
Negi A, Pare A, Meenatchi R. Emerging techniques for adulterant authentication in spices and spice products. Food Control. 2021;127:108113. Available from: https://doi.org/10.1016/j.foodcont.2021.108113
Paulson A, Ravishankar S. AI Based indigenous medicinal plant identification. In: 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA). IEEE; 2020. pp 57–63. Available from: https://doi.org/10.1109/ACCTHPA49271.2020.9213224
Soofi AA, Awan A. Classification techniques in machine learning: applications and issues. Journal of Basic and Applied Science. 2017;13:459–465. Available from: https://doi.org/10.6000/1927-5129.2017.13.76
Sharma S, Naman S, Dwivedi J, Baldi A. Artificial intelligence-based smart identification system using herbal images: Decision making using various machine learning models. In: Applications of Optimization and Machine Learning in Image Processing and IoT. Chapman and Hall/CRC; pp 123–155. Available from:https://doi.org/10.1201/9781003364856
Naman S, Sharma S, Kumar M, Kumar M, Baldi A. Developing a CNN-based machine learning model for cardamom identification: A transfer learning approach. Latin American Journal of Pharmacy. 2023;42(6):565–574.
Naman S, Sharma S, Baldi A. Machine learning based identification of spices: A case study of chilli. Latin American Journal of Pharmacy: A Life Science Journal 2023;42(10):248–261.
Sun X, Qian H, Xiong Y, Zhu Y, Huang Z, Yang F. Deep learning-enabled mobile application for efficient and robust herb image recognition. Science Report. 2022;12(1):6579. Available from:https://doi.org/10.1038/s41598-022-10449-9
Kaur PP, Singh S. Classification of herbal plant and comparative analysis of SVM KNN classifier models on the leaf features using machine learning. In: Soft Computing for Intelligent Systems. Springer; 2021. pp 227–239. Available from: https://doi.org/10.1007/978-981-16-1048-6_17
Shailendra R, Jayapalan A, Velayutham S, Baladhandapani A, Srivastava A, Gupta KS, Kumar M.An IoT and machine learning based intelligent system for the classification of therapeutic plants. Neural Processing Letter. 2022;54(5):4465–4493. Available from: https://doi.org/10.1007/s11063-022-10818-5
Almazaydeh L, Alsalameen R, Elleithy K. Herbal leaf recognition using mask-region convolutional neural network (Mask R-CNN). Journal of Theoretical and Applied Information Technology. 2022;100(11): 3664-3671. Available from: http://www.jatit.org/
Liu S, Chen W, Li Z, Dong X. Chinese herbal classification based on image segmentation and deep learning methods. In: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery: Proceedings of the ICNC-FSKD 2021 17. Springer; 2022. pp 267–75. Available From: https://doi.org/10.1007/978-3-030-89698-0_28
Abdollahi J. Identification of medicinal plants in ardabil using deep learning: Identification of medicinal plants using deep learning. In: 2022 27th International Computer Conference, Computer Society of Iran (CSICC). IEEE; 2022. pp 1–6. Available From: https://doi.org/10.1109/CSICC55295.2022.9780493
Lozada AJM, Monsanto NL, Pepito GB. Comparative study on image filtering for herbal plant identification using Xception based convolutional neural network. In: 2022 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE; 2022. pp 268–272. Available from: https://doi.org/10.1109/iSemantic55962.2022.9920424
Gao W, Cheng N, Xin G, Khantong S, Ding C. TCM2Vec: A detached feature extraction deep learning approach of traditional chinese medicine for formula efficacy prediction. Multimedia Tools Application. 2023;1–18. Available from: https://doi.org/10.1007/s11042-023-14701-w
Chen W, Tong J, He R, Lin Y, Chen P, Chen Z, Liu X. An easy method for identifying 315 categories of commonly-used chinese herbal medicines based on automated image recognition using AutoML platforms. Informatics in Medicine Unlocked. 2021;25:100607. Available From:https://doi.org/10.1016/j.imu.2021.100607
Duong-Trung N, Quach LD, Nguyen CN. Learning deep transferability for several agricultural classification problems. International Journal of Advanced Computer Science and Applications. 2019;10(1):58-67. Available From: https://doi.org/10.14569/ijacsa.2019.0100107
Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering. 2010;22(10):1345–1359. Available from: https://doi.org/10.1109/TKDE.2009.191
Muneer A, Fati SM. Efficient and automated herbs classification approach based on shape and texture features using deep learning. IEEE Access. 2020;8:196747–64. Available from: https://doi.org/10.1109/ACCESS.2020.3034033
Robins A, Rountree J, Rountree N. Learning and teaching programming: A review and discussion. Computer Science Education. 2003;13(2):137–172. Available from: https://doi.org/10.1076/csed.13.2.137.14200
Lisin DA, Mattar MA, Blaschko MB, Learned-Miller EG, Benfield MC. Combining local and global image features for object class recognition. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops. IEEE; 2005. pp 47. Available from: https://doi.org/10.1109/CVPR.2005.433
Satpute MR, Jagdale SM. Color, size, volume, shape and texture feature extraction techniques for fruits: a review. International Research Journal of Engineering and Technology. 2016;3:703–708. Available from: https://courseware.cutm.ac.in/wp-content/uploads/2020/06/4.2-Feature-extraction-based-on-size-shape-and-colour.pdf
Sun X, Qian H. Chinese herbal medicine image recognition and retrieval by convolutional neural network. PLoS One. 2016;11(6):e0156327. Available from: https://doi.org/10.1371/journal.pone.0156327
Omondiagbe DA, Veeramani S, Sidhu AS. Machine learning classification techniques for breast cancer diagnosis. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing; 2019. pp 012033. Available from: 10.1088/1757-899X/495/1/012033
Kesavaraj G, Sukumaran S. A study on classification techniques in data mining. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT). IEEE; 2013. pp 1–7.Available from: https://doi.org/10.1109/ICCCNT.2013.6726842
Hassan SM, Maji AK, Jasiński M, Leonowicz Z, Jasińska E. Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics (Switzerland). 2021;10(12):1388. Available from: https://doi.org/10.3390/electronics10121388
Espejo-Garcia B, Mylonas N, Athanasakos L, Fountas S, Vasilakoglou I. Towards weeds identification assistance through transfer learning. Computer and Electronics inAgriculture. 2020;171. Available from: https://doi.org/10.1016/j.compag.2020.105306
Thangaraj R, Anandamurugan S, Kaliappan VK. Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. Journal of Plant Diseases and Protection 2021;128(1):73–86. Available from: https://doi.org/10.1007/s41348-020-00403-0
Mukti IZ, Biswas D. Transfer learning-based plant diseases detection using resnet50. In: 2019 4th International Conference on Electrical Information and Communication Technology, EICT 2019. Institute of Electrical and Electronics Engineers Inc.; 2019. Available from: https://doi.org/10.1109/EICT48899.2019.9068805
Son J, Shin JY, Kim HD, Jung KH, Park KH, Park SJ. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology. 2020;127(1):85–94. Available from: https://doi.org/10.1016/j.ophtha.2019.05.029
Zhao Y, Sun Z, Tian E, Hu C, Zong H, Yang F. A CNN model for herb identification based on part priority attention mechanism. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE; 2020. page 2565–2571.Available from: https://doi.org/10.1109/SMC42975.2020.9283189
Mukherjee G, Tudu B, Chatterjee A. A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: Case studies with Neem, Tulsi and Kalmegh leaves. Soft Computing. 2021;25(22):14119–14138. Available from: https://doi.org/10.1007/s00500-021-06139-9
Bharathan K, Deepasree VP. Tulsi leaves classification system. In: 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). IEEE; 2018. page 1–5. Available from: https://doi.org/10.1109/ICCSDET.2018.8821069
Aakif A, Khan MF. Automatic classification of plants based on their leaves. Biosystem Engineering. 2015;139:66–75.Available from: https://doi.org/10.1016/j.biosystemseng.2015.08.003
Anushya DA. Herbal leaves image clustering via K-Means. International Journal for Research in Applied Science & Engineering Technology. 2020;8(1):282–285. Available from: http://www.ijraset.com/
Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R. Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing. 2020;86:105933. Available from: https://doi.org/10.1016/j.asoc.2019.105933
Harish BS, Hedge A, Venkatesh O, Spoorthy DG, Sushma D. Classification of plant leaves using Morphological features and zernike moments. In: Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI),. Mysore: 2013. pp 1827–1831. Available from: https://doi.org/10.1109/ICACCI.2013.6637459
Chanyal H, Yadav RK, Saini DKJ. Classification of medicinal plants leaves using deep learning technique: A review. International Journal of Intelligent Systems and Applications in Engineering. 2022;10(4):78–87. Available from: https://ijisae.org/index.php/IJISAE/article/view/2199
Velázquez R, Rodríguez A, Hernández A, Casquete R, Benito MJ, Martín A. Spice and herb frauds: Types, incidence, and detection: The state of the art. Foods. 2023;12(18):3373. Available from: https://doi.org/10.3390/foods12183373
Galvin-King P, Haughey SA, Elliott CT. Herb and spice fraud; The drivers, challenges and detection. Food Control. 2018;88:85–97. Available from: https://doi.org/10.1016/j.foodcont.2017.12.031
Zhu H, Zhao M. Study on the microscopic identification of the adulterated plant origin powdered seasonings. Discourse Journal of Agriculture and Food Sciences. 2014;2(9):264–269. Available from:https://cabidigitallibrary.org/
Available from: http://www.resjournals.org/JAFS/PDF/2014/Sept/Zhu_and_Zhao.pdf
Osman AG, Raman V, Haider S, Ali Z, Chittiboyina AG, Khan IA. Overview of analytical tools for the identification of adulterants in commonly traded herbs and spices. Journal of AOAC International. 2019;102(2):376–385. Available from: https://doi.org/10.5740/jaoacint.18-0389
Negi A, Pare A, Meenatchi R. Emerging techniques for adulterant authentication in spices and spice products. Food Control. 2021;127:108113. Available from: https://doi.org/10.1016/j.foodcont.2021.108113
Ryu WK, Kim HW, Kim GD, Rhee HI. Rapid determination of capsaicinoids by colorimetric method. Journal of Food and Drug Analysis. 2017;25(4):798–803. Available from: https://doi.org/10.1016/j.jfda.2016.11.007
da Silva Antonio A, Wiedemann LSM, da Veiga Junior VF. Food pungency: The evolution of methods for capsaicinoid analysis. Food Analytical Methods. 2019;12:1327–1345. Available from: https://doi.org/10.1007/s12161-019-01470-2
Wall MM, Bosland PW. Analytical methods for color and pungency of chiles (capsicums). Developments in Food Science. 1998;39:347–373. Available from: https://doi.org/10.1016/S0167-4501(98)80014-9
Oliveira MM, Cruz‐Tirado JP, Barbin DF. Nontargeted analytical methods as a powerful tool for the authentication of spices and herbs: A review. Comprehensive Reviews in Food Science and Food Safety. 2019;18(3):670–689. Available from: https://doi.org/10.1111/1541-4337.12436
Modupalli N, Naik M, Sunil CK, Natarajan V. Emerging non-destructive methods for quality and safety monitoring of spices. Trends in Food Science & Technology. 2021;108:133–147. Available from: https://doi.org/10.1016/j.tifs.2020.12.021
DalL’asta C. Authentication of spices. Food authentication using bioorganic molecules. DesTech Publication 2013.
Khan MH, Saleem Z, Ahmad M, Sohaib A, Ayaz H, Mazzara M, Raza RA. Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants. Neural Computing and Applications. 2021;33(21):14507–14521. Available from: https://doi.org/10.1007/s00521-021-06094-4
Jyothirmayi T, Rao GN, Rao DG. Physicochemical changes during processing and storage of green chili (Capsicum annuum) powders. Journal of Food Processing and Preservation. 2008;32(5):868–80. Available from: https://doi.org/10.1111/j.1745-4549.2008.00219.x
Pandiselvam, R., Ashokkumar, C., Ramesh, S.V., Kaavya, R., Cozzolino, D., Mohammed, M., Dakshayani, R., Kothakota, A.Application of infrared spectroscopy techniques for the assessment of quality and safety in spices: A review. Applied Spectroscopy Reviews. 2020;55(7):593–611. Available from:https://doi.org/10.1080/05704928.2020.1713801
Oliveira MM, Cruz‐Tirado JP, Barbin DF. Nontargeted analytical methods as a powerful tool for the authentication of spices and herbs: A review. Comprehensive Reviews in Food Science and Food Safety. 2019;18(3):670–689. Available from: https://doi.org/10.1111/1541-4337.12436
Essuman EK, Teye E, Sam-Amoah LK, Amuah CLY. Rapid and non-destructive prediction of adulterated chilli powder by employing pocket-sized NIR spectrometer and chemometrics. Infrared Physics & Technology.2023;135:104961. Available from: https://doi.org/10.1016/j.infrared.2023.104961
Lohumi S, Joshi R, Kandpal LM, LeeH, Kim MS, Cho H, Mo C, Seo YW, Rahman A, Cho BK. Quantitative analysis of sudan dye adulteration in paprika powder using FTIR spectroscopy. Food Additives & Contaminants: Part A 2017;34(5):678–686. Available from: https://doi.org/10.1080/19440049.2017.1290828
Kumar P, Tripathi S, Islam Z, Shaida B. Detection of adulteration in spices. International Journal of Medical Toxicology and Legal Medicne. 2023;26(3and4):138–142. Available from: http://dx.doi.org/10.5958/0974-4614.2023.00061.X
Essuman EK, Teye E, Dadzie RG, Sam-Amoah LK. Consumers’ knowledge of food adulteration and commonly used methods of detection. Journal of Food Quality. 2022;2022:1–10. Available from: https://doi.org/10.1155/2022/2421050
Sudhabindu K, Samal LC. Common adulteration in spices and Do-at-home tests to ensure the purity of spices. Food and Scientific Reports. 2020;1:66–8.
Sharma S, Kushwaha RS, Naman S, Patil UK, Baldi A. Optimized extraction of oleoresin capsicum and analytical method validation for capsaicin using HPLC. Indian Journal of Natural Products 2021;35(1):38-45.Available from: http://dx.doi.org/10.5530/ijnp.2021.1.8
Dejmkova H, Morozova K, Scampicchio M. Estimation of Scoville index of hot chili peppers using flow injection analysis with electrochemical detection. Journal of Electroanalytical Chemistry. 2018;821:82–86. Available from: https://doi.org/10.1016/j.jelechem.2018.01.056
Santos P, Aguiar AC, Barbero GF, Rezende CA, Martínez J. Supercritical carbon dioxide extraction of capsaicinoids from malagueta pepper (Capsicum frutescens L.) assisted by ultrasound. Ultrasonics Sonochemistry. 2015;22:78–88. Available from: https://doi.org/10.1016/j.ultsonch.2014.05.001
Ekwere MR, Udoh DE. Extraction and comparative analysis of moisture and capsaicin contents of capsicum peppers. Journal of Pain & Relief. 2016;5(268):2167–846. Available from: 10.4172/2167-0846.1000268
Chinn MS, Sharma-Shivappa RR, Cotter JL. Solvent extraction and quantification of capsaicinoids from Capsicum chinense. Food and Bioproducts Processing. 2011; 89(4):340–345. Available from:https://doi.org/10.1016/j.fbp.2010.08.003
Kuzma M, Fodor K, Boros B, Perjési P. Development and validation of an HPLC-DAD analysis for pharmacopoeial qualification of industrial capsicum extracts. Journal of Chromatographic Sciences. 2015;53(1):16–23. Available from: https://doi.org/10.1093/chromsci/bmu004
Nag M, Chanda J, Biswas R, Al-Dhabi NA, Duraipandiyan V, Banerji P, Mukherjee PK. Validation of capsaicin in Indian capsicum species through RP-HPLC. Indian Journal of Pharmaceutical Education and Research. 2017;51(2):337–42.
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