Abstract
Food adulteration occurs globally, in many facets, and affects almost all food commodities. Adulteration is not just a crucial economic problem, but it may also lead to serious health problems for consumers. Turmeric (Curcuma longa) is a world-class spice commonly contaminated with various chemicals and colors. It has also been used extensively in many Asian curries, sauces, and medications. Different traditional approaches, such as chemical and physical methods, are available for detecting adulterants in turmeric. These approaches are rather time-consuming and inaccurate methods. Therefore, it is of utmost importance to identify the adulterants in turmeric accurately and instantly. A cloud-based system was developed to detect adulteration in adulterated turmeric. The dataset consists of spectral images of turmeric with tartrazine-colored rice flour adulterant. Adulterants in weight percentages of 0%, 5%, 10%, and 15% were mixed with turmeric. A convolutional neural network (CNN) was implemented to detect adulteration, which achieved 100% accuracy for training and 94.35% accuracy for validation. The deep CNN (DCNN) models, namely, VGG16, DenseNet201, and MobileNet, were implemented to detect adulteration. The proposed CNN model outperforms DCNN models in terms of accuracy and layers. The CNN model is deployed to the platform as a service (PaaS) cloud. The deployed model link can be accessed using a smartphone. Uploading the adulterated turmeric image to a cloud link can analyze and detect adulteration.
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References
Akbar A, Kuanar A, Patnaik J et al (2018) Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.). Comput Electron Agric 148:160–178. https://doi.org/10.1016/j.compag.2018.03.002
Amani M, Kakooei M, Moghimi A, Ghorbanian A, Ranjgar B, Mahdavi S, Davidson A, Fisette T, Rollin P, Brisco B, Mohammadzadeh A (2020) Application of Google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. Remote Sens 12:3561. https://doi.org/10.3390/rs12213561
Ashok V, Agrawal N, Durgbanshi A, Esteve-Romero J, Bose D (2015) A novel micellar chromatographic procedure for the determination of metanil yellow in foodstuffs. Anal Methods 7:9324–9330. https://doi.org/10.1039/C5AY02377G
Bandara C (2019) Multispectral images of adulterated turmeric powder [Calibration Data]. https://data.mendeley.com/datasets/b7cwddkcjm/3; https://doi.org/10.17632/b7cwddkcjm.3
Bandara WGC, Prabhath GWK, Dissanayake DWSCB, Herath VR, Godaliyadda GMRI, Bandara Ekanayake MP, Demini D, Madhujith T (2020) Validation of multispectral imaging for the detection of selected adulterants in turmeric samples. J Food Eng 266:109700. https://doi.org/10.1016/j.jfoodeng.2019.109700
Bertelli D, Plessi M, Sabatini A, Lolli M, Grillenzoni F (2007) Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS). Food Chem 101:1565–1570. https://doi.org/10.1016/j.foodchem.2006.04.010
Bhowmik D, Chiranjib KKPS, Chandira M, Jayakar B. Direct CAB. https://www.cabdirect.org/?target=%2fcabdirect%2fabstract%2f20103252001. Accessed 16 Aug 2021
Boureau Y-L, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Omnipress, Haifa, pp 111–118
Chawki EB, Ahmed A, Zakariae T (2018) IaaS cloud model security issues on behalf cloud provider and user security behaviors. Procedia Comput Sci 134:328–333. https://doi.org/10.1016/j.procs.2018.07.180
Chen L, Hu J, Zhang W, Zhang J, Guo P, Sun C (2015) Simultaneous determination of nine banned azo dyes in foodstuffs and beverages by high-performance capillary electrophoresis. Food Anal Methods 8:1903–1910. https://doi.org/10.1007/s12161-014-0074-6
Dhakal S, Chao K, Schmidt W, Qin J, Kim M, Chan D (2016) Evaluation of turmeric powder adulterated with metanil yellow using FT-Raman and FT-IR Spectroscopy. Foods 5:36. https://doi.org/10.3390/foods5020036
Di Anibal CV, Odena M, Ruisánchez I, Callao MP (2009) Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV–visible spectroscopy and multivariate classification techniques. Talanta 79:887–892. https://doi.org/10.1016/j.talanta.2009.05.023
Di Anibal CV, Ruisánchez I, Callao MP (2011) High-resolution 1H Nuclear Magnetic Resonance spectrometry combined with chemometric treatment to identify adulteration of culinary spices with Sudan dyes. Food Chem 124:1139–1145. https://doi.org/10.1016/j.foodchem.2010.07.025
Dong T, Liu J, Shang J, Qian B, Huffman T, Zhang Y, Champagne C, Daneshfar B (2016) Assessing the impact of climate variability on cropland productivity in the canadian prairies using time series MODIS FAPAR. Remote Sens 8:281. https://doi.org/10.3390/rs8040281
Ennis R, Schiller F, Toscani M, Gegenfurtner KR (2018) Hyperspectral database of fruits and vegetables. J Opt Soc Am A 35:B256. https://doi.org/10.1364/JOSAA.35.00B256
Estimate Computation Costs - MATLAB &, Simulink. https://www.mathworks.com/help/physmod/simscape/ug/estimate-computation-costs.html. Accessed 17 Aug 2021
Fadda E, Manerba D, Cabodi G et al (2021) Comparative analysis of models and performance indicators for optimal service facility location. Transp Res E 145:102174. https://doi.org/10.1016/j.tre.2020.102174
Fadda E, Manerba D, Cabodi G et al (2021) Evaluation of Optimal Charging Station Location for Electric Vehicles: An Italian Case-Study. In: Fidanova S et al (eds) Recent Advances in Computational Optimization. Springer International Publishing, Cham, pp 71–87
Fuh M (2002) Determination of sulphonated azo dyes in food by ion-pair liquid chromatography with photodiode array and electrospray mass spectrometry detection. Talanta 56:663–671. https://doi.org/10.1016/S0039-9140(01)00625-7
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524 [cs]
Guan Q, Wang Y, Ping B, Li D, Du J, Qin Y, Lu H, Wan X, Xiang J (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10:4876–4882. https://doi.org/10.7150/jca.28769
Hatcher H, Planalp R, Cho J, Torti FM, Torti SV (2008) Curcumin: From ancient medicine to current clinical trials. Cell Mol Life Sci 65:1631–1652. https://doi.org/10.1007/s00018-008-7452-4
He K, Sun J (2014) Convolutional neural networks at constrained time cost. arXiv:14121710 [cs]
He K, Zhang X, Ren S, Sun J (2014) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision – ECCV 2014. Springer International Publishing, Cham, pp 346–361
Hierarchical Convolutional Deep Learning in Computer Vision - ProQuest. https://www.proquest.com/openview/62c046242f67ce115a76b9224e66a69c/1?cbl=18750&diss=y&pq-origsite=gscholar. Accessed 17 Aug 2021
How fast is my model? https://machinethink.net/blog/how-fast-is-my-model/. Accessed 17 Aug 2021
Hu L, Yin C, Ma S, Liu Z (2018) Assessing the authenticity of black pepper using diffuse reflectance mid-infrared Fourier transform spectroscopy coupled with chemometrics. Comput Electron Agric 154:491–500. https://doi.org/10.1016/j.compag.2018.09.029
Huang G, Liu Z, van der Maaten L, Weinberger KQ (2018) Densely connected convolutional networks. arXiv:1608.06993 [cs]
Izquierdo M, Lastra-Mejías M, González-Flores E, Cancilla JC, Aroca-Santos R, Torrecilla JS (2020) Deep thermal imaging to compute the adulteration state of extra virgin olive oil. Comput Electron Agric 171:105290. https://doi.org/10.1016/j.compag.2020.105290
Jayaprakasha GK, Jagan Mohan Rao L, Sakariah KK (2002) Improved HPLC method for the determination of curcumin, demethoxycurcumin, and bisdemethoxycurcumin. J Agric Food Chem 50:3668–3672. https://doi.org/10.1021/jf025506a
Khodabakhshian R, Emadi B, Khojastehpour M, Golzarian MR (2017) Determining quality and maturity of pomegranates using multispectral imaging. J Saudi Soc Agric Sci 16:322–331. https://doi.org/10.1016/j.jssas.2015.10.004
Kiani S, Minaei S, Ghasemi-Varnamkhasti M (2017) Integration of computer vision and electronic nose as non-destructive systems for saffron adulteration detection. Comput Electron Agric 141:46–53. https://doi.org/10.1016/j.compag.2017.06.018
Kiani S, van Ruth SM, Minaei S, Ghasemi-Varnamkhasti M (2018) Hyperspectral imaging, a non-destructive technique in medicinal and aromatic plant products industry: Current status and potential future applications. Comput Electron Agric 152:9–18. https://doi.org/10.1016/j.compag.2018.06.025
Kim D, Schaffer HE, Vouk MA (2017) About PaaS security. IJCC 6:325. https://doi.org/10.1504/IJCC.2017.090200
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90. https://doi.org/10.1145/3065386
Kumar N, Dahiya AK, Kumar K (2020) Image Restoration Using a Fuzzy-Based Median Filter and Modified Firefly Optimization Algorithm. Int J Adv Sci Technol 29:1471–1477
Kumar N, Dahiya AK, Kumar K (2020) Modified Median Filter for Image Denoising. Int J Adv Sci Technol 29:1495–1502
Kwan C (2019) Methods and challenges using multispectral and hyperspectral images for practical change detection applications. Information 10:353. https://doi.org/10.3390/info10110353
Lan H, Zheng X, Torrens PM (2018) Spark sensing: a cloud computing framework to unfold processing efficiencies for large and multiscale remotely sensed data, with examples on Landsat 8 and MODIS Data. J Sens 1–12. https://doi.org/10.1155/2018/2075057
Lee B-H, Dewi EK, Wajdi MF (2018) Data security in cloud computing using AES under HEROKU cloud. In: 2018 27th Wireless and Optical Communication Conference (WOCC). IEEE, Hualien, pp 1–5
Lee K, Silva BN, Han K (2021) Algorithmic implementation of deep learning layer assignment in edge computing based smart city environment. Comput Electr Eng 89:106909. https://doi.org/10.1016/j.compeleceng.2020.106909
Liu C, Hao G, Su M, Chen Y, Zheng L (2017) Potential of multispectral imaging combined with chemometric methods for rapid detection of sucrose adulteration in tomato paste. J Food Eng 215:78–83. https://doi.org/10.1016/j.jfoodeng.2017.07.026
Liu J, Zhou X, Huang J, Liu S, Li H, Wen S, Liu J (2017) Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine. Multimed Syst 23:95–104. https://doi.org/10.1007/s00530-015-0455-8
Malapela T. Is there a potential in adopting Artificial Intelligence in food and agriculture sector, and can it transform food systems and with what impact? | E-Agriculture. http://www.fao.org/e-agriculture/news/there-potential-adopting-artificial-intelligence-food-and-agriculture-sector-and-can-it. Accessed 17 Aug 2021
McNairn H, Brisco B (2004) The application of C-band polarimetric SAR for agriculture: a review. Can J Remote Sens 30:525–542. https://doi.org/10.5589/m03-069
Morajkar PP, Naik AP, Bugde ST, Naik BR (2019) Photocatalytic and microbial degradation of Amaranth dye. Advances in Biological Science Research. Elsevier, Amsterdam, pp 327–345
Mujtaba H. An introduction to Rectified Linear Unit (ReLU) | What is RelU? https://www.mygreatlearning.com/blog/relu-activation-function/. Accessed 16 Aug 2021
Naik AP, Salkar AV, Majik MS, Morajkar PP (2017) Enhanced photocatalytic degradation of Amaranth dye on mesoporous anatase TiO 2: evidence of C–N, NN bond cleavage and identification of new intermediates. Photochem Photobiol Sci 16:1126–1138. https://doi.org/10.1039/C7PP00090A
Naik AP, Sawant JV, Mittal H, Al Alili A, Morajkar PP (2021) Facile synthesis of 2D nanoflakes and 3D nanosponge-like Ni1–xO via direct calcination of Ni (II) coordination compounds of imidazole and 4-nitrobenzoate: Adsorptive separation kinetics and photocatalytic removal of Amaranth dye contaminated wastewater. J Mol Liquids 325:115235. https://doi.org/10.1016/j.molliq.2020.115235
Naz S, Ashraf A, Zaib A (2021) Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimed Syst. https://doi.org/10.1007/s00530-021-00797-3
Oquab M, Bottou L, Laptev I, Sivic J (2015) Is object localization for free? - Weakly-supervised learning with convolutional neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Boston, pp 685–694
Ouyang W, Wang X, Zeng X, Qiu S, Luo P, Tian Y, Li H, Yang S, Wang Z, Loy C-C, Tang X (2015) DeepID-Net: Deformable deep convolutional neural networks for object detection. arXiv:1412.5661 [cs]
Parvathy VA, Swetha VP, Sheeja TE, Sasikumar B (2015) Detection of plant-based adulterants in turmeric powder using DNA barcoding. Pharm Biol 53:1774–1779. https://doi.org/10.3109/13880209.2015.1005756
Prabhath GWK, Bandara WGC, Dissanayake DWSCB, Hearath HMVR, Godaliyadda GMRI, Ekanayake MPB, Demini SMD, Madhujith T (2019) Multispectral imaging for detection of adulterants in turmeric powder. Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors). p. HTu3B.3. OSA, San Jose
Product Profiles of TURMERIC. http://apeda.in/agriexchange/Market%20Profile/one/TURMERIC.aspx. Accessed 14 Aug 2021
Ropodi AI, Panagou EZ, Nychas G-JE (2017) Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat. Food Control 73:57–63. https://doi.org/10.1016/j.foodcont.2016.05.048
Salmerón-García JJ, van den Dries S, Díaz-del-Río F, Morgado-Estevez A, Sevillano-Ramos JL, van de Molengraft MJG (2019) Towards a cloud-based automated surveillance system using wireless technologies. Multimedia Syst 25:535–549. https://doi.org/10.1007/s00530-017-0558-5
Sha O, Zhu X, Feng Y, Ma W (2014) Determination of sunset yellow and tartrazine in food samples by combining ionic liquid-based aqueous two-phase system with high performance liquid chromatography. J Anal Methods Chem 1–8. https://doi.org/10.1155/2014/964273
Shafiee S, Polder G, Minaei S, Moghadam-Charkari N, van Ruth S, Kuś PM (2016) Detection of honey adulteration using hyperspectral imaging. IFAC-PapersOnLine 49:311–314. https://doi.org/10.1016/j.ifacol.2016.10.057
Shah R (2017) Identification and estimation of non-permitted food colours (metanil yellow and aniline dyes) in turmeric powder by rapid color test and thin layer chromatography. WJPPS, 2034–2045. https://doi.org/10.20959/wjpps20178-9867
Sinha RK, Pandey R, Pattnaik R (2018) Deep learning for computer vision tasks: A review. arXiv:1804.03928 [cs]
Su W-H, Sun D-W (2016) Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportion. Comput Electron Agric 125:113–124. https://doi.org/10.1016/j.compag.2016.04.034
Sunyaev A (2020) Cloud computing. Internet computing. Springer International Publishing, Cham, pp 195–236
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Boston, pp 1–9
Tateo F, Bononi M (2004) Fast determination of Sudan I by HPLC/APCI-MS in hot chilli, spices, and oven-baked foods. J Agric Food Chem 52:655–658. https://doi.org/10.1021/jf030721s
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci: 1–13. https://doi.org/10.1155/2018/7068349
Wu L (2021) Analysis of food Additives. In: Innovative Food Analysis. Elsevier, Amsterdam, pp 157–180
Wu H, Gu X (2015) Max-pooling dropout for regularization of convolutional neural networks. arXiv:1512.01400 [cs]
Xu P, Hu R, Su S (2013) Research on resource management in PaaS based on IaaS environment. In: Su J, Zhao B, Sun Z, Wang X, Wang F, Xu K (eds) Frontiers in Internet Technologies. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 145–157
Yang X, Luo N, Tan Z, Jia Z, Liao X (2017) A fluorescence probe for tartrazine detection in foodstuff samples based on fluorescence resonance energy transfer. Food Anal Methods 10:1308–1316. https://doi.org/10.1007/s12161-016-0691-3
Zhang L, Yong W, Liu J, Wang S, Chen Q, Guo T, Zhang J, Tan T, Su H, Dong Y (2015) Determination of dicyandiamide in powdered milk using direct analysis in real time quadrupole time-of-flight tandem mass spectrometry. J Am Soc Mass Spectrom 26:1414–1422. https://doi.org/10.1007/s13361-015-1142-x
Zhao S, Yin J, Zhang J, Ding X, Wu Y, Shao B (2012) Determination of 23 dyes in chili powder and paste by high-performance liquid chromatography–electrospray ionization tandem mass spectrometry. Food Anal Methods 5:1018–1026. https://doi.org/10.1007/s12161-011-9337-7
Zheng X, Fu M, Chugh M (2017) Big data storage and management in SaaS applications. J Commun Inf Netw 2:18–29. https://doi.org/10.1007/s41650-017-0031-9
Zoughi S, Faridbod F, Amiri A, Ganjali MR (2021) Detection of tartrazine in fake saffron containing products by a sensitive optical nanosensor. Food Chem 350:129197. https://doi.org/10.1016/j.foodchem.2021.129197
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Mr. Madhusduan Lanjewar1: Methodology, Software, Validation, Investigation, Writing - Original Draft.
Dr. Pranay P. Morajkar2: Conceptualization, visualization, corrections, modifications and rewriting of the original manuscript.
Dr. Jivan Parab*3: Methodology, Investigation, Performance analysis, and modifications of the original manuscript.
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Lanjewar, M.G., Morajkar, P.P. & Parab, J. Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud. Multimed Tools Appl 81, 16537–16562 (2022). https://doi.org/10.1007/s11042-022-12392-3
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DOI: https://doi.org/10.1007/s11042-022-12392-3