Abstract
Food adulteration imposes a significant health concern on the community. Being one of the key ingredients used for spicing up food dishes. Red chilli powder is almost used in every household in the world. It is also common to find chilli powder adulterated with brick powder in global markets. We are amongst the first research attempts to train a machine learning-based algorithms to detect the adulteration in red chilli powder. We introduce our dataset, which contains high quality images of red chilli powder adulterated with red brick powder at different proportions. It contains 12 classes consists of 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% adulterant. We applied various image color space filters (RGB, HSV, Lab, and YCbCr). Also, extracted features using mean and histogram feature extraction techniques. We report the comparison of various classification and regression models to classify the adulteration class and to predict the percentage of adulteration in an image, respectively. We found that for classification, the Cat Boost classifier with HSV color space histogram features and for regression, the Extra Tree regressor with Lab color space histogram features have shown the best performance.
This is a preview of subscription content, access via your institution.
Data Availability
All the data used in the manuscript are available in the tables and figures.
Code Availability
Not applicable.
References
Al-Awadhi MA, Deshmukh RR (2021) Detection of adulteration in coconut milk using infrared spectroscopy and machine Learning. Sana'a, Yemen, In: 2021 Int Conf Modern Trends Inf Commun Technol Ind (MTICTI) 1–4. https://doi.org/10.1109/MTICTI53925.2021.9664764
Al-Awadhi MA, Deshmukh RR (2022) Honey Adulteration detection using hyperspectral imaging and machine learning. Vijayawada, India, In: 2022 2nd Int Conf Artif Intell Signal Proc (AISP) 1–5. https://doi.org/10.1109/AISP53593.2022.9760585
Anami BS, Malvade NN, Palaiah S (2019) Automated recognition and classification of adulteration levels from bulk paddy grain samples. Inf Process Agric 6:47–60. https://doi.org/10.1016/j.inpa.2018.09.001
Ayob O, Hussain PR, Suradkar P et al (2021) Evaluation of chemical composition and antioxidant activity of Himalayan red chilli varieties. LWT 146:111413. https://doi.org/10.1016/j.lwt.2021.111413
Bansal S, Singh A, Mangal M et al (2017) Food adulteration: sources, health risks, and detection methods. Crit Rev Food Sci Nutr 57:1174–1189. https://doi.org/10.1080/10408398.2014.967834
Bao P, Zhang L (2003) Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Trans Med Imaging 22:1089–1099. https://doi.org/10.1109/TMI.2003.816958
Bianchini D, De Antonellis V, Franceschi N, Melchiori M (2016) PREFer: a prescription-based food recommender system. Comput Stand Interfaces 54:64–75. https://doi.org/10.1016/j.csi.2016.10.010
Boateng AA, Sumaila S, Lartey M et al (2022) Evaluation of chemometric classification and regression models for the detection of syrup adulteration in honey. LWT 163:113498. https://doi.org/10.1016/j.lwt.2022.113498
Breiman L (2001) Random Forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Brighty SPS, Harini GS, Vishal N (2021) Detection of adulteration in fruits using machine learning. Chennai, India, In: 2021 Sixth Int Conf Wirel Commun, Signal Proc Netw (WiSPNET) 37–40. https://doi.org/10.1109/WiSPNET51692.2021.9419402
Calle JLP, Barea-Sepúlveda M, Ruiz-Rodríguez A et al (2022a) Rapid Detection and quantification of adulterants in fruit juices using machine learning tools and spectroscopy data. Sensors (Basel) 22:. https://doi.org/10.3390/s22103852
Calle JLP, Ferreiro-González M, Ruiz-Rodríguez A et al (2022b) Detection of adulterations in fruit juices using machine learning methods over FT-IR Spectroscopic data. Agron 12. https://doi.org/10.3390/agronomy12030683
Carolina CPD, David NTD (2014) Classification of oranges by maturity, using image processing techniques. In: 2014 III Int Congr Eng Mechatron Autom (CIIMA) 1–5. https://doi.org/10.1109/CIIMA.2014.6983466
Chen T, He T (2017) Xgboost: extreme gradient boosting. R package version 0.4-2 1, no. 4:1–4
Dorogush A, Gulin A, Gusev G et al (2017) Fighting biases with dynamic boosting
Dorogush AV, Ershov V, Gulin A (2018) CatBoost: gradient boosting with categorical features support. ArXiv abs/1810.1
Drucker H (1997) Improving Regressors Using Boosting Techniques. Proc 14th Int Conf Mach Learn
Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407–499. https://doi.org/10.1214/009053604000000067
Fatima N, Areeb QM, Khan IM, Khan MM (2021) Siamese network-based computer vision approach to detect papaya seed adulteration in black peppercorns. J Food Process Preserv n/a:e16043. https://doi.org/10.1111/jfpp.16043
Fayyazi S, Abbaspour-Fard M, Rohani A, et al (2017) Identification and classification of three iranian rice varieties in mixed bulks using image processing and MLP neural network. Int J Food Eng 13:. https://doi.org/10.1515/ijfe-2016-0121
Fix E, Hodges JL (1989) Discriminatory analysis. nonparametric discrimination: consistency properties. Int Stat Rev / Rev Int Stat 57:238–247
Freund Y, Schapire RE (1997) A Decision-Theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139. https://doi.org/10.1006/jcss.1997.1504
Friedman JH (2001) Greedy function approximation: A grADIENT BOOsting machine. Ann Stat 29:1189–1232. https://doi.org/10.1214/aos/1013203451
Gao W, Zhang X, Yang L, Liu H (2010) An improved Sobel edge detection. In: 2010 3rd Int Conf Comput Sci Inf Technol 67–71. https://doi.org/10.1109/ICCSIT.2010.5563693
Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3–42. https://doi.org/10.1007/s10994-006-6226-1
Goyal K, Kumar P, Verma K (2022) Food Adulteration detection using artificial intelligence: a systematic review. Arch Comput Methods Eng 29:397–426. https://doi.org/10.1007/s11831-021-09600-y
Hans C (2012) Elastic Net regression modeling with the orthant normal prior. J Am Stat Assoc 106:1383–1393. https://doi.org/10.1198/jasa.2011.tm09241
Hastie T, Tibshirani R, Friedman J (2009) Overview of supervised learning. In: Hastie T, Tibshirani R, Friedman J (eds) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, New York, NY, pp 9–41
Hendrawan Y, Widyaningtyas S, Fauzy MR (2022) Deep Learning to detect and classify the purity level of Luwak Coffee green beans. Pertanika J Sci Technol 30:1–18
Huang B, Liu J, Jiao J et al (2022) Applications of machine learning in pine nuts classification. Sci Rep 12:8799. https://doi.org/10.1038/s41598-022-12754-9
Huang W, Guo L, Kou W et al (2022) Identification of adulterated milk powder based on convolutional neural network and laser-induced breakdown spectroscopy. Microchem J 176:107190. https://doi.org/10.1016/j.microc.2022.107190
Huber PJ, Ronchetti EM (2009) Robust Statistics concomitant scale estimate. Wiley
Hussein WB, Moaty AA, Hussein MA, Becker T (2011) A novel edge detection method with application to the fat content prediction in marbled meat. Pattern Recognit 44:2959–2970. https://doi.org/10.1016/j.patcog.2011.04.028
Jahanbakhshi A, Abbaspour-Gilandeh Y, Heidarbeigi K, Momeny M (2021) Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning. Comput Biol Med 136:104764. https://doi.org/10.1016/j.compbiomed.2021.104764
Jahanbakhshi A, Abbaspour-Gilandeh Y, Heidarbeigi K, Momeny M (2021) A novel method based on machine vision system and deep learning to detect fraud in turmeric powder. Comput Biol Med 136:104728. https://doi.org/10.1016/j.compbiomed.2021.104728
Jamaluddin F, Noranizan MA, Mohamad Azman E et al (2022) A review of clean-label approaches to chilli paste processing. Int J Food Sci Technol 57:763–773. https://doi.org/10.1111/ijfs.15293
Januaviani TMA, Gusriani N, Joebaedi K et al (2019) The best model of LASSO with the LARS (least angle regression and shrinkage) Algorithm Using Mallow’s Cp
Jin H, Wang Y, Lv B et al (2022) Rapid detection of avocado oil adulteration using low-Field nuclear magnetic resonance. Foods 11. https://doi.org/10.3390/foods11081134
Kamruzzaman M, Makino Y, Oshita S (2016) Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. J Food Eng 170:8–15. https://doi.org/10.1016/j.jfoodeng.2015.08.023
Ke G, Meng Q, Finley T et al (2017) LightGBM: A highly efficient gradient boosting decision tree. In: Guyon I, Luxburg U Von, Bengio S, et al. (eds) Advances in Neural Information Processing Systems. Curran Associates, Inc
Khan N, Ahmed MJ, Shah SZA (2019) Comparative analysis of mineral content and proximate composition from chilli pepper (Capsicum annuum L.) germplasm. Pure Appl Biol 8. https://doi.org/10.19045/bspab.2019.80075
Khosa I, Pasero E (2014) Defect detection in food ingredients using multilayer perceptron neural network. In: 2014 World Symp Comput Appl Res WSCAR 2014
Kleinbaum DG, Klein M (2002) Important special cases of the logistic model. In: Kleinbaum DG, Klein M (eds) Logistic Regression: A Self-Learning Text. Springer, New York, New York, NY, pp 39–72
Kobek JA (2017) Vision based model for identification of adulterants in milk. http://su-plus.strathmore.edu/handle/11071/5652
Lakhwani K, Murarka P, Narendra M (2015) Color space transformation for visual enhancement of noisy color image. IET Image Process
Lanjewar MG, Morajkar PP, Parab J (2022) 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. https://doi.org/10.1007/s11042-022-12392-3
Lapcharoensuk R, Danupattanin K, Kanjanapornprapa C, Inkawee T (2020) Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya. E3S Web Conf 187:4001. https://doi.org/10.1051/e3sconf/202018704001
Li Y, Huang J-B, Ahuja N, Yang M-H (2016) Deep joint image filtering BT - Computer Vision – ECCV 2016. In: Leibe B, Matas J, Sebe N, Welling M (eds). Springer International Publishing, Cham, 154–169
Lim DK, Long NP, Mo C et al (2017) Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice. Food Res Int 100:814–821. https://doi.org/10.1016/j.foodres.2017.08.006
Ma J, Sun D-W, Qu J-H et al (2016) Applications of computer vision for assessing quality of agri-food products: a review of recent research advances. Crit Rev Food Sci Nutr 56:113–127. https://doi.org/10.1080/10408398.2013.873885
MacKay DJC (1992) Bayesian interpolation. Neural Comput 4:415–447. https://doi.org/10.1162/neco.1992.4.3.415
Manasha S, Janani M (2016) Food Adulteration and its problems (Intentional, Accidental and Natural Food Adulteration). Int J Res Financ Int J Res Financ Mark 6:2231–5985
McDonald GC (2009) Ridge regression. WIREs. Comput Stat 1:93–100. https://doi.org/10.1002/wics.14
Mi S, Zhang X, Wang Y et al (2022) Effect of different genotypes on the fruit volatile profiles, flavonoid composition and antioxidant activities of chilli peppers. Food Chem 374:131751. https://doi.org/10.1016/j.foodchem.2021.131751
Nandi C (2014) Computer Vision Based Mango Fruit Grading System. https://doi.org/10.15242/IIE.E1214004
Nascimento CF, Santos PM, Pereira-Filho ER, Rocha FRP (2017) Recent advances on determination of milk adulterants. Food Chem 221:1232–1244. https://doi.org/10.1016/j.foodchem.2016.11.034
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Pinheiro Claro Gomes W, Gonçalves L, Barboza da Silva C, Melchert WR (2022) Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee. Comput Electron Agric 198:107097. https://doi.org/10.1016/j.compag.2022.107097
Platt J (2000) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10
Pourreza A, Pourreza H, Abbaspour-Fard M-H, Sadrnia H (2012) Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Comput Electron Agric 83:102–108. https://doi.org/10.1016/j.compag.2012.02.005
Pradana-López S, Pérez-Calabuig AM, Cancilla JC, Torrecilla JS (2022) Standard photographs convolutionally processed to indirectly detect gluten in chickpea flour. J Food Compos Anal 110:104547. https://doi.org/10.1016/j.jfca.2022.104547
Pratibha N, Hemlata M, Krunali M (2017) Analysis and identification of rice granules using image processing and neural network. International Journal of Electronics and Communication Engineering 10:25–33
Ranstam J, Cook JA (2018) LASSO regression. Br J Surg 105:1348
Rateni G, Dario P, Cavallo F (2017) Smartphone-based food diagnostic technologies: a review. Sensors (Basel) 17:. https://doi.org/10.3390/s17061453
Rong D, Rao X, Ying Y (2017) Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Comput Electron Agric 137:59–68. https://doi.org/10.1016/j.compag.2017.02.027
Ropodi AI, Pavlidis DE, Mohareb F et al (2015) Multispectral image analysis approach to detect adulteration of beef and pork in raw meats. Food Res Int 67:12–18. https://doi.org/10.1016/j.foodres.2014.10.032
Sadhukhan T, Chatterjee S, Das RK et al (2019) Efficient Removal of noise from an image using HSV filtering. In: 2019 Global Conf Adv Technol (GCAT), Bangalore, pp 1–4
Salim NOM, Zeebaree SRM, Sadeeq MAM et al (2021) Study for food recognition system using deep learning. J Phys Conf Ser 1963:12014. https://doi.org/10.1088/1742-6596/1963/1/012014
Sarkar T, Mukherjee A, Chatterjee K et al (2022) Edge detection aided geometrical shape analysis of Indian gooseberry (Phyllanthus emblica) for freshness classification. Food Anal Methods. https://doi.org/10.1007/s12161-021-02206-x
Schneider A, Hommel G, Blettner M (2010) Linear regression analysis: part 14 of a series on evaluation of scientific publications. Dtsch Arztebl Int 107:776–782. https://doi.org/10.3238/arztebl.2010.0776
Shafiee S, Polder G, Minaei S et al (2016) Detection of honey adulteration using hyperspectral imaging. IFAC-PapersOnLine 49:311–314. https://doi.org/10.1016/j.ifacol.2016.10.057
Soltani Firouz M, Rashvand M, Omid M (2021) Rapid identification and quantification of sesame oils adulteration using low frequency dielectric spectroscopy combined with chemometrics. LWT 140:110736. https://doi.org/10.1016/j.lwt.2020.110736
Song Y, Liang J, Lu J, Zhao X (2017) An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing 251:26–34. https://doi.org/10.1016/j.neucom.2017.04.018
Sowmya N, Ponnusamy V (2021) Development of spectroscopic sensor system for an IoT application of adulteration identification on milk using machine learning. IEEE Access 9:53979–53995. https://doi.org/10.1109/ACCESS.2021.3070558
Srinath K, Kiranmayee AH, Bhanot S, Panchariya PC (2022) Detection of palm oil adulteration in sunflower oil using ATR-MIR spectroscopy coupled with chemometric algorithms. Mapan. https://doi.org/10.1007/s12647-022-00558-1
Subashini P (2010) Comparison of filters used for underwater image pre-processing. Int J Comput Sci Netw Secur 10:58–64
Tharwat A (2016) Linear vs. quadratic discriminant analysis classifier: a tutorial. Int J Appl Pattern Recognit 3:145. https://doi.org/10.1504/IJAPR.2016.079050
van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T; scikit-image contributors (2014) scikit-image: Image processing in Python. PeerJ 2:e453. https://doi.org/10.7717/peerj.453
VijaykumarVanathiKanagasabapathy VRPTP (2010) Fast and efficient algorithm to remove gaussian noise in digital images. IAENG Int J Comput Sci 37:78–84
Vincent L (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2:176–201. https://doi.org/10.1109/83.217222
Zhang Y, Zheng M, Zhu R, Ma R (2022) Detection of adulteration in mutton using digital images in time domain combined with deep learning algorithm. Meat Sci 192:108850. https://doi.org/10.1016/j.meatsci.2022.108850
Zhu J, Rosset S, Zou H, Hastie T (2006) Multi-class AdaBoost. Stat Interface 2:. https://doi.org/10.4310/SII.2009.v2.n3.a8
Acknowledgements
We acknowledge Mr. Aniket Ghosh, Mr. Gopal Roy, Mr. Prasun Kumar Saha final year student (2022 batch) of department of Food Processing Technology and Sri Snehashis Guha, PIC Malda polytechnic, Malda for their support to conduct this study. Thanks to GAIN (Axencia Galega de Innovación) for supporting this research (grant number IN607A2019/01).
Funding
Funding for open access charge: Universidade de Vigo/CISUG.
Author information
Authors and Affiliations
Contributions
T.R.: conceptualization, methodology, investigation, validation, formal analysis, writing-original draft preparation; T.C.: methodology, investigation, validation, formal analysis, contribution in writing; V.R.A.: methodology, investigation, validation, formal analysis, and contribution in writing in relevant section; M.K.: data analysis, writing-review and editing, final draft supervision, and monitoring; M.A.S.: review and editing, final draft supervision, and monitoring; J.M.L.: review and editing, final draft supervision and monitoring. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics Approval
Not applicable.
Consent to Participate
All authors has given their full consent to participate.
Consent for Publication
All authors has given their full consent for publication.
Conflict of Interest
Tanmay Sarkar declares that she has no conflict of interest. Tanupriya Choudhury declares that she has no conflict of interest. Nikunj Bansal declares that he has no conflict of interest. VR Arunachalaeshwaran declares that she has no conflict of interest. Mars Khayrullin declares that she has no conflict of interest. Mohammad Ali Shariati declares that he has no conflict of interest. Jose Manuel Lorenzo declares that he has no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sarkar, T., Choudhury, T., Bansal, N. et al. Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder. Food Anal. Methods (2023). https://doi.org/10.1007/s12161-023-02445-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12161-023-02445-0
Keywords
- Food fraud
- Machine learning
- Computer vision
- Image analysis
- Food authentication