Abstract
This paper presents a freshness classification model for determining the quality of amla (Phyllanthus emblica) using features extracted from the progressively deteriorating shapes. Estimation of the peripheral distance from the centroid is carried out to develop key features regarding the shape of the samples, followed by developing two classifier models using support vector machine (SVM) and artificial neural network (ANN) to segment the samples into Good and Deteriorated classes. The proposed algorithm is simple in analysis as it includes geometry-based computation for identifying the surface irregularities and shape changes occurring due to aging the samples. Canny edge detection model is used to obtain the peripheral edges, followed by analyzing the same using “ConvexImage.” High accuracy of classification, exceeding 90%, is achieved in most cases, using three different feature parameters so developed, either as univariate or multivariate schemes of analysis. Besides, the sample images are captured using smartphones only. Thus, high accuracy of freshness classification, along with ease of analysis and image capturing using the phone camera itself, makes the algorithm suitable for implementing in low memory devices such as smartphones, which would also make the proposed model more widely exploring.
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References
Adebo OA, Molelekoa T, Makhuvele R et al (2021) A review on novel non-thermal food processing techniques for mycotoxin reduction. Int J Food Sci Technol 56:13–27. https://doi.org/10.1111/ijfs.14734
Angerosa F, Di GL, Vito R, Cumitini S (1996) Sensory evaluation of virgin olive oils by artificial neural network processing of dynamic head-space gas chromatographic data. J Sci Food Agric 72:323–328. https://doi.org/10.1002/(SICI)1097-0010(199611)72:3%3c323::AID-JSFA662%3e3.0.CO;2-A
Asif MJ, Shahbaz T, Rizvi STH, Iqbal S (2018) Rice grain identification and quality analysis using image processing based on principal component analysis. In: 2018 International Symposium on Recent Advances in Electrical Engineering (RAEE). pp 1–6. https://doi.org/10.1109/RAEE.2018.8706891
Bhargava A, Bansal A (2020) Automatic detection and grading of multiple fruits by machine learning. Food Anal Methods 13:751–761. https://doi.org/10.1007/s12161-019-01690-6
Burkapalli VCPC, Patil (2020) Food image segmentation using edge adaptive based deep-CNNs. Int J Intell Unmanned Syst 8:243–252. https://doi.org/10.1108/IJIUS-09-2019-0053
Caballero D, Pérez-Palacios T, Caro A et al (2017) Prediction of pork quality parameters by applying fractals and data mining on MRI. Food Res Int 99:739–747. https://doi.org/10.1016/j.foodres.2017.06.048
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI-8:679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Chaphalkar R, Apte KG, Talekar Y et al (2017) Antioxidants of Phyllanthus emblica L bark extract provide hepatoprotection against ethanol-induced hepatic damage: a comparison with silymarin. Oxid Med Cell Longev 2017:3876040. https://doi.org/10.1155/2017/3876040
Dang H, Song J, Guo Q (2010) A fruit size detecting and grading system based on image processing. In: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. pp 83–86. https://doi.org/10.1109/IHMSC.2010.120
Dowlati M, Mohtasebi SS, Omid M et al (2013) Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. J Food Eng 119:277–287. https://doi.org/10.1016/j.jfoodeng.2013.05.023
Dutta Gupta S, Pattanayak AK (2017) Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato. Vitr Cell Dev Biol - Plant 53:520–526. https://doi.org/10.1007/s11627-017-9825-6
Gago J, Landín M, Gallego P (2010) Strengths of artificial neural networks in modeling complex plant processes. Plant Signal Behav 5:743–745. https://doi.org/10.4161/psb.5.6.11702
Gaikwad B, Manza R (2012) Use of edge detection operators for agriculture video scene feature extraction from mango fruits. Adv Comput Res 4:50–53
Gantait S, Mahanta M, Bera S, Verma SK (2021) Advances in biotechnology of Emblica officinalis Gaertn. syn. Phyllanthus emblica L a nutraceuticals-rich fruit tree with multifaceted ethnomedicinal uses. 3 Biotech 11:62. https://doi.org/10.1007/s13205-020-02615-5
Hetal NP, Jain R, Joshi M (2011) Fruit detection using improved multiple features based algorithm. Int J Comput Appl 13:1–5
Hoang N-D, Nguyen Q-L (2018) Metaheuristic optimized edge detection for recognition of concrete wall cracks: a comparative study on the performances of Roberts, Prewitt, Canny, and Sobel algorithms. Adv Civ Eng 2018:7163580. https://doi.org/10.1155/2018/7163580
Hu J, Zhou C, Zhao D et al (2020) A rapid, low-cost deep learning system to classify squid species and evaluate freshness based on digital images. Fish Res 221:105376. https://doi.org/10.1016/j.fishres.2019.105376
Hussain A, Pu H, Sun D-W (2018) Innovative nondestructive imaging techniques for ripening and maturity of fruits – a review of recent applications. Trends Food Sci Technol 72:144–152. https://doi.org/10.1016/j.tifs.2017.12.010
Hussain Hassan NM, Nashat AA (2019) New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques. Multidimens Syst Signal Process 30:571–589. https://doi.org/10.1007/s11045-018-0573-5
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
Kapoor MP, Suzuki K, Derek T et al (2020) Clinical evaluation of Emblica officinalis Gatertn (Amla) in healthy human subjects: health benefits and safety results from a randomized, double-blind, crossover placebo-controlled study. Contemp Clin Trials Commun 17:100499. https://doi.org/10.1016/j.conctc.2019.100499
Koyama K, Tanaka M, Cho B-H et al (2021) Predicting sensory evaluation of spinach freshness using machine learning model and digital images. PLoS One 16:e0248769
Limsiroratana S, Ikeda Y (2005) Detection of fruits in natural background (part 2). J Japanese Soc Agric Mach 67:54–60. https://doi.org/10.11357/jsam1937.67.5_54
Liu X, Jiang Y, Shen S et al (2015) Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures. LWT - Food Sci Technol 60:142–147. https://doi.org/10.1016/j.lwt.2014.09.030
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
Mahale B, Korde S (2014) Rice quality analysis using image processing techniques. In: International Conference for Convergence for Technology-2014. pp 1–5. https://doi.org/10.1109/I2CT.2014.7092300
Manickavasagan A, Al-Shekaili HN, Thomas G et al (2014) Edge detection features to evaluate hardness of dates using monochrome images. Food Bioprocess Technol 7:2251–2258. https://doi.org/10.1007/s11947-013-1219-0
Mendoza F, Aguilera JM (2004) Application of image analysis for classification of ripening bananas. J Food Sci 69:E471–E477. https://doi.org/10.1111/j.1365-2621.2004.tb09932.x
Mendoza F, Dejmek P, Aguilera JM (2007) Colour and image texture analysis in classification of commercial potato chips. Food Res Int 40:1146–1154. https://doi.org/10.1016/j.foodres.2007.06.014
Mukherjee A, Chatterjee K, Sarkar T (2022) Entropy-aided assessment of amla (Emblica officinalis) quality using principal component analysis Biointerface. Res Appl Chem 12:2162–2170. https://doi.org/10.33263/BRIAC122.21622170
Mukherjee A, Sarkar T, Chatterjee K (2021) Freshness assessment of Indian gooseberry (Phyllanthus emblica) using probabilistic neural network. J Biosyst Eng. https://doi.org/10.1007/s42853-021-00116-8
Musoromy Z, Ramalingam S, Bekooy N (2010) Edge detection comparison for license plate detection. In: 2010 11th International Conference on Control Automation Robotics & Vision. pp 1133–1138. https://doi.org/10.1109/ICARCV.2010.5707935
Mustafa NBA, Fuad NA, Ahmed SK, et al (2008) Image processing of an agriculture produce: determination of size and ripeness of a banana. In: 2008 International Symposium on Information Technology. pp 1–7. https://doi.org/10.1109/ITSIM.2008.4631636
Narendra V, VHareesh K. (2011) Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision. Int J Agric Biol Eng 4:83–90
Navotas IC, Santos CN V, Balderrama EJM, et al (2018) Fish identification and freshness classification through image processing using artificial neural network. J Eng Appl 13(18):4912–4922.
Nga TTK, Pham TV, Tam DM et al (2021) Combining binary particle swarm optimization with support vector machine for enhancing rice varieties classification accuracy. IEEE Access 9:66062–66078. https://doi.org/10.1109/ACCESS.2021.3076130
Pedreschi F, León J, Mery D, Moyano P (2006) Development of a computer vision system to measure the color of potato chips. Food Res Int 39:1092–1098. https://doi.org/10.1016/j.foodres.2006.03.009
Péneau S, Brockhoff PB, Escher F, Nuessli J (2007) A comprehensive approach to evaluate the freshness of strawberries and carrots. Postharvest Biol Technol 45:20–29. https://doi.org/10.1016/j.postharvbio.2007.02.001
Pratibha N, Hemlata M, Krunali M (2017) Analysis and identification of rice granules using image processing and neural network. Int J Electron Commun 10:25–33.
Priyadumkol J, Kittichaikarn C, Thainimit S (2017) Crack detection on unwashed eggs using image processing. J Food Eng 209:76–82. https://doi.org/10.1016/j.jfoodeng.2017.04.015
Przybylak A, Boniecki P, Koszela K et al (2016) Estimation of intramuscular level of marbling among Whiteheaded Mutton Sheep lambs. J Food Eng 168:199–204. https://doi.org/10.1016/j.jfoodeng.2015.07.035
Rabby MKM, Chowdhury B, Kim JH (2018) A modified canny edge detection algorithm for fruit detection & classification. In: 2018 10th International Conference on Electrical and Computer Engineering (ICECE). pp 237–240. https://doi.org/10.1109/ICECE.2018.8636811
Sanahuja S, Fédou M, Briesen H (2018) Classification of puffed snacks freshness based on crispiness-related mechanical and acoustical properties J. Food Eng 226:53–64. https://doi.org/10.1016/j.jfoodeng.2017.12.013
Sarkar T, Mukherjee A, Chatterjee K (2021a) Supervised learning aided multiple feature analysis for freshness class detection of Indian gooseberry (Phyllanthus emblica). J Inst Eng Ser A. https://doi.org/10.1007/s40030-021-00585-2
Sarkar T, Saha S, Saluddin M, Chakraborty R (2021b) Drying kinetics, Fourier-transform infrared spectroscopy analysis and sensory evaluation of sun, hot-air, microwave and freeze dried mango leather. J Microbiol Biotechnol Food Sci 10:1–7
Sarkar T, Salauddin M, Pati S, et al (2021c) The fuzzy cognitive map–based shelf-life modelling for food storage. Food Anal Methods. https://doi.org/10.1007/s12161-021-02147-5
Satone M, Diwakar S, Joshi V (2017) Automatic bruise detection in fruits using thermal images. Int J Adv Res Comput Sci Soft Eng 7:727–732. http://doi.org/10.23956/ijarcsse/SV7I5/0116
Siswantoro J, Prabuwono AS, Abdullah A, Idrus B (2015) Automatic image segmentation using sobel operator and k-means clustering: a case study in volume measurement system for food products. In: 2015 International Conference on Science in Information Technology (ICSITech). pp 13–18. https://doi.org/10.1109/ICSITech.2015.7407769
Suktanarak S, Teerachaichayut S (2017) Non-destructive quality assessment of hens’ eggs using hyperspectral images. J Food Eng 215:97–103. https://doi.org/10.1016/j.jfoodeng.2017.07.008
Thendral R, Suhasini A, Senthil N (2014) A comparative analysis of edge and color based segmentation for orange fruit recognition. In: 2014 International Conference on Communication and Signal Processing. pp 463–466. https://doi.org/10.1109/ICCSP.2014.6949884
Uddin MS, Al MA, Hossain MS et al (2016) Exploring the effect of Phyllanthus emblica L. on cognitive performance, brain antioxidant markers and acetylcholinesterase activity in rats: promising natural gift for the mitigation of Alzheimer’s disease. Ann Neurosci 23:218–229. https://doi.org/10.1159/000449482
Wada Y, Tsuzuki D, Kobayashi N et al (2007) Visual illusion in mass estimation of cut food. Appetite 49:183–190. https://doi.org/10.1016/j.appet.2007.01.009
Waliyansyah RR, Hasbullah UHA (2021) Comparison of tree method, support vector machine, naïve Bayes, and logistic regression on coffee bean image. Emit Int J Eng Technol 9:126–136. https://doi.org/10.24003/emitter.v9i1.536
Zhu L, Spachos P (2021) Support vector machine and YOLO for a mobile food grading system. Internet of Things 13:100359. https://doi.org/10.1016/j.iot.2021.100359
Zhu H, Yang L, Fei J et al (2021) Recognition of carrot appearance quality based on deep feature and support vector machine. Comput Electron Agric 186:106185. https://doi.org/10.1016/j.compag.2021.106185
Sarkar T, Mukherjee A, Chatterjee K, Shariati, MA et al (2021d) Comparative Analysis of Statistical and Supervised Learning Models for Freshness Assessment of Oyster Mushrooms. Food Anal Methods https://doi.org/10.1007/s12161-021-02161-7
Acknowledgements
We acknowledge the authority, staff, and students of Malda polytechnic, Malda, to being with us throughout the study.
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Thanks to GAIN (Axencia Galega de Innovación) for supporting this review (grant number IN607A2019/01).
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Tanmay Sarkar declares that he has no conflict of interest. Alok Mukherjee declares that he has no conflict of interest. Kingshuk Chatterjee declares that he has no conflict of interest. Vladimir Ermolaev declares that he has no conflict of interest. Dmitry Piotrovsky declares that he has no conflict of interest. Kristina Vlasova declares that she has no conflict of interest. Mohammad Ali Shariati declares that he has no conflict of interest. Paulo E. S. Munekata declares that he has no conflict of interest. Jose M. Lorenzo declares that he has no conflict of interest.
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Sarkar, T., Mukherjee, A., Chatterjee, K. et al. Edge Detection Aided Geometrical Shape Analysis of Indian Gooseberry (Phyllanthus emblica) for Freshness Classification. Food Anal. Methods 15, 1490–1507 (2022). https://doi.org/10.1007/s12161-021-02206-x
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DOI: https://doi.org/10.1007/s12161-021-02206-x