Non-destructive techniques for grading of mango: a review
Keywords:
Maturity, quality, grading, mango, non-destructive techniquesAbstract
Postharvest grading of fruits is vital to maximizing the profit, as it offers a variety of choices to the customers. This paper presents the recent developments and future scopes in non-destructive techniques for grading of agricultural products like fruit, especially mango. The significant technologies that associated with non-destructive techniques are machine vision, electronic nose (e-nose), near-infrared (NIR) spectroscopy, and multispectral and hyperspectral imaging. In this paper, different non-destructive techniques to assess the quality and safety parameters of fruits as applied by the researchers in the recent past have been discussed. These techniques work on different fruit characteristics like colour, aroma, firmness, defects and chemical compositions, etc. The limitations, challenges and future scopes of research of these techniques, along with a suitable comparison, are presented in this paper. It is concluded that hyperspectral imaging is a great and promising technique for estimation of quality and safety of fruits and food products. Moreover, machine vision is the most widespread and versatile technique for maturity and visual quality detection of fruit at high speed and low-cost applications.
References
Aghilinategh, N., Dalvand, M.J., and Anvar, A. 2020. Detection of ripeness grades of berries using an Electronic nose. Food Science and Nutrition, 1-10, https://doi.org/10.1002/fsn3.1788
Bisson, M.M.A., Kessenbrock, M., Müller, L., Hofmann, A., Schmitz, F., Cristescu, S.M., and Groth, G. 2016. Peptides interfering with protein-protein interactions in the ethylene signaling pathway delay tomato fruit ripening. Scientific Reports, 6: 30634, https://doi.org/10.1038/srep30634
Bhargava, A., and Bansal, A. 2018. Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University - Computer and Information Sciences, 33(4): 1-15, https://doi.org/10.1016/j.jksuci.2018.06.00
.Bresilla, K., Perulli, G.D., Boini, A., Morandi, B., Grappadelli, L.C., Manfrini, L. 2019. Single-shot convolution neural networks for real-time fruit detection within the tree. Frontiers in Plant Science, https://doi.org/10.3389/fpls.2019.00611.21
Bureau, S., Cozzolino, D., Clark, C.J. 2019. Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review. Postharvest Biology and Technology, 148: 1-14, https://doi.org/10.1016/j.postharvbio.2018.10.003.
Celis, M.M.E., Yahia, E.M., Bedoya, R., Landázuri, P., Loango, N., Aguillón, J., Restrepo, B., Ospina, J.C.G. 2019. Review; Chemical composition of mango (Mangifera indica L.) fruit: nutritional and phytochemical compounds. Frontiers in Plant Science., https://doi.org/10.3389/fpls.2019.01073
Chilo, J., Sebastia, J.P., Cupane, M. 2016. E-nose application to food industry production. IEEE Instrumentation & Measurement Magazine, 19 (1): 27 - 33, https://doi.org/10.1109/MIM.2016.7384957
Cortés, V., Ortiz, C., Aleixos, N., Blasco, J., Cubero, S., Talens, P. 2016. A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy. Postharvest Biology and Technology, 118: 148-158, https://doi.org/10.1016/j.postharvbio.2016.04.011 .
Daisy, L.L., Nduko. J.M., Joseph, W.M., Richard, S.M. 2020. Effect of edible gum Arabic coating on the shelf life and quality of mangoes (Mangifera indica) during storage. Journal of Food Science and Technology, 57: 79-85, https://doi.org/10.1007/s13197-019-04032-w .
Feygenberg, O., Keinan, A., Kobiler, I., Falik, E., Pesis, E., Lers, A., Prusky, D. 2014. Improved management of mango fruit though orchard and packinghouse treatments to reduce lenticel discoloration and prevent decay, Postharvest Biology and Technology, 91, 128-133, https://doi.org/10.1016/j.postharvbio.2014.01.001.
Gabriëls, S.H.E.J., Mishra, P., Mensink, M.G.J., Spoelstra, P., Woltering, E.J. 2020. Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis. Postharvest Biology and Technology, 166, 111206, https://doi.org/10.1016/j.postharvbio.2020.111206 .
Gross, J., 1987. Pigments in Fruits. London: Academic Press.
Gwanpua, S.G., Jabbar, A., Tongonya, J., Nicholson, S., East, A.R. 2018. Measuring ethylene in postharvest biology research using the laser based ETD-300 ethylene detector, Plant Methods, 14: 1-17, https://doi.org/10.1186/s13007-018-0372- x.
Huang, H., Liu, L., Ngadi, M.O. 2014. Reecent developments in hyperspectral imaging for assessment of food quality and safety. Sensors (Basel), 14(4): 7248-7276, https://doi.org/10.3390/s140407248 .
Jha, S.N., Narsaiah, K., Sharma, A.D., Singh, M., Bansal, S., Kumar, R. 2010. Quality parameters of mango and potential of non-destructive techniques for their measurement - a review. Journal of Food Science and Technology, 47(1): 1-14, https://doi.org/10.1007/s13197-010-0004-6 .
Jha, S.N., Narsaiah, K., Jaiswal, P., Bhardwaj, R., Gupta, M., Kumar, R., Sharma, R. (2014). Nondestructive prediction of maturity of mango using near infrared spectroscopy. Journal of Food Engineering., 124, 152-157, https://doi.org/10.1016/j.jfoodeng.2013.10.012 .
Kitthawee, U., Pathaveerat, S., Slaughter, D.C., Mitcham, E.J. 2012. Nondestructive sensing of maturity and ripeness in mango. Acta Horticulturae, 943(40): 287-296, https://doi.org/10.17660/ActaHortic.2012.943.40 .
Li, S., Luo, H., Hu, M., Zhang, M., Feng, J., Liu, Y., Dong, Q., Liu, B. 2019. Optical non-destructive techniques for small berry fruits: A review. Artificial Intelligence in Agriculture, 2: 85-98 https://doi.org/10.1016/j.aiia.2019.07.002 .
Li, Z., Wang, N., Raghavan, G.S.V., Vigneault, C. 2009. Ripeness and rot evaluation of ‘Tommy Atkins’ mango fruit through volatiles detection. Journal of Food Engineering, 91(2): 319-324, https://doi.org/10.1016/j.jfoodeng.2008.09.009 .
Londhe, D., Nalawade, S., Pawar, G., Atkari, V., Wandkar, S. 2013. Grader: A review of different methods of grading for fruits and Vegetables., Agricultural Engineering International: CIGR Journal, 15(3): 217-230.
Ma, J., Sun, D.W., Pu, H., Cheng, J.H., Wei, Q. 2019. Advanced techniques for hyperspectral imaging in the food industry: principles and recent applications. Annual Review of Food Science and Technology, 10: 197-220, https://doi.org/10.1146/annurev-food-032818-121155 .
Martin, M., He, Q. 2008. Major mango polyphenols and their potential significance to human health. Comprehensive Reviews in Food Science and Food Safety, 7(4): 309 - 319, https://doi.org/10.1111/j.1541-4337.2008.00047.x .
Mavridou, E., Vrochidou, E., Papakostas, G.A., Pachidis, T., Kaburlasos, V.G. 2019. Review; Machine vision systems in precision agriculture for crop farming. Journal of Imaging, 5(89): 1-32 https://doi.org/10.3390/jimaging5120089 .
Mogollón, R., Contreras, C., Neta, M.L.S., Marques, E.J.N., Zoffoli, J.P., Freitas, S.T. 2020. Non-destructive prediction and detection of internal physiological disorders in 'Keitt' mango using a hand-held VIS-NIR spectrometer. Postharvest Biology and Technology, 167: 1-7, https://doi.org/10.1016/j.postharvbio.2020.111251.
Momin, M.A., Rahman, M.T., Sultana, M.S., Igathinathane, C., Ziauddin, A.T.M., Grift, T.E. 2017.Geometry-based mass grading of mango fruits using image processing. Information Processing in Agriculture, 4(2): 150-160, https://doi.org/10.1016/j.inpa.2017.03.003 .
Nandi, C.S., Tudu, B., Koley, C. 2014. A machine vision based maturity prediction system for sorting of harvested mangoes. IEEE Transactions on Instrumentation and Measurement, 63 (7): 1722-1731, https://doi.org/10.1109/TIM.2014.2299527.
Nandi, C.S., Tudu, B., Koley, C. 2016. A machine vision technique for grading of harvested mangoes based on maturity and quality. IEEE Sensors Journal, 16(16): 6387-6396, https://doi.org/10.1109/JSEN.2016.2580221
Neto, J.P.S., Assis, M.W.D., Casagrande, I.P., Júnior, L.C.C., Teixeira, G.H.A. 2017. Determination of ‘palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer. Postharvest Biology and Technology, 130: 75-80, https://doi.org/10.1016/j.postharvbio.2017.03.009.
Patel, K.K., Kar, A., Jha, S.N., Khan, M.A. 2012. Machine vision system: A tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 49(2): 123-141, https://doi.org/10.1007/s13197-011-0321-4 .
Penchaiya, P., Tijskens, L.M.M., Uthairatanakij, A., Srilaong, V., Tansakul, A., Kanlayanarat, S. 2020. Modelling quality and maturity of ‘Namdokmai Sithong’ mango and their variation during storage. Postharvest. Biology and Technology, 159, 111000, https://doi.org/10.1016/j.postharvbio.2019.111000.
Popa, C. 2009. Ethylene measurements from sweet fruits flowers using photoacoustic spectroscopy. Molecules, https://doi.org/10.3390/molecules24061144 .
Pu, Y.Y., Feng, Y.Z., Sun, D.W. 2015. Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: A review, comprehensive reviews in food science and food safety, 14: 176-188, https://doi.org/10.1111/1541-4337.12123 .
Qin, J., Kim, M.S., Chao, K., Chan, D.E., Delwiche, S.R., Cho, B.K. 2017. Review: Line-scan hyperspectral imaging techniques for food safety and quality applications. Applied Science. 7(125): 1-22, http://dx.doi.org/10.3390/app7020125 .
Ribeiro, S.M.R., Queiroz, J.H., Queiroz, M.E.L.R.D., Campos, F.M., Sant'ana, H.M.P. 2007. Antioxidant in mango (Mangifera indica L.) pulp. Plant Foods for Human Nutrition, 62: 13-17, http://dx.doi.org/10.1007/s11130-006-0035-3 .
Rungpichayapichet, P., Mahayothee, B., Nagle, M., Khuwijitjaru, P., Müller, J. 2016. Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biology and Technology, 111: 31-40, https://doi.org/10.1016/j.postharvbio.2015.07.006 .
Raghavendra, A, Guru, D.S, Raoa, M. K. 2021. Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy. Artificial Intelligence in Agriculture, 5: 43-51, https://doi.org/10.1016/j.aiia.2021.01.005.
Schulze, K., Nagle, M., Spreer, W., Mahayothee, B., Müller, J. 2015. Development and assessment of different modeling approaches for size-mass estimation of mango fruits (Mangifera Indica L., cv. ‘Nam Dokmai’), Computers and Electronics in Agriculture, 114: 269-276, https://doi.org/10.1016/j.compag.2015.04.013 .
Shirmohammadi, S., Ferrero, A. 2014, Camera as the instrument: The rising trend of vision based Measurement. IEEE Instrumentation & Measurement Magazine, 17(3): 41-47, https://doi.org/10.1109/MIM.2014.6825388.
Sommerburg, O., Keunen, J.E.E., Bird, A.C., Kuijk, F.J.G.M.V. 1998. Fruits and vegetables that are sources for lutein and zeaxanthin: the macular pigment in human eyes, British Journal of Ophthalmology, 82(8): 907-910, https://doi.org/10.1136/bjo.82.8.907 .
Steinbrener, J., Posch, K., Leitner, R. 2019. Hyperspectral fruit and vegetable classification using convolutional neural networks. Journal of Computers and Electronics in Agriculture, 162: 364-372, https://doi.org/10.1016/j.compag.2019.04.019 .
Taing, M.W., Pierson, J.T., Shaw, P.N., Dietzgen, R.D., Thomson, S,J.R., Gidley, M.J., Monteith, G.R. 2015. Mango fruit extracts differentially affect proliferation and intracellular calcium signalling in MCF-7 human breast cancer cells. Journal of Chemistry, Hindawi Publishing Corporation, 201: 1-10.
Travers, S., Bertelsen, M.G., Petersen, K.K, Kucheryavskiy, S.V. 2014. Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy, LWT - Food Science and Technology, 59(2): 1107-1113, https://doi.org/10.1016/j.lwt.2014.04.048
Valente, M., Leardi, R., Self, G., Luciano, G., Pain, J.P. 2009. Multivariate calibration of mango firmness using Vis/NIR spectroscopy and acoustic impulse method. Journal of Food Engineering, 94: 7-13, https://doi.org/10.1016/j.jfoodeng.2009.02.020
Vasconcelos, O.C.M., Ferreira, G.J.B.C., Silva, J.C., Mederos, B.J.T., Freitas, S.T. 2019. Development of an artificial fruit prototype for monitoring mango skin and flesh temperatures during storage and transportation, Postharvest Biology and Technology, 158, 110956, https://doi.org/10.1016/j.postharvbio.2019.110956.
Walsh, K.B., Blasco, J., Sasse, M.Z., Sun, X. 2020. Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biology and Technology, 168, 111246, https://doi.org/10.1016/j.postharvbio.2020.111246.
Watanawan, C., Wasusri, T., Srilaong, V., Aree, C.W., Kanlayanarat, S. 2014. Near infrared spectroscopic evaluation of fruit maturity and quality of export Thai mango (Mangifera indica L. var. Namdokmai). International Food Research Journal, 21(3): 1109-1114.
Wu, D., Sun, D.W. 2013. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review - Part II: Applications., Innovative Food Science and Emerging Technology., 19: 15-28, https://doi.org/10.1016/j.ifset.2013.04.016 .
Zhang, H., Wu, J., Ma, H. 2019. Acoustic firmness measurement of differently shaped pears: Comparison of resonance indices with propagation indices, Postharvest Biology and Technology, 148: 151-157, https://doi.org/10.1016/j.postharvbio.2018.11.002
Zhen, O.P., Hashima, N., Maringgala, B. 2020. Quality evaluation of mango using non-destructive approaches: A review. Journal of Agriculture and Food Engineering, 1: 1-8, http://doi.org/10.37865/jafe.2020.0003.
Zheng, H., Lu, H. 2012. A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.). Journal of Computers and Electronics in Agriculture, 83: 47-51, https://doi.org/10.1016/j.compag.2012.01.012.
