https://doi.org/10.4081/jae.2023.1421 Anthocyanins estimation in homogeneous bean landrace (Phaseolus vulgaris L.) using probabilistic representation and convolutional neural networks
Published: 1 August 2023
Abstract Views: 646
PDF: 293
HTML: 10
HTML: 10
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Studying chemical components in food of natural origin allows us to understand their nutritional contents. However, nowadays, this analysis is performed using invasive methods that destroy the sample under study. These methods are also expensive and time-consuming. Computer vision is a non-invasive alternative to determine the nutritional contents through digital image processing to obtain the colour properties. This work employed a probability mass function (PMF) in colour spaces HSI (hue, saturation, intensity) and CIE L*a*b* (International Commission on Illumination) as inputs for a convolutional neural network (CNN) to estimate the anthocyanin contents in landraces of homogeneous colour. This proposal is called AnthEstNet (Anthocyanins Estimation Net). Before applying the CNN, a methodology was used to take digital images of the bean samples and extract their colourimetric properties represented by PMF. AnthEstNet was compared against regression methods and artificial neural networks (ANN) with different characterisation in the same colour spaces. The performance was measured using precision metrics. Results suggest that AnthEstNet presented a behaviour statistically equivalent to the invasive method results (pH differential method). For probabilistic representation in channels H and S, AnthEstNet obtained a precision value of 87.68% with a standard deviation of 10.95 in the test set of samples. As to root mean square error (RMSE) and R2, this configuration was 0.49 and 0.94, respectively. On the other hand, AnthEstNet, with probabilistic representations on channels a* and b* of the CIE L*a*b* colour model, reached a precision value of 87.49% with a standard deviation of 11.84, an RMSE value of 0.51, and an R2 value of 0.93.
Aquino-Bolaños E.N., García-Díaz Y.D., Chavez-Servia J.L., Carrillo-Rodríguez J.C., Vera-Guzmán A.M., Heredia-García, E. (2016). Anthocyanins, polyphenols, flavonoids and antioxidant activity in common bean (Phaseolus vulgaris L.) landraces. Emirates J. Food Agric. 581-8.
Ataie-Jafari A., Hosseini S., Karimi F., Pajouhi M. 2008. Effects of sour cherry juice on blood glucose and some cardiovascular risk factors improvements in diabetic women: a pilot study. 38:355-60. DOI: https://doi.org/10.1108/00346650810891414
Bowen-Forbes C.S., Zhang Y., Nair M.G. 2010. Anthocyanin content, antioxidant, anti-inflammatory and anticancer properties of blackberry and raspberry fruits. J. Food Compos. Analysis 23:554-60. DOI: https://doi.org/10.1016/j.jfca.2009.08.012
Chai T., Draxler R.R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7:1247-50. DOI: https://doi.org/10.5194/gmd-7-1247-2014
Chávez-Servia J.L., Heredia-García E., Mayek-Pérez N., Aquino-Bolaños E.N., Hernández-Delgado S., Carrillo-Rodríguez J.C., Gill-Langarica H.R., Vera-Guzmán A.M. 2016. Diversity of common bean (Phaseolus vulgaris L.) landraces and the nutritional value of their grains. In: A.K. Goyal (Ed.), Grain
legumes. IntechOpen. Availble from: https://doi.org/10.5772/63439 DOI: https://doi.org/10.5772/63439
Chen S., Zhang F., Ning J., Liu X., Zhang Z., Yang S. 2015. Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem. 172:788-93. DOI: https://doi.org/10.1016/j.foodchem.2014.09.119
Chen Y., Zheng L., Wang M., Wu M., Gao W. 2020. Prediction of chlorophyll and anthocyanin contents in purple lettuce based on image processing 2020 ASABE Annual International Virtual Meeting, St. Joseph, MI, USA. DOI: https://doi.org/10.13031/aim.202000544
del Valle J.C., Gallardo-López A., Buide M.L., Whittall J.B., Narbona E. 2018. Digital photography provides a fast, reliable, and noninvasive method to estimate anthocyanin pigment concentration in reproductive and vegetative plant tissues. Ecol. Evol. 8:3064-76. DOI: https://doi.org/10.1002/ece3.3804
Farrell N., Norris G., Lee S.G., Chun O.K., Blesso C.N. 2015. Anthocyanin-rich black elderberry extract improves markers of HDL function and reduces aortic cholesterol in hyperlipidemic mice [10.1039/C4FO01036A]. Food Funct. 6:1278-87. DOI: https://doi.org/10.1039/C4FO01036A
Fernandes A.M., Franco C., Mendes-Ferreira A., Mendes-Faia A., Costa P.L.D., Melo-Pinto P. 2015. Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks. Comput. Electron. Agric. 115:88-96. DOI: https://doi.org/10.1016/j.compag.2015.05.013
Garzón G.A. 2008. Las antocianinas como colorantes naturales y compuestos bioactivos: revisión. Acta Biol. Colomb. 13:27-36.
Giusti M.M., Wrolstad R.E. 2001. Characterization and measurement of anthocyanins by UV-visible spectroscopy. Curr Protocols Food Analyt. Chem. 00:F1.2.1-F1.2.13. DOI: https://doi.org/10.1002/0471142913.faf0102s00
Gonzalez R.C., Woods R.E. 2002. Digital image processing. Prentice Hall Upper Saddle River, NJ, USA.
Goodwin P., Lawton R. 1999. On the asymmetry of the symmetric MAPE. Int. J. Forecast. 15:405-8. DOI: https://doi.org/10.1016/S0169-2070(99)00007-2
Grimes K.L., Stuart C.M., McCarthy J.J., Kaur B., Cantu E. J., Forester S.C. 2018. Enhancing the cancer cell growth inhibitory effects of table grape anthocyanins. J. Food Sci. 83:2369-74. DOI: https://doi.org/10.1111/1750-3841.14294
Hidalgo M., Martin-Santamaria S., Recio I., Sanchez-Moreno C., de Pascual-Teresa B., Rimbach G., de Pascual-Teresa S.J.G. 2012. Potential anti-inflammatory, anti-adhesive, anti/estrogenic, and angiotensin-converting enzyme inhibitory activities of anthocyanins and their gut metabolites. Nutrition 7:295-306. DOI: https://doi.org/10.1007/s12263-011-0263-5
Horbowicz M., Kosson R., Grzesiuk A., Dębski H. 2008. Anthocyanins of Fruits and vegetables - their occurrence. Analy. Role Human Nutr. 68:5. DOI: https://doi.org/10.2478/v10032-008-0001-8
Kim P. 2017. Convolutional neural network. pp. 121-147 in MATLAB deep learning. Springer. DOI: https://doi.org/10.1007/978-1-4842-2845-6_6
Korytkowski P., Olejnik-Krugly A. 2017. Precise capture of colors in cultural heritage digitization. Color Res. Appl. 42:333-6. DOI: https://doi.org/10.1002/col.22092
Morales-Reyes J.L., Acosta-Mesa H.G., Aquino-Bolaños E.N., Herrera-Meza S., Cruz-Ramírez N., Chávez-Servia J.L., 2021. Classification of bean (Phaseolus vulgaris L.) landraces with heterogeneous seed color using a probabilistic representation 2021 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico, 2021, pp. 1-7, doi: 0.1109/ROPEC53248.2021.9668106. DOI: https://doi.org/10.1109/ROPEC53248.2021.9668106
Nirumand M.C., Hajialyani M., Rahimi R., Farzaei M.H., Zingue S., Nabavi S.M., Bishayee A. 2018. Dietary plants for the prevention and management of kidney stones: preclinical and clinical evidence and molecular mechanisms. Int. J. Mol. Sci. 19:765. DOI: https://doi.org/10.3390/ijms19030765
Paul M.S. 2000. MAPE (mean absolute percentage error). In: P.M. Swamidass (Ed.), Encyclopedia of production and manufacturing management. Springer US, pp. 462-462. DOI: https://doi.org/10.1007/1-4020-0612-8_580
Pishro-Nik H. 2016. Introduction to probability, statistics, and random processes. Kappa Research LLC, 2014, available from: https://www.probabilitycourse.com
Singh B., Singh S. 2018. Advances in postharvest technologies of vegetable crops. Apple Academic Press. DOI: https://doi.org/10.1201/9781315161020
Steinmetz K.A., Potter J.D. 1996. Vegetables, fruit, and cancer prevention: a review. J. Am. Diet. Assoc. 96:1027-39. DOI: https://doi.org/10.1016/S0002-8223(96)00273-8
Taghadomi-Saberi S., Omid M., Emam-Djomeh Z., Ahmadi H. 2014. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing. J. Sci. Food Agricult. 94:95-101. DOI: https://doi.org/10.1002/jsfa.6202
Tang J. 2010. A color image segmentation algorithm based on region growing. 2nd International Conference on Computer Engineering and Technology, Chengdu, China, 2010, pp. V6-634-V6-637, doi: 10.1109/ICCET.2010.5486012. DOI: https://doi.org/10.1109/ICCET.2010.5486012
Wrolstad R.E. 1993. Color and pigment analyses in fruit products. Agricultural Experiment Station, Oregon State University. Station Bulletin, 624.
Xu B.J., Yuan S.H., Chang S.K.C. 2007. Comparative analyses of phenolic composition, antioxidant capacity, and color of cool season legumes and other selected food legumes. J. Food Sci. 72:S167-77. DOI: https://doi.org/10.1111/j.1750-3841.2006.00261.x
Yoshioka Y., Nakayama M., Noguchi Y., Horie H. 2013. Use of image analysis to estimate anthocyanin and UV-excited fluorescent phenolic compound levels in strawberry fruit. Breed Sci. 63:211-7. DOI: https://doi.org/10.1270/jsbbs.63.211
Zhang C., Wu W., Zhou L., Cheng H., Ye X., He Y. 2020. Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging. Food Chem. 319:126536. DOI: https://doi.org/10.1016/j.foodchem.2020.126536
How to Cite
Morales-Reyes, J. L. (2023) “Anthocyanins estimation in homogeneous bean landrace (<em>Phaseolus vulgaris</em> L.) using probabilistic representation and convolutional neural networks”, Journal of Agricultural Engineering, 54(2). doi: 10.4081/jae.2023.1421.
Copyright (c) 2023 the Author(s) 
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- José F. Reyes, Elías Contreras, Christian Correa, Pedro Melin, Image analysis of real-time classification of cherry fruit from colour features , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Roberto Romaniello, Alessandro Leone, Giorgio Peri, Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Dario Friso, Lucia Bortolini, Influence of the trajectory angle and nozzle height from the ground on water distribution radial curve of a sprinkler , Journal of Agricultural Engineering: Vol. 43 No. 1 (2012)
- Ugo Lazzaro, Caterina Mazzitelli, Benedetto Sica, Paola Di Fiore, Nunzio Romano, Paolo Nasta, On evaluating the hypothesis of shape similarity between soil particle-size distribution and water retention function , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Yusuf Uzun, Mehmet Resit Tolun, Halil Tanyer Eyyuboglu, Filiz Sarı, An intelligent system for detecting Mediterranean fruit fly [Medfly; Ceratitis capitata (Wiedemann)] , Journal of Agricultural Engineering: Vol. 53 No. 3 (2022)
- Dhanashree Barbole, Parul M. Jadhav, Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Kai Tian, Jiefeng Zeng, Tianci Song, Zhuliu Li, Asenso Evans, Jiuhao Li, Tomato leaf diseases recognition based on deep convolutional neural networks , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- H. Kursat Celik, Ibrahim Akinci, Nuri Caglayan, Allan E.W. Rennie, Structural strength analysis of a rotary drum mower during harvesting , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Yanmei Meng, Tingting Zhang, Jin Wei, Jinlai Zhang, Xulei Zhai, Design and experiments of an automatic pipe winding machine , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Wei Deng, Chunjiang Zhao, Liping Chen, Xiu Wang, Constant pressure control for variable-rate spray using closed-loop proportion integration differentiation regulation , Journal of Agricultural Engineering: Vol. 47 No. 3 (2016)
You may also start an advanced similarity search for this article.
