Abstract
Large numbers of grain varieties are exploited for different purposes and their characteristics contribute in varied manners to the type, quality of the product. The variety of the grain is an important factor in deciding the value of the product. Identifying these varieties and cultivars is a crucial procedure in a food supply system. Various methods are employed to identify the variety of grains based on morphological, biochemical, or genotypic characteristics of the grain. This note discusses the need for identifying the variety of grains and looks upon various methods that have been brought into action to meet the purpose. Each method of identification has its own set of benefits and drawbacks, making it possible to choose the appropriate one for cereal grains at different stages of the supply chain. The methods of identification described in the review present an alternative to conventional manual methods. Rapid detection and accuracy are expected from the identification methods. Lab-on-a-chip, web portals, and QR codes are just a few of the recent solutions that have the ability to overcome the drawbacks and make variety identification an uncomplicated job.
[1]
Vithu P, Moses JA. Machine vision system for food grain quality evaluation: A review. Trends Food Sci Technol 2016; 56: 13-20.
[http://dx.doi.org/10.1016/j.tifs.2016.07.011]
[http://dx.doi.org/10.1016/j.tifs.2016.07.011]
[2]
Howitt CA, Miskelly D. Identification of grain variety and quality type.In:Cereal Grains: Assessing and Managing Quality 2nd Edi. Elsevier Ltd 2017; pp. 453-92.
[3]
Bao Y, Mi C, Wu N, Liu F, He Y. Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics. Appl Sci 2019; 9(19): 4119.
[http://dx.doi.org/10.3390/app9194119]
[http://dx.doi.org/10.3390/app9194119]
[4]
Santhiya S, Kowshiga K, Abisha D, Sarankumar C, Kavithamani D, Selvi B, et al. SSR Marker-based DNA fingerprinting and morphological characterization for varietal identification in popular sorghum varieties of Tamil Nadu. Electron J Plant Breed 2020; 11(2): 521-7.
[5]
Chen X, Xun Y, Li W, Zhang J. Combining discriminant analysis and neural networks for corn variety identification. Comput Electr Agricul 2010; 71 (Supple. 1): S48-53.
[http://dx.doi.org/10.1016/j.compag.2009.09.003]
[http://dx.doi.org/10.1016/j.compag.2009.09.003]
[6]
Giménez MJ, Real A, García-Molina MD, Sousa C, Barro F. Characterization of celiac disease related oat proteins: Bases for the development of high quality oat varieties suitable for celiac patients. Sci Rep 2017; 7(1): 42588.
[http://dx.doi.org/10.1038/srep42588] [PMID: 28209962]
[http://dx.doi.org/10.1038/srep42588] [PMID: 28209962]
[7]
Korir NK, Han J, Shangguan L, et al. Plant variety and cultivar identification: advances and prospects. Crit Rev Biotechnol 2013; 33(2): 111-25.
[http://dx.doi.org/10.3109/07388551.2012.675314] [PMID: 22698516]
[http://dx.doi.org/10.3109/07388551.2012.675314] [PMID: 22698516]
[8]
Kozłowski M, Górecki P, Szczypiński PM. Varietal classification of barley by convolutional neural networks. Biosyst Eng 2019; 184: 155-65.
[http://dx.doi.org/10.1016/j.biosystemseng.2019.06.012]
[http://dx.doi.org/10.1016/j.biosystemseng.2019.06.012]
[9]
Yafutri MIS, Ratama FP, Yaiful FS, Aizal AF. Science direct effects of varieties and cooking methods on physical and chemical characteristics of cooked rice. Rice Sci 2016; 23(5): 282-6.
[10]
Nazir S, Iqbal MZ, Rahman SU. Molecular identification of genetically modified crops of iosafety and legitimacy of trangenes.In:Gene Editing. Technologies and Applications. InechOpen 2019; pp. 1-18.
[11]
Jayas DS, Paliwal J, Visen NS. Multi-layer neural networks for image analysis of agricultural products. J Agric Eng Res 2000; 77(2): 119-28.
[http://dx.doi.org/10.1006/jaer.2000.0559]
[http://dx.doi.org/10.1006/jaer.2000.0559]
[12]
Saini M, Singh J, Prakash NR. Analysis of wheat grain varieties using image processing: A review. Int J Sci Res (IJSR) 2014; 3(6): 490-5.
[13]
Dubey BP, Bhagwat SG, Shouche SP, Sainis JK. Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosyst Eng 2006; 95(1): 61-7.
[http://dx.doi.org/10.1016/j.biosystemseng.2006.06.001]
[http://dx.doi.org/10.1016/j.biosystemseng.2006.06.001]
[14]
Lopes JF, Ludwig L, Barbin DF, Victória M, Grossmann E, Barbon S. Computer vision classification of barley flour based on spatial pyramid partition ensemble. Sensors 2019; 19(13): 2953.
[http://dx.doi.org/10.3390/s19132953]
[http://dx.doi.org/10.3390/s19132953]
[15]
Laabassi K, Belarbi MA, Mahmoudi S, Mahmoudi SA, Ferhat K. Wheat varieties identification based on a deep learning approach. J Saudi Soc Agric Sci 2021; 20(5): 281-9.
[http://dx.doi.org/10.1016/j.jssas.2021.02.008]
[http://dx.doi.org/10.1016/j.jssas.2021.02.008]
[16]
Cinar I, Koklu M. Classification of rice varieties using artificial intelligence methods. Int J Intell Sys Appl Engin 2019; 7(3): 188-94.
[http://dx.doi.org/10.18201/ijisae.2019355381]
[http://dx.doi.org/10.18201/ijisae.2019355381]
[17]
Lingwal S, Bhatia KK, Tomer MS. Image-based wheat grain classification using convolutional neural network. Multimedia Tools Appl 2021; 80(28-29): 35441-65.
[http://dx.doi.org/10.1007/s11042-020-10174-3]
[http://dx.doi.org/10.1007/s11042-020-10174-3]
[18]
Poudel A, Devkota B. Varietal identification of rice seed using deep convolutional neural network. J Eng Sci 2022; 1(1): 1-5.
[19]
ElMasry G, Mandour N, Al-Rejaie S, Belin E, Rousseau D. Recent applications of multispectral imaging in seed phenotyping and quality monitoring—An overview. Sensors 2019; 19(5): 1090.
[http://dx.doi.org/10.3390/s19051090] [PMID: 30836613]
[http://dx.doi.org/10.3390/s19051090] [PMID: 30836613]
[20]
Mahesh S, Jayas DS, Paliwal J, White NDG. Hyperspectral imaging to classify and monitor quality of agricultural materials. J Stored Prod Res 2015; 61: 17-26.
[http://dx.doi.org/10.1016/j.jspr.2015.01.006]
[http://dx.doi.org/10.1016/j.jspr.2015.01.006]
[21]
Wang L, Sun D, Pu H, Zhu Z. Application of hyperspectral imaging to discriminate the variety of maize seeds. Food Anal Meth 2015; 9: 225-34.
[22]
Wu N, Zhang Y, Na R, et al. Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network. RSC Advances 2019; 9(22): 12635-44.
[http://dx.doi.org/10.1039/C8RA10335F] [PMID: 35515879]
[http://dx.doi.org/10.1039/C8RA10335F] [PMID: 35515879]
[23]
Zhang L, Ji H. Identification of wheat grain in different states based on hyperspectral imaging technology. Spectrosc Lett 2019; 52(6): 356-66.
[http://dx.doi.org/10.1080/00387010.2019.1639762]
[http://dx.doi.org/10.1080/00387010.2019.1639762]
[24]
Chu X, Wang W, Ni X, Li C, Li Y. Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging. Infrared Phys Technol 2020; 105(2): 103242.
[http://dx.doi.org/10.1016/j.infrared.2020.103242]
[http://dx.doi.org/10.1016/j.infrared.2020.103242]
[25]
Fabiyi SD, Vu H, Tachtatzis C, et al. Varietal classification of rice seeds using RGB and hyperspectral images. IEEE Access 2020; 8: 22493-505.
[http://dx.doi.org/10.1109/ACCESS.2020.2969847]
[http://dx.doi.org/10.1109/ACCESS.2020.2969847]
[26]
Shewry PR, Halford NG. Cereal seed storage proteins: Structures, properties and role in grain utilization. J Exp Bot 2002; 53(370): 947-58.
[http://dx.doi.org/10.1093/jexbot/53.370.947] [PMID: 11912237]
[http://dx.doi.org/10.1093/jexbot/53.370.947] [PMID: 11912237]
[27]
Wrigley CW. Identification of cereal varieties by gel electrophoresis of the grain proteins. In: Linskens HF, Jackson JF, Eds. Seed Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg 1992; pp. 17-41.
[http://dx.doi.org/10.1007/978-3-662-01639-8_2]
[http://dx.doi.org/10.1007/978-3-662-01639-8_2]
[28]
Smith JSC, Smith OS. Fingerprinting crop varieties. Adv Agron 1992; 47(C): 85-140.
[http://dx.doi.org/10.1016/S0065-2113(08)60489-7]
[http://dx.doi.org/10.1016/S0065-2113(08)60489-7]
[29]
Singh G, Vig AP, Verma R. Varietal identification in paddy (Oryza sativa L.) and moong (Phaseolus mungo L.) by gel electrophoresis of soluble seed proteins. J New Seeds 2004; 6(1): 91-9.
[http://dx.doi.org/10.1300/J153v06n01_06]
[http://dx.doi.org/10.1300/J153v06n01_06]
[30]
Shewry PR, Napier JA, Tatham AS. Seed storage proteins: Structures and biosynthesis. Plant Cell 1995; 7(7): 945-56.
[PMID: 7640527]
[PMID: 7640527]
[31]
Balázs G, Tömösközi S, Harasztos A, et al. Advantages and limitation of lab-on-a-chip technique in the analysis of wheat proteins. Cereal Res Commun 2012; 40(4): 562-72.
[http://dx.doi.org/10.1556/CRC.40.2012.0015]
[http://dx.doi.org/10.1556/CRC.40.2012.0015]
[32]
Amane D, Ananthanarayan L. Application of two-dimensional gel electrophoresis technique for protein profiling of Indian black gram varieties and detection of adulteration in black gram-based food products using comparative proteomics. Food Chem X 2019; 3(6): 100051.
[http://dx.doi.org/10.1016/j.fochx.2019.100051] [PMID: 31517297]
[http://dx.doi.org/10.1016/j.fochx.2019.100051] [PMID: 31517297]
[33]
Beom HR, Kim JS, Jang YR, Lim SH, Kim CK, Lee CK, et al. Proteomic analysis of low-molecular-weight glutenin subunits and relationship with their genes in a common wheat variety. 3 Biotech 2018; 20188(1): 1-9.
[http://dx.doi.org/10.1007/s13205-017-1081-2]
[http://dx.doi.org/10.1007/s13205-017-1081-2]
[34]
Galindo-Luján R, Pont L, Sanz-Nebot V, Benavente F. Classification of quinoa varieties based on protein fingerprinting by capillary electrophoresis with ultraviolet absorption diode array detection and advanced chemometrics. Food Chem 2021; 341(Pt 1): 128207.
[http://dx.doi.org/10.1016/j.foodchem.2020.128207] [PMID: 33035861]
[http://dx.doi.org/10.1016/j.foodchem.2020.128207] [PMID: 33035861]
[35]
Su W. Genetic diversity analysis of volunteer wheat based on microsatellite simple sequence repeats (SSR) markers. Preprint 2022. [https://europepmc.org/article/ppr/ppr588295
[36]
Lyubimova A, Eremin D. Laboratory varietal control as a guarantee of successful work of agribusiness in Russia. MATEC Web Conf 2018; 170: 1-7.
[http://dx.doi.org/10.1051/matecconf/201817004015]
[http://dx.doi.org/10.1051/matecconf/201817004015]
[37]
Nikolić Z, Đorđević V, Torbica A, Mikić A. Legumes seed storage proteins characterization by SDS-PAGE and Lab-on-a-Chip electrophoresis. J Food Compos Anal 2012; 28(2): 75-80.
[http://dx.doi.org/10.1016/j.jfca.2012.08.005]
[http://dx.doi.org/10.1016/j.jfca.2012.08.005]
[38]
Dilber S, Muhammad K, Nadeem MS, Abbasi M, et al. Evaluation of rice varieties using proteomic approach. Int J Biosci 2013; 3(10): 8-14.
[http://dx.doi.org/10.12692/ijb/3.10.8-14]
[http://dx.doi.org/10.12692/ijb/3.10.8-14]
[39]
Sunilkumar BA, Leonova S, Olsson O, Leonova S, Olsson O. Indentification and characterization of high protein oat lines from a mutagenised oat population. J Cereal Sci 2017; 75: 100-7.
[40]
Banta N, Himachal PK, Pradesh H, Singh I, Singh N. Comparative protein profile analysis by SDS-PAGE of different grain cereals. Pharma Innov J 2021; 10(9): 104-8.
[41]
Yilmaz B, Yilmaz F. Lab-on-a-Chip technology and its applications. Omi Technol Bio-engineering Towar Improv Qual Life 2018; 1: 145-53.
[42]
Lee S-M, Shin D, Cha J, Lee S, Kabange NR. Rapid and easy high-molecular-weight glutenin subunit identification system by lab on a. Chip 2020; 2020: 1-14.
[43]
Uthayakumaran S, Batey IL, Wrigley CW. On-the-spot identification of grain variety and wheat-quality type by Lab-on-a-chip capillary electrophoresis. J Cereal Sci 2005; 41(3): 371-4.
[http://dx.doi.org/10.1016/j.jcs.2004.12.001]
[http://dx.doi.org/10.1016/j.jcs.2004.12.001]
[44]
Marchetti-Deschmann M, Lehner A, Peterseil V, Sövegjarto F, Hochegger R, Allmaier G. Fast wheat variety classification by capillary gel electrophoresis-on-a-chip after single-step one-grain high molecular weight glutenin extraction. Anal Bioanal Chem 2011; 400(8): 2403-14.
[http://dx.doi.org/10.1007/s00216-011-4717-9] [PMID: 21298418]
[http://dx.doi.org/10.1007/s00216-011-4717-9] [PMID: 21298418]
[45]
Jang YR, Cho K, Kim SW, et al. Development of an optimized MALDI-TOF-MS method for high-throughput identification of high-molecular-weight glutenin subunits in wheat. Molecules 2020; 25(18): 4347.
[http://dx.doi.org/10.3390/molecules25184347] [PMID: 32971947]
[http://dx.doi.org/10.3390/molecules25184347] [PMID: 32971947]
[46]
Berky R, Sipkó E, Balázs G, Harasztos AH, Kemény S, Fekete J. Coupled-column RP-HPLC in combination with chemometrics for the characterization and classification of wheat varieties. Chromatographia 2016; 79(13-14): 811-21.
[http://dx.doi.org/10.1007/s10337-016-3091-3]
[http://dx.doi.org/10.1007/s10337-016-3091-3]
[47]
Soller M, Beckmann JS. Genetic polymorphism in varietal identification and genetic improvement. Theor Appl Genet 1983; 67(1): 25-33.
[http://dx.doi.org/10.1007/BF00303917]
[http://dx.doi.org/10.1007/BF00303917]
[48]
Tanwar H. Morphological and molecular methods of varietal identification. Agrobios 2018; (7): 1-4.
[49]
Valentina VASILE. DNA-based methods used for varietal purity detection in wheat cultivars. AgroLife Sci J 2020; 9(1): 342-54.
[50]
Tahir NA-R. Genetic variability evaluation among iraqi rice (oryza sativa L) varieties using RAPD markers and protein profiling. Jordan J Biol Sci 2014; 7(1): 13-8.
[http://dx.doi.org/10.12816/0008207]
[http://dx.doi.org/10.12816/0008207]
[51]
Ganopoulos I, Argiriou A, Tsaftaris A. Adulterations in Basmati rice detected quantitatively by combined use of microsatellite and fragrance typing with High Resolution Melting (HRM) analysis. Food Chem 2011; 129(2): 652-9.
[http://dx.doi.org/10.1016/j.foodchem.2011.04.109] [PMID: 30634282]
[http://dx.doi.org/10.1016/j.foodchem.2011.04.109] [PMID: 30634282]
[52]
Little DP. A DNA mini-barcode for land plants. Mol Ecol Resour 2014; 14(3): 437-46.
[http://dx.doi.org/10.1111/1755-0998.12194] [PMID: 24286499]
[http://dx.doi.org/10.1111/1755-0998.12194] [PMID: 24286499]
[53]
Bhattramakki D, Dong J, Chhabra AK, Hart GE. An integrated SSR and RFLP linkage map of Sorghum bicolor (L.). Moench Genome 2000; 43(6): 988-1002.
[http://dx.doi.org/10.1139/g00-074] [PMID: 11195353]
[http://dx.doi.org/10.1139/g00-074] [PMID: 11195353]
[54]
Singh R, Iquebal MA, Mishra CN, et al. Development of model web-server for crop variety identification using throughput SNP genotyping data. Sci Rep 2019; 9(1): 5122.
[http://dx.doi.org/10.1038/s41598-019-41204-2] [PMID: 30914659]
[http://dx.doi.org/10.1038/s41598-019-41204-2] [PMID: 30914659]
