Digital image analysis of diverse Mexican rice cultivars

Journal of the Science of Food and Agriculture

Volumn 92, Issue 13, 2012, Pages 2709-2714

  Camelo Méndez, Gustavo Adolfo ^a,b   Camacho Díaz, Brenda Hildeliza ^b   del Villar Martínez, Alma Angélica ^b   Arenas Ocampo, Martha Lucia ^b   Bello Pérez, Luis Arturo ^b   Jiménez Aparicio, Antonio Ruperto ^b  

^a UNIVERSIDAD DE LA SABANA   (Colombia)

^b INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

Author keywords

Cluster analysis; Digital image analysis; Morphometric parameters; Principal component analysis; Rice

Indexed keywords

ARTICLE; CLASSIFICATION; COLOR; HISTOLOGY; MATHEMATICAL PHENOMENA; MEXICO; MULTIVARIATE ANALYSIS; PHENOTYPE; PLANT SEED; PRINCIPAL COMPONENT ANALYSIS; RICE; SPECIES DIFFERENCE;

COLOR; MATHEMATICAL CONCEPTS; MEXICO; MULTIVARIATE ANALYSIS; ORYZA SATIVA; PHENOTYPE; PRINCIPAL COMPONENT ANALYSIS; SEEDS; SPECIES SPECIFICITY;

EID: 84866174615     PISSN: 00225142     EISSN: 10970010     Source Type: Journal    
DOI: 10.1002/jsfa.5693     Document Type: Article

References (35)

1. Brondani C, Borba TCO, Rangel PHN and Brondani RPV, Determination of genetic variability of traditional varieties of Brazilian rice using microsatellite markers. Genet Mol Biol 29: 676-684 (2006).
2. Narciso JO and Hattori K, Genotypic differences in morphology and ultrastructures of callus derived from selected rice varieties. Philippine Sci Lett 3: 59-65 (2010).
3. Hernández AL and Tavitas F, Plan Nacional de Investigación y Apoyos a la Transferencia de Tecnología: Cadena Agroalimentaria Arroz (in Spanish). INIFAP, Centro de Investigación Regional del Centro Campo Experimental "Zacatepec". Zacatepec, Morelos, México, p. 42 (2005).
4. Salcedo J, Morelos A-98 Variedad de Arroz Para Morelos y Otros Estados de la República (in Spanish). INIFAP, Centro de Investigación Regional del Centro Campo Experimental "Zacatepec". Zacatepec, Morelos, México, p. 21 (2006).
5. Chávez-Murillo CE, Wang JY, Quintero-Gutiérrez AG and Bello-López LA, Physicochemical, textural and nutritional characterization of Mexican rice cultivars. Cereal Chem 88: 245-252 (2011).
6. Ogawa Y, Quality evaluation of rice, in Computer Vision Technology for Food Quality Evaluation, ed. by Sun D-W, Elsevier, New York, USA, pp. 377-400 (2008).
7. Menesatti P, Costa C, Paglia G, Pallottino F, D'Andrea S, Rimatori V, et al, Shape-based methodology for multivariate discrimination among Italian hazelnut cultivars. Biosyst Eng 101: 417-424 (2008).
8. Xiao H, Jiang N, Schaffner E, Van der Stockinger EJ and Knaap E, A retrotransposon-mediated gene duplication underlies morphological variation of tomato fruit. Science 319: 1527-1530 (2008).
9. Sun DW, Costa C and Menesatti P, Advantages of using quantitative shape descriptors in protocols for plant cultivar and postharvest product quality assessment. Food Bioprocess Technol 5: 1-2 (2012).
10. Costa C, Antonucci F, Pallottino F, Aguzzi J, Sun DW and Menesatti P, Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food Bioprocess Technol 4: 673-692 (2011).
11. Igathinathane C, Pordesimo LO and Batchelor WD, Major orthogonal dimensions measurement of food grains by machine vision using Image J. Food Res Int 42: 76-84 (2009).
12. Firatligil-Durmus E, Sarka E, Bubnik Z, Schejbal M and Kadlec P, Size properties of legume seeds of different varieties using image analysis. J Food Eng 99: 445-451 (2010).
13. Medina W, Skurtys O and Aguilera JM, Study on image analysis application for identification Quinoa seeds (Chenopodium quinoa Willd) geographical provenance. LWT-Food Sci Technol 43: 238-246 (2010).
14. Sakai N, Yonekawa S and Matsuzaki A, Two-dimensional image analysis of the shape of rice and its application to separating varieties. J Food Eng 27: 397-407 (1996).
15. Ting M, Hai X, Quan X, Yan-hua G, Chun-jie Z, Kai C, et al, Comparison of morphological and genetic differentiations in filial generation of cross between indica and japonica rice. Rice Sci 17: 82-86 (2011).
16. Du CJ and Sun DW, Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15: 230-249 (2004).
17. Zheng CX, Sun DW and Zheng LY, Recent developments and applications of image features for food quality evaluation and inspection-A review. Trends Food Sci Technol 17: 642-655 (2006).
18. Razavi Seyed MA, Emadzadeh B, Rafe A and Mohammad Amini A, The physical properties of pistachio nut and its kernel as a function of moisture content and variety: Part I. Geometrical properties. J Food Eng 81: 209-217 (2007).
19. Suchowilska E and Wiwart M, Multivariate analysis of image descriptors of common wheat (Triticum aestivum) and spelt (T. spelta) grain infected by Fusarium culmorum. Int Agrophys 20: 345-351 (2006).
20. Zapotoczny P, Zielinska M and Nita Z, Application of image analysis for the geographical provenance of barley: Morphological features. J Cereal Sci 48: 104-110 (2008).
21. Vaughn MR, van Oorschot RAH and Baindur-Hudson S, A comparison of hair colour measurement by digital image analysis with reflective spectrophotometry. Forensic Sci Int 183: 97-101 (2009).
22. Hintze JL, NCSS Help System. Kaysville, Utah (2007).
23. Wiesnerová D and Wiesner L, Computer image analysis of seed shape and seed color for flax cultivar description. Comput Electron Agric 61: 126-135 (2008).
24. Mather P, Computational Methods of Multivariate Analysis in Physical Geography. David Fulton Publishers Ltd, London, UK, (1976).
25. Srinivas T and Desikachar H, A simple inexpensive device for detecting and estimating cracks in intact paddy grains. J Food Sci Technol 10: 197-199 (1973).
26. Hendriks C and van Vliet L, Segmentation-free estimation of length distributions using sieves and RIA morphology, in Proceedings of the Third International Conference Scale-Space. Vancouver, Canada, pp. 398-406 (2001).
27. Cnossen A, Jiménez M and Siebenmorgen T, Rice fissuring response to high drying and tempering temperatures. J Food Eng 59: 61-69 (2003).
28. Van Dalen G, Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Res Int 37: 51-58 (2004).
29. Courtois F, Faessel M and Bonazzi C, Assessing breakage and cracks of parboiled rice kernels by image analysis techniques. Food Control 21: 567-572 (2010).
30. Yadav BK and Jindal VK, Monitoring milling quality of rice by image analysis. Comput Electron Agric 33: 19-33 (2001).
31. Yadav BK and Jindal VK, Modeling changes in milled rice (Oryza sativa L.) kernel dimensions during soaking by image analysis. J Food Eng 80: 359-369 (2007).
32. Shimizu N, Haque M, Andersson M and Kimura T, Measurement and fissuring of rice kernels during quasi-moisture sorption by image analysis. J Cereal Sci 48: 98-103 (2008).
33. Tian-Yun J, Hui L, Tao G, Xiao-Lu L, Ning S, Fu-Qing W, et al, Analysis of physiological and biochemical characteristics of six mutants with stable high percentage of chalkiness in rice grains. Acta Agron Sin 36: 121-132 (2010).
34. Shen Y, Jin L, Xiao P, Lu Y and Bao JS, Total phenolics, flavonoids, antioxidant capacity in rice grain and their relations to grain color, size and weight. J Cereal Sci 49: 106-111 (2009).
35. Mohammadi SA and Prassanna BM, Analysis of genetic diversity in crop plants-salient statistical tools and considerations. Crop Sci 43: 1235-1248 (2003).