Accurate prediction of nutritional value of sorghum grain using image analysis

Ebadi, M. R.; Sedghi, Mohamad; Kakhki, R. Akbari Moghaddam

doi:10.1080/00071668.2018.1562157

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…Image analysis can complement chemical analysis as an early screening for sorghums grain processing (Fox et al, 2020). It can be used for the assessment of the chemical composition and nutrient digestibility as an alternative to more expensive and time‐consuming chemical and bioassay methods (Ebadi et al, 2019; Sedghi et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

“…Image analysis has been implemented for the identification of several plants (Gresta et al, 2016;Lo Bianco et al, 2015;Smykalova, Grillo, Bjelkova, Hybl, & Venora, 2011;Ucchesu et al, 2015) confirming its performance as an analytical approach for seed authentic- (Ebadi, Sedghi, & Akbari Moghaddam Kakhki, 2019;Sedghi et al, 2012). The chemical composition of sorghum grain depends on the variety.…”

Section: Introductionmentioning

confidence: 94%

“…Sakamoto et al (2019) evaluated seed shape variations in a sorghum germplasm collection to analyze the association between shape variations and genome‐wide single‐nucleotide polymorphisms by genomic prediction and genome‐wide association studies. There are some reports on the relationship between the seed color and chemical composition of sorghum grains (Ebadi, Sedghi, & Akbari Moghaddam Kakhki, 2019; Sedghi et al, 2012). The chemical composition of sorghum grain depends on the variety.…”

Section: Introductionmentioning

confidence: 99%

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Identification of sorghum genotypes using a machine vision system

Nazari

Shaker

Karimi

et al. 2021

J Food Process Engineering

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The aim of this study was to develop an innovative machine vision system based on seed texture and geometric parameters to evaluate their efficacy in the identification of sorghum genotypes. The images of sorghum grain were acquired by a digital camera. Texture parameters were calculated for images converted to individual color channels. The geometric features including length, width, perimeter, area as well as 10 shape factors were determined. The seeds of individual genotypes were classified using the discriminative classifiers. In the case of discriminant analysis performed using texture parameters, the total accuracy reached 95.6% for the model built based on textures selected from all color channels. The complete separation of some genotypes was confirmed by scatterplots. The results of discrimination based on textures selected from color space Lab revealed a total accuracy of up to 94.8%. Discriminative models based on textures selected from color space RGB resulted in a total accuracy of up to 93.5%. Models built based on geometric features provided the accuracy of up to 99% for comparison selected genotypes. Practical Applications The classification models developed in this study can efficiently be used for the identification of sorghum seeds. The results of the studies can be used in practice for the assessment of the authentication of seeds of sorghum cultivars in a rapid, nondestructive and cost‐effective way and it can be an alternative to chemical or molecular methods, which are more expensive, long‐lasting and more complicated. Therefore, the application of image analysis is a great novelty of the research for the identification of cereal grain genotype.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Identification of sorghum genotypes using a machine vision system

Nazari

Shaker

Karimi

et al. 2021

J Food Process Engineering

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“…A consistent study on the color parameters (L* a* b* values) of germinating seeds, leaf buds, pericarp, and testa lead to identifying bio fingerprints inherited to different sorghum cultivars. Correlations among the tannin content and colorimetric parameters (L* value) have been developed by Ebadi et al (2019). Therefore, by measuring the L* value by a chromameter, a general idea about the tannin content of the grain can be perceived.…”

Section: Future Trendsmentioning

confidence: 99%

Potential food applications of sorghum (Sorghum bicolor) and rapid screening methods of nutritional traits by spectroscopic platforms

Thilakarathna

Madhusankha

Navaratne

2021

Journal of Food Science

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Sorghum is a drought-resistant crop widely spread in tropical regions of the American, African, and Asian continents. Sorghum flour is considered the main alternative for wheat flour, and it exhibits gluten-free nature. Generally, conventional wet chemical methods are used to analyze the nutritional profile of sorghum. Since many sorghum plants are available in breeding grounds, the application of conventional methods has limitations due to high cost and time consumption. Therefore, rapid screening protocols have been introduced as nondestructive alternatives. The current review highlights novel and portable devices that can be used to analyze the nutritional composition, color parameters, and pest resistance. Sorghum is often a traditional food item with minimal processing, and the review elaborates on emerging food applications and feasible food product developments from sorghum. The demand for gluten-free products has been rapidly increasing in developed countries. In order to develop food products according to market requirements, it is necessary to screen high-quality sorghum plants. Rapid analysis techniques effectively select the best sorghum types, and the novel tools have outperformed existing conventional methods.

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“…Aliado à sua cultura, ser tolerado a períodos de secas, o sorgo traz um grão de cultivo facilitado. No entanto, possui fatores antinutricionais, como os taninos, que podem prejudicar nutricionalmente a dieta em si (EBADI et al, 2019).…”

Section: Introductionunclassified

Obtenção De Oócitos De Gatas Domésticas Como Estratégia Para Preservação De Felídeos Selvagens

Cavalcante¹,

Motta²,

Abreu³

et al. 2020

Geração E Difusão De Conhecimento Científico Na Zootecnia

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Accurate prediction of nutritional value of sorghum grain using image analysis

Cited by 8 publications

References 24 publications

Identification of sorghum genotypes using a machine vision system

Identification of sorghum genotypes using a machine vision system

Potential food applications of sorghum (Sorghum bicolor) and rapid screening methods of nutritional traits by spectroscopic platforms

Obtenção De Oócitos De Gatas Domésticas Como Estratégia Para Preservação De Felídeos Selvagens

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