The distribution of phosphorus, carotenoids and tocochromanols in grains of four Chinese maize (Zea mays L.) varieties

Sun, Xiaohong; Ma, Lei; Lux, Peter E.; Wang, Xuan; Stuetz, Wolfgang; Frank, Jan; Liang, Jiankun

doi:10.1016/j.foodchem.2021.130725

Cited by 21 publications

(12 citation statements)

References 36 publications

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“… 1 T corresponds to tocopherols and T3 corresponds to tocotrienols; 2 ND = not detected. The percentages reported in Table 2 correspond to the average values of distribution percentages reported in previously published reports [ 54 , 58 , 72 , 73 , 74 ]. …”

Section: Figuresupporting

confidence: 76%

“…The levels of each tocopherol and tocotrienol isomer quantified in whole kernels vary according to the cereal type ( Figure 3 ). In maize kernels, γ-tocopherol, α-tocopherol, and γ-tocotrienol were the major tocochromanols, which correspond to 34, 23 and 22% of the total tocochromanols ( Figure 3 a), respectively, and β-tocotrienol, β-tocopherol, δ-tocotrienol, δ-tocopherol, and α-tocotrienol account for 1 to 12% of the total tocochromanol content [ 56 , 57 , 58 , 59 ]. A different distribution pattern was observed in small-grain cereals.…”

Section: Tocochromanols In Cerealsmentioning

confidence: 99%

“…The major isomers found in the different parts of kernels differ depending on the considered cereal. In maize, the γ- isomers of both tocopherol and tocotrienol are predominant in all parts of kernels, with a prevalence of γ-tocopherol in the germ and pericarp (65% and 39%, respectively, of total tocochromanol content) and a majority of γ-tocotrienol in the endosperm (45%) [ 58 , 72 , 73 ].…”

Section: Tocochromanols In Cerealsmentioning

confidence: 99%

“… Tocochromanol profile of major cereal crops (( a ) Maize; ( b ) Oat; ( c ) Rice; ( d ) Barley; ( e ) Wheat). The percentages were calculated using average concentration of tocochromanols reported in previously published studies [ 38 , 52 , 54 , 56 , 57 , 58 , 59 , 61 , 62 , 63 , 65 , 66 , 67 , 68 ]. …”

Section: Figurementioning

confidence: 99%

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Role of Tocochromanols in Tolerance of Cereals to Biotic Stresses: Specific Focus on Pathogenic and Toxigenic Fungal Species

Savignac

Atanasova‐Pénichon

Chéreau

et al. 2022

IJMS

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Fungal pathogens capable of producing mycotoxins are one of the main threats to the cultivation of cereals and the safety of the harvested kernels. Improving the resistance of crops to fungal disease and accumulation of mycotoxins is therefore a crucial issue. Achieving this goal requires a deep understanding of plant defense mechanisms, most of them involving specialized metabolites. However, while numerous studies have addressed the contribution of phenylpropanoids and carotenoids to plant chemical defense, very few have dealt with tocochromanols. Tocochromanols, which encompass tocopherols and tocotrienols and constitute the vitamin E family, are widely distributed in cereal kernels; their biosynthetic pathway has been extensively studied with the aim to enrich plant oils and combat vitamin E deficiency in humans. Here we provide strong assumptions arguing in favor of an involvement of tocochromanols in plant–fungal pathogen interactions. These assumptions are based on both direct effects resulting from their capacity to scavenge reactive oxygen species, including lipid peroxyl radicals, on their potential to inhibit fungal growth and mycotoxin yield, and on more indirect effects mainly based on their role in plant protection against abiotic stresses.

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Section: Figuresupporting

confidence: 76%

Section: Tocochromanols In Cerealsmentioning

confidence: 99%

Section: Tocochromanols In Cerealsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

Role of Tocochromanols in Tolerance of Cereals to Biotic Stresses: Specific Focus on Pathogenic and Toxigenic Fungal Species

Savignac

Atanasova‐Pénichon

Chéreau

et al. 2022

IJMS

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“…Corn, one of the basic grain products, is a staple food for millions of people all over the world, particularly in Latin America, Asia, and Africa. Corn is used by being processed in various food products directly as human food such as corn flour, semolina, starch, snacks, breakfast cereals as well as it is used in the production of animal feed [ 1 ]. Corn, or maize, which can be harvested once a year, is an agricultural product that ranks third after wheat and rice in terms of cultivation area throughout the world [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

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Metabolic Engineering for Carotenoids Enrichment of Plants

Butnariu¹

2023

Plants as Bioreactors for Industrial Molecules

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The distribution of phosphorus, carotenoids and tocochromanols in grains of four Chinese maize (Zea mays L.) varieties

Cited by 21 publications

References 36 publications

Role of Tocochromanols in Tolerance of Cereals to Biotic Stresses: Specific Focus on Pathogenic and Toxigenic Fungal Species

Role of Tocochromanols in Tolerance of Cereals to Biotic Stresses: Specific Focus on Pathogenic and Toxigenic Fungal Species

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Metabolic Engineering for Carotenoids Enrichment of Plants

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