Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Leite, Daniel Carvalho; Corrêa, Aretha Arcenio Pimentel; Júnior, Luís Carlos Cunha; Lima, Kássio M. G.; Morais, Camilo L. M.; Vianna, Viviane Formice; Teixeira, Gustavo Henrique de Almeida; Mauro, Antônio Orlando Di; Unêda-Trevisoli, Sandra Helena

doi:10.1016/j.jfca.2020.103536

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…The near infrared spectroscopy (NIRS) is a very efficient method that has been widely used for high-throughput determine various chemical and biochemical structures of agricultural crop (Washburn et al, 2013). For instances, NIRS has been used for high-throughput predicting fiber and nutrient content of dryland cereal cultivars (Brenna and Berardo, 2004;Stubbs et al, 2010), phenotyping of moisture and amylose content in maize (Wang et al, 2019;Dong et al, 2021), evaluating the composition of carbohydrates in soybean (Leite et al, 2020;Singh et al, 2021), detecting biomass of plant root mixtures (Roumet et al, 2006), analyzing available P contents in soils to aid fertilization (Patzold et al, 2020), as well as determining the internal quality and physiological maturity in the fruit (Cunha et al, 2016;de Carvalho et al, 2019;Minas et al, 2021). In our previous studies, the NIRS has been successfully applied for stalk quality determination (Wang et al, 2021), cell wall features and lignocellulose digestibility characterization in sugarcane Adnan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

IPPS 2022 - Plant Phenotyping for a Sustainable Future

Schurr,

Kaiser,

Clarke

et al. 2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Introductionmentioning

confidence: 99%

IPPS 2022 - Plant Phenotyping for a Sustainable Future

Schurr,

Kaiser,

Clarke

et al. 2024

Frontiers Research Topics

View full text Add to dashboard Cite

show abstract

“…The near infrared spectroscopy (NIRS) is a very efficient method that has been widely used for high-throughput determine various chemical and biochemical structures of agricultural crop ( Washburn et al., 2013 ). For instances, NIRS has been used for high-throughput predicting fiber and nutrient content of dryland cereal cultivars ( Brenna and Berardo, 2004 ; Stubbs et al., 2010 ), phenotyping of moisture and amylose content in maize ( Wang et al., 2019 ; Dong et al., 2021 ), evaluating the composition of carbohydrates in soybean ( Leite et al., 2020 ; Singh et al., 2021 ), detecting biomass of plant root mixtures ( Roumet et al., 2006 ), analyzing available P contents in soils to aid fertilization ( Patzold et al., 2020 ), as well as determining the internal quality and physiological maturity in the fruit ( Cunha et al., 2016 ; de Carvalho et al., 2019 ; Minas et al., 2021 ). In our previous studies, the NIRS has been successfully applied for stalk quality determination ( Wang et al., 2021 ), cell wall features and lignocellulose digestibility characterization in sugarcane ( Li et al., 2021 ; Adnan et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm

Shen

et al. 2023

Front. Plant Sci.

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Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond.

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“…Na cultura da soja a espectroscopia NIR tem sido especialmente usada para predição do teor de óleo e proteínas (Saito et al, 2021;Leite et al, 2020), monitoramento do teor de ácidos graxos (Zhao et al, 2022), detecção de compostos com isoflavonas e saponinas (Berhow et al, 2020), identificação de sementes quebradas (Wang et al, 2021), classificação de genótipos quanto a disponibilidade hídrica (Crusiol et al, 2021), dentre outras utilidades.…”

Section: Introductionunclassified

Estratégias e seleção de cultivares de soja em estádios iniciais para tolerância ao déficit hídrico

Duarte

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A soja é uma importante oleaginosa em todo o mundo e apresenta sensibilidade ao deficit hídrico, especialmente na fase inicial de desenvolvimento. Por isso, avaliar diferentes estratégias buscando selecionar cultivares de soja no início do desenvolvimento é extremamente necessário. Nesse sentido, são objetivos deste estudo: i) selecionar cultivares de soja tolerantes ao deficit hídrico utilizando os índices de seleção FAI-BLUP (Factor analysis and ideotype- design - Best Linear Unbiased Prediction) e o MGIDI (multi-trait genotype–ideotype distance index), além de indicar cultivares tolerantes ao deficit hídrico na fase inicial de desenvolvimento; ii) predizer o conteúdo de água nas folhas de soja e classificá-las quanto a condição hídrica, por meio de dados de espectroscopia NIR (near infrared) e diferentes modelos de machine learning. Dois experimentos foram avaliados envolvendo duas formas de imposição de estresse por deficit hídrico (em solo e em areia) em dois estádios diferentes (germinação e V1) e repetidos por duas épocas. Os experimentos envolveram 100 cultivares de soja, as quais foram submetidas a duas condições de disponibilidade hídrica (condição controle e condição estresse). Em ambos os experimentos, o estresse permaneceu por 20 dias. Os índices de seleção FAI-BLUP e MGIDI possibilitaram a seleção de 15 cultivares de soja. Foram selecionadas 12 cultivares comuns aos dois índices. As cultivares M 9144 RR, BMX TITAN RR foram as que mais se aproximaram do ideótipo. Em relação aos modelos de machine learning, todos os quatro modelos utilizados apresentaram boas performances ao realizar as tarefas de classificação e regressão. Os modelos PLS (Partial Least Squares) e SVM (Support Vector Machine) apresentaram os melhores resultados para classificar folhas de soja quanto à condição hídrica. Já para a tarefa de regressão, os modelos PLS e PCR (principal component regression) apresentaram os melhores desempenhos. Palavras-chave: Índices de seleção. Machine learning. Espectroscopia NIR. Seca.

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Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Cited by 12 publications

References 19 publications

IPPS 2022 - Plant Phenotyping for a Sustainable Future

IPPS 2022 - Plant Phenotyping for a Sustainable Future

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm

Estratégias e seleção de cultivares de soja em estádios iniciais para tolerância ao déficit hídrico

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