Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy

Burns, Michael J.; Renk, Jonathan S.; Eickholt, David; Gilbert, Amanda M.; Hattery, Travis J.; Holmes, Mark; Anderson, Nickolas; Waters, Amanda J.; Kalambur, Sathya; Flint-García, Sherry; Yandeau‐Nelson, Marna D.; Annor, George Amponsah; Hirsch, Candice N.

doi:10.1007/s00122-021-03926-8

Cited by 5 publications

(3 citation statements)

References 27 publications

(41 reference statements)

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“…A set of 501 diverse inbred lines from the Wisconsin Diversity Panel (Hansey et al ., 2011; Renk et al ., 2021; Burns et al ., 2021) were grown in the summers of 2018, 2019, 2020, and 2021. These trials were planted on May 14, 2018, May 30, 2019, May 7, 2020, and May 6, 2021 at the Minnesota Agricultural Experiment Station in St. Paul, MN.…”

Section: Methodsmentioning

confidence: 99%

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Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Sweet,

Tirado,

Cooper

et al. 2024

Preprint

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Plant height can be an indicator of plant health across environments and used to identify superior genotypes or evaluate abiotic stress factors. Typically plant height is measured at a single time point when plants have reached terminal height for the season. Evaluating plant height using unoccupied aerial vehicles (UAVs) is faster, allowing for measurements throughout the growing season, which facilitates a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected weekly for a panel of ∼500 diverse maize inbred lines over four growing seasons. The variation in plant height throughout the season was significantly explained by genotype, year, and genotype-by-year interactions to varying extents throughout development. Genome-wide association studies revealed significant SNPs associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth that would not be identified by terminal height associations alone. When plant height growth rates were compared to growth rates estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

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Section: Methodsmentioning

confidence: 99%

“…Genome-wide association studies were completed as previously described (Renk et al ., 2021; Burns et al ., 2021). In brief, GAPIT v.3 (Wang and Zhang, 2021) was used to transform genomic data into numeric format, generate a genotypic map dataset, and a PCA covariates dataset.…”

Section: Methodsmentioning

confidence: 99%

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Sweet,

Tirado,

Cooper

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…A GWAS was performed to find MTA for all trait-model-time phenotypes as previously described (Burns et al, 2021; Renk et al, 2021). Intra- and inter-year BLUPs, AVAMGE values, and FW slopes were used as phenotypes.…”

Section: Methodsmentioning

confidence: 99%

Dissecting the temporal phenomics and genomics of maize canopy cover using UAV mediated image capture

Cooper,

Sweet,

Tirado

et al. 2024

Preprint

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Canopy cover is an important agronomic trait influencing photosynthesis, weed suppression, biomass accumulation, and yield. Conventional methods to quantify canopy cover are time and labor-intensive. As such, little is known about how canopy cover develops over time, the stability of canopy cover across environments, or the genetic architecture of canopy cover. We used unoccupied aerial vehicle-mediated image capture to quantify plot-level canopy coverage in maize throughout the growing season. Images of 501 diverse inbred lines were acquired between 300 and 1300 growing degree days in the 2018-2021 growing seasons. We observed that the maize canopy developed following a logistic curve. Phenotypic variation in percent canopy coverage and canopy growth rate was explained by genetic and environmental factors and genotype-by-environment interactions, however the percent of variance explained by each factor varied throughout the growing season. Environmental factors explained the largest portion of trait variance during the adult vegetative growth stage and had a larger impact on canopy growth rates than percent canopy coverage. We conducted multiple genome wide association studies and found that canopy cover is a complex, polygenic trait with a diverse range of marker trait associations throughout development. The change in associations indicated that single time point phenotyping was insufficient to capture the full phenomic and genetic diversity of canopy cover in maize.

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Robust and Accurate Classification of Mutton Adulteration Under Food Additives Effect Based on Multi-Part Depth Fusion Features and Optimized Support Vector Machine

Huang

Wang

et al. 2023

Food Anal. Methods

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Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy

Abstract: Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.

Cited by 5 publications

References 27 publications

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Dissecting the temporal phenomics and genomics of maize canopy cover using UAV mediated image capture

Robust and Accurate Classification of Mutton Adulteration Under Food Additives Effect Based on Multi-Part Depth Fusion Features and Optimized Support Vector Machine

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