A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits

Varela, Jose I.; Miller, Nathan D.; Infante, Valentina; Kaeppler, Shawn M.; León, Natalia de; Spalding, Edgar P.

doi:10.1016/j.foodchem.2022.133264

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…A potentially insightful follow-up to this study might be to scan finely ground kernel samples with NIRS to see if the scanning methodology improves the predictive ability of the PP models (M2 and/or M3). Alternatively, to avoid the laborious process of grinding samples, hyperspectral imaging via novel methods developed by Varela et al (2022) may allow researchers to obtain accurate estimations of kernel composition traits nondestructively, in addition to obtaining morphometric features such as width and length of the kernels. An additional limitation to the present study's findings on the use of PP includes the limited effects of G × E for GY and KW, as the interaction effect between pedigree and environment accounted for only 4.04% and 3.57% for GY and KW, respectively (Figure 1A).…”

Section: Discussionmentioning

confidence: 99%

Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

DeSalvio,

Adak,

Murray

et al. 2024

The Plant Genome

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For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across‐environment sparse genomic prediction models. One phenomic data modality is whole grain near‐infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500‐kernel weight (KW) across 2 years (2011–2012) and two management conditions (water‐stressed and well‐watered) were conducted using combinations of reflectance data obtained from high‐throughput, F2 whole‐kernel scans and genomic data obtained from genotyping‐by‐sequencing within four different cross‐validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024—genomic vs. 0.612 ± 0.045—phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034—genomic vs. 0.617 ± 0.145—phenomic). Multi‐kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single‐kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single‐kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.

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Section: Discussionmentioning

confidence: 99%

Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

DeSalvio,

Adak,

Murray

et al. 2024

The Plant Genome

View full text Add to dashboard Cite

show abstract

“…A potentially insightful follow-up to this study might be to scan finely ground kernel samples with NIRS to see if the scanning methodology can "rescue" the predictive ability of the phenomic prediction models (M2) using the same sample genetics and growing environments. Alternatively, to avoid the laborious process of grinding samples, hyperspectral imaging via novel methods developed by Varela et al (2022) could allow researchers to obtain accurate estimations of kernel composition traits nondestructively, in addition to obtaining morphometric features such as width and length of the kernels. Additional limitations to the present study could include the small sample size of 145 hybrids common across all four growing environments as well as limited effects of G×E owing to similarities between WS and WW environments across years.…”

Section: Discussionmentioning

confidence: 99%

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

DeSalvio,

Adak,

Murray

et al. 2023

Preprint

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For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e.,MADS69,ZCN8, sh1, wx1, du1) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes.Key messageGenomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.

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“…Currently, near-infrared spectroscopy is the mainstream method for detecting the hardness of single grains. Near-infrared spectroscopy can reliably predict the protein content, density, and endosperm vitrification inside grains with a high accuracy [16]. Biological and infrared imaging technology can quickly and non-destructively evaluate the quality parameters of maize as well as reliably predict the protein content and density with a high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test

Lin,

Song,

Dai

et al. 2024

Agriculture

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Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we developed machine learning models (MLMs) to predict the hardness of Jinsui No.4 maize seeds. The input variables of the MLM were loading speed, loading depth, and different types of indenters, and the output variable was the slope of the linear segment. Using the Latin square design, 100 datasets were generated. Four different types of MLMs, a genetic algorithm (GA), support vector machine (SVM), random forest (RF), and long short-term memory network (LSTM), were used for our data analysis, respectively. The result indicated that the GA model had a high accuracy in predicting hardness values, the R2 of the GA model training set and testing set reached 0.98402 and 0.92761, respectively, while the RMSEs were 1.4308 and 2.8441, respectively. The difference between the predicted values and the actual values obtained by the model is relatively small. Furthermore, in order to investigate the relationship between hardness and morphology after compression, scanning electron microscopy was used to observe the morphology of the maize grains. The result showed that the more complex the shape of the indenter, the more obvious the destruction to the internal polysaccharides and starch in the grain, and the number of surface cracks also significantly increases. The results of this study emphasize the potential of MLMs in determining the hardness of agricultural cereal grains, leading to improved industrial processing efficiency and cost savings. Additionally, combining grain hardness prediction models with the operating mechanisms of industry machinery would provide valuable references and a basis for the parameterization of seed grain processing machinery.

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A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits

Cited by 6 publications

References 30 publications

Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test

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