Rapid, Simple, and High-Throughput Nutritional Phenotyping of Pulse Crops

Importance of the Topic

Pulse crops offer high levels of plant protein, dietary fiber and essential micronutrients. Rapid and reliable phenotyping of nutritional traits is critical to support breeding programs aimed at improving crop quality and accelerating varietal development. Traditional assays are slow, resource intensive and often destructive, creating bottlenecks in data acquisition and delaying genetic gains.

Objectives and Study Overview

This overview evaluates the capabilities of the Agilent Cary 630 FTIR spectrometer equipped with a diamond ATR module and chemometric modeling software for high-throughput phenotyping of chickpea, lentil and dry pea flours. Key aims include predicting total protein content, amino acid digestibility, fatty acid composition and starch fractions, and comparing FTIR-based predictions to reference methods.

Methodology

• Sample preparation: minimal milling of individual seed flour, no reagents or extensive pretreatment required.
• Spectral acquisition: 1–2 minutes per sample using Cary 630 FTIR with diamond ATR.
• Chemometric analysis: development of partial least squares regression models in Agilent MicroLab Expert, calibrated against conventional assays (HPLC, GC-MS, PDCAAS enzyme assay, Megazyme starch assay).

Instrumentation Used

• Agilent Cary 630 FTIR spectrometer with diamond ATR accessory
• Agilent MicroLab Expert software for model development
• Agilent MicroLab software for routine guided analysis
• Reference platforms: combustion nitrogen analyzer, Agilent HPLC, Agilent GC-MS, PDCAAS assay system, Megazyme resistant starch kit

Key Results and Discussion

• Protein models achieved R² values between 0.84 and 0.95 for total protein and sulfur amino acids, with no significant bias versus reference assays.
• Protein digestibility predictions reached R² ≥ 0.88, matching PDCAAS enzyme assay outcomes.
• Fatty acid models in chickpea delivered R² > 0.90 for total, unsaturated and saturated fatty acids relative to GC-MS data.
• Resistant and total starch models in all three crops showed R² ≥ 0.91 with acceptable prediction errors.
• Multiple nutrient parameters can be assessed simultaneously from a single FTIR spectrum, reducing analysis time, cost and sample consumption.

Benefits and Practical Applications

FTIR phenotyping provides analytical results in under two minutes with minimal sample destruction and no hazardous reagents. The compact footprint, low operating cost and intuitive guided software minimize training requirements. High throughput screening enables rapid generation of large phenotypic datasets, supporting marker-assisted selection and breeding program acceleration.

Future Trends and Potential Applications

• Deployment of portable FTIR units for on-site phenotyping in breeding fields.
• Extension of chemometric models to additional quality markers such as polyphenols and anti-nutrients.
• Integration of FTIR data with genomic selection and machine learning for predictive breeding.
• Adoption in resource-limited regions to support local crop improvement initiatives.

Conclusion

The Agilent Cary 630 FTIR spectrometer combined with robust chemometric modeling offers a rapid, cost-effective and reliable platform for comprehensive nutritional phenotyping of pulse crops. By substantially reducing analysis time and expense while maintaining accuracy, this approach empowers breeding programs to accelerate the development of nutritionally enhanced varieties.

References

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4. Sab S et al. Front Nutr. 2020; QTL mapping for iron and zinc in chickpea.
5. Madurapperumage A et al. ACS Food Sci Technol. 2023; FTIR for chickpea fatty acids.
6. Johnson N et al. Plant Phenome J. 2023; FTIR for starch analysis.
7. Madurapperumage A et al. Plant Phenome J. 2022; FTIR for protein quality.
8. Madurapperumage A et al. Crop Sci. 2024; FTIR for protein digestibility.