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

Importance of the topic

Pulse crops such as chickpeas, lentils, and dry peas play a vital role in global nutrition due to their high protein, fiber, and micronutrient content. Efficient phenotyping of these crops is essential for plant breeding programs aiming to improve nutritional quality and accelerate varietal development. Traditional analytical workflows based on enzymatic assays, GC/MS, or HPLC are costly, time-consuming, and labor-intensive, creating bottlenecks in data acquisition. Fourier-transform infrared (FTIR) spectroscopy offers a rapid, low-cost, and minimally destructive alternative that addresses these challenges and supports high-throughput screening in both laboratory and field settings.

Objectives and study overview

This technical overview evaluates the application of the Agilent Cary 630 FTIR spectrometer with a diamond ATR module for high-throughput nutritional phenotyping of pulse crops. Key goals include:

• Developing chemometric models to predict protein quality, protein digestibility, fatty acid profiles, and starch (including resistant starch) content from FTIR spectra.
• Comparing FTIR-predicted values with reference data obtained via conventional methods (combustion nitrogen analysis, HPLC, GC/MS, PDCAAS enzyme assay, Megazyme starch assay).
• Demonstrating a streamlined workflow—from sample preparation to color-coded results—to reduce turnaround time and training requirements.

Instrumentation used

• Agilent Cary 630 FTIR Spectrometer with diamond ATR module
• Agilent MicroLab Expert software (Calibration Wizard and Chemometric Model engine)
• Agilent MicroLab software for routine analyses
• Reference platforms: Agilent 1100 HPLC, Agilent 8860 GC with 5977B MSD, combustion nitrogen analyzer, PDCAAS enzyme kit, Megazyme resistant starch assay

Methodology

Sample flours from individual seeds were lightly milled and directly analyzed by FTIR (~2 minutes per sample) without chemical treatments. Spectra were collected and processed in MicroLab Expert, where partial least squares regression (PLSR) models were built and cross-validated against conventional assay results. Validated chemometric models were then deployed in MicroLab software, which guides operators through picture-driven workflows and outputs color-coded, actionable data.

Main results and discussion

– Protein quality and digestibility models achieved calibration R² values ≥0.84 and prediction errors comparable to reference assays, with no significant bias (α=0.05).
– Sulfur amino acids, total protein, and in vitro protein digestibility predictions in chickpea, lentil, and pea flours showed R² ≥0.88.
– Fatty acid models for chickpea (total fatty acids, unsaturated and saturated fractions) delivered R² ≥0.91 versus GC/MS, confirming high predictive accuracy.
– Starch and resistant starch models for pea, chickpea, and lentil flours achieved R² values up to 0.96, matching Megazyme assay performance.

Benefits and practical applications

• Turnaround time reduction from days to minutes enables rapid decision-making in breeding programs.
• Minimal sample destruction allows re-analysis when needed.
• Compact, affordable instrumentation supports deployment in resource-limited settings.
• Easy-to-use software with guided workflows lowers training barriers and human error.

Future trends and potential uses

– Integration with field-deployable FTIR units for on-site screening.
– Expansion of chemometric libraries to include additional nutritional and anti-nutritional compounds (polyphenols, phytic acid, flavor volatiles).
– Coupling FTIR data with genomic selection tools and machine learning models for predictive breeding.
– Adoption in quality control for post-harvest processing, food formulation, and regulatory compliance.

Conclusion

The Agilent Cary 630 FTIR spectrometer coupled with MicroLab software provides a robust, high-throughput platform for nutritional phenotyping of pulse crops. Chemometric models deliver accuracy comparable to traditional assays while drastically reducing analysis time, cost, and complexity. This approach empowers breeding programs and laboratories with rapid insights, facilitating accelerated development of nutrient-enhanced varieties and supporting global food security initiatives.

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