Understanding sorghum grain quality is essential for breeding, food innovation, and industrial applications. Traits such as protein, starch composition, oil content, tannins, and phenolic compounds directly influence nutritional value, processing behavior, and end use. However, traditional laboratory assays are slow, destructive, expensive, and difficult to apply at the scale required for modern breeding programs.
Recent advances in nondestructive, high-throughput analytical methods—including spectroscopy, hyperspectral imaging, image analysis, and machine learning—are transforming how grain quality can be measured. This blog synthesizes insights from nine recent studies that collectively demonstrate how these technologies are enabling rapid, scalable, and data-rich assessment of sorghum grain composition and identity.
Spectral Approaches for Functional Quality Traits
Several recent studies demonstrate the power of near-infrared (NIR) and visible–near-infrared (VIS–NIR) spectroscopy, which measure how grain reflects light across specific wavelengths related to chemical bonds in starches, proteins, and lipids.
Wu et al. (2025) showed that VIS–NIR hyperspectral imaging—which collects both spectral and spatial information—can be used to estimate crude protein, tannin, and crude fat contents in sorghum grain without destroying samples. By combining spectral preprocessing with variable-selection techniques and regression and neural-network models, the authors demonstrated that different nutritional traits require distinct wavelength subsets and modeling strategies. This highlights the importance of trait-specific calibration when applying spectral tools at scale.
🔗 https://doi.org/10.1038/s41598-025-90892-6
Complementing this work, Zhou et al. (2025) focused on single sorghum kernels, developing reliable laboratory methods for measuring total starch and apparent amylose at the individual-kernel level and then training near-infrared spectroscopy models to predict these traits nondestructively. The ability to measure starch composition in single kernels opens the door to kernel-level sorting and selection, which is especially valuable for breeding programs targeting specific processing or end-use qualities.
🔗 https://doi.org/10.1016/j.carbpol.2025.124257
Huang et al. (2021) applied hyperspectral imaging to predict amylose and amylopectin, the two main components of starch that strongly influence sorghum’s suitability for fermentation and liquor production. By combining wavelength selection with advanced machine-learning models, the study achieved high prediction accuracy across multiple sorghum varieties. This work illustrates how hyperspectral imaging can capture subtle biochemical differences that affect processing performance.
🔗 https://doi.org/10.1016/j.foodchem.2021.129954
Mendoza et al. (2023) extended hyperspectral imaging to oil content prediction, demonstrating that near-infrared hyperspectral data can match the performance of established single-kernel reflectance instruments while also providing spatial information. Oil content is an important trait for food, feed, and bioenergy uses, and this study shows that hyperspectral imaging can serve as a practical quality-control and breeding tool.
🔗 https://doi.org/10.1002/cche.10656
Image-Based Screening and Machine Learning
Not all high-throughput approaches rely on spectroscopy. Several studies demonstrate that standard digital images, when analyzed using texture features and machine-learning models, can serve as proxies for chemical composition.
Nazari et al. (2021) examined correlations between grain surface texture features extracted from multiple color spaces and laboratory measurements of protein, tannin, and total phenolic compounds. Strong correlations were identified for specific texture features, and regression models explained a substantial proportion of the variation in these traits.
🔗 https://doi.org/10.1007/s00217-020-03625-6
Building on this idea, Nazari et al. (2022) combined image-derived texture features with artificial neural networks to predict the same chemical traits. The resulting models showed high agreement between predicted and measured values, suggesting that low-cost image analysis can support rapid screening, especially in early breeding stages or large germplasm collections.
🔗 https://doi.org/10.1002/cche.10542
These image-based approaches are particularly attractive where access to spectroscopic instruments is limited, and they highlight how visible grain characteristics can encode information about internal composition.
Advanced Modeling for Multi-Trait Prediction
As data volumes increase, modeling strategies are becoming more sophisticated. Zhao et al. (2025) introduced multi-output Gaussian process regression, a probabilistic machine-learning approach that predicts multiple traits simultaneously. Using near-infrared spectroscopy data, the authors achieved very high accuracy for both tannin and protein content, outperforming traditional regression and neural-network models.
🔗 https://doi.org/10.1016/j.jfca.2025.107326
This study illustrates an important trend: joint modeling of correlated traits can improve prediction accuracy and better reflect the biological relationships among grain quality components.
Beyond Composition: Variety Identification and Quality Control
High-throughput grain analysis is not limited to composition alone. Bu et al. (2023) demonstrated that hyperspectral imaging combined with deep learning can accurately identify sorghum varieties at the single-grain level, achieving classification accuracies above 95%. This capability is particularly relevant for varietal purity assessment, where unintended mixing can affect processing quality, especially in liquor production.
🔗 https://doi.org/10.1002/jsfa.12344
Connecting Grain Quality to Food Applications
Thilakarathna et al. (2022) provide a broader review linking rapid screening technologies with emerging food applications of sorghum, particularly in gluten-free products. The review highlights portable spectroscopic tools and image-based methods as key enablers for selecting high-quality sorghum types that meet market and nutritional demands.
🔗 https://doi.org/10.1111/1750-3841.16008