ArticleJournal of food science2025
Near-Infrared Spectroscopy Prediction of Dry Matter and Starch Content in Cassava Using Optimized Calibration Models.
Article in Journal of food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Establishment of a Near-Infrared Spectroscopy-Based Screening Framework for Key Quality Indicators of Cassava Varieties.Foods (Basel, Switzerland) · 2026Article
- Near-Infrared Spectroscopy Prediction of Dry Matter and Starch Content in Cassava Using Optimized Calibration Models.Journal of food science · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Dry matter content (DMC) and starch content (StC) are key quality traits in cassava breeding, yet traditional phenotyping methods are time-consuming and limit scalability. This study aimed to develop and compare predictive models for DMC and StC using near-infrared (NIR) spectroscopy, evaluating two devices-a benchtop spectrometer (Büchi NIRFlex N-500; 1000-2500 nm) and a portable device (QualitySpec Trek; 350-2500 nm)-and assessing the influence of sample type (fresh vs. processed). A total of 3,391 cassava clones from the Embrapa breeding program were analyzed from 2018 to 2023. Reference values were obtained via gravimetric analysis (DMCg), oven drying (DMCo), and manual StC extraction. Spectral data were used to train and validate models using Partial Least Squares (PLS), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB). PLS consistently delivered the highest predictive accuracy across traits and devices. KNN slightly outperformed PLS for DMCg using the benchtop device, while XGB was comparable to PLS in select scenarios (e.g., StC with the benchtop: 0.88 vs. 0.89; DMCo with the portable: 0.92 vs. 0.95). Processed samples yielded higher model accuracy than fresh ones. The portable NIR device showed better performance with processed samples and even surpassed the benchtop for DMCg and StC in external validation (0.74 and 0.76 vs. 0.71 and 0.72, respectively). Overall, processed sample preparation significantly improved model performance, and the portable spectrometer proved to be a practical, accurate, and scalable alternative for high-throughput phenotyping in cassava breeding.
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