ArticleFrontiers in plant science2022
Predicting starch content in cassava fresh roots using near-infrared spectroscopy.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 29 citations in OpenAlex.
- 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
- From root to result: Portable NIRS-based non-destructive prediction of cassava quality traits.PloS one · 2025Article
- Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour.Scientific reports · 2024Article
- Cassava Breeding and Cultivation Challenges in Thailand: Past, Present, and Future Perspectives.Plants (Basel, Switzerland) · 2024Review
- Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava.The plant genome · 2024Article
- Genetic Variation and Heritability for Hydrogen Cyanide in Fresh Cassava Roots: Implications for Low-Cyanide Cassava Breeding.Plants (Basel, Switzerland) · 2024Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Quantifying Microalgae Growth by the Optical Detection of Glucose in the NIR Waveband.Molecules (Basel, Switzerland) · 2023Article
- Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm.Frontiers in nutrition · 2023Article
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec
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