ArticleFrontiers in plant science2024
Near infrared spectroscopy for cooking time classification of cassava genotypes.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 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
- Multisource spectral fusion combined with variable selection for rapid geographical origin discrimination ofFrontiers in chemistry · 2025Article
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5 authors.
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Abstract
Cooking time is a crucial determinant of culinary quality of cassava roots and incorporating it into the early stages of breeding selection is vital for breeders. This study aimed to assess the potential of near-infrared spectroscopy (NIRS) in classifying cassava genotypes based on their cooking times. Five cooking times (15, 20, 25, 30, and 40 minutes) were assessed and 888 genotypes evaluated over three crop seasons (2019/2020, 2020/2021, and 2021/2022). Fifteen roots from five plants per plot, featuring diameters ranging from 4 to 7 cm, were randomly chosen for cooking analysis and spectral data collection. Two root samples (15 slices each) per genotype were collected, with the first set aside for spectral data collection, processed, and placed in two petri dishes, while the second set was utilized for cooking assessment. Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high (
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