ArticleFoods (Basel, Switzerland)2024
Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- NIRS chemometrics for rapid nutritional profiling of vegetable pea (Pisum sativum L.) germplasm.Scientific reports · 2025Article
- Near-Infrared Spectroscopy Prediction of Dry Matter and Starch Content in Cassava Using Optimized Calibration Models.Journal of food science · 2025Article
- Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis-NIR-SWIR Spectroscopy: A Machine Learning Approach.Foods (Basel, Switzerland) · 2025Article
- Construction and optimization of quantitative analysis models for pigments in broccoli (Food chemistry: X · 2025Article
- Biochemical and agro-morphological traits-based mining for Malt Barley Germplasm.Frontiers in nutrition · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQ
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