ArticleScientific reports2023
Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content.
Article in Scientific reports, 2023. 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, 10 citations in OpenAlex.
- Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods.Plants (Basel, Switzerland) · 2026Article
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Nutritional profiling of horse gram through NIRS-based multi-trait prediction modelling.Scientific reports · 2025Article
- Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fourier transform mid infrared (FT-MIR) spectroscopy combined with modeling techniques has been studied as a useful tool for multivariate chemical analysis in agricultural research. A drawback of this method is the sample preparation requirement, in which samples must be dried and fine ground for accurate model calibrations. For research involving large sample sets, this may dramatically increase the time and cost of analysis. This study investigates the effect of fine grinding on model performance using leaf tissue from a variety of crop species. Dried leaf samples (N = 300) from various environmental conditions were obtained with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques. Scanning was repeated after fine grinding for 2, 5, and 10 min. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations. All analytes except for boron, iron, and zinc were well-modeled (average R
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