ArticleAnimals : an open access journal from MDPI2021
Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Convolutional Neural Network-Based Models for Near-Infrared Prediction of Nutritional Quality in Multi-Product Animal Feeds.Animals : an open access journal from MDPI · 2026Article
- Spectral sensing for forage nutritive value determination of cool season, grass pastures during the grazing season.Journal of animal science · 2025Article
- Unlocking the synergistic potential of sensor technologies in grassland research.Discover sensors · 2025Review
- Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Discriminant Analysis as a Tool to Classify Grasslands Based on Near-Infrared Spectra.Animals : an open access journal from MDPI · 2024Article
- Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Article
- Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters.Micromachines · 2022Article
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4 authors.
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Abstract
Near-infrared spectroscopy (NIRS) and closed spectroscopy methods have been applied to analyse the quality of forage and animal feed. However, grasslands are linked to variability factors (e.g., site, year, occurring species, etc.) which restrict the prediction capacity of the NIRS. The aim of this study is to test the Fourier transform NIRS application in order to determine the chemical characteristics of fresh, undried and unground samples of grassland located in north-central Apennine. The results indicated the success of FT-NIRS models for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF) and acid detergent lignin (ADL) on fresh grassland samples (R2 > 0.90, in validation). The model can be used to quantitatively determine CP and ADF (residual prediction deviation-RPD > 3 and range error ratio- RER > 10), followed by DM and NDF that maintain a RER > 10, and are sufficient for screening for the lignin fraction (RPD = 2.4 and RER = 8.8). On the contrary, models for both lipid and ash seem not to be usable at a practical level. The success of FT-NIRS quantification for the main chemical parameters is promising from the practical point of view considering both the absence of samples preparation and the importance of these parameters for diet formulation.
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