SpectrifyAI
Research PipelineCrude Fibre - Feed Inputs
Sector 01 · Food & AgricultureEarly Exploration

Crude Fibre - Feed Inputs

Crude fibre is a key nutritional parameter for agricultural feed quality certification - used by feed mills, livestock farms, and regulatory bodies to verify that raw material inputs (rice straw, sugarcane bagasse, coconut pith, etc.) meet formulation specifications. Current measurement by Weende proximate analysis takes 6–8 hours. NIR-based crude fibre prediction is well-established for homogeneous matrices (e.g., single-grain feeds) but less characterised for the heterogeneous mixed-input matrices common in Sri Lankan and South Asian livestock farming.

NIRFeedFibreLivestockAgriculture

Findings

What We've Established

Technical Feasibility Assessment

Published NIR calibrations for crude fibre in standardised feed matrices (wheat straw, hay) show R² > 0.90. Heterogeneous inputs with highly variable particle size and composition present greater calibration challenges and may require particle size normalisation or matrix-specific sub-models.

Methodology

Technical Approach

Feasibility review is ongoing. The proposed approach is to begin with one feed input type (likely rice straw or coconut pith, due to volume and availability) before expanding. Reference method is ISO 6865 crude fibre determination. Calibration dataset requirements are estimated at 150–300 samples for a single matrix type.

Status

Where We Stand

Early Exploration

Early feasibility exploration. No partner commitments and no dataset collection. The primary objective at this stage is to identify one or two feed mills or livestock operations willing to serve as data collection and validation partners.

Roadmap

Next Steps

1

Define target feed input matrices in priority order

2

Identify feed mill or research institute partners for sample access

3

Conduct a small-scale spectral feasibility scan on representative samples before committing to full calibration

R&D Partnerships

Interested in This Research?

If you have relevant data, domain expertise, or a measurement problem in this area, we're open to research collaboration and data-sharing agreements.