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Related Experiment Video

Updated: Feb 22, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Protein content prediction in single wheat kernels using hyperspectral imaging.

Nicola Caporaso¹, Martin B Whitworth², Ian D Fisk³

¹Campden BRI, Chipping Campden, Gloucestershire GL55 6LD, UK; Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK.

|September 27, 2017

Summary

This summary is machine-generated.

Hyperspectral imaging (HSI) accurately predicts single wheat kernel protein content, revealing variations within batches. This technology enables detailed analysis of protein distribution, crucial for quality assessment.

Keywords:

Cereals Chemical imaging Hyperspectral imaging Near-infrared spectroscopy Rapid measurement Single kernel assessment Wheat protein

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Last Updated: Feb 22, 2026

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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Published on: August 8, 2017

Area of Science:

Agricultural Science
Analytical Chemistry
Spectroscopy

Background:

Protein content is a key quality indicator in wheat, influencing baking properties and market value.
Existing methods often analyze bulk samples, masking significant protein variations at the single kernel level.
Understanding single kernel protein distribution is vital for precise quality control and breeding programs.

Purpose of the Study:

To quantify protein content distribution in individual wheat kernels.
To develop and validate hyperspectral imaging (HSI) models for single kernel protein prediction.
To assess the feasibility of HSI for analyzing protein variation within wheat batches.

Main Methods:

Wheat samples from multiple harvests were analyzed using HSI in the 980-2500nm range.
Partial Least Squares (PLS) regression models were developed using single kernel spectra.
Protein content was determined using the Dumas combustion method for calibration and validation.

Main Results:

HSI models achieved good prediction performance for single kernel protein content (R² of 0.79-0.82, RMSE of 0.86-0.94%).
The study successfully quantified and visualized protein distribution within and between wheat kernels.
HgCdTe detectors showed superior performance over InGaAs detectors for this application.

Conclusions:

Hyperspectral imaging is a viable technique for non-destructive, single kernel protein analysis in wheat.
HSI provides valuable insights into protein variation, complementing traditional bulk analysis methods.
This technology has potential applications in wheat quality assessment, breeding, and milling industries.