[Study of quantitative analysis of protein in barley using OSC-PLS algorithm]

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Spectroscopy

Context:

• Accurate protein content determination is crucial for barley quality assessment.
• Near-infrared (NIR) spectroscopy offers a rapid, non-destructive analytical technique.
• Traditional methods may be time-consuming or require sample destruction.

Purpose:

• To develop and validate a robust method for predicting protein content in barley using NIR spectroscopy.
• To evaluate the efficacy of orthogonal signal correction (OSC) as a preprocessing step for NIR data.
• To compare the performance of the OSC-assisted partial least squares (OSC-PLS) model against a standard PLS model.

Summary:

• Spectra of barley were analyzed using dispersive near-infrared (NIR) spectroscopy.
• Orthogonal signal correction (OSC) was applied to preprocess spectral data, removing uncorrelated variables.
• The OSC-PLS model demonstrated strong predictive performance, with R2 = 0.901 and a validation set correlation coefficient of 0.9717.
• The OSC-PLS method improved model interpretability and reduced complexity compared to the regular PLS model.

Impact:

• The OSC-PLS method provides a reliable and efficient approach for fast protein content analysis in barley.
• This technique can meet the demands for rapid quality assessment in the agricultural industry.
• Improved analytical models enhance the value and usability of spectroscopic data for agricultural products.