Determination of wheat moisture using terahertz spectroscopy combined with the tabu search algorithm

Area of Science:

• Agricultural Science
• Spectroscopy
• Chemometrics

Background:

• Accurate wheat moisture detection is crucial for grain storage and classification.
• Traditional methods can be time-consuming and less precise.

Purpose of the Study:

• To develop a rapid and accurate method for quantitative analysis of moisture content in wheat.
• To evaluate the effectiveness of terahertz spectroscopy combined with chemometric algorithms for this purpose.

Main Methods:

• Terahertz (THz) time-domain spectroscopy was applied to 240 wheat samples.
• Spectral data underwent pretreatment (S-G, MSC, SNV) and variable selection using Tabu Search (TS).
• Quantitative models were built and validated using Partial Least Squares (PLS) regression.

Main Results:

• High correlation coefficients for calibration (Rc=0.9522) and prediction (Rp=0.9531) were achieved.
• Low root mean square errors for calibration (RMSEC=0.4730) and prediction (RMSEP=0.5396) indicate model accuracy.
• The S-G + MSC + TS + PLS model demonstrated effective prediction of wheat moisture content.

Conclusions:

• Terahertz time-domain spectroscopy, coupled with the TS algorithm, provides an accurate quantitative analysis of wheat moisture.
• This integrated approach offers a rapid and reliable method for detecting moisture in wheat samples, benefiting grain management.