Abstract

Partial least-squares regression (PLSR) was used to generate wheat moisture content predictive models from eight-frequency microwave attenuation (A) and phase (P) spectra in the 10.36 to 18.0 GHz range, as obtained by a free-space technique with a 10.4 cm thick sample. Spectra (n = 379) were measured for a set of grain samples that had been treated to span the agriculturally practical ranges of moisture content (M) (10.6 to 19.2% g/g wet), temperature (K) (- 1 to 42 C), and bulk density (D) (0.72 to 0.88 g/mL). The sample property space formed by M, K, and D was used to prune redundant samples and select representative subsets for calibration (n = 279), cross-validation (n = 40 segments), and testing (n = 31). Twelve model types are reported and vary from attenuation or phase alone to the combination of attenuation, phase, temperature, and density (i.e., APKD). For optimization of each PLSR model, the raw spectral, temperature, and density data were preprocessed with variable ratios, mathematical transformations, and/or variable scaling. The lowest moisture prediction errors were for temperature and density-corrected models with variables AKD or APKD; these produced root-mean-square cross-validation and prediction errors (RMSECV and RMSEP) of 0.19 to 0.20% in moisture content units. The more practical unifrequency models, APK at 15.2 GHz, and AK at 18.0 GHz, yielded RMSECV values of 0.21% and 0.35%, respectively. Addition of temperature to dielectric data always substantially reduced the model error. However, the multiplicative effect of density is well corrected by using the ratio A/P, or partly corrected by using the features in the attenuation spectra. Data trends suggest that dual-frequency PK models might benefit from a wider frequency range, and unifrequency AK models might be better at frequencies higher than 18.0 GHz. The results presented make it possible to evaluate a wide variety of instrumental configurations that might be proposed to suit particular engineering criteria such as measurement accuracy, range of operating conditions, and hardware complexity.

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