Comparison of moisture uniformity between microwave-vacuum and hot-air dried ginger slices using hyperspectral information combined with semivariogram

Abstract

This study aimed to compare the moisture content (MC) uniformity of ginger slices subjected to microwave-vacuum drying (MVD) and hot-air drying (HAD) and discriminate MVD ginger slices from HAD ones (MC < 20 g/100 g) by NIR hyperspectral imaging (HSI). MC maps were visualized by a simplified partial least squares regression (PLSR) model with the determination coefficient in the prediction of 0.971. The hole effect of the semivariogram in MC maps indicated the moisture difference between the center and the edge. Furthermore, principal component analysis (PCA) of the semivariogram indicated a similar degree of moisture dissimilarity between MVD and HAD ginger slices with MC lower than 60 g/100 g. Partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) classifiers were respectively applied to the datasets of spectral reflectance at full wavelengths and nine optimum wavelengths selected by PCA, and variances in E-W and N-S directions to build classification models. The highest correct classification rate (CCR) of 95.1% was achieved by applying PLS-DA or SVM to the dataset of full wavelengths, while CCR of 90.3% was obtained for SVM model based on the dataset of nine selected wavelengths, confirming the potential of HSI spectra for MVD and HAD ginger slices discrimination.

Keywords:

• Hyperspectral imaging
• ginger
• microwave-vacuum drying
• hot-air drying
• semivariogram

Additional information

Funding

The authors would like to acknowledge University College Dublin (UCD) and China Scholarship Council (CSC, China) for providing financial support to this research under the UCD-CSC funding scheme.