Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics

Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC2) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety.

Natural Hybridization between Persian Walnut and Chinese Walnut Revealed by Simple Sequence Repeat Markers

Hybridization between species of the genus Juglans is common because of weak reproductive isolation mechanisms between closely related species with sympatric distributions. In this research, we investigated the possibility of naturally occurring interspecific hybrids between two species in the genus Juglans: persian walnut (Juglans regia) and chinese walnut (Juglans cathayensis). We used 12 pairs of microsatellite markers to analyze introgression between the two species. All amplified microsatellites were polymorphic in the two species. The result of Bayesian admixture analyses showed that introgression between the two species is rare; only three of nine individuals tentatively identified as hybrids, based on intermediate morphological characteristics, were defined as mixed genotypes. The other six putative hybrids and 156 morphologically pure individuals showed no sign of introgression.

Removal of Pb(II) by adsorption onto Chinese walnut shell activated carbon

The excessive discharge of Pb(II) into the environment has increasingly aroused great concern. Adsorption is considered as the most effective method for heavy metal removal. Chinese walnut shell activated carbon (CWSAC) was used as an adsorbent for the removal of Pb(II) from aqueous solution. Batch experiments were conducted by varying contact time, temperature, pH, adsorbent dose and initial Pb(II) concentration. Adsorption equilibrium was established within 150 min. Although temperature effect was insignificant, the Pb(II) adsorption was strongly pH dependent and the maximum removal was observed at pH 5.5. The Pb(II) removal efficiency increased with increasing CWSAC dosage up to 2.0 g/L and reached a maximum of 94.12%. Langmuir and Freundlich adsorption isotherms were employed to fit the adsorption data. The results suggested that the equilibrium data could be well described by the Langmuir isotherm model, with a maximum adsorption capacity of 81.96 mg/g. Adsorption kinetics data were fitted by pseudo-first- and pseudo-second-order models. The result indicated that the pseudo-first-order model best describes the adsorption kinetic data. In summary, CWSAC could be a promising material for the removal of Pb(II) from wastewater.