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This study proposed a method using hyper-spectral imaging technology in determining eggs' quality in term of freshness from a biochemical perspective by estimating the S-ovalbumin content. This method has the potential in assessing eggs' quality rapidly and non-destructively. Hyper-spectral image of egg was captured using a hyper-spectral imaging system and regression model was built to estimate the S-ovalbumin content. The successive projections algorithm (SPA) was used to select significant wavebands followed by building a partial least squares regression (PLSR) model and a multiple linear regression (MLR) model. The MLR model could predict S-ovalbumin content better than PLSR model with a higher correlation coefficient (0.922) and lower root mean square error (0.086) of the calibration set, a higher correlation coefficient (0.911) and lower root mean square error (0.119) of the validation set, and a higher residual predictive deviation (2.348). The regression equation from the MLR model was used to compute each pixel of the image in the validation set and visualisation of S-ovalbumin content distribution in the egg was obtained using pseudo-color image. The findings implied that the proposed hyper-spectral imaging system with the regression model developed has the potential in determining and visualising the eggs' quality. [ABSTRACT FROM AUTHOR]

Understanding factors that shape host selection has been a classic issue in ecology, evolutionary biology, and epidemiological investigation. During the survey from 2000 to 2009, a total of 11,216 individuals of small mammals were captured from Yunnan Province in southwestern China. The captured small mammalian hosts belong to five orders, ten families, 35 genera, and 65 species and from their body surface, 38,885 individuals of ectoparasitic sucking lice were collected, which represent five families, seven genera, and 31 species. Based on niche overlap of dominant sucking lice on their primary hosts, we used hierarchical cluster analysis to sort different sucking louse species' resource utilizations of similar kind into respective categories. Given λ < 5, there are only two groups clustered, however, sucking louse species' resource utilization was sorted into eight respective categories at λ = 15. The results revealed that most species of sucking lice usually had high host specificity and a certain species of sucking louse usually restricted to one or few small mammalian species as their dominant hosts. Correspondence analysis was used to visualize associations between parasitic sucking lice and their small mammalian hosts, which suggested three different patterns of host resource utilization: species specialists, genera generalists, and multiple selections. For example, Sathrax durus (Johnson) only parasitized on species of Tupaia belangeri (Wagner), Hoplopleura edentula (Fahredholz) predominatly on genus of Eothenomys, and Polyplax reclinata (Nitzsch) on Family of Soricidae. Our results demonstrate that sucking lice have high host specificity and this might be due to coevolution between sucking lice and their hosts. [ABSTRACT FROM AUTHOR]