Assessment of Solidago×niederederi Origin Based on the Accumulation of Phenolic Compounds in Plant Raw Materials

This study provides the first phytochemical characterization of the morphologically identified natural hybrid Solidago×niederederi Khek compared with the native Solidago virgaurea and two invasive species, Canada goldenrod (Solidago canadensis L.) and giant goldenrod (Solidago gigantea Aiton). The phenolic compounds, namely, chlorogenic acid, rutin, isoquercitrin, hyperoside, and quercitrin, were detected in leaves and inflorescences by the high-performance liquid chromatography–photodiode array detector/ultraviolet (PAD/UV) method. All analyzed Solidago species contained all of the phenolic compounds investigated. The quantitative phytochemical differentiation among Solidago taxa was shown by principal component analysis. The results indicated that S. gigantea plants were characterized by significantly different quantities of phenolic compounds compared with three other Solidago taxa, which formed a separate cluster in the space of the principal component model, indicating the high similarity of their profiles. An additional multivariate analysis of the three species studied revealed a chemical gradient from S. canadensis to S. virgaurea with a slightly overlapping zone on the score plots presented by S.×niederederi and S. virgaurea accessions. The results showed that S.×niederederi was closely related to S. virgaurea. This result is suggestive of a hybrid origin with significant contributions from the native species. However, S.×niederederi was significantly different from its parental species with respect to chlorogenic acid and quercitrin in leaves and rutin with isoquercitrin in inflorescences. Conversely, samples indicating intermediate chemical composition between native S. virgaurea and invasive S. gigantea were not distinguished. The comparison of phenolic compound accumulation in Solidago plants supported the additional identification of the origin of S.×niederederi.

Comparison of Principal Component Solutions in Two Populations

When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Comprehensive Evaluation of the Level of Consumption Based on Principal Component Analysis and Cluster Analysis

The residents' consuming level of the various provinces in China is not balanced, so accurate analysis of the various provinces and cities in China's consumer spending and identifying the key factors that affect the level of consuming are beneficial for the promotion of the construction of the country's overall development. The paper used principal component analysis, established a comprehensive evaluation of the principal component model, and combined cluster analysis with the analysis of the differences in consumption of the different regions of China. Finally, the paper carried on a comprehensive evaluation of the 31 provinces and cities in the level of consumption and offered a proposal for the evaluation results.

Use of a (MSPCA) and (SVM) Method for Diagnosis of Motor Faults

This paper uses the combination between support vector machine and multi-scale principal component analysis. For motor fault detection, the principal component model can be established in various scales. Through T2 and Q statistic judgment whether motor can run normally. The experimental results show that the method of combination vector machine and multi-scale principal component analysis is supported to diagnose motor fault. This offers a new method and idea to diagnose motor. This method improves the accuracy of motor fault detection and practical significance.