RESUMO
Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.
RESUMO
Listeria monocytogenes (L. monocytogenes) is a pathogen that is transmitted through contaminated food and causes the illness known as listeriosis. The virulence factor InlA plays a crucial role in the invasion of L. monocytogenes into the human intestinal epithelium. In addition, InlA enhances the pathogenicity of host strains, and different strains of L. monocytogenes contain varying variations of InlA. Our study analyzed a total of 4393 published L. monocytogenes genomes from 511 sequence types (STs) of diverse origins. We identified 300 unique InlA protein sequence types (PSTs) and revealed 45 highly mutated amino acid sites. The leucine-rich repeat (LRR) region was found to be the most conserved among the InlA, while the protein A (PA) region experienced the highest mutation rate. Two new types of mutations were identified in the B-repeat region of InlA. Correspondence analysis (CA) was used to analyze correlations between the lineages or 10 most common sequence types (STs) and amino acid (aa) sites. ST8 was strongly correlated with site 192_F, 454_T. ST7 exhibited a strong correlation with site 51_A, 573_E, 648_S, and 664_A, and it was also associated with ST6 and site 544_N, 671_A, 738_B, 739_B, 740_B, and 774_Y. Additionally, a strong correlation between ST1 and site 142_S, 738_N, ST2 and site 2_K, 142_S, 738_N, as well as ST87 and site2_K, 738_N was demonstrated. Our findings contribute significantly to the understanding of the distribution, composition, and conservation of InlA in L. monocytogenes. These findings also suggest a potential role of InlA in supporting molecular epidemiological tracing efforts.