This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0–100% w/w at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA–KM–Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R2) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon.
Members of the caveolin family played important roles during fundamental cellular processes, such as regulation of cell morphology, migration, and gene expression in muscle cells. In this study, caveolin-1 (Cav-1), one of the caveolins, was identified from longissimus dorsi muscle of Large Yorkshire pig and Chinese indigenous Lantang pig based on the results of mRNA differential display analysis. The deduced amino acids sequence of the porcine Cav-1 contained a caveolin domain, and was very conservative among different species. The Cav-1 mRNA was widely expressed in the eight tissues in this study, including heart, liver, kidney, encephalon, spleen, lung, longissimus dorsi muscle, and back fat, and the highest expression quantity was found in back fat of the two pig breeds. The expression quantity of porcine Cav-1 in back fat and longissimus dorsi muscle of Lantang pig was significantly higher than that of Large Yorkshire (P<0.01, and P<0.05, respectively). These results suggested that the Cav-1 might be a candidate gene for carcass traits, and might provide valuable information for understanding the mechanism of caveolae signaling in fat deposition by using the animal model of pig.
This article is the 14th in the Fungal Diversity Notes series, wherein we report 98 taxa distributed in two phyla, seven classes, 26 orders and 50 families which are described and illustrated. Taxa in this study were collected from Australia, Brazil, Burkina Faso, Chile, China, Cyprus, Egypt, France, French Guiana, India, Indonesia, Italy, Laos, Mexico, Russia, Sri Lanka, Thailand, and Vietnam. There are 59 new taxa, 39 new hosts and new geographical distributions with one new combination. The 59 new species comprise Angustimassarina kunmingense , Asterina lopi , Asterina brigadeirensis , Bartalinia bidenticola , Bartalinia caryotae , Buellia pruinocalcarea , Coltricia insularis , Colletotrichum flexuosum , Colletotrichum thasutense , Coniochaeta caraganae , Coniothyrium yuccicola , Dematipyriforma aquatic , Dematipyriforma globispora , Dematipyriforma nilotica , Distoseptispora bambusicola , Fulvifomes jawadhuvensis , Fulvifomes malaiyanurensis , Fulvifomes thiruvannamalaiensis , Fusarium purpurea , Gerronema atrovirens , Gerronema flavum , Gerronema keralense , Gerronema kuruvense , Grammothele taiwanensis , Hongkongmyces changchunensis , Hypoxylon inaequale , Kirschsteiniothelia acutisporum , Kirschsteiniothelia crustaceum , Kirschsteiniothelia extensum , Kirschsteiniothelia septemseptatum , Kirschsteiniothelia spatiosum , Lecanora immersocalcarea , Lepiota subthailandica , Lindgomyces guizhouensis , Marthe asmius pallidoaurantiacus , Marasmius tangerinus , Neovaginatispora mangiferae , Pararamichloridium aquisubtropicum , Pestalotiopsis piraubensis , Phacidium chinaum , ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.