Computational QSAR models have gradually been preferred for retention time prediction in data mining of emerging environmental contaminants using liquid chromatography coupled with mass spectrometry. Generally, the model performance relies on the components such as machine learning algorithms, chemical features, and example data. In this study, we evaluated the performances of four algorithms on three feature sets, using 321 and 77 pesticides as the training and validation sets, respectively. The results were varied with different combinations of algorithms on distinct feature sets. Two strategies including enhancing the complexity of chemical features and enlarging the size of the training set were proved to improve the results. XGBoost, Random Forest, and lightGBM algorithms exhibited the best results when built on a large-scale chemical descriptors, while the Keras algorithm preferred fingerprints. These four models have comparable prediction accuracies that at least 90% of pesticides in validation set can be successfully predicted with ΔRT <1.0 min. Meanwhile, a blended prediction strategy using average results from four models presented a better result than any single model. This strategy was used for assisting identification of pesticides and pesticide transformation products in 120 strawberry samples from a national survey of food contamination. Twenty pesticides and twelve pesticide transformation products were tentatively identified, where all pesticides and two pesticide transformation products (bifenazate diazene and spirotetramat-enol) were confirmed by standard materials. The outcome of this study suggested that retention time prediction is a valuable approach in compound identification when integrated with in silico MS spectra and other MS identification strategies.
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http://dx.doi.org/10.1016/j.chemosphere.2020.129447 | DOI Listing |
J Food Sci Technol
January 2025
Agri Business Incubator, Department of Agricultural Engineering, College of Agriculture, Kerala Agricultural University, Thrissur, 680656 India.
Unlabelled: The present work investigates the impact of pressure (; 300-600 MPa) and holding time (; 5-20 min) on the quality attributes and microbial stability of jackfruit shreds. The results revealed that the and had significantly affected physico-chemcial and bioactive composition of the jackfruit shreds. Higher levels of and increased the firmness of the shreds.
View Article and Find Full Text PDFFront Plant Sci
January 2025
Department of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju, Republic of Korea.
Kiwifruit ()-derived actinidin, a cysteine protease, is renowned for its meat-tenderizing and milk-clotting activities. Despite its potential in various biotechnological applications, an efficient expression platform for actinidin production has not yet been developed. Instead, actinidin has traditionally been purified directly from the fruits of various plants.
View Article and Find Full Text PDFPhytochem Anal
January 2025
School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China.
Objective: This study aimed to qualitatively study the main chemical components of apple peel in APORT, Kazakhstan, by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) and to compare the components of apple peels with different provenances.
Methods: An ACQUITY UPLC HSS T3 (100 mm × 2.1 mm, 1.
JMIR Med Educ
January 2025
Department of Anesthesiology, Washington University School of Medicine, 660 S Euclid Avenue, Saint Louis, MO, United States, 1 3149565620.
Background: Mentoring, advising, and coaching are essential components of resident education and professional development. Despite their importance, there is limited literature exploring how anesthesiology faculty perceive these practices and their role in supporting residents.
Objective: This study aims to investigate anesthesiology faculty perspectives on the significance, implantation strategies, and challenges associated with mentorship, advising, and coaching in resident education.
Int J Biol Macromol
January 2025
Tianjin Key Laboratory of Pulp and Paper, Tianjin University of Science and Technology, Tianjin 300457, China. Electronic address:
A multifunctional hydrogel with outstanding mechanical properties and excellent ionic conductivity holds immense potential for applications in various fields, such as healthcare monitoring, and various devices, such as wearable devices and flexible electronics. However, developing hydrogels that combine high mechanical strength with efficient electrical conductivity remains a considerable challenge. Herein, an ion-conductive hydrogel with excellent mechanical properties and ionic conductivity is successfully created.
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