This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.saa.2017.10.052 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning-based prediction of illness course in major depression: The relevance of risk factors.
J Affect Disord
January 2025
Department of Psychiatry and Psychotherapy, University of Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg, Germany.
Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.
Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).
Integrating machine learning, suspect and nontarget screening reveal the interpretable fates of micropollutants and their transformation products in sludge.
J Hazard Mater
January 2025
School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430074, China; National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address:
Activated sludge enriches vast amounts of micropollutants (MPs) when wastewater is treated, posing potential environmental risks. While standard methods typically focus on target analysis of known compounds, the identity, structure, and concentration of transformation products (TPs) of MPs remain less understood. Here, we employed a novel approach that integrates machine learning for the quantification of nontarget TPs with advanced target, suspect, and nontarget screening strategies.
View Article and Find Full Text PDFComparison of data augmentation and classification algorithms based on plastic spectroscopy.
Anal Methods
January 2025
Jiangsu Beier Machinery Co. Ltd, Jiangsu, 215600, China.
Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFNon-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach.
Adv Appl Bioinform Chem
January 2025
Department of Information Technology, Mutah University, Al-Karak, Jordan.
Purpose: The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages.
Patients And Methods: We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!