ArticleSensors (Basel, Switzerland)2021
Research on the Prediction of Green Plum Acidity Based on Improved XGBoost.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 26 citations in OpenAlex.
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- Enhanced machine learning-ensemble method for estimation of oil formation volume factor at reservoir conditions.Scientific reports · 2023Article
- Evaluation of Dual-Band Near-Infrared Spectroscopy and Chemometric Analysis for Rapid Quantification of Multi-Quality Parameters of Soy Sauce Stewed Meat.Foods (Basel, Switzerland) · 2023Article
- Enhanced intelligent approach for determination of crude oil viscosity at reservoir conditions.Scientific reports · 2023Article
- Investigation of the Impact of Damaged Smartphone Sensors' Readings on the Quality of Behavioral Biometric Models.Sensors (Basel, Switzerland) · 2022Article
- Maturity Stage Discrimination ofMolecules (Basel, Switzerland) · 2022Article
- CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models.Frontiers in plant science · 2022Article
- A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The acidity of green plum has an important influence on the fruit's deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis-linear discriminant analysis-extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.