Non‐destructive detection of milk nutritional components based on hyperspectral imaging DOI Open Access

Yuanpu Zhang,

Jiangping Liu

Journal of Food Science, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 28, 2024

Abstract As consumers increasingly prioritize food safety and nutritional value, the dairy industry faces a pressing need for rapid accurate methods to detect essential components in milk, such as fat, protein, lactose. Hyperspectral imaging (HSI) technology, known its non‐destructive, fast, precise nature, shows great promise quality assessment. However, high dimensionality of HSI data poses challenges effective band selection model optimization. Additionally, prior studies primarily focus on predicting single without addressing simultaneous multi‐component detection. To overcome these challenges, this study presents comprehensive approach that integrates moving average smoothing first derivative (MA‐FD) preprocessing, improved coati optimization algorithm (ICOA), CatBoost multi‐target regression. ICOA incorporates good point set strategy, dynamic opposition‐based learning, golden sine algorithm, which significantly enhance global search capability convergence speed selection. Combined with CatBoost's prediction capability, method enables detection lactose levels milk. Experimental results demonstrate accuracy, calibration achieving an coefficient determination (MultiR 2 ) 0.9992 root mean square error (MultiRMSE) 0.0240, while yielded MultiR 0.9797 MultiRMSE 0.1181. Prediction R values were 0.9658, 0.9910, 0.9825, respectively. The proposed demonstrates robust predictive accuracy reliability milk assessment, potential application broader assessments is substantial. Practical Application This provides rapid, non‐destructive assessing by detecting key through hyperspectral imaging, combined MA‐FD selection, offers reliable, non‐invasive solution supports control helps safeguard consumer health.

Language: Английский

EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model DOI Creative Commons
Wenping He, Xizhe Li, Yujin Wan

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 2156 - 2156

Published: Feb. 18, 2025

Shale gas is a critical energy resource, and estimating its ultimate recoverable reserves (EUR) key indicator for evaluating the development potential effectiveness of wells. To address challenges in accurately predicting shale EUR, this study analyzed production data from 200 wells CN block. Sixteen factors influencing EUR were considered, geological, engineering, identified using Spearman correlation analysis mutual information methods to exclude highly linearly correlated variables. An attention mechanism was introduced weight input features prior model training, enhancing interpretability feature contributions. The hyperparameters optimized Rabbit Optimization Algorithm (ROA), 10-fold cross-validation employed improve stability reliability evaluation, mitigating overfitting bias. performance four machine learning models compared, optimal selected. results indicated that ROA-CatBoost-AM exhibited superior both fitting accuracy prediction effectiveness. This subsequently applied identifying primary controlling productivity, providing effective guidance practices. dominant forecasts determined by offer valuable references optimizing block strategies.

Language: Английский

Citations

0
Interpretable phase structure and hardness prediction of multi-principal element alloys through ensemble learning DOI
Xiaohui Li,

Zicong Li,

Chunxi Hou

et al.

Applied Physics A, Journal Year: 2025, Volume and Issue: 131(3)

Published: Feb. 27, 2025

Language: Английский

Citations

0
Effect of cyclic wetting on lateritic clay subgrade settlement and train-track dynamic response of high-speed railway DOI
Weizheng Liu, Jiming Tan, Jun Wu

et al.

Transportation Geotechnics, Journal Year: 2025, Volume and Issue: unknown, P. 101541 - 101541

Published: March 1, 2025

Language: Английский

Citations

0
Machine learning for time series prediction of valley deformation induced by impoundment for high arch dams DOI

Hang‐Hang Zang,

Dianqing Li, Xiaosong Tang

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(4)

Published: March 17, 2025

Language: Английский

Citations

0
Concrete Carbonization Prediction Method Based on Bagging and Boosting Fusion Framework DOI Creative Commons
Qingfu Li,

A. Xu

Buildings, Journal Year: 2025, Volume and Issue: 15(8), P. 1349 - 1349

Published: April 18, 2025

Concrete carbonation is an important factor causing corrosion of steel reinforcement, which leads to damage reinforced concrete structures. To address the problem depth prediction, this paper proposes a prediction model. The framework synergistically integrates Bagging and Boosting algorithms, specifically replacing original Random Forest base learner with gradient variants (LightGBM (version 4.1.0), XGBoost 2.1.1), CatBoost 1.2.5)). This hybrid approach exploits strengths all three algorithms reduce variance bias, further improve accuracy, Bayesian optimization were used fine-tune hyperparameters, resulting in hybrid-integrated models: Forest–LightGBM Fusion Framework, Forest–XGBoost Forest–CatBoost Framework. These models trained on dataset containing 943 case sets six input variables (FA, t, w/b, B, RH, CO2). comprehensively evaluated using comprehensive scoring formula Taylor diagrams. results showed that model outperformed single model, RF–CatBoost fusion having highest test set performance (R2 = 0.9674, MAE 1.4199, RMSE 2.0648, VAF 96.78%). In addition, Framework identified exposure t CO2 concentration as most features. demonstrates applicability predictive based predicting carbonation, providing valuable insights into durability design concrete.

Language: Английский

Citations

0
Research on dynamic response characteristics of red clay low embankment with different road structures under vehicle load DOI
Jinhong Li, Hongyuan Fu, Xiang Qiu

et al.

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: 49, P. 101427 - 101427

Published: Nov. 1, 2024

Language: Английский

Citations

2
Static and dynamic characteristics of cement-treated and untreated aeolian sand from the Tengger desert hinterland: Laboratory tests and prediction models DOI
Weizheng Liu, Xuanjia Huang, Wenhua Yin

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 458, P. 139733 - 139733

Published: Dec. 25, 2024

Language: Английский

Citations

2
Mapping Soil Properties in Tropical Rainforest Regions Using Integrated UAV-Based Hyperspectral Images and LiDAR Points DOI Open Access
Yiqing Chen,

Tiezhu Shi,

Qipei Li

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2222 - 2222

Published: Dec. 17, 2024

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve management practices replace time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability properties over linear models, their practical automated application for predicting using remote sensing data requires further assessment. Therefore, this study aims integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images Light Detection Ranging (LiDAR) points predict indirectly in two mountains (Diaoluo Limu) Hainan Province, China. A total 175 features, including texture indices, forest parameters, were extracted from sites. Six ML Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Decision Trees (GBDT), Extreme (XGBoost), Multilayer Perceptron (MLP), constructed properties, acidity (pH), nitrogen (TN), organic carbon (SOC), phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced obtain optimal hyperparameters. The results showed that compared default parameter tuning method, BOA always improved models’ performances achieving average R2 improvements 202.93%, 121.48%, 8.90%, 38.41% pH, SOC, TN, TP, respectively. In general, effectively determined complex interactions between hyperparameters prediction leading an performance models. GBDT generally outperformed other pH while XGBoost achieved highest accuracy SOC TP. fusion LiDAR resulted better each single source. models utilizing integration features derived those relying on one summary, highlights promising combination UAV-based advance digital property forested areas, monitoring.

Language: Английский

Citations

1
Non‐destructive detection of milk nutritional components based on hyperspectral imaging DOI Open Access

Yuanpu Zhang,

Jiangping Liu

Journal of Food Science, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 28, 2024

Abstract As consumers increasingly prioritize food safety and nutritional value, the dairy industry faces a pressing need for rapid accurate methods to detect essential components in milk, such as fat, protein, lactose. Hyperspectral imaging (HSI) technology, known its non‐destructive, fast, precise nature, shows great promise quality assessment. However, high dimensionality of HSI data poses challenges effective band selection model optimization. Additionally, prior studies primarily focus on predicting single without addressing simultaneous multi‐component detection. To overcome these challenges, this study presents comprehensive approach that integrates moving average smoothing first derivative (MA‐FD) preprocessing, improved coati optimization algorithm (ICOA), CatBoost multi‐target regression. ICOA incorporates good point set strategy, dynamic opposition‐based learning, golden sine algorithm, which significantly enhance global search capability convergence speed selection. Combined with CatBoost's prediction capability, method enables detection lactose levels milk. Experimental results demonstrate accuracy, calibration achieving an coefficient determination (MultiR 2 ) 0.9992 root mean square error (MultiRMSE) 0.0240, while yielded MultiR 0.9797 MultiRMSE 0.1181. Prediction R values were 0.9658, 0.9910, 0.9825, respectively. The proposed demonstrates robust predictive accuracy reliability milk assessment, potential application broader assessments is substantial. Practical Application This provides rapid, non‐destructive assessing by detecting key through hyperspectral imaging, combined MA‐FD selection, offers reliable, non‐invasive solution supports control helps safeguard consumer health.

Language: Английский

Citations

0