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Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm

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Artificial Intelligence in Agriculture 2020年第1期4卷 48-57页

作者： Xi Tian Jiangbo Li Shilai Yi Guoqiang Jin Xiaoying Qiu Yongjie Li College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Beijing Research Center of Intelligent Equipment for Agriculture Beijing 100097China Citrus Research Institute Southwest UniversityChongqing 400712China Co-innovation Center of Citrus Industrial Technology in Linhai Linhai 31700China Station of Popularizing Speciality Technology in Linhai Linhai 31700China

Nondestructive determination the internal quality of thick-skin fruits has always been a *** order to investigate the prediction ability of full transmittance mode on the soluble solid content(SSC)in thick-skin fruits,the full transmittance spectra of citrus were collected using a visible/near infrared(Vis/NIR)portable spectrograph(550–1100 nm).Three obvious absorption peakswere found at 710,810 and 915 nmin the original spectra *** spectral preprocessing methods including Smoothing,multiplicative scatter correction(MSC),standard normal variate(SNV)and first derivativewere employed to improve the quality of the original ***,the effective wavelengths of SSC were selected from the original and pretreated spectra with the algorithms of successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)and genetic algorithm(GA).Finally,the prediction models of SSC were established based on the full wavelengths and *** showed that SPA performed the best performance on eliminating the useless information variable and optimizing the number of effective *** optimal predictionmodel was established based on 10 characteristic variables selected from the spectra pretreated by SNV with the algorithmof SPA,with the correlation coefficient,root mean square error,and residual predictive deviation for prediction set being 0.9165,0.5684°Brix and 2.5120,***,the full transmittance mode was feasible to predict the internal quality of thick-skin fruits,like ***,the combination of spectral preprocessing with a variable selection algorithmwas effective for developing the reliable *** conclusions of this study also provide an alternative method for fast and real-time detection of the internal quality of thick-skin fruits using Vis/NIR spectroscopy.

关键词： Full transmittance spectrum Spectral preprocessing Thick-skin fruits Soluble solids content variable selection algorithm

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Multi-sensor fusion and deep learning for batch monitoring and real-time warning of apple spoilage

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FOOD CONTROL 2025年 172卷

作者： Guo, Zhiming Zhang, Yiyin Xiao, Haidi Jayan, Heera Majeed, Usman Ashiagbor, Kwami Jiang, Shuiquan Zou, Xiaobo Jiangsu Univ Sch Food & Biol Engn China Light Ind Key Lab Food Intelligent Detect & Zhenjiang 212013 Peoples R China Jiangsu Univ Int Joint Res Lab Intelligent Agr & Agriprod Proc Zhenjiang 212013 Peoples R China Jiangsu Kaiyi Intelligent Technol Co Ltd Natl Profess Res & Dev Ctr Fruit & Vegetable Proc Wuxi 214174 Peoples R China

Apples are highly susceptible to contamination from environmental factors and fungi during storage and logistics, leading to spoilage and economic losses. The study proposes a method for batch monitoring of apple quality and real-time spoilage warning system, based on multi-sensor fusion technology (MSFT), deep learning (DL), and variable selection algorithms (VSA). Volatile gases from apples infected with Colletotrichum acutatum (C.a), Botrytis cinerea (B.c), and Botryosphaeria sarmentorum (B.s) were collected using gas sensors. Key feature variables were optimized through VSA, and a prediction model for spoilage was developed using chemometrics and DL. Models based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) demonstrated strong predictive performance, with correlation coefficients (R) exceeding 0.88. Optimal performance was observed in the Synergy Interval-Gated Recurrent Unit (Si-GRU) model for B.s, with R of 0.9473 for calibration (Rc) and 0.9430 for prediction (Rp). A vehicle-mounted terminal using MSFT was developed to acquire temperature, humidity, vibration, and gas data, and transmitted this information in real time to a remote monitoring and warning platform. The Si-GRU model was identified as optimal for constructing a multi-environmental factor spoilage warning model, achieving R above 0.98, indicating robustness and accuracy. The optimal model was implemented on the platform, providing visual displays and graded alerts for apple spoilage. Results indicated that key quality indicators were effectively captured by MSFT, and primary spoilage factors were identified by VSA, enabling accurate DL-based monitoring and real-time warning of apple spoilage.

关键词： Multi-sensor fusion Deep learning variable selection algorithm Batch monitoring Vehicle-mounted monitoring terminal

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variable selection wrapper in presence of correlated input variables for random forest models

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第1期40卷 297-312页

作者： Rotari, Marta Kulahci, Murat Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark Lulea Univ Technol Dept Business Adm Technol & Social Sci Lulea Sweden

In most data analytic applications in manufacturing, understanding the data-driven models plays a crucial role in complementing the engineering knowledge about the production process. Identifying relevant input variables, rather than only predicting the response through some "black-box" model, is of great interest in many applications. There is, therefore, a growing focus on describing the contributions of the input variables to the model in the form of "variable importance", which is readily available in certain machine learning methods such as random forest (RF). Once a ranking based on the importance measure of the variables is established, the question of how many variables are truly relevant in predicting the output variable rises. In this study, we focus on the Boruta algorithm, which is a wrapper around the RF model. It is a variable selection tool that assesses the variable importance measure for the RF model. It has been previously shown in the literature that the correlation among the input variables, which is often a common occurrence in high dimensional data, distorts and overestimates the importance of variables. The Boruta algorithm is also affected by this resulting in a larger set of input variables deemed important. To overcome this issue, in this study, we propose an extension of the Boruta algorithm for the correlated data by exploiting the conditional importance measure. This extension greatly improves the Boruta algorithm in the case of high correlation among variables and provides a more precise ranking of the variables that significantly contribute to the response. We believe this approach can be used in many industrial applications by providing more transparency and understanding of the process.

关键词： additive manufacturing Boruta algorithm conditional importance random forest variable selection algorithm

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variable selection in Near-Infrared Spectra for Modeling of Hemoglobin Content in Bio-Water Solutions

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JOURNAL OF APPLIED SPECTROSCOPY 2024年第4期91卷 928-935页

作者： Fang, Renjie Han, Xin Li, Xiangxian Tong, Jingjing Gao, Minguang Wang, Yang Chinese Acad Sci Hefei Inst Phys Sci Hefei Peoples R China Univ Sci & Technol China Hefei Peoples R China

The background differences in water content of different samples have a very strong influence on the robustness of near-infrared spectroscopy (NIRS). For this reason, this study simulated typical biological water matrix samples with formulated hemoglobin (Hb), glucose (Glc), and distilled water, and attempted to use four different intelligent spectral variable selection algorithms [Competitive Adaptive Reweighted Sampling (CARS), Randomized Frog Hopping algorithm (RF), Genetic algorithm (GA), and variable Projection Importance algorithm (VIP)] to perform the Hb water interference-resistant feature band preferences, while combining partial least squares (PLS) in parallel to build a robust quantitative model of Hb. In addition, the applicability and validity of the model were validated using three prediction sets P1, P2, P3 with different water backgrounds (the formulation method and composition were kept the same, and only the water content increased sequentially). The results showed that RF, GA, and VIP could effectively screen out the characteristic wavelengths of Hb with low sensitivity to water changes and successfully correct the water effect, but due to the large number of characteristic variables they screened out and the existence of a large number of redundant and water interference variables, this ultimately made the model's robustness less than ideal. The CARS algorithm performed the best, and the RMSEP of the three prediction sets were 0.016, 0.017, and 0.038, which is closer to the RMSECV of the calibration set. Therefore, NIRS combined with the variable selection can reduce the effect of water on model robustness and improve the prediction accuracy of the model by the method of selecting effective wave number intervals, and CARS may be one of the ideal algorithms to solve such problems.

关键词： near-infrared spectroscopy hemoglobin variable selection algorithm water robust model

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A Framework for Retrieving Soil Organic Matter by Coupling Multi-Temporal Remote Sensing Images and variable selection in the Sanjiang Plain, China

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REMOTE SENSING 2023年第12期15卷 3191-3191页

作者： Ma, Haiyi Wang, Changkun Liu, Jie Wang, Xinyi Zhang, Fangfang Yuan, Ziran Yao, Chengshuo Pan, Xianzhang Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Peoples R China Univ Chinese Acad Sci Coll Adv Agr Sci Beijing 100049 Peoples R China

Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in this region. Therefore, Baoqing County of the Sanjiang Plain, an important grain production area, was considered the study area. In the study, we proposed a framework for high-accuracy SOM retrieval by coupling multi-temporal remote sensing (RS) images and variable selection algorithms. A total of 73 surface soil samples (0-20 cm) were collected in 2010, and Landsat 5 images acquired during the bare soil period (April, May, and June) were selected from 2008 to 2011. Three variable selection algorithms, namely, Genetic algorithm, Random Frog and Competitive Adaptive Reweighted Sampling (CARS), were combined with Partial Least Squares Regression (PLSR) to build SOM retrieval models on the spectral bands and indices of the images. The results using a single-date image showed that the combination of variable selection algorithms and PLSR outperformed using PLSR alone, and CARS showed the best performance (R-2 = 0.34, RMSE = 15.66 g/kg) among all the algorithms. Therefore, only CARS was applied to SOM retrieval in the different year interval groups. To investigate the effect of the image acquisition time, all images were divided into various year interval groups, and the resulting images were then stacked. The results using multi-temporal images showed that the SOM retrieval accuracy improved as the year interval lengthened. The optimal result (R-2 = 0.59, RMSE = 11.81 g/kg) was obtained from the 2008-2011 group, wherein the difference indices derived from the images of 2009, 2010, and 2011 dominated the selected spectral variables. Moreover, the spatial prediction of SOM based on the optimal model was consistent with the distribution of SOM. Our study suggested that the proposed framework that couples stacked multi-temporal RS

关键词： soil organic matter multi-temporal remote sensing spectral index variable selection algorithm soil mapping

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Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 182卷 106031-106031页

作者： Lao, Congcong Chen, Junying Zhang, Zhitao Chen, Yinwen Ma, Yu Chen, Haorui Gu, Xiaobo Ning, Jifeng Jin, Jiming Li, Xianwen Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Dept Foreign Languages Yangling 712100 Shaanxi Peoples R China China Inst Water Resources & Hydropower Res Dept Irrigat & Drainage Beijing 100048 Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China

This study aimed to improve the potential of visible-near infrared (VIS-NIR) spectroscopy in predicting the contents of salt and major soluble ions in the topsoil at Hetao Irrigation District in Inner Mongolia, China. A total of 120 soil samples (sampling depth is 0-20 cm) were collected from the field. Fractional-order derivatives (FODs) (with intervals of 0.05 and range of 0-2) were used for soil spectral pretreatment. Coefficient of determination (R-2) was used to determine the optimal FOD spectrums of the soil salt and major soluble ions. Extreme learning machine (ELM) models were calibrated with such spectral parameters as band reflectance, newly constructed two-band indices, and three-band indices from the optimal FOD spectrum, and the models were subsequently applied to estimate the contents of soil salt and soluble ions. Different algorithms including Monte Carlo uninformative variable elimination (MCUVE), iteratively retaining informative variables (IRIV), and bootstrapping soft shrinkage (BOSS) were applied to variable selection. The results indicated that the correlation between the contents of soil salt and soluble ions and FOD spectra increased as the derivative order increased. The optimal FOD spectrum of soil salt content (SSC), K+, Na+ and SO42- were the 0.9-order derivative spectrum while the optimal FOD spectrum of Ca2+, Mg2+, CO32-, HCO3- and Cl were 1.65, 0.85, 1.90, 0.80 and 1.55 order derivative spectrum, respectively. The newly proposed three-band index (TBI4) showed a better correlation with ions content (e.g., TBI4 of Mg2+ had the best effect, and its R-2 was 0.92). In most cases, the IRIV-ELM models outperformed MCUVE-ELM and BOSS-ELM models in estimating the contents of soil salt and soluble ions. Difference existed among the optimal prediction accuracy of the contents of soil salt and different ions: the prediction accuracy of SSC, Ca2+, Na+, Mg2+, Cl and SO42- was very high while that of CO32- was high and K+ and HCO32- was relatively lo

关键词： VIS-NIR Soil salinization Fractional-order derivative (FOD) Spectral indices variable selection algorithm Extreme learning machine (ELM)

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Database building and statistical methods to predict sailing yacht hydrodynamics

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OCEAN ENGINEERING 2014年 90卷 21-33页

作者： Huetz, Lionel Guillerm, Pierre Emmanuel Marc Lombard Yacht Design Grp La Rochelle France Ecole Cent Nantes Nantes France

The aim of the paper is to build a database of numerical simulation results and a parametrical model able to describe the evolution of the hydrodynamic behaviour depending on the hull shape through variables describing the hull geometry. This type of approach using meta-models can be used in various fields to approximate a complex behaviour in a very short computational time. This paper presents the parametrical tool used to produce the hulls and a specific hydrostatic routine featuring new coefficients and measurements. An emphasis is made on the statistical methodology developed to produce the database and then select the relevant predictive variables to build generalized linear regressions in the context of yachts hydrodynamics. The regressions allowing the prediction of the bare hull forces, moments and running attitude of racing monohulls are detailed and compared with numerical simulations. Finally, the implementation of these formulations in a six degrees of freedom VPP including appendage and aerodynamic models is presented. The increase in accuracy allowed by these new formulations is discussed, especially the central role of the formulations approximating the running attitude of the bare hull. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Sailing yacht Systematic series Parametric design variable selection algorithm Generalized linear regression VPP

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Extending exploratory diagnostic classification models: Inferring the effect of covariates

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2023年第2期76卷 372-401页

作者： Yigit, Hulya Duygu Culpepper, Steven Andrew Univ Illinois Comp Applicat BldgRoom 152 605 E Springfield Ave Champaign IL 61820 USA Univ Illinois Dept Stat Champaign IL 61820 USA

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.

关键词： Bayesian statistics covariates Markov chain Monte Carlo (MCMC) methods variable selection algorithm

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Validation and Implementation of Customer Classification System using Machine Learning

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MEASUREMENT-INTERDISCIPLINARY RESEARCH AND PERSPECTIVES 2024年第2期22卷 131-140页

作者： Yoon, Hyemin Kim, HyunJin Kim, Sangjin Dong A Univ Dept Management Informat Syst Busan South Korea Dong A Univ Grad Sch Business Adm Dept Digital Finance Busan South Korea

We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial institution to financial institution. In this study, we create a machine learning prediction model using items and added items that are based on the current customer grade of our bank,- and the purpose is an optimal model that considers the adequacy of existing variables and the validity of additional variables through comparison between models. Using Lasso, Elastic net and Multinomial Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, we propose that the best model be found and gradually applied to customer grade calculation criteria.

关键词： Customer grade variable selection algorithm classification algorithm machine learning lasso elastic net multinomial logistic regression decision tree random forest support vector machine

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