scholarly journals Comprehensive model optimization in pulp quality prediction: a machine learning approach

2017 ◽  
Author(s):  
Chin-Wei Huang ◽  
Luc Baron ◽  
Marek Balazinski ◽  
Sofiane Achiche
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Special Importance ◽  
Quality Prediction ◽  
Inference System

Feature selection in machine learning is of great interest since it is reckoned as creating more efficient predictive models in several engineering domains. It is even of special importance in the pulp and paper transformation industry as the knowledge of this particular process is generally very limited. In this paper, we first compared the performance of rule-based genetic algorithm and that of adaptive neuro-fuzzy inference system; the latter is found to be more precise in predicting the pulp quality. We then combined several data mining algorithms such as genetic algorithm-partial least square regression, along with other statistical methods, to explore the relevance of all the potential variables that could be used to predict the pulp ISO brightness, an important property that is usually linked to model performance and hence pulp quality prediction. A few highly relevant variables are thereby determined, and the full set of 79 variables obtained from a Chip Management System was trimmed down to an optimized combination of 3 inputs depending on their relevancy. Peroxide charge (P), average luminance (L) and hue (H) were chosen as the optimal subset to describe the ISO brightness of the pulp and the model was simplified without losing much of its accuracy. Finally, we derived the numbers of membership functions for each variable to further refine the fuzzy logic-based prediction model. The error then reached 2.18%. The loss on accuracy was compensated by adjusting to the fittest membership function numbers

scholarly journals Comprehensive model optimization in pulp quality prediction: a machine learning approach

2017 ◽  
Author(s):  
Chin-Wei Huang ◽  
Luc Baron ◽  
Marek Balazinski ◽  
Sofiane Achiche
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Special Importance ◽  
Quality Prediction ◽  
Inference System

Feature selection in machine learning is of great interest since it is reckoned as creating more efficient predictive models in several engineering domains. It is even of special importance in the pulp and paper transformation industry as the knowledge of this particular process is generally very limited. In this paper, we first compared the performance of rule-based genetic algorithm and that of adaptive neuro-fuzzy inference system; the latter is found to be more precise in predicting the pulp quality. We then combined several data mining algorithms such as genetic algorithm-partial least square regression, along with other statistical methods, to explore the relevance of all the potential variables that could be used to predict the pulp ISO brightness, an important property that is usually linked to model performance and hence pulp quality prediction. A few highly relevant variables are thereby determined, and the full set of 79 variables obtained from a Chip Management System was trimmed down to an optimized combination of 3 inputs depending on their relevancy. Peroxide charge (P), average luminance (L) and hue (H) were chosen as the optimal subset to describe the ISO brightness of the pulp and the model was simplified without losing much of its accuracy. Finally, we derived the numbers of membership functions for each variable to further refine the fuzzy logic-based prediction model. The error then reached 2.18%. The loss on accuracy was compensated by adjusting to the fittest membership function numbers

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Error propagation in spectrometric functions of soil organic carbon

SOIL ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 275-288
Author(s):  
Monja Ellinger ◽  
Ines Merbach ◽  
Ulrike Werban ◽  
Mareike Ließ
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Error Propagation ◽  
Field Experiments ◽  
Model Building ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Measurement Protocol

Abstract. Soil organic carbon (SOC) plays a major role concerning chemical, physical, and biological soil properties and functions. To get a better understanding of how soil management affects the SOC content, the precise monitoring of SOC on long-term field experiments (LTFEs) is needed. Visible and near-infrared (Vis–NIR) reflectance spectrometry provides an inexpensive and fast opportunity to complement conventional SOC analysis and has often been used to predict SOC. For this study, 100 soil samples were collected at an LTFE in central Germany by two different sampling designs. SOC values ranged between 1.5 % and 2.9 %. Regression models were built using partial least square regression (PLSR). In order to build robust models, a nested repeated 5-fold group cross-validation (CV) approach was used, which comprised model tuning and evaluation. Various aspects that influence the obtained error measure were analysed and discussed. Four pre-processing methods were compared in order to extract information regarding SOC from the spectra. Finally, the best model performance which did not consider error propagation corresponded to a mean RMSEMV of 0.12 % SOC (R2=0.86). This model performance was impaired by ΔRMSEMV=0.04 % SOC while considering input data uncertainties (ΔR2=0.09), and by ΔRMSEMV=0.12 % SOC (ΔR2=0.17) considering an inappropriate pre-processing. The effect of the sampling design amounted to a ΔRMSEMV of 0.02 % SOC (ΔR2=0.05). Overall, we emphasize the necessity of transparent and precise documentation of the measurement protocol, the model building, and validation procedure in order to assess model performance in a comprehensive way and allow for a comparison between publications. The consideration of uncertainty propagation is essential when applying Vis–NIR spectrometry for soil monitoring.

scholarly journals A non-destructive method to quantify leaf starch content in red clover

2020 ◽  
Author(s):  
Lea Antonia Frey ◽  
Philipp Baumann ◽  
Helge Aasen ◽  
Bruno Studer ◽  
Roland Kölliker
Keyword(s):  
Energy Content ◽  
Starch Content ◽  
Model Performance ◽  
Red Clover ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Test Set ◽  
Independent Test ◽  
Non Destructive

Abstract Background Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes.Results We assessed prediction performance of partial least square regression models (PLSR) and validated model performance with an independent test set. Starch content of the training set ranged from 0.1 to 120.3 mg g -1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g -1 DW. Model performance decreased when applied to the independent test set (RMSE = 29 mg g -1 DW, R 2 = 0.36). Different filtering methods did not increase model performance.Conclusion The non-destructive spectral method presented here, provides a tool to detect large differences in leaf starch content of red clover. Breeding material can be sampled and selected according to their starch content without destroying the plant.

scholarly journals Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

2020 ◽  
Vol 412 (26) ◽  
pp. 7085-7097
Author(s):  
Rebecca Brendel ◽  
Sebastian Schwolow ◽  
Sascha Rohn ◽  
Philipp Weller
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Detection System ◽  
Drift Time ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Learning Tools

Abstract For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops.

scholarly journals A non-destructive method to quantify leaf starch content in red clover

2020 ◽  
Author(s):  
Lea Antonia Frey ◽  
Philipp Baumann ◽  
Helge Aasen ◽  
Bruno Studer ◽  
Roland Kölliker
Keyword(s):  
Energy Content ◽  
Starch Content ◽  
Model Performance ◽  
Red Clover ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Test Set ◽  
Independent Test ◽  
Non Destructive

Abstract Background: Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes. Results: We assessed prediction performance of partial least square regression models (PLSR) using cross-validation, and validated model performance with an independent test set. Starch content of the training set ranged from 0.1 to 120.3 mg g-1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g-1 DW. Model performance decreased when applying the trained model on the independent test set (RMSE = 29 mg g-1 DW, R2 = 0.36). Different variable selection methods did not increase model performance. Conclusion: The non-destructive spectral method presented here, provides a tool to detect large differences in leaf starch content of red clover. The major benefit of the method is that it can be repeatedly applied to the same plants, thus providing a means to follow starch concentrations over time and over a broad range of environments.

Application of Coupling Model of Projection Pursuit Partial Least-Square Regression Based on Real Coded Accelerating Genetic Algorithm in Land Use Change Forecasting

2011 ◽  
Vol 347-353 ◽  
pp. 1774-1777 ◽  
Author(s):  
Hai Tao Wang ◽  
Zhen Wen Xu ◽  
Bin Wang ◽  
Heng Li
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Change ◽  
Projection Pursuit ◽  
Coupling Model ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Accelerating Genetic Algorithm

In study of Land Use Change forecasting, lots of methods have been developed ,such as Markov model、BP algorithm、Canonical correlation analysis, least-squares regression analysis ,but these methods have deficiency in decision and often inadequate in sample size. In response to these deficiencies，projection pursuit Partial Least-Square Regression based on real coded accelerating genetic algorithm model is developed to analyze and predict land use change in Yanji City. The computation results show that the relative error of Coupling Model of Partial Least-Square Regression Based on Projection Pursuit is smaller than traditional Partial Least-Square Regression model’s, and it has improved the prediction precision evidently.

scholarly journals A non-destructive method to quantify leaf starch content in red clover

2020 ◽  
Author(s):  
Lea Antonia Frey ◽  
Philipp Baumann ◽  
Helge Aasen ◽  
Bruno Studer ◽  
Roland Kölliker
Keyword(s):  
Energy Content ◽  
Starch Content ◽  
Model Performance ◽  
Red Clover ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Test Set ◽  
Independent Test ◽  
Non Destructive

Abstract Background: Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes.Results: We assessed prediction performance of partial least square regression models (PLSR) and validated model performance with an independent test set. Starch content of the training set ranged from 0.1 to 120.3 mg g-1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g-1 DW. Model performance decreased when applied to the independent test set (RMSE = 29 mg g-1 DW, R2 = 0.36). Different filtering methods did not increase model performance.Conclusion: The non-destructive spectral method presented here, provides a tool to detect large differences in leaf starch content of red clover. Breeding material can be sampled and selected according to their starch content without destroying the plant.

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