Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions

Geoderma ◽  
2022 ◽  
Vol 411 ◽  
pp. 115678
Author(s):  
Alla Nasonova ◽  
Guy J. Levy ◽  
Oshri Rinot ◽  
Gil Eshel ◽  
Mikhail Borisover
Keyword(s):  
Organic Matter ◽  
Ir Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Bulk Soil ◽  
Soil Extracts

scholarly journals Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4822
Author(s):  
Shifang Wang ◽  
Xu Cheng ◽  
Decong Zheng ◽  
Haiyan Song ◽  
Ping Han ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Water Absorption ◽  
Correlation Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Moist Soil ◽  
Absorption Bands ◽  
Soil System

This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.

scholarly journals Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4882
Author(s):  
Mahamed Lamine Guindo ◽  
Muhammad Hilal Kabir ◽  
Rongqin Chen ◽  
Fei Liu
Keyword(s):  
Organic Matter ◽  
Particle Swarm Optimization ◽  
Limit Of Detection ◽  
Organic Fertilizer ◽  
Particle Swarm ◽  
Partial Least Square ◽  
Least Square ◽  
Organic Fertilizers ◽  
Support Vector ◽  
Swarm Optimization

Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R2) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R2p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R2p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer.

Near-Infrared Spectroscopic Measurement of Organic Matter in Soil

2012 ◽  
Vol 499 ◽  
pp. 414-418
Author(s):  
Tao Pan ◽  
Zhen Tao Wu ◽  
Jie Mei Chen
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Modeling Prediction ◽  
Validation Set ◽  
Selection Of

Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

scholarly journals Storage and transformation of organic matter fractions in cryoturbated permafrost soils across the Siberian Arctic

2015 ◽  
Vol 12 (3) ◽  
pp. 2697-2743 ◽  
Author(s):  
N. Gentsch ◽  
R. Mikutta ◽  
R. J. E. Alves ◽  
J. Barta ◽  
P. Čapek ◽  
...  
Keyword(s):  
Organic Matter ◽  
Active Layer ◽  
Soil Depth ◽  
Stable Carbon Isotope ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Small Scale ◽  
Siberian Arctic ◽  
Permafrost Soils

Abstract. In permafrost soils, the temperature regime and the resulting cryogenic processes are decisive for the storage of organic carbon (OC) and its small-scale spatial variability. For cryoturbated soils there is a lack in the assessment of pedon-scale heterogeneity in OC stocks and the transformation of functionally different organic matter (OM) fractions such as particulate and mineral-associated OM. Therefore, pedons of 28 Turbels across the Siberian Arctic were sampled in five meter wide soil trenches in order to calculate OC and total nitrogen (TN) stocks within the active layer and the upper permafrost based on digital profile mapping. Density fractionation of soil samples was performed to distinguish particulate OM (light fraction, LF, <1.6 g cm−3), mineral associated OM (heavy fraction, HF, >1.6 g cm−3), and a mobilizable dissolved pool (mobilizable fraction, MoF). Mineral-organic associations were characterized by selective extraction of pedogenic Fe and Al oxides and the clay composition was analyzed by X-ray diffraction. Organic matter transformation in bulk soil and density fractions was assessed by the stable carbon isotope ratio (δ13C) and element contents (C and N). Across all investigated soil profiles, total OC stocks were calculated to 20.2 ± 8.0 kg m−2 (mean ± SD) to 100 cm soil depth. Of this average, 54% of the OC was located in active layer horizons (annual summer thawing layer) showing evidence of cryoturbation, and another 35% was present in the permafrost. The HF-OC dominated the overall OC stocks (55%) followed by LF-OC (19% in mineral and 13% in organic horizons). During fractionation about 13% of the OC was released as MoF, which likely represents the most bioavailable OM pool. Cryogenic activity combined with an impaired biodegradation in topsoil horizons (O and A horizons) were the principle mechanisms to sequester large OC stocks in the subsoil (16.4 ± 8.1 kg m−2; all mineral B, C, and permafrost horizons). About 22% of the subsoil OC stock can be attributed to LF material subducted by cryoturbation, whereas migration of soluble OM along freezing gradients appeared as principle source for the dominating HF (63%) in the subsoil. The large proportion of MoF (15%) in the subsoil suggests a pool of weaker mineral-organic associations as result of the low acidity and presence of basic cations, reductive dissolution of Fe(III) oxides, and the frequent freezing-thawing cycles. Despite the unfavourable abiotic conditions, substantial microbial OM transformation in the subsoil was indicated by low C/N ratios and high δ13C values but this was not reflected in altered LF and HF pool sizes. Partial least square regression analyses suggest that OC accumulates in the HF fraction due to coprecipitation with multivalent cations (Al, Fe) and association with poorly crystalline Fe oxides and clay minerals. Our data show that across all permafrost pedons, mineral-associated OM represents the most important OM fraction but the reactivity of this pool under changing future environmental conditions warrants further attention.

scholarly journals Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1809
Author(s):  
Chunxu Li ◽  
Jinghan Zhao ◽  
Yaoxiang Li ◽  
Yongbin Meng ◽  
Zheyu Zhang
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Near Infrared ◽  
Scatter Correction ◽  
Organic Matter Content ◽  
Model Performance ◽  
Matter Content ◽  
Partial Least Square ◽  
Least Square ◽  
Successive Projections Algorithm

In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.

MOTIVASI MEMEDIASI PENGARUH PELATIHAN DAN LINGKUNGAN KERJA TERHADAP KINERJA KARYAWAN PT. TELKOM WITEL YOGYAKARTA

2020 ◽  
Vol 28 (1) ◽  
pp. 71-88
Author(s):  
Tyas Tunjung Sari ◽  
Pandu Nuansa Luhur
Keyword(s):  
Work Environment ◽  
Significant Influence ◽  
Structural Equation ◽  
Employee Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Primary Data ◽  
Version 2.0 ◽  
Analyze Data

This study aims to determine the motivation of work to mediate the effect of training and work environment on employee performance at PT. Telkom Witel Yogyakarta Yogyakarta. The purpose of this study is to determine and analyze 1) the effect of training on employee performance at PT. Telkom Witel Yogyakarta 2) the effect of training on employee performance through motivation at PT. Telkom Witel Yogyakarta 3) the influence of the work environment on employee performance at PT. Telkom Witel Yogyakarta 4) the influence of the work environment on employee performance through motivation at PT. Telkom Witel Yogyakarta. This study uses primary data through research on 62 respondents. Structural Equation is used to analyze data, using PLS (Partial Least Square) version 2.0. The results of this study indicate that there are 1) positive and significant influence of training on employee performance 2) positive and significant influence of work environment on employee performance 3) positive and significant effect of training on employee performance through motivation 4) positive and significant influence of work environment on employee performance through motivation.

Locus Of Control Sebagai Varibel Moderasi Hubungan Kompleksitas Tugas Dan Kinerja Auiditor

2018 ◽  
Vol 16 (2) ◽  
pp. 113
Author(s):  
Sri Hastuti ◽  
Siti Sundari
Keyword(s):  
Locus Of Control ◽  
Big Five ◽  
Partial Least Square ◽  
Least Square

Research Objectives to prove the influence of the complexity of the tasks faced by the Auditor on performance in carrying out duties as an Auditor. The complexity of tasks related to various problems in the company requires locus of control from internal and external to maintain independence and competence.The first auditor performance case occurred in 2002 with the disclosure of the Enron case involving the KAP in the big five, Athur Anderson. In 2008 the Telkom case affected the closure of KAP Edy Priyanto, and there were still many other cases which were violations of the accountant's code of ethics.This research is in the form of quantitative, with proof of the complexity of the task and locus of control on the performance of the auditor. Sample 46 Junior auditors from several KAPs in Surabaya, using the Partial Least Square test, the result that the complexity of the task affects the performance of the Auditor and the interaction of the complexity of the task with locus of control does not affect the performance of the Auditor.

ANALYSIS THE IMPACT OF THE IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM (QMS) AND SAFETY AND HEALTHY AT WORK MANAGEMENT SYSTEM (SHWMS / SMK3) ON THE JOB PERFORMANCE OF EMPLOYEES

2018 ◽  
Vol 3 (01) ◽  
pp. 45
Author(s):  
Nur Hidayat ◽  
Indah Kusuma Hayati
Keyword(s):  
Quality Management ◽  
Job Performance ◽  
Latent Variables ◽  
Management System ◽  
Quality Management System ◽  
Partial Least Square ◽  
Least Square ◽  
Global Challenges ◽  
The Impact ◽  
Work Management

Recently, the evolvement of globalization era has been the global challenges that cannot be avoided either by private or government sectors, and they are requested to be survived encountering such the condition. The implementation of Quality Management System (QMS) in the operational company is the way how to guarantee the quality of products or services offered to the people. One of the purposes of QMS implementation is to provide a prime satisfaction to the customers. The impact of QMS implementation is expected to increase job performance of the employees. Besides the implementation of Quality Management System (QMS), the impact of global challenges has been increasing the competitive efforts to execute more effective production process. However, it has required manpower protection accordingly. This research aims to find out whether the implementation of quality management system and safety and healthy at work management system have impacted on the job performance of employees. Objects of this research are the employees in the production department at PT Guna Senaputra Sejahtera Plant 1 Bogor. Data analysis technique of this research has applied software Smart PLS (Partial Least Square). PLS has estimated a model of correlation among the latent variables and correlation between latent variables and its indicators. Result of data processing has indicated that the implementation of Quality Management System (QMS) and system of safety and healthy at work have positively and significantly impacted job performance of employees.Keywords : Quality Management System (QMS), Safety and Healthy at Work System ( SHWS / SMK3), and Job Performance of Employees

A Consuequence of Statisfaction and Loyalty: How Brand nd Purchase Intention Affect Its (An empirical study of Bengkulu)

2020 ◽  
Vol 7 (2) ◽  
pp. 61-70
Author(s):  
Fachri Eka Saputra ◽  
Fedyah Anggriani
Keyword(s):  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Purchase Intention ◽  
Partial Least Square ◽  
Least Square ◽  
Primary Data ◽  
Price Fairness ◽  
Online Questionnaire ◽  
Positive Effect ◽  
Descriptive Method

The purpose of this study as to determine how the effect of waterpark image and price fairness on customer satisfaction and its implications for customer loyalty at Waterpark Wahana Surya Bengkulu. The measurement of this study uses 14 indicator items which are distributed using an online questionnaire. The number of samples in this study were 136 respondents and the data were analyzed using SEM PLS (Partial Least Square). Date were collected using a questionnaire using a Likert scale. This research used descriptive method with a quantitative approach. The type of data used in this study is primary data. The results of this study prove that 1. waterpark image has a positive effect on price fairness, 2. Waterpark image has a positive effect on customer satisfaction, 3. Fairness of price has a positive effect on customer satisfaction, 4. Waterpark image has a positive effect on customer loyalty, 5. Fairness of price has a positive effect on customer loyalty, 6. Customer satisfaction has no effect on customer loyalty.

Factors Influencing Green Purchase Behavior among Young Consumers in Saudi Arabia

2020 ◽  
Vol 8 (1) ◽  
pp. 51-73
Author(s):  
Abdulalem Mohammed ◽  
Abdo Homaid ◽  
Wail Alaswadi
Keyword(s):  
Saudi Arabia ◽  
Peer Pressure ◽  
Partial Least Square ◽  
Least Square ◽  
Purchase Behavior ◽  
Self Identity ◽  
Factors Influencing ◽  
Purchasing Intention ◽  
Young Consumers ◽  
Green Purchasing

For environmental and business reasons, understanding the consumer behaviour of the young towards green products is very important. Therefore, the main purpose of this study is to investigate the factors influencing green product buying intention and behaviour among young consumers in Saudi Arabia. The study has developed a set of hypotheses utilising the theory of planned behaviour (TPB) as a guiding principle. They were tested based on data collected from 257 individuals through the use of the Partial Least Square (PLS) method. The findings showed that a culture of collectivism was the best way to predict the green purchasing intentions of young Saudis, followed by a willingness to pay, environmental self-identity and peer pressure. Additionally, purchasing intention is a major factor influencing actual green purchasing behaviour.

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