scholarly journals Multi-parameter calibration of a UV/Vis spectrometer for online monitoring of sewer systems

2020 ◽  
Vol 82 (5) ◽  
pp. 927-939
Author(s):  
Micaela Pacheco Fernández ◽  
Thorsten Knutz ◽  
Matthias Barjenbruch
Keyword(s):  
Online Monitoring ◽  
Oxygen Demand ◽  
Partial Least Square ◽  
Least Square ◽  
Data Sets ◽  
Validation Data ◽  
Sewer Systems ◽  
Calibration Methods ◽  
Almost All ◽  
Parameter Dependency

Abstract UV/Vis spectrometers are powerful tools for online monitoring of wastewater constituents and processes. However, most studies only focus on typical parameters such as chemical oxygen demand (COD) and total suspended solids. This work presents a multi-parameter approach for calibration of a UV/Vis spectrometer for online monitoring of sewer systems. Parameters studied include soluble and total COD, nitrate, ammonium, sulphate and orthophosphate, as well as total dissolved sulphide, bisulphide and hydrogen sulphide, because they are one of the main causes for odour and corrosion in sewer systems. Two calibration methods are compared: multiple linear regression included in the manufacturer's software, and partial least square (PLS) computed using the pls package of the R library. Performance of the methods is evaluated for calibration and validation data sets employing four different criteria: relative root mean square error (RMSErel), RMSE-observations standard deviation ratio, Nash–Sutcliffe efficiency and percentage bias. A method-parameter dependency was revealed during the calibration phase but, when predicting new data, the PLS method showed higher robustness for almost all parameters. Both methods were able to predict concentration trends associated with sewer processes, some of which are strongly correlated to the sulphide species.

scholarly journals Development and characterisation of a new fluorescence sensor for online monitoring of bioprocesses

2018 ◽  
Vol 7 (2) ◽  
pp. 461-467 ◽  
Author(s):  
Jan C. König ◽  
Tobias Steinwedel ◽  
Dörte Solle ◽  
Patrick Lindner ◽  
Ingo de Vries ◽  
...  
Keyword(s):  
Online Monitoring ◽  
Principal Component ◽  
Fluorescence Sensor ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Invasive Technique ◽  
Data Sets ◽  
Fluorescence Measurements ◽  
Set Up

Abstract. Fluorescence spectroscopy is a highly sensitive and non-invasive technique for the identification of characteristic process states and for the online monitoring of substrate and product concentrations. Nevertheless, fluorescence sensors are mainly used in academic studies and are not well implemented for monitoring of industrial production processes. In this work, we present a newly developed robust online fluorescence sensor that facilitates the analysis of fluorescence measurements. The set-up of the sensor was miniaturised and realised without any moveable part to be robust enough for application in technical environments. It was constructed to measure only the three most important biologic fluorophores (tryptophan, NADH and FAD/FMN), resulting in a significant data reduction compared to conventional a 2-D fluorescence spectrometer. The sensor performance was evaluated by calibration curves and selectivity tests. The measuring ranges were determined as 0.5–50 µmol L−1 for NADH and 0.0025–7.5 µmol L−1 for BSA and riboflavin. Online monitoring of batch cultivations of wild-type Escherichia coli K1 in a 10 L bioreactor scale were performed. The data sets were analysed using principal component analysis and partial least square regression. The recorded fluorescence data were successfully used to predict the biomass of an independent cultivation (RMSEP 4.6 %).

scholarly journals Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin

ISRN Ecology ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Maliha S. Nash ◽  
Deborah J. Chaloud
Keyword(s):  
Surface Water ◽  
Small Sample Size ◽  
Surface Water Quality ◽  
Spatial Location ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Data Sets ◽  
Savannah River ◽  
Remotely Sensed Data

Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In this paper, we present the results of applying PLS to explore relationships among biotic indicators of surface water quality and landscape conditions accounting for the above problems. Available field sampling and remotely sensed data sets for the Savannah Basin are used. We were able to develop models and compare results for the whole basin and for each ecoregion (Blue Ridge, Piedmont, and Coastal Plain) in spite of the data constraints. The amount of variability in surface water biota explained by each model reflects the scale, spatial location, and the composition of contributing landscape metrics. The landscape-biota model developed for the whole basin using PLS explains 43% and 80% of the variation in water biota and landscape data sets, respectively. Models developed for each of the three ecoregions indicate dominance of landscape variables which reflect the geophysical characteristics of that ecoregion.

Model Optimization for near-Infrared Spectroscopy Analysis of Chemical Oxygen Demand of Wastewater

2012 ◽  
Vol 500 ◽  
pp. 832-837 ◽  
Author(s):  
Tao Pan ◽  
Wei Wei Chen ◽  
Wen Jie Huang ◽  
Rui Tuo Qu
Keyword(s):  
Chemical Oxygen Demand ◽  
Near Infrared ◽  
Oxygen Demand ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Simultaneous Optimization ◽  
Model Optimization ◽  
Spectroscopy Analysis ◽  
Smoothing Parameters

A directly rapid quantitative analysis method for chemical oxygen demand (COD) of wastewater samples was established by near-infrared (NIR) spectroscopy and partial least square (PLS) method. The optimization of Savitzky-Golay (SG) smoothing modes combined with PLS factor was applied to optimize the model of NIR spectroscopy analysis here. The waveband used for modeling was the combination of 400-1878 nm and 2088-2338 nm. The optimal smoothing parameters were the 5th derivative smoothing, 5th degree polynomial, 17 smoothing points, the optimal PLS factor, root mean squared error of predication (RMSEP) and correlation coefficient of predication (RP) were 7, 33.2 (mg/L) and 0.929 respectively, which was obviously superior to the direct PLS model without SG smoothing and ones based on the whole spectral collecting region 400-2500 nm. This demonstrated that NIR spectroscopy can be applied to the rapid determination of COD of wastewater, large-scale simultaneous optimization selection of SG smoothing parameters and PLS factor can be effectively applied to the model optimization of NIR analysis.

scholarly journals Modeling Academic Performance of the Students by using Partial Least Square

2020 ◽  
Vol 8 (5) ◽  
pp. 5438-5443
Keyword(s):  
Academic Achievement ◽  
Socioeconomic Status ◽  
School Environment ◽  
Latent Variables ◽  
Latent Variable ◽  
Partial Least Square ◽  
Least Square ◽  
Learning Motivation ◽  
Inner Model ◽  
Almost All

This study aims to model the relationship between predictor variables consisting of learning motivation (LM), parents' socioeconomic status (SS), and school environment (SE) which are all latent variables to academic achievement (AC) which are not latent variables. Modeling is done by the method of partial least square (PLS) which is expected to explore the various effects found in the inner model and also confirm the questionnaire items forming the latent variables. With a real level of 5%, almost all loading values on each latent variable are significant. Likewise, a simple linear relationship consisting of 5 models has a coefficient that has a significant effect. The influence of learning motivation (LM), parents' socioeconomic status (SS), school environment (SE) on academic achievement are 0.270, 0.249, and 0.320, respectively. while learning motivation (LM), socioeconomic status of parents (SS) contributing to academic achievement (AC) were 17.7% and 3.13%, respectively..

Spectroscopy quantitative analysis cotton content of blend fabrics

2016 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xudong Sun ◽  
Mingxing Zhou ◽  
Yize Sun
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Svm Model ◽  
Calibration Methods ◽  
Learning Machine ◽  
Mean Square Errors

Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively. Findings – The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics. Originality/value – It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

scholarly journals INOVASI FINANCIAL TECHNOLOGY TERHADAP PENINGKATAN INKLUSI KEUANGAN

2020 ◽  
Vol 18 (2) ◽  
pp. 155
Author(s):  
Dian Ratnasari Yahya ◽  
Rika Rahayu
Keyword(s):  
Survey Methods ◽  
Total Sample ◽  
Partial Least Square ◽  
Financial Inclusion ◽  
Least Square ◽  
Intention To Adopt ◽  
Digital Era ◽  
Almost All ◽  
Customer Trust ◽  
Financial Technology

People’s lifestyle in the digital era is very varied, almost all stratum of society use technology to gain information, data, loans, transactions, and others practically and quickly. This study aims to find customer trust on intention to adopt fintect performance, find the effect of fintect promotion on the intention to adopt fintect, find the effect of intention to adopt fintect on financial inclusion. This study used survey methods through questionnaires for respondents who used fintect products (OVO applications) in Surabaya with a total sample of 265 respondents. To find out the responses of the respondents toward fintect products, it uses a Likert scale. The data processing performed by using Partial Least Square (PLS). The results show that there was an influence between customer trust on intention to adopt fintect, there was an influence between fintect promotion on intention to adopt fintect, and there was an influence between intention to adopt fintect on financial inclusion.

Online monitoring of inorganic cooking chemicals in white liquor by pulse voltammetry

TAPPI Journal ◽  
2010 ◽  
Vol 9 (8) ◽  
pp. 49-53
Author(s):  
ROBERT B. BJORKLUND ◽  
DAN LOUTHANDER ◽  
PER MÅRTENSSON ◽  
STAFFAN ANDERSSON ◽  
ERLAND KVIST ◽  
...  
Keyword(s):  
Online Monitoring ◽  
Pulp Mill ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Mean Squared Errors ◽  
White Liquor ◽  
Pulse Voltammetry ◽  
Relative Root ◽  
Shift Personnel

White liquor parameters in the recovery area of a kraft pulp mill were monitored for a 1-year period using rhodium as an electrode material in a sensor system based on pulse voltammetry. Shift personnel performed offline titration analysis of the liquor every 4 hours. The results for effective alkali, sulfidity, and total titratable alkali were used to train and validate the sensor for online monitoring. Partial least square regression models developed from 150 reference titration results for each parameter from the first month of the study predicted concentrations for the following 11 months. Validation of the models using titration results indicated that overall relative root mean squared errors for prediction of the parameters were 3.7% for effective alkali, 3.4% for sulfidity, and 5.1% for total titratable alkali. Process stops that exposed the sensor to temperature excursions or acid washings resulted in temporary periods of poor prediction.

VIS-NIR wavelength importance in SOC models

2020 ◽  
Author(s):  
Javier Reyes ◽  
Mareike Ließ
Keyword(s):  
Field Data ◽  
Nir Spectroscopy ◽  
Field Measurements ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Data Sets ◽  
Mineral Fertilization ◽  
Soil Matrix ◽  
Model Interpretation

<p>Soil organic carbon (SOC) is a key soil property attracting special interest in the study of agricultural systems. Striving towards more effective SOC data acquisition, the use of VIS-NIR spectroscopy has increased over the last years. The interpretation of the recorded signal information with regards to SOC is not trivial as spectral absorption features are caused by the stretching and bending of structural molecule groups which are embedded in a complex soil matrix. The aim of this study was to assess spectral wavelength importance in partial least square regression (PLSR) models for SOC prediction. Surface soil samples were obtained from a long-term field experiment (LTFE) located in the state of Saxony-Anhalt, Germany. The LTFE presented a high variability of SOC values due to the different organic and mineral fertilization treatments. Data sets of Vis-NIR spectra were acquired in the lab and field using an ASD field spectrometer. Then, different preprocessing methods were applied before building the models. Finally, the wavelength importance was observed using regression coefficients (RC) and variable importance projections (VIP). As expected, lab data showed higher accuracy in SOC predictions (RMSE: 0.10-0.12%) compared with field data (RMSE: 0.18-0.20%). Overall, the VIP indicator provided more identifiable peaks at specific wavelength ranges, and it was more consistent among the preprocessing methods compared with RC for both lab and field data. Although model uncertainties were higher with field measurements, the importance of some wavelengths was maintained. Overall, this information is essential for model interpretation and tells us about disturbance effects in spectral field measurements compared to lab measurements.</p>

scholarly journals Implementing the CRISPR/Cas9 Technology in Eucalyptus Hairy Roots Using Wood-Related Genes

2020 ◽  
Vol 21 (10) ◽  
pp. 3408 ◽  
Author(s):  
Ying Dai ◽  
Guojian Hu ◽  
Annabelle Dupas ◽  
Luciano Medina ◽  
Nils Blandels ◽  
...  
Keyword(s):  
Hairy Roots ◽  
Functional Characterization ◽  
Eucalyptus Grandis ◽  
Basic Research ◽  
Partial Least Square ◽  
Least Square ◽  
Knock Out ◽  
Large Deletions ◽  
Partial Least Square Analysis ◽  
Almost All

Eucalypts are the most planted hardwoods worldwide. The availability of the Eucalyptus grandis genome highlighted many genes awaiting functional characterization, lagging behind because of the lack of efficient genetic transformation protocols. In order to efficiently generate knock-out mutants to study the function of eucalypts genes, we implemented the powerful CRISPR/Cas9 gene editing technology with the hairy roots transformation system. As proofs-of-concept, we targeted two wood-related genes: Cinnamoyl-CoA Reductase1 (CCR1), a key lignin biosynthetic gene and IAA9A an auxin dependent transcription factor of Aux/IAA family. Almost all transgenic hairy roots were edited but the allele-editing rates and spectra varied greatly depending on the gene targeted. Most edition events generated truncated proteins, the prevalent edition types were small deletions but large deletions were also quite frequent. By using a combination of FT-IR spectroscopy and multivariate analysis (partial least square analysis (PLS-DA)), we showed that the CCR1-edited lines, which were clearly separated from the controls. The most discriminant wave-numbers were attributed to lignin. Histochemical analyses further confirmed the decreased lignification and the presence of collapsed vessels in CCR1-edited lines, which are characteristics of CCR1 deficiency. Although the efficiency of editing could be improved, the method described here is already a powerful tool to functionally characterize eucalypts genes for both basic research and industry purposes.

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