scholarly journals Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

2019 ◽  
Vol 11 (5) ◽  
pp. 506 ◽  
Author(s):  
Kensuke Kawamura ◽  
Yasuhiro Tsujimoto ◽  
Tomohiro Nishigaki ◽  
Andry Andriamananjara ◽  
Michel Rabenarivo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Soil Phosphorus ◽  
Predictive Ability ◽  
Lowland Rice ◽  
Pls Regression ◽  
Proximal Sensing ◽  
Extractable P

As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

scholarly journals Evaluation of Chlorophyll-a Estimation Approaches Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression and Several Traditional Algorithms from Field Hyperspectral Measurements in the Seto Inland Sea, Japan

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2656 ◽  
Author(s):  
Zuomin Wang ◽  
Yuji Sakuno ◽  
Kazuhiko Koike ◽  
Shizuka Ohara
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Chlorophyll A ◽  
Predictive Ability ◽  
Seto Inland Sea ◽  
Band Model ◽  
Pls Regression ◽  
Chl A ◽  
The Seto Inland Sea ◽  
Two Band Model

Harmful algal blooms (HABs) occur frequently in the Seto Inland Sea, bringing significant economic and environmental losses for the area, which is well known as one of the world’s most productive fisheries. Our objective was to develop a quantitative model using in situ hyperspectral measurements in the Seto Inland Sea to estimate chlorophyll a (Chl-a) concentration, which is a significant parameter for detecting HABs. We obtained spectra and Chl-a data at six stations from 12 ship-based surveys between December 2015 and September 2017. In this study, we used an iterative stepwise elimination partial least squares (ISE-PLS) regression method along with several empirical and semi-analytical methods such as ocean chlorophyll, three-band model, and two-band model algorithms to retrieve Chl-a. Our results showed that ISE-PLS using both the water-leaving reflectance (RL) and the first derivative reflectance (FDR) had a better predictive ability with higher coefficient of determination (R2), lower root mean squared error (RMSE), and higher residual predictive deviation (RPD) values (R2 = 0.77, RMSE = 1.47 and RPD = 2.1 for RL; R2 = 0.78, RMSE = 1.45 and RPD = 2.13 for FDR). However, in this study the ocean chlorophyll (OC) algorithms had poor predictive ability and the three-band and two-band model algorithms did not perform well in areas with lower Chl-a concentrations. These results support ISE-PLS as a potential coastal water quality assessment method using hyperspectral measurements.

scholarly journals FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 827
Author(s):  
Najeeb Ur Rehman ◽  
Ahmed Al-Harrasi ◽  
Ricard Boqué ◽  
Fazal Mabood ◽  
Muhammed Al-Broumi ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Near Infrared ◽  
Pls Regression ◽  
Least Squares Regression ◽  
Daily Consumption ◽  
Limit Of Quantification ◽  
Scientific Attention

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm−1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm−1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

Feature Selection Method Based on the Adaptive Genetic Algorithm-Kernel Partial Least Squares for High Dimensional Data

2012 ◽  
Vol 468-471 ◽  
pp. 1762-1766 ◽  
Author(s):  
Dong Yan ◽  
Shao Wei Liu ◽  
Jian Tang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
High Dimensional Data ◽  
Feature Selection Method ◽  
High Dimensional ◽  
Adaptive Genetic Algorithm ◽  
Kernel Partial Least Squares

Feature selection for modeling the high dimensional data, such as the near-infrared spectrum (NIR) is very important. A novel modeling approach combined the adaptive genetic algorithm-kernel partial least squares (AGA-KPLS) is proposed. The KPLS algorithm is used to construct nonlinear models with the popular kernel based modeling technology. The AGA is used to select the optimal feature sub-set from the original high dimensional data, which also used to select the kernel parameters of the KPLS algorithm simultaneously. The experimental results based on the vibration frequency spectrum show that the proposed approach has better prediction performance than the normal GA-PLS method.

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