scholarly journals Building soil spectral library of the Czech soils for quantitative digital soil mapping

2011 ◽  
Vol 6 (No. 4) ◽  
pp. 165-172 ◽  
Author(s):  
L. Brodský ◽  
A. Klement ◽  
V. Penížek ◽  
R. Kodešová ◽  
L. Borůvka
Keyword(s):  
Diffuse Reflectance Spectroscopy ◽  
Biological Properties ◽  
Parent Material ◽  
Soil Mapping ◽  
Partial Least Square ◽  
Least Square ◽  
Digital Soil Mapping ◽  
Partial Least Square Regression ◽  
Spectral Library ◽  
Wide Range

  Spectral libraries are the data archives of spectral signatures measured on natural and/or man-made materials. Here, the objective is to build a soil spectral library of the Czech soils (SSL-CZ). Further on, the overall aim is to apply diffuse reflectance spectroscopy as a tool for digital soil mapping. An inevitable part of the library is a metadata database that stores the corresponding auxiliary information on the soils: type of material (soil, parent material), sample preparation, location of the sample with geographic coordinates, soil classification, morphological features, soil laboratory measurements – chemical, physical, and potential biological properties, geophysical features of and climatological information on the sample location. The metadata database consists of seven general tables (General, Spatial, Soil class, Environmental, Auxiliary, Analytical and Spectra) relationally linked together. The stored information allows for a wide range of analyses and for modelling developments of digital soil mapping applications. An example of partial least-square regression (PLSR) modelling for soil pH and clay content with 0.84 and 0.68 coefficients of determination is provided on the subset of the collected data. Currently, the SSL-CZ database contains more than 500 records in the first phase of development. Spectral reflectance signatures are stored in the range of 350 to 2500 nm with a step of 1 nm measured by ASD FieldSpec 3. The soil spectral library developed is fully compatible with Global Soil Spectral Library (Soil Spectroscopy Group).

scholarly journals Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 261 ◽  
Author(s):  
Maria Marques ◽  
Ana Álvarez ◽  
Pilar Carral ◽  
Iris Esparza ◽  
Blanca Sastre ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Prediction Ability ◽  
Greenhouse Gas Inventories ◽  
Starting Point

Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

scholarly journals Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

2019 ◽  
Vol 8 (3) ◽  
pp. 7876-7881
Keyword(s):  
Statistical Analysis ◽  
Soil Texture ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression

The texture of soil i.e. Sand, Silt and Clay are the most important physical properties of soil for agricultural management. In the agricultural practices to increase the productivity of soil, moisture-holding capacity, aeration and to support the agronomic decisions the knowledge of soil texture is an essential task. For this purpose, the present research gives better results and fast acquisition of soil information with the use of Visible and Near Infrared (Vis- NIR) Diffuse Reflectance Spectroscopy. A total of 30 soil samples from two different locations from Aurangabad, Maharashtra, India were collected and analyzed for soil texture. To detect the soil texture the Vis-NIR DRS has shown levels of accurate results compared to the traditional laboratory method with less time, cost and effort. To measure the reflectance of soil the ASD FieldSpec4 Spectroradiometer (350-2500nm) was used. By the observation of captured spectra by using Spectroradiometer it showed that on the basis of different textural classes the soil samples could be spectrally separable. For database collection and pre-processing, we have used RS3 and ViewSpec Pro software respectively. The statistical analysis by using the combination of Principal Component Analysis (PCA) and Partial Least Square Regression method gives accurate results. To determine the texture of soil sample thirteen features were calculated. The main goal of this research was to determine the soil texture by using statistical methods and to test the performance of VNIR-SWIR reflectance spectroscopy by using the ASD FieldSpec4 Spectroradiometer for estimation of the texture of the soil. The results showed that R2 = 0.99 gives maximum accuracy for clay content and R2 = 0.988 for silt content and R2 = 0.989 for sand. The Root Mean Square Values (RMSE) for clay, silt, and sand are 0.02392, 0.02399 and 0.02289 respectively. With the use of reflectance spectroscopy and statistical analysis by using regression models we can determine the soil properties accurately in very less time.

scholarly journals Predicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2016 ◽  
Vol 9 (2) ◽  
pp. 441-454 ◽  
Author(s):  
Matteo Reggente ◽  
Ann M. Dillner ◽  
Satoshi Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change, and reduced visibility. Typically OC and EC concentrations are measured using thermal–optical methods such as thermal–optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the functional group composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2 = 0.97 and R2 = 0.95 for OC and EC respectively), and for samples from 9 of the 11 sites not included in the calibration set (R2 = 0.96 and R2 = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error; we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

scholarly journals Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dan Peng ◽  
Yali Liu ◽  
Jiasheng Yang ◽  
Yanlan Bi ◽  
Jingnan Chen
Keyword(s):  
Moisture Content ◽  
Spectral Data ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector

The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.

Evaluating near infrared spectroscopy for field prediction of soil properties

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 664 ◽  
Author(s):  
Budiman Minasny ◽  
Alex B. McBratney ◽  
Leo Pichon ◽  
Wei Sun ◽  
Michael G. Short
Keyword(s):  
Soil Properties ◽  
Near Infrared ◽  
Agricultural Land ◽  
Diffuse Reflectance Spectroscopy ◽  
Soil Samples ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Conditions ◽  
Spectral Library ◽  
Field Samples

This paper demonstrates the application of near infrared diffuse reflectance spectroscopy (NIR-DRS) measurements as part of digital soil mapping. We also investigate whether calibration functions developed from a spectral library can be used for rapid characterisation of soil properties in the field. Soil samples were collected along 24 toposequences in the Pokolbin irrigation district, ~7 km2 of predominantly agricultural land in the Hunter Valley, NSW, Australia. Soil samples at 2 depths: 0–0.10 and 0.40–0.50 m were collected. The soil samples were scanned using NIR under 3 different conditions: field condition, dried unground, and dried ground. A separate spectral library containing soil laboratory measurements was used to develop functions to predict 3 main soil properties from NIR spectra (total C content, clay content, and sum of exchangeable cations). The absorbance spectra were found to be different for the 3 soil conditions. The field spectra appear to have higher absorbance, followed by dried unground samples and then dried ground samples. Although most spectral signatures or peaks were similar for the 3 soil conditions, field samples appear to have higher absorbance, particularly at 1400 nm and 1900 nm. The convex hull of the first 2 principal components of the soil spectra is an easy tool to evaluate the similarity of spectra from a calibration set to an observation. For field prediction, samples need to be calibrated using field samples. Finally, this study shows that NIR-DRS measurement is a useful part of digital soil mapping.

scholarly journals Sensory Drivers of Consumer Acceptance, Purchase Intent and Emotions toward Brewed Black Coffee

Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 180
Author(s):  
Ammaraporn Pinsuwan ◽  
Suntaree Suwonsichon ◽  
Penkwan Chompreeda ◽  
Witoon Prinyawiwatkul
Keyword(s):  
Positive Emotions ◽  
Bitter Taste ◽  
Partial Least Square ◽  
Least Square ◽  
Consumer Acceptance ◽  
Sensory Characteristics ◽  
Partial Least Square Regression ◽  
Sensory Profile ◽  
Purchase Intent ◽  
Wide Range

The link between coffee aroma/flavor and elicited emotions remains underexplored. This research identified key sensory characteristics of brewed black coffee that affected acceptance, purchase intent and emotions for Thai consumers. Eight Arabica coffee samples were evaluated by eight trained descriptive panelists for intensities of 26 sensory attributes and by 100 brewed black coffee users for acceptance, purchase intent and emotions. Results showed that the samples exhibited a wide range of sensory characteristics, and large differences were mainly described by the attributes coffee identity (coffee ID), roasted, bitter taste, balance/blended and fullness. Differences also existed among the samples for overall liking, purchase intent and most emotion terms. Partial least square regression analysis revealed that liking, purchase intent and positive emotions, such as active, alert, awake, energetic, enthusiastic, feel good, happy, jump start, impressed, pleased, refreshed and vigorous were driven by coffee ID, roasted, ashy, pipe tobacco, bitter taste, rubber, overall sweet, balanced/blended, fullness and longevity. Contrarily, sour aromatic, sour taste, fruity, woody, musty/earthy, musty/dusty and molasses decreased liking, purchase intent and positive emotions, and stimulated negative emotions, such as disappointed, grouchy and unfulfilled. This information could be useful for creating or modifying the sensory profile of brewed black coffee to increase consumer acceptance.

scholarly journals Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2015 ◽  
Vol 8 (11) ◽  
pp. 12433-12474 ◽  
Author(s):  
M. Reggente ◽  
A. M. Dillner ◽  
S. Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change and reduced visibility. Typically OC and EC concentrations are measured using thermal optical methods such as thermal optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2=0.97 and R2=0.95 for OC and EC respectively), and for samples from nine of the 11 sites not included in the calibration set (R2=0.96 and R2=0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error: we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

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 Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Tanvir Tazul Islam ◽  
Md Sajid Ahmed ◽  
Md Hassanuzzaman ◽  
Syed Athar Bin Amir ◽  
Tanzilur Rahman
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Quantitative Estimation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Frequency Noise ◽  
Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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