Mid-infrared spectroscopy as a potential tool for monitoring the success of tropical peatland restoration.

Predicting the decomposability of arctic tundra soil organic matter with mid infrared spectroscopy

QUANTIFYING PEATLAND CARBON DYNAMICS USING MECHANISTICALLY-BASED BIOGEOCHEMISTRY MODELS

Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy

Salts in the root zone have high spatial variability, change rapidly and adversely affect soil quality and crop productivity. In contrast to the time-intensive traditional methods for measuring electrical conductivity (EC), visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy provide faster alternatives that can assist in creating strategies to reduce negative impacts on soil and plants. Soils were collected from the Indo-Gangetic Plains and analysed for EC1:2.5 using conventional method. There was a wide variation in EC measured by the conventional method. So Partial Least Squares Regression (PLSR) was used to predict soil EC from spectral data, with the data divided into calibration (70%) and validation (30%) datasets. The partial least square regression (PLSR), random forest (RF), support vector regression (SVR) and multivariate adaptive regression splines (MARS) both in Vis-NIR and MIR region during calibration. The predictive performance of PLSR, RF, SVR, and MARS models for EC1:2.5 in the Vis-NIR range showed PLSR as the best model (R2 = 0.84, RMSE = 0.21, RPD = 2.44). In the MIR range, RF was considered fairly good (R2 = 0.52, RMSE = 0.20, RPD = 1.43). Vis-NIR spectroscopy with PLSR algorithm predicted EC better than MIR spectroscopy and would be the method of choice for rapid estimation and prediction of EC in the study region.

The successful estimation of soil organic matter (SOM) and soil total nitrogen (TN) contents with mid-infrared (MIR) reflectance spectroscopy depends on selecting appropriate variable selection techniques and multivariate methods for regression analysis. This study aimed to explore the potential of combining a multivariate method and spectral variable selection for soil SOM and TN estimation using MIR spectroscopy. Five hundred and ten topsoil samples were collected from Quzhou County, Hebei Province, China, and their SOM and TN contents and reflectance spectra were measured using DRIFT-MIR spectroscopy (diffuse reflectance infrared Fourier transform in the mid-infrared range, MIR, wavenumber: 4000–400 cm−1; wavelength: 2500–25,000 nm). Two multivariate methods (partial least-squares regression, PLSR; multiple linear regression, MLR) combined with two variable selection techniques (stability competitive adaptive reweighted sampling, sCARS; bootstrapping soft shrinkage approach, BOSS) were used for model calibration. The MLR model combined with the sCARS method yielded the most accurate estimation result for both SOM (Rp2 = 0.72 and RPD = 1.89) and TN (Rp2 = 0.84 and RPD = 2.50). Out of the 2382 wavenumbers in a full spectrum, sCARS determined that only 31 variables were important for SOM estimation (accounting for 1.30% of all variables) and 27 variables were important for TN estimation (accounting for 1.13% of all variables). The results demonstrated that sCARS was a highly efficient approach for extracting information on wavenumbers and mitigating redundant wavenumbers. In addition, the current study indicated that MLR, which is simpler than PLSR, when combined with spectral variable selection, can achieve high-precision prediction of SOM and TN content. As such, DRIFT-MIR spectroscopy coupled with MLR and sCARS is a good alternative for estimating the SOM and TN of soils.

Developing a routine and cost effective capability for measuring soil organic carbon (SOC) content and composition will allow identification of land management practices with a potential to maintain or enhance SOC stocks. Coupling SOC content data and mid-infrared (MIR) spectra through the application of partial least-squares regression (PLSR) analyses has been used to develop such a prediction capability. The objective of this study was to determine whether MIR/PLSR analyses provide accurate estimates of the content and composition of SOC that can be used to quantify SOC stocks and its potential vulnerability to loss. Soil was collected from a field trial incorporating a range of land use (pasture, arable cropping and bare fallow) and tillage (intensive, minimum and no tillage) treatments over a nine-year period. The SOC content was measured by dry combustion analysis. Particulate organic carbon was separated from other forms of carbon on the basis of particle size (SOC in the >50 µm fraction). Resistant organic carbon was quantified using solid-state 13C nuclear magnetic resonance. The MIR/PLSR algorithms were successfully developed to predict the natural logarithms of the contents of SOC and POC in the collected soils. With initial calibration, a single MIR analysis could be used in conjunction with PLSR algorithms to predict the content of SOC and its allocation to component fractions. The MIR/PLSR predicted SOC contents provided reliable estimates of the impact of agricultural management on the 0–25-cm SOC stocks, as well as an indication of the vulnerability of SOC to loss. Development of this capability will facilitate the rapid and cost effective collection of SOC content data for detecting the impact of agricultural management treatments on SOC stocks, composition and potential vulnerability to change.

The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis

NIR and MIR spectral data fusion for rapid detection of Lonicera japonica and Artemisia annua by liquid extraction process

Detection of minced beef adulteration with turkey meat by UV–vis, NIR and MIR spectroscopy

The data presented in this article relates to “Determination of Total Carbon in Biosolids using MID-Infrared Spectroscopy” published in Science of the Total Environment. In this new article, we present the data used for the development of the methodology using Partial Least Squares (PLS) combined with MID-Infrared (MID-IR) spectroscopy for the determination of total carbon in biosolids. Based on the data used, MID-IR combined with PLS was found to be an acceptable alternative and inexpensive method to determine the total C of biosolids compared to conventional methods such as the Dumas combustion method using a LECO C analyser.

Peatland degradation indicated by stable isotope depth profiles and soil carbon loss

Mid-infrared spectroscopy (MIRS) is a well-established analytical tool for qualitative and quantitative analysis of soil samples. However, effects of soil sample grinding procedures on the prediction accuracy of MIR models and on qualitative spectral information have not been well investigated and, in consequence, not standardized up to now. Further, the effects of soil sample selection on the accuracy of MIR prediction models has not been quantified yet. This study investigated these effects by using 180 well-characterized soil samples that were ground for different times (0, 2 or 4 minutes) and then used for MIR measurements. To study the impact of sample preparation, soil spectra were subjected to principal component analyses (PCA), multiple regression and partial least square (PLS) analysis. The results indicate that the prediction accuracy of MIR models for soil organic carbon (SOC) and pH and the qualitative spectral information were better overall for lightly ground (2 minutes) soil samples compared with intensively (4 minutes) or unground soil samples. Whereas the grinding procedure did not show any effect on spectra of clay minerals, spectral information for quartz and for SOC was modified. Even though it is difficult to recommend a global standardized soil sample grinding procedure for MIR measurements because of different mill types available within laboratories, we highly recommend using an internally standardized grinding procedure. Moreover, we show that neither land use nor soil sampling depth influences the prediction of the SOC content. However, sand and clay content substantially affect the score vectors used by the PLS algorithm to predict the SOC content. Thus, we recommend using soil samples similar in texture for more precise SOC calibration models for MIR spectroscopy.

Prediction of fatty acid profiles in cow, ewe, and goat milk by mid-infrared spectrometry

Raman spectroscopy for wine analyses: A comparison with near and mid infrared spectroscopy

This paper proposes extended Generalized Hough Transform (GHT) to introduce training process by using Partial Least Squares (PLS) regression analysis. Hough transform can robustly detect patterns against noise and occlusions, and GHT is adapted to perform the generic object detection. In this study, we introduced training process to determine the voting weight of GHT by using PLS regression analysis. Thereby, it becomes possible to generic object detection, while maintaining the framework of Hough-based object detection. In this paper, we applied PLS Hough transform to the vehicle detection from satellite images. In addition, we compared PLS Hough transform with the previous approach (original GHT) on the vehicle detection, and our proposed method achieved high detection accuracy.