Mid-Infrared Variable Selection for Soil Organic Matter Fractions Based on Soil Model Systems and Permutation Importance Algorithm

In this research, an attempt was made to classify soil samples according to the different fractions of soil organic matter (SOM) using model systems in which the ratio of the fractions of SOM is chemically mimicked. A mixture of starch and nicotinamide was used for the labile organic matter model, while a standard of humic acid was used for the stabile organic matter. Changing the threshold value in the selected ranges after a permutation importance algorithm is conducted using train models and test data set, a list of selected important wavelengths and their importance scores were obtained. Three regions for the classification of soil fractions within the estimated probability density function are most prominent: 800-1200 cm-1, 0.48-0.55; 1800-2000 cm-1, 0.52-0.62; and 2500-3200 cm-1, 0.48-0.62, where the first component represents the spectral range while the second component covers the range of the importance score. Obtained wavelength ranges indicate the importance of the aliphatic stretching and bending vibration region, as well as the total soil reflectance (mineral content) for the characterization of organic matter fractions. A comparative evaluation with literature data found that the obtained wavelengths have a potential for application in methods of proximal and remote detection/calibration of existing and development of new sensors for Advanced Spaceborne Thermal Emission and Reflection Radiometer satellites, specifically in the shortwave infrared and thermal infrared ranges.

Single-Cell and Bulk Fluorescence Excitation Signatures of Seven Phytoplankton Species During Nitrogen Depletion and Resupply

Phytoplankton play a vital role as primary producers in aquatic ecosystems. One common approach to classifying phytoplankton is fluorescence excitation spectroscopy, which leverages the variation in types and concentrations of pigments among different phytoplankton taxonomic groups. Here, we used a fluorescence imaging photometer to measure excitation ratios ("signatures") of single cells and bulk cultures of seven differently pigmented phytoplankton species as they progressed from nitrogen N-replete to N-depleted conditions. Our objective was to determine whether N depletion alters the fluorescence excitation signature of each species and, if so, how quickly they recover when N (as nitrate) was resupplied, because these factors affect our ability to classify the species correctly. Of the seven species studied, only Proteomonas sulcata, a marine cryptophyte, showed measurable changes in single-cell fluorescence excitation ratios and bulk fluorescence excitation spectra. These changes were likely due to decreases in the cellular concentration of phycoerythrin, a N-rich pigment, as N became scarce. Within 3 h of resupply of N, fluorescence signatures began returning to pre-depletion values and were indistinguishable from N-replete cells by 80 h after resupply. These data suggest that our classification approach is robust for non-PE containing phytoplankton. PE-containing phytoplankton might exhibit systematic changes in their signatures depending on their level of N depletion, but this could be detected and the phytoplankton re-classified following a few hours of incubation in N replete conditions.

A New Multivariate Optical Computing Microelement and Miniature Sensor for Spectroscopic Chemical Sensing in Harsh Environments: Design, Fabrication, and Testing

Multivariate optical computing (MOC) is a compressed sensing technique with the ability to provide accurate spectroscopic compositional analysis in a variety of different applications to multiple industries. Indeed, recent developments have demonstrated the successful deployment of MOC sensors in downhole/well-logging environments to interrogate the composition of hydrocarbon and other chemical constituents in oil and gas reservoirs. However, new challenges have necessitated sensors that operate at high temperatures and pressures (up to 230 °C and 138 MPa) as well as even smaller areas that require the miniaturization of their physical footprint. To this end, this paper details the design, fabrication, and testing of a novel miniature-sized MOC sensor suited for harsh environments. A micrometer-sized optical element provides the active spectroscopic analysis. The resulting MOC sensor is no larger than two standard AAA batteries yet is capable of operating in high temperature and pressure conditions while providing accurate spectroscopic compositional analysis comparable to a laboratory Fourier transform infrared spectrometer.

Hydrogen Sulfide Gas Detection via Multivariate Optical Computing

Hydrogen-sulfide gas is a toxic, colorless gas with a pungent odor that occurs naturally as a decomposition by-product. It is critical to monitor the concentration of hydrogen sulfide. Multivariate optical computing (MOC) is a method that can monitor analytes while minimizing responses to interferences. MOC is a technique by which an analogue calculation is performed entirely in the optical domain, which simplifies instrument design, prevents the drift of a calibration, and increases the strength and durability of spectroscopic instrumentation against physical perturbation when used for chemical detection and identification. This paper discusses the detection of hydrogen-sulfide gas in the ultraviolet (UV) spectral region in the presence of interfering gaseous species. A laboratory spectroscopic measurement system was set up to acquire the UV spectra of H₂S and interference gas mixtures in high-pressure/high-temperature (HPHT) conditions. These spectra were used to guide the design and fabrication of a multivariate optical element (MOE), which has an expected measurement relative accuracy of 3.3% for H₂S, with a concentration in the range of 0–150 nmol/mL. An MOC validation system with the MOE was used to test three samples of H₂S and mercaptans mixtures under various pressures, and the relative accuracy of H₂S measurement was determined to be 8.05%.