The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.

The image of a bone-dry surface in the Moon's non-polar regions impinged by the Apollo missions was changed by the detection of widespread absorption near 3 mu m in 2009, interpreted as a signature of hydration. However, debates persist on the relative contribution of molecular water (H2O) and other hydroxyl (OH) compounds to this hydration feature, as well as the cause of the potential temperature-dependence of the OH/H2O abundance. Resolving these debates will help to estimate the inventory of water on the Moon, a crucial resource for future space explorations. In this study, we measured the abundance and isotope composition of hydrogen within the outermost micron of Chang'e-5 soil grains, collected from the lunar surface and from a depth of 1 m. These measurements, combined with our laboratory simulation experiments, demonstrate that solar-wind-induced OH can be thermally retained in lunar regolith, with an abundance of approximately 48-95 ppm H2O equivalent. This abundance exhibits small latitude dependence and no diurnal variation. By integrating our results with published remote sensing data, we propose that a high amount of molecular water (similar to 360 +/- 200 ppm H2O) exists in the subsurface layer of the Moon's non-polar regions. The migration of this H2O accounts for the observed latitude and diurnal variations in 3 mu m band intensity. The inventory of OH and H2O proposed in this study reconciles the seemingly conflicting observations from various instruments, including infrared/ultraviolet spectroscopies and the Neutral Mass Spectrometer (NMS). Our interpretation of the distribution and dynamics of lunar hydration offers new insights for future lunar research and space missions.

Vegetable oils contain traces of heavy metals that can cause irreversible damage to human health. The present study employed near-infrared spectroscopy and variable selection in conjunction with partial least squares (PLS) for the rapid determination of Cd content in peanut oil. Firstly, the spectral data of peanut oil test samples were preprocessed by different preprocessing methods, and the best preprocessing method was selected according to the results obtained by the PLS regression model. Then, PLS regression models were established to determine Cd content in peanut oil by variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator (MFE-LASSO), and bootstrap soft shrinkage (BOSS), respectively. The results show that all four feature optimization algorithms could improve the prediction accuracy of the model. Among them, the CARS-PLS model had high prediction accuracy. Its prediction coefficient of determination (R2P) was 0.9666, the root mean square error of prediction (RMSEP) was 2.8207 mg/kg, and the relative prediction deviation (RPD) was 5.4705, respectively. In summary, near-infrared spectroscopy combined with chemometrics could be used for rapid quantitative detection of Cd in peanut oil.

The astrochemistry of CO2 ice analogues has been a topic of intensive investigation due to the prevalence of CO2 throughout the interstellar medium and the Solar System, as well as the possibility of it acting as a carbon feedstock for the synthesis of larger, more complex organic molecules. In order to accurately discern the physicochemical processes in which CO2 plays a role, it is necessary to have laboratory-generated spectra to compare against observational data acquired by ground-and space-based telescopes. A key factor which is known to influence the appearance of such spectra is temperature, especially when the spectra are acquired in the infrared and ultraviolet. In this present study, we describe the results of a systematic investigation looking into: (i) the influence of thermal annealing on the mid-IR and VUV absorption spectra of pure, unirradiated CO2 astrophysical ice analogues prepared at various temperatures, and (ii) the influence of temperature on the chemical products of electron irradiation of similar ices. Our results indicate that both mid-IR and VUV spectra of pure CO2 ices are sensitive to the structural and chemical changes induced by thermal annealing. Furthermore, using mid-IR spectroscopy, we have successfully identified the production of radiolytic daughter molecules as a result of 1 keV electron irradiation and the influence of temperature over this chemistry. Such results are directly applicable to studies on the chemistry of interstellar ices, comets, and icy lunar objects and may also be useful as reference data for forthcoming observational missions.

The paper presents an acousto-optic lunar infrared spectrometer (LIS) intended for mineralogical analysis and assessment of the hydration of the lunar surface regolith near the lander. Its optical layout, characteristics, results of calibrations and laboratory measurements are given. The LIS is designed to measure the spectrum of solar radiation reflected by the lunar surface; it will function as part of the load complex of the Luna-25 lander. The instrument is mounted on the manipulator of the lander in such a way that its field of view is within the field of view of the television support stereo cameras of the robotic arm working zone (TV RPM). The instrument operates in the spectral range of 1.15-3.4 mu m, including the OH/H2O absorption bands, with a spectral resolution of approximately 25 cm(-1). The principle of operation of the device is based on acousto-optic spectral filtering of optical radiation.

Vast amounts of soil organic matter (SOM) have been preserved in arctic soils over millennia time scales due to the limiting effects of cold and wet environments on decomposer activity. With the increase in high latitude warming due to climate change, the potential decomposability of this SOM needs to be assessed. In this study, we investigated the capability of mid infrared (MIR) spectroscopy to quickly predict soil carbon and nitrogen concentrations and carbon (C) mineralized during short-term incubations of tundra soils. Active layer and upper permafrost soils collected from four tundra sites on the North Slope of Alaska were incubated at 1, 4, 8 and 16 C for 60 days. All incubated soils were scanned to obtain the MIR spectra and analyzed for total organic carbon (TOC) and total nitrogen (TN) concentrations, and salt-extractable organic matter carbon (SEOM). Partial least square regression (PLSR) models, constructed using the MIR spectral data for all soils, were excellent predictors of soil TOC and TN concentrations and good predictors of mineralized C for these tundra soils. We explored whether we could improve the prediction of mineralized C by splitting the soils into the groups defined by the influential factors and thresholds identified in a principal components analysis: (1) TOC > 10%, (2) TOC 0.6%, (5) acidic tundra, and (6) non-acidic tundra. The best PLSR mineralization models were found for soils with TOC < 10% and TN < 0.6%. Analysis of the PLSR loadings and beta coefficients from these models indicated a small number of influential spectral bands. These bands were associated with clay content, phenolics, aliphatics, silicates, carboxylic acids, and amides. Our results suggest that MIR could serve as a useful tool for quickly and reasonably estimating the initial decomposability of tundra soils, particularly for mineral soils and the mixed organic-mineral horizons of cryoturbated soils.