Dataset Information

Application of FTIR-PAS in Rapid Assessment of Rice Quality under Climate Change Conditions.

ABSTRACT: Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS), versus attenuated total reflectance spectroscopy (FTIR-ATR) and diffuse reflectance spectroscopy (DRIFT), was firstly applied in quick assessment of rice quality in response to rising CO₂/temperature instead of conventional time-consuming chemical methods. The influences of elevated CO₂ and higher temperature were identified using FTIR-PAS spectra by principal component analysis (PCA). Variations in the rice functional groups are crucial indicators for rice identification, and the ratio of the intensities of two selected spectral bands was used for correlation analysis with starch, protein, and lipid content, and the ratios all showed a positive linear correlation (R² = 0.9103, R² = 0.9580, and R² = 0.9246, respectively). Subsequently, changes in nutritional components under future environmental conditions that encompass higher CO₂ and temperature were evaluated, which demonstrated the potential of FTIR-PAS to detect the responses of rice to climate change, providing a valuable technique for agricultural production and food security.

SUBMITTER: Wei L

PROVIDER: S-EPMC7828744 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Application of FTIR-PAS in Rapid Assessment of Rice Quality under Climate Change Conditions.

Wei Lianlian L Ma Fei F Du Changwen C

Foods (Basel, Switzerland) 20210114 1

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS), versus attenuated total reflectance spectroscopy (FTIR-ATR) and diffuse reflectance spectroscopy (DRIFT), was firstly applied in quick assessment of rice quality in response to rising CO<sub>2</sub>/temperature instead of conventional time-consuming chemical methods. The influences of elevated CO<sub>2</sub> and higher temperature were identified using FTIR-PAS spectra by principal component analysis (PCA). Variations in the rice ...[more]

PMID: 33466600

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Project description:As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Dataset Information

Application of FTIR-PAS in Rapid Assessment of Rice Quality under Climate Change Conditions.

Publications

Application of FTIR-PAS in Rapid Assessment of Rice Quality under Climate Change Conditions.

Similar Datasets

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