Movements of moisture and acid in gastric milk clots during gastric digestion: Spatiotemporal mapping using hyperspectral imaging
By: Siqi Li, Yash Dixit, Marlon M. Reis, Harjinder Singh, Aiqian Ye
Abstract
Ruminant milk is known to coagulate into structured clots during gastric digestion. Hyperspectral imaging was used to investigate the movements of moisture and acid in skim milk clots formed during dynamic gastric digestion and the effects of milk type (regular or calcium-rich) and the presence/absence of pepsin.
NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager
By: Bruce D. Cook, Lawrence A. Corp, Ross F. Nelson, Elizabeth M. Middleton, Douglas C. Morton, Joel T. McCorkel, Jeffrey G. Masek, Kenneth J. Ranson, Vuong Ly, Paul M. Montesano
Abstract
The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system.
NASA Operation IceBridge 2018
By: Nathan Kurtz
Abstract
Ice surface elevation data over ice sheets, glaciers, and sea ice to bridge the gap between ICESat and ICESat-2 missions • New measurements critical to ice sheet models: bed topography, grounding line position, ice and snow thickness
Non-Destructive Detection & Monitoring of Mold on Cannabis with Hyperspectral Imaging
By: John Margeson, Lynn Chandler Ph.D.
Abstract
Hyperspectral imaging is poised to make a significant impact on the cannabis industry because of its ability to simultaneously utilize the value of both spatial and spectral information. Current cannabis testing methods rely on spot sampling and do not capture enough data to measure the variability within a sample. Hyperspectral imaging is non-destructive and enables real-time test results, allowing rapid and complete product testing without the need to remove samples from product and prepare those samples for testing.
Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors
By: Ivan Simko, Ryan J. Hayes, Robert T. Furbank
Abstract
The objective of the present study was to test the feasibility of using non-destructive phenotyping with optical sensors for the evaluations of lettuce plants in early stages of development. We performed the series of experiments to determine if hyperspectral imaging and chlorophyll fluorescence imaging can determine phenotypic changes manifested on lettuce plants subjected to the extreme temperature and salinity stress treatments.
Non-Invasive Survey of Old Paintings using VNIR Hyperspectral Sensor
By: E. Matouskova, K. Pavelka, Z. Svadlenkova
Abstract
This paper shows first results of the project on painting documentation field as well as used instrument. Hyperspec VNIR by Headwall Photonics was used for this analysis. It operates in the spectral range between 400 and 1000 nm. Comparison with infrared photography is discussed. The goal of this contribution is a non-destructive deep exploration of specific paintings.
Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging
By: Anisur Rahman, Lalit Mohan Kandpal, Santosh Lohumi, Moon S. Kim, Hoonsoo Lee, Changyeun Mo, Byoung-Kwan Cho
Abstract
The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000–1550 nm.
The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky–Golay (S–G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model.
These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes.
Pedogenic hematitic concretions from the Triassic New Haven Arkose, Connecticut, Implications for understanding Martian diagenetic processes
By: J.H. Wilson, S.M. McLennan, T.D. Glotch, E.T. Rasbury, E.H. Gierlowski-Kordesch, R.V. Tappero
Abstract
We examine pedogenic sedimentary concretions from the New Haven Arkose. We use spectroscopic and geochemical methods to characterize the concretions. New Haven concretions consist of ~ 20% hematite, quartz, goethite and montmorillonite. Differences from other concretions are a negative Ce anomaly and lack of abundant Mn. New Haven concretions possess a pattern of Ni enrichment, similar to the Martian “blueberries.”
Performance & Feasibility of Drone-Mounted Imaging Spectroscopy for Invasive Aquatic Vegetation Detection
By: Erik A. Bolch, Erin L. Hestir, Shruti Khanna
Abstract
Invasive plants are non-native species that can spread rapidly, leading to detrimental economic, ecological, or environmental impact. In aquatic systems such as the Sacramento-San Joaquin River Delta in California, USA, management agencies use manned aerial vehicles (MAV) imaging spectroscopy missions to map and track annual changes in invasive aquatic plants. Advances in unmanned aerial vehicles (UAV) and sensor miniaturization are enabling higher spatial resolution species mapping, which is promising for early detection of invasions before they spread over larger areas.
Photon migration of Raman signal in bone as measured with spatially offset Raman spectroscopy
By: Kay Sowoidnich, John H. Churchwell, Kevin Buckley, Allen E. Goodship, Anthony W. Parker, Pavel Matousek
Abstract
Spatially offset Raman spectroscopy (SORS) is currently being developed as an in vivo tool for bone disease detection, but to date, information about the interrogated volume as influenced by the light propagation and scattering characteristics of the bone matrix is still limited. This paper seeks to develop our general understanding of the sampling depths of SORS in bone specimens as a function of the applied spatial offset.
