Likelihood Transformations and Artificial Mixtures

2020 ◽  
pp. 191-209
Author(s):  
Alex Tsodikov ◽  
Lyrica Xiaohong Liu ◽  
Carol Tseng
Keyword(s):  
Artificial Mixtures

Quantitative Phase Analysis By Linear Regression of Chemistry on X-Ray Diffraction Intensity

1987 ◽  
Vol 2 (2) ◽  
pp. 96-98 ◽  
Author(s):  
Jacques Renault
Keyword(s):  
Linear Regression ◽  
Quantitative Information ◽  
Quantitative Phase Analysis ◽  
X Ray Diffraction ◽  
Diffraction Intensity ◽  
Mineralogical Analysis ◽  
X Ray ◽  
Reduction Program ◽  
Speed Of Analysis ◽  
Artificial Mixtures

AbstractXRF and XRD measurements made on a single pressed powder briquet can be combined to give more quantitative information than either technique employed alone. Speed of analysis and simplification of sample preparation are also enhanced. The algorithm presented here uses multiple linear regression of the concentrations of one or more elements on the corrected X-ray diffraction intensities of the phases containing them. The data reduction program runs on a microcomputer. Data are presented to show its application to mineralogical analysis of artificial mixtures of quartz, microcline (a feldspar) and calcite.

Analysis of artificial mixtures of pure chemicals by near-infrared reflectance

1990 ◽  
Vol 114 (3) ◽  
pp. 253-257 ◽  
Author(s):  
G. Z. Wetherill ◽  
I. Murray ◽  
C. A. Glasbey
Keyword(s):  
Near Infrared ◽  
Compositional Analysis ◽  
Principal Component ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Chemical Methods ◽  
Wet Chemical ◽  
Linear Regressions ◽  
Unexplained Variation ◽  
Artificial Mixtures

SUMMARYCompositional analysis of feeds and other materials by near-infrared reflectance (NIR) has been proposed as a cheap and rapid alternative to traditional wet chemical methods. A theoretical basis for NIR measurements is needed and may be obtained from the study of artificial mixtures of pure chemicals.Mixtures of lactose, casein and sodium oleate, in widely differing concentrations, were analysed by NIR. Principal component analysis was used to study the variations between spectra, and multiple linear regressions gave predictors of sample compositions from the spectra. Optical densities at most combinations of wavelengths gave good predictions of sample compositions because there was much less unexplained variation between NIR spectra than would occur between natural samples.

Factors affecting the ethanol stability of bovine milk: II. The origin of the pH transition

1981 ◽  
Vol 48 (2) ◽  
pp. 285-291 ◽  
Author(s):  
David S. Horne ◽  
Thomas G. Parker
Keyword(s):  
Bovine Milk ◽  
Skim Milk ◽  
Soluble Phosphate ◽  
Ph Profile ◽  
Factors Affecting ◽  
Milk Serum ◽  
Ethanol Stability ◽  
Artificial Mixtures

SummaryDialysing milk against phosphate-free sera showed that the transition in the ethanol (EtOH) stability/pH profile was associated with the soluble phosphate component of the milk serum. Sigmoidal behaviour similar to that of milk was reproduced when the EtOH stability of artificial mixtures of casein, Ca and phosphate was measured as a function of pH. A mechanism for the s coagulation of skim-milk is discussed.

Neural coding of quality of complex olfactory stimuli in lobsters

1988 ◽  
Vol 60 (1) ◽  
pp. 303-324 ◽  
Author(s):  
M. N. Girardot ◽  
C. D. Derby
Keyword(s):  
Neural Coding ◽  
Spatial Configuration ◽  
Dimensional Space ◽  
Absolute Magnitude ◽  
Neural Population ◽  
Population Responses ◽  
Natural Extracts ◽  
Stimulus Concentration ◽  
Standardization Procedure ◽  
Artificial Mixtures

1. Extracellular responses to complex biologically relevant stimuli were recorded from 30 primary olfactory cells from excised antennules of spiny lobsters. The stimulus types were natural extracts of crab, mullet, oyster, and shrimp and artificial mixtures of crab, mullet, oyster and shrimp based on the chemical composition of the related extracts. All stimulus types were presented at the following three concentrations: 0.005, 0.05, and 0.5 mM. 2. The responses were expressed as number of spikes per 5 s. Response magnitude increased significantly as a function of concentration. It was significantly greater for the natural extracts than for the related artificial mixtures but was not significantly different among stimulus types within either natural extracts or artificial mixtures. 3. The cells were broadly tuned to all stimuli. Tuning slightly, but significantly, broadened as a function of stimulus concentration. 4. Multidimensional scaling (MDS) was used to evaluate similarities and dissimilarities among stimuli based on population responses. The artificial mixtures and the natural extracts were analyzed separately. Dimensionality of spatial configuration was based on the following three criteria: stress values, squared correlation values, and relevance to quality coding. 5. When applied to the original data, MDS distributed the stimuli in a two-dimensional space where the location of each stimulus was based mainly on stimulus concentration. The results of a simple standardization procedure showed that this distribution resulted mostly from the significant effect of concentration on one of the two features of population responses, which is the absolute magnitude. This standardization procedure equalized the three concentrations in terms of absolute magnitude of evoked response. Consequently, the neural population responses of the 12 stimuli (4 types X 3 concentrations) could be compared based only on their across-neuron patterns (ANPs) (relative amount of activity across neurons). 6. When stress and squared correlation values were used as criteria for dimensionality, the configuration of the artificial mixtures space was best derived from dimensions 1, 2, and 3 of the three-dimensional resolution. When relevance to quality coding was used, the configuration of the artificial mixtures space was best derived from dimensions 1, 3, and 4 of the four-dimensional resolution. Whether stress and squared correlation values or relevance to quality coding were used, the four types of stimuli occupied nonoverlapping spaces.(ABSTRACT TRUNCATED AT 400 WORDS)

High-frequency absorbance measurements for sediment source apportionment – laboratory and field assessment

2021 ◽  
Author(s):  
Niels Lake ◽  
Núria Martínez-Carreras ◽  
Peter Shaw ◽  
Adrian Collins
Keyword(s):  
Particle Size ◽  
Source Apportionment ◽  
High Frequency ◽  
Soil Samples ◽  
Sediment Source ◽  
Water Tank ◽  
Sediment Sources ◽  
Environmental Objectives ◽  
Set Up ◽  
Artificial Mixtures

<p>To manage effectively excessive sediment inputs to rivers and streams, it is crucial to have detailed and reliable information on key sediment sources. Such evidence is important for implementing targeted measures for improving ecosystem functioning and meeting environmental objectives. Although sediment fingerprinting is increasingly adopted worldwide to provide such evidence, current procedures do not provide detailed information on how sediment sources can change over both short (e.g., events and in between events) and long (e.g., over seasons or years) time scales. These limitations are mainly due to the conventional methods used for target sediment sampling and the high workloads and costs associated with laboratory analyses for tracers, which limit both high-frequency and longer duration sampling campaigns. To address this issue, we propose the use of a submersible spectrophotometer, which measures absorbance in the UV-VIS range in situ and at high frequency (e.g., minutes) to trace suspended sediment sources. In our proof of concept investigation, the approach was first tested in a laboratory setting, using soil samples and artificial mixtures with known proportions of two, three and four soil source samples in an experimental water tank. A total of six soil samples were collected, which were sieved to different fractions to investigate the influence of particle size on the sensor absorbance readings. Both soil samples and artificial mixtures were suspended in the laboratory tank set-up at different concentrations to investigate the effects on: (i) absorbance, and; (ii) un-mixing accuracy. The results showed that absorbance was linearly additive and could be used to predict dominant samples in the artificial mixtures correctly using a Bayesian tracer un-mixing model, largely regardless of particle size and of the concentration inside the experimental tank. This approach is currently being tested in a field experiment in the Attert River Basin (Luxembourg) to investigate if the results found in the laboratory experiments hold under natural field conditions. Our preliminary insights into the use of absorbance for sediment source apportionment in the field will be presented.</p>

Geochemical fingerprints for tracing sediments in the sub-catchment Durazno del Medio, Argentina 

2021 ◽  
Author(s):  
Yanina Garcias ◽  
Romina Torres Astorga ◽  
Gisela Borgatello ◽  
Samuel Tejeda-Vega ◽  
Sergio de los Santos-Villalobos ◽  
...  
Keyword(s):  
Protected Areas ◽  
Annual Rainfall ◽  
Agricultural Fields ◽  
Tree Plantation ◽  
Natural Sediment ◽  
Sediment Samples ◽  
X Ray ◽  
Exotic Tree ◽  
Native Woodland ◽  
Artificial Mixtures

<p>Soil erosion is one of the most serious environmental problems caused by land-use changes in semi-arid regions of central Argentina. Hence, to understand the erosive dynamics in these regions becomes fundamental. </p><p>Sub-catchment Durazno del Medio (6.56 km<sup>2</sup>) is located 21 km northeast of San Luis City (S 33º 08’ 16” – W 66º 09’ 18”; S 33º 11’ 44” – W 66º 08’ 06”), in the central region of Argentina. The average annual temperature is 17 ºC. Annual rainfall ranges from 600 to 800 mm, with a tendency to increase in the last years. Rainfall varies seasonally, with a dry season from May to October and a rainy season from November to April. This agricultural catchment has been researched to identify critical hot spots of land degradation by applying sediment source fingerprinting techniques. </p><p>In the studied area, exotic tree plantations in protected areas (in a state of youth development), native woodland, roads (dirt and paved), agricultural fields, and channel banks were identified as sources of sediments. Most of the sources were found on quaternary deposits (loessoid deposits), except native forests and some roads, which were found in gneiss and migmatites. The sub-catchment has a drainage network formed by two water courses that converge into the main one. Channel sediments (mixtures) were collected at the end of the main channel.</p><p>The energy dispersive X-ray fluorescence (EDXRF) analytical technique and the MixSIAR unmixing model were implemented to estimate the contribution of sediment sources in mixtures. The sources and mixtures (tablets) were analyzed in triplicate using a Si-Li detector SDD (resolution 145 keV, Kalpha Mn) with a X ray tube of 50 W and 50 kV. The tracers were analyzed using a fundamental parameters method. Since the selection of correct fingerprints has been proven to be an essential stage in the analysis, before unmixing the natural sediment samples, two artificial mixtures were made using known quantities of soil collected in the identified sources to test the precision and robustness of the tracers selection procedure. These analyses showed that the use of most of the tracers (25 elements) increases the exactitude of calculated proportions. The first mixture was made using two sources, exotic tree plantation in protected areas (35.6 %) and agricultural fields (64.4 %). The second mixture was created using different proportions of 4 different sources including native woodland (30 %), exotic tree plantation in protected areas (20 %), agricultural fields (5 %) and channel banks (45 %). A low mean absolute error (MAE) of 2 % and 4 %, respectively, was obtained when reconstructing the 2 artificial mixtures. This outcome indicates that the selection process was effective.</p><p>Once the tracers were properly selected, the natural sediment samples were analyzed. Hence, for the catchment mixture, the main sources of sediments were exotic tree plantation in protected areas (96.7 %) and roads (1.8 %). The application of the fingerprint technique highlighted that forests are one of the largest contributors of sediment, followed by dirt roads.</p>

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