Unity Scientific LLC

Brookfield, CT, United States

Unity Scientific LLC

Brookfield, CT, United States

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Lavine B.K.,Oklahoma State University | Nuguru K.,Oklahoma State University | Mirjankar N.,Oklahoma State University | Workman Jr. J.,Unity Scientific LLC | Workman Jr. J.,University of San Diego
Applied Spectroscopy | Year: 2012

Pattern recognition methods have been used to develop search prefilters for infrared (IR) library searching. A two-step procedure has been employed. First, the wavelet packet tree is used to decompose each spectrum into wavelet coefficients that represent both the high and low frequency components of the signal. Second, a genetic algorithm for pattern recognition analysis is used to identify wavelet coefficients characteristic of functional group. Even in challenging trials involving carboxylic acids, compounds that possess both carbonyl and hydroxyl functionalities can be readily differentiated from carboxylic acids. The proposed search prefilters allow for the use of more sophisticated and correspondingly more time-consuming algorithms in IR spectral library matching because the size of the library can be culled down for a specific match using information from the search prefilter about the presence or absence of specific functional groups in the unknown. © 2012 Society for Applied Spectroscopy.


Lavine B.K.,Oklahoma State University | Nuguru K.,Oklahoma State University | Mirjankar N.,Oklahoma State University | Workman J.,Unity Scientific LLC | Workman J.,National University of La Jolla
Microchemical Journal | Year: 2012

435 infrared (IR) absorbance spectra of 140 carboxylic acids and 295 noncarboxylic acids which included aldehydes, ketones, esters, amides as well as compounds containing both carbonyls and alcohols were preprocessed using the wavelet packet tree to enhance subtle but important features in the data. Wavelet coefficients that optimized the separation of the spectra by functional group in a plot of the two largest principal components of the data were identified using a genetic algorithm (GA) for pattern recognition analysis. Because principal components maximize variance, the bulk of the information encoded by the wavelet coefficients selected by the pattern recognition GA is characteristic of the carboxylic acid functional group. The carboxylic acid search prefilter developed as part of this study was successfully validated using two external validation sets. The first validation set consisted of 24 carboxylic acids and 61 noncarboxylic acids and the second validation set consisted of 264 carboxylic acids and 72 noncarboxylic acids. © 2012 Elsevier B.V..


Workman J.,Unity Scientific LLC | Workman J.,University of San Diego | Workman J.,Liberty University | Mark H.,Mark Electronics
Spectroscopy (Santa Monica) | Year: 2015

When using any regression technique, either linear or nonlinear, there is a rational process that allows the researcher to select the best model. One question often arises: Which regression method (or model) is better or best when compared to others? This column discusses a mathematical and rational process that is useful for selecting the best predictive model when using regression methods for spectroscopic quantitative analysis. © 2015 Advanstar Communications, Inc. All rights reserved.


Workman Jr. J.,Unity Scientific LLC | Workman Jr. J.,University of San Diego | Workman Jr. J.,Liberty University | Mark H.,Mark Electronics
Spectroscopy (Santa Monica) | Year: 2013

This column is a continuation from our previous two columns on the subject of multivariate calibration transfer (or calibration transfer) for spectroscopy. As we noted in the previous columns, calibration transfer is a series of approaches or techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, to two or more instruments. Those instruments may be of like or different technical design. In this installment, we review the mathematical approaches and issues related to the calibration transfer process.


Mark H.,Mark Electronics | Workman J.,Unity Scientific LLC | Workman J.,University of San Diego
Spectroscopy (Santa Monica) | Year: 2015

The science of statistics are concerned with the effects of the random portion of the uncertainty, not least because it can help discern and, even better, calculate the systematic errors so that they can be corrected. Over the years, statisticians have discovered much about the nature and behavior of random errors, and have learned how to specify bounds for what can legitimately be said about the data that are subject to these errors. One of the more important findings is that if the data are subject to fluctuations because of random error, then anything you calculate from those data will also be subject to fluctuations. To the statistician, a value calculated from a set of data subject to random fluctuations is what is known as a statistic. An important difference between samples and statistics is the distribution of the variations. The distribution of values of multiple measurements from a sample, or the distribution of values of measurements from multiple samples can be almost anything, and is determined by the physics or chemistry (or other applicable discipline) of the situation governing the behavior of the samples.


Workman J.,Unity Scientific LLC | Workman J.,University of San Diego | Workman J.,Liberty University | Mark H.,Mark Electronics
Spectroscopy (Santa Monica) | Year: 2016

This column addresses the issue of degrees of freedom (df) for regression models. It seems there is some confusion about the use of df for the various calibration and prediction situations - the standard error parameters should be-comparable and are related to the total independent samples, data channels containing information (that is, wavelengths or wave numbers), and number of factors or terms in the regression. By convention everyone could just choose a definition, but there is a more correct one that should be verified and discussed for each case. The problem is computing the standard deviation using different degrees of freedom without a more rigorous explanation and then putting so much emphasis on the actual number derived for the standard error of the estimate (SEE) and the standard error of cross validation (SECV), rather than on the computed confidence intervals. © 2016 Advanstar Communications, Inc. All rights reserved.


Workman J.,Unity Scientific LLC | Workman J.,University of San Diego | Workman J.,Liberty University | Mark H.,Mark Electronics
Spectroscopy (Santa Monica) | Year: 2014

Photometric accuracy and precision, as reproducibility and repeatability, respectively, are essential for building consistent large databases over time for use in qualitative searches or quantitative multivariate analysis. If the spectrophotometer in use is inconsistent in terms of linearity and photometric accuracy, the analytical precision and accuracy will be jeopardized over time. Photometric accuracy and linearity drift over time within a single instrument or between instruments and create errors and variation in the accuracy of measurements using databases collected with different photometric registrations. How do current commercial instruments vary with respect to photometric accuracy and precision over time? What are potential solutions to this challenge? © 2014, Advanstar Communications Inc. All rights reserved.


Workman Jr. J.,Unity Scientific LLC | Workman Jr. J.,University of San Diego | Workman Jr. J.,Liberty University | Mark H.,Mark Electronics
Spectroscopy (Santa Monica) | Year: 2014

Different units of measurement have different relationships to the spectral values, for reasons having nothing to do with the spectroscopy. The experimental finding that electromagnetic spectroscopy is sensitive to the volume percent (or, strictly speaking, the volume fraction) of materials in a sample is the resulting conclusion. There are a variety of measurement errors because of variation in sample presentation and instrumentation, as described above, but fundamentally the spectroscopy is relating to volume fraction and not weight percent. There are both graphical methods available and numeric methods. It made sense to use both approaches for the comparison, if for no other reason than that is standard procedure when performing calibrations with the other algorithms, and we decided to examine our CLS results as closely as is normally done. Researchers started with the numeric approach, and computed the root mean square differences and correlation coefficients between the concentration values from the CLS method and the concentration values obtained using other units.


Mark H.,Mark Electronics | Workman Jr. J.,Unity Scientific LLC
Spectroscopy (Santa Monica) | Year: 2013

The results we found from our previous subseries about classical least squares analysis provides the mechanism for understanding when and why calibration transfer can be done easily or when it will be difficult. Those results also provide a basis for a modified understanding of what calibration transfer means and how we can tell whether or not such a transfer can be performed, for any given analysis.


Mark H.,Unity Scientific LLC | Workman J.,Jr.
Spectroscopy (Santa Monica) | Year: 2015

Howard Mark, Jerome Workman discuss the findings of their study regarding units of measure in spectroscopy. The authors found that Beer's law and the CLS algorithm are not the same is also easily seen when considering the units used in the two concepts. When discussing results with various people, there would inevitably be a discussion about the units used in Beer's law, and the fact that the absorption coefficient used in Beer's law is assigned units that cancel the units of concentration used. This way, the net units from the Beer's law expression A = abc cancel out, which they must since the absorbance (A) is dimensionless. Also significant, the expression for Beer's law has a, the absorption coefficient, on its right-hand side, while the expression for CLS has A, the absorbance on the right-hand side. An important characteristic of the volume % that makes it useful for calibration with other algorithms is the fact that, by virtue of being equal to the spectroscopic measurement. The calibration process needs to approximate the nonlinear relations between the spectral measurements and the weight percents so often used to express the analyte concentrations. To demonstrate calibration transfer, it is necessary to show that the same samples give the same results when measured on the different instruments.

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