A Chemometrics Module for an Undergraduate Instrumental Analysis Chemistry Course

Msimanga, Huggins Z.; Elkins, Phet; Tata, Segmia K.; Smith, Dustin R.

doi:10.1021/ed082p415

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…In this paper, we present a method for doing the PCA analysis using R, a light-weight, free, and open source cross-platform piece of software, and we offer an addition to already existing laboratory experiments dealing with PCA analysis. ,− …”

Section: Introductionmentioning

confidence: 99%

Teaching Principal Component Analysis Using a Free and Open Source Software Program and Exercises Applying PCA to Real-World Examples

Sidou

Borges

2020

J. Chem. Educ.

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Principal component analysis (PCA) is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. Running a PCA results in two main elements, the score plot and the loading plot; the score plot provides the location of the samples, and the loading plot indicates correlations among variables, the trends in the grouping of samples, and the most important variables. In the past 10 years teaching chemometrics, we have struggled with not having free software with an easy to use graphical user interface for data handling and calculations. In this paper, we provide a series of examples that students used to carry PCA in R-Project, a free and open source software program. In the first example, students used PCA to find correlations among chemical properties of chemical elements and relate these properties with the periodic distribution of the elements. In the second example, meat samples were grouped using 14 variables, and students could observe how outlier samples might influence the PCA model; in this case, they were also taught how to use the t test to choose the variables that were significant to the PCA model. In the third example, healthy patients were differentiated from diabetic patients using 163 lipid concentrations. In the fourth example, Atlantic salmon samples were differentiated from catfish samples. In the fifth and sixth examples, students were able to observe how data treatment affects the classification of natural products and edible oils, respectively.

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Section: Introductionmentioning

confidence: 99%

Teaching Principal Component Analysis Using a Free and Open Source Software Program and Exercises Applying PCA to Real-World Examples

Sidou

Borges

2020

J. Chem. Educ.

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“…While work on quantitative analysis of acetaminophencontaining medicines is readily available in the literature, [23][24][25] the authors are not aware of any citations involving classification of Tylenol brands using MVSATs. Using the Tylenol formulations as a model for illicit drugs, our goal was to determine whether these brands could be distinguished from one another and to identify possible variables responsible for such distinction.…”

Section: Introductionmentioning

confidence: 99%

Discerning Some Tylenol Brands Using Attenuated Total Reflection Fourier Transform Infrared Data and Multivariate Analysis Techniques

Msimanga

Ollis

2010

Appl Spectrosc

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Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify acetaminophen-containing medicines using their attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectra. Four formulations of Tylenol (Arthritis Pain Relief, Extra Strength Pain Relief, 8 Hour Pain Relief, and Extra Strength Pain Relief Rapid Release) along with 98% pure acetaminophen were selected for this study because of the similarity of their spectral features, with correlation coefficients ranging from 0.9857 to 0.9988. Before acquiring spectra for the predictor matrix, the effects on spectral precision with respect to sample particle size (determined by sieve size opening), force gauge of the ATR accessory, sample reloading, and between-tablet variation were examined. Spectra were baseline corrected and normalized to unity before multivariate analysis. Analysis of variance (ANOVA) was used to study spectral precision. The large particles (35 mesh) showed large variance between spectra, while fine particles (120 mesh) indicated good spectral precision based on the F-test. Force gauge setting did not significantly affect precision. Sample reloading using the fine particle size and a constant force gauge setting of 50 units also did not compromise precision. Based on these observations, data acquisition for the predictor matrix was carried out with the fine particles (sieve size opening of 120 mesh) at a constant force gauge setting of 50 units. After removing outliers, PCA successfully classified the five samples in the first and second components, accounting for 45.0% and 24.5% of the variances, respectively. The four-component PLS-DA model (R(2)=0.925 and Q(2)=0.906) gave good test spectra predictions with an overall average of 0.961 +/- 7.1% RSD versus the expected 1.0 prediction for the 20 test spectra used.

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“…Despite its popularity in many industrial fields, multivariate calibration is not usually taught in chemistry careers . Previous papers in this Journal have described examples with data sets produced by students in experimental courses. − The most common model for NIR spectra is partial least-squares (PLS), which is incorporated in all computers operating NIR spectrometers. The theory of PLS has been described in the relevant literature .…”

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confidence: 99%

Developing and Implementing an R Shiny Application to Introduce Multivariate Calibration to Advanced Undergraduate Students

Antonelli

Olivieri

2020

J. Chem. Educ.

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During a short chemometrics course in the seventh semester of the chemistry undergraduate program, students receive a brief theoretical introduction to multivariate calibration, focused on partial least-squares regression as the most commonly employed data processing tool. The theory is complemented with the use of MVC1_R, an easy-to-use software developed in-house as an R Shiny application. The present report describes student activities with the latter software in the development of mathematical models to predict quality parameters of corn seeds from near-infrared spectra. Subsequently, an experimental project is carried out involving near-infrared spectral measurements, which are widely used in several industrial fields for quality control. To process the obtained data, students apply the knowledge acquired during the theoretical/software sessions.

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A Chemometrics Module for an Undergraduate Instrumental Analysis Chemistry Course

Cited by 12 publications

References 10 publications

Teaching Principal Component Analysis Using a Free and Open Source Software Program and Exercises Applying PCA to Real-World Examples

Teaching Principal Component Analysis Using a Free and Open Source Software Program and Exercises Applying PCA to Real-World Examples

Discerning Some Tylenol Brands Using Attenuated Total Reflection Fourier Transform Infrared Data and Multivariate Analysis Techniques

Developing and Implementing an R Shiny Application to Introduce Multivariate Calibration to Advanced Undergraduate Students

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