Quantitative XRD Analysis by Partial Least Squares Application in a Commercial Product

1991 ◽  
Vol 35 (A) ◽  
pp. 117-126
Author(s):  
Arthur G. Mateos
Keyword(s):  
Data Compression ◽  
Least Squares ◽  
Diffraction Pattern ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Xrd Analysis ◽  
Commercial Product ◽  
Calibration Model ◽  
New Approach ◽  
Data Points

AbstractA new approach to quantitative XRD by Partial Least Squares (PLS) used region(s) or the entirety of the diffraction pattern of calibration standards (also called a training set) in the model. The basic concept of this approach states that the information in many observed variables, expressed as matrix I = (i1, i2 … , ik,) is concentrated onto a few underlying latent variables, called factors, by the process of data compression. In XRD, the data points of the diffraction pattern are compressed to few factors T, computed according to their ability to explain the variation in the diffraction pattern or matrix I. The procedure incorporates into the model that part of I that is correlated to C concentrations. Data compression preserves the redundancy between variables due to collinearity and stabilizies the predictions against noise in I. The resulting calibration model allows for detection of outliers. Another important effect of data reduction is the ability to analyze muticomponent systems even when lines of the components are overlapped, Examples of quantitative analysis by PLS are demonstrated in the analysis of a commercial product.

Smart shopping: the adoption of grocery shopping apps

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lee-Andra Bruwer ◽  
Nkosivile Welcome Madinga ◽  
Nqobile Bundwini
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Moderating Effect ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Grocery Shopping ◽  
Intention To Use ◽  
Content Type

PurposeThe purpose of this paper is to determine the key factors influencing the adoption of grocery shopping and to examine the moderating effect of education between antecedents of the adoption of grocery shopping apps and user attitude and intention to purchase.Design/methodology/approachThis study adopted partial least squares structural equation modeling (PLS-SEM) to evaluate the relationship between the latent variables: perceived usefulness, perceived ease of use, attitude and intention to use grocery shopping apps. Partial least squares multigroup analysis (PLS-MGA) was used to examine the moderating effect of education. A total of 305 grocery shopping apps users were surveyed using a structural questionnaire.FindingsThe results indicated that all the factors considered in the framework were significant in predicting the intention to use the grocery shopping apps. The findings show that education has no significant impact on any relationship.Practical implicationsA better understanding of the factors that affect the acceptance of mobile grocery shopping apps is important for developing better strategic management plans.Originality/valueThis is one of the first studies to research the adoption of grocery shopping apps in a developing country, as well as the first to focus on consumers in South Africa.

scholarly journals Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection

2019 ◽  
Vol 5 (1) ◽  
pp. 10 ◽  
Author(s):  
Ahmed Rady ◽  
Daniel Guyer ◽  
William Kirk ◽  
Irwin R Donis-González
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Prediction Models ◽  
Calibration Model ◽  
Data Sets ◽  
Least Squares Regression ◽  
Potato Tubers ◽  
Russet Norkotah ◽  
Systems Models

The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the prediction of primordial leaf count as a significant sign for tubers sprouting. Electronic and lab measurements were conducted on whole tubers of Frito Lay 1879 (FL1879) and Russet Norkotah (R.Norkotah) potato cultivars. The interval partial least squares (IPLS) technique was adopted to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR), and the best calibration model was chosen using four-fold cross-validation. Then the prediction models were obtained using separate test data sets. Prediction results were enhanced compared with those obtained from individual systems’ models. The values of the correlation coefficient (the ratio between performance to deviation, or r(RPD)) were 0.95(3.01) and 0.9s6(3.55) for FL1879 and R.Norkotah, respectively, which represented a feasible improvement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R.Norkotah, respectively. The proposed study shows the possibility of building a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers.

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

Noninvasive Alcohol Detection Using Near-Infrared Spectroscopy Based on Partial Least Squares and Monte-Carlo

2012 ◽  
Vol 229-231 ◽  
pp. 1308-1311
Author(s):  
Si Te Luo ◽  
Guo Qiang Chen ◽  
Ruo Fei Cui ◽  
Wei Wei Zhou ◽  
Li Qian Lu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Blood Alcohol Concentration ◽  
Test Method ◽  
Calibration Model ◽  
Good Prediction ◽  
Good Prediction Accuracy

The objective of this study was to assess the feasibility of noninvasive alcohol testing in vivo with near-infrared (NIR) spectroscopy. The suitable distance between light source and detector was determined by Monte-Carlo simulation. The NIR spectra signals of alcohol in vitro and in vivo were measured, and the blood alcohol concentration (BAC) was measured with breath test method. Wavelet de-noising and partial least squares (PLS) method were used to establish the quantitative calibration model of alcohol. The results indicate that alcohol spectra had two absorption peaks at range of 2200nm～2400nm. The optimal principal component number of PLS model is 3, RMSEP=9.29, MREP=3.5%，R=0.9802. The model has good prediction accuracy. NIRS might provide a new method to the measurement of alcohol in vivo.

Detection of the Contents of the Ingredient of Food by Using the NIR Spectroscopy and the Backward Interval Partial Least-Squares

2013 ◽  
Vol 726-731 ◽  
pp. 4337-4341
Author(s):  
Yong Fu Liu ◽  
Xi Chen ◽  
Bin Zheng ◽  
Ze Yu Xu ◽  
Guo Tian He
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Speed ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Set Partitioning ◽  
New Approach ◽  
Orthogonal Signal Correction ◽  
The Relationship ◽  
Sample Set

Near-infrared spectroscopy (NIRS), with the characteristics of high speed, non-destructiveness, high precision and reliable detection data etc., is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for the discrimination of the ingredients of corn (moisture, oil, protein, starch) by means of NIR spectroscopy (1100-2498 nm) was developed in this work. The relationship between the reflectance spectra and the ingredients of corn was established. The data were spilt into training and testing subsets by sample set partitioning based on join x-y distance (SPXY),the spectral data was compressed by orthogonal signal correction (OSC), wavelength was selected by backward interval partial least-squares (biPLS),the 60 samples to build PLS mode, the model was used to predict the varieties of 20 unknown samples. The standard error of prediction (SEP) was 0.173; the relative error of prediction (PRE) was 0.55%; the correlation coefficient (R) was 0.98. The way to detect the ingredient of food is simply, reliable.

scholarly journals An Improvised SIMPLS Estimator Based on MRCD-PCA Weighting Function and Its Application to Real Data

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Siti Zahariah ◽  
Habshah Midi ◽  
Mohd Shafie Mustafa
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Weighting Function ◽  
Real Data ◽  
Least Squares Regression ◽  
Regression Problem ◽  
Leverage Points ◽  
Explanatory Variables ◽  
Diagnostic Plot ◽  
Data Points

Multicollinearity often occurs when two or more predictor variables are correlated, especially for high dimensional data (HDD) where p>>n. The statistically inspired modification of the partial least squares (SIMPLS) is a very popular technique for solving a partial least squares regression problem due to its efficiency, speed, and ease of understanding. The execution of SIMPLS is based on the empirical covariance matrix of explanatory variables and response variables. Nevertheless, SIMPLS is very easily affected by outliers. In order to rectify this problem, a robust iteratively reweighted SIMPLS (RWSIMPLS) is introduced. Nonetheless, it is still not very efficient as the algorithm of RWSIMPLS is based on a weighting function that does not specify any method of identification of high leverage points (HLPs), i.e., outlying observations in the X-direction. HLPs have the most detrimental effect on the computed values of various estimates, which results in misleading conclusions about the fitted regression model. Hence, their effects need to be reduced by assigning smaller weights to them. As a solution to this problem, we propose an improvised SIMPLS based on a new weight function obtained from the MRCD-PCA diagnostic method of the identification of HLPs for HDD and name this method MRCD-PCA-RWSIMPLS. A new MRCD-PCA-RWSIMPLS diagnostic plot is also established for classifying observations into four data points, i.e., regular observations, vertical outliers, and good and bad leverage points. The numerical examples and Monte Carlo simulations signify that MRCD-PCA-RWSIMPLS offers substantial improvements over SIMPLS and RWSIMPLS. The proposed diagnostic plot is able to classify observations into correct groups. On the contrary, SIMPLS and RWSIMPLS plots fail to correctly classify observations into correct groups and show masking and swamping effects.

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