Evaluation of the effect of data pre-treatment procedures on classical pattern recognition and principal components analysis: a case study for the geographical classification of tea

Abstract

A simple transformation that uses the half-range and central value has been used as a data pre-treatment procedure for principal component analysis (PCA) and pattern recognition techniques. The results obtained have been compared with the results from classical normalisation of data (mean normalisation, maximum normalisation and range normalisation), autoscaling and the minimum-maximum transformation. Three data sets were used in the study. The first was formed by determining 17 elements in 53 tea samples (901 pieces of data). The second and third data sets arose from two long-term drift studies performed to examine instrumental stability at standard and robust conditions. The instruments used were an inductively coupled plasma atomic emission spectrometer and an inductively coupled plasma mass spectrometer. Each drift diagnosis experiment consisted of replicate determinations of a test solution containing 15 analytes at 10 mg l-1 over 8 h without recalibration. Twenty-nine emission lines were determined 99 times, thus, each data set was formed by 2881 pieces of data. Data pre-treatment was applied to the three data sets prior to the use of principal component analysis, cluster analysis, linear discrimination analysis and soft independent modelling of class analogy. The study revealed that the half-range and central value transformation resulted in a better classification of the tea samples than that achieved using the classical normalisation. The loadings in the PCA for the long-term stability study, under both standard and robust conditions, were found to be similar to the drift trends only when the minimum-maximum transformation and the mean or maximum normalizations were used as data pre-treatments.

Future of Liquid Chromatography–Mass Spectrometry in Metabolic Profiling and Metabolomic Studies for Biomarker Discovery

“…However, investigators have also applied high performance liquid chromatography (HPLC) coupled with UV detection [49], pyrolysis-mass spectrometry (MS) [50,51], and inductively-coupled plasma (ICP) atomic emission and ICP-MS [52] among other techniques. Current metabolomics and metabolic profiling studies rely almost exclusively on 1 H NMR, GC-MS and LC-MS due to the technological maturity of the corresponding instrumentation, the recent independent advancements made in all three fields, and the realization by the instrumentation industry that these three approaches offer the most potential for successful results.…”

Water Quality Modelling Using Multivariate Statistical Analysis and Remote Sensing in South Florida

“…Due to differences in units of the studied water quality parameters in the dataset, pre-treatment of data is required. Although studies done by Moreda-Piñeiro et al [29] and Reid and…”

“…Many researchers have used the visible the solar spectrum from blue to near infrared region in their studies to find significant correlations between reflection from water surface and physical and biogeochemical constituents, such as turbidity, chlorophyll concentration, and the organic matters and suspended sediments in different waterbodies [9,10,18,[22][23][24][25][26][27][28][29][30]. Although the capabilities of remote sensing to assess water quality has been proved by many studies, the application of this technique alone without using the traditional samplings and field observations is not sufficiently precise [29]. In other words, to obtain a better insight, an integrated use of remote sensing, in-situ measurements and computer water quality modelling may lead to an increased knowledge of the water quality of water systems.…”

“…Curran, et al [26] and Novo, et al [27] showed that single band algorithms can be applied where TSM increases coincides with the reflectance values increase. However, the complex substances in water change the reflectance of the water body and hence, cause variation in colors, and thus, different spectral bands can be used for TSS retrievals [25,28,29]. Therefore, the advantage of using signal band or band ratios can be employed to obtain more accurate results in different concentrations in waterbodies.…”

“…The collection of remotely-sensed data occurs in digital form and therefore can be easily used for computer processing. Since the 1970s, remote sensing application to monitor and evaluate the water quality status of various waterbodies has tremendously increased [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”