Investigation of sport supplements quality by Raman spectroscopy and principal component analysis

In this work, sport supplements were investigated by Raman spectroscopy. Samples were obtained from health foods shops, gyms and sports centers covering a wide range of available supplement powders. A systematic comparison of Raman spectra of the analyzed supplements allowed identifying the supplement type through the characteristic vibrational modes of carbohydrates and proteins. The protein supplements were identified by Raman bands at 1650, 1250 and 1004 cm⁻¹, while the spectral range between 1200 and 800 cm⁻¹ was useful to identify the carbohydrate supplements. Due to the diversity in composition of sport supplements, a chemometric tool such as principal component analysis (PCA) was employed to assist in the interpretation of Raman spectra, allowing also the identification of compounds present in sport supplements. Especially, the Raman scattering of aromatic and aliphatic amino acids residues contributes to the existence of bands characteristic for the different types of proteins. This kind of information is very important for the quality control of these products, for detecting the presence of fraud or a sample composition in disagreement with the label, thus ensuring the provenance of the supplements.     

Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400–2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.     

Identifying constituents in commercial gasoline using Fourier transform-infrared spectroscopy and independent component analysis

A new method is proposed that enables the identification of five refinery fractions present in commercial gasoline mixtures using infrared spectroscopic analysis. The data analysis and interpretation was carried out based on independent component analysis (ICA) and spectral similarity techniques. The FT-IR spectra of the gasoline constituents were determined using the ICA method, exclusively based on the spectra of their mixtures as a blind separation procedure, i.e. assuming unknown the spectra of the constituents. The identity of the constituents was subsequently determined using similarity measures commonly employed in spectra library searches against the spectra of the constituent components. The high correlation scores that were obtained in the identification of the constituents indicates that the developed method can be employed as a rapid and effective tool in quality control, fingerprinting or forensic applications, where gasoline constituents are suspected.     

A new regression method based on independent component analysis

Based on independent component analysis (ICA), a new regression method, independent component regression (ICR), was developed to build the model of NIR spectra and the routine components of plant samples. It is found that ICR and principal component regression (PCR) are completely equivalent when they are applied in quantitative prediction. However, independent components (ICs) can give more chemical explanation than principal components (PCs) because independence is a high-order statistic that is a much stronger condition than orthogonality. Three ICs are obtained by ICA from the NIR spectra of plant samples; it is found that they are strongly correlated to the NIR spectra of water, hydrocarbons and organonitrogen compounds, respectively. Therefore, ICA may be a promising tool to retrieve both quantitative and qualitative information from complex chemical data sets.     

Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares,principal component analysis,and related techniques

Quantitative analysis with laser-induced breakdown spectroscopy traditionally employs calibration curves that are complicated by chemical matrix effects. These chemical matrix effects influence the laser-induced breakdown spectroscopy plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, laser-induced breakdown spectroscopy calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis techniques are employed to analyze the laser-induced breakdown spectroscopy spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis and Soft Independent Modeling of Class Analogy are employed to generate a model and predict the rock type of the samples. These Multivariate Analysis techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.     

Benefits of applying combined diffuse reflectance FTIR spectroscopy and principal component analysis for the study of blue tempera historical painting

This paper explores the application of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) to the examination of historic blue pigments and blue tempera paintings commonly found on works of art. The discussion is mainly focused on the practical benefits of using this technique joined to principal component analysis (PCA), a powerful multivariate analysis tool. Thanks to the study of several replica samples that contain either pure blue pigments (azurite, lapis lazuli and smalt), or pure binder (rabbit glue) and mixtures of each of the pigments with the binder (tempera samples), different aspects of these benefits are highlighted. Comparative results of direct spectra and multivariate analysis using transmittance-Fourier transform infrared spectroscopy (T-FTIR) are discussed throughout this study. Results showed an excellent ability of PCA on DRIFT spectra for discriminating replica samples according to differing composition. Several IR regions were tested with this aim; the fingerprint IR region exhibited the best ability for successfully clustering the samples. The presence of the binder was also discriminated. Only using this approach it was possible to completely separate all the studied replica samples. This demonstrates the potential benefits of this approach in identifying historical pigments and binders for conservation and restoration purposes in the field of Cultural Heritage.     

Principal component analysis versus fuzzy principal component analysis A case study: the quality of danube water (1985-1996)

Principal component analysis (PCA) is a favorite tool in environmetrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approach to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of the outliers. In this paper we discuss and apply a robust fuzzy PCA algorithm (FPCA). The efficiency of the new algorithm is illustrated on a data set concerning the water quality of the Danube River for a period of 11 consecutive years. Considering, for example, a two component model, FPCA accounts for 91.7% of the total variance and PCA accounts only for 39.8%. Much more, PCA showed only a partial separation of the variables and no separation of scores (samples) onto the plane described by the first two principal components, whereas a much sharper differentiation of the variables and scores is observed when FPCA is applied.     

Multivariate concentration determination using principal component regression with residual analysis

Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method.     

Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis

Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes.     

Characterization of some new polysiloxane stationary phases by principal component analysis

Summary In this study, several new stationary phases were characterized by principal component analysis. Fourteen new stationary phases, including substituted phenyl and oligoethyleneoxide functionalities on polysiloxane polymers, were tested and compared to three well known stationary phases. The main features of these phases were studied using a series of test solutes of varying chemical characteristics representing the data set for principal component analysis. Two principal compounds were found to account for 99.20% of the variance (the first accounted for 94.96% and the second for 4.24%). The data were represented as a two-dimensional map for visual representation of the characteristics of these stationary phases. The first principal component represented a selectivity based on polarity (r²=0.998), while the second showed Lewis acid-base characteristics of the phases. Polarizable and amphoteric characteristics of these phases also became evident using this evaluation method.     

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39–14.14 mg cm⁻². The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R²), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20 s without sample destruction.     

Preliminary study of UV ageing process of proteinaceous paint binder by FT-IR and principal component analysis

This work presents a preliminary study on the ageing process of proteinaceous binder materials used in painting under UV light. With this aim, two sets of model samples were prepared: samples prepared using a single protein material and complex samples prepared in a similar way to the sequence of layers in a real painting from lowest to highest complexity (protein, drying oils, pigment and varnish). The study focuses on acquiring information about the possible degradation process of proteinaceous binders due to ageing and how this process be affected by the presence of characteristic non-proteinaceous painting materials, such as lipids from linseed oil, terpenic compounds from varnish and inorganic pigments. Samples simulated the accelerated ageing process, as did the UV light exposition. The FT-IR spectra were recorded after 100, 500, 1000 and 1500 h of exposition. The study of the accelerated ageing process was performed by means of principal component analysis (PCA) using the FT-IR spectra obtained. Loadings from the significant principal components were analysed to find the FT-IR frequency (cm⁻¹) involved in the degradation process. The study showed the lack of any relevant modification on the proteins in the single model samples. On the contrary, the complex model samples showed the ageing process. The accelerated ageing process can be explained by a principal component from PCA. The most affected IR region was 2900-3600 cm⁻¹, where the amide band was included.     

Simultaneous determination of iridoids, phenolic acids, flavonoids, and saponins in Flos Lonicerae and Flos Lonicerae Japonicae by HPLC-DAD-ELSD coupled with principal component analysis

A method, HPLC coupled with diode-array and evaporative light scattering detectors (HPLC-DAD-ELSD), was newly developed to evaluate the quality of Flos Lonicerae (FL) and Flos Lonicerae Japonicae (FLJ), through a simultaneous determination of multiple types of bioactive components. By employing DAD, the detection wavelengths were set at 240 nm for the determination of iridoids, 330 nm for phenolic acids, and 360 nm for flavonoids, respectively. While ELSD, connected in series after DAD, was applied to the determination of saponins. This assay was fully validated with respect to precision, repeatability, and accuracy. Moreover, principal component analysis (PCA) was used for the similarity evaluation of different samples, and it was proven straightforward and reliable to differentiate FL and FLJ samples from different origins. For PCA, two principal components have been extracted. Principal component 1 (PC1) influences the separation between different sample sets, capturing 54.598% variance, while principal component 2 (PC2) affects differentiation within sample sets, capturing 12.579% variance. In conclusion, simultaneous quantification of bioactive components by HPLC-DAD-ELSD coupled with PCA would be a well-acceptable strategy to differentiate the sources and to comprehensively control the quality of the medicinal plants FL and FLJ.     

Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram

Near infrared(NIR) spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis(HCA) performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.     

Study of spatial distribution for the active ingredient in ibuprofen tablet based on near-infrared micro-imaging technology

The NIR micro-images of ibuprofen tablets were collected in this research.Compare correlation imaging and principal component analysis(PC A) with histogram were applied to acquire the spatial distribution of ibuprofen granule.The result indicated that a similar distribution trend can be acquired by both of the two methods mentioned above;the information of PC2 results from ibuprofen mainly since the correlation coefficient between PC2 loading vector and the NIR spectrum of ibuprofen is 0.9930.The result of PCA indicated that the information of PC2 results from ibuprofen mainly for both the low and the high content of ibuprofen in the tablets.The correlation coefficient between the data of the two PC2 loading vectors of the low and the high content of ibuprofen in the tablets is 0.9998,which indicates that the result of PCA is stable and reliable.     

Gas chromatographic evaluation of wool wax alcohols supported by principal component analysis

Summary Capillary gas chromatography with flame ionization detection has been applied to the separation of the components of wool wax alcohols. Twenty-six commercial and non-commercial (laboratory) samples were investigated. Twenty-eight components found in the samples were used as variables for further characterization by a chemometric procedure. Principal component analysis was applied to the differentiation of samples from different sources and obtained by different technologies.     

偏最小二乘-近红外漫反射光谱法测定西米替丁药片

研究了应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对西米替丁片剂药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品。     

Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach

A novel strategy for building and maintaining calibration models has been developed for use when the future boundaries of the sample set are unknown or likely to change. Such a strategy could have an impact on the economics and time required to obtain and maintain a calibration model for routine analysis. The strategy is based on both principal component analysis (PCA) and partial least squares (PLS) multivariate techniques. The principal action of the strategy is to define how “similar” a new sample is to the samples currently defining the calibration dataset. This step is performed by residuals analysis, following PCA. If the new sample is considered to have a spectrum “similar” to previously available spectra, then the model is assumed able to predict the analyte concentration. Conversely, if the new sample is considered “dissimilar”, then there is new information in this sample, which is unknown to the calibration model and the new sample is added automatically to the calibration set in order to improve the model. The strategy has been applied to a real industrial dataset provided by BP Amoco Chemicals. The data consists of spectra of 102 sequential samples of a raw material. The strategy produced an accurate calibration model for both target components starting with only the first four samples, and required a further 17 reference measurements to maintain the model for the whole sampling sequence, which was over a 1-year period.     

Study of gas-liquid partitioning of alkane solutes in several organic solvents by using principal component analysis and linear solvation energy relationships

Principal component analysis (PCA) was used to extract the number of factors which can describe the 737 gas-liquid partition coefficients of five linear, four branched, and two cyclic alkanes in 67 common solvents. Based on the reconstruction of partition coefficient data matrix, we concluded that the experimental dataset could readily be reduced to two relevant factors. Using only these two factors, there were no errors larger than 3%, 7 cases had errors larger than 2%, and in 34 cases, errors were between 1 and 2%. n-Hexane and ethylcyclohexane were chosen as the test factors, and all other partition coefficients were expressed in terms of these two test factors. Prediction of the logarithmic partition coefficient of these alkanes in seven chemically different solvents, which were originally excluded from the data matrix, was excellent: the root mean square error was 0.064, only in 11 cases the errors were larger than 1%, and only 3 had errors larger than 4%.Linear solvation energy relationships (LSERs) using both theoretical and empirical solvent parameters were used to explain the molecular interactions responsible for partition. Several combinations of parameters were tried but the standard deviations were not less than 0.31. This could be attributed to the model itself, imprecisions in the data matrix or in some of the LSER parameters. Solvent cohesive parameters and surface tension in combination with polarity-polarizability or dispersion parameters perform the best.Finally, the two principal component factors were rotated onto the most relevant physicochemical parameters that control the gas-liquid partitioning phenomena.     

Characterization of nano FeIII-tannic acid modified polyacrylate in controlled-release coated urea by Fourier transform infrared photoacoustic spectroscopy and laser-induced breakdown spectroscopy

Polyacrylate polymer (PA) has been widely applied in coating products for decades. Recently, it has been used in controlled-release fertilizers. Nano Fe^III-tannic acid modified PA (PA-Fe) provides a better nutrient controlled release performance than conventional PA. In this work, a preliminary database of molecular and elemental information about the polymer was obtained using FTIR-PAS (Fourier transform infrared photoacoustic spectroscopy) and LIBS (laser-induced breakdown spectroscopy), respectively. The PA-Fe polymer contained more hydrophobic groups (–CH₃) and fewer hydrophilic groups (–COOR, –COOH) than PA. More elements were detected for PA-Fe than PA. LIBS was useful to identify and classify PA and PA-Fe samples using principal component analysis. The combination of spectroscopic results and a film formation process model explained the lower nutrient release rate of PA-Fe. These results showed the strong analytical capabilities of FTIR-PAS combined with LIBS for identifying and characterizing modified PA.