Two-dimensional correlation localized surface plasmon resonance spectroscopy for analysis of the interaction between metal nanoparticles and bovine serum albumin

Time-resolved extinction spectra assisted with two-dimensional correlation spectroscopy (2DCOS) analysis and principal component analysis (PCA) were employed to investigate the interaction between bovine serum albumin (BSA) and metal nanoparticles (NPs). A series of localized surface plasmon resonance (LSPR) spectra of metal NPs were measured just after a small amount of BSA was added into metal colloids. Through 2DCOS analysis, remarkable changes in the intensities of the LSPR were observed. The interaction process was totally divided into three periods according to the PCA. Transmission electron microscopy, dynamic light scattering, and ζ-potential measurements were also employed to characterize the interaction between BSA and metal NPs. The addition of BSA brings silver NPs to aggregate through the electrostatic interaction between them, but it has less effect on gold NPs. In a gold and silver mixed system, gold NPs can affect the interaction of silver NPs and BSA, leading it to weaken. The combination of 2DCOS analysis and LSPR spectroscopy is powerful for exploring the LSPR spectra of the metal NP involved systems. This combined technique holds great potential in LSPR sensing through analysis of slight, slim spectral changes of metal colloids

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Nonnegative blind source separation by sparse component analysis based on determinant measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Rapid discrimination and determination of polyunsaturated fatty acid composition in marine oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n - 3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R 2≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs. © 2014 Springer Science+Business Media New York.

Synchrotron-FTIR microspectroscopy enables the distinction of lipid accumulation in thraustochytrid strains through analysis of individual live cells

The superior characteristics of high photon flux and diffraction-limited spatial resolution achieved by synchrotron-FTIR microspectroscopy allowed molecular characterization of individual live thraustochytrids. Principal component analysis revealed distinct separation of the single live cell spectra into their corresponding strains, comprised of new Australasian thraustochytrids (AMCQS5-5 and S7) and standard cultures (AH-2 and S31). Unsupervised hierarchical cluster analysis (UHCA) indicated close similarities between S7 and AH-7 strains, with AMCQS5-5 being distinctly different. UHCA correlation conformed well to the fatty acid profiles, indicating the type of fatty acids as a critical factor in chemotaxonomic discrimination of these thraustochytrids and also revealing the distinctively high polyunsaturated fatty acid content as key identity of AMCQS5-5. Partial least squares discriminant analysis using cross-validation approach between two replicate datasets was demonstrated to be a powerful classification method leading to models of high robustness and 100% predictive accuracy for strain identification. The results emphasized the exceptional S-FTIR capability to perform real-time in vivo measurement of single live cells directly within their original medium, providing unique information on cell variability among the population of each isolate and evidence of spontaneous lipid peroxidation that could lead to deeper understanding of lipid production and oxidation in thraustochytrids for single-cell oil development.

Linking the serotonin transporter gene, family environments, hippocampal volume and depression onset: a prospective imaging gene × environment analysis

A single imaging gene-environment (IGxE) framework that is able to simultaneously model genetic, neurobiological, and environmental influences on psychopathology outcomes is needed to improve understanding of how complex interrelationships between allelic variation, differences in neuroanatomy or neuroactivity, and environmental experience affect risk for psychiatric disorder. In a longitudinal study of adolescent development we demonstrate the utility of such an IGxE framework by testing whether variation in parental behavior at age 12 altered the strength of an imaging genetics pathway, involving an indirect association between allelic variation in the serotonin transporter gene to variation in hippocampal volume and consequent onset of major depressive disorder by age 18. Results were consistent with the presence of an indirect effect of the serotonin transporter S-allele on depression onset via smaller left and right hippocampal volumes that was significant only in family environments involving either higher levels of parental aggression or lower levels of positive parenting. The previously reported finding of S-allele carriers' increased risk of depression in adverse environments may, therefore, be partly because of the effects of these environments on a neurobiological pathway from the serotonin transporter gene to depression onset that proceeds through variation in hippocampal volume. 

A new process capability index for multiple quality characteristics based on principal components

This paper presents a new multivariate process capability index (MPCI) which is based on the principal component analysis (PCA) and is dependent on a parameter (Formula presented.) which can take on any real number. This MPCI generalises some existing multivariate indices based on PCA proposed by several authors when (Formula presented.) or (Formula presented.). One of the key contributions of this paper is to show that there is a direct correspondence between this MPCI and process yield for a unique value of (Formula presented.). This result is used to establish a relationship between the capability status of the process and to show that under some mild conditions, the estimators of this MPCI is consistent and converge to a normal distribution. This is then applied to perform tests of statistical hypotheses and in determining sample sizes. Several numerical examples are presented with the objective of illustrating the procedures and demonstrating how they can be applied to determine the viability and capacity of different manufacturing processes.

Role of dietary pattern analysis in determining cognitive status in elderly Australian adults

Principal Component Analysis (PCA) was used to determine the association between dietary patterns and cognitive function and to examine how classification systems based on food groups and food items affect levels of association between diet and cognitive function. The present study focuses on the older segment of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) sample (age 60+) that completed the food frequency questionnaire at Wave 1 (1999/2000) and the mini-mental state examination and tests of memory, verbal ability and processing speed at Wave 3 (2012). Three methods were used in order to classify these foods before applying PCA. In the first instance, the 101 individual food items asked about in the questionnaire were used (no categorisation). In the second and third instances, foods were combined and reduced to 32 and 20 food groups, respectively, based on nutrient content and culinary usage—a method employed in several other published studies for PCA. Logistic regression analysis and generalized linear modelling was used to analyse the relationship between PCA-derived dietary patterns and cognitive outcome. Broader food group classifications resulted in a greater proportion of food use variance in the sample being explained (use of 101 individual foods explained 23.22% of total food use, while use of 32 and 20 food groups explained 29.74% and 30.74% of total variance in food use in the sample, respectively). Three dietary patterns were found to be associated with decreased odds of cognitive impairment (CI). Dietary patterns derived from 101 individual food items showed that for every one unit increase in ((Fruit and Vegetable Pattern: p = 0.030, OR 1.061, confidence interval: 1.006–1.118); (Fish, Legumes and Vegetable Pattern: p = 0.040, OR 1.032, confidence interval: 1.001–1.064); (Dairy, Cereal and Eggs Pattern: p = 0.003, OR 1.020, confidence interval: 1.007–1.033)), the odds of cognitive impairment decreased. Different results were observed when the effect of dietary patterns on memory, processing speed and vocabulary were examined. Complex patterns of associations between dietary factors and cognition were evident, with the most consistent finding being the protective effects of high vegetable and plant-based food item consumption and negative effects of ‘Western’ patterns on cognition. Further long-term studies and investigation of the best methods for dietary measurement are needed to better understand diet-disease relationships in this age group.

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography–mass spectrometry (GC–MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC–MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ingredient classification according to the digestible amino acid profile: an exploratory analysis

This study aimed: 1) to classify ingredients according to the digestible amino acid (AA) profile; 2) to determine ingredients with AA profile closer to the ideal for broiler chickens; and 3) to compare digestible AA profiles from simulated diets with the ideal protein profile. The digestible AA levels of 30 ingredients were compiled from the literature and presented as percentages of lysine according to the ideal protein concept. Cluster and principal component analyses (exploratory analyses) were used to compose and describe groups of ingredients according to AA profiles. Four ingredient groups were identified by cluster analysis, and the classification of the ingredients within each of these groups was obtained from a principal component analysis, showing 11 classes of ingredients with similar digestible AA profiles. The ingredients with AA profiles closer to the ideal protein were meat and bone meal 45, fish meal 60 and wheat germ meal, all of them constituting Class 1; the ingredients from the other classes gradually diverged from the ideal protein. Soybean meal, which is the main protein source for poultry, showed good AA balance since it was included in Class 3. on the contrary, corn, which is the main energy source in poultry diets, was classified in Class 8. Dietary AA profiles were improved when corn and/or soybean meal were partially or totally replaced in the simulations by ingredients with better AA balance.