Orthogonal Projections To Latent Structures Model Research Articles

Towards objective measurements of habitual dietary intake patterns: comparing NMR metabolomics and food frequency questionnaire data in a population-based cohort

BackgroundLow-quality, non-diverse diet is a main risk factor for premature death. Accurate measurement of habitual diet is challenging and there is a need for validated objective methods. Blood metabolite patterns reflect direct or enzymatically diet-induced metabolites. Here, we aimed to evaluate associations between blood metabolite patterns and a priori and data-driven food intake patterns.Methods1, 895 participants in the Northern Sweden Health and Disease Study, a population-based prospective cohort study, were included. Fasting plasma samples were analyzed with 1H Nuclear Magnetic Resonance. Food intake data from a 64-item validated food frequency questionnaire were summarized into a priori Healthy Diet Score (HDS), relative Mediterranean Diet Score (rMDS) and a set of plant-based diet indices (PDI) as well as data driven clusters from latent class analyses (LCA). Orthogonal projections to latent structures (OPLS) were used to explore clustering patterns of metabolites and their relation to reported dietary intake patterns.ResultsAge, sex, body mass index, education and year of study participation had significant influence on OPLS metabolite models. OPLS models for healthful PDI and LCA-clusters were not significant, whereas for HDS, rMDS, PDI and unhealthful PDI significant models were obtained (CV-ANOVA p < 0.001). Still, model statistics were weak and the ability of the models to correctly classify participants into highest and lowest quartiles of rMDS, PDI and unhealthful PDI was poor (50%/78%, 42%/75% and 59%/70%, respectively).ConclusionAssociations between blood metabolite patterns and a priori as well as data-driven food intake patterns were poor. NMR metabolomics may not be sufficiently sensitive to small metabolites that distinguish between complex dietary intake patterns, like lipids.

Screening of bioactive components endowing hawthorn with turbidity-eliminating and lipid-lowering functions and development of quality control method of hawthorn

Hawthorn has the efficacy of eliminating turbidity and lowering the blood lipid level, and it is used for treating hyperlipidemia in clinic. However, the bioactive components of hawthorn are still unclear. In this study, the spectrum-effect relationship was employed to screen the bioactive components of hawthorn in the treatment of hyperlipidemia, and then the bioactive components screened out were verified in vivo. Furthermore, the quality control method for hawthorn was developed based on liquid chromatography-mass spectrometry(LC-MS). The hyperlipidemia model of rats was built, and different polar fractions of hawthorn extracts and their combinations were administrated by gavage. The effects of different hawthorn extract fractions on the total cholesterol(TC), triglycerides(TG), and low-density lipoprotein-cholesterol(LDL-C) in the serum of model rats were studied. The orthogonal projections to latent structures(OPLS) algorithm was used to establish the spectrum-effect relationship model between the 24 chemical components of hawthorn and the pharmacodynamic indexes, and the bioactive components were screened out and verified in vivo. Finally, 10 chemical components of hawthorn, including citric acid and quinic acid, were selected to establish the method for evaluating hawthorn quality based on LC-MS. The results showed that different polar fractions of hawthorn extracts and their combinations regulated the TG, TC, and LDL-C levels in the serum of the model rats. The bioactive components of hawthorn screened by the OPLS model were vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, rutin, citric acid, malic acid, and quinic acid. The 10 chemical components of hawthorn, i.e., citric acid, quinic acid, rutin, gallic acid, vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, malic acid, vanillic acid, neochlorogenic acid, and fumaric acid were determined, with the average content of 38, 11, 0.018, 0.009 5, 0.037, 0.017, 8.1, 0.009 5, 0.073, and 0.98 mg·g~(-1), respectively. This study provided a scientific basis for elucidating the material basis of hawthorn in treating hyperlipidemia and developed a content determination method for evaluating the quality of hawthorn.

Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.

Wide QRS-T angles are associated with markers of increased inflammatory activity independently of hypertension and diabetes.

BackgroundWide QRS‐T angles and inflammatory activity are markers of future cardiovascular events including sudden cardiac death (SCD). The association between wide QRS‐T angles and inflammatory activation is however not fully understood.Methods1,094 study participants of both sexes, 50–64 years old, were included from a randomly selected population‐based cohort as a part of the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot study. Serum samples were analyzed for markers of inflammation, cardiac wall stress/injury, and the metabolic syndrome. Wide QRS‐T angles were defined using Frank vectorcardiography. Variables were analyzed through unsupervised principal component analysis (PCA) as well as Orthogonal Projections to Latent Structures (OPLS) modeling. In addition, a subset of study participants was analyzed in a post hoc matched group design.ResultsWide QRS‐T angles correlated positively with markers of inflammation, cardiac wall stress/injury, the metabolic syndrome, and male sex in both PCA and OPLS models. In the matched post hoc analysis, participants with wide QRS‐T angles had significantly higher counts of white blood cells (WBC) and neutrophils in comparison with matched controls. WBC as well as the number of neutrophils, monocytes, basophils, eosinophils and levels of C‐reactive protein, IL‐1, IL‐4, IL‐6, TNF‐α, and NT‐pro‐BNP were also significantly higher in comparison with healthy controls.ConclusionsMarkers of inflammatory activation and cardiac injury/wall stress were significantly higher in the presence of wide QRS‐T angles. These results corroborate an association between abnormal electrophysiological function and inflammatory activation and may have implications for the prediction of SCD.

Determination of physicochemical properties of petroleum using 1H NMR spectroscopy combined with multivariate calibration

This paper proposes a methodology to characterize the following petroleum properties: UOP characterization factor (K), nitrogen content, solubility parameter, sulfur content, maximum and minimum pour point, and kinematic viscosity (at 20 °C, 30 °C, 40 °C and 50 °C), in petroleum within a single 1H NMR (proton nuclear magnetic resonance) spectra. Multivariate calibration tools, such as partial least squares (PLS), orthogonal projections to latent structures (OPLS) (with variable selection), interval PLS (iPLS), synergy interval PLS (siPLS) and model population analysis (MPA), were applied to 138 samples. The models were evaluated by coefficient of determination (R2), root mean square error of prediction (RMSEP), and residuals. In general, model population analysis had better results in the determination of petroleum's physicochemical properties than PLS and OPS. Thus, MPA provided models with better accuracy in the determination of the following properties: UOP characterization factor (0.06), nitrogen content (0.048 wt%), kinematic viscosity at 40 °C (relative error of 21.9%) and pour point (14.58 °C and 19.2 °C, respectively). The sulfur content was estimated with a 0.09 wt% mean error of prediction, using the PLS model, and the solubility parameter with a 1.06 (MPa)1/2 mean error of prediction, using the OPLS model.

Lipidomics Profiles are Related to Preterm Birth and Depressive Symptoms in Pregnant African American Women

African American women have a higher preterm birth rate (PTB, birth before 37 weeks) compared to non‐Hispanic white women. Exposure to chronic stressors has been related to higher rates of PTB (Latendresse et al. 2009 &amp; Misra et al. 2010). This project is a part of a larger research study to investigate the social and biological mechanisms responsible for higher PTB and other pregnancy complications in African American women. We hypothesized that the lipidomic profiles will be related to gestational age at birth and depressive symptoms among pregnant African American women. Thirty women were recruited at the prenatal clinic site in Metropolitan Detroit. Women completed the questionnaires and plasma samples were collected between 8 to 16 weeks of gestation. The lipidomic profiles were determined by “shotgun” Orbitrap high resolution/accurate mass spectrometry. Plasma triglycerides, glycerolipids, phospholipids, sphingolipids, sterols, fatty acids, and signaling lipids were measured. The data were analyzed by multivariate analysis, Principal Component Analysis (PCA), Orthogonal Projections to Latent Structures (OPLS), and OPLS Discriminant Analysis (OPLS‐DA) using SIMCA P+ software. PCA was used to identify the outliers and patterns in the data. OPLS DA score plot showed good separation and groupings of PTB and full‐term birth. The corresponding S‐plot, loading plot, and Variable Importance for the Projection (VIP) plot/list were used to identify the lipids responsible for the groupings. We identified a panel of lipid markers of PTB. Higher levels of triglycerides species TG (53:5), TG (53:4), TG (54:5), TG (54:6), TG (52:6) and TG (52:4) and lower levels of phospholipid species PC (37:1), PC (41:6) and PC (39:3) were associated with PTB. Plasma lipidomics profiles showed a good correlation with the Center for Epidemiologic Studies Depression Scale (CES‐D) scores in the OPLS model. There was a clear separation in the OPLS DA score plot between women with CES‐D scores ≥ 23 compared to women with CES‐D scores &lt; 23. Phosphatidylcholine PC (37:1) levels were also lower among women with CES‐D scores ≥ 23 compared to women with CES‐D scores &lt; 23 pointing toward a possible connection between depressive symptoms and PTB. We also investigated the differences in lipidomics profiles based on Body Mass Index (BMI). Although the overweight pregnant women group (pre‐pregnancy BMI &gt;25 kg/m2) showed higher levels of triglycerides, there was no clear overlap with the PTB model. Our data suggest the role of specific triglycerides and phosphatidylcholines in PTB. Further investigation of the association of PTB and depressive symptoms with lipidomics profiles and phospholipid metabolism in a larger sample of pregnant African American women may lead to the identification of non‐invasive lipid biomarkers of PTB and intervention strategies for these women.Support or Funding InformationR01 MD011575, Social Stressors and Inflammation: A Mixed Methods Approach to Preterm Birth. National Institute on Minority Health and Health DisparitiesThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Metabolomic and lipidomic assessment of the metabolic syndrome in Dutch middle-aged individuals reveals novel biological signatures separating health and disease

BackgroundWe aimed to identify novel metabolite and lipid signatures connected with the metabolic syndrome in a Dutch middle-aged population.Methods115 individuals with a metabolic syndrome score ranging from 0 to 5 [50 cases of the metabolic syndrome (score ≥ 3) and 65 controls] were enrolled from the Leiden Longevity Study, and LC/GC–MS metabolomics and lipidomics profiling were performed on fasting plasma samples. Data were analysed with principal component analysis and orthogonal projections to latent structures (OPLS) to study metabolite/lipid signatures associated with the metabolic syndrome. In addition, univariate analyses were done with linear regression, adjusted for age and sex, for the study of individual metabolites/lipids in relation to the metabolic syndrome.ResultsData was available on 103 metabolites and 223 lipids. In the OPLS model with metabolic syndrome score (Y-variable), 9 metabolites were negatively correlated and 26 metabolites (mostly acylcarnitines, amino acids and keto acids) were positively correlated with the metabolic syndrome score. In addition, a total of 100 lipids (mainly triacylglycerides) were positively correlated and 10 lipids from different lipid classes were negatively correlated with the metabolic syndrome score. In the univariate analyses, the metabolic syndrome (score) was associated with multiple individual metabolites (e.g., valeryl carnitine, pyruvic acid, lactic acid, alanine) and lipids [e.g., diglyceride(34:1), diglyceride(36:2)].ConclusionIn this first study on metabolomics/lipidomics of the metabolic syndrome, we identified multiple novel metabolite and lipid signatures, from different chemical classes, that were connected to the metabolic syndrome and are of interest to cardiometabolic disease biology.

A multi-omics approach reveals function of Secretory Carrier-Associated Membrane Proteins in wood formation of\u200b \u200b\u200bPopulus\u200b\u200b \u200btrees

BackgroundSecretory Carrier-Associated Membrane Proteins (SCAMPs) are highly conserved 32–38 kDa proteins that are involved in membrane trafficking. A systems approach was taken to elucidate function of SCAMPs in wood formation of Populus trees. Phenotypic and multi-omics analyses were performed in woody tissues of transgenic Populus trees carrying an RNAi construct for Populus tremula x tremuloides SCAMP3 (PttSCAMP3; Potri.019G104000).ResultsThe woody tissues of the transgenic trees displayed increased amounts of both polysaccharides and lignin oligomers, indicating increased deposition of both the carbohydrate and lignin components of the secondary cell walls. This coincided with a tendency towards increased wood density as well as significantly increased thickness of the suberized cork in the transgenic lines. Multivariate OnPLS (orthogonal projections to latent structures) modeling of five different omics datasets (the transcriptome, proteome, GC-MS metabolome, LC-MS metabolome and pyrolysis-GC/MS metabolome) collected from the secondary xylem tissues of the stem revealed systemic variation in the different variables in the transgenic lines, including changes that correlated with the changes in the secondary cell wall composition. The OnPLS model also identified a rather large number of proteins that were more abundant in the transgenic lines than in the wild type. Several of these were related to secretion and/or endocytosis as well as both primary and secondary cell wall biosynthesis.ConclusionsPopulus SCAMP proteins were shown to influence accumulation of secondary cell wall components, including polysaccharides and phenolic compounds, in the woody tissues of Populus tree stems. Our multi-omics analyses combined with the OnPLS modelling suggest that this function is mediated by changes in membrane trafficking to fine-tune the abundance of cell wall precursors and/or proteins involved in cell wall biosynthesis and transport. The data provides a multi-level source of information for future studies on the function of the SCAMP proteins in plant stem tissues.

Relationships between POPs, biometrics and circulating steroids in male polar bears (Ursus maritimus) from Svalbard

The aim of this study was to determine the effects of persistent organic pollutants (POPs) and biometric variables on circulating levels of steroid hormones (androgens, estrogens and progestagens) in male polar bears (Ursus maritimus) from Svalbard, Norway (n = 23). Levels of pregnenolone (PRE), progesterone (PRO), androstenedione (AN), dehydroepiandrosterone (DHEA), testosterone (TS), dihydrotestosterone (DHT), estrone (E1), 17α-estradiol (αE2) and 17β-estradiol (βE2) were quantified in polar bear serum by gas chromatography tandem mass spectrometry (GC-MS/MS), while POPs were measured in plasma. Subsequently, associations between hormone concentrations (9 steroids), POPs (21 polychlorinated biphenyls (PCBs), 8 OH-PCBs, 8 organochlorine pesticides (OCPs) and OCP metabolites, and 2 polybrominated diphenyl ethers (PBDEs)) and biological variables (age, head length, body mass, girth, body condition index), capture date, location (latitude and longitude), lipid content and cholesterol levels were examined using principal component analysis (PCA) and orthogonal projections to latent structures (OPLS) modelling. Average concentrations of androgens, estrogens and progestagens were in the range of 0.57–83.7 (0.57–12.4 for subadults, 1.02–83.7 for adults), 0.09–2.69 and 0.57–2.44 nmol/L, respectively. The steroid profiles suggest that sex steroids were mainly synthesized through the Δ-4 pathway in male polar bears. The ratio between androgens and estrogens significantly depended on sexual maturity with androgen/estrogen ratios being approximately 60 times higher in adult males than in subadult males. PCA plots and OPLS models indicated that TS was positively related to biometrics, such as body condition index in male polar bears. A negative relationship was also observed between POPs and DHT. Consequently, POPs and body condition may potentially affect the endocrinological function of steroids, including development of reproductive tissues and sex organs and the general condition of male polar bears.

Evaluation of calibration transfer methods using the ATR-FTIR technique to predict density of crude oil

Multivariate calibration combined with infrared technique is an alternative to traditional methods of determination of physicochemical parameters in crude oil. However, a multivariate model can only be applied for the instrument in which the spectra were measured. In case of equipment upkeep or change of instrument, transferring the calibration model is necessary for the new instrumental condition or new instrument. In this study, Fourier transform infrared spectra (FTIR) were measured in the mid-infrared region (MIR) in two different instruments for 96 crude oil samples with API gravity ranging from 11.2 to 54.0. Multivariate calibration models by PLS (Partial Least Squares) and OPLS (Orthogonal Projections to Latent Structures) were developed and forefront techniques in the calibration transfer area were tested, namely SBC (Slope and Bias Correction), DS (Direct Standardization) and PDS (Piecewise Direct Standardization). The OPLS method stands out for not requiring transfer samples, although it compromises the accuracy of prediction. Applying spectra transferred by PDS to the OPLS model resulted in accuracy statistically equal to that of the PLS original model.

SENYAWA INHIBITOR α-GLUKOSIDASE DAN ANTIOKSIDAN DARI KUMIS KUCING DENGAN PENDEKATAN METABOLOMIK BERBASIS FTIR

Plant is well known as an excellent source for bioactive compounds. Metabolomics was reported as a potential tool to accelerate plant acitive compounds identification. In this research, FTIR-based metabolomics method was used to identify active compounds with α-glucosidase inhibitory and antioxidant activity in aerial parts of Orthosiphon stamineus (OS) extract and its fractions. Chemical profile of OS methanolic extracts and hexane, chloroform, butanol, and water fractions were analyzed using infrared spectroscopy. OS extracts and fractions showed inhibitory activity against α-glucosidase enzymes with IC50 value 154.07±30.60-465.83±85.34 µg/mL and antioxidant activity with IC50 value 7.41±0.02-19.35±0.09 µg/mL. Butanol fraction was the fraction with the highest α-glucosidase inhibitory activity and moderate antioxidant activity with IC50 value between 154.07±30.60 µg/mL and 10.84±0.54 µg/mL, respectively. The correlation between the biological activity and chemical composition data were analyzed using Orthogonal Projections to Latent Structures (OPLS). Based on the VIP (variable influence on projection), the coefficient value of the respective OPLS models, and IR database of compounds previously identified in OS, it was suggested that methoxy flavonoid (sinensitin and 5,6,7,3’-tetramethoxy-4’-hydroxy-8-C-preny-lflavone), diterpenes (orthosiphols, orthoarisins, neoorthosiphols, staminols, and staminolactones) and triterpenes (ursolic acid, oleanolic acid, betulinic acid, hydroxybetulinic acid, maslinic acid, α-amyrin and β-amyrin) were identified as responsible compounds for the α-glucosidase inhibitory activity. Meanwhile phenolic (rosmarinic acid), methoxy flavonoid (eupatorin, sinensetin, 5-hydroxy-6,7,3’,4’-tetramethoxyflavone, salvigenin, 6-hydroxy-5,7,3’-trimethoxyflavone and 5,6,7,3’-tetramethoxy-4’-hydroxy-8-C-prenylflavone), diterpenes (orthosiphols, orthoarisins, neoortho-siphols, staminols, and staminolactones) and triterpenes (ursolic acid, oleanolic acid, betulinic acid, hydroxybetulinic acid, maslinic acid, α-amyrin and β-amyrin) were identified as responsible compounds for the antioxidant activity.

Antioxidant activities of Pistacia atlantica extracts modeled as a function of chromatographic fingerprints in order to identify antioxidant markers

Pistacia atlantica belongs to the Anacardiaceae family, and originated from the Atlas mountains of north Africa, which has been employed in traditional medicine for the treatment of various diseases. There are many limitations regarding their activities. The bioactive compounds present in the extract of P. atlantica leaves are not well defined. For these identifications HPLC fingerprint methodology can be used. In the present study, 28 extracts were obtained from male and female leaves of P. atlantica, collected monthly during 6months, in two different growing regions. The total antioxidant capacity (TAC) values of P. atlantica extracts were determined using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical method and the potassium ferricyanide (PFC) reduction method. The fingerprints were pre-treated using several techniques, such as column centering; normalization followed by column centering, and standard normal variate (SNV) transformation followed by column centering; each after initial alignment of the chromatographic profiles with Correlation Optimized Warping (COW). The multivariate calibration methods, Partial Least Squares (PLS) and Orthogonal Projections to Latent Structures (O-PLS), were employed to model TAC. The regression coefficients of the best models were evaluated to indicate the peaks probably responsible for the antioxidant activity. The O-PLS model seems preferable because of its better predictive and describing abilities, and its good interpretability of the contribution of compound peaks to the TAC. Several peaks in the chromatograms were recognized as importantly contributing to the model due to their large regression coefficients. The structural elucidation of the relevant peaks was achieved by negative ionization LC–QTOF–MS. The phenolic compounds galloylquinic acid, quinic acid and gallic acid mainly seemed to be responsible for scavenging the DPPH radical, while glucogallin, trigalloylglucose, gallic acid and tetragalloylquinic acid were considered specifically important for the prediction of TAC determined by the PFC method.

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

Abstract For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R 2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.

Metabolomics study of fatigue in patients with rheumatoid arthritis naïve to biological treatment.

Fatigue occurs in all chronic inflammatory diseases, in cancer, and in some neurological conditions. Patients often regard fatigue as one of their most debilitating problems, but currently there is no established treatment and the mechanisms that lead to and regulate fatigue are incompletely understood. Our objective was to more completely understand the physiology of this phenomenon. Twenty-four patients with rheumatoid arthritis (RA) naïve to treatment with biological drugs were enrolled for the study. Fatigue was measured with a fatigue visual analogue scale (fVAS). Ethylenediaminetetraacetic acid (EDTA) plasma samples were subjected to gas chromatography-time-of-flight mass spectrometry (GC/MS-TOF)-based metabolite profiling. Obtained metabolite data were evaluated by multivariate data analysis with orthogonal projections to latent structures (OPLS) method to pinpoint metabolic changes related to fatigue severity. A significant multivariate OPLS model was obtained between the fVAS scores and the measured metabolic levels. Increasing fatigue scores were associated with a metabolic pattern characterized by down-regulation of metabolites from the urea cycle, fatty acids, tocopherols, aromatic amino acids, and hypoxanthine. Uric acid levels were increased. Apart from fatigue, we found no other disease-related variables that might be responsible for these changes. Our MS-based metabolomic approach demonstrated strong associations between fatigue and several biochemical patterns related to oxidative stress.

Coronary Artery Calcification Is Related to Inflammation in Rheumatoid Arthritis: A Long-Term Follow-Up Study.

Objective. A long-term follow-up of patients with rheumatoid arthritis (RA) to evaluate factors related to coronary artery calcification (CAC). Methods. All 22 eligible patients (4 males/18 females, mean age 65 years, and RA-duration 30–36 years) from the original (baseline; n = 39) study of atherosclerosis were included. Inflammation, cardiovascular risk factors, and biomarkers were measured at baseline. At follow-up 13 years later, CAC was assessed by computed tomography (CT) and the grade of inflammation was measured. Multivariate analysis of differences between patients with low (0–10) and high CAC (>10) was done by orthogonal projection to latent structures (OPLS). Results. Ten patients had CAC 0–10 and 12 had >10 (range 18–1700). Patients with high CAC had significantly higher ESR (24.3 versus 9.9 mm/h) and swollen joint count (2 versus 0). The OPLS models discriminated between patients having high or low CAC. With only baseline variables, the sensitivity was 73% and the specificity 82%. The model that also included inflammatory variables from follow-up had a sensitivity of 89% and a specificity of 85%. Exclusion of baseline intima media thickness and plaque from the latter model modestly reduced the accuracy (sensitivity 80% and specificity 83%). Conclusions. CAC is related to inflammation in patients with RA.

Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis

Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.

SAT0122 Prediction of Coronary Artery Calcification and Association with Inflammation in Rheumatoid Arthritis: A Follow-Up Study

BackgroundWe previously reported increased prevalence of atherosclerosis in patients with RA (1).ObjectivesTo evaluate (i) the relation between inflammatory markers, measures of atherosclerosis and potential biomarkers of atherosclerosis at baseline and...

OPLS methods for the analysis of hyperspectral images—comparison with MCR‐ALS

Two new, orthogonal projections to latent structures (OPLS) based methods were proposed to analyze hyperspectral images, enabling the visualization of multiple chemical compounds in one matrix without the need of extensive preprocessing. Both proposed methods delivered images representing the chemical distribution in the ribbon similar to the more traditional multivariate curve resolution–alternating least squares (MCR‐ALS) method, but their image background was less dynamic resulting in a stronger chemical contrast. This indicated that the methods successfully removed structured variation orthogonal to the chemical information (pure spectra of individual compounds), which was confirmed by the fact that physical scattering effects caused by grooves and edges were captured in the images visualizing the orthogonal components of the model. Hereby, the OPLS‐based method employing the pure spectra as weights in the OPLS algorithm was more successful in distinguishing compounds with a similar spectral signal than the transposed OPLS algorithm (pure spectra of individual compounds were used as response in OPLS model). It should be noted that for the main compounds, the MCR‐ALS method enabled easier visual interpretation compared to the OPLS‐based methods by setting all values below zero to zero, resulting in a higher contrast between pixels containing the studied compound and pixels not containing that compound. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Towards a complete identification of orthogonal variation in multiple regression from a PLS1 modeling point of view: including OPLS by a change of orthogonal basis

It is well known that the predictions of the single response orthogonal projections to latent structures (OPLS) and the single response partial least squares regression (PLS1) regression are identical in the single‐response case. The present paper presents an approach to identification of the complete y‐orthogonal structure by starting from the viewpoint of standard PLS1 regression. Three alternative non‐deflating OPLS algorithms and a modified principal component analysis (PCA)‐driven method (including MATLAB code) is presented. The first algorithm implements a postprocessing routine of the standard PLS1 solution where QR factorization applied to a shifted version of the non‐orthogonal scores is the key to express the OPLS solution. The second algorithm finds the OPLS model directly by an iterative procedure. By a rigorous mathematical argument, we explain that orthogonal filtering is a ‘built‐in’ property of the traditional PLS1 regression coefficients. Consequently, the capabilities of OPLS with respect to improving the predictions (also for new samples) compared with PLS1 are non‐existing. The PCA‐driven method is based on the fact that truncating off one dimension from the row subspace of X results in a matrix Xorth with y‐orthogonal columns and a rank of one less than the rank of X. The desired truncation corresponds exactly to the first X deflation step of Martens non‐orthogonal PLS algorithm. The significant y‐orthogonal structure of X found by PCA of Xorth is split into two fundamental parts: one part that is significantly contributing to correct the first PLS score toward y and one part that is not. The third and final OPLS algorithm presented is a modification of Martens non‐orthogonal algorithm into an efficient dual PLS1–OPLS algorithm. Copyright © 2014 John Wiley &amp; Sons, Ltd.

A pilot study of NMR-based sensory prediction of roasted coffee bean extracts

Nuclear magnetic resonance (NMR) spectroscopy can be considered a kind of “magnetic tongue” for the characterisation and prediction of the tastes of foods, since it provides a wealth of information in a nondestructive and nontargeted manner. In the present study, the chemical substances in roasted coffee bean extracts that could distinguish and predict the different sensations of coffee taste were identified by the combination of NMR-based metabolomics and human sensory test and the application of the multivariate projection method of orthogonal projection to latent structures (OPLS). In addition, the tastes of commercial coffee beans were successfully predicted based on their NMR metabolite profiles using our OPLS model, suggesting that NMR-based metabolomics accompanied with multiple statistical models is convenient, fast and accurate for the sensory evaluation of coffee.