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Fulltext Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis

Shabri A, Samsudin R

ScientificWorldJournal, 2014;2014:854520.
PMID: 24895666 DOI: 10.1155/2014/854520

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Matched MeSH terms: Linear Models*
Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.

J Integr Neurosci, 2017;16(3):275-289.
PMID: 28891512 DOI: 10.3233/JIN-170016

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Matched MeSH terms: Linear Models
Deep learning for healthcare applications based on physiological signals: A review

Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR

Comput Methods Programs Biomed, 2018 Jul;161:1-13.
PMID: 29852952 DOI: 10.1016/j.cmpb.2018.04.005

BACKGROUND AND OBJECTIVE: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.
METHODS: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.
RESULTS: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.
CONCLUSIONS: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

Matched MeSH terms: Linear Models
Demography and the canonical ensemble

Smith JD

Math Biosci, 1998 Nov;153(2):151-61.
PMID: 9825637

The Gibbs canonical ensemble of statistical mechanics is used to describe the probability distribution of the age classes of mothers of new-borns in an age-structured population. The Malthusian parameter emerges as a Lagrange multiplier corresponding to a generation time constraint, while a new perturbation parameter appears as the Lagrange multiplier corresponding to a maternity constraint. Classical Lotka stability reduces to the unperturbed case of the more general canonical ensemble model. The model is used in a case study of the female (peninsular) Malaysian population of 1970. The Malthusian parameter and perturbation are calculated easily by linear regression. Use of the model identifies an anomaly in the population due to the effects of World War II.

Matched MeSH terms: Linear Models
Fulltext Detection of butter adulteration with lard using differential scanning calorimetry

Nurrulhidayah, A.F., Arieff, S.R., Rohman, A., Amin, I., Shuhaimi, M., Khatib, A.

International Food Research Journal, 2015;22(2):832-839.
MyJurnal

Differential scanning calorimetry (DSC) is developed and used for detection of butter adulteration with lard. Butter has the similar characteristics to lard makes lard a desirable adulterant in butter. DSC provides unique thermal profiling for lard and butter. In the heating thermogram of the mixture, there was one major endothermic peak (peak A) with a smaller shoulder peak embedded in the major peak that gradually smoothed out to the major peak as the lard percent increased. In the cooling thermogram, there were one minor peak (peak B) and two major exothermic peaks, peak C which increased as lard percent increased and peak D which decreased in size as the lard percent increased. From Stepwise Multiple Linear Regression (SMLR) analysis, two independent variables were found to be able to predict lard percent adulteration in butter with R2 (adjusted) of 95.82. The SMLR equation of lard percent adulteration in butter is 293.1 - 11.36 (Te A) - 2.17 (Tr D); where Te A is the endset of peak A and Tr D is the range of thermal transition for peak D. These parameters can serve as a good measurement parameter in detecting lard adulteration in butter. DSC is a very useful means for halal screening technique to enhance the authenticity of Halal process.

Matched MeSH terms: Linear Models
Fulltext Determinants of posterior corneal biometric measurements in a multi-ethnic Asian population

Ang M, Chong W, Huang H, Wong TY, He MG, Aung T, et al.

PLoS One, 2014;9(7):e101483.
PMID: 25006679 DOI: 10.1371/journal.pone.0101483

To describe the corneal and anterior segment determinants of posterior corneal arc length (PCAL) and posterior corneal curvature (PCC).

Matched MeSH terms: Linear Models
Determinants of waiting time to third pregnancy using censored linear regression

Awang H

J Biosoc Sci, 2003 Jan;35(1):59-70.
PMID: 12537156

The intervals between pregnancies have important effects on fertility and maternal and infant health outcomes. This study uses linear regression with censored observation to assess the determinants of the waiting time to third pregnancy. The analysis is applied to data from the Second Malaysian Family Life Survey consisting of 1172 women who had their second delivery ending in a live birth. Contraceptive use, age of the woman, duration of breast-feeding, length of previous pregnancy interval and education of the woman all affect the waiting time to third pregnancy significantly.

Matched MeSH terms: Linear Models*
Fulltext Determination of polycyclic aromatic hydrocarbons in fresh milk by hollow fiber liquid-phase microextraction-gas chromatography mass spectrometry

Sanagi MM, Loh SH, Wan Ibrahim WA, Hasan MN, Aboul Enein HY

J Chromatogr Sci, 2013 Feb;51(2):112-6.
PMID: 22776739 DOI: 10.1093/chromsci/bms113

In this work, a two-phase hollow fiber liquid-phase microextraction (HF-LPME) method combined with gas chromatography-mass spectrometry (GC-MS) is developed to provide a rapid, selective and sensitive analytical method to determine polycyclic aromatic hydrocarbons (PAHs) in fresh milk. The standard addition method is used to construct calibration curves and to determine the residue levels for the target analytes, fluorene, phenanthrene, fluoranthene, pyrene and benzo[a]pyrene, thus eliminating sample pre-treatment steps such as pH adjustment. The HF-LPME method shows dynamic linearity from 5 to 500 µg/L for all target analytes with R(2) ranging from 0.9978 to 0.9999. Under optimized conditions, the established detection limits range from 0.07 to 1.4 µg/L based on a signal-to-noise ratio of 3:1. Average relative recoveries for the determination of PAHs studied at 100 µg/L spiking levels are in the range of 85 to 110%. The relative recoveries are slightly higher than those obtained by conventional solvent extraction, which requires saponification steps for fluorene and phenanthrene, which are more volatile and heat sensitive. The HF-LPME method proves to be simple and rapid, and requires minimal amounts of organic solvent that supports green analysis.

Matched MeSH terms: Linear Models
Fulltext Determination of quality parameters in Cavendish banana during ripening by NIR spectroscopy

Liew, C.Y., Lau, C.Y.

International Food Research Journal, 2012;19(2):751-758.
MyJurnal

Studies have been carried out to determine the chemical (soluble solid content) and physical (firmness) parameters of locally grown Cavendish banana by near infrared (NIR) spectroscopy. NIR spectra in the wavelength region of 680-2500 nm were obtained from a total of 408 Cavendish bananas of different ripeness indices. Chemometrics using multiple linear regression (MLR) was applied to develop calibration models for prediction of firmness and soluble solid content (SSC) of Cavendish banana. Results showed that NIR spectroscopy had the feasibility for non-destructive determination of the quality of Cavendish banana. The coefficient of determination (R2) for firmness and SSC calibration models at different ripeness indices ranged from 0.78 to 0.86 and 0.75 to 0.96, respectively. The calibration models were validated using independent sets of data and prediction models developed with the root mean square error of prediction (RMSEP) ranged from 0.01 to 0.26 kgf and 0.039 to 0.788 Brix for firmness and SSC, respectively. The multi-index models showed considerable robustness but higher prediction error with RMSEP of 0.336 kgf for firmness and 0.937% Brix for SSC compared to index specific model.

Matched MeSH terms: Linear Models
Determination of quinocide as impurity in primaquine tablets by capillary zone electrophoresis

Elbashir AA, Saad B, Ali AS, Saleh MI, Aboul-Enein HY

Biomed Chromatogr, 2009 May;23(5):464-71.
PMID: 19016231 DOI: 10.1002/bmc.1137

A capillary zone electrophoretic method has been developed and validated for the determination of the impurity quinocide (QC) in the antimalarial drug primaquine (PQ). Different buffer additives such as native cyclodextrins and crown ethers were evaluated. Promising results were obtained when either beta-cyclodextrin (beta-CD) or 18-crown-6 ether (18C6) were used. Their separation conditions such as type of buffer and its pH, buffer additive concentration, applied voltage capillary temperature and injection time were optimized. The use of 18C6 offers slight advantages over beta-CD such as faster elution times and improved resolution. Nevertheless, migration times of less than 5 min and resolution factors (R(s)) in the range of 2-4 were obtained when both additives were used. The method was validated with respect to selectivity, linearity, limits of detection and quantitation, analytical precision (intra- and inter-day variability) and repeatability. Concentrations of 2.12 and 2.71% (w/w) of QC were found in pharmaceutical preparations of PQ from two different manufacturers. A possible mechanism for the successful separation of the isomers is also discussed.

Matched MeSH terms: Linear Models
Determination of tamoxifen and its metabolites using micelle to solvent stacking in nonaqueous capillary electrophoresis

Thang LY, See HH, Quirino JP

Electrophoresis, 2016 05;37(9):1166-9.
PMID: 26873060 DOI: 10.1002/elps.201600010

Micelle to solvent stacking was implemented for the recently established NACE-C(4) D method to determine tamoxifen and its metabolites in standard samples and human plasma of breast cancer patients. For stacking, the standard samples and extract after liquid-liquid extraction (LLE) were prepared in methanol and the resulting sample solution was pressure injected after a micellar plug of SDS. Factors that affected the stacking such as SDS concentration, micelle, and sample plug length were examined. The sensitivity enhancement factor (peak height from stacking/peak height from typical injection of sample in BGE) was 15-22. The method detection limits with LLE were in the range of 5-10 ng/mL, which was lower than the established method (where the LLE extract was also prepared in methanol) with reported method detection limits of 25-40 ng/mL. The intraday and interday repeatability were in the range of 1.0-3.4% and 3.8-6.5%, respectively.

Matched MeSH terms: Linear Models
Determination of the binding constants of modafinil enantiomers with sulfated β-cyclodextrin chiral selector by capillary electrophoresis using three different linear plotting methods

Al Azzam KM, Saad B, Aboul-Enein HY

Electrophoresis, 2010 Sep;31(17):2957-63.
PMID: 20690150 DOI: 10.1002/elps.201000266

Binding constants for the enantiomers of modafinil with the negatively charged chiral selector sulfated-β-CD (S-β-CD) using CE technique is presented. The calculations of the binding constants employing three different linearization plots (double reciprocal, X-reciprocal and Y-reciprocal) were performed from the electrophoretic mobility values of modafinil enantiomers at different concentrations of S-β-CD in the BGE. The highest inclusion affinity of the modafinil enantiomers were observed for the S-enantiomer-S-β-CD complex, in agreement with the computational calculations performed previously. Binding constants for each enantiomer-S-β-CD complex at different temperatures, as well as thermodynamic parameters for binding, were calculated. Host-guest binding constants using the double reciprocal fit showed better linearity (r(2)>0.99) at all temperatures studied (15-30°C) and compared with the other two fit methods. The linear van't Hoff (15-30°C) plot obtained indicated that the thermodynamic parameters of complexation were temperature dependent for the enantiomers.

Matched MeSH terms: Linear Models
Determination of triazine herbicides using membrane-protected carbon nanotubes solid phase membrane tip extraction prior to micro-liquid chromatography

See HH, Marsin Sanagi M, Ibrahim WA, Naim AA

J Chromatogr A, 2010 Mar 12;1217(11):1767-72.
PMID: 20138287 DOI: 10.1016/j.chroma.2010.01.053

A novel microextraction technique termed solid phase membrane tip extraction (SPMTE) was developed. Selected triazine herbicides were employed as model compounds to evaluate the extraction performance and multiwall carbon nanotubes (MWCNTs) were used as the adsorbent enclosed in SPMTE device. The SPMTE procedure was performed in semi-automated dynamic mode and several important extraction parameters were comprehensively optimized. Under the optimum extraction conditions, the method showed good linearity in the range of 1-100 microg/L, acceptable reproducibility (RSD 6-8%, n=5), low limits of detection (0.2-0.5 microg/L), and satisfactory relative recoveries (95-101%). The SPMTE device could be regenerated and reused up to 15 analyses with no analyte carry-over effects observed. Comparison was made with commercially available solid phase extraction-molecular imprinted polymer cartridge (SPE-MIP) for triazine herbicides as the reference method. The new developed method showed comparable or even better results against reference method and is a simple, feasible, and cost effective microextraction technique.

Matched MeSH terms: Linear Models
Determination of underivatized long chain fatty acids using RP-HPLC with capacitively coupled contactless conductivity detection

Makahleh A, Saad B, Siang GH, Saleh MI, Osman H, Salleh B

Talanta, 2010 Apr 15;81(1-2):20-4.
PMID: 20188881 DOI: 10.1016/j.talanta.2009.11.030

A reversed-phase high-performance liquid chromatographic method with capacitively coupled contactless conductivity detector (C(4)D) has been developed for the separation and the simultaneous determination of five underivatized long chain fatty acids (FAs), namely myristic, palmitic, stearic, oleic, and linoleic acids. An isocratic elution mode using methanol/1mM sodium acetate (78:22, v/v) as mobile phase with a flow rate of 0.6 mL min(-1) was used. The separation was effected by using a Hypersil ODS C(18) analytical column (250 mm x 4.6 mm x 5 microm) and was operated at 45 degrees C. Calibration curves of the five FAs were well correlated (r(2)>0.999) within the range of 5- 200 microg mL(-1) for stearic acid, and 2-200 microg mL(-1) for the other FAs. The proposed method was tested on four vegetable oils, i.e., pumpkin, soybean, rice bran and palm olein oils; good agreement was found with the standard gas chromatographic (GC) method. The proposed method offers distinct advantages over the official GC method, especially in terms of simplicity, faster separation times and sensitivity.

Matched MeSH terms: Linear Models
Developing a validated liquid chromatography-mass spectrometric method for the simultaneous analysis of five bioactive quassinoid markers for the standardization of manufactured batches of Eurycoma longifolia Jack extract as antimalarial medicaments

Teh CH, Murugaiyah V, Chan KL

J Chromatogr A, 2011 Apr 8;1218(14):1861-77.
PMID: 21367427 DOI: 10.1016/j.chroma.2011.02.014

An extensive comparative study on the electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) mass spectrometry using automated flow injection analysis (FIA), was performed on eurycomanone (1), 13α(21)-epoxyeurycomanone (2), eurycomanol (3), eurycomanol-2-O-β-d-glucopyranoside (4), and 13,21-dihydroeurycomanone (5), the bioactive markers isolated from Eurycoma longifolia. The effects of eluent mixture (methanol or acetonitrile in water) and acidic modifiers (acetic acid, formic acid and trifluoroacetic acid) on the ionization efficiency of the markers were also investigated. The ESI in the positive ion mode with methanol containing 0.1% (v/v) acetic acid was selected for the subsequent optimization of nebulizer pressure, dry gas flow, dry gas temperature and capillary voltage to improve the sensitivity of the total ion chromatogram (TIC). Fragmentation of the analytes was further investigated by varying the capillary exit offset voltage and fragmentation amplitude in positive mode of ESI. The detection limits (LODs) were determined in isolation mode (selected ion monitoring, SIM). Their limits of detection (LODs) ranged between 0.03 and 0.1μgmL(-1) while the intra-day and inter-day precisions were less than 5.72% and 4.82%, respectively. The method was next applied for the simultaneous analysis of the markers to standardize various batches of manufactured extracts of E. longifolia for potential use as antimalarial products. Multiple Reaction Monitoring (MRM) mode was used for the quantification of analytes which gave protonated molecular ion, [M+H](+). For those without pseudo-molecular ions, SIM mode was used to quantify the analytes. The batches contained 5.65-9.95% of eurycomanone (1), 5.21-19.75% of eurycomanol (3) and 7.59-19.95% of eurycomanol-2-O-β-d-glucopyranoside (4) as major quassinoids whereas, 13α(21)-epoxyeurycomanone (2), and 13,21-dihydroeurycomanone (5) were much lower in concentrations of 0.78-3.90% and 0.47-1.76%, respectively.

Matched MeSH terms: Linear Models
Developing correlation equations for converting among coliforms, E. coli and HPC for rainwater disinfection by sunlight

M.T. Amin, M.Y. Han, Tschung-il Kim, A.A. Alazba, M.N. Amin

Sains Malaysiana, 2013;42:1273-1281.

The application of solar disinfection for treating stored rainwater was investigated by the authors using indicator organisms. The multiple tube fermentation technique and pour plate method were used for the detection of microbial quality indicators like total and fecal coliforms, E. coli and heterotrophic plate count. These techniques have disadvantages mainly that these are laborious and time consuming. The correlation of total coliform with that of exposure time is proposed under different factors of weather, pH and turbidity. Statistical tools like root mean square error and coefficient of determination were used to validate these proposed equations. The correlation equations of fecal coliform, E. coli and heterotrophic plate count with total coliform are suggested by using four regression analysis including Reciprocal Quadratic, Polynomial Regression (2 degree), Gaussian Model and Linear Regression in order to reduce the tedious experimental work in similar types of experiments and treatment systems.

Matched MeSH terms: Linear Models
Fulltext Development of HPLC Method for Quantification of Sinigrin from Raphanus sativus Roots and Evaluation of Its Anticancer Potential

Nair AB, Gandhi D, Patel SS, Morsy MA, Gorain B, Attimarad M, et al.

Molecules, 2020 Oct 26;25(21).
PMID: 33114598 DOI: 10.3390/molecules25214947

Sinigrin, a precursor of allyl isothiocyanate, present in the Raphanus sativus exhibits diverse biological activities, and has an immense role against cancer proliferation. Therefore, the objective of this study was to quantify the sinigrin in the R. sativus roots using developed and validated RP-HPLC method and further evaluated its' anticancer activity. To achieve the objective, the roots of R. sativus were lyophilized to obtain a stable powder, which were extracted and passed through an ion-exchange column to obtain sinigrin-rich fraction. The RP-HPLC method using C18 analytical column was used for chromatographic separation and quantification of sinigrin in the prepared fraction, which was attained using the mobile phase consisting of 20 mM tetrabutylammonium: acetonitrile (80:20%, v/v at pH 7.0) at a flow rate of 0.5 mL/min. The chromatographic peak for sinigrin was showed at 3.592 min for pure sinigrin, where a good linearity was achieved within the concentration range of 50 to 800 µg/mL (R2 > 0.99), with an excellent accuracy (-1.37% and -1.29%) and precision (1.43% and 0.94%), for intra and inter-day, respectively. Finally, the MTT assay was performed for the sinigrin-rich fraction using three different human cancer cell lines, viz. prostate cancer (DU-145), colon adenocarcinoma (HCT-15), and melanoma (A-375). The cell-based assays were extended to conduct apoptotic and caspase-3 activities, to determine the mechanism of action of sinigrin in the treatment of cancer. MTT assay showed IC50 values of 15.88, 21.42, and 24.58 µg/mL for DU-145, HCT-15, and A-375 cell lines, respectively. Increased cellular apoptosis and caspase-3 expression were observed with sinigrin-rich fraction, indicating significant increase in overexpression of caspase-3 in DU-145 cells. In conclusion, a simple, sensitive, fast, and accurate RP-HPLC method was developed for the estimation of sinigrin in the prepared fraction. The data observed here indicate that sinigrin can be beneficial in treating prostate cancer possibly by inducing apoptosis.

Matched MeSH terms: Linear Models
Fulltext Development of Linear Regression Model for tile waste generation in Malaysian construction industry

Siti Hafizan Hassan, Hamidi Abdul Aziz, Mohd Samsudin Abdul Hamid, Siti Rashidah Mohd Nasir, Suhailah Mohamed Noor

ESTEEM Academic Journal, 2019;15(2):11-23.
MyJurnal

The effect of unmanageable construction waste is an unstable land settlement and groundwater pollution. In addition to environmental pollution, construction waste could incur construction cost. The most construction waste is the material used at sites and tile is also a part of the waste generated in construction. The objectives of this study are to determine the tile waste generated in construction stages and linear regression analysis for the amount of tile waste generated. The method used in this study was the Linear Regression Model. The regression model established in the sample data reported an R2 value of 0.793; therefore, the model can predict approximately 79.3% of the factor (area) of tile waste generation. The linear regressions can be applied as tools to predict the tile waste generated at construction sites and help the contractor to track the sources of missing waste.

Matched MeSH terms: Linear Models
Development of QSAR model to predict the ecotoxicity of Vibrio fischeri using COSMO-RS descriptors

Ghanem OB, Mutalib MIA, Lévêque JM, El-Harbawi M

Chemosphere, 2017 Mar;170:242-250.
PMID: 28006757 DOI: 10.1016/j.chemosphere.2016.12.003

Ionic liquids (ILs) are class of solvent whose properties can be modified and tuned to meet industrial requirements. However, a high number of potentially available cations and anions leads to an even increasing members of newly-synthesized ionic liquids, adding to the complexity of understanding on their impact on aquatic organisms. Quantitative structure activity∖property relationship (QSAR∖QSPR) technique has been proven to be a useful method for toxicity prediction. In this work,σ-profile descriptors were used to build linear and non-linear QSAR models to predict the ecotoxicities of a wide variety of ILs towards bioluminescent bacterium Vibrio fischeri. Linear model was constructed using five descriptors resulting in high accuracy prediction of 0.906. The model performance and stability were ascertained using k-fold cross validation method. The selected descriptors set from the linear model was then used in multilayer perceptron (MLP) technique to develop the non-linear model, the accuracy of the model was further enhanced achieving high correlation coefficient with the lowest value being 0.961 with the highest mean square error of 0.157.

Matched MeSH terms: Linear Models
Fulltext Development of a Healthy Dietary Habits Index for New Zealand Adults

Wong JE, Haszard JJ, Howe AS, Parnell WR, Skidmore PML

Nutrients, 2017 May 03;9(5).
PMID: 28467392 DOI: 10.3390/nu9050454

Healthful dietary habits are individually associated with better nutrient intake and positive health outcomes; however, this information is rarely examined together to validate an indicator of diet quality. This study developed a 15-item Healthy Dietary Habits Index (HDHI) based on self-reported dietary habits information collected in the 2008/09 New Zealand Adult Nutrition Survey. The validity of HDHI as a diet quality index was examined in relation to sociodemographic factors, 24-diet recall derived nutrient intakes, and nutritional biomarkers in a representative sample of adults aged 19 years and above. Linear regression models were employed to determine associations between HDHI quintiles and energy-adjusted nutrient data and nutritional biomarkers. Significantly higher HDHI scores were found among women, older age groups, Non-Māori or Pacific ethnic groups, and less socioeconomically-deprived groups (all p < 0.001). Increasing quintiles of HDHI were associated with higher intakes of dietary fibre and seven micronutrients including calcium, iron, and vitamin C, and lower intakes of energy, macronutrients, sodium, zinc, vitamins B6 and B12. Associations in the expected directions were also found for urinary sodium, whole blood folate, serum and red blood cell folate, and plasma selenium (all p < 0.001). The present findings suggest that the HDHI is a valid measure of diet quality as it is capable of discerning quality of diets of subgroups and ranking nutrient intakes among NZ adults.

Matched MeSH terms: Linear Models

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