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Squares Regression (square + regression)
Kinds of Squares Regression Terms modified by Squares Regression Selected AbstractsSimultaneous Quantitative Determination of Cadmium, Lead, and Copper on Carbon-Ink Screen-Printed Electrodes by Differential Pulse Anodic Stripping Voltammetry and Partial Least Squares RegressionELECTROANALYSIS, Issue 23 2008Michael Cauchi Abstract Water is a vital commodity for every living entity on the planet. However, water resources are threatened by various sources of contamination from pesticides, hydrocarbons and heavy metals. This has resulted in the development of concepts and technologies to create a basis for provision of safe and high quality drinking water. This paper focuses on the simultaneous quantitative determination of three common contaminants, the heavy metals cadmium, lead and copper. Multivariate calibration was applied to voltammograms acquired on in-house printed carbon-ink screen-printed electrodes by the highly sensitive electrochemical method of differential pulse anodic stripping voltammetry (DPASV). The statistically inspired modification of partial least squares (SIMPLS) algorithm was employed to effect the multivariate calibration. The application of data pretreatment techniques involving range-scaling, mean-centering, weighting of variables and the effects of peak realignment are also investigated. It was found that peak realignment in conjunction with weighting and SIMPLS led to the better overall root mean square error of prediction (RMSEP) value. This work represents significant progress in the development of multivariate calibration tools in conjunction with analytical techniques for water quality determination. It is the first time that multivariate calibration has been performed on DPASV voltammograms acquired on carbon-ink screen-printed electrodes. [source] A new holistic exploratory approach to Systems Biology by Near Infrared Spectroscopy evaluated by chemometrics and data inspectionJOURNAL OF CHEMOMETRICS, Issue 10-11 2007Lars Munck Abstract There is a need for an improved biological and theoretical interpretation of Near Infra-Red Spectral (NIRS) fingerprints from tissues that could contribute with holistic overview to fine-grained detail modelled in Systems Biology. The concept of gene expression in self-organised networks was experimentally tested in a barley endosperm model with molecularly defined and undefined mutants. Surprisingly reproducible gene-specific NIRS fingerprints were observed directly in log1/R MSC pre-treated spectra that could not be accurately represented by destructive mathematical models. A mutant spectrum in an isogenic background represents the physiochemical expression of the gene in the whole network (tissue). The necessary holistic overview that is needed experimentally to introduce Ilya Prigogine's theory on self-organisation in Systems Biology was supplied by defining the spectral phenome. Interval spectral information on genotypes and environment was classified by interval Extended Canonical Variates Analysis (iECVA). Genetic changes in spectra were interpreted by interval Partial Least Squares Regression (iPLSR) correlations to chemical variables. A new pathway regulation was detected. The finely grained ,bottom up' modelling of molecular and chemical data from pathways requires a coarsely grained exploratory ,top down' overview by NIRS to account for the outcome of self-organisation. The amplification of expression from a gene to the phenome (pleiotropy) can now for the first time be quantified as a whole reproducible phenomenological pattern by NIRS and compared to other gene spectra. It explains published findings that transformed respectively mutated genes in genetically modified organisms (GMOs) and cancer patients can be detected unsupervised from tissues by spectroscopy, chemometrics and data inspection. Copyright © 2007 John Wiley & Sons, Ltd. [source] Relating Descriptive Sensory Analysis to Gas Chromatography/Olfactometry Ratings of Fresh Strawberries Using Partial Least Squares RegressionJOURNAL OF FOOD SCIENCE, Issue 7 2004K.F. Schulbach ABSTRACT: Sensory properties of 5 strawberry varieties were related to gas chromatography/olfactometry (GC/ O) analysis using partial least squares regression (PLS). The sour and green sensory aspects were strongly associated with titratable acidity, hexanal, and E-2 hexenal. The caramel/sweet character was differentiated from the strawberry/fruity character by its stronger association with Furaneol, which had a high score in the 2nd PLS dimension. The sensory scores for peach and the GC/O ratings for the peach-like lactones were also associated. The fruity sensory scores and the floral sensory scores were not well correlated with compounds having fruity or floral character. This lack of relationship could partially be explained by covariance among the sensory ratings for the samples. [source] INFLUENCE OF UNIAXIAL COMPRESSION RATE ON RHEOLOGICAL PARAMETERS AND SENSORY TEXTURE PREDICTION OF COOKED POTATOESJOURNAL OF TEXTURE STUDIES, Issue 1 2000ANETTE KISTRUP THYBO ABSTRACT The effect of uniaxial compression rate (20,1000 mm/min) on the parameters: Stress (,ftrue), strain (,fHencky) and work to fracture (Wf), modulus of deformability (Ed), maximum slope before fracture (Emax) and work during 75% compression (Wtotal) was investigated for ten potato varieties. Multivariate data analysis was used to study the correlation between and within the sensory and nonsensory measurements by Principal Component Analysis (PCA) which showed ,ftrue, Emax, Wf, and Wtotal to explain the same type of information in the data, and ,fHencky versus Ed another type of information in the data. The deformation rate had a large effect on ,fHencky. Nine sensory texture attributes covering the mechanical, geometrical and moistness attributes were evaluated. Relationships between uniaxial compression data at various deformation rates and the sensory texture attributes were studied by Partial Least Squares Regression (PLSR). A minor effect of deformation rate on the correlation with the sensory texture properties was obtained. Mechanical properties were predicted to a higher extent than the geometrical attributes and moistness. The prediction of the mechanical, geometrical and moistness attributes increased largely by using uniaxial compression supplemented by chemical measures such as dry matter and pectin methylesterase, but here no relevant effect of deformation rate was obtained. [source] Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AESCHINESE JOURNAL OF CHEMISTRY, Issue 11 2007Mohamad KHAYATZADEH MAHANI Abstract Simultaneous determination of several elements (U, Ta, Mn, Zr and W) with inductively coupled plasma atomic emission spectrometry (ICP-AES) in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network (ANN) and partial least squares regression (PLS) for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line (263.553 nm) with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 (input, hidden and output neurons) network was used with linear transfer function after both hidden and output layers. The PLS model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions (REP) in test set were 3.75% and 3.56% for ANN and PLS respectively. [source] Speciation mirrors geomorphology and palaeoclimatic history in African laminate-toothed rats (Muridae: Otomyini) of the Otomys denti and Otomys lacustris species-complexes in the ,Montane Circle' of East AfricaBIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 4 2009PETER J. TAYLOR We adopted an integrated systematic approach to delimit evolutionary species and describe phylogeographic, morphometric and ecological relationships in Otomys denti (from the Albertine Rift, Southern Rift in Malawi and the northern Eastern Arc Mountains) and Otomys lacustris (from the Southern Rift in Tanzania and Zambia, and the southern Eastern Arc Mountains). Molecular [cytochrome (cyt) b sequences, 1143 bp, N = 18], craniometric (classical, N = 100 and geometric, N = 60) and ecological (Partial Least Squares regression of shape and ecogeographic variables) approaches show a profound, parallel disjunction between two groups: (1) Eastern Arc and Southern Rift (including the Malawi Rift) (O. lacustris and Otomys denti sungae) and (2) Albertine Rift (Otomys denti denti and Otomys denti kempi) taxa. Within both groups, cyt b sequences or craniometric analysis provided evidence for the differentiation of both southern and northern Eastern Arc from Southern Rift lineages (across the so-called Makambako Gap). Within the Albertine Rift (denti,kempi) lineage, populations from individual mountain ranges differed significantly in skull shape (but not size), but were similar genetically. Over-reliance in the past on very few morphological characters (e.g. number of molar laminae) and a polytypic species concept has obscured phylogenetic relationships and species discrimination in this group. We recognize at least three species in this group, and distinct lineages within two of these species. Each species or lineage was endemic to one of three regions: the Albertine Rift, the Malawi Rift or the Eastern Arc. Our result echo conclusions of recent studies of other mammalian and bird taxa and reflect the geomorphology and palaeoclimatic history of the region. © 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 96, 913,941. [source] SENSORY CHARACTERISTICS OF TRADITIONAL FIELD GROWN TOMATO GENOTYPES IN SOUTHERN ITALYJOURNAL OF FOOD QUALITY, Issue 6 2007FIORELLA SINESIO ABSTRACT This study was conducted with the aim to characterize the diversity of fruit sensory quality of traditional tomato genotypes, grown in open fields, by means of descriptive profile analysis. It gives the results from sensory profiling of fresh tomato genotypes San Marzano, Vesuviano, Corbarino and Sorrento, originating from Southern Italy, and their respective commercial hybrids over 3 years of harvesting. The effects of genotypes, year of production (2002, 2003, 2004) and fields located in different geographical areas on sensory data were analyzed using principal component analysis and multivariate analysis of variance partial least square regression. For most sensory characteristics, the greatest variation was caused by differences in genotypes, suggesting that there was considerable level of genetic diversity. Minor effects were given to year of harvest and experimental fields. PRACTICAL APPLICATIONS Tomato is one of the most frequently consumed vegetables in many countries. Italy is one of the main tomato producers in the world, where the genetic variability among traditional tomato genotypes, hybrid and wild varieties in terms of variability in shape, dimension and sensorial attributes is enormous. A feasible area of improvement of tomato production is toward the increase or changing the original flavor. The knowledge of the effect of variety and season on sensory-perceived quality and the selection by breeding of genotypes with improved aroma and flavor profile is a tool to better orientate the tomato production. [source] Rapid Profiling of Swiss Cheese by Attenuated Total Reflectance (ATR) Infrared Spectroscopy and Descriptive Sensory AnalysisJOURNAL OF FOOD SCIENCE, Issue 6 2009N.A. Kocaoglu-Vurma ABSTRACT:, The acceptability of cheese depends largely on the flavor formed during ripening. The flavor profiles of cheeses are complex and region- or manufacturer-specific which have made it challenging to understand the chemistry of flavor development and its correlation with sensory properties. Infrared spectroscopy is an attractive technology for the rapid, sensitive, and high-throughput analysis of foods, providing information related to its composition and conformation of food components from the spectra. Our objectives were to establish infrared spectral profiles to discriminate Swiss cheeses produced by different manufacturers in the United States and to develop predictive models for determination of sensory attributes based on infrared spectra. Fifteen samples from 3 Swiss cheese manufacturers were received and analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). The spectra were analyzed using soft independent modeling of class analogy (SIMCA) to build a classification model. The cheeses were profiled by a trained sensory panel using descriptive sensory analysis. The relationship between the descriptive sensory scores and ATR-IR spectra was assessed using partial least square regression (PLSR) analysis. SIMCA discriminated the Swiss cheeses based on manufacturer and production region. PLSR analysis generated prediction models with correlation coefficients of validation (rVal) between 0.69 and 0.96 with standard error of cross-validation (SECV) ranging from 0.04 to 0.29. Implementation of rapid infrared analysis by the Swiss cheese industry would help to streamline quality assurance. [source] Electronic Nose Technology in Quality Assessment: Predicting Volatile Composition of Danish Blue Cheese During RipeningJOURNAL OF FOOD SCIENCE, Issue 6 2005Jeorgos Trihaas ABSTRACT This work describes for the 1st time the use of an electronic nose (e-nose) for the determination of changes of blue cheeses flavor during maturation. Headspace analysis of Danish blue cheeses was made for 2 dairy units of the same producer. An e-nose registered changes in cheeses flavor 5, 8, 12, and 20 wk after brining. Volatiles were collected from the headspace and analyzed by gas chromatography-mass spectrometry (GC-MS). Features from the chemical sensors of the e-nose were used to model the volatile changes by multivariate methods. Differences registered during ripening of the cheeses as well as between producing units are described and discussed for both methods. Cheeses from different units showed significant differences in their e-nose flavor profiles at early ripening stages but with ripening became more and more alike. Prediction of the concentration of 25 identified aroma compounds by e-nose features was possible by partial least square regression (PLS-R). It was not possible to create a reliable predictive model for both units because cheeses from 1 unit were contaminated by Geotrichum candidum, leading to unstable ripening patterns. Correction of the e-nose features by multiple scatter correction (MSC) and mean normalization (MN) of the integrated GC areas made correlation of the volatile concentration to the e-nose signal features possible. Prediction models were created, evaluated, and used to reconstruct the headspace of unknown cheese samples by e-nose measurements. Classification of predicted volatile compositions of unknown samples by their ripening stage was successful at a 78% and 54% overall correct classification for dairy units 1 and 2, respectively. Compared with GC-MS, the application of the rapid and less demanding e-nose seems an attractive alternative for this type of investigation. [source] Assessment of Relationships between Sensory and Instrumental Quality of Controlled-atmosphere-stored ,Fuji' Apples by Multivariate AnalysisJOURNAL OF FOOD SCIENCE, Issue 9 2004G. Echeverría ABSTRACT: Physicochemical parameters, sensory attributes, and total aroma emission of ,Fuji' apples (Malus×domestica Borkh.) were studied in relation to storage conditions, storage duration, and shelf life period. Commercially ripe fruit were analyzed after 3, 5, and 7 mo of cold storage in normal atmosphere (AIR) (210 L/m3 O2+ 0.3 L/m3 CO2) or under 3 different controlled atmosphere (CA) treatments (10 L/m3 O2+ 10 L/m3 CO2, 20 L/m3 O2+ 20 L/m3 CO2, or 10 L/m3 O2+ 30 L/m3 CO2), after which apples were kept at 20 °C for 1, 5, and 10 d. Data were subjected to partial least square regression (PLSR) analysis. Physicochemical parameters were well preserved throughout storage, especially in CA-stored apples; however, these apples showed lower total aroma emission. Sensory acceptability was also higher for CA-stored fruit after 7 mo of storage, whereas no significant differences were found for shorter storage periods. Accordingly, greater scores in sensory firmness, sensory flavor, sensory acidity, and appearance were observed for fruit stored in 10 L/m3 O2+ 10 L/m3 CO2 after long storage. Two PLSR models were established, 1 for relating physicochemical parameters to overall acceptability, and another for assessing the correlation between sensory acidity and instrumentally measured titratable acidity. The 1st PLSR model indicated that soluble solids concentration, titratable acidity, flesh firmness, and background color of the shaded side have a positive influence on acceptability. The 2nd model indicated that sensory acidity also showed an excellent correlation to instrumentally measured titratable acidity. [source] Texture and Chemical Characteristics of Soy Protein Meat Analog Extruded at High MoistureJOURNAL OF FOOD SCIENCE, Issue 2 2000S. Lin ABSTRACT: The relationships among extruder responses, texture, and protein solubility of soy protein meat analogs were studied. Soy protein isolate and wheat starch at 9:1 ratio were extruded at 60%, 65%, and 70% moisture contents and 137.8, 148.9, and 160°C cooking temperatures. The results showed that moisture content was a more important factor on the overall product texture than cooking temperature. Lower moisture content resulted in higher die pressure, harder texture, and lower total protein solubility. At a fixed moisture content, a higher cooking temperature resulted in a softer and less chewy product but only slightly changed the protein solubility. According to partial least square regression, the data from Texture Profile Analysis, protein solubility, and extruder responses correlated well and could be used to predict each other. [source] Ab initio energy calculations and macroscopic rate modeling of hydroformylation of higher alkenes by Rh-based catalystAICHE JOURNAL, Issue 12 2009Maizatul S. Shaharun Abstract Ab initio quantum chemical computations have been done to determine the energetics and reaction pathways of hydroformylation of higher alkenes using a rhodium complex homogeneous catalyst. Calculation of fragments of the potential energy surfaces of the HRh(CO)(PPh3)3 -catalyzed hydroformylation of 1-decene, 1-dodecene, and styrene were performed by the restricted Hartree-Fock method at the second-order MØller-Plesset (MP2) level of perturbation theory and basis set of 6-31++G(d,p). Geometrically optimized structures of the intermediates and transition states were identified. Three generalized rate models were developed on the basis of above reaction path analysis as well as experimental findings reported in the literature. The kinetic and equilibrium parameters of the models were estimated by nonlinear least square regression of available literature data. The model based on H2 -oxidative addition fitted the data best; it predicts the conversion of all the alkenes quite satisfactorily with an average deviation of 7.6% and a maximum deviation of 13%. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] On the importance of patch attributes, environmental factors and past human impacts as determinants of perennial plant species richness and diversity in Mediterranean semiarid steppesDIVERSITY AND DISTRIBUTIONS, Issue 1 2004Fernando T. Maestre ABSTRACT Richness and diversity of perennial plant species were evaluated in 17 Stipa tenacissima steppes along a degradation gradient in semiarid SE Spain. The main objective of the study was to evaluate the relative importance of historical human impacts, small-scale patch attributes and environmental factors as determinants of perennial plant species richness and diversity in S. tenacissima steppes, where vegetation is arranged as discrete plant patches inserted on a bare ground matrix. Partial least squares regression was used to determine the amount of variation in species richness and diversity that could be significantly explained by historical human impacts, patch attributes, and environmental factors together and separately. They explained up to 89% and 69% of the variation in species richness and diversity, respectively. In both cases, the predictive power of patch attributes models was higher than that of models consisting of abiotic characteristics and variables related to human impact, suggesting that patch attributes are the major determinants of species richness and diversity in semiarid S. tenacissima steppes. However, patch attributes alone are not enough to explain the observed variation in species richness and diversity. The area covered by late-successional sprouting shrubs and the distance between consecutive patches were the most influencing individual variables on species richness and diversity, respectively. The implications of these results for the management of S. tenacissima steppes are discussed. [source] Predicting pasture root density from soil spectral reflectance: field measurementEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2010B. H. KUSUMO This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350,2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g,1 soil. The average root density in the Fluvial soil (13.21 mg g,1) was twice that in the Allophanic soil (6.88 mg g,1). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r2 = 0.83, ratio prediction to deviation (RPD ) = 2.44, root mean square error of cross-validation (RMSECV ) = 1.96 mg g ,1) than in the Fluvial soil (r2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g ,1). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change. [source] Multivariate calibration of hyperspectral ,-ray energy spectra for proximal soil sensingEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2007R. A. Viscarra Rossel Summary The development of proximal soil sensors to collect fine-scale soil information for environmental monitoring, modelling and precision agriculture is vital. Conventional soil sampling and laboratory analyses are time-consuming and expensive. In this paper we look at the possibility of calibrating hyperspectral ,-ray energy spectra to predict various surface and subsurface soil properties. The spectra were collected with a proximal, on-the-go ,-ray spectrometer. We surveyed two geographically and physiographically different fields in New South Wales, Australia, and collected hyperspectral ,-ray data consisting of 256 energy bands at more than 20 000 sites in each field. Bootstrap aggregation with partial least squares regression (or bagging-PLSR) was used to calibrate the ,-ray spectra of each field for predictions of selected soil properties. However, significant amounts of pre-processing were necessary to expose the correlations between the ,-ray spectra and the soil data. We first filtered the spectra spatially using local kriging, then further de-noised, normalized and detrended them. The resulting bagging-PLSR models of each field were tested using leave-one-out cross-validation. Bagging-PLSR provided robust predictions of clay, coarse sand and Fe contents in the 0,15 cm soil layer and pH and coarse sand contents in the 15,50 cm soil layer. Furthermore, bagging-PLSR provided us with a measure of the uncertainty of predictions. This study is apparently the first to use a multivariate calibration technique with on-the-go proximal ,-ray spectrometry. Proximally sensed ,-ray spectrometry proved to be a useful tool for predicting soil properties in different soil landscapes. [source] ON THE OPPORTUNITY FOR SEXUAL SELECTION, THE BATEMAN GRADIENT AND THE MAXIMUM INTENSITY OF SEXUAL SELECTIONEVOLUTION, Issue 7 2009Adam G. Jones Bateman's classic paper on fly mating systems inspired quantitative study of sexual selection but also resulted in much debate and confusion. Here, I consider the meaning of Bateman's principles in the context of selection theory. Success in precopulatory sexual selection can be quantified as a "mating differential," which is the covariance between trait values and relative mating success. The mating differential is converted into a selection differential by the Bateman gradient, which is the least squares regression of relative reproductive success on relative mating success. Hence, a complete understanding of precopulatory sexual selection requires knowledge of two equally important aspects of mating patterns: the mating differential, which requires a focus on mechanisms generating covariance between trait values and mating success, and the Bateman gradient, which requires knowledge of the genetic mating system. An upper limit on the magnitude of the selection differential on any sexually selected trait is given by the product of the standard deviation in relative mating success and the Bateman gradient. This latter view of the maximum selection differential provides a clearer focus on the important aspects of precopulatory sexual selection than other methods and therefore should be an important part of future studies of sexual selection. [source] Gamma regression improves Haseman-Elston and variance components linkage analysis for sib-pairsGENETIC EPIDEMIOLOGY, Issue 2 2004Mathew J. Barber Abstract Existing standard methods of linkage analysis for quantitative phenotypes rest on the assumptions of either ordinary least squares (Haseman and Elston [1972] Behav. Genet. 2:3,19; Sham and Purcell [2001] Am. J. Hum. Genet. 68:1527,1532) or phenotypic normality (Almasy and Blangero [1998] Am. J. Hum. Genet. 68:1198,1199; Kruglyak and Lander [1995] Am. J. Hum. Genet. 57:439,454). The limitations of both these methods lie in the specification of the error distribution in the respective regression analyses. In ordinary least squares regression, the residual distribution is misspecified as being independent of the mean level. Using variance components and assuming phenotypic normality, the dependency on the mean level is correctly specified, but the remaining residual coefficient of variation is constrained a priori. Here it is shown that these limitations can be addressed (for a sample of unselected sib-pairs) using a generalized linear model based on the gamma distribution, which can be readily implemented in any standard statistical software package. The generalized linear model approach can emulate variance components when phenotypic multivariate normality is assumed (Almasy and Blangero [1998] Am. J. Hum Genet. 68: 1198,1211) and is therefore more powerful than ordinary least squares, but has the added advantage of being robust to deviations from multivariate normality and provides (often overlooked) model-fit diagnostics for linkage analysis. Genet Epidemiol 26:97,107, 2004. © 2004 Wiley-Liss, Inc. [source] Can a publicly funded home care system successfully allocate service based on perceived need rather than socioeconomic status?HEALTH & SOCIAL CARE IN THE COMMUNITY, Issue 2 2007A Canadian experience Abstract The present quantitative study evaluates the degree to which socioeconomic status (SES), as opposed to perceived need, determines utilisation of publicly funded home care in Ontario, Canada. The Registered Persons Data Base of the Ontario Health Insurance Plan was used to identify the age, sex and place of residence for all Ontarians who had coverage for the complete calendar year 1998. Utilisation was characterised in two dimensions: (1) propensity , the probability that an individual received service, which was estimated using a multinomial logit equation; and (2) intensity , the amount of service received, conditional on receipt. Short- and long-term service intensity were modelled separately using ordinary least squares regression. Age, sex and co-morbidity were the best predictors (P < 0.0001) of whether or not an individual received publicly funded home care as well as how much care was received, with sicker individuals having increased utilisation. The propensity and intensity of service receipt increased with lower SES (P < 0.0001), and decreased with the proportion of recent immigrants in the region (P < 0.0001), after controlling for age, sex and co-morbidity. Although the allocation of publicly funded home care service was primarily based on perceived need rather than ability to pay, barriers to utilisation for those from areas with a high proportion of recent immigrants were identified. Future research is needed to assess whether the current mix and level of publicly funded resources are indeed sufficient to offset the added costs associated with the provision of high-quality home care. [source] Language and Regional Differences in Evaluations of Medicare Managed Care by HispanicsHEALTH SERVICES RESEARCH, Issue 2 2008Robert Weech-Maldonado Objectives. This study uses the Consumer Assessments of Healthcare Providers and Systems (CAHPS®) survey to examine the experiences of Hispanics enrolled in Medicare managed care. Evaluations of care are examined in relationship to primary language (English or Spanish) and region of the country. Data Sources. CAHPS 3.0 Medicare managed care survey data collected in 2002. Study Design. The dependent variables consist of five CAHPS multi-item scales measuring timeliness of care, provider communication, office staff helpfulness, getting needed care, and health plan customer service. The main independent variables are Hispanic primary language (English or Spanish) and region (California, Florida, New York/New Jersey, and other states). Ordinary least squares regression is used to model the effect of Hispanic primary language and region on CAHPS scales, controlling for age, gender, education, and self-rated health. Data Collection/Extraction Methods. The analytic sample consists of 125,369 respondents (82 percent response rate) enrolled in 181 Medicare managed care plans across the U.S. Of the 125,369 respondents, 8,463 (7 percent) were self-identified as Hispanic. The survey was made available in English and Spanish, and 1,353 Hispanics completed one in Spanish. Principal Findings. Hispanic English speakers had less favorable reports of care than whites for all dimensions of care except provider communication. Hispanic Spanish speakers reported more negative experiences than whites with timeliness of care, provider communication, and office staff helpfulness, but better reports of care for getting needed care. Spanish speakers in all regions except Florida had less favorable scores than English-speaking Hispanics for provider communication and office staff helpfulness, but more positive assessments for getting needed care. There were greater regional variations in CAHPS scores among Hispanic Spanish speakers than among Hispanic English speakers. Spanish speakers in Florida had more positive experiences than Spanish speakers in other regions for most dimensions of care. Conclusions. Hispanics in Medicare managed care face barriers to care; however, their experiences with care vary by language and region. Spanish speakers (except FL) have less favorable experiences with provider communication and office staff helpfulness than their English-speaking counterparts, suggesting language barriers in the clinical encounter. On the other hand, Spanish speakers reported more favorable experiences than their English-speaking counterparts with the managed care aspects of their care (getting needed care and plan customer service). Medicare managed care plans need to address the observed disparities in patient experiences among Hispanics as part of their quality improvement efforts. Plans can work with their network providers to address issues related to timeliness of care and office staff helpfulness. In addition, plans can provide incentives for language services, which have the potential to improve communication with providers and staff among Spanish speakers. Finally, health plans can reduce the access barriers faced by Hispanics, especially among English speakers. [source] Fatty acid composition, antioxidants and lipid oxidation in chicken breasts from different production regimesINTERNATIONAL JOURNAL OF FOOD SCIENCE & TECHNOLOGY, Issue 4 2004Kishowar Jahan Summary Chicken breast from nine products and from the following production regimes: conventional (chilled and frozen), organic and free range, were analysed for fatty acid composition of total lipids, preventative and chain breaking antioxidant contents and lipid oxidation during 5 days of sub-ambient storage following purchase. Total lipids were extracted with an optimal amount of a cold chloroform methanol solvent. Lipid compositions varied, but there were differences between conventional and organic products in their contents of total polyunsaturated fatty acids and n-3 and n-6 fatty acids and n-6:n-3 ratio. Of the antioxidants, , -tocopherol content was inversely correlated with lipid oxidation. The antioxidant enzyme activities of catalase, glutathione peroxidase and glutathione reductase varied between products. Modelling with partial least squares regression showed no overall relationship between total antioxidants and lipid data, but certain individual antioxidants showed a relationship with specific lipid fractions. [source] Healthcare Cost Differences with Participation in a Community-Based Group Physical Activity Benefit for Medicare Managed Care Health Plan MembersJOURNAL OF AMERICAN GERIATRICS SOCIETY, Issue 8 2008Ronald T. Ackermann MD OBJECTIVES: To determine whether participation in a physical activity benefit by Medicare managed care enrollees is associated with lower healthcare utilization and costs. DESIGN: Retrospective cohort study. SETTING: Medicare managed care. PARTICIPANTS: A cohort of 1,188 older adult health maintenance organization enrollees who participated at least once in the EnhanceFitness (EF) physical activity benefit and a matched group of enrollees who never used the program. MEASUREMENTS: Healthcare costs and utilization were estimated. Ordinary least squares regression was used, adjusting for demographics, comorbidity, indicators of preventive service use, and baseline utilization or cost. Robustness of findings was tested in sensitivity analyses involving continuous propensity score adjustment and generalized linear models with nonconstant variance assumptions. RESULTS: EF participants had similar total healthcare costs during Year 1 of the program, but during Year 2, adjusted total costs were $1,186 lower (P=.005) than for non-EF users. Differences were partially attributable to lower inpatient costs (,$3,384; P=.02), which did not result from high-cost outliers. Enrollees who attended EF an average of one visit or more per week had lower adjusted total healthcare costs in Year 1 (,$1,929; P<.001) and Year 2 (,$1,784; P<.001) than nonusers. CONCLUSION: Health plan coverage of a preventive physical activity benefit for seniors is a promising strategy to avoid significant healthcare costs in the short term. [source] Responses of riparian plants to flooding in free-flowing and regulated boreal rivers: an experimental studyJOURNAL OF APPLIED ECOLOGY, Issue 6 2002M. E. Johansson Summary 1The long history of river regulation has resulted in extensively changed ecosystem structures and processes in rivers and their associated environments. This fact, together with changing climatic and hydrological conditions, has increased the need to recover the natural functions of rivers. To develop guidelines for river restoration, comparative ecological experiments at contrasting water-level regimes are needed. We compared growth and survival of transplanted individuals of four riparian plant species (Betula pubescens, Carex acuta, Filipendula ulmaria and Leontodon autumnalis) over 2 years on four free-flowing and four regulated riverbank sites in northern Sweden. The species were chosen as representatives of dominating life-forms and species traits on different elevations of the riverbanks. 2In Betula and Filipendula, mean proportional growth rates were significantly higher at free-flowing sites than at regulated sites, whereas no consistent differences between free-flowing and regulated sites were found in Carex and Leontodon. Differences among species were generally in accordance with natural distribution patterns along riverbank elevation gradients and with experimental evidence on flooding tolerance, although plants of all species survived and even showed positive growth rates on elevations below their natural range of occurrence. 3Partial least squares regression was used to relate plant performance (growth and survival) to duration, frequency and timing of flooding at the different sites. Flood duration and frequency typically reduced performance in all species and during all time periods, although to various degrees. Flood events early in the experiment determined the outcome to a high degree at all sites. Variables indicating a regulated regime were mostly negatively related to plant performance, whereas free-flowing regime variables were positively related to plant performance. 4We used two of the regression models generated from our data with an acceptably high predictive power to simulate a hypothetical re-regulation scenario in run-of-river impoundments. With an overall reduction in flooding duration and frequency of 50,75%, plant performance of Filipendula at low riverbank elevations showed predicted increases of about 20,30%, levelling off to zero at the highest elevations. Reductions in summer floods represented about one-third to half of this increase. 5We conclude that for a range of species individual plant performance is clearly reduced on banks of impoundments and storage reservoirs due to changes in the water-level regime. Furthermore, our model simulation suggests that rather substantial reductions of flood duration and frequency are needed to improve plant performance on riverbanks upstream from dams in impounded rivers. River restoration principles should, however, be based on a combination of experimental data on plant performance of individual species and observed long-term changes in plant communities of regulated rivers. Consequently, successful re-regulation schemes in boreal rivers should include both reductions of summer and winter floods as well as re-introduced spring floods. [source] Patterns of woody plant species richness in the Iberian Peninsula: environmental range and spatial scaleJOURNAL OF BIOGEOGRAPHY, Issue 10 2008Ole R. Vetaas Abstract Aim, Climate-based models often explain most of the variation in species richness along broad-scale geographical gradients. We aim to: (1) test predictions of woody plant species richness on a regional spatial extent deduced from macro-scale models based on water,energy dynamics; (2) test if the length of the climate gradients will determine whether the relationship with woody species richness is monotonic or unimodal; and (3) evaluate the explanatory power of a previously proposed ,water,energy' model and regional models at two grain sizes. Location, The Iberian Peninsula. Methods, We estimated woody plant species richness on grid maps with c. 2500 and 22,500 km2 cell size, using geocoded data for the individual species. Generalized additive models were used to explore the relationships between richness and climatic, topographical and substrate variables. Ordinary least squares regression was used to compare regional and more general water,energy models in relation to grain size. Variation partitioning by partial regression was applied to find how much of the variation in richness was related to spatial variables, explanatory variables and the overlap between these two. Results, Water,energy dynamics generate important underlying gradients that determine the woody species richness even over a short spatial extent. The relationships between richness and the energy variables were linear to curvilinear, whereas those with precipitation were nonlinear and non-monotonic. Only a small fraction of the spatially structured variation in woody species richness cannot be accounted for by the fitted variables related to climate, substrate and topography. The regional models accounted for higher variation in species richness than the water,energy models, although the water,energy model including topography performed well at the larger grain size. Elevation range was the most important predictor at all scales, probably because it corrects for ,climatic error' due to the unrealistic assumption that mean climate values are evenly distributed in the large grid cells. Minimum monthly potential evapotranspiration was the best climatic predictor at the larger grain size, but actual evapotranspiration was best at the smaller grain size. Energy variables were more important than precipitation individually. Precipitation was not a significant variable at the larger grain size when examined on its own, but was highly significant when an interaction term between itself and substrate was included in the model. Main conclusions, The significance of range in elevation is probably because it corresponds to several aspects that may influence species diversity, such as climatic variability within grid cells, enhanced surface area, and location for refugia. The relative explanatory power of energy and water variables was high, and was influenced by the length of the climate gradient, substrate and grain size of the analysis. Energy appeared to have more influence than precipitation, but water availability is also determined by energy, substrate and topographic relief. [source] Species,area relationships in Mediterranean-climate plant communitiesJOURNAL OF BIOGEOGRAPHY, Issue 11 2003Jon E. Keeley Abstract Aim To determine the best-fit model of species,area relationships for Mediterranean-type plant communities and evaluate how community structure affects these species,area models. Location Data were collected from California shrublands and woodlands and compared with literature reports for other Mediterranean-climate regions. Methods The number of species was recorded from 1, 100 and 1000 m2 nested plots. Best fit to the power model or exponential model was determined by comparing adjusted r2 values from the least squares regression, pattern of residuals, homoscedasticity across scales, and semi-log slopes at 1,100 m2 and 100,1000 m2. Dominance,diversity curves were tested for fit to the lognormal model, MacArthur's broken stick model, and the geometric and harmonic series. Results Early successional Western Australia and California shrublands represented the extremes and provide an interesting contrast as the exponential model was the best fit for the former, and the power model for the latter, despite similar total species richness. We hypothesize that structural differences in these communities account for the different species,area curves and are tied to patterns of dominance, equitability and life form distribution. Dominance,diversity relationships for Western Australian heathlands exhibited a close fit to MacArthur's broken stick model, indicating more equitable distribution of species. In contrast, Californian shrublands, both postfire and mature stands, were best fit by the geometric model indicating strong dominance and many minor subordinate species. These regions differ in life form distribution, with annuals being a major component of diversity in early successional Californian shrublands although they are largely lacking in mature stands. Both young and old Australian heathlands are dominated by perennials, and annuals are largely absent. Inherent in all of these ecosystems is cyclical disequilibrium caused by periodic fires. The potential for community reassembly is greater in Californian shrublands where only a quarter of the flora resprout, whereas three quarters resprout in Australian heathlands. Other Californian vegetation types sampled include coniferous forests, oak savannas and desert scrub, and demonstrate that different community structures may lead to a similar species,area relationship. Dominance,diversity relationships for coniferous forests closely follow a geometric model whereas associated oak savannas show a close fit to the lognormal model. However, for both communities, species,area curves fit a power model. The primary driver appears to be the presence of annuals. Desert scrub communities illustrate dramatic changes in both species diversity and dominance,diversity relationships in high and low rainfall years, because of the disappearance of annuals in drought years. Main conclusions Species,area curves for immature shrublands in California and the majority of Mediterranean plant communities fit a power function model. Exceptions that fit the exponential model are not because of sampling error or scaling effects, rather structural differences in these communities provide plausible explanations. The exponential species,area model may arise in more than one way. In the highly diverse Australian heathlands it results from a rapid increase in species richness at small scales. In mature California shrublands it results from very depauperate richness at the community scale. In both instances the exponential model is tied to a preponderance of perennials and paucity of annuals. For communities fit by a power model, coefficients z and log c exhibit a number of significant correlations with other diversity parameters, suggesting that they have some predictive value in ecological communities. [source] Robust partial least squares regression: Part II, new algorithm and benchmark studiesJOURNAL OF CHEMOMETRICS, Issue 1 2008Uwe Kruger Abstract This paper presents the second part of the work on robust partial least squares (RPLS) regression and develops a new RPLS algorithm based on the concept laid out in Part I. The paper also contrasts the new algorithm with existing work using two simulation examples. This comparison highlights (i) the impact of the flaws in existing RPLS work and (ii) the compromised sensitivity resulting from introducing simplifications to the determination of the Stahel,Donoho estimator (SDE). The paper finally presents an evaluation of the computational complexity of RPLS algorithms and examines the impact of the signal-to-noise ratio (SNR) upon the sensitivity of detecting outliers. The third part of this work will examine practical aspects of RPLS applications based on the analysis of experimental data. Copyright © 2007 John Wiley & Sons, Ltd. [source] Theory of net analyte signal vectors in inverse regressionJOURNAL OF CHEMOMETRICS, Issue 12 2003Rasmus Bro Abstract The net analyte signal and the net analyte signal vector are useful measures in building and optimizing multivariate calibration models. In this paper a theory for their use in inverse regression is developed. The theory of net analyte signal was originally derived from classical least squares in spectral calibration where the responses of all pure analytes and interferents are assumed to be known. However, in chemometrics, inverse calibration models such as partial least squares regression are more abundant and several tools for calculating the net analyte signal in inverse regression models have been proposed. These methods yield different results and most do not provide results that are in accordance with the chosen calibration model. In this paper a thorough development of a calibration-specific net analyte signal vector is given. This definition turns out to be almost identical to the one recently suggested by Faber (Anal. Chem. 1998; 70: 5108,5110). A required correction of the net analyte signal in situations with negative predicted responses is also discussed. Copyright © 2004 John Wiley & Sons, Ltd. [source] Non-parametric statistical methods for multivariate calibration model selection and comparison,JOURNAL OF CHEMOMETRICS, Issue 12 2003Edward V. Thomas Abstract Model selection is an important issue when constructing multivariate calibration models using methods based on latent variables (e.g. partial least squares regression and principal component regression). It is important to select an appropriate number of latent variables to build an accurate and precise calibration model. Inclusion of too few latent variables can result in a model that is inaccurate over the complete space of interest. Inclusion of too many latent variables can result in a model that produces noisy predictions through incorporation of low-order latent variables that have little or no predictive value. Commonly used metrics for selecting the number of latent variables are based on the predicted error sum of squares (PRESS) obtained via cross-validation. In this paper a new approach for selecting the number of latent variables is proposed. In this new approach the prediction errors of individual observations (obtained from cross-validation) are compared across models incorporating varying numbers of latent variables. Based on these comparisons, non-parametric statistical methods are used to select the simplest model (least number of latent variables) that provides prediction quality that is indistinguishable from that provided by more complex models. Unlike methods based on PRESS, this new approach is robust to the effects of anomalous observations. More generally, the same approach can be used to compare the performance of any models that are applied to the same data set where reference values are available. The proposed methodology is illustrated with an industrial example involving the prediction of gasoline octane numbers from near-infrared spectra. Published in 2004 by John Wiley & Sons, Ltd. [source] Robust methods for partial least squares regressionJOURNAL OF CHEMOMETRICS, Issue 10 2003M. Hubert Abstract Partial least squares regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm, this being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variables and the regressors and on linear least squares regression, the results are affected by abnormal observations in the data set. Two robust methods, RSIMCD and RSIMPLS, are constructed from a robust covariance matrix for high-dimensional data and robust linear regression. We introduce robust RMSECV and RMSEP values for model calibration and model validation. Diagnostic plots are constructed to visualize and classify the outliers. Several simulation results and the analysis of real data sets show the effectiveness and robustness of the new approaches. Because RSIMPLS is roughly twice as fast as RSIMCD, it stands out as the overall best method. Copyright © 2003 John Wiley & Sons, Ltd. [source] A robust PCR method for high-dimensional regressorsJOURNAL OF CHEMOMETRICS, Issue 8-9 2003Mia Hubert Abstract We consider the multivariate calibration model which assumes that the concentrations of several constituents of a sample are linearly related to its spectrum. Principal component regression (PCR) is widely used for the estimation of the regression parameters in this model. In the classical approach it combines principal component analysis (PCA) on the regressors with least squares regression. However, both stages yield very unreliable results when the data set contains outlying observations. We present a robust PCR (RPCR) method which also consists of two parts. First we apply a robust PCA method for high-dimensional data on the regressors, then we regress the response variables on the scores using a robust regression method. A robust RMSECV value and a robust R2 value are proposed as exploratory tools to select the number of principal components. The prediction error is also estimated in a robust way. Moreover, we introduce several diagnostic plots which are helpful to visualize and classify the outliers. The robustness of RPCR is demonstrated through simulations and the analysis of a real data set. Copyright © 2003 John Wiley & Sons, Ltd. [source] Real-time forecasting of photosmog episodes: the Naples case studyJOURNAL OF CHEMOMETRICS, Issue 7 2001A. Riccio Abstract In this paper we analysed the ozone time series data collected by the local monitoring network in the Naples urban area (southern Italy) during the spring/summer period of 1996. Our aim was to identify a reliable and effective model that could be used for the real-time forecasting of photosmog episodes. We studied the applicability of seasonal autoregressive integrated moving average models with some exogenous variables (ARIMAX) to our case study. The choice of exogenous variables,temperature, [NO2]/[NO] ratio and wind speed,was based on physical reasoning. The forecasting performance of all models was evaluated with data not used in model development, by means of an array of statistical indices: the comparison between observed and forecast means and standard deviations; intercept and slope of a least squares regression of forecast variable on observed variable; mean absolute and root mean square errors; and 95% confidence limits of forecast variable. The assessment of all models was also based on their tendency to forecast critical episodes. It was found that the model using information from the temperature data set to predict peak ozone levels gives satisfactory results, about 70% of critical episodes being correctly predicted by the 24,h ahead forecast function. Copyright © 2001 John Wiley & Sons, Ltd. [source] | ||||||||||||||||||||||||||||