Search results for "STICS"

showing 10 items of 21128 documents

Comparison of odour sensory profiles performed by two independent trained panels following the same descriptive analysis procedures

2000

Odour sensory profiling of 28 associations of cheese ripening micro-organisms was performed by two panels of 10 assessors on two different sites. Sample preparation, training protocols and references, tasting procedures and scoring were similar in the two laboratories. Panel 2 used 10 attributes and panel 1 used these terms plus 4 extra descriptors. Analysis of variance and multivariate methods (canonical variate analysis, generalised procrustes analysis and STATIS) exhibited differences between assessors within a panel and between panels concerning the use of the scoring scale and the strength of product discrimination by attribute. Panel 1 was more sensitive to fruity notes and panel 2 to…

0303 health sciencesMultivariate statisticsNutrition and DieteticsDescriptive statistics030309 nutrition & dieteticsSensory system04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineering040401 food scienceSensory analysisGeneralised procrustes analysis03 medical and health sciences0404 agricultural biotechnologyCanonical variate analysisStatistics[SDV.IDA]Life Sciences [q-bio]/Food engineeringEconometricsWine tastingAnalysis of varianceComputingMilieux_MISCELLANEOUSFood ScienceMathematics
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Risk tables for discrimination tests

1993

Abstract Duo-trio and triangle test are often used in the food industry for the purpose of declaring two products non-distinguishable. In that situation, it is much more important to control the power of the test rather than the type 1 error risk. This paper makes available by e-mail a SAS ® macro, called BINRISKS, for computing type 1 and type 2 risks for any one-tailed binomial test and for any level of the percentage above chance to be detected. Using this macro, two sets of tables have been compiled. The first table includes for any total number of responses below 50, for any number of correct responses and for three levels of the percentage above chance to be detected, the correspondin…

0303 health sciencesNutrition and Dietetics030309 nutrition & dieteticsBinomial test04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineering040401 food scienceDiscrimination testingTest (assessment)03 medical and health sciences0404 agricultural biotechnology[SDV.IDA]Life Sciences [q-bio]/Food engineeringStatisticsEconometricsTable (database)MacroComputingMilieux_MISCELLANEOUSFood ScienceTriangle testMathematicsType I and type II errorsFood Quality and Preference
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The MAM-CAP table: A new tool for monitoring panel performances

2014

Abstract Assessor performances in sensory analysis are usually represented by three indicators: repeatability, discrimination and agreement. However, assessors can also differ on the range of their scores, the so-called “scaling effect”. Brockhoff, Schlich, and Skovgaard (2013) proposed the mixed assessor model (MAM) which, as the original assessor model ( Brockhoff & Skovgaard, 1994 ), takes this effect into account, but also allows for the product effect to be tested against a new interaction free of the scaling effect. The present paper proposes a unified system for monitoring assessor and panel performances based on the MAM. In addition to the product effect (tested at panel and individ…

0303 health sciencesNutrition and Dietetics030309 nutrition & dieteticsComputer science[ SDV.AEN ] Life Sciences [q-bio]/Food and Nutritionscalingpanel performance04 agricultural and veterinary sciencesRepeatabilitymixed assessor model040401 food scienceSensory analysisUnified system03 medical and health sciences0404 agricultural biotechnologyScaling effectStatisticsRange (statistics)Table (database)Scaling[SDV.AEN]Life Sciences [q-bio]/Food and NutritionFood Science
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What are the sensory differences among coffees? Multi-panel analysis of variance and FLASH analysis

1998

International audience

0303 health sciencesNutrition and Dietetics030309 nutrition & dieteticsSensory system04 agricultural and veterinary sciencesVariance (accounting)[SDV.IDA] Life Sciences [q-bio]/Food engineering040401 food scienceSensory analysis03 medical and health sciencesFlash (photography)0404 agricultural biotechnologyPanel analysis[SDV.IDA]Life Sciences [q-bio]/Food engineeringStatisticsmedia_common.cataloged_instanceAnalysis of varianceEuropean unionComputingMilieux_MISCELLANEOUSFood ScienceDemographymedia_commonMathematicsFood Quality and Preference
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Uses of change-over designs and repeated measurements in sensory and consumer studies

1993

Abstract The paper illustrates two statistical methods, the design and analysis of sensory experiments taking into account the effects of serving order and previously assessed treatment and the analysis of experiments with time repeated measurements. Change-over design experiments balance both presentation order and carry-over effects. The proper analysis of variance allows the testing of these effects and the estimation of product means adjusted for carry-over effect. Repeated measurements occur when groups are being compared over time. Either a corrected split-plot or a multivariate analysis of variance (MANOVA) with measurements at different times forming the variable should be adopted t…

0303 health sciencesNutrition and Dietetics030309 nutrition & dieteticsSensory system04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineering040401 food scienceSensory analysis03 medical and health sciencesVariable (computer science)0404 agricultural biotechnologyMultivariate analysis of varianceStatistics[SDV.IDA]Life Sciences [q-bio]/Food engineeringGroup effectMain effectAnalysis of variancePsychologyComputingMilieux_MISCELLANEOUSFood ScienceBalance (ability)
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Building an Optimal WSD Ensemble Using Per-Word Selection of Best System

2006

In Senseval workshops for evaluating WSD systems [1,4,9], no one system or system type (classifier algorithm, type of system ensemble, extracted feature set, lexical knowledge source etc.) has been discovered that resolves all ambiguous words into their senses in a superior way. This paper presents a novel method for selecting the best system for target word based on readily available word features (number of senses, average amount of training per sense, dominant sense ratio). Applied to Senseval-3 and Senseval-2 English lexical sample state-of-art systems, a net gain of approximately 2.5 – 5.0% (respectively) in average precision per word over the best base system is achieved. The method c…

0303 health sciencesWord-sense disambiguationComputer scienceSample (material)Speech recognition02 engineering and technologyBase (topology)SemanticsSupport vector machine03 medical and health sciencesPattern recognition (psychology)Classifier (linguistics)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingWord (computer architecture)030304 developmental biology
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Efficient Online Laplacian Eigenmap Computation for Dimensionality Reduction in Molecular Phylogeny via Optimisation on the Sphere

2019

Reconstructing the phylogeny of large groups of large divergent genomes remains a difficult problem to solve, whatever the methods considered. Methods based on distance matrices are blocked due to the calculation of these matrices that is impossible in practice, when Bayesian inference or maximum likelihood methods presuppose multiple alignment of the genomes, which is itself difficult to achieve if precision is required. In this paper, we propose to calculate new distances for randomly selected couples of species over iterations, and then to map the biological sequences in a space of small dimension based on the partial knowledge of this genome similarity matrix. This mapping is then used …

0303 health sciences[STAT.AP]Statistics [stat]/Applications [stat.AP]Computer scienceDimensionality reductionComputationDimension (graph theory)Complete graphMinimum spanning treeBayesian inferenceQuantitative Biology::Genomics03 medical and health sciencesComputingMethodologies_PATTERNRECOGNITION0302 clinical medicine[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]Algorithm030217 neurology & neurosurgeryEigenvalues and eigenvectorsDistance matrices in phylogenyComputingMilieux_MISCELLANEOUS030304 developmental biology
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EHRtemporalVariability: delineating temporal dataset shifts in electronic health records

2020

AbstractBackgroundTemporal variability in healthcare processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal dataset shifts can present as trends, abrupt or seasonal changes in the statistical distributions of data over time, being particularly complex to address in multi-modal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large historical data from EHRs, there is a need for spec…

0303 health scienceseducation.field_of_studybusiness.industryComputer sciencePopulationReuseHealth recordsData science3. Good health03 medical and health sciencesIdentification (information)0302 clinical medicineSoftwareData qualityRange (statistics)030212 general & internal medicineUser interfacebusinesseducation030304 developmental biology
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Defining sensory descriptors: towards writing rules based on terminology

2007

International audience; Descriptive analysis relies upon the use of sensory descriptors. They are words generally associated to a definition aimed at helping their understanding. However, the writing rules for such definitions remain implicit. The present work is a collaborative attempt from sensory analysts and linguists to get further insight into how definitions are elaborated.Definition formulations were analyzed according to linguistic criteria, syntactic (type and number of nouns, verbs and adjectives) as well as semantic ones (relations of synonymy, metaphor or analogy between the descriptors and their definitions). Such a linguistic analysis was performed on one hundred descriptor d…

030309 nutrition & dieteticsMetaphorComputer sciencemedia_common.quotation_subjectrègles d'écritureAnalogycomputer.software_genreSemanticsPsycholinguisticsTerminology03 medical and health sciences0404 agricultural biotechnologyNounterminologyterminologie[SHS.LANGUE]Humanities and Social Sciences/LinguisticsSet (psychology)ComputingMilieux_MISCELLANEOUSmedia_commonStructure (mathematical logic)0303 health sciencesNutrition and Dieteticsbusiness.industry[SCCO.NEUR]Cognitive science/Neurosciencelinguisticswriting rules04 agricultural and veterinary sciences040401 food scienceLinguistics[ SCCO.NEUR ] Cognitive science/Neuroscience[SCCO.PSYC]Cognitive science/Psychology[SCCO.PSYC] Cognitive science/PsychologyArtificial intelligencebusinesscomputerNatural language processinglinguistiqueFood Science
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Comparison of conventional descriptive analysis and a citation frequency-based descriptive method for odor profiling: An application to Burgundy Pino…

2010

International audience; The limitations of intensity scoring when describing the odor characteristics of a complex product have been documented in the literature. In the present work, the odor properties of 12 Burgundy Pinot noir wines were described by two independent panels performing, respectively, an intensity-based (conventional descriptive analysis) and a citation frequency-based method. Methods were compared according to three criteria: similarity of the sensory maps, control of panel performance and practical aspects. Intensity scoring and citation frequency data were analyzed, respectively, by Principal Components Analysis (PCA) and Correspondence Analysis (CA) followed by Hierarch…

030309 nutrition & dietetics[ SDV.AEN ] Life Sciences [q-bio]/Food and NutritionSensory analysisCorrespondence analysis03 medical and health sciences0404 agricultural biotechnologySENSORY ANALYSISStatistics[SDV.IDA]Life Sciences [q-bio]/Food engineeringCluster analysisComputingMilieux_MISCELLANEOUSMathematicsWinePINOT NOIR0303 health sciencesFREQUENCY OF CITATIONNutrition and DieteticsDescriptive statisticsbusiness.industryDESCRIPTIVE PROFILEWINE04 agricultural and veterinary sciencesCONVENTIONAL DA040401 food scienceHierarchical clusteringOdorPrincipal component analysisArtificial intelligencebusiness[SDV.AEN]Life Sciences [q-bio]/Food and NutritionMETHOD COMPARISONFood Science
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