Partial least squares and principal components analysis of wine vintage by high performance liquid chromatography with chemiluminescence detection

HPLC with acidic potassium permanganate chemiluminescence detection was employed to analyse 17 Cabernet Sauvignon wines across a range of vintages (1971–2003). Partial least squares regression analysis and principal components analysis was used in order to investigate the relationship between wine composition and vintage. Tartaric acid, vanillic acid, catechin, sinapic acid, ethyl gallate, myricetin, procyanadin B and resveratrol were found to be important components in terms of differences between the vintages.

Modelling for injection moulding process based on principal component regression method

Determination of the optimal operating condition for moulding process has been of special interest for many researchers. To determine the optimal setting, one has to derive the model of injection moulding process first which is able to map the relationship between the input process control factors and output responses. One of most popular modeling techniques is the linear least square regression due to its effectiveness and completeness. However, the least square regression was found to be very sensitive to the outliers and failed to provide a reliable model if the control variables are highly related with each other. To address this problem, a new modeling method based on principal component regression was proposed in this paper. The distinguished feature of our proposed method is it does not only consider the variance of covariance matrix of control variables but also consider the correlation coefficient between control variables and target variables to be optimised. Such a modelling method has been implemented into a commercial optimisation software and field test results demonstrated the performance of the proposed modelling method.

Snapshots of complexity: using motion capture and principal component analysis to reconceptualise dance

This article brings together the disparate worlds of dance practice, motion capture and statistical analysis. Digital technologies such as motion capture offer dance artists new processes for recording and studying dance movement. Statistical analysis of these data can reveal hidden patterns in movement in ways that are semantically ‘blind’, and are hence able to challenge accepted culturo-physical ‘grammars’ of dance creation. The potential benefit to dance artists is to open up new ways of understanding choreographic movement. However, quantitative analysis does not allow for the uncertainty inherent in emergent, artistic practices such as dance. This article uses motion capture and principal component analysis (PCA), a common statistical technique in human movement recognition studies, to examine contemporary dance movement, and explores how this analysis might be interpreted in an artistic context to generate a new way of looking at the nature and role of movement patterning in dance creation.

Learning over sets using boosted manifold principal angles (BoMPA)

In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.

Principal led school improvement and teacher capacity building in the Barwon South Network : final report, May 2013

The period of interest for this report is the beginning of 2011 to the end of 2012. The period commenced when the Regional Network Leader of the Barwon South Network of schools in the Barwon South Region of the Department of Education and Early Childhood contacted the School of Education at Deakin University, Waurn Ponds Campus Geelong. The Regional Network Leader outlined a desire to engage with Deakin University to research a short-term-cycle model of school improvement to be implemented in the region. While the model was expected to be taken on by all schools in the region the research was limited to the 23 schools in the Barwon South Network with four schools to be investigated more closely for each of two years (2001 & 2012) – eight focus schools in total.

Many positive outcomes flowed from the implementation of short-term-cycle school improvement plans and their associated practices but there was wide variation in the nature and degrees of success and of the perception of the process. The research team asked the following questions of the data:

1. What aspects of the School Improvement Plan (SIP) approach were important for initiating and supporting worthwhile change?
2. What might we take from this, to provide guidance on how best to support change in teaching and learning processes in schools?

The School Improvement Plan (SIP) worked in a range of ways. At one level it was strongly focused on school leadership, and a need to improve principals’ capacity to initiate worthwhile teaching and learning processes in their schools. Underlying this intent, one might think an assumption is operation is that the leadership process involves top down decision-making and a willingness to hold staff accountable for the quality of their practice.

The second strong focus was on the translation into practice and the consequent effect on student learning, involving an emphasis on data and evidence led practice. Hence, along with the leadership focus there was a demand for the process of school improvement to reach down into students and classrooms. Thus, the SIP process inevitably involved a chain of decision-making by which student learning quality drove the intervention, and teachers responsible for this had a common view. The model therefore should not be seen as an intervention only on the principal, but rather on the school decision-making system and focus. Even though it was the principal receiving the SIP planning template, and reporting to the network, the reporting was required to include description of the operation of the school processes, of classroom processes, and of student learning. This of course placed significant constraints on principals, which may help explain the variation in responses and outcomes described above.

The findings from this study are based on multiple data sources: analysis of both open and closed survey questions which all teachers in the 23 schools in the network were invited to complete; interviews with principals, teachers and leaders in the eight case study schools; some interviews with students in the case study schools; and interviews with leaders who worked in the regional network office; and field notes from network meetings including the celebrations days. Celebrations days occurred each school term when groups of principals came together to share and celebrate the improvements and processes happening in their schools. Many of the themes emerging from the analysis of the different data sources were similar or overlapping, providing some confidence in the evidence-base for the findings.

The study, conducted over two years of data collection and analysis, has demonstrated a range of positive outcomes in at the case study schools relating to school communication and collaboration processes, professional learning of principals, leadership teams and classroom teachers. There was evidence in the survey responses and field notes from ‘celebration days’ that these outcomes were also represented in other schools in the network. The key points of change concerned the leadership processes of planning for improvement, and the rigorous attention to student data in framing teaching and learning processes. This latter point of change had the effect of basing SIP processes on a platform of evidence-based change. The research uncovered considerable anecdotal and observational evidence of improvements in student learning, in teacher accounts in interview, and presentations of student work. Interviews with students, although not as representative as the team would have liked, showed evidence of student awareness of learning goals, a key driver in the SIP improvement model. It was, however, not possible over this timescale to collect objective comparative evidence of enhanced learning outcomes.

A number of features of the short-term-cycle SIP were identified that supported positive change across the network. These were: 1) the support structures represented by the network leader and support personnel within schools, 2) the nature of the SIP model – focusing strongly on change leadership but within a collaborative structure that combined top-down and bottom-up elements, 3) the focus on data-led planning and implementation that helped drill down to explicit elements of classroom practice, and 4) the accountability regimes represented by network leader presence, and the celebration days in which principals became effectively accountable to their peers. We found that in the second year of the project, momentum was lost in the case study schools, as the network was dismantled. This raised issues also for the conduct of research in situations of systemic change.

Alongside the finding of evidence of positive outcomes in the case study schools overall, was the finding that the SIP processes and outcomes varied considerably across schools. A number of contextual factors were identified that led to this variation, including school histories of reform, principal management style, and school size and structure that made the short-term-cycle model unmanageable. In some cases there was overt resistance to the SIP model, at least in some part, and this led to an element of performativity in which the language of the SIP was conscripted to other purposes. The study found that even with functioning schools the SIP was understood differently and the processes performed differently, raising the question of whether in the study we are dealing with one SIP or many. The final take home message from the research is that schools are complex institutions, and models of school improvement need to involve both strong principled features, and flexibility in local application, if all schools’ interests in improving teaching and learning processes and outcomes are to be served.

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. © 2010 World Scientific Publishing Company.

Cross-sectional averages versus principal components

In spite of the increased use of factor-augmented regressions in recent years, little is known regarding the relative merits of the two main approaches to estimation and inference, namely, the cross-sectional average and principal component estimators. By providing a formal comparison of the approaches, the current paper fills this gap in the literature.

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

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

A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians

BACKGROUND: Despite increased use of dietary pattern methods in nutritional epidemiology, there have been few direct comparisons of methods. Older adults are a particularly understudied population in the dietary pattern literature. This study aimed to compare dietary patterns derived by principal component analysis (PCA) and cluster analysis (CA) in older adults and to examine their associations with socio-demographic and health behaviours. METHODS: Men (n = 1888) and women (n = 2071) aged 55-65 years completed a 111-item food frequency questionnaire in 2010. Food items were collapsed into 52 food groups and dietary patterns were determined by PCA and CA. Associations between dietary patterns and participant characteristics were examined using Chi-square analysis. The standardised PCA-derived dietary patterns were compared across the clusters using one-way ANOVA. RESULTS: PCA identified four dietary patterns in men and two dietary patterns in women. CA identified three dietary patterns in both men and women. Men in cluster 1 (fruit, vegetables, wholegrains, fish and poultry) scored higher on PCA factor 1 (vegetable dishes, fruit, fish and poultry) and factor 4 (vegetables) compared to factor 2 (spreads, biscuits, cakes and confectionery) and factor 3 (red meat, processed meat, white-bread and hot chips) (mean, 95 % CI; 0.92, 0.82-1.02 vs. 0.74, 0.63-0.84 vs. -0.43, -0.50- -0.35 vs. 0.60 0.46-0.74, respectively). Women in cluster 1 (fruit, vegetables and fish) scored highest on PCA factor 1 (fruit, vegetables and fish) compared to factor 2 (processed meat, hot chips cakes and confectionery) (1.05, 0.97-1.14 vs. -0.14, -0.21- -0.07, respectively). Cluster 3 (small eaters) in both men and women had negative factor scores for all the identified PCA dietary patterns. Those with dietary patterns characterised by higher consumption of red and processed meat and refined grains were more likely to be Australian-born, have a lower level of education, a higher BMI, smoke and did not meet physical activity recommendations (all P < 0.05). CONCLUSIONS: PCA and CA identified comparable dietary patterns within older Australians. However, PCA may provide some advantages compared to CA with respect to interpretability of the resulting dietary patterns. Older adults with poor dietary patterns also displayed other negative lifestyle behaviours. Food-based dietary pattern methods may inform dietary advice that is understood by the community.

Effective and efficient detection of premature ventricular contractions based on variation of principal directions

Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use "Replacing" strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.