Dry mixing of spice powders - investigation of effect of powder properties on mixture quality of binary powder mixtures

Dry mixing of binary food powders was conducted in a 2L lab-scale paddle mixer. Different types of food powders such as paprika, oregano, black pepper, onion powder and salt were used for the studies. A novel method based on a digital colour imaging system (DCI) was developed to measure the mixture quality (MQ) of binary food powder mixtures. The salt conductivity method was also used as an alternative method to measure the MQ. In the first part of the study the DCI method was developed and it showed potential for assessing MQ of binary powder mixes provided there was huge colour difference between the powders. In the second and third part of the study the effect of composition, water content, particle size and bulk density on MQ was studied. Flowability of powders at various moisture contents was also investigated. The mixing behaviour was assessed using coefficient of variation. Results showed that water content and composition influence the mixing behavior of powders. Good mixing was observed up to size ratios of 4.45 and at higher ratios MQ disimproved. The bulk density had a larger influence on the MQ. In the final study the MQ evaluation of binary and ternary powder mixtures was compared by using two methods – salt conductivity method and DCI method. Two binary food and two quaternary food powder mixtures with different coloured ingredients were studied. Overall results showed that DCI method has a potential for use by industries and it can analyse powder mixtures with components that have differences in colour and that are not segregating in nature.

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.