W. T. O'Hare

The age estimation of blood stains up to 30 days old using visible wavelength hyperspectral image analysis and linear discriminant analysis

A novel application of visible wavelength hyperspectral image analysis has been applied to determine the age of blood stains up to 30 days old. Reflectance spectra from selected locations within the hyperspectral image, obtained from a portable instrument, were subjected to spectral pre-processing. This was followed by the application of a linear discriminant classification model, making estimations possible with an average error of ±0.27days for the first 7 days and an overall average error of ±1.17days up to 30 days. This is also the first reported study of the determination of the age of fresh blood stains (less than one day old) with an error of ±0.09h. The studies have been made under controlled conditions and represent, at this stage, proof of concept results but also are the most accurate age estimation results for measurements between 0 and 30 days reported to date. The results are consistent with well-established kinetic processes suggesting that the pre-processing stages described are revealing spectroscopic changes which are reliably following the time dependent oxidation of HbO2. The potential for parameterisation of environmental factors to make the method generally applicable at crime scenes is discussed, along with the developments required to further improve classification and to make the instrument genuinely portable.

Fluoridated toothpaste: usage and ingestion of fluoride by 4‐ to 6‐yr‐old children in England

Fluoridated toothpaste is effective for dental caries control, yet may be a risk factor for dental fluorosis. This study aimed to quantify fluoride ingestion from toothpaste by children and to investigate the effects of age, gender, and social class on the amount of fluoride ingested per toothbrushing session. Sixty-one children, 4-6 yr of age, were recruited: 38 were from low socio-economic (LSE) areas of Newcastle, UK, and 23 were from high socio-economic (HSE) areas of Newcastle, UK. All expectorated saliva, rinse water (if used), and residual toothpaste were collected after brushing at home and were analysed for fluoride. Of the children, 74% and 69% from HSE and LSE areas, respectively, claimed that they brushed twice per day. The mean (SD) weight of toothpaste dispensed was 0.67 (0.36) g. The mean (SD) amount of fluoride ingested per toothbrushing session and per day was 17.0 (14.7) and 29.3 (32.8) μg kg(-1) of body weight, respectively. Daily fluoride intake per kilogram of body weight did not differ significantly between children from LSE and HSE areas. Fluoride intake per toothbrushing session was significantly influenced by weight of toothpaste, its fluoride concentration, and the childs age. Whilst the average amount of toothpaste used per toothbrushing session was more than twice the recommended amount (of 0.25 g), only one child had a daily fluoride intake that exceeded the tolerable upper intake level of 0.1 mg kg(-1) of body weight for this age group.

Detection of bacterial contaminated milk by means of a quartz crystal microbalance based electronic nose

Headspace analysis by means of sensor arrays has been successfully applied to a wide range of qualitative applications. In this study, a six element array of coated Quartz Crystal Microbalance (QCM) sensors was used for the headspace analysis of milk volatiles. The sensors were exposed to uncontaminated samples of milk and samples contaminated with Pseudomonas fragi (Ps. fragi) or Escherichia coli (E. coli). Principal component analysis (PCA) was used to analyse the sensor array responses. No discrimination between uncontaminated milk samples and those contaminated with Ps. fragi was observed. This can be explained by Ps. fragi being a poor fermenter of milk. However, encouraging results were found for the discrimination between the milk samples and those contaminated with E. coli.

Total luminescence spectroscopy with pattern recognition for classification of edible oils

Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.

The application of visible wavelength reflectance hyperspectral imaging for the detection and identification of blood stains

Current methods of detection and identification of blood stains rely largely on visual examination followed by presumptive tests such as Kastle-Meyer, Leuco-malachite green or luminol. Although these tests are useful, they can produce false positives and can also have a negative impact on subsequent DNA tests. A novel application of visible wavelength reflectance hyperspectral imaging has been used for the detection and positive identification of blood stains in a non contact and non destructive manner on a range of coloured substrates. The identification of blood staining was based on the unique visible absorption spectrum of haemoglobin between 400 and 500 nm. Images illustrating successful discrimination of blood stains from nine red substances are included. It has also been possible to distinguish between blood and approximately 40 other reddish stains. The technique was also successfully used to detect latent blood stains deposited on white filter paper at dilutions of up to 1 in 512 folds and on red tissue at dilutions of up to 1 in 32 folds. Finally, in a blind trial, the method successfully detected and identified a total of 9 blood stains on a red T-shirt.

Radial basis neural network for the classification of fresh edible oils using an electronic nose

An electronic nose utilising an array of six-bulk acoustic wave polymer coated Piezoelectric Quartz (PZQ) sensors has been developed. The nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), Non-virgin olive oil (OI) and Sunflower oil (SFO), were used over a period of 30 days. The sensor responses were then analysed producing an architecture for the Radial Basis Function Artificial Neural Network (RBF). It was found that the RBF results were excellent, giving classifications of above 99% for the vegetable oil test samples.

Discrimination of Sri Lankan black teas using fluorescence spectroscopy and linear discriminant analysis.

BACKGROUND The quality of teas is currently graded using trained tea tasters, whose evaluation can sometimes be subjective. In this study the simple fluorescence-based technique of total luminescence spectroscopy (TLS) in conjunction with data classification using principal component analysis (PCA) was applied to discriminate between teas from 11 different Sri Lankan plantations. Solvent extraction of the tea samples was followed by TLS to record excitation-emission matrices in the excitation range 250-590 nm and emission range 300-700 nm. RESULTS The application of PCA and linear discriminant analysis (LDA) allowed the successful classification of all 11 teas using only the first two principal components. LDA demonstrated how the technique was able to discriminate between all teas correctly with 100% classification. CONCLUSION Further development of this work could lead to a simple device that could be used by tea manufacturers instead of or alongside trained tea tasters to grade teas.

The motivations of young people moving into medical waste scavenging as a street career

This paper discusses the impact of sociocultural exclusion influences and biographical disruptions on adolescents moving into street involvement associated with hazardous medical waste scavenging. Data were collected in Dhaka, Bangladesh, using a variety of qualitative techniques, including adaptive sampling for roaming populations. Observation distinguished a distinct group of people (‘medical waste scavengers’) who were involved in unauthorised scavenging and reselling of medical waste. This trade is linked to homelessness, sexual abuse and drug use. These individuals have given accounts of underprivileged family backgrounds, exclusion due to ethnicity or caste, and of horrific experiences through early childhood and adolescence. From this difficult position, they have demonstrated flexibility and resilience to develop the street competence required to survive by specialising in scavenging extremely hazardous items to be repackaged and resold to the community.

Gas-sensing System Using an Array of Coated Quartz Crystal Microbalances with a Fuzzy Inference System

Quartz crystal microbalances have high mass sensitivities. Their application in gas sensing has been limited because they are required to have both high selectivity and reversibility. Yet by the inherent nature of their operation these properties are mutually exclusive. One approach to this problem is to use an array of quartz crystal microbalances. We have used an array of six coated quartz crystal microbalances for the classification of methanol, propan-1-ol, butan-1-ol, hexane, heptane and toluene. A novel classification scheme using fuzzy membership functions was found to be highly efficient.

Organic vapour sensing using a coated piezoelectric quartz crystal sensor array

The pattern of responses from a four sensor array have been used for the classification of methanol, propanol, butanol, hexane, heptane and toluene using artificial intelligence (AI) based pattern recognition methods. A feedforward forward network with backpropagation was trained using sensor array data with approximately 300 training vectors and 100 test cases and covering a period of four months. The network consisting of four input nodes, six output nodes, learning rate of 0.1 and momentum of 0 was built using a commercial package (NeuroShell). A classification success rate of 75% was achieved. The bulk of the mis-classifications arose from propanol being classified as butanol and hexane being classified as heptane. These mis-classifications are rational since the respective compounds are very similar in nature. A fuzzy logic algorithm where class membership functions are developed using the mean frequency change and standard deviation of individual sensors was developed for classification of the vapors. In this particular case, classification using the developed fuzzy logic Gaussian algorithm was not as good as the feedforward network with backpropagation, but the Gaussian membership function offers a more rational approach than the previously published trapezoidal membership function.