Cormac Sheridan

A comparison of CIE L*a*b* and spectral methods for the analysis of fading in sliced cured ham

In the modern retail environment, the appearance of a product is frequently the only quality indicator available to consumers. This is especially true of products such as sliced ham that have been sealed into packages to maintain product freshness. It has been shown that sliced ham products undergo discolouration from their original pink colour to a pale grey colour when exposed to a combination of oxygen and light. This is unappealing to consumers who expect a pink colour for sliced ham. An investigation is made into a sensor that would monitor the initial colour status of cured ham before packaging in order to determine the amount of time left before the ham fades to an unsatisfactory colour. For this sensor to operate, appropriate analysis of the appearance of the meat is required. Two methods for the measurement of the fading were investigated—CIE L*a*b* measurements and analysis of the spectral reflectance of the colour of the ham. Several sliced ham products with differing amounts of fading were examined using both methods. It was observed that the products used covered a wide range of variation in colour. Reproducibility of CIE L*a*b* values proved to be quite difficult and significant overlapping of the L* (lightness) and a* (redness) values measured for pink and grey coloured ham was observed. The variations in these values can be attributed to differences in the intensity of reflected light for different products. L*a*b* measurements are sensitive to light intensity and pigment concentration. Analysis of the spectral reflectance readings did not encounter these problems as the spectral response was normalized (to reduce intensity errors) before data analysis was carried out on the spectral shape or pattern using principal component analysis (PCA) and artificial neural networks (ANN). A classifier based on PCA and ANN was successfully implemented that can discriminate different stages of fading for the ham slices. A case study was carried out on ham slices that had two different initial colours—light and dark. The results of the case study show that the sensor system can better discriminate between a light initial colour and a dark faded colour than the CIE L*a*b* colour measurement system.

Monitoring food quality using an optical fibre based sensor system—a comparison of Kohonen and back-propagation neural network classification techniques

This paper reports on two methods of classifying the spectral data from an optical fibre based sensor system as used in the food industry. The first method uses a feed-forward back-propagation artificial neural network while the second method involves using Kohonen self-organizing maps. The sensor monitors the food colour online as the food cooks by examining the reflected light from both the surface and the core of the product. The combination of using principal component analysis and back-propagation neural networks has been successfully investigated previously. In this paper, results obtained using this method are compared with results obtained using a self-organizing map trained on the principal components. The principal components used to train both classifiers are ordered in a colourscale—a scale developed to allow several products of similar colour to be tested using a single network that had been trained using the colourscale. The results presented show that both classifiers perform well, and that any differences that arise occur at the boundaries of the classes.

Development of a fibre optic sensor for the detection of harmful algae bloom and in particular domoic acid

Research into an early warning harmful algae bloom (HAB) sensing system for use in underwater monitoring applications is presented. The sensor proposed by the authors utilises principal component analysis (PCA) to establish the complex linkages between ocean colour, absorption and scattering, algae pigmentation and cell size, along with depth of bloom layers. The authors are proposing an optical fibre sensor based method of detecting the associated neurotoxins that cause harmful algal blooms. The sensing mechanism utilised in this system is based on a combination of absorption and reflection spectroscopy and principle component analysis (PCA) signal processing. Preliminary results are presented from the interrogation of chlorophyll, yeast and saline solutions.

Online Optical Fiber Sensor for Detecting Premature Browning in Ground Beef Using Pattern Recognition Techniques and Reflection Spectroscopy

This paper examines the design of an optical fiber sensor that monitors ground beef online, as it cooks, in order to determine the quality of the meat; in particular, if premature browning has occurred. The experimental work involved cooking fresh meat and meat that has been stored in a freezer for, one week, one month and three months, and recording the reflected spectra and temperature during the cooking process in order to develop a classifier, based on pattern recognition techniques that can determine premature browning and the degree to which the meat has been cooked. A comparison of this sensor is made with traditional research methods of detecting premature browning, to demonstrate that it would be more commercially viable as an online solution.

Development of an inexpensive optical fiber based Harmful Algae Bloom sensor

Research into the development of an Early Warning Harmful Algae Bloom (HAB) Sensing System for use in Underwater Monitoring Applications is presented. The sensor proposed by the authors utilises the complex ties between ocean colour, absorption and scattering, along with algae pigmentation. The objective is to develop a robust inexpensive sensor for use in an early warning system for the detection and possible identification of Harmful Algae Blooms. The sensing mechanism utilised in this system is based on a combination of absorption and reflection spectroscopy and Principle Component Analysis (PCA) signal processing. Spectroscopy is concerned with the production, measurement, and interpretation of electromagnetic spectra arising from either emission or absorption of radiant energy by various substances (or HABs in this application). Preliminary results are presented from the interrogation of chlorophyll, yeast and saline solutions, as well as levels of absorption obtained utilising two dyes Blue (brilliant Blue (E133) and Carmoisine (E122) mix) and Red (Ponceau (E124) and Sunset yellow (E110) mix).

An examination of ham colour fading using optical fibre methods

Sliced ham products undergo significant discolouration and fading when placed in retail display cabinets. This is due to factors such as illumination of the display cabinet, packaging, i.e. low OTR (Oxygen Transmission Rate) or very low OTR packaging, product to headspace ratio and percentage of residual oxygen. This paper presents initial investigations into the development of a sensor to measure rate of colour fading in cured ham, in order to predict an optimum colour sell-by-date. An investigation has been carried out that shows that spectral reflections offer more reproducibility than CIE L*a*b* readings, which are, at present, most often used to measure meat colour. Self-Organising Maps were then used to classify the data into five colour fading stages, from very pink to grey. The results presented here show that this classifier could prove an effective system for determining the rate of colour fading in ham.

Design of a slim-line integrated probe using optical fibre technology that is suitable for microwave environments and measures reflection spectroscopy and temperature

The work presented describes the development of a novel integrated optical sensor system for the simultaneous and online measurement of colour and temperature of food as it cooks in a large-scale microwave and hybrid oven systems. The integrated probe contains two different sensor concepts, one to monitor temperature, based on Fibre Bragg Grating (FBG) technology and a second for food quality, based on reflection spectroscopy in the visible wavelength range. The in-house designed probe was used to obtain measurements of product temperature in a conventional and microwave oven. The performance of the FBG temperature sensor within the combined optical probe was thus evaluated for use in conventional and microwave ovens.

Detection of premature browning in ground beef using an optical-fibre-based sensor

This paper reports on an optical fibre based sensor system to detect the occurrence of premature browning in ground beef. Premature browning (PMB) occurs when, at a temperature below the pasteurisation temperature of 71°C, there are no traces of pink meat left in the patty. PMB is more frequent in poorer quality beef or beef that has been stored under imperfect conditions. The experimental work pertaining to this paper involved cooking fresh meat and meat that has been stored in a freezer for, 1 week, 1 month and 3 months and recording the reflected spectra and temperature at the core of the product, during the cooking process, in order to develop a classifier based on the spectral response and using a Self-Organising Map (SOM) to classify the patties into one of four categories, based on their colour. The combination of both the classifier and temperature data can be used to determine the presence of PMB for a given patty and can thus be used for Quality Control by food producers.

An Integrated Probe Design For Measuring Food Quality In A Microwave Environment

The work presented describes the development of a novel integrated optical sensor system for the simultaneous and online measurement of the colour and temperature of food as it cooks in a large-scale microwave and hybrid oven systems. The integrated probe contains two different sensor concepts, one to monitor temperature and based on Fibre Bragg Grating (FBG) technology and a second for meat quality, based on reflection spectroscopy in the visible wavelength range. The combination of the two sensors into a single probe requires a careful configuration of the sensor approaches in the creation of an integrated probe design.

Detection of premature browning in ground beef with an integrated optical-fibre based sensor using reflection spectroscopy and fibre Bragg grating technology

This paper reports on an optical fibre based sensor system to detect the occurrence of premature browning in ground beef. Premature browning (PMB) occurs when, at a temperature below the pasteurisation temperature of 71°C, there are no traces of pink meat left in the patty. PMB is more frequent if poorer quality beef or beef that has been stored under imperfect conditions. The experimental work pertaining to this paper involved cooking fresh meat and meat that has been stored in a freezer for, 1 week, 1 month and 3 months and recording the reflected spectra and temperature at the core of the product, during the cooking process, in order to develop a classifier based on the spectral response and using a Self-Organising Map (SOM) to classify the patties into one of four categories, based on their colour. Further tests were also carried out on developing an all-optical fibre sensor for measuring both the temperature and colour in a single integrated probe. The integrated probe contains two different sensor concepts, one to monitor temperature, based on Fibre Bragg Grating (FBG) technology and a second for meat quality, based on reflection spectroscopy in the visible wavelength range.