Biospectroscopy & Food Quality
A key research focus of the Prepared Foods Department is the development of rapid methods for directly confirming the authenticity and quality of food and food ingredients. These are based on the use of near and mid-infrared spectroscopy coupled with advanced mathematical data manipulation techniques. In all cases, the desired end-product is a simple, fast and inexpensive procedure for monitoring the quality of raw materials, in-process and finished goods. Current priorities include the following:
Food authenticity
Products such as honey, extra virgin olive oil and fruit purées are in demand, command premium prices and are, therefore, vulnerable to economic adulteration. The potential for adulteration is escalating due to increasing globalisation of the food industry and the greater separation of producers and consumers. Traditional methods of authenticity confirmation rely on the detection of the presence or absence of specific marker molecules in food; they are slow, expensive and usually destroy the sample being tested. In contrast, spectroscopic techniques combined with powerful multivariate mathematical procedures are fast, non-destructive and easy to use and spectra act as “fingerprints”. This has allowed detection of: (a) honey samples adulterated with sugar mixtures, (b) soft fruit (strawberry and raspberry) purées which have been extended with apple, and (c) olive oils adulterated with sunflower oil (see more…). Geographic origin of olive oils has also been confirmed. Specroscopic procedures have been applied successfully to the identification of flour grade (e.g. baker’s vs hi-ratio vs retail) and meat species (see more…). This work is being extended to include the development of infrared models for the identification of extra virgin olive oil and honey from defined PDO (Protected Designation of Origin) regions as part of the TRACE EU-funded integrated project (www.trace.eu.org). Research enabling the authentication of oil and honey from a single PDO in Italy is in progress and the derived model will be incorporated into a traceability system for olive oils and honey which will be available to the food industry. (gerard.downey@teagasc.ie)
Cheese composition and texture
Organoleptic properties of hard and processed cheeses vary according to process, formulation and storage time. The main aim of a recently-completed project was to monitor the development of desirable organoleptic properties in both cheese types during storage and to generate spectroscopic models for the prediction of these properties. In this project, co-ordinated by Moorepark Food Research Centre, a series of experimental cheeses (Cheddar and processed) were produced on a pilot-plant scale and stored at 4°C for varying time periods. At specific times, cheese samples were removed from storage and analysed using standard instrumental texture procedures and also by a trained taste panel. Contemporaneously, samples were scanned at Ashtown Food Research Centre using a near infrared spectrophotometer and the datasets generated were analysed mathematically to produce models for the prediction of a range of sensory properties. In the case of Cheddar cheese, age (over the range 0 to 9 months) was predicted with a root mean square error of cross-validation (RMSECV) of 0.61; sensory attributes successsfully modelled and their associated RMSECV values were “crumbly” (2.3), “rubbery” (3.4), “chewy” (4.0), “mouthcoating” (5.0) and “massforming” (4.1). These models are sufficiently accurate to be industrially useful (see more...).(gerard.downey@teagasc.ie)
Statistical methods for analysing near-infrared spectra in food authenticity studies.
“Is a sample of a particular type or not?” is the key question in food authenticity studies. The answer is difficult because while one sample type (authentic) is known, the alternative sample types (adulterated, mislabelled, etc) may not be defined at the outset. The focus of this statistical project is to address this core issue. Novel statistical methodologies that directly analyse near-infrared spectroscopic data in food authenticity studies will be developed. This approach will avoid the data reduction step, e.g. principal component analysis, which underlies previous studies. Methods that have high performance, even with very little training data, have recently been developed and these will be extended to the proposed direct classification and clustering methods. (gerard.downey@teagasc.ie)
Investigation of the potential use of near infra-red technology to detect and enumerate microbial pathogens.
This 3 year project commenced in 2005 and is investigating the suitability of near infrared technology for the detection and enumeration of microbial pathogens in food systems. It seeks to discover whether the advantages of NIR analysis, including rapidity, specificity, reliability and ease of use, that have been documented in studies into food quality can be exploited in the area of food safety. To achieve this, a number of elements will be addressed including (a) can NIR enumerate and differentiate microbes in a pure system?; (b) if so, can the system be scaled up to simple food systems and retain the ability to detect and differentiate micro-organisms, and (c) does the complexity of consumer foods adversely affect the specificity of near infrared spectroscopy as a detection system. (gerard.downey@teagasc.ie)
