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P. 63-69 /

Benefits and challenges in applying Raman spectroscopy as a non-destructive tool in the analysis of food quality

University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark


In recent years there has been an increasing focus from the consumers on food quality i.e. unwanted substances such as bacteria, pesticides, drug residues and additives as well as on food composition including nutritional value, healthy additives, antioxidants and the contents of selected fatty acids (1). This is also reflected in the increasing interest for organic food products. It is therefore highly relevant to develop substance specific, non-destructive and fast measuring techniques for monitoring different properties of food products, which can be used close to the consumer. Raman spectroscopy is an example of a fast, non-destructive and molecule specific technique. In the present paper three case studies are discussed each highlighting different benefits and challenges, when implementing Raman spectroscopy, i.e. case study 1) The revelation of a pork content in minced lamb, 2) Detection of pesticides and 3) The distinction between closely related bio- molecules.


When we talk about food quality, this refers to a very broad designation, i.e. the occurrences of healthy substances present in high concentrations, but also to the presence of non-wanted substances such as pesticides, fungus etc., which might be present in only minute amounts. Another example is the presence of meat from other animals than those indicated on the commodity or the familiar aspect of aging of a commodity. This also raises the need for a technique characterized by a high molecular specificity, since often very similar substances, seen on a molecular scale, should be told apart. Hence, the quality parameter should be paired with the application and type of Raman spectroscopy, because a variety of different techniques exist and may even be combined in a lot of different ways to suit the requirements of the specific task. Today it is possible to tailor a specific setup according to the application on hand but also to decide whether mathematical tools such as multivariate analysis should be applied. The challenge most often being that the implementation within the food industry requires a fast and non-destructive execution of the R ...

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