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Hyperspectral imaging (HSI) combines conventional imaging and spectroscopy to simultaneously acquire both spatial and spectral information from an object. This technology has recently emerged as a powerful process analytical tool for rapid, non-contact and non-destructive food analysis. In this study, the potential application of HSI for damage detection on the caps of white mushrooms (Agaricus bisporus) was investigated. Mushrooms were damaged by controlled vibration to simulate damage caused by transportation. Hyperspectral images were obtained using a pushbroom line-scanning HSI instrument, operating in the wavelength range of 400-1000 nm with spectroscopic resolution of 5 nm. The effective resolution of the CCD detector was 580 T 580 pixels by 12 bits. Two data reduction methods were investigated: in the first, principal component analysis (PCA) was applied to the hypercube of each sample, and the second PC (PC 2) scores image was used for identification of bruise-damaged regions on the mushroom surface; in the second method PCA was applied to a dataset comprising of average spectra from regions normal and bruise-damaged tissue. In this case it was observed that normal and bruised tissue were separable along the resultant first principal component (PC 1) axis. Multiplying the PC 1 eigenvector by the hypercube data allowed reduction of the hypercube to a 2-D image, which showed maximal contrast between normal and bruise-damaged tissue. The second method performed better than the first when applied to a set of independent mushroom samples. The results from this study could be used for the development of a non-destructive monitoring system for rapid detection of damaged mushrooms on the processing line.

Non-destructive food testing is becoming increasingly important due to expanding automation and the incorporation of new and more efficient processes in the food industry. The quality and safety of food are the main points of interest. It is important to have a technology which will allow for a high throughput and a short response time to increase process efficiency and reduce waste. In order for this equipment to be integrated with the existing infrastructure, it should be robust and capable of functioning in varying environments. Near infrared (NIR) spectroscopy provides several advantages compared with traditional analytical methods; it is fast and non-destructive, it requires little or no sample preparation, it can provide simultaneous determination of multiple components per measurement, it has a remote sampling capability and it can provide real-time information in a process stream. Thus, NIR spectroscopy provides the ideal technology needed for fast and efficient food analysis. This review reports recently published (in the last 10 years) applications of NIR spectroscopy in both raw and prepared foods. It highlights the ability of NIR spectroscopy to assess food and beverage composition, functional properties, quality attributes, regional and varietal differences and contribute to food safety and consumer confidence.

Food authenticity is an issue of concern to food processors, retailers, regulatory authorities and consumers alike. Near infrared (NIR) spectroscopy has many potential advantages as an authenticity testing tool and has already been applied to a number of authentication problems using a range of sample presentation and chemometric techniques. This review outlines the principles of the statistical procedures used so far, and summarises the work reported to-date on a range of foods and food ingredients.

An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what they pay for. Classification methods are an important tool in food authenticity studies where they are used to assign food samples of unknown type to known types. A classification method is developed where the classification rule is estimated by using both the labelled and the unlabelled data, in contrast with many classical methods which use only the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering. A missing data formulation of the mixture model is used and the models are fitted by using the EM and classification EM algorithms. The methods are applied to the analysis of spectra of food-stuffs recorded over the visible and near infra-red wavelength range in food authenticity studies. A comparison of the performance of model-based discriminant analysis and the method of classification proposed is given. The classification method proposed is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis. Copyright 2006 Royal Statistical Society.

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