Food quality assessment by NIR hyperspectral imaging

Martin B. Whitworth; Samuel J. Millar; Astor Chau

doi:10.1117/12.852170

20 April 2010 Food quality assessment by NIR hyperspectral imaging

Martin B. Whitworth, Samuel J. Millar, Astor Chau

Author Affiliations +

Proceedings Volume 7676, Sensing for Agriculture and Food Quality and Safety II; 767605 (2010) https://doi.org/10.1117/12.852170
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Near infrared reflectance (NIR) spectroscopy is well established in the food industry for rapid compositional analysis of bulk samples. NIR hyperspectral imaging provides new opportunities to measure the spatial distribution of components such as moisture and fat, and to identify and measure specific regions of composite samples. An NIR hyperspectral imaging system has been constructed for food research applications, incorporating a SWIR camera with a cooled 14 bit HgCdTe detector and N25E spectrograph (Specim Ltd, Finland). Samples are scanned in a pushbroom mode using a motorised stage. The system has a spectral resolution of 256 pixels covering a range of 970-2500 nm and a spatial resolution of 320 pixels covering a swathe adjustable from 8 to 300 mm. Images are acquired at a rate of up to 100 lines s^-1, enabling samples to be scanned within a few seconds. Data are captured using SpectralCube software (Specim) and analysed using ENVI and IDL (ITT Visual Information Solutions). Several food applications are presented. The strength of individual absorbance bands enables the distribution of particular components to be assessed. Examples are shown for detection of added gluten in wheat flour and to study the effect of processing conditions on fat distribution in chips/French fries. More detailed quantitative calibrations have been developed to study evolution of the moisture distribution in baguettes during storage at different humidities, to assess freshness of fish using measurements of whole cod and fillets, and for prediction of beef quality by identification and separate measurement of lean and fat regions.

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Martin B. Whitworth, Samuel J. Millar, and Astor Chau "Food quality assessment by NIR hyperspectral imaging", Proc. SPIE 7676, Sensing for Agriculture and Food Quality and Safety II, 767605 (20 April 2010); https://doi.org/10.1117/12.852170

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