multispectral imaging (4)


Arabica coffee beans have twice the value, or more, compared to Robusta beans, and consequently are susceptible to substitution. In this study, MSI was applied to discriminate roasted Arabica and Robusta coffee beans and perform a quantitative prediction of Arabica coffee bean adulteration with Robusta. Using selected spectral and morphological features from individual coffee beans, and applying an OPLS-DA (orthogonal partial least squares discriminant analysis) model, a 100% correct classification of the two coffee species in the test dataset was achieved. In addition, the OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. 

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10005082061?profile=RESIZE_400xThis research provides validated methods for specific food adulteration scenarios, guidance on general MSI validation, and recommendations on technology transfer and feasibility of developing additional MSI related resources (e.g. an MSI database). The project builds on a previous FSA (Food Standards Agency) project, which demonstrated proof-of-principle on the applicability of MSI as a rapid, multi-analyte, non-targeted and non-invasive screening approach for food and feed analysis. In this project, 5 validated methods were successfully developed: adulteration in oregano, presence of offal in meat, ground peanut in ground almond, presence of pork in beef products, and presence of almond in commercial paprika samples, where the application, scope and key performance characteristics were captured for each application. A sixth single fully validated method for determination of multiple fish species was not successful, thought mainly due to the broad scope of the method and associated data used to build the models. The report also identifies six further areas of work that can give greater applicability of imaging technology to food and feed analysis.

The report is available here

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Multispectral Imaging for Plant Food Quality

This article is a comprehensive review of the use of multispectral imaging combined with chemometrics to determine the composition and quality of various plant-based foods; cereals (wheat, maize), legumes (soybean, peanut), tubers (potato, sweet potato), fruits (apple, pear, kiwi), and vegetables (white radish, sugar beet).  The method is rapid and can give a visualisation of for example the distribution fructose, glucose and sucrose in unripe and ripe fruit. Future developments should make it a useful industrial tool to control raw materials and production. 

Read the full article at: Multispectral imaging of plant foods

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Determination and quantification of durum wheat adulteration with common wheat has been successfully developed using DNA methodology. However, this assay is time consumer and requires specialist equipment. A feasibility study of determining durum wheat adulteration with common wheat grains using multispectral imaging (MSI) and hyperspectral imaging (HSI) has been carried out. The two techniques have been successful in rapidly distinguishing durum wheat from common wheat grains, and permitting quantitative determination of the amount of common what present.

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