hyperspectral imaging (4)

 

 

10588856055?profile=RESIZE_584xHyperspectral imaging (HSI) is now widely applied in research studies with the help of machine learning methods for detecting various components of different meat products. This review presents a fresh look at the current status of HSI research in both the scope and the applicability of HSI in meat quality evaluation. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed.

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This review investigates the feasibility of different non-destructive techniques used for authenticating meat products, which could provide real-time monitoring in the near future. The spectroscopic techniques reviewed are NIR (near infrared), MIR (mid-infrared), FTIR (Fourier transform infrared), and Raman. The imaging techniques discussed are colour imaging, hyperspectral imaging and Xray imaging with computed technology. The advantages of these techniques is that they can be applied in-situ, and they give rapid results, but calibration procedures are laborious. In addition, the results are influenced by scanning times, sample to detector distance and environmental factors such as ambient temperature, humidity, illumination conditions, and sample temperature, the latter can differ in meat processing facilities. However, it is hoped that the application of these techniques will be easier with the improvement in instrumental technology, the availability of high-speed computers with appropriate storage capacity, and the development of appropriate chemometric procedures.

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Dutch researchers used a new handheld hyperspectral imaging system to obtain information about nutmeg powder samples in the wavelength region of 400–1000 nm. The samples used to develop the method were 15 authentic samples, seven adulterant materials (i.e. 1 pericarp, 1 shell, and 5 spent samples) and 31 retail samples. Furthermore, another set of adulterated nutmeg samples were artificially prepared by mixing authentic material with spent powder (5–60%). Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN) models were applied to the spectral data to construct the models, and authenticate the retail samples. The PCA showed successful spatial separation of authentic samples from adulterant materials. The ANN model predicted and showed the ability to detect adulteration at levels as low as 5% of added product-own material, which was more accurate than the PLS-DA model. Microscopic analysis was applied for comparison with hyperspectral imaging and to verify possible sample modification. It was concluded that method has good potential for the development of a visual quality control procedure for nutmeg powder authentication.

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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.

Read the full paper at:  http://file.scirp.org/pdf/FNS_2016042814342947.pdf 

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