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Huang, M.,Kim, M.S.,Delwiche, S.R.,Chao, K.,Qin, J.,Mo, C.,Esquerre, C.,Zhu, Q. Applied Science Publishers 2016 Journal of food engineering Vol.181 No.-
<P>Since 2008, the detection of the adulterant melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02-1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938-1654 nm) of melamine powder, whole milk powder, nonfat milk powder, and mixtures of melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure melamine. Band ratio (13144711455) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing melamine and melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of melamine in milk powders. Published by Elsevier Ltd.</P>
Hyperspectral near-infrared imaging for the detection of physical damages of pear
Lee, W.H.,Kim, M.S.,Lee, H.,Delwiche, S.R.,Bae, H.,Kim, D.Y.,Cho, B.K. Applied Science Publishers 2014 Journal of food engineering Vol.130 No.-
Bruise damage on pears is one of the most crucial internal quality factors, which needs to be detected in postharvest quality sorting processes. Near-infrared imaging techniques (NIR) have effective potentials for identifying and detecting bruises since bruises result in the rupture of internal cell walls due to defects on agricultural materials. In this study, a novel NIR technique, hyperspectral imaging with beyond NIR range of 950-1650nm, was investigated for detecting bruise damages underneath the pear skin, which has never been examined in the past. A classification algorithm based on F-value was applied for analysis of image to find the optimal waveband ratio for the discrimination of bruises against sound surface. The result demonstrated that the best threshold waveband ratio detected bruises with the accuracy of 92%, illustrating that the hyperspectral infra-red imaging technique with the region beyond NIR could be a potential detection method for pear bruises.
Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging
Mo, C.,Kim, M.S.,Kim, G.,Lim, J.,Delwiche, S.R.,Chao, K.,Lee, H.,Cho, B.K. Elsevier Science B.V., Amsterdam 2017 BIOSYSTEMS ENGINEERING Vol.159 No.-
<P>A partial least squares regression (PLSR) model to map internal soluble solids content (SSC) of apples using visible/near-infrared (VNIR) hyperspectral imaging was developed. The reflectance spectra of sliced apples were extracted from hyperspectral absorbance images obtained in the 400-1000 nm range. Prediction models for SSC mapping were developed for three different measurement/sampling designs that varied in the number and size of the regions of interest (ROIs) used for apple SSC measurement and spectral averaging. Case 1 used 29 small ROIs per apple, Case II used 9 moderate-size ROIs per apple, and Case III used 5 large ROIs per apple. The optimal pre-treatment of the spectra extracted from the hyperspectral images was investigated to enhance the performance of the prediction models. The coefficients of determination and root mean square errors of the best-performing models were, respectively, 0.802 and +/- 0.674 degrees Brix for Case I, 0.871 and +/- 0.524 degrees Brix for Case II, and 0.876 and +/- 0.514 degrees Brix for Case III. The accuracy of the PLSR models was enhanced by using the spectra and SSC measured/averaged from the fewer but larger areas of the apples rather than from more numerous but smaller areas. PLS images of SSC showed the predicted internal distribution of SSC within the apples. The overall results demonstrate that hyperspectral absorbance imaging techniques may be useful for mapping internal soluble solids content of apples. (C) 2017 IAgrE. Published by Elsevier Ltd. All rights reserved.</P>
Qin, J.,Kim, M.S.,Chao, K.,Schmidt, W.F.,Cho, B.K.,Delwiche, S.R. Applied Science Publishers 2017 Journal of food engineering Vol.198 No.-
<P>Both surface and subsurface food inspection is important since interesting safety and quality attributes can be at different sample locations. This paper pregents a multipurpose line-scan Raman platform for food safety and quality research, which can be configured for Raman chemical imaging (RCI) mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In the RCI mode, macro-scale imaging was achieved using a 785 nm line laser up to 24 cm long with a push broom method. In the SORS mode, a 785 nm point laser was used and a complete set of SORS data was collected in an offset range of 0-36 mm with a spatial interval of 0.07 mm using one CCD exposure. The RCI and SOPS modes share a common detection module including a dispersive imaging spectrograph and a CCD camera, covering a Raman shift range from 674 to 2865 cm(-1). A pork shoulder and an orange carrot were used to test large-field-of-view (230 min wide) and high-spatial-resolution (0.07 mm/pixel) settings of the RCI mode for food surface evaluation. Fluorescence-corrected images at selected Raman peak wavenumbers Were used to view Raman-active analytes on the whole sample surfaces (e.g., fat on the pork shoulder and carotenoids over the carrot cross section). Also, three layered samples, which were created by placing carrot slices with thicknesses of 2, 5, and 8 mm on top of melamine powder, were used to test the SORS mode for subsurface food evaluation. Raman spectra from carrot and melamine were successfully resolved for all three layered samples using self-modeling mixture analysis. The line-scan Raman imaging and spectroscopy platform provides a new tool for surface and subsurface inspection for food safety and quality. Published by Elsevier Ltd.</P>