Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using...

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Main Authors: Byoung-Kwan Cho, Changyeun Mo, Moon S. Kim, Hoonsoo Lee, Lalit Mohan Kandpal
Format: Article
Language:English
Published: MDPI AG 2013-09-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/13/10/13289
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spelling doaj-c30cb447ed7e4deb87f52e0dc229db332020-11-25T00:45:26ZengMDPI AGSensors1424-82202013-09-011310132891330010.3390/s131013289Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken BreastByoung-Kwan ChoChangyeun MoMoon S. KimHoonsoo LeeLalit Mohan KandpalSpectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum  of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an  of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat.http://www.mdpi.com/1424-8220/13/10/13289hyperspectral imagingchicken breastmoisture contentcooking ovenPLSR
collection DOAJ
language English
format Article
sources DOAJ
author Byoung-Kwan Cho
Changyeun Mo
Moon S. Kim
Hoonsoo Lee
Lalit Mohan Kandpal
spellingShingle Byoung-Kwan Cho
Changyeun Mo
Moon S. Kim
Hoonsoo Lee
Lalit Mohan Kandpal
Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast
Sensors
hyperspectral imaging
chicken breast
moisture content
cooking oven
PLSR
author_facet Byoung-Kwan Cho
Changyeun Mo
Moon S. Kim
Hoonsoo Lee
Lalit Mohan Kandpal
author_sort Byoung-Kwan Cho
title Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast
title_short Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast
title_full Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast
title_fullStr Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast
title_full_unstemmed Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast
title_sort hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2013-09-01
description Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum  of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an  of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat.
topic hyperspectral imaging
chicken breast
moisture content
cooking oven
PLSR
url http://www.mdpi.com/1424-8220/13/10/13289
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AT moonskim hyperspectralreflectanceimagingtechniqueforvisualizationofmoisturedistributionincookedchickenbreast
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