scholarly journals Daily quantification of myoglobin forms on beef longissimus lumborum steaks over 7-days of display using near-infrared diffuse reflectance spectroscopy

2021 ◽  
Author(s):  
Daqing Piao ◽  
Morgan L. Denzer ◽  
Gretchen Mafi ◽  
Ranjith Ramanathan
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Moderate Correlation ◽  
Surface Color ◽  
Redox States ◽  
Apparent Decrease ◽  
Longissimus Lumborum ◽  
Daily Changes

Oxymyoglobin (OxyMb), deoxymyoglobin (DeoxyMb), andmetmyoglobin (MetMb) content on beef longissimus lumborum muscles weredetermined using a near-infrared (NIR) diffuse reflectance spectroscopy (DRS).  Approximately eighteen wavelengths spanningfrom 480-650 nm were utilized to develop an algorithm to quantify myoglobinforms. Daily changes in subsurface myoglobin redox states were evaluated forseven days using NIR-DRS and compared with surface color assessed by HunterLab MiniScanspectrophotometer. Both measurements revealed that MetMb increased steadilyover the duration of display, showing a high correlation (R2 = 0.91)between the two methods. Comparatively, while NIR-DRS revealed the OxyMb tohave decreased steadily over the period of display, the HunterLab MiniScanspectrophotometer indicated a much later onset of the apparent decrease ofOxyMb than NIR-DRS reading, resulting in a moderate correlation (R2= 0.64) between the two methods. No correlation was found between the twomethods regarding the changes of DeoxyMb over the duration of display. NIR rayscan penetrate deeper into the muscle sub-surface than visible light and probemyoglobin forms. The newly developed NIR-DRS algorithm has potential as analternative method for the color assessment in post-rigor skeletal muscle.

Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113840 ◽  
Author(s):  
Jean-Martial Johnson ◽  
Elke Vandamme ◽  
Kalimuthu Senthilkumar ◽  
Andrew Sila ◽  
Keith D. Shepherd ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Rice Fields ◽  
Sub Saharan Africa ◽  
Mid Infrared ◽  
Sub Saharan

scholarly journals Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4568 ◽  
Author(s):  
Yong Hao ◽  
Pei Geng ◽  
Wenhui Wu ◽  
Qinhua Wen ◽  
Min Rao
Keyword(s):  
Transgenic Rice ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Genetically Modified ◽  
Reflectance Spectroscopy ◽  
Genetically Modified Foods ◽  
Optimal Model ◽  
Rice Varieties ◽  
Svm Model

Background: In recent years, genetically modified technology has developed rapidly, and the potential impact of genetically modified foods on human health and the ecological environment has received increasing attention. The currently used methods for testing genetically modified foods are cumbersome, time-consuming, and expensive. This paper proposed a more efficient and convenient detection method. Methods: Near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with multivariate calibration methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), were used for identification of different rice varieties and transgenic (Bt63)/non-transgenic rice. Spectral pretreatment methods, including Norris–Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay 1st derivative (SG 1st-Der), were used for spectral noise reduction and effective information enhancement. Accuracy was used to evaluate the qualitative discriminant models. Results: The results showed that the SG 1st-Der pretreatment method, combined with the SVM, provided the optimal model to distinguish different rice varieties. The accuracy of the optimal model was 98.33%. For the discrimination model of transgenic/non-transgenic rice, the SNV-SVM model, MSC-SVM model, and SG 1st-Der-PLS-DA model all achieved good analysis results with the accuracy of 100%. Conclusion: The results showed that portable NIR spectroscopy combined with chemometrics methods could be used to identify rice varieties and transgenic characteristics (Bt63) due to its fast, non-destructive, and accurate advantages.

Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement

2002 ◽  
Vol 66 (2) ◽  
pp. 640-646 ◽  
Author(s):  
G. W. McCarty ◽  
J. B. Reeves ◽  
V. B. Reeves ◽  
R. F. Follett ◽  
J. M. Kimble
Keyword(s):  
Soil Carbon ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared
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