scholarly journals Fusion of visible-to-near-infrared and mid-infrared spectroscopy to estimate soil organic carbon

2022 ◽  
Vol 217 ◽  
pp. 105284
Author(s):  
Yongsheng Hong ◽  
Muhammad Abdul Munnaf ◽  
Angela Guerrero ◽  
Songchao Chen ◽  
Yaolin Liu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Generic Prediction of Soil Organic Carbon in Alfisols Using Diffuse Reflectance Fourier-Transform Mid-Infrared Spectroscopy

2011 ◽  
Vol 75 (6) ◽  
pp. 2358-2360 ◽  
Author(s):  
Mercy Kamau-Rewe ◽  
Frank Rasche ◽  
Juan Guillermo Cobo ◽  
Gerd Dercon ◽  
Keith D. Shepherd ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Diffuse Reflectance ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

scholarly journals Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy

CATENA ◽  
2020 ◽  
Vol 189 ◽  
pp. 104452 ◽  
Author(s):  
Myriam Allo ◽  
Pierre Todoroff ◽  
Magali Jameux ◽  
Mathilde Stern ◽  
Louis Paulin ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stocks ◽  
Volcanic Soil ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Soil Organic Carbon Stocks

scholarly journals An application of near-infrared and mid-infrared spectroscopy to the study of selected minerals

2010 ◽  
Vol 165 (3) ◽  
pp. 193-205 ◽  
Author(s):  
Ray L. Frost ◽  
B. Jagannadha Reddy ◽  
Eloise C. Keeffe
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Dairy Product Analysis: Identification of Microorganisms by Mid-Infrared Spectroscopy and Determination of Constituents by Raman Spectroscopy

1995 ◽  
Vol 78 (6) ◽  
pp. 1537-1541 ◽  
Author(s):  
Angela Fehrmann ◽  
Monika Franz ◽  
Andreas Hoffmann ◽  
Lutz Rudzik ◽  
Eberhard Wüst
Keyword(s):  
Raman Spectroscopy ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Dairy Product ◽  
Milk Powder ◽  
Solid Content ◽  
Product Analysis ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Microbiological Methods

Abstract Identification of microorganisms by traditional microbiological methods is time consuming. The German Federal Health Office has developed a method using mid-infrared spectroscopy to identify microorganisms rapidly. This method has been modified for application to microorganisms important in the dairy industry. Mid- and near-infrared spectroscopies are well-established methods for quantitative measurements of fat, protein, lactose, and solid content in a variety of products. A disadvantage of both methods is the huge absorption due to water; extraction of other components is complicated and can be achived only statistically. With Raman spectroscopy, water causes less absorption. We investigated the use of Raman spectroscopy as a quantitative method for milk powder.

Vibrational spectroscopy in practice: Detection of mineral oil in sunflower oil with near- and mid-infrared spectroscopy

NIR news ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 6-11 ◽  
Author(s):  
Michael K-H Pfister ◽  
Bettina Horn ◽  
Janet Riedl ◽  
Susanne Esslinger ◽  
Carsten Fauhl-Hassek
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Sunflower Oil ◽  
Near Infrared ◽  
Limit Of Detection ◽  
Model Performance ◽  
Mineral Oil ◽  
Food Ingredient ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

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