DocumentCode :
1969815
Title :
Nondestructive moisture sensing in peanuts by NIR reflectance
Author :
Kandala, Chari V. ; Sundaram, Jaya
Author_Institution :
ARS, Nat. Peanut Res. Lab., USDA, Dawson, GA, USA
fYear :
2010
fDate :
23-25 Feb. 2010
Firstpage :
149
Lastpage :
153
Abstract :
Near Infrared reflectance spectroscopy, measuring reflectance value of the energy incident on a peanut sample over the wavelengths 400 nm to 2500 nm, is used to estimate the moisture content (MC) of in-shell peanuts non-destructively. A sample of Valencia type in-shell peanuts weighing about 150g was filled into the sample cup of the instrument and NIR light reflected from the sample was collected in the form of an absorbance spectrum. A calibration model was developed with the measured spectral values and their MC values, determined earlier by standard air-oven method, using partial least square (PLS) regression methods. Peanuts were then shelled, and similar measurements were made on the kernels also. MC values of peanut samples in the moisture range of 6% to 21%, not used in the calibration, were predicted by the model and compared with their standard air-oven values for validation. This method being nondestructive and rapid will have considerable application in the drying and storage processes of corn, wheat, peanuts and similar food products.
Keywords :
calibration; drying; food products; food technology; infrared spectroscopy; least squares approximations; moisture measurement; nondestructive testing; storage; NIR reflectance; absorbance spectrum; calibration model; drying; food products; near infrared reflectance spectroscopy; nondestructive moisture sensing; partial least square regression methods; peanuts; standard air-oven method; storage processes; Calibration; Energy measurement; Infrared spectra; Instruments; Measurement standards; Moisture measurement; Reflectivity; Spectroscopy; Standards development; Wavelength measurement; NIR reflectance spectroscopy; in-shell peanuts; kernels; moisture content; non-destructive; partial least square regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sensors Applications Symposium (SAS), 2010 IEEE
Conference_Location :
Limerick
Print_ISBN :
978-1-4244-4988-0
Electronic_ISBN :
978-1-4244-4989-7
Type :
conf
DOI :
10.1109/SAS.2010.5439411
Filename :
5439411
Link To Document :
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