Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

Abstract The possibility of rapid estimation of moisture, protein, fat, free fatty acid (FFA), and peroxide value (PV) content in peanut kernel was studied by Fourier transform near-infrared spectroscopy (FTNIR) in the diffuse reflectance mode along with chemometric technic. The moisture, fat and protein of fresh and damaged seeds of peanuts ranging from 3 to 9 %, 45 to 57 % and 23 to 27 % respectively, were used for the calibration model building based on partial least squares (PLS) regression. The peanut samples had major peaks at wavenumbers 53.0853, 4954.98, 4464.03, 4070.85, 74.75.63, 8230.21, and 6178.13 in per cm. First and second derivate mathematical preprocessing was also applied in order to eliminate multiple baselines for different chemical quality parameters of peanut. The FFA had the lowest value of calibration and validation errors (0.579 and 0.738) followed by the protein (0.736 and 0.765). The quality of peanut seeds with lowest root mean square error of cross validation of 0.76 and maximum correlation coefficient (R2) of 96.8 was obtained. The comprehensive results signify that FT-NIR spectroscopy can be used for rapid, non-destructive quantification of quality parameters in peanuts.

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Multivariate Calibration Model for a Voltammetric Electronic Tongue Based on a Multiple Output Wavelet Neural Network

Biologically Inspired Signal Processing for Chemical Sensing - Studies in Computational Intelligence ◽

10.1007/978-3-642-00176-5_9 ◽

2009 ◽

pp. 137-167 ◽

Cited By ~ 1

Author(s):

R. Cartas ◽

L. Moreno-Barón ◽

A. Merkoçi ◽

S. Alegret ◽

M. del Valle ◽

...

Keyword(s):

Neural Network ◽

Multivariate Calibration ◽

Electronic Tongue ◽

Wavelet Neural Network ◽

Calibration Model ◽

Voltammetric Electronic Tongue

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Implementation of Non Destructive FTNIR Method for Quick Estimation of Peanut Quality Based on FFA and Peroxide Value

10.21203/rs.3.rs-412189/v1 ◽

2021 ◽

Author(s):

Rakesh Kumar Kumar Raigar ◽

Shubhangi Srivast ◽

Hari Niwas Mishra

Keyword(s):

Peroxide Value ◽

Near Infrared ◽

Model Building ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Multiple Baseline ◽

Calibration Model ◽

Peanut Kernel ◽

Non Destructive

Abstract The possibility of rapid estimation of moisture, protein, fat, free fatty acid (FFA), and peroxide value (PV) content in peanut kernel was studied by Fourier transform near-infrared spectroscopy (FTNIR) in the diffuse reflectance mode along with chemometric technic. The moisture, fat and protein of fresh and damaged seeds of peanuts ranging from 3 to 9 %, 45 to 57 % and 23 to 27 % respectively, were used for the calibration model building based on partial least squares (PLS) regression. The peanut samples had major peaks at wavenumbers 53.0853, 4954.98, 4464.03, 4070.85, 74.75.63, 8230.21, and 6178.13 in per cm. First and second derivate mathematical preprocessing was also applied in order to eliminate multiple baseline for different chemical quality parameters of peanut. The FFA had the lowest value of calibration and validation errors (0.579 and 0.738) followed by the protein (0.736 and 0.765). The quality of peanut seeds with lowest root mean square error of cross validation of 0.76 and maximum correlation coefficient (R2) of 96.8 was obtained. The comprehensive results signify that FT-NIR spectroscopy can be used for rapid, non destructive quantification of quality parameters in peanut.

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Determination of Fat,Moisture, and Protein in Meat and Meat Products by Using the FOSS FoodScan Near-Infrared Spectrophotometer with FOSS Artificial Neural Network Calibration Model and Associated Database: Collaborative Study

Journal of AOAC International ◽

10.1093/jaoac/90.4.1073 ◽

2007 ◽

Vol 90 (4) ◽

pp. 1073-1083 ◽

Cited By ~ 57

Author(s):

Shirley Anderson ◽

S Aldana ◽

M Beggs ◽

J Birkey ◽

A Conquest ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Standard Deviation ◽

Near Infrared ◽

Collaborative Study ◽

Meat Products ◽

Calibration Model ◽

Relative Standard ◽

Infrared Spectrophotometer

Abstract A collaborative study was conducted to evaluate the repeatability and reproducibility of the FOSS FoodScan near-infrared spectrophotometer with artificial neural network calibration model and database for the determination of fat, moisture, and protein in meat and meat products. Representative samples were homogenized by grinding according to AOAC Official Method 983.18. Approximately 180 g ground sample was placed in a 140 mm round sample dish, and the dish was placed in the FoodScan. The operator ID was entered, the meat product profile within the software was selected, and the scanning process was initiated by pressing the start button. Results were displayed for percent (g/100 g) fat, moisture, and protein. Ten blind duplicate samples were sent to 15 collaborators in the United States. The within-laboratory (repeatability) relative standard deviation (RSDr) ranged from 0.22 to 2.67% for fat, 0.23 to 0.92% for moisture, and 0.35 to 2.13% for protein. The between-laboratories (reproducibility) relative standard deviation (RSDR) ranged from 0.52 to 6.89% for fat, 0.39 to 1.55% for moisture, and 0.54 to 5.23% for protein. The method is recommended for Official First Action.

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