Abstract
Detecting the peroxide value (PV) in oil is significant for people in everyday life, especially as a fast, convenient, and on-site method. To tackle this challenge, the near-infrared (NIR) spectra of oil were collected by a Viavi MicroNIR 1700 handheld NIR spectrometer and a liquid sample transmission accessory. Subsequently to the spectral pretreatment method of standard normal variate (SNV), the sensitive wavelength variables were optimized by the algorithms of competitive adaptive reweighted sampling (CARS), genetic algorithms (GA), and random frog (RF). The results showed that CARS was the best, and the selected variables were used to build the partial least squares (PLS) regression model. The root mean square error (RMSE) values for cross-validation (RMSECV) and prediction (RMSEP) were 0.984 mmol/ kg and 0.950 mmol/kg, respectively, and the corresponding R2cv and R2P were 0.875, and 0.867, respectively. Therefore, the PV of edible oil can be determined easily and quickly with a handheld NIR spectrometer.
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