Moisture content of Panax notoginseng taproot predicted using near infrared spectroscopy

Abstract

The rapid determination of moisture content in Panax notoginseng taproot (PNT) was determined using a portable near infrared spectrometer (900∼1700 nm). First, to reduce baseline offset of the spectra Savitzky-Golay and standard normal variate transformation were combined to preprocess the original spectral data. Then, competitive adaptive reweighting sampling and bootstrapping soft shrinkage (BOSS) were employed to extract feature wavelengths that could characterize the moisture content information of PNT respectively. Finally, the least square support vector regression (LSSVR) model was established based on feature spectra and full spectra. To improve the prediction accuracy of the model, a LSSVR model based on the arithmetic optimization algorithm (AOA) was proposed, and the optimization results were compared with those of the snake optimizer and particle swarm optimization. The results indicated that the best prediction model was BOSS-AOA-LSSVR, with r2 and RMSEP values of 0.96 and 0.03%, respectively. Thus, it is feasible to predict the moisture content of Panax notoginseng taproot by portable near infrared spectroscopy in combination with BOSS-AOA-LSSVR. The results show that portable near infrared spectroscopy can be used to predict the moisture content of Panax notoginseng taproot, which provides a theoretical basis for the rapid and non-destructive detection of the moisture content of Panax notoginseng taproots.