Rapid detection of yeast growth status based on molecular spectroscopy fusion (MSF) technique

Abstract

This work innovatively proposes a novel method for quantitative detection of yeast growth status based on molecular spectroscopy fusion (MSF) technique. First, the Raman spectra and near-infrared (NIR) spectra of the samples during the yeast culture process were collected by spectrometers, and the raw spectra were respectively preprocessed using Savitzky-Golay (SG) smoothing + standard normal variate (SNV). Then, the two kinds of molecular spectra after preprocessing were respectively optimized for characteristic wavelength using variable combination population analysis (VCPA), and feature fusion was performed on the feature layer. Finally, support vector machine (SVM) models based on the fusion features were established to achieve quantitative detection of yeast growth status. The experimental results showed that, compared with the best VCPA-SVM model based on single-molecule spectral features, the best VCPA-SVM model based on the fusion features could obtain better prediction performance. The root mean square error of prediction (RMSEP), coefficient of determination (RP2) and relative percent deviation (RPD) of the best model were 0.47, 0.98, and 6.69, respectively. The results obtained reveal that the MSF technique can effectively improve the detection accuracy of yeast growth status; in addition, the VCPA method has a certain potential in feature wavelength mining, which can effectively reduce the dimensionality of fusion features.