Multisource information fusion strategies of mass spectrometry and Fourier transform infrared spectroscopy data for authenticating the age and parts of Vietnamese ginseng

Abstract

Aiming at two different classification tasks in the field of quality evaluation of valuable Chinese herbal medicine, the applicability of data fusion strategy based on different complementary analysis techniques was studied. In this study, attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and ultra-performance liquid chromatography quadrupole timeof-flight mass spectrometry (UPLC-QTOF/MS) were used to analyze the different parts (including roots, stems, leaves, and fibrils) of 2-to 5-year-old Vietnamese ginseng. The multivariate classification models (orthogonal partial least squares discrimination analysis, OPLS-DA; support vector machine, SVM) were established using low-level and mid-level data fusion methods to identify different parts and age of Vietnamese ginseng. The OPLS-DA model have shown that the fusion data matrix with low-level fusion processing could separate Vietnamese ginseng samples with different growth years to the greatest extent, whereas the single data matrix had partial overlap. But 100% of prediction set classification accuracy could be achieved. The SVM model combined with two parameter optimization algorithms (grid search, GS; genetic algorithm, GA) was used to identify Vietnamese ginseng samples with different ages. The mid-level fusion strategy based on the recursive feature elimination (RFE) features variable extraction method was more suitable for SVM model. In the model established by combining the two parameter optimization methods, the identification effect can reach 83.33%. The results showed that data fusion, as an effective strategy, could distinguish different ages and parts of Vietnamese ginseng.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

“…Autoscaling could be an appropriate solution for the fusion of univariate sensors with multivariate data, which frequently occurs in chemical or pharmaceutical processes [ 90 , 91 , 92 , 93 ]. The min-max normalization is suitable for MS [ 94 ] and some vibrational spectroscopic data [ 86 , 95 ]. It is typical to use normalization methods or elemental peak ratios for LIBS data to minimize the variability of replicates [ 96 ].…”

“…The use of genetic algorithm (GA), iteratively retained informative variables (IRIV), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), recursive feature extraction (RFE), univariate filter (UF), and ordered predictors selection (OPS) has also been reported [ 78 , 86 , 94 , 108 , 110 , 112 , 113 ]. GAs have been used in spectroscopic applications for optimal wavelength selection, multicollinearity, and noise reduction [ 108 ].…”