Gender Discrimination of Flower Buds of Mature Populus tomentosa by HPLC Fingerprint Combined with Chemometrics

Abstract

A high performance liquid chromatography-diode array detector (HPLC-DAD) was used to establish the HPLC fingerprint. Chemometrics methods were used to discriminate against the gender of flower buds of Populus tomentosa based on areas of common peaks calibrated with the HPLC fingerprint. The score plot of principal component analysis (PCA) showed a clear grouping trend (R2X, 0.753; Q2, 0.564) between female and male samples. Two groups were also well discriminated with orthogonal partial least squares-discriminant analysis (OPLS-DA) (R2X, 0.741; R2Y, 0.980; Q2, 0.970). As the hierarchical clustering analysis (HCA) heatmap showed, all samples were separated into two groups. Four compounds were screened out by S-plot and variable importance in projection (VIP > 1.0). Two of them were identified as siebolside B and tremulacin. This study demonstrated that HPLC fingerprints combined with chemometrics can be applied to discriminate against dioecious plants and screen differences, providing a reference for identifying the gender of dioecious plants.

Research on quality marker based on the processing from Aconiti lateralis radix praeparata to Heishunpian

“…The mass spectrometry data of all batches of ALRP and HSP were imported into Markerview software for normalization of the detected peak ion intensity, and the processed data was imported into SIMCA for analysis. The R 2 X, R 2 Y, and Q 2 of this OPLS‐DA model were 0.826, 1, and 0.876, indicating the good fitting and predictive ability of the model 54 . As shown in Figure 7A, the ALRP and HSP were well distinguished, located on both sides of the X‐axis, respectively.…”

“…The R 2 X, R 2 Y, and Q 2 of this OPLS-DA model were 0.826, 1, and 0.876, indicating the good fitting and predictive ability of the model. 54 As shown in Figure 7A, the ALRP and HSP were well distinguished, located on both sides of the X-axis, respectively. S-plot was displayed for selecting differential variables, as shown in Figure 7B.…”

Modeling of soluble solid content of PE‐packaged blueberries based on near‐infrared spectroscopy with back propagation neural network and partial least squares (BP–PLS) algorithm

“…The result showed that the R 2 and Q 2 values on the left were less than the values they had originally on the right. The intercept of the regression line of Q 2 on the longitudinal axis was less than zero, thus suggesting that the OPLS‐DA model was not overfitting and was reliable (Z. Li, Wu, et al., 2022).…”

“…The intercept of the regression line of 𝑄 2 on the longitudinal axis was less than zero, thus suggesting that the OPLS-DA model was not overfitting and was reliable (Z. Li, Wu, et al, 2022).…”

“…The detection efficiency is significantly increased by the fact that the NIR spectrum is typically collected without sample preparation or the use of chemical reagents (Park et al., 2020). NIR detection technology has been widely used in agriculture, forestry, food, petroleum, chemical, pharmaceutical, and other research fields (C. Li, Li, et al., 2022; Q. Liu et al., 2021; J. Xue et al., 2019; Zhang et al., 2022). However, with the rapid development of NIR technology, there are already some portable NIR spectrometers as well as online NIR devices available for quick detection under industrial conditions (Porep et al., 2015).…”