Representative FT-IR spectral data obtained after area normalization.

<p>Representative FT-IR spectral data obtained after area normalization.</p

Assignment of major bands in a representative Fourier-transform infrared (FT-IR) spectrum of <i>Panax ginseng</i> samples.

<p>Assignment of major bands in a representative Fourier-transform infrared (FT-IR) spectrum of <i>Panax ginseng</i> samples.</p

Selection of partial-least-squares–discriminant analysis (PLS-DA) models according to various normalization and scaling methods and numbers of PLS components for discriminating cultivation ages and parts of <i>P</i>. <i>ginseng</i> samples.

<p>Selection of partial-least-squares–discriminant analysis (PLS-DA) models according to various normalization and scaling methods and numbers of PLS components for discriminating cultivation ages and parts of <i>P</i>. <i>ginseng</i> samples.</p

Selected normalization and variable influence on projection (VIP) cutoff values for model construction for discriminating various parts of ginseng samples, and the permutation parameters derived from the PLSR prediction models.

<p>Selected normalization and variable influence on projection (VIP) cutoff values for model construction for discriminating various parts of ginseng samples, and the permutation parameters derived from the PLSR prediction models.</p

PLS-DA-derived score plots from 70% methanol (A), and 100% <i>n</i>-hexane (B) extracts of cultivated <i>C</i>. <i>bassiana</i> at various development stages.

<p>stage 1; □, stage 2; •, stage 3; △, stage 4; ♦.</p

Metabolites identified in 70% methanol extractsof <i>C. bassiana</i> fruiting bodies using GC-MS.

*<p>No annotation in enrichment analysis.</p

The free-radical scavenging activity according to developmental stage of <i>C. bassiana</i> (10,000 mg/L).

<p>Different letters in the same column indicate significant differences at <i>p</i><0.05. Data are mean ± STD values for triplicate measurements.</p

Metabolites identified in 100% <i>n</i>-hexane extracts of <i>C. bassiana</i> fruiting bodies using GC-MS.

*<p>No annotation in enrichment analysis.</p

Free radical scavenging activities and total purine contents (A) and correlation (B) to various development stages of cultivated <i>C</i>. <i>bassiana</i>.

<p>Free radical scavenging activities and total purine contents (A) and correlation (B) to various development stages of cultivated <i>C</i>. <i>bassiana</i>.</p

Discrimination and prediction of cultivation age and parts of <i>Panax ginseng</i> by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

<div><p><i>Panax ginseng</i> C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of <i>P</i>. <i>ginseng</i>. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of <i>P</i>. <i>ginseng</i>, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of <i>P</i>. <i>ginseng</i> using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.</p></div