Non-Destructive Analysis of Phenolic and Flavonoid Contents in Medicinal Plant Powders Using Hyperspectral Imaging and Variable Selection.
Hyperspectral imaging combined with chemometrics shows promise for non-destructive analysis of phenolic and flavonoid contents in medicinal plants, but further validation is needed.
Where it sits
this study against the rest of the vip (vasoactive intestinal polypeptide) corpusSummary and findings
This study measured phenolic and flavonoid contents in 12 powder samples of medicinal plants using hyperspectral imaging. The optimized variable importance in projection (VIP) combined with partial least squares regression (PLSR) models yielded coefficients of determination of 0.985 and 0.996. Standard root mean square error of prediction values were reported as 0.165 and 1.150 mg/g for the respective compounds.
Abstract
Phenolic and flavonoid contents in medicinal plants are essential to their growth and development and provide numerous health benefits, yet their quantification using traditional wet chemistry is labor-intensive and time-consuming. This study utilized the combination of two benchtop hyperspectral imaging (HSI) systems, namely short-wave infrared (SWIR) and visible near-infrared, for the non-destructive quantitative estimation of the phenolic and flavonoid contents present in 12 powder samples of medicinal plants. For quantitative analysis, partial least squares regression (PLSR) prediction models used several spectral preprocessing techniques. To further enhance performance, variable importance in projection (VIP) selected the most informative spectral variables and combined them with PLSR to improve predictions. The optimized VIP-PLSR models further improved the predictive performance, yielding coefficients of determination (R 2) of 0.985 and 0.996 and even lower standard root mean square error of prediction values of 0.165 and 1.150 mg/g for the respective compounds. Additionally, chemical imaging provided a clear spatial visualization of both compounds within the samples. These results underscore the potential of SWIR-HSI combined with chemometrics as an effective tool for screening and quality control in medicinal plant powders.
Background
This paper addresses the challenge of quantifying phenolic and flavonoid contents in medicinal plants, which are crucial for their growth and potential health benefits. Traditional methods for quantification are labor-intensive and time-consuming. The study aims to explore a non-destructive method using hyperspectral imaging combined with chemometrics, which could streamline the analysis process.
Methods
The study utilized two benchtop hyperspectral imaging systems: short-wave infrared (SWIR) and visible near-infrared. A total of 12 powder samples of medicinal plants were analyzed. Partial least squares regression (PLSR) models were developed using various spectral preprocessing techniques, and variable importance in projection (VIP) was employed to select the most informative spectral variables.
Results
The optimized VIP-PLSR models yielded coefficients of determination of 0.985 and 0.996 for phenolic and flavonoid contents, respectively. The standard root mean square error of prediction values were 0.165 mg/g for phenolic content and 1.150 mg/g for flavonoid content. Chemical imaging provided spatial visualization of the compounds within the samples.
Interpretation
The findings suggest that the combination of SWIR-HSI and chemometrics can effectively estimate phenolic and flavonoid contents in medicinal plant powders. The high R² values indicate strong predictive performance, but the clinical significance of these findings in practical applications remains unclear. Limitations include the absence of longitudinal data and potential confounding factors not addressed in the study.
Key findings
- R² of 0.985 for phenolic content prediction.
- R² of 0.996 for flavonoid content prediction.
- Standard root mean square error of prediction of 0.165 mg/g for phenolic content.
- Standard root mean square error of prediction of 1.150 mg/g for flavonoid content.
Limitations
- Not reported in abstract.
- Small sample size of 12 plant powders.
- No longitudinal data provided.
- Potential confounding factors not addressed.