Quality Control of Jati Belanda Leaves (Guazuma ulmifolia) using Image Analysis and Chemometrics

The quality of medicinal plants, such as Guazuma ulmifolia (jati belanda, JB), affects the quality of the herbal material derived from them, and can be determined using image analysis. The objective of this study is to investigate the possibility of using an image-generated spectrum and chemometrics as a method for quality control of Jati belanda leaves. Three different quality levels of JB leaves were determined, based on their harvesting time, and confirmed by total flavonoid content analysis. The images of JB samples were collected and reconstructed as a reflection spectrum using the Wiener estimation. The reconstructed spectrum had a goodness-of-fit coefficient of 0.9576 and a root-mean-square-error (RMSE) of 36.65%, compared to the experimental spectrum. Principal Component Analysis (PCA) was used to classify the JB reconstructed spectrum based on its quality. A score plot of two PCs that represented 98% variance was able to group the JB spectrum. Further analysis using Partial Least Squares-Discriminant Analysis (PLSDA) showed that the method can result in around 90% prediction success rate with external validation. This study indicates that image analysis and chemometrics could be used as quality control methods for herbal material.

Quality Control of Jati Belanda Leaves (Guazuma ulmifolia) using Image Analysis and Chemometrics

The quality of medicinal plants, such as Guazuma ulmifolia (jati belanda, JB), affects the quality of the herbal material derived from them, and can be determined using image analysis. The objective of this study is to investigate the possibility of using an image-generated spectrum and chemometrics as a method for quality control of Jati belanda leaves. Three different quality levels of JB leaves were determined, based on their harvesting time, and confirmed by total flavonoid content analysis. The images of JB samples were collected and reconstructed as a reflection spectrum using the Wiener estimation. The reconstructed spectrum had a goodness-of-fit coefficient of 0.9576 and a root-mean-square-error (RMSE) of 36.65%, compared to the experimental spectrum. Principal Component Analysis (PCA) was used to classify the JB reconstructed spectrum based on its quality. A score plot of two PCs that represented 98% variance was able to group the JB spectrum. Further analysis using Partial Least Squares-Discriminant Analysis (PLSDA) showed that the method can result in around 90% prediction success rate with external validation. This study indicates that image analysis and chemometrics could be used as quality control methods for herbal material.