Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra -highperformance liquid chromatography (UPLC) and high -performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre -processed data. Subsequently, orthogonal partial least squares -discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre -processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spec- troscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.