Zhong Zhao

Caulis Sinomenii is the dried plant stems of Sinomenium acutum and Sinomenium acutum var. cinereum and has been used in Chinese medicine for treating rheumatic diseases for over a thousand years. Previous studies have demonstrated that sinomenine is a major active constituent in both plants and can be utilized as an indicator of quality of the medicinal herb Caulis Sinomenii. Currently, S. acutum and S. acutum var. cinereum are growing over a wide geographical range in China, with equally wide variations in growing conditions. The objectives of this research were to determine whether there were difference between the species and varieties, and whether the different growing conditions could result in different quality by determining the content of sinomenine in different samples. A modified HPLC method using a diode array detector (DAD) has been developed for efficiently quantifying sinomenine in the plants. Using this method, fourteen samples of S. acutum var. cinereum and eleven samples of S. acutum from growing regions as well as eighteen herbal samples of Caulis Sinomenii from wholesale herbal markets were evaluated. The results showed that there was no marked difference in the content of sinomenine between the species and varieties collected from growing regions; however, a very large variation was found among the samples collected from different regions. Moreover, the content of sinomenine in the plants of large size (stem diameter>3 cm) was much higher than those of small size (stem diameter<1 cm). This implies that the growing region has greater impact on the quality of Caulis Sinomenii in terms of the content of sinomenine than the species and varieties. The results also showed that the content of sinomenine in commercial Caulis Sinomenii was markedly lower than that in the plants collected directly from growing regions. This suggests that to obtain the herb with higher content of sinomenine and thus ensure greater efficacy, both in clinical applications and in pharmacological investigations, the plant of Caulis Sinomenii with controlled stem size collected directly from growing regions is preferable.

The side effects in spectral multivariate modeling caused by the uneven distribution of sample numbers in the region of the calibration set and validation set were analyzed, and the "average" phenomenon that samples with small property values are predicted with larger values, and those with large property values are predicted with less values in spectral multivariate calibration is showed in this paper. Considering the distribution feature of spectral space and property space simultaneously, a new method of optimal sample selection named Rank-KS is proposed. Rank-KS aims at improving the uniformity of calibration set and validation set. Y-space was divided into some regions uniformly, samples of calibration set and validation set were extracted by Kennard-Stone (KS) and Random-Select (RS) algorithm respectively in every region, so the calibration set was distributed evenly and had a strong presentation. The proposed method were applied to the prediction of dimethylcarbonate (DMC) content in gasoline with infrared spectra and dimethylsulfoxide in its aqueous solution with near infrared spectra. The "average" phenomenon showed in the prediction of multiple linear regression (MLR) model of dimethylsulfoxide was weakened effectively by Rank-KS. For comparison, the MLR models and PLS1 models of MDC and dimethylsulfoxide were constructed by using RS, KS, Rank-Select, sample set partitioning based on joint X- and Y-blocks (SPXY) and proposed Rank-KS algorithms to select the calibration set, respectively. Application results verified that the best prediction was achieved by using Rank-KS. Especially, for the distribution of sample set with more in the middle and less on the boundaries, or none in the local, prediction of the model constructed by calibration set selected using Rank-KS can be improved obviously.