粉末样品颗粒大小对花椒挥发油近红外光谱定量预测的影响研究

为了快速有效评定花椒质量等级,应用近红外光谱分析技术,采用偏最小二乘法,对141份花椒样品粉碎成八种不同颗粒大小的粉末,对近红外光谱分别建立挥发油含量预测模型,当粉末样品颗粒大小为40目时,建立的模型最优,交叉验证测定系数r2141为0.9364,交叉验证误差均方根RMSECV141为0.421。使用105份40目粉末样品近红外光谱所建立的模型对36份样品的预测集进行预测,光谱预处理采用Meancentering vector normalization,谱区在6100.1~5774.2cm-1及4601.6~4424.2cm-1,则预测测定系数r326为0.9862,预测集验证误差均方根RMSEP36为0.192,预测相对标准差RSD36为4.95%,预测相对分析误差RPD36为8.517。研究结果表明,对花椒进行近红外光谱扫描前,粉碎到40目时所建立的近红外光谱模型最佳,使用近红外光谱技术快速有效检测花椒挥发油含量是可行的。

Effect of Powder's Particle Size on the Quantitative Prediction of Volatile Oil Content in Zanthoxylum Bungeagum by NIR Technique

The traditional chemical methods to measure the volatile oil content of zanthoxylum bungeagum encounter some problems such as long time and low efficiency, so it is difficult to achieve rapid detection. One hundred forty-one samples including 74 zanthoxylum bungeagum maxim and 67 zanthoxylum schinifolium Sieb. et zucc were collected, from many provinces in China such as Shan Xi, Si Chuan, Gan Su, Chong Qing, Yun Nan, etc. Each sample was crushed and sorted to 8 kinds of powder samples according to the particle size of 120-mesh, 100-mesh, 80-mesh, 60-mesh, 40-mesh, 20-mesh, 10-mesh, respectively, including the material retained by the 10-mesh sieve. Then, each powder sample was labeled by one of the following serial numbers: 120, 100, 080, 060, 040, 020, 010 and 000. For each sample, the NIR spectra of 8 different kinds of particle size powders were measured using a Bruker MATRIX-I FT-NIR spectrometer. Then, the 8 different kinds of particle size powders of each sample were mixed uniformly. The volatile oil content was measured in each sample according to the distillation stipulated by the Forestry Standard of PRC-Quality Classify of Prickly Ash (LY/T 1652-2005). Based on near infrared spectroscopy technique and partial least squares (PLS), 8 calibration models of predicting volatile oil content were established by 141 powder samples with 8 different kinds of particle size. Experiments indicatd that the model was the best with the powder's particle size of 40-mesh and the determination coefficient (r2(141)) and the root mean square error of cross validation (RMSECV141) were 0.9364 and 0.421, respectively. The model was established by the calibration set with 105 samples with particle size of 40-mesh. Applying the model to the test set with 36 samples, the determination coefficient (r2(36)), the root mean square error of prediction (RMSEP36), the relative standard deviation (RSD36), and the ratio of prediction to deviation (RPD36) were 0.9233, 0.452, 11.66%, and 3.624, respectively. The model, based on the same sample set but optimized by OPUS 5.0, was developed by spectral data pretreatment of the Mean Centering+Vector Normalization in the spectral region of 6 100.1-5 774.2 cm(-1) and 4 601.6-4 424.2 cm(-1). Using the model to predict the test set, r2(36), RMSEP36, RSD36, and RPD36 were 0.9862, 0.192, 4.95%, and 8.517, respectively. The results showed that the model built by samples passed through 40-mesh screen was the best and rapid detection of volatile oil content in zanthoxylum bungeagum by NIR was feasible and efficient.