目的 探討近紅外光譜（near infrared spectroscopy，NIRS）技術(shù)在天麻Gastrodia elata質(zhì)量評(píng)價(jià)中的應(yīng)用，通過(guò)對(duì)不同蒸制程度的天麻進(jìn)行判別分析，進(jìn)一步構(gòu)建天麻有效成分含量預(yù)測(cè)模型，為天麻質(zhì)量評(píng)價(jià)提供新方法。方法 建立天麻中天麻素（gastrodin，GAS）、對(duì)羥基苯甲醇（p-hydroxybenzyl alcohol，HBA）、巴利森苷B（parishin B，PB）、巴利森苷C（parishin C，PC）、巴利森苷A（parishin A，PA）含量測(cè)定的高效液相色譜法，并以其測(cè)定值為參比。采集天麻樣品的NIRS，結(jié)合線(xiàn)性判別分析（linear discriminant analysis，LDA）算法，建立不同蒸制時(shí)間的天麻定性判別模型。選擇偏最小二乘法（partial least squares，PLS）等化學(xué)計(jì)量學(xué)方法建立有效成分的測(cè)定值與NIRS的定量校正模型，對(duì)建模過(guò)程的各個(gè)階段進(jìn)行優(yōu)化，構(gòu)建天麻各有效成分的最優(yōu)PLS定量模型。結(jié)果 基于LDA算法建立的天麻蒸制程度的定性判別模型準(zhǔn)確度達(dá)到96.2%，混淆矩陣圖與接收器工作特性（receiver operating characteristic，ROC）曲線(xiàn)評(píng)價(jià)模型的預(yù)測(cè)性能良好；天麻近紅外原始光譜經(jīng)標(biāo)準(zhǔn)正態(tài)變換（standard normal variate，SNV）或平滑法（savitzky-golay，SG）預(yù)處理后，以競(jìng)爭(zhēng)性自適應(yīng)重加權(quán)抽樣-偏最小二乘法（competitive adaptive eeweighted sampling-partial least squares，CARS-PLS）構(gòu)建的定量模型準(zhǔn)確度較高，建立的GAS、HBA、PB、PC、PA最佳PLS定量模型的校正決定系數(shù)（R²c）分別為0.975 3、0.986 4、0.970 0、0.963 6、0.965 9，預(yù)測(cè)決定系數(shù)（R²p）分別為0.970 4、0.984 0、0.977 9、0.978 6、0.985 5，5個(gè)定量模型的預(yù)測(cè)偏差（residual prediction deviation，RPD）均大于6。表明NIRS定量模型預(yù)測(cè)值與測(cè)定值具有良好的線(xiàn)性關(guān)系，模型預(yù)測(cè)效果良好。結(jié)論 所建立的天麻近紅外LDA定性和CARS-PLS定量模型準(zhǔn)確、可靠，可實(shí)現(xiàn)天麻蒸制程度的定性鑒別以及GAS、HBA、PB、PC、PA 5個(gè)有效成分含量的快速定量分析，為天麻的質(zhì)量評(píng)價(jià)與控制提供新的參考。

Objective To explore the application of near infrared spectroscopy (NIRS) technology in the quality evaluation of Tianma (Gastrodiae Rhizoma), a qualitative analysis was conducted to distinguish the degree of steaming of Gastrodiae Rhizoma. Furthermore, a prediction model was established to determine the contents of active components, aiming to provide a new method for quality evaluation of Gastrodiae Rhizoma. Methods A high-performance liquid chromatography (HPLC) method was established to measure the contents of gastrodin (GAS), p-hydroxybenzyl alcohol (HBA), parishin B (PB), parishin C (PC) and parishin A (PA) in Gastrodiae Rhizoma, which were used as the reference value. The near infrared spectroscopy of Gastrodiae Rhizoma with different degrees of steaming were collected. Linear discriminant analysis (LDA) was used to establish the discrimination model of the degree of steaming of Gastrodiae Rhizoma. The quantitative calibration model between the near infrared spectrum and the contents of active components to be measured was established by partial least squares (PLS) and other chemometrics methods. Each part of the modeling process was optimized respectively to construct the optimal PLS quantitative model for the active ingredients of Gastrodiae Rhizoma. Results The accuracy of the discrimination model based on LDA algorithm reached 96.2%, and the performance of model evaluated by the confusion matrix diagram and ROC curve was good. After pretreatment by standard normal variate (SNV) or Savitzky-Golay (SG), the quantitative analysis model constructed by competitive adaptive reweighted sampling - partial least squares regression (competitive adaptive reweighted sampling-partial least squares, CARS-PLS) had high accuracy. The correction determination coefficient (R²c) of GAS, HBA, PB, PC and PA models was 0.975 3, 0.986 4, 0.970 0, 0.963 6, and 0.965 9; The prediction determination coefficient (R²p) was 0.970 4, 0.984 0, 0.977 9, 0.978 6, and 0.985 5. The residual prediction deviation (RPD) values for all five quantitative models exceeded 6. The predicted values of NIRS models and the measured values of HPLC showed a good linear relation, which presented a great prediction ability of the models. Conclusion The established NIR-LDA qualitative model and CARS-PLS quantitative model were accurate and reliable, effectively identifying the steaming degree of Gastrodiae Rhizoma and nondestructively determining the contents of active components. These findings provide a novel reference for quality evaluation and control during the process of Gastrodiae Rhizoma.

R286.2

特色炮制技術(shù)規(guī)律發(fā)掘——煨制（GZY-KJS-2022-050）;江蘇省研究生科研創(chuàng)新計(jì)劃項(xiàng)目（KYCX23-2023）