Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Near-Infrared Spectroscopy Coupled with Kernel Partial Least Squares-Discriminant Analysis for Rapid Screening Water Containing Malathion

Near-infrared spectroscopy coupled with kernel partial least squares-discriminant analysis was used to rapidly screen water containing malathion. In the wavenumber of 4348 cm-1 to 9091 cm-1, the overall correct classification rate of kernel partial least squares-discriminant analysis was 100% for training set, and 100% for test set, with the lowest concentration detected malathion residues in water being 1 μg·ml-1. Kernel partial least squares-discriminant analysis was able to have a good performance in classifying data in nonlinear systems. It was inferred that Near-infrared spectroscopy coupled with the kernel partial least squares-discriminant analysis had a potential in rapid screening other pesticide residues in water.

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

基于OPLS-DA模型分析不同养殖方式下宁都黄鸡肌肉关键挥发性风味物质

为研究不同养殖方式下宁都黄鸡肌肉关键挥发性风味物质,将试验鸡随机分为笼养组和平养组,饲喂同一日粮。试验鸡达上市日龄时对鸡肉进行感官品尝评价和挥发性风味物质检测,并采用正交偏最小二乘-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)方法筛选与不同养殖方式相关的差异性风味物质。结果表明:平养组和笼养组共有的挥发性风味物质27种,主要为酚类、醇类和烃类。挥发性风味物质中,己醛、1-辛烯-3-醇、E-2-壬烯醛、正己醇、壬醛、2,3-戊二酮、癸醛、2,3-辛二酮、E-2-辛烯醛为具有显著性差异的挥发性风味物质。综上,这一研究可为地方鸡肉品质基于风味物质的评价提供科学依据。

基于PLS-DA和LS-SVM的可见/短波近红外光谱鉴定港种四九、十月红和九月鲜菜心种子的可行性研究

目前市面上菜心的品种复杂,不同菜心种子的品质与发芽率不同,但菜心种子单从外观上差别不大,因此区分菜心种子的类别成为了一大难题。为了实现菜心种子类别的快速区分,探究了基于可见/短波近红外光谱分析菜心种子类别的可行性。从南昌市种子交易场所购买了港种四九、十月红和九月鲜三个品种的菜心种子,从中挑选出品相较好且大小适中的子粒,将每种菜心种子均匀分为30份,按照2∶1划分为建模集和预测集,所有样本共计90份。通过近红外光谱仪获取采样间隔为1 nm的菜心种子的光谱反射率,波长覆盖范围325~1075 nm,将原始光谱数据采用多元散射校正(MSC)、卷积平滑(S-G)和标准正态变换(SNV)三种预处理方法进行预处理,预处理后的光谱变量建立偏最小二乘回归(PLSR)模型,确定了SNV是最佳预处理方法。采用主成分分析(PCA)对菜心种子进行了聚类分析,从前三个主成分因子(PCs)得分图可知三种菜心种子存在光谱特征差异。将原始光谱变量、前三个PCs(累计贡献97.15%)和基于随机蛙跳(RF)算法挑选的13个特征波长作为偏最小二乘判别(PLS-DA)和最小二乘支持向量机(LS-SVM)模型的输入变量,从模型结果可知:三种输入变量中,采用RF筛选特征波长作为模型输入变量时,模型预测效果最好,PCs建立的模型最差,相比于PCA分析,采用RF筛选出的特征波长更能够反映原始光谱信息。比较不同模型预测效果,LS-SVM模型比PLS-DA模型得到的预测精度更好,其中RF-LS-SVM模型是所有模型中最佳的预测模型,建模集和预测集均为100%。采用可见/短波近红外光谱研究菜心种子的类别可行,并且能够获得很好地预测效果,为菜心种子的快速区分提供了理论依据。

美国户外休闲产业发展特征及预测模型构建研究——基于PLS和PLS-DA方法的分析

户外休闲产业是美国经济的支柱性产业,分析其发展特征和构建预测模型对于我国健身休闲产业健康、可持续发展有重要借鉴价值。研究收集了美国50个州2021年户外休闲产业相关数据,选取27个影响指标,运用PLS和PLS-DA方法,对美国户外休闲产业发展特征进行分析并构建预测模型。研究表明:(1)美国州域户外休闲产业可分为4种发展模式,即总量大占比小型、总量小占比大型、总量较大占比较小型、总量较小占比较大型,4种发展模式在空间上呈现一定集聚分布特征。(2)构建的预测模型有效且精度较高,并筛选出12个VIP指标,经济、人口和社会因素似乎更多地影响美国户外休闲产业发展,而自然环境因素的影响相对较弱。

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined with supervised pattern recognition based on partial least-squares discriminant analysis （PLSDA） was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct （MSC） fingerprint and second derivative （2nd derivative） fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces ofA. catechu.

基于microRNA表达谱初步构建PLS-DA体液识别模型

针对外周血、唾液、精液、月经血、阴道分泌物这五种法医学常见的体液样本,采用二代测序(NGS)平台检测获得microRNA(miRNA)表达谱,使用偏最小二乘判别分析(PLS-DA)建立基于这五种体液的识别模型,并探讨PLS-DA在法医体液溯源中的应用价值。经优化小分子RNA文库制备过程,采用Ion Torrent S5 XL测序系统对前述法医学五种体液样本(每种10例)进行小RNA测序,以PLS-DA构建体液识别模型,评估不同数量miRNA标记组合下预测的准确性。本研究获得法医学常见五种体液样本的miRNA表达谱,外周血与月经血中表达量前10名的miRNAs有6个重叠;唾液和阴道分泌物中表达量前10名的miRNAs有4个重叠。基于全数据集、107个和11个miRNAs构建的体液来源识别模型的准确率分别为0.95、0.94、0.89。本研究通过NGS测序分析获得了五种体液样本的miRNA组(miRNome),利用PLS-DA初步构建了体液识别模型,对于应用miRNome进行体液识别的相关研究具有参考价值。

基于HS-SPME-GC-MS和PLS-DA分析不同季节早白尖红茶挥发性风味物质

采集四川省筠连县春、夏、秋三季共15份早白尖红茶样品,采用顶空固相萃取-气相色谱-质谱技术对红茶样品的香气成分进行测定,运用偏最小二乘-判别分析(partial least square-discriminant analysis,PLS-DA)建立不同季节茶叶判别模型,绘制层次聚类的树状热图确定关键香气成分在不同季节样品中的分布规律。结果表明,春季样品醇类(113.05μg/g)和酯类物质(34.92μg/g)含量明显高于夏秋两季样品,而醛类物质(23.85μg/g)明显低于夏秋两季样品,且所建PLS-DA模型可将春和夏秋两季样品明显区分。进一步分析后的分层聚类的树状热图显示,苯乙醛、橙花醇和香叶醇是春季样品区别于其它两季茶样的特征香气化合物,在此基础上可通过芳樟醇、芳樟醇氧化物Ⅰ和Ⅱ对夏、秋两季样品进行进一步区分。该研究为解析不同季节早白尖红茶香气物质提供基础研究数据,也为进一步探究筠连早白尖红茶关键香气形成机制奠定基础。

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

黄花败酱HPLC多组分定量控制及PCA、OPLS-DA联合GRA法综合质量评价

目的建立HPLC法同时测定黄花败酱中常春藤皂苷元、齐墩果酸、熊果酸、黄花败酱皂苷C、原儿茶酸、绿原酸、咖啡酸、秦皮乙素、东莨菪内酯、芦丁、槲皮素和山奈酚的含量,探索主成分分析(PCA)、正交偏最小二乘法-判别分析(OPLSDA)及灰色关联度分析(GRA)模型在黄花败酱质量评价中的应用。方法采用HPLC法,色谱柱为Waters Atlantis T3 C_(18)柱(250 mm×4.6 mm,5μm),以乙腈-0.1%磷酸为流动相,梯度洗脱;检测波长为210,260,360 nm;通过对多指标成分含量结果进行PCA、OPLS-DA和GRA分析,综合评价黄花败酱的整体质量。结果外标法方法学验证结果均符合要求;12种成分在各自范围内线性关系良好(r>0.9990),平均加样回收率在96.82%~100.16%之间(RSD<2.0%,n=9)。PCA、OPLS-DA结果显示齐墩果酸、熊果酸、绿原酸和槲皮素是影响黄花败酱产品质量的主要潜在标志物。GRA结果显示相对关联度在0.3806~0.5714之间,黄花败酱存在一定批间差异。结论HPLC法同时测定黄花败酱质量中12个成分,方法简便实用。PCA、OPLS-DA和GRA可用于评价黄花败酱的整体质量。

基于电子鼻和气质联用分析萌芽大蒜挥发性物质差异

为探究萌芽期大蒜挥发性物质的差异,采用电子鼻、捕集阱顶空-气质联用仪(Trap head space-gas chromatography-mass spectrometry,HS-Trap-GC-MS)结合正交偏最小二乘法判别分析(Orthogonal partial least squares discriminant analysis,OPLS-DA)、香气活度值、差异性热图、相关性分析分析大蒜萌芽在0、24、48、72、96 h挥发性物质的差异。电子鼻结合OPLS-DA建立预测模型其预测能力达96.00%。GC-MS分析表明:含硫化合物是不同萌芽期大蒜的主要共有挥发性物质,含硫化合物的相对含量随萌芽时间的延长而呈递减趋势,而种类呈现出递增趋势;二烯丙基二硫醚是样品在萌芽过程中含量降低最多的物质。二烯丙基四硫醚、烯丙硫醇是样品共有关键化合物。差异性热图分析显示:除共有物质含量差异外,硫化丙烯、己醛、叠氮二羧酸二叔丁酯、丙烯醇、6-甲基-2-庚炔、5-甲基噻二唑、2-亚乙基-1,3-二硫烷、2-丙-2-炔基磺酰基丙烷、2,5-二甲基噻吩、2,5-二甲基呋喃、1-戊烯-3-醇、1,3-二噻烷的缺失进一步加大了未萌芽和萌芽大蒜气味的差异。萌芽大蒜主要共有挥发性物质的种类随萌芽时间的延长呈现递增趋势。大蒜主要挥发性物质与电子鼻大多数传感器存在显著相关性。大蒜的气味强度会随萌芽时间的延长而逐步减弱。

红芪搓条前后主要次级代谢产物变化规律研究

目的 考察红芪搓条前后主要次级代谢产物的变化规律。方法 UPLC-MS/MS法测定芒柄花素、芒柄花苷、毛蕊异黄酮、毛蕊异黄酮苷、美迪紫檀素、染料木素、木犀草素、甘草素、异甘草素、香草酸、阿魏酸、γ-氨基丁酸、腺苷、甜菜碱的含量,聚类分析、主成分分析、正交偏最小二乘判别分析进行化学模式识别以寻找差异性成分。结果 搓条后,芒柄花素、毛蕊异黄酮、甘草素、γ-氨基丁酸含量升高,芒柄花苷、毛蕊异黄酮苷、香草酸含量降低。搓条、未搓条药材聚为2类,毛蕊异黄酮苷、芒柄花素、γ-氨基丁酸、香草酸、毛蕊异黄酮、芒柄花苷为差异性成分。结论 本实验阐明红芪搓条前后化学成分差异,可为其他药材搓条机制研究提供参考。

基于化学计量学和指纹图谱的辽宁道地药材北五味子质量评价研究

目的 采用指纹图谱与化学计量学相结合的方法,评价辽宁岫岩产北五味子的质量。方法 采用HPLC法,柱温30℃,流速1 mL/min,流动相采用水-乙腈梯度洗脱,检测波长220 nm,对10批辽宁岫岩五味子基地生产的北五味子建立指纹图谱,运用聚类分析(hierarchical cluster analysis, HCA)、主成分分析(principal component analysis, PCA)及正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)进行化学模式识别分析。结果 建立了岫岩产北五味子的指纹图谱,相似度为0.970^0.999,共标定了29个共有峰,指认了14个成分;HCA分析10批北五味子可分为2类;PCA共得到6个主要成分,其累计方差贡献率为95.5%;OPLS-DA表明五味子甲素、戈米辛G、五味子丙素、五味子醇乙等11个成分可能是影响北五味子质量的差异性标志物。结论 研究所建立的指纹图谱结合化学模式识别分析,方法准确、稳定、可靠,可用于北五味子药材的质量控制研究。

基于单料烟的加热卷烟与传统卷烟香气成分释放差异分析

将10种单料烟分别制备成加热卷烟和传统卷烟两种类型卷烟样品,采用GC-MS分析和正交偏最小二乘法判别分析(OPLS-DA)模型评价加热卷烟气溶胶和传统卷烟主流烟气中香气成分的释放差异。结果表明:加热卷烟气溶胶和传统卷烟主流烟气中分别鉴定出53种和77种香气成分;两者共有香气成分47种,其中13种香气成分在加热卷烟气溶胶中的释放量较为突出,除乙酸、γ-丁内酯外,其他11种均是醛酮类化合物;筛选出两种卷烟中存在显著释放差异的香气成分53种,包含吡嗪吡啶类7种、酮类12种、呋喃类4种、酸类9种、酯类3种、酚类8种、烃类9种、醇类1种,从差异性香气成分在该类别总鉴定成分的占比来看,酚类香气成分在两种卷烟中的释放差异性最为显著,其余依次为烃类、吡嗪吡啶类、酸类、酯类、酮类、呋喃类、醇类。

基于HPLC-QAMS多组分定量联合多元统计分析及加权TOPSIS法的畲药树参综合品质评价

目的建立同步检测畲药树参中紫丁香苷、绿原酸、芥子醛葡萄糖苷、松柏醇、芦丁、山柰酚-3-O-芸香糖苷、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸含量的高效液相色谱一测多评(HPLC-QAMS)方法,并采用多元统计分析及加权优劣解距离(technique for order preference by similarity to ideal solution method,TOPSIS)法对其品质进行综合评价。方法以Waters Xbridge C 18色谱柱;乙腈-0.05%甲酸溶液为流动相,梯度洗脱;检测波长260 nm。以山柰酚-3-O-芸香糖苷为参照物,建立内参物与其他8个待测成分的相对校正因子(relative correction factor,RCF),进行RCF耐用性考察及色谱峰定位,同时与外标法实测结果进行对比,验证HPLC-QAMS法准确性和可靠性。运用主成分分析(principal component analysis,PCA)、正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)等多元统计分析以及W-TOPSIS法对9个成分HPLC-QAMS法含量结果的相关性进行分析,挖掘影响畲药树参产品质量的主要潜在标志物,建立畲药树参综合质量优劣评价方法。结果9种成分分别在3.27~81.75μg/mL、9.85~246.25μg/mL、0.43~0.75μg/mL、0.31~7.75μg/mL、1.58~39.50μg/mL、0.59~14.75μg/mL、1.26~31.50μg/mL、4.55~113.75μg/mL和1.98~49.50μg/mL范围内线性关系良好,平均加样回收率96.82%~100.07%(RSD<2.0%);HPLC-QAMS和外标法(ESM)含量测定结果差异无统计学意义(P>0.05),HPLC-QAMS法可用于畲药树参多组分定量控制;多元统计分析结果显示,前2个主成分累计方差贡献率89.589%,绿原酸、紫丁香苷、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸是影响畲药树参产品质量的主要潜在标志物;加权TOPSIS法结果显示浙江地区所得畲药树参质量最优,其次为江西、安徽、湖南和湖北产树参,云南和贵州产树参位于排名后4位。结论所建立的HPLC-QAMS多组分定量控制方法,操作便捷、结果准确;多元统计分析联合加权TOPSIS法全面客观,可用于畲药树参品质的综合评价。

基于UPLC-Q-TOF-MS分析江西特色炮制技术对中药升麻化学成分的影响

目的比较江西特色炮制技术对升麻化学成分的影响,筛选优质饮片品种。方法采用超高效液相色谱-四极杆-飞行时间串联质谱(ultra performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry,UPLC-Q-TOF-MS)技术,在正、负离子模式下分析升麻不同炮制品的化学成分,通过对照品、相对分子质量、质谱裂解规律和文献信息进行鉴定。利用SIMCA-P13.0软件建立升麻各炮制品主成分分析(principal component analysis,PCA)和偏最小二乘法-判别分析(partial least squares discriminant analysis,PLS-DA)模型,获取PCA得分图、PLA-DA得分图和变量重要性投影(variable importance plot,VIP)值,筛选造成升麻炮制前后主要差异的物质基础。利用MetaboAnatyst网页绘图工具,制作得到热图,可更直观地观察升麻化学成分经炮制后的变化趋势。结果鉴定出71个化学成分,PCA显示经不同方法炮制后升麻组间差异性大,PLS-DA筛选出VIP值>1的33个化学成分作为炮制前后差异性的主要化学标记物。其中生品和蜜炙升麻中三萜类含量较高,蜜麸、蜜糠炒升麻中酚酸类物质含量较高,蜜麸升麻中阿魏酸含量较高。结论酚酸类和三萜皂苷类是区分升麻不同炮制品最重要的化合物类别,为江西特色升麻饮片的药效物质基础及优势品种研究提供了依据。