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.

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.

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.

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。

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.

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。

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GC-QTOF-MS对物理回收的食品接触用再生高密度聚乙烯中迁移物的非靶向筛查

以3家企业提供的21?种食品接触用再生高密度聚乙烯(recycled high-density polyethylene,rHDPE)样品在60℃条件下与两种代表性食品模拟物(95%乙醇、4%乙酸溶液)接触10 d作为迁移实验条件,利用气相色谱-串联四极杆飞行时间质谱检测迁移到食品模拟物中的物质。被筛查出的161种物质根据其毒性进行分级(由低至高分为Ⅰ~Ⅳ级),其中毒性Ⅲ和Ⅳ级的有59种,且其预测辛醇/水分配系数大于毒性Ⅰ、Ⅱ级的物质。被筛查的物质中苯及取代衍生物占比最高。邻苯类增塑剂、抗氧剂降解产物以及多环芳烃等物质需要特别关注。使用正交偏最小二乘判别分析迁移物在不同阶段样品中的迁移量变化,发现终产品相较母粒样品中物质的迁移量有所提升。该研究可以为食品接触用r HDPE中迁移物的分析及安全风险评估提供理论基础。

经典名方旋覆代赭汤乙酸乙酯部位指纹图谱及化学模式识别研究

目的:建立旋覆代赭汤乙酸乙酯部位UPLC指纹图谱,结合化学模式识别分析不同产地饮片对旋覆代赭汤质量的影响。方法:采用Waters UPLC BEH C_(18)(2.1 mm×150 mm,1.7μm)色谱柱,流动相为乙腈-甲醇-0.4%甲酸水溶液,体积流量0.1 mL·min^(-1),梯度洗脱,检测波长350 nm;建立13批不同产地饮片提取的旋覆代赭汤样品指纹图谱,进行相似度评价,并结合聚类分析(CA)、主成分分析(PCA)和正交偏最小二乘法-判别分析(OPLS-DA),对旋覆代赭汤指纹图谱数据进行分析。结果:13批旋覆代赭汤指纹图谱确定22个共有特征峰,指认出12个多酚类成分,其中11个多酚类成分来自于旋覆花,其相似度均在0.9以上;通过CA发现,13批样品可按旋覆花产地分为三类;PCA与CA结果基本一致,并提取出4个主成分;OPLS-DA筛选出影响分类的差异性质量标志物,其中已指认出的成分有异绿原酸、异槲皮苷、异鼠李素-3-O-葡萄糖苷、异绿原酸B、1,5-二咖啡酰奎宁酸、绿原酸、槲皮万寿菊苷、槲皮素。结论:通过指纹图谱结合化学模式识别技术的分析策略,可快速有效地筛选不同批次旋覆代赭汤中多酚类成分中的差异质量标志物,为后续旋覆代赭汤的药效物质基础研究和质量评价提供参考。

不同干燥方式对柳蒿芽山野菜挥发性风味的影响研究

为研究不同干燥方式对柳蒿芽山野菜中的挥发性风味化合物影响的差异性,采用气相色谱-离子迁移谱联用(GC-IMS)技术对3种不同干燥处理的柳蒿芽样品进行挥发性化合物检测分析。从样品中共检出103种挥发性有机化合物(volatile organic compounds,VOCs);经归一化处理,结合指纹图谱分析,真空冷冻干燥样品中93种VOCs得到了有效的保留,显著高于电热鼓风干燥和自然干燥组,根据气味描述数据库检索可知冷冻干燥对柳蒿芽中的甜香、焦香气味有较好的保留。正交偏最小二乘法判别分析(OPLS-DA)结果筛选出了12种对区分不同干燥方式的柳蒿芽挥发性风味物质贡献较大的变量,分别为十二醛、2-甲基异冰片、1-辛醇、薄荷脑、(E,E)-2,4-庚二烯醛、乙酸冰片酯、香茅醛、DL-2-羟基-4-甲基戊酸乙酯、1-石竹烯、三亚乙基二胺。

基于GC-IMS结合多元统计方法研究掺入不同比例玉米油对橄榄辣椒油风味的影响

为分析掺入不同比例玉米油对橄榄辣椒油风味的影响,文章采用感官评分、气相色谱-离子迁移谱(GC-IMS)和正交偏最小二乘-判别分析(OPLS-DA)等方法对辣椒油样品的风味成分进行检测和多元统计分析。结果表明,6种辣椒油的色泽和滋味无显著性差异,但香气差异显著(P<0.05),其中GLY1香气最佳,GLY2和GLY6香气和谐。通过GC-IMS共检测出81种挥发性有机化合物(VOCs),其中醇类、酯类、酸类、醛类、杂环类和酮类化合物在6种辣椒油中的相对含量较高。通过变量投影重要性(VIP)筛选出异丁醛、丙酮、乙酸-D、乙酸甲酯等29种VOCs为6种辣椒油的关键风味差异标志物(VIP>1.0)。OPLS-DA的因子载荷结果与热图聚类分析、感官评价结果一致。因此,GC-IMS结合OPLS-DA和热图聚类分析能将6种辣椒油进行有效区分。该研究结果可为橄榄辣椒油的鉴伪和风味多样化辣椒油产品的生产提供一定理论支撑。

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。

早期高血压弦脉脉象特点及其瞬时波强技术参数特征分析

目的:应用瞬时波强(WI)技术探测早期高血压弦脉患者颈总动脉各参数,分析早期高血压弦脉的脉象特点;提取弦脉脉象判别的特征参数,探讨早期高血压弦脉患者颈总动脉WI参数的特点,以期为弦脉的精准分型及信息解读提供客观依据。方法:选择52例早期原发性高血压弦脉、50例生理性弦脉、50例平脉受试者,分析其颈总动脉的WI参数,总结早期高血压弦脉的脉象特点,并运用SIMCA14.1统计软件提取脉象分型的主参数。结果:早期高血压弦脉组与平脉组及生理性弦脉组比较,瞬时加速度波强(W1)、负向波面积(NA)值增高,W1-W2间期降低(均P<0.01);而平脉组与生理性弦脉组W1、NA、W1-W2间期比较,差异均无统计学意义(均P>0.05)。平脉组、生理性弦脉组、早期高血压弦脉组的血管压力应变弹性模量(EP)、脉搏波传导速度(PWV)、血管硬化参数(β)数值均逐渐增高(均P<0.01)。平脉组血管顺应性(AC)高于其他2组(均P<0.01),生理性弦脉组AC高于早期高血压弦脉组(P<0.05)。3组R-W1间期比较差异无统计学意义(P>0.05)。基于主成分分析(PCA)和正交偏最小二乘法判别分析(OPLS-DA),生理性弦脉组与平脉组样本区分明显,表明2组WI各参数比较差异均有统计学意义(均P<0.05),特征性的WI参数[投影重要性(VIP值)>1]为EP、PWV、β、AC,其中贡献率为EP>PWV>β>AC。基于PCA和OPLS-DA,生理性弦脉组与早期高血压弦脉组样本区分明显,表明2组WI各参数差异均有统计学意义(均P<0.05),特征性的WI参数(VIP值>1)为EP、PWV、NA、W1、β,其中贡献率为EP>PWV>NA>W1>β。结论:区分早期高血压弦脉组与生理性弦脉组的WI特征性参数为EP、PWV、β、NA、W1。区分平脉组与生理性弦脉组特征性参数为EP、PWV、β、AC。WI技术可为脉象的精准分型和信息解读提供客观依据,值得进一步推广应用。