Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

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.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

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.

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

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.

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。

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。

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。

基于地下结构整体损伤表征的复合地震动参数构造及其性能验证

在地下结构抗震设计中,不同的输入地震动引起的地下结构响应有显著差异,因此,合理通过地震动参数选择输入地震动是正确开展地下结构抗震设计的重要前提。针对单一地震动参数难以表征地下结构地震动潜在破坏势问题,文章构造了能更好表征地下结构损伤破坏的复合地震动参数。具体开展了以下工作:提出基于变形与滞回耗能的地下结构整体损伤指标作为结构需求参数,以定量化评估地下结构的整体破坏状态。选取64条真实地震动记录作为输入地震动,开展四层三跨地铁车站地震弹塑性动力时程分析。基于分析结果提供的数据样本,采用偏最小二乘法从统计角度构造复合地震动参数。最后,选用100条真实地震动记录开展两层三跨地铁车站弹塑性动力时程分析,对文章所构造的复合地震动参数进行验证。对比分析复合地震动参数、12个常用地震动参数与地下结构整体损伤指数的回归统计特征。结果表明:复合地震动参数与结构需求数之间具有更好拟合优度值,其Pearson相关性、有效性也优于单一地震动参数。

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.

基于电子鼻、HS-SPME-GC-MS和HS-GC-IMS评价不同制油工艺对大豆油品质及风味的影响

为探究不同制油工艺(冷榨法、浸出法、冷榨-浸出法)对三级大豆油品质及风味的影响,测定了大豆油的理化性质及脂肪酸组成,分别采用电子鼻、顶空-固相微萃取-气相色谱-质谱(headspace-solid phase microextraction-gaschromatography-massspectrometry,HS-SPME-GC-MS)法和顶空-气相色谱-离子迁移谱(headspace-gas chromatography-ion mobility spectroscopy,HS-GC-IMS)法鉴定3种豆油中挥发性化合物,并借助聚类热图、主成分分析(principal component analysis,PCA)和正交偏最小二乘判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)对3种豆油的挥发性化合物数据进行差异分析。结果表明,冷榨型豆油的水分含量最低,浸出型豆油过氧化值显著偏高且油脂色泽最深;浓香型豆油中亚油酸含量最多,营养价值更高;被检出的挥发性组分中,醇类、醛类以及吡嗪类化合物为豆油风味的形成做出主要贡献,明晰了部分风味化合物形成的原因。最终通过OPLS-DA筛选出45种贡献较大的挥发性化合物,同时构建可靠的用以鉴别浓香型豆油的模型。此外,发现豆油的品质与风味之间存在一定的相关性。

香附饮片与4种醋制香附饮片的快速鉴别

目的建立香附饮片与4种醋制香附饮片的傅里叶变换红外光谱图,快速鉴别香附饮片与4种醋制香附饮片;建立香附饮片与4种醋制香附饮片的高效液相指纹图谱,并测定α-香附酮、香附烯酮、5-羟甲基糠醛(5-HMF)、对香豆酸、阿魏酸、木犀草素的含量。方法建立25批香附样品的红外光谱图,利用OMNIC 9.2软件分析其平均红外光谱间的差异,采用Spectrum for Window 3.02软件标定共有峰,计算相对峰高;采用《中药色谱指纹图谱相似度评价系统(2012版)》建立香附饮片与4种醋制香附饮片的高效液相指纹图谱,进行相似度评价,确定共有峰个数;采用SIMCA 14.1软件进行聚类分析、主成分分析、正交偏最小二乘法-判别分析,判断红外光谱图结果与高效液相指纹图谱结果是否相互验证;测定α-香附酮、香附烯酮、5-HMF、对香豆酸、阿魏酸、木犀草素的含量。结果25批香附样品的红外光谱图的相关系数为0.9347~0.9829,共标定16个共有峰,1500~1300 cm^(-1)波段处差异明显。正态分布分析结果显示,2928、1649、995 cm^(-1)波段处可分别将醋煮品、醋炙品、醋蒸品与饮片区分;高效液相指纹图谱结果显示25批样品共有21个共有峰,相似度均大于0.9。指认出3号峰为5-HMF,7号峰为对香豆酸,8号峰为阿魏酸,14号峰为木犀草素,19号峰为香附烯酮,21号峰为α-香附酮;红外光谱图与高效液相指纹图谱聚类分析结果、主成分分析结果与正交偏最小二乘法-判别分析结果一致,均可聚为五类,即能够相互验证;5-HMF的含量经炮制后均有增加。对香豆酸、阿魏酸、木犀草素在饮片中含量最低,经炮制后均有增加。香附烯酮经醋炙、醋煮、醋蒸、醋煮蒸后均有减少,在香附饮片中含量最高。α-香附酮经醋炙后含量略有增加,经醋煮、醋蒸、醋煮蒸后均减少。结论香附饮片与4种醋制香附饮片的红外光谱图之间存在明显差异,能够快速鉴别香附饮片与4种醋制香附饮片;所建立的高效液相指纹图谱与含量测定方法操作简单、准确。

十里香夏季花香型红茶加工工艺优化及其品质分析

为优化十里香夏季花香型红茶的加工工艺,提升其品质,该研究采用感官审评、化学品质分析、聚类分析和偏最小二乘判别分析(partial least squares discrimination analysis,PLS⁃DA)对不同工艺加工的茶样品质进行综合评价。结果表明,16个工艺加工的十里香夏季花香型红茶的感官品质得分为88.3~93.8,其中有8个产品的得分超过90,整体表现较优;水浸出物、茶多酚、游离氨基酸、茶黄素、茶红素、茶褐素、咖啡碱含量以及儿茶素总量范围分别为30.39%~43.38%、9.69%~22.68%、1.55%~2.96%、0.06%~0.66%、0.86%~6.71%、0.29%~8.93%、5.18%~5.96%、5.79%~12.72%;系统聚类以欧式平方距离为15时可将16个茶样聚为3大类;PLS⁃DA可通过品质成分得分图、拟合曲线、载荷图及其变异权重参数预测型将不同茶样进行区分。十里香夏季花香型红茶的最优加工工艺为萎凋时间12 h、发酵时间2 h、足火温度80℃。

气相色谱-离子迁移谱结合化学计量学分析对新会陈皮的鉴别

采用气相色谱-离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)技术对包括新会陈皮在内的10个产地陈皮的风味成分进行测定,运用主成分分析和偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)方法对GC-IMS检出的75种风味成分进行分析,以建立新会陈皮的鉴别方法。结果表明,该方法可将新会陈皮与其他陈皮区分开,实现对新会陈皮的有效鉴别。同时,分析变量投影重要性可进一步筛选出20种对有效区分新会陈皮和其他产地陈皮发挥关键作用的特征标志物。本研究通过引入GC-IMS技术和PLSDA方法实现了新会陈皮与其他产地陈皮的准确鉴别,可为新会陈皮的国家地理标志产品保护和产地溯源提供新的技术参考。

花生籽仁黄酮含量近红外分析检测方法

黄酮是影响花生籽仁营养价值的重要指标,常用的分光光度法及色谱法测定黄酮含量费时费力,不适于育种过程中大批量的检测。构建其近红外测定模型可为快速检测籽仁黄酮含量提供重要的技术保障。该研究以290份不同黄酮含量的花生种质为材料,Al^(3+)显色法测定的黄酮含量在46.96~140.18 mgRT(RT:rutin)·(100 g)^(-1)之间。使用瑞典波通DA7250型号的近红外分析仪(950~1650 nm)扫描和采集花生籽仁的近红外光谱值,选用全波长光谱范围内偏最小二乘回归法(PLSR),对比单一和复合不同的预处理方法,比较不同模型的相关系数和误差来预测最佳模型,确定黄酮含量近红外光谱定标模型的最佳光谱预处理方法为“Savitzky-Golay Derivative+Baseline+De-trending”,校正集相关系数(R_(c))为0.884,标准误差(RMSEC)为4.998。模型构建过程中,采用含有Savitzky-Golay Derivative的组合光谱预处理方法可以显著提高模型预测的相关系数。利用50份花生样品对该模型进行外部验证,预测的相关系数R_(p)为0.904,而预测的均方根误差RMSEP为1.122。本研究所构建的近红外光谱模型能够无损、高效的测定花生籽仁中的黄酮含量,为选育高黄酮含量的花生品种奠定基础,并为μg·g^(-1)级物质含量的近红外模型构建提供借鉴。

咽痒咳合剂质量评价

目的评价咽痒咳合剂质量。方法建立HPLC指纹图谱,进行聚类分析、主成分分析和偏最小二乘判别分析。测定甘草苷、射干苷、迷迭香酸、异甘草苷、野鸢尾黄素、甘草酸单铵盐、次野鸢尾黄素、和厚朴酚、厚朴酚的含量,分析采用Agilent ZORBAX SB-C_(18)色谱柱(5μm,250 mm×4.6 mm);流动相0.1%磷酸-乙腈,梯度洗脱;体积流量1 mL/min;柱温35℃;多波长检测。结果12批样品指纹图谱中有10个共有峰,相似度均大于0.9。各批样品聚为3类,3种主成分累积方差贡献率达87.448%,峰5、14(和厚朴酚)、3(甘草苷)、11(甘草酸单铵盐)、15(细辛脂素)为质量标志物。9种成分在各自范围内线性关系良好(r=0.9999),平均加样回收率98.23%~101.63%,RSD 1.05%~2.92%。结论该方法稳定可靠,可为咽痒咳合剂质量控制提供依据。