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.

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.

PARTIAL LEAST-SQUARES(PLS)REGRESSION AND SPECTROPHOTOMETRY AS APPLIED TO THE ANALYSIS OF MULTICOMPONENT MIXTURES

The UV absorption spectra of o-naphthol,α-naphthylamine,2,7-dihydroxy naphthalene,2,4-dimethoxy ben- zaldehyde and methyl salicylate,overlap severely;therefore it is impossible to determine them in mixtures by traditional spectrophotometric methods.In this paper,the partial least-squares(PLS)regression is applied to the simultaneous determination of these compounds in mixtures by UV spectrophtometry without any pretreatment of the samples.Ten synthetic mixture samples are analyzed by the proposed method.The mean recoveries are 99.4%,996%,100.2%,99.3% and 99.1%,and the relative standard deviations(RSD) are 1.87%,1.98%,1.94%,0.960% and 0.672%,respectively.

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.

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.

Application of near-infrared spectroscopy for the rapid analysis of Lonicerae Japonicae Flos solution extracted by water

A rapid quantitative analytical method for three components of Lonicerae Japornicae Flos solution(Lonicera Japonica Thumb.)extracted by water was developed using near-infrared(NIR)spectroscopy and the partial least-squares(PLS)method.The NIR spectra of 81 samples collected from a production line were obtained.The concentrations of secologanic acid,chlorogenicacid and galuteolin were detemmined by using high-performance liquid chromatography-diodearray detection as the reference method.Several pretreatment methods for the NIR spectra wereusedi during PLS calibration.The most appropriate latent variable number of the PLS factor wasselected based on the standard error of cross-validation(SECV).The performance of the finalPLS models was evaluated according to SECV,standard error of predliction(SEP)and deter-mination coeficient(R^(2)).The compounds secologanic acid,chlorogenic acid and galuteolin hadSEP values of 0.030,0.061 and 1.668μg/mL,respectively and R^(2) values over 0.85.This workshows that NIR spectroscopy is a rapid and convenient method for the analysis of LoniceraeJaponicae Flos solution extracted by water.The proposed method can help in the application ofprocs analytical technology in the pha maceutical industry,particularly in tra ditional Chinesemedicine injections.

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.

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。

五种产香酵母对发酵耙豌豆挥发性风味物质的影响

为探究产香酵母菌对耙豌豆挥发性风味物质的影响,该文用5种酵母(安琪酵母、季也蒙毕赤酵母、酿酒酵母、鲁氏酵母和近平滑假丝酵母)对耙豌豆进行固态发酵5 d。采用电子鼻结合GC-MS对发酵耙豌豆挥发性风味物质进行鉴定分析,通过主成分分析和偏最小二乘-判别分析(partial least square-discriminant analysis,PLS-DA)对不同酵母菌发酵耙豌豆的香气进行差异分析。电子鼻分析表明酵母菌发酵对耙豌豆挥发性风味有影响,酵母发酵耙豌豆与未发酵耙豌豆整体风味差异较大。GC-MS共检测出69种挥发性风味物质,系以醇类和酸类为主,其次为酯类和酮类。5种不同酵母发酵耙豌豆的挥发性香气成分差异显著(P<0.05),假丝酵母发酵耙豌豆样品中的挥发性风味物质种类最丰富,含38种。PLS-DA模型筛选得到12种关键风味物质(VIP>1),主要是异丙醇、异戊醇、2-甲基丁醇和乙酸乙酯等物质,赋予发酵耙豌豆醇香、果香和麦芽香。因此,产香酵母能降低耙豌豆的豆腥味,显著改善耙豌豆的风味。综上,该研究为酵母发酵耙豌豆工业化生产提供数据支持,为开发发酵型耙豌豆产品提供理论基础。

不同产地太白贝母中11种核苷与碱基类成分分析及产地差异研究

目的对10批采自重庆、云南、陕西等5个省(市)的太白贝母样品中11种核苷和碱基类成分的含量进行测定,采用化学计量学分析法比较太白贝母中的核苷和碱基类成分的含量差异,对其质量进行综合评价,为规范化种植和产地优选提供参考。方法水超声提取太白贝母中的核苷和碱基类成分,采用高效液相色谱-二极管阵列检测器(HPLC-DAD)法测定样品中各成分含量,并采用主成分分析(Principal component analysis,PCA)、层次聚类分析(Hierarchical cluster analysis,HCA)对产地进行划分,偏最小二乘判别分析(Partial least squares discriminant analysis,PLS-DA)确定太白贝母中差异性的指标成分,比较指标性成分在不同产地样品间的含量差异。结果11种核苷和碱基类成分在不同产地太白贝母中存在显著差异;主成分分析和层次聚类分析可将样品聚为4类;PLS-DA鉴定出5个指标性成分,分别为尿嘧啶、胞嘧啶、尿苷、肌苷、腺苷,以重庆、湖北产地样品所含核苷和碱基成分相对较高,质量相对较优。结论该方法操作简单、重复性好、准确可靠,筛选出了鉴定不同产地太白贝母中的特征性核苷和碱基类成分,可用于初步阐明不同产地样品的差异性,并能够较好地反映太白贝母的品质,为太白贝母药材采购产地选择和质量控制提供参考。

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。

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。