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 t...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.展开更多
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 classi...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.展开更多
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 Laojia...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.展开更多
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 id...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.展开更多
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) spe...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 (C<sub>5</sub>H<sub>10</sub>NNaO<sub>5</sub>) and caramel color (C<sub>6</sub>H<sub>8</sub>O<sub>3</sub>). 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 (RAR<sub>V</sub>) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RAR<sub>V</sub> = 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.展开更多
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 ...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.展开更多
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...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。展开更多
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 p...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.展开更多
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 par...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.展开更多
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...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。展开更多
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...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。展开更多
文摘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.
文摘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.
文摘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.
文摘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.
文摘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 (C<sub>5</sub>H<sub>10</sub>NNaO<sub>5</sub>) and caramel color (C<sub>6</sub>H<sub>8</sub>O<sub>3</sub>). 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 (RAR<sub>V</sub>) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RAR<sub>V</sub> = 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.
基金supported by the National Natural Science Foundation of China(Nos.21205145,21276006,21036009)the Open Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics of Hunan University(No.201111)+1 种基金the Special Fund for Basic Scientific Research of Central Colleges,South-Central University for Nationalities(Nos.CZZ10005 and CZQ11012)the 'Five-twelfth' National Science and Technology Support Program (No.2012BAI27B00)
文摘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.
文摘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。
文摘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.
文摘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.
文摘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。
文摘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。
