Isolation and purification of honokiol and magnolol from cortex Magnoliae officinalis by high-speed counter-current chromatography

High-speed counter-current chromatography was used to isolate and purify honokiol and magnolol from cortex Magnoliae Officinalis (Magnolia officinalis Rehd. et Wils.), a plant used in the traditional Chinese medicine. A crude sample, 150 mg, was successfully separated with a two-phase solvent system composed of n-hexane-ethyl acetate-methanol-water (1:0.4:1:0.4, v/v), and the fractions were analyzed by high-performance liquid chromatography. The separation produced 80 and 45 mg of honokiol and magnolol with purities of 99.2 and 98.2%, respectively, in 2.5 h.     

Enhancing Classification Performance of Multisensory Data through Extraction and Selection of Features

Linear discriminant analysis (LDA) has been widely used in the classification of multi sensor data fusion. This paper discusses the performance of LDA when the classifications were performed based on feature extraction and feature selection methods. Comparisons were also made based on single sensor modality. These strategies were studied using a honey dataset along with two types of sugar concentration collected from two types of sensors namely electronic nose (e-nose) and electronic tongue (e-tongue). Assessment of error rate was achieved using the leave-one-out procedure.     

化学模式识别技术评价中药厚朴质量的研究

本文对不同产地和市售的二十四种厚朴药材及饮片的萃取液进行了高效液相色谱、气相色谱及紫外光谱分析;对所得数据用主成分分析进行分类,概括出不同样品化学成分的异同,认为大叶木兰可做为厚朴的代用品,而在陕西安康购得的样品不应做厚朴使用,后者经生药学鉴定为厚朴伪品凹叶木兰。化学模式识别技术可为寻找药材的代用品、评价中药质量及鉴定伪劣药材提供依据。     

Wheat lipids to discriminate species, varieties, geographical origins and crop years

The knowledge of lipid components of wheat finds a precious information in order to differentiate between Triticum durum (TD) and Triticum aestivum (TA). The determination of the percentages of methyl esters of the differently unsaturated fatty acids with 18-carbon atoms (C₁₈), of sterol fraction and of the other components is of particular weight. In this paper, the classification methods of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were applied in order to measure the classification and prediction abilities of the determined (percentages of the) components of the lipid fraction of wheat in differentiating among species, origins, varieties and crops. By univariate feature selection method (Fisher weights (FW)) and linear discriminant analysis, it was found that the only oleate is able to distinguish between the two species with a prediction rate of 100%. Inside the species Triticum durum, it was obtained a prediction rate of 83.9% while discriminating between the different origins, a prediction rate of 82.2% while discriminating among varieties and a prediction rate of 94.3% among crop years.     

Bio-inspired Sensor Fusion for Quality Assessment of Harumanis Mangoes

In recent years, there have been a number of reported studies on the use of non-destructive technique to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose (e-nose), acoustics, CCD, IR sensor or by other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using data fusion of the electronic nose (e-nose) and acoustic sensor and combine with CCD and IR sensor. A Fourier-based shape separation method was developed from CCD camera images to grade mango by its shape and able to correctly classify 100%. Colour intensity from infrared image was used to distinguish and classify the level of maturity and ripeness of the fruits. The finding shows 92% correct classification of maturity levels by using infrared vision Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved.     

GC-MS联用技术结合化学计量学方法分析厚朴叶挥发油成分

利用水蒸气蒸馏法提取湖南产厚朴叶的挥发油成分,采用GC-MS联用技术结合化学计量学方法(直观推导式演进特征投影法和选择性离子法)进行分析,同时结合程序升温保留指数辅助定性。结果共鉴定出54种化合物,占挥发油总量的84.95%。主要化学成分为α-、β-和γ-桉油醇,含量分别为13.10%、28.21%和14.67%。此外,含量较高的化合物还有α-蒎烯(2.96%)、芳樟醇(1.71%)、丹皮酚(1.88%)、石竹烯(2.04%)、佛术烯(3.60%)、α-瑟林烯(3.84%)和[1AR-(1Aα,4α,4Aβ,7Bα)]-1A,2,3,4,4A,5,6,7B-八氢化-1,1,4,7-四甲基-1H-环丙烯并[E]奥(1.34%)。研究表明,将化学计量学方法用于中药挥发油成分的分析可提高定性分析的准确性。所得结果可为厚朴药植物资源的开发利用提供依据。     

A comparison of different chemometrics approaches for the robust classification of electronic nose data

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365–372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56 % accuracy) and SVM with a polynomial kernel (91.66 % accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.     

Comprehensive quality evaluation of Fructus Schisandrae using electrospray ionization ion trap multiple-stage tandem mass spectrometry coupled with chemical pattern recognition techniques

Electrospray ionization ion trap multiple-stage tandem mass spectrometry (ESI-MS(n)) was used to evaluate Fructus Schisandrae of similar species (Schisandra chinensis (Turcz.) Baill. fruits and Schisandra sphenanthera Rehd. et Wils. fruits) and different growth characteristics (color, shape, etc.). The application of chemical pattern recognition in the ESI-MS(n) data analysis was carried out by principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). Then the antioxidant activity of different Fructus Schisandrae samples were determined by an LC-ESI-MS method and ferric reducing antioxidant power (FRAP) assay. Using the ESI-MS(n) method coupled with chemical pattern recognition analysis and correlated with the antioxidant activity evaluation, the two similar species were successfully distinguished, thus improving the therapeutic safety and effectiveness. The superior characteristics of Schisandra chinensis (Turcz.) Baill. fruits were obtained and made the selection and breeding of Chinese medicine materials more scientific. This study indicates that ESI-MS(n) is a valuable tool for the authentication of botanical origin and can also be useful for the quality control of Chinese medicinal herbs.     

Breathing Rhythm Variations during Wash-In Do Not Influence Exhaled Volatile Organic Compound Profile Analyzed by an Electronic Nose

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in.     

Discrimination and classification techniques applied on Mallotus and Phyllanthus high performance liquid chromatography fingerprints

Mallotus and Phyllanthus genera, both containing several species commonly used as traditional medicines around the world, are the subjects of this discrimination and classification study. The objective of this study was to compare different discrimination and classification techniques to distinguish the two genera (Mallotus and Phyllanthus) on the one hand, and the six species (Mallotus apelta, Mallotus paniculatus, Phyllanthus emblica, Phyllanthus reticulatus, Phyllanthus urinaria L. and Phyllanthus amarus), on the other. Fingerprints of 36 samples from the 6 species were developed using reversed-phase high-performance liquid chromatography with ultraviolet detection (RP-HPLC-UV). After fingerprint data pretreatment, first an exploratory data analysis was performed using Principal Component Analysis (PCA), revealing two outlying samples, which were excluded from the calibration set used to develop the discrimination and classification models. Models were built by means of Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification and Regression Trees (CART) and Soft Independent Modeling of Class Analogy (SIMCA). Application of the models on the total data set (outliers included) confirmed a possible labeling issue for the outliers. LDA, QDA and CART, independently of the pretreatment, or SIMCA after “normalization and column centering (N_CC)” or after “Standard Normal Variate transformation and column centering (SNV_CC)” were found best to discriminate the two genera, while LDA after column centering (CC), N_CC or SNV_CC; QDA after SNV_CC; and SIMCA after N_CC or after SNV_CC best distinguished between the 6 species. As classification technique, SIMCA after N_CC or after SNV_CC results in the best overall sensitivity and specificity.     

A chemometric approach to characterization of ionic liquids for gas chromatography

A chemometric study was carried out to characterize three ionic liquid types (ILs) with hexacationic imidazolium, polymeric imidazolium, and phosphonium cationic cores, using a range of contra-anions such as halogens, thiocyanate, boron anions, triflate, and bistriflimide. The solvation parameter model developed by Abraham et al., unsupervised techniques as cluster analysis (CA), and supervised techniques as linear discriminant analysis (LDA), step-LDA, quadratic discriminant analysis (QDA), and multivariate regression techniques as discriminant partial least squares (D-PLS), or multiple linear regression (MLR) were used to characterize the functionalized ILs above. CA established two main groups of phases, those with an acidic H-bond and those with basic ones. Once detected, the two natural groups, a linear and quadratic delimiters with good classification (>96 %) and prediction (>92 %) capacities were computed. The use of step-LDA technique allowed us to establish that a, b, and s solvation parameters were the most discriminant variables. These variables were used for modeling purposes, and a D-PLS and MLR models were constructed using a binary response. The explained variance of categorical variable by the model validated by cross-validation was 65 %, and 94.5 % of ILs were correctly predicted. IL characterization carried out would allow the appropriate selection of phases for gas chromatography (GC).     

Characterization and classification of the aroma of beer samples by means of an MS e-nose and chemometric tools

An electronic nose based on coupling of headspace (HS) with a mass spectrometer (MS) has been used in this study to classify and characterize a series of beers according to their production site and chemical composition. With this objective, we analyzed 67 beers of the same brand and preparation process but produced in different factories. The samples were also subjected to sensory evaluation by a panel of experts. Linear discriminant analysis (LDA) was used as the classification technique and stepwise LDA based on Wilk’s lambda criterion was used to select the most discriminating variables. To interpret the aroma characteristics of the beers from the m/z ions obtained, score and loading bi-plots were obtained by applying canonical variables. Because the beers analyzed were marketed with the same name and brand, we expected to be working with the same product irrespective of its origin. However, results from both sensory evaluation and use of the e-nose revealed differences between factories. With the e-nose it was possible to relate these differences to the presence (and abundance) of characteristic ions of different compounds typically found in beer. These results demonstrate that the HS–MS e-nose is not only an aroma sensor capable to classify and/or differentiate samples but it can also provide information about the compounds responsible for this differentiation.     

Classification of Venezuelan spirituous beverages by means of discriminant analysis and artificial neural networks based on their Zn, Cu and Fe concentrations

Copper, zinc and iron concentrations were determined in “aguardiente de Cocuy de Penca” (Cocuy de Penca firewater), a spirituous beverage very popular in the North-Western region of Venezuela, by flame atomic absorption spectrometry (FAAS). These elements were selected for their presence can be traced to the (illegal) manufacturing process of the aforementioned beverages. Linear and quadratic discriminant analysis (QDA), and artificial neural networks (ANNs) trained with the backpropagation algorithm were employed for estimating if such beverages can be distinguished based on the concentrations of these elements in the final product, and whether it is possible to assess the geographic location of the manufacturers (Lara or Falcón states) and the presence or absence of sugar in the end product. A linear discriminant analysis (LDA) performed poorly, overall estimation and prediction rates being 51.7% and 50.0%, respectively. A QDA showed a slightly better overall performance, yet unsatisfactory (estimation: 79.2%, prediction: 72.5%). Various ANNs, comprising a linear function (L) in the input layer, a sigmoid function (S) in the hidden layer(s) and a hyperbolic tangent function (T) in the output layer, were evaluated. Of the networks studied, the (3L:5S:7S:4T) gave the highest estimation (overall: 96.5%) and prediction rates (overall: 97.0%), demonstrating the superb performance of ANNs for classification purposes.     

Cephasinenoside A,a new cephalotane diterpenoid glucoside from Cephalotaxus sinensis

A new cephalotane diterpenoid glucoside, cephasinenoside A (1), was isolated from the twigs and leaves of Cephalotaxus sinensis (Rehd. et Wils.) Li. The structure of 1 was established on the basis of spectroscopic analyses and ECD calculations. Compound 1 represents the first cephalotane diterpenoid glycoside which inhibits the proliferation of human acute myeloid leukemia HL-60 cells with a GI₅₀ value of 7.17 ± 1.03 μM.     

南五味子中五味子甲素的薄层扫描色谱定量分析

南五味子是2000年中国药典开始收录的中药材。南五味子与北五味子的性状完全不同,所含化学成分亦不同,功能主治各有侧重。南五味子为木兰科植物华中五味子的干燥成熟果实,性味酸、甘、温,具有收敛固涩,益气生津,补肾宁心之功效。现代研究表明,南五味子的活性成分主要包括五味子甲素和五味子酯甲等木脂素成分。五味子甲素具有显著的功效,但药典仅有五味子酯甲的含量测定,未有五味子甲素的含量测定,相关研究报道也很少,因此有必要研究建立南五味子中五味子甲素含量测定方法。     

Determination of volatile components in peptic powder by gas chromatography-mass spectrometry and chemometric resolution

Gas chromatography-mass spectrometry (GC-MS) coupled with chemometric resolution upon two-dimensional data was proposed as a method for the analysis of volatile components in a traditional Chinese medicinal preparation peptic powder which contains Rhizoma Atractylodis, Pericarpium Citri Reticulatae, Cortex Magnoliae Officinalis and Radix Glycyrrhizae. Ninety-three components were separated and 65 of them were qualitatively and quantitatively analyzed which represented about 90.28% of the total content. With the help of chemometric resolution, the data were resolved into a pure chromatogram and a mass spectrum of each chemical component. The accuracy of qualitative and quantitative results was greatly improved by using the two-dimensional comprehensive information of chromatograms and mass spectra. The example showed that chemometric resolution could greatly enhance separation ability. This makes it possible to analyze complicated practical systems like traditional Chinese medicinal preparations with the help of coupled instruments and chemometric resolution methods.     

Authentication of Iranian Saffron (Crocus sativus) Using Stable Isotopes δ13C and δ2H and Metabolites Quantification

Saffron is a very high value-added ingredient used in the food supplement market and contains a high level of safranal. Adding synthetic safranal to saffron, which is significantly cheaper, and falsifying the origin of saffron may represent recurrent fraud. Saffron from different countries was analyzed to determine the stable isotope ratios δ¹³C and δ²H from safranal by gas chromatography coupled with isotope-ratio mass spectrometry (GC-C/P-IRMS) and the concentration of saffron metabolites with ultra-high performance liquid chromatography coupled with diode array detector (UHPLC-DAD). The isotopic analysis highlighted a higher ratio of δ²H in synthetic safranal than in natural safranal; the mean values were 36‰ (+/− 40) and −210‰ (+/− 35), respectively. The δ¹³C between Iranian, Spanish and other saffron was significantly different and represents median values of −28.62‰, −30.12‰ and −30.70‰, respectively. Moreover, linear and quadratic discriminant analyses (LDA and QDA) were computed using the two isotope ratios of safranal and the saffron metabolites. A first QDA showed that trans-crocetin and the δ¹³C of safranal, picrocrocin, and crocin C3 concentrations clearly differentiated Iranian saffron from other origins. A second model identified δ¹³C, trans-crocetin, crocin C2, crocin C3, and picrocrocin as good predictors to discriminate saffron samples from Iran, Spain, or other origins, with a total ability score classification matrix of 100% and a prediction matrix of 82.5%. This combined approach may be a useful tool to authenticate the origin of unknown saffron.     

Magnolol‐induced inhibition of tumor growth in systemic malignancies

The commentary illustrates the significant tumor attenuating effects of magnolol and refers to the article on “Screening active anti‐breast cancer compounds from Cortex Magnolia officinalis by 2D LC–MS” (X. Hou, et al., J. Sep. Sci. 2013, 36 (4), 706–712). On the basis of the literature, there is definitively need for further studies to fully understand the magnolol reation pathways and to harness the antineoplastic effects.     

Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils

An “electronic nose” has been used for the detection of adulterations of virgin olive oil. The system, comprising 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, feature selection techniques were employed to choose a set of optimally discriminant variables. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and artificial neural networks (ANN) were applied. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils and it was even possible to identify the type of oil used in the adulteration. Promising results were also obtained as regards quantification of the percentages of adulteration.     

Variation of the Lignan Content of Schisandra chinensis (Turcz.) Baill. and Schisandra sphenanthera Rehd. et Wils.

A rapid and convenient extraction technique followed by HPLC analysis has been developed for determination of lignans in Schisandra chinensis (Turcz.) Baill. and Schisandra sphenanthera Rehd. et Wils. Under the optimized conditions the lignans schisandrin, schisantherin, deoxyschisandrin, and γ-schisandrin were extracted from ten samples collected from different regions in China and then quantified by HPLC. The lignan content of the ten samples is different. S. sphenanthera Rehd. et Wils. is richer in deoxyschisandrin but there is less schisandrin and γ-schisandrin is not found. S. chinensis (Turcz.) Baill. is usually richer in γ-schisandrin, the maximum amount being 4.263 mg g⁻¹, although one sample contained none. This method is simple, rapid, sensitive, and repeatable, and is suitable for identification and differentiation of samples from different regions of China.