Application of chemometric analysis using physicochemical and chromatographic data to differentiate the origin of plant protection products containing trinexapac-ethyl

Authentication and distinction of Shenmai injection with HPLC fingerprint analysis assisted by pattern recognition techniques

Radix Angelicae Sinensis, known as Danggui in China, is an effective and wide applied material in Traditional Chinese Medicine (TCM) and it is used in more than 80 composite formulae. Danggui from Minxian County, Gansu Province is the best in quality. To rapidly and nondestructively discriminate Danggui from the authentic region of origin from that from an unauthentic region, an electronic nose coupled with multivariate statistical analyses was developed. Two different feature extraction methods were used to ensure the authentic region and unauthentic region of Danggui origin could be discriminated. One feature extraction method is to capture the average value of the maximum response of the electronic nose sensors (feature extraction method 1). The other one is to combine the maximum response of the sensors with their inter-ratios (feature extraction method 2). Multivariate statistical analyses, including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and hierarchical clustering analysis (HCA) were employed. Nineteen samples were analyzed by PCA, SIMCA and HCA. Then the remaining samples (GZM1, SH) were projected onto the SIMCA model to validate the models. The results indicated that, in the use of feature extraction method 2, Danggui from Yunnan Province and Danggui from Gansu Province could be successfully discriminated using the electronic nose coupled with PCA, SIMCA and HCA, which suggested that the electronic-nose system could be used as a simple and rapid technique for the discrimination of Danggui between authentic and unauthentic region of origin.

Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial

Saccharomyces cerevisiae is a widely studied and highly utilized eukaryotic organism, ideally suited to high throughput metabolic analysis, being a powerful model for understanding basic cell biology. This study compares the models developed by two supervised methods, such as the partial least squares‐discriminant analysis (PLS‐DA) and soft independent modeling of class analogy (SIMCA), using mid‐infrared (MIR) spectra registered during the growth of S. cerevisiae in bioreactor. The spectra were analyzed using the principal component analysis (PCA), with resolution in five different classes, which were well defined in terms of their biochemical parameters. The SIMCA model showed a significant fitting, 99%, validation, 98%, and prediction parameters, 97%, comparatively with PLS‐DA model. Regarding accuracy, sensitivity, and specificity parameters, a value between 83% and 100% was achieved for both methods, but the SIMCA method showed significant specificity and sensitivity values, 98%–100%, representing a suitable classification tool of yeast cells. According to these results, the MIR spectra associated with chemometric tools can be considered a valued strategy for a classification and detailed analysis for an accurate control, allowing to predict the evolution of the corrected process in advance, avoiding losses of time and costs associated with new fermentations, identifying a significant number of samples in any biotechnological process.

Coumarin and alkaloids are the major bioactive constituents of Toddalia asiatica, playing an important role in various biological activities such as anti-inflammatory, analgesic, anti-bacterial and anti-tumour. To establish a method that will simultaneously determine the coumarins and alkaloids compounds in T. asiatica and identify their characteristic fragmentation patterns, while combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of T. asiatica samples. Qualitative characterisation of coumarins and alkaloids compounds in the methanol extracts of T. asiatica was determined by ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS). Quantitative analysis relies on high-performance liquid chromatography with a diode array detector (HPLC-DAD). A total of 59 components were characterised by UPLC-QTOF-MS/MS, including 29 coumarin, 25 alkaloids, one phenolic acid and four flavonoids. While the 19 characteristic components out of 23 common peaks in the chromatographic fingerprints of T. asiatica were confirmed. Quantitative analysis of seven major compounds from 18 samples were simultaneously detected by HPLC-DAD at wavelengths of 280nm. The samples were classified into three groups by hierarchical clustering analysis (HCA) combined with principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) which screened out the main chemical markers responsible for the samples differences. Fingerprints combined with chemometrics and chemical identification are a simple, rapid and effective method for the quality control of T. asiatica.

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

Chromatographic impurity fingerprinting of genuine and counterfeit Cialis® as a means to compare the discriminating ability of PDA and MS detection

We report soft independent modeling of class analogy (SIMCA) analysis of laser-induced plasma emission spectra of edible salts from 12 different geographical origins for their classification model. The spectra were recorded by using a simple laser-induced breakdown spectroscopy (LIBS) device. Each class was modeled by principal component analysis (PCA) of the LIBS spectra. For the classification of a separate test data set, the SIMCA model showed 97% accuracy in classification. An additional insight could be obtained by comparing the SIMCA classification result with that of partial least squares discriminant analysis (PLS-DA). Different from SIMCA, the PLS-DA classification accuracy seems to be sensitive to addition of new sample classes to the whole data set. This indicates that the individual modeling approach (SIMCA) can be an alternative to global modeling (PLS-DA), particularly for the classification problems with a relatively large number of sample classes.

The peak areas from a high-performance liquid chromatography-diode array (HPLC-DAD) analysis of biophenols extracted from olive leaves have been used as chemotaxonomic markers to construct chemometric models in order to discriminate and classify (1) 13 varieties of Olea europaea olive trees, namely, Alameño, Arbequina, Azulillo, Chorna, Hojiblanca, Lechín, Manzanillo, Negrillo, Nevadillo, Ocal, Pierra, Sevillano, and Tempranillo, from the same cultivation zone and (2) Arbequina samples from six different geoghaphical origins, namely, Córdoba, Mallorca (north and south), Ciudad Real, Lleida, and Navarra. Models based on principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for discrimination between samples as a function of the tree varieties and cultivation zone, whereas K nearest neighbors (KNN) and soft independent modeling of class analogy (SIMCA) models were generated to classify the samples used to validate the models into one of the groups previously established by PCA and HCA. KNN classified correctly 93 and 92% of the samples into the variety and cultivation zone, respectively; meanwhile, the SIMCA models predicted 85 and 92%, respectively.

Rapid and accurate screening for toxicants/chemicals in a broad range is an important element in systematic toxicological analysis (STA). Herein, we report a novel method for the rapid screening of 61 central nervous system (CNS) drugs in plasma, using a solid-phase extraction (SPE) column termed, weak cation exchange (WCX) and high performance liquid chromatography with a diode array detector (HPLC-DAD). The SPE column was preconditioned sequentially with 3 ml of acetonitrile, 1 ml of water and, 2 ml of buffer solution. The pretreated plasma was loaded onto the column, which was then washed with 2 ml of water, followed by 2 ml of acetonitrile, and the acetonitrile elution was collected as the neutral/acid fraction. Subsequently, 3 ml of trifluoroacetic acid-acetonitrile (2+98) was then used to elute the column and the elution was collected as basic fraction. The collected fractions were evaporated at 60°C under a nitrogen stream until about 100 μl of solvent remained. The final volume was then adjusted to 1 ml with 5% of acetonitrile. The HPLC separation was accomplished on an Agilent TC-C18 column (250 × 4.6 mm, 5 µm) with acetonitrile and phosphate buffer solution as mobile phase, by gradient elution at a flow rate of 1.5 ml/min. The detection wavelength was 210 nm, and the full spectra were recorded from 200 to 364 nm. The absolute recoveries of 55 drugs tested, exceeded 50%; 42 of them exceeded 80%. In conclusion, the WCX SPE preparation combined with HPLC-DAD, is suitable for a broad drug screening for CNS drugs.   Key words: Solid phase extraction (SPE), weak cation exchange (WCX), high performance liquid chromatography with diode array detection (HPLC-DAD), systematic toxicological analysis (STA), drug screening.

The ability to analytically differentiate and match intact apple (Malus domestica, Borkh) fruit and fruit juice extracts from different apple cultivars is of interest to the food industry. This study tested the feasibility of detecting the difference among volatile gases evolved from intact ‘McIntosh (Buhr),’ ‘Delicious,’ and ‘Gala’ apples and their extracted juice using a prototype 32-array polymeric detector chemical sensor. All data were first processed to obtain principal components. PCA analysis clearly separated whole ‘McIntosh,’ ‘Gala,’ and ‘Delicious’ samples from juiced on day 1. PCA analysis of day 2 samples showed clustering of whole vs. juiced for all three cultivars, although there was some overlap between the clusters. A soft independent modeling of class analogy (SIMCA) class discrimination of the sensor principal component data sets was then performed to determine the degree of difference. SIMCA analysis of the same samples showed a pronounced difference (SIMCA value >3.00) for only the ‘McIntosh’ samples. SIMCA values between 2.00 and 3.00 were found for the other two cultivars on day 1. For day 2 samples, no SIMCA values greater than 2.00 were found for any cultivar whole vs. juiced. PCA analysis showed clear separation between cultivars for day 1 whole samples. SIMCA analysis showed that there was a difference between ‘Delicious’ and ‘McIntosh’ and between ‘Delicious’ and ‘Gala.’ Neither PCA nor SIMCA showed good separation between day 2 whole cultivars, nor between juiced cultivars on either day. As a reference, the same sample headspace volatile gases were analyzed with a mass spectrometer. A hierarchical cluster analysis (HCA) of the principal components from the mass spectrometer data sets revealed five clusters that discriminated differences among intact apple and apple juice samples but did not discriminate between samples from different apple cultivars.

Chemical analysis in conjunction with multivariate data evaluation methods was used to study elemental profiles and geographical origin of wines from central Balkan countries (Serbia, Montenegro, and Macedonia). Nine elements (Na, K, Mg, Ca, Fe, Mn, Zn, Cu, and Pb) chosen as chemical descriptors were analyzed in 41 commercial wine samples. Unsupervised pattern recognition methods—principal component analysis (PCA) and factor analysis—identified the main factors controlling the data variability, while the application of hierarchical cluster analysis (HCA) highlighted a differentiation between sample groups belonging to different variable inputs. Three PCs were shown to be the most significant, together accounting for 70.8% of the total variance. Supervised pattern recognition methods—linear discriminant analysis (LDA), k-nearest neighbor (kNN), soft independent modeling of class analogy (SIMCA), and artificial neural network (ANN)—applied to the classification of wine samples demonstrated different recognition and prediction abilities. The recognition rate for LDA was 97.6%, and the percentage of classification obtained by kNN, SIMCA, and ANN was 100%. However, the LDA method produced the best prediction rate of 83.3%, whereas kNN, SIMCA, and ANN gave much lower percentages of correctly classified samples, at 72.2, 61.1, and 55.6%, respectively. Trace elements seem to be suitable descriptors for wine samples studied by classification methods, since their concentrations comprising both natural and other sources of influence are attributed to grapegrowing and winemaking sites. Comparison of pattern recognition methods reveals the difference in their classification power.

Independent neural network modeling of class analogy for classification pattern recognition and optimization

Discrimination of Brazilian artisanal and inspected pork sausages: Application of unsupervised, linear and non-linear supervised chemometric methods

Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post-mortem. The traditional methods for the detection of K. thyrsites infected fish are time-consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near-infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS-DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD-DA classification models. SIMCA and PLS-DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS-DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS-DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS-DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy.