Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data

Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from the tip of the stem in each of four strains, was analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Twenty-four extracts from mulberry leaf showed independent spectra by 1H NMR. The separation of leaf extraction data due to the difference at six locations was achieved in the PCA score plot as correlation PC1 (86.1%) and PC3 (4.6%) and showed two loading plots, suggesting classification by leaf position as an independent variable in the loading plot. Moreover, the difference among six locations clarified the seven highest discrimination powers by the SIMCA method. Meanwhile, the PCA score plot obtained classification by the variety of mulberry strains with three loading plots, but the SIMCA method did not give a peak by classification.     

Determination of phospholipids in dairy products by SPE/HPLC/ELSD

The aim of this work was to evaluate the performance of different methods for both milk lipid extraction and phospholipids separation. As far as the lipid extraction procedure is concerned, the Folch method showed a higher phospholipid recovery with respect to the Rose-Gottlieb method. Different SPE cartridges and solvent phases were tested to carry out the separation of phospholipids from fat. The yield of extraction was evaluated by isolating phospholipids from both milk fat and synthetic fat; Standard Addition Method was applied as well. The isolation of the phospholipids by SPE silica column and subsequent analysis by HPLC/ELSD was shown to be an accurate and reproducible analytical method for the determination of phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidylcholine and sphingomyelin in milk fat extracted by Folch method.     

Determination of fat in leather by the use of supercritical fluid extraction combined with on-line piezoelectric detection.

Leather samples were prepared and characterized as 'in house' matrix standards for the determination of fat. The Soxhlet standard method was used to establish the reference fat content in every standard sample. Sample homogeneity and stability were examined under specific storing conditions. The materials were subsequently used as matrix standards for the determination of fat in leather samples, using supercritical fluid extraction (SFE) with on-line piezoelectric detection. Real samples were weighed in the extraction SFE thimble, previously loaded with 1 g of diatomaceous earth. A temperature of 45 degrees C and a CO2 fluid density of 0.85 g ml-1 were used for extraction. The linear calibration range thus achieved was 0.001-0.040% m/m total fat (related to the weight of the leather) and the relative standard deviation +/- 3% (n = 11; P = 0.05). The results were compared with those obtained with the Soxhlet method and no significant differences were found.     

Development and application of an analytical method for the determination of storage lipids, fatty acids and fatty alcohols in Calanus finmarchicus

A method was developed for the determination of the major storage lipids, wax ester and triglycerides, in the copepod Calanus finmarchicus. A variation of the Folch method was used to extract the lipid. The method was scaled down to enable the extraction of either pooled (-1 mg) or individual (approximately 200 microg) copepods. The major lipid classes were identified using TLC and quantified using HPLC coupled with evaporative light scattering detection. Analysis of laboratory reference materials indicated that this method underestimated the minor triglyceride component, but gave a good estimate of the major wax ester component. The fatty acid and fatty alcohol composition of the C. finmarchicus were determined following trans-esterification of the lipid extract in methanol. Fatty acids and fatty alcohols were initially identified by comparison with authentic standard and by mass spectroscopy. Using GC with flame ionisation detection the normalised area percentage of the fatty alcohols and fatty acid methyl esters was determined simultaneously in one run for either pooled or individual copepod samples. These methods were applied to C. finmarchicus collected from the Irminger Sea, North Atlantic in 2001 and 2002.     

Multivariate analysis for 1H-NMR spectra of two hundred kinds of tea in the world.

NMR measurements coupled with pattern-recognition analysis offer a powerful mixture-analysis tool for latent-feature extraction and sample classification. As fundamental applications of this analysis for mixtures, the 1H spectra of 176 kinds of green, black, oolong and other tea infusions were acquired by a 500 MHz NMR spectrometer. Each spectrum pattern was analyzed by a multivariate statistical pattern-recognition method where Principal Component Analysis (PCA) was used in combination with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA effectively selected variables that contribute to tea categorization. The final PCA resulted in clear classification reflecting the fermentation and processing of each tea, and revealed marker variables that include catechin and theanine peaks.     

Pressurized liquid extraction of lipids for the determination of oxysterols in egg-containing food

Pressurized liquid extraction (PLE, ASE) was compared with the Folch procedure (a solid-liquid extraction with chloroform/methanol 2:1, v/v) for the lipid extraction of egg-containing food; the accuracy of PLE for the quantitative determination of oxysterols in whole egg powder was evaluated. Samples of spray-dried whole egg, an Italian vanilla cake (Pandoro) and egg noodles were used. Two different extraction solvents (chloroform/methanol 2:1, v/v, and hexane/isopropanol 3:2, v/v) were tested at different extraction temperatures and pressures (60 degrees C at 15 MPa, 100 degrees C at 15 MPa, 120 degrees C at 20 MPa). No significant differences in the lipid recovery of the egg powder sample using PLE were found. However, PLE of the vanilla cake and egg noodles with the chloroform/methanol mixture was not selective enough and led to the extraction of a non-lipid fraction, including nitrogen-containing compounds. In the same samples, the pressurized hexane/isopropanol mixture gave a better recovery result, comparable to that obtained using the Folch method. Cholesterol oxidation products of the Folch extract and the pressurized liquid extract of spray dried egg powder (obtained with hexane/isopropanol 3:2, v/v, at 60 degrees C and 15 MPa) were determined by gas chromatography. PLE performed under these conditions is suitable to replace the Folch extraction, because the differences between the two methods tested were not statistically significant. Moreover, PLE shows important advantages, since the analysis time was shortened by a factor of 10, the solvent costs were reduced by 80% and the use of chlorinated solvents was avoided.     

Comparison of extraction and derivatization methods for fatty acid analysis in solid environmental matrixes

This study involved comparison of different extraction and derivatization methods for determining FAs in soil and in four highly organic matrixes (cattle manure, pig slurry, compost, and vermicompost), by application of a multifactor categorical design. Although some studies have been carried out regarding the application of FA analysis to highly organic matrixes, comparison and verification are still required to test which methods of extraction and derivatization of FAs function best for these matrixes. We compared three extraction methods (one in which the same extraction mixture as used in the Folch method was employed, a modification of the Bligh and Dyer method, and a microwave-assisted extraction) and two derivatization procedures (alkaline methanolysis and derivatization with trimethylsulfonium hydroxide (TMSH)). The highest yields of FAs belonging to different structural classes, and of individual FAs used as microbial biomarkers were obtained by application of the same extraction mixture as in the Folch method and use of TMSH as derivatization agent. These methods also involved a significant reduction in the complexity and time involved in sample preparation.     

Validation of green‐solvent extraction combined with chromatographic chemical fingerprint to evaluate quality of Stevia rebaudiana Bertoni

An approach that combined green‐solvent methods of extraction with chromatographic chemical fingerprint and pattern recognition tools such as principal component analysis (PCA) was used to evaluate the quality of medicinal plants. Pressurized hot water extraction (PHWE) and microwave‐assisted extraction (MAE) were used and their extraction efficiencies to extract two bioactive compounds, namely stevioside (SV) and rebaudioside A (RA), from Stevia rebaudiana Bertoni (SB) under different cultivation conditions were compared. The proposed methods showed that SV and RA could be extracted from SB using pure water under optimized conditions. The extraction efficiency of the methods was observed to be higher or comparable to heating under reflux with water. The method precision (RSD, n = 6) was found to vary from 1.91 to 2.86% for the two different methods on different days. Compared to PHWE, MAE has higher extraction efficiency with shorter extraction time. MAE was also found to extract more chemical constituents and provide distinctive chemical fingerprints for quality control purposes. Thus, a combination of MAE with chromatographic chemical fingerprints and PCA provided a simple and rapid approach for the comparison and classification of medicinal plants from different growth conditions. Hence, the current work highlighted the importance of extraction method in chemical fingerprinting for the classification of medicinal plants from different cultivation conditions with the aid of pattern recognition tools used.     

Focused microwave-assisted Soxhlet extraction of acorn oil for determination of the fatty acid profile by GC–MS. Comparison with conventional and standard methods

A study of the feasibility of focused microwave-assisted Soxhlet extraction of acorn oil and comparison of results from analysis of trans fatty acids in the oil thus obtained with those for oils obtained by use of other methods commonly used for oil extraction are reported here. The proposed method was optimized by means of a 21-experiment screening design to determine, by means of a reduced number of experiments, which factors affect both extraction efficiency and the degree of unsaturation of the fatty acids in the oil. The proposed method enables total extraction of the fatty acids in 30 min, which is much less than the time required by the Folch (4.5 h), Soxhlet (16 h), and ISO (8 h) reference methods and the stirring–extraction method (56 h). The efficiency of extraction achieved by use of the proposed method is statistically equivalent to that achieved by use of the other methods; the composition of the extracts obtained by use of the proposed method and the Folch and stirring reference methods are also statistically similar. No trans fatty acids were present in the extracts obtained by use of the Folch, stirring, and proposed methods but they were detected in the extracts obtained by use of both the Soxhlet and ISO methods.     

Direct and two-stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue-Effect of supervised feature extraction

A novel strategy of data analysis for artificial taste and odour systems is presented in this work. It is demonstrated that using a supervised method also in feature extraction phase enhances fruit juice classification capability of sensor array developed at Warsaw University of Technology. Comparison of direct processing (raw data processed by Artificial Neural Network (ANN), raw data processed by Partial Least Squares-Discriminant Analysis (PLS-DA)) and two-stage processing (Principal Components Analysis (PCA) outputs processed by ANN, PLS-DA outputs processed by ANN) is presented. It is shown that considerable increase of classification capability occurred in the case of the new method proposed by the authors.     

Focused microwave-assisted Soxhlet extraction: a convincing alternative for total fat isolation from bakery products

An approach for automated fast extraction of the fat content in bakery products based on focused microwave-assisted Soxhlet extraction (FMASE) and gravimetric determination is proposed. The main factors affecting the extraction efficiency—namely, power of irradiation, number of cycles and irradiation time—were optimized using experimental design methodology. The proposed method was applied to six samples, which were classified in two groups—namely, snacks and cookies. The results obtained agree with those provided by the AOAC 920.39 reference extraction method. No significant differences in the extraction efficiency of the fat content in bakery samples using FMASE versus the official method were found. Moreover, a drastic reduction in both the extraction time (60 and 35 min versus 16 and 8 h, respectively, for the two above commented groups) and sample handling are achieved with similar precision (expressed as repeatability and within-laboratory reproducibility standard deviation) to that provided by the AOAC 920.39 method. In addition, the proposed method is cleaner than the reference method as 75-80% of the extractant is recycled.     

Optimized extraction of phospholipids and lysophospholipids for nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry

The efficiencies of four different methods for the extraction of phospholipids (PLs) and lysophospholipids (LPLs) from human plasma samples were examined by comparing extraction recovery values using nanoflow liquid chromatography-electrospray ionization-mass spectrometry (nLC-ESI-MS). For recovery measurements, six PL and six LPL standards of different head groups were spiked into a human plasma sample, and the peak areas of each individual species after extraction were measured from the chromatograms of the nLC-ESI-MS runs. Recovery was calculated by comparing the peak area of an extracted standard species with that of the same species' spike after extraction of the same plasma sample. For lipid extraction, four different extraction methods were examined: three based on the Folch method with different organic solvents such as CHCl(3), methyl-tert-butyl ether (MTBE), and MTBE/CH(3)OH, and one relatively fast method involving CH(3)OH only. Evaluations of recovery showed that the modified Folch method with MTBE/CH(3)OH proposed in this study was effective for extracting most PL and LPL standards. Then, the four extraction methods were compared with the identified numbers of plasma PLs and LPLs, of which molecular structures can be confirmed by data-dependent, collision-induced dissociation experiments during nLC-ESI-MS-MS. These results demonstrated that the proposed method yielded the identification of 54 LPLs and 66 PLs from a plasma sample, which was the highest identification rate among the four methods.     

Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.     

Automatic microgravimetric determination of fats in milk products by use of supercritical fluid extraction with on-line piezoelectric detection

An on-line supercritical fluid extraction-piezoelectric detection system was developed and applied to the quantitative gravimetric determination of total fat in food samples (skimmed milk and cocoa). The proposed assembly provides all the advantages of an on-line system as regards automation, in addition to acceptable sensitivity and precision. Its strength lies in the design of the interface between the supercritical fluid extractor and the piezoelectric detector. Samples of skimmed milk and cocoa are weighed in the extraction thimble, previously loaded with I g of diatomaceous earth. A temperature of 100 degrees C and a CO2 fluid density of 0.60 mg/ml are used for extraction. The linear calibration range thus achieved is 0.005-0.07% w/w total fat, and the relative standard deviation is +/-2.3% (n=11; P=0.05). The throughput is six samples h(-1) (for the overall process). The proposed method was used to determine the total fat in food samples (milk, cocoa), the results being competitive with those of the Soxhlet methods for the same purpose.     

Wavelength selection framework for classifying food and pharmaceutical samples into multiple classes

Near infrared (NIR) spectroscopy is an efficient, low‐cost analytical technique widely applied to identify the origin of food and pharmaceutical products. NIR spectra‐based classification strategies typically use thousands of equally spaced wavelengths as input information, some of which may not carry relevant information for product classification. When that is the case, the performance of predictive and exploratory multivariate techniques may be undermined by such noisy information. In this paper, we propose an iterative framework for selecting subsets of NIR wavelengths aimed at classifying samples into categories. For that matter, we integrate Principal Components Analysis (PCA) and three classification techniques: k‐Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA). PCA is first applied to NIR data, and a wavelength importance index is derived based on the PCA loadings. Samples are then categorized using the wavelength with the highest index and the classification accuracy is calculated; next, the wavelength with the second highest index is inserted into the dataset and a new classification is performed. This forward‐based iterative procedure is carried out until all original wavelengths are inserted into the dataset used for classification. The subset of wavelengths leading to the maximum accuracy is chosen as the recommended subset. Our propositions performed remarkably well when applied to four datasets related to food and pharmaceutical products. Copyright © 2016 John Wiley & Sons, Ltd.     

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical,Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.     

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.     

Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis

This paper proposes a simple and non-expensive electroanalytical methodology for classification of edible vegetable oils with respect to type (canola, sunflower, corn and soybean) and conservation state (expired and non-expired shelf life). The proposed methodology employs an alcoholic extraction procedure followed by square wave voltammetry (SWV). Two chemometric methods were compared for classification of the resulting voltammograms, namely Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors in a set of samples not included in the modelling process. The best results were obtained with the SPA-LDA method, which correctly classified all samples in terms of type and conservation state.     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

Study of the feasibility of distinguishing cigarettes of different brands using an Adaboost algorithm and near-infrared spectroscopy

The feasibility of utilizing an Adaboost algorithm in conjuction with near-infrared (NIR) spectroscopy to automatically distinguish cigarettes of different brands was explored. Simple linear discriminant analysis (LDA) was used as the base algorithm to train all weak classifiers in Adaboost. Both principal component analysis (PCA) and its kernel version (kernel principal component analysis, KPCA) were used for feature extraction and were also compared to each other. The influence of the training set size on the final classification model was also investigated. Using a case study, it was demonstrated that Adaboost coupled with PCA or KPCA can obviously improve the ability to discriminate between samples that cannot be separated by a single linear classifier. However, in term of the overall performance, KPCA appears preferable to PCA for feature extraction, especially when the samples used for training are relatively small. The results also indicate that more training samples should be applied, if possible, in order to fully demonstrate the superiority of Adaboost. It seems that the use of an Adaboost algorithm in conjunction with NIR spectroscopy in combination with KPCA for feature extraction comprises a promising tool for distinguishing cigarettes of different brands, especially in situations where there is an obvious overlap between the NIR spectra afforded by cigarettes of different brands.