Novel molecular tumour classification using MALDI–mass spectrometry imaging of tissue micro-array

The development of tissue micro-array (TMA) technologies provides insights into high-throughput analysis of proteomics patterns from a large number of archived tumour samples. In the work reported here, matrix-assisted laser desorption/ionisation–ion mobility separation–mass spectrometry (MALDI–IMS–MS) profiling and imaging methodology has been used to visualise the distribution of several peptides and identify them directly from TMA sections after on-tissue tryptic digestion. A novel approach that combines MALDI–IMS–MSI and principal component analysis–discriminant analysis (PCA–DA) is described, which has the aim of generating tumour classification models based on protein profile patterns. The molecular classification models obtained by PCA–DA have been validated by applying the same statistical analysis to other tissue cores and patient samples. The ability to correlate proteomic information obtained from samples with known and/or unknown clinical outcome by statistical analysis is of great importance, since it may lead to a better understanding of tumour progression and aggressiveness and hence improve diagnosis, prognosis as well as therapeutic treatments. The selectivity, robustness and current limitations of the methodology are discussed.     

One- and two-dimensional gas chromatography–mass spectrometry and high performance liquid chromatography–diode-array detector fingerprints of complex substances: A comparison of classification performance of similar,complex Rhizoma Curcumae samples with the aid of chemometrics

Many complex natural or synthetic products are analysed either by the GC–MS (gas chromatography–mass spectrometry) or HPLC–DAD (high performance liquid chromatography–diode-array detector) technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC–MS and HPLC–DAD analyses of complex, similar substances represented by the three common types of the TCM (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA (Principal component analysis), which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC–MS and HPLC–DAD variables were observed. LDA (linear discriminant analysis), BP-ANN (back propagation-artificial neural networks) and LS-SVM (least squares-support vector machine) chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all training models indicated that several samples would be misclassified; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

A chromatochemometric approach for evaluating and selecting the perfume maceration time

A chemometric treatment of the data obtained by gas chromatography (GC) with flame ionization detector (FID) has been proposed to study the maceration time involved in perfumes manufacture with the final purpose of reducing this time but preserving the organoleptic characteristics of the perfume that is being elaborated. In this sense, GC–FID chromatograms were used as a fingerprint of perfume samples subjected to different maceration times, and data were treated by linear discriminant analysis (LDA), by comparing to a set of samples known to be macerated or not, which were used as calibration objects. The GC–FID methodology combined with the treatment of data by LDA has been applied successfully to seven different perfumes. The constructed LDA models exhibited excellent Wilks’ lambdas (0.013–0.118, depending on the perfume), and up to a reduction of 57% has been achieved with respect to the maceration time initially established.     

Recognition of beer brand based on multivariate analysis of volatile fingerprint

Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography–time-of-flight mass spectrometry (GC–TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models “Trappist vs. non-Trappist beers” with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and “Rochefort 8 vs. the rest” with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach.     

Systematic ratio normalization of gas chromatography signals for biological sample discrimination and biomarker discovery

The present paper introduces a new gas chromatography data processing procedure dubbed systematic ratio normalization (SRN) enabling to improve both sample set discrimination and biomarker identification. SRN consists in (1) calculating, for each sample, all the log-ratios between abundances of chromatography-analyzed compounds, then (2) selecting the log-ratio(s) that best maximize the discrimination between sample-sets. The relevance of SRN was evaluated on two data sets acquired through gas chromatography–mass spectrometry as part of separate studies designed (i) to discriminate source-origins between vegetable oils analyzed via an analytical system exposed to instrument drift (data set 1) and (ii) to discriminate animal feed between meat samples aged for different durations (data set 2). Applying SRN to raw data made it possible to obtain robust discrimination models for the two data sets by enhancing the contribution to the data variance of the factor-of-interest while stabilizing the contribution of the disturbance factor. The most discriminant log-ratios were shown to employ the most relevant biomarkers presenting relative independence of the factor-of-interest as well as co-behavior of the disturbance effects potentially biasing the discrimination, such as instrument drift or sample biochemical changes. SRN can be run a posteriori on any data set, and might be generalizable to most of separating methods.     

The Comparison of Physicochemical Parameters,Antioxidant Activity and Proteins for the Raw Local Polish Honeys and Imported Honey Blends

Many imported honeys distributed on the Polish market compete with local products mainly by lower price, which can correspond to lower quality and widespread adulteration. The aim of the study was to compare honey samples (11 imported honey blends and 5 local honeys) based on their antioxidant activity (measured by DPPH, FRAP, and total phenolic content), protein profile obtained by native PAGE, soluble protein content, diastase, and acid phosphatase activities identified by zymography. These indicators were correlated with standard quality parameters (water, HMF, pH, free acidity, and electrical conductivity). It was found that raw local Polish honeys show higher antioxidant and enzymatic activity, as well as being more abundant in soluble protein. With the use of principal component analysis (PCA) and stepwise linear discriminant analysis (LDA) protein content and diastase number were found to be significant (p < 0.05) among all tested parameters to differentiate imported honey from raw local honeys.     

Analysis of trace levels of domoic acid in seawater and plankton by liquid chromatography without derivatization,using UV or mass spectrometry detection

Quantitation of trace levels of domoic acid (DA) in seawater samples usually requires labour-intensive protocols involving chemical derivatization with 9-fluorenylmethylchloroformate and liquid chromatography with fluorescence detection (FMOC–LC–FLD). Procedures based on LC–MS have been published, but time-consuming and costly solid-phase extraction pre-concentration steps are required to achieve suitable detection limits. This paper describes an alternative, simple and inexpensive LC method with ultraviolet detection (LC–UVD) for the routine analysis of trace levels of DA in seawater without the use of sample pre-concentration or derivatization steps. Qualitative confirmation of DA identity in dubious samples can be achieved by mass spectrometry (LC–MS) using the same chromatographic conditions. Addition of an ion-pairing/acidifying agent (0.15% trifluoroacetic acid) to sample extracts and the use of a gradient elution permitted the direct analysis of large sample volumes (100 μl), resulting in both high selectivity and sensitivity (limit of detection = 42 pg ml⁻¹ by LC–UVD and 15 pg ml⁻¹ by LC–MS). Same-day precision varied between 0.4 and 5%, depending on the detection method and DA concentration. Mean recoveries of spiked DA in seawater by LC–UVD were 98.8% at 0.1–10 ng ml⁻¹ and 99.8% at 50–1000 ng ml⁻¹. LC–UVD exhibited strong correlation with FMOC–LC–FLD during inter-laboratory analysis of Pseudo-nitzschia multiseries cultures containing 60–2000 ng DA ml⁻¹ (r² > 0.99), but more variable results were obtained by LC–MS (r² = 0.85). This new technique was used to confirm the presence of trace DA levels in low-toxicity Pseudo-nitzschia spp. isolates (0.2–1.6 ng ml⁻¹) and in whole-water field samples (0.3–5.8 ng ml⁻¹), even in the absence of detectable Pseudo-nitzschia spp. cells in the water column.     

Discrimination of Chinese traditional soy sauces based on their physico-chemical properties

This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces. Nine variables of physico-chemical properties (density, pH, dry matter, ashes, electric conductivity, amino nitrogen, salt, viscosity and total acidity) of 53 soy sauce samples were measured. The measured data was submitted to such pattern recognition as cluster analysis (CA), principal component analysis (PCA), discrimination partial least squares (DPLS), linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands. Two clusters corresponding to the two categories were obtained, and each cluster was divided into three subsets corresponding to three brands by the CA method. The variables for LDA and KNN were selected by the Fisher F-ratio approach. The prediction ability of all classifiers was evaluated by cross-validation. For the three supervised discrimination analyses, LDA and KNN gave 100% predications according to the sample category and brand.     

Geographical Characterization of Greek Olive Oils Using Rare Earth Elements Content and Supervised Chemometric Techniques

Different ANNs models [Multi-layer Perceptrons (MLPs) and Radial Basis Function (RBF)] were developed and evaluated for the discrimination of olive oils produced in four Greek regions according to their geographical origin. For this purpose, ninety-seven samples were analyzed for 10 rare earth elements (REE) by ICP-MS. Moreover, two additional supervised techniques, discriminant analysis (DA) and classification trees (CTs), were applied to the same set for the data pre-treatment and for comparison purposes. In addition, two approaches were used for models' training and evaluation: the classical random choice of samples for the learning data set and an innovative one, which used the two linear discriminant functions (LDFs) of the preceding DA to choose the most representative learning sample set. The results were very satisfactory for the new ANNs classifiers. Over-fitting phenomena were overcome and the prediction ability was 73%, as evaluated by an independent test sample set. The results are encouraging for the ANNs efficiency even in demanding data bases, as the one under consideration.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resources: Additional figures and tables.]     

Optimization of solid-phase extraction and liquid chromatography–tandem mass spectrometry for the determination of domoic acid in seawater,phytoplankton, and mammalian fluids and tissues

We previously reported a solid-phase extraction (SPE) method for determination of the neurotoxin domoic acid (DA) in both seawater and phytoplankton by liquid chromatography–tandem mass spectrometry (LC–MS/MS) with the purpose of sample desalting without DA pre-concentration. In the present study, we optimized the SPE procedure with seawater and phytoplankton samples directly acidified with aqueous formic acid without addition of organic solvents, which allowed sample desalting and also 20-fold pre-concentration of DA in seawater and phytoplankton samples. In order to reduce MS contamination, a diverter valve was installed between LC and MS to send the LC eluant to waste, except for the 6-min elution window bracketing the DA retention time, which was sent to the MS. Reduction of the MS turbo gas temperature also helped to maintain the long-term stability of MS signal. Recoveries exceeded 90% for the DA-negative seawater and the DA-positive cultured phytoplankton samples spiked with DA. The SPE method for DA extraction and sample clean-up in seawater was extended to mammalian fluids and tissues with modification in order to accommodate the fluid samples with limited available volumes and the tissue extracts in aqueous methanol. Recoveries of DA from DA-exposed laboratory mammalian samples (amniotic fluid, cerebrospinal fluid, plasma, placenta, and brain) were above 85%. Recoveries of DA from samples (urine, feces, intestinal contents, and gastric contents) collected from field stranded marine mammals showed large variations and were affected by the sample status. The optimized SPE–LC–MS method allows determination of DA at trace levels (low pg mL⁻¹) in seawater with/without the presence of phytoplankton. The application of SPE clean-up to mammalian fluids and tissue extracts greatly reduced the LC column degradation and MS contamination, which allowed routine screening of marine mammalian samples for confirmation of DA exposure and determination of fluid and tissue DA concentrations in experimental laboratory animals.     

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.     

Evaluation and discrimination of cortex Magnoliae officinalis produced in Zhejiang Province (Wen-Hou-Po) by UPLC-DAD-TOF-MS fingerprint

An ultra performance liquid chromatography tandem TOF mass spectrometric (UPLC-DAD-TOF-MS) fingerprinting method was developed for the quality control and source discrimination of Cortex magnoliae officinalis produced in Zhejiang Province (Wen-Hou-Po). Twelve samples of Wen-Hou-Po collected from two species in five areas in Zhejiang Province of China were used to establish the fingerprint. Data were evaluated statistically using similarity analysis, hierarchical cluster analysis (HCA) and discriminant analysis (DA) in order to establish a similarity standard of fingerprint for quality control of Wen-Hou-Po, then to classify the Wen-Hou-Po samples and to identify key categorizing parameters. The similarity indexes were all above 0.95 between the reference chromatogram and that of each sample. By comparing the UV and MS data with those of the authentic standards and literature, nine main peaks in the fingerprints were identified. The result of hierarchical cluster analysis showed that the samples from two species in five areas could be divided into two distinct groups (the same as the groups of the samples divided by their species) based on their compositional fingerprints. A rapid and convenient discriminant function was then established to discriminate the species of unknown Wen-Hou-Po, and the cross validation result was 100%. In this study, the methods established are reliable, and could be used to evaluate the quality and to identify the species of Wen-Hou-Po in the future.     

费谢尔判别法鉴别原油/燃料油

以多环芳烃作为变量,建立了原油、燃料油属性鉴别的费谢尔判别法。分别测定了来自不同国家和地区的26个原油样品和25个燃料油样品中8种多环芳烃的含量,并将它们作为判别变量。借助SPSS 16.0进行费谢尔判别分析,建立费谢尔判别函数。将未知样品的判别变量值代入后,可以快速地得知样品的类别。结果表明,以多环芳烃作为判别变量进行原油、燃料油费谢尔判别快速而准确。     

Rapid classification of White Oak (Quercus alba) and Northern Red Oak (Quercus rubra) by using pyrolysis direct analysis in real time (DART™) and time-of-flight mass spectrometry

Thirty-four samples of Red Oak (Quercus rubra) and fifty samples of White Oak (Quercus alba) were analyzed by pyrolytic direct analysis in real time (DART) ionization coupled with time-of-flight (TOF) mass spectrometry. Although significant differences were not observed in the positive-ion mass spectra, the negative-ion mass spectra showed clear differences. Principal component analysis (PCA) and linear discriminant analysis (LDA) were calculated for the relative abundances of 11 peaks in the negative-ion mass spectra including peaks tentatively assigned as representing deprotonated acetic, malic, gallic, dimethoxycinnamic, and ellagic acids. Leave one out cross validation (LOOCV) was 100% successful in classifying the samples for both PCA and LDA.     

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.     

Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis

The secondary metabolites of different Ephedra plants are various. Therefore, the discrimination of different Ephedra plants is significant. An objective, easy-to-use, rapid and pollution-free approach is proposed for discriminating Ephedra plants of different species, habitats and picking times on the basis of diffuse reflectance Fourier transform near infrared spectroscopy (FT-NIRS) measurements and multivariate analysis. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from 37 pulverized samples of Ephedra plants put in glass vials in the near infrared (NIR) region between 10 000 and 4000 cm⁻¹, averaging 64 scans per spectrum at a resolution of 4 cm⁻¹. After spectra processing and data pre-processing, spectral data were analyzed respectively with three multivariate analysis techniques: discriminant analysis (DA), self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The proposed method could distinguish not only the Ephedra plants of three species and two habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The performance indexes of the DA model were 84.2-91.9% and the prediction accuracies of both the SOM and the BP-ANN models reached 93.3-100.0%.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Feasibility study on the use of infrared spectroscopy for the direct authentication of Iberian pig fattening diet

The feasibility of using both middle- and near-infrared spectroscopy for discrimination between subcutaneous fat of Iberian pigs reared on different fattening diets has been evaluated. The sample set was formed by subcutaneous fat of pigs fattened outdoors (extensively) with natural resources (montanera) and pigs fattened on commercial feeds, either with standard feed or with especial formulations with higher content in oleic acid (HO-formulated feed). Linear discriminant analysis was used to classify the samples according to the fattening diet using the scores obtained from principal component analysis of near- and middle-infrared spectra as variables to construct the discriminant functions. The most influential variables were identified using a stepwise procedure. The discriminant potential of each spectral region was investigated. Best results were obtained with the combination of both regions with 91.7% of the standard feed and 100% of montanera and HO-formulated feed samples correctly classified. Chemical explanations are provided based on the correlation of these variables with fatty acid content in the samples.