产酸丙酸杆菌FS1171产丙酸发酵动力学研究

对产酸丙酸杆菌(Propionibacterium acidogenes)FS1171产丙酸的分批发酵动力学进行研究,基于经典发酵动力学模型(Logistic、Luedeking-Piret、Dose-Resp方程)及Origin软件优选模型(Boltzmann方程)两种方法分别构建丙酸发酵过程中菌体生长、丙酸合成及底物甘油消耗随时间变化的动力学模型,软件分析表明,经典发酵动力学构建的模型拟合度整体不如Boltzmann方程构建的动力学模型,但前者所构建的3个动力学模型之间有较好的关联性,两种方法构建的模型拟合值和实验值能较好的吻合,说明所构建的产酸丙酸杆菌的发酵动力学能较好地反应丙酸杆菌的发酵过程,为优化发酵过程和工业上放大生产丙酸提供参考。     

Neurofeedback—The Significance of Reinforcement and the Search for an Appropriate Strategy for the Success of Self-regulation

Nine healthy children took part in five sessions of feedback and instrumental conditioning of slow cortical potentials (SCPs). The feedback conditions (the relation between the feedback signal and amplitude of SCP) were inverted after two sessions. Neither the children nor the therapists were aware of this change. The adjustment of the children to the new feedback setting and the self-regulation strategies employed were investigated. The results were as follows: (a) Healthy children achieved control over cortical negativity within two sessions. (b) The change of feedback conditions worsened the regulation abilities, which then improved again within the following three sessions. (c) After the first two sessions, the participants were able to describe strategies that were successful during different phases of self-regulation. (d) Following the change in the feedback conditions, the children re-evaluated the way they influenced their SCPs. However, they did not alter the cognitive or behavioral strategies. The study demonstrated that positive and negative reinforcement and the knowledge of results are more important for successful self-regulation than the search for effective strategies. The relevance of these findings is discussed.     

Microarray segmentation methods significantly influence data precision

Little consideration has been given to the effect of different segmentation methods on the variability of data derived from microarray images. Previous work has suggested that the significant source of variability from microarray image analysis is from estimation of local background. In this study, we used Analysis of Variance (ANOVA) models to investigate the effect of methods of segmentation on the precision of measurements obtained from replicate microarray experiments. We used four different methods of spot segmentation (adaptive, fixed circle, histogram and GenePix) to analyse a total number of 156 172 spots from 12 microarray experiments. Using a two-way ANOVA model and the coefficient of repeatability, we show that the method of segmentation significantly affects the precision of the microarray data. The histogram method gave the lowest variability across replicate spots compared to other methods, and had the lowest pixel-to-pixel variability within spots. This effect on precision was independent of background subtraction. We show that these findings have direct, practical implications as the variability in precision between the four methods resulted in different numbers of genes being identified as differentially expressed. Segmentation method is an important source of variability in microarray data that directly affects precision and the identification of differentially expressed genes.     

A comparison of two methods for prediction of response and rates of inbreeding in selected populations with the results obtained in two selection experiments

Selection programmes are mainly concerned with increasing genetic gain. However, short-term progress should not be obtained at the expense of the within-population genetic variability. Different prediction models for the evolution within a small population of the genetic mean of a selected trait, its genetic variance and its inbreeding have been developed but have mainly been validated through Monte Carlo simulation studies. The purpose of this study was to compare theoretical predictions to experimental results. Two deterministic methods were considered, both grounded on a polygenic additive model. Differences between theoretical predictions and experimental results arise from differences between the true and the assumed genetic model, and from mathematical simplifications applied in the prediction methods. Two sets of experimental lines of chickens were used in this study: the Dutch lines undergoing true truncation mass selection, the other lines (French) undergoing mass selection with a restriction on the representation of the different families. This study confirmed, on an experimental basis, that modelling is an efficient approach to make useful predictions of the evolution of selected populations although the basic assumptions considered in the models (polygenic additive model, normality of the distribution, base population at the equilibrium, etc.) are not met in reality. The two deterministic methods compared yielded results that were close to those observed in real data, especially when the selection scheme followed the rules of strict mass selection: for instance, both predictions overestimated the genetic gain in the French experiment, whereas both predictions were close to the observed values in the Dutch experiment.     

Migration and the genetic covariance between habitat preference and performance

Studies of the genetic covariance between habitat preference and performance have reported conflicting outcomes ranging from no covariance to strong covariance. The causes of this variability remain unclear. Here we show that variation in the magnitude of genetic covariance can result from variability in migration regimes. Using data from walking stick insects and a mathematical model, we find that genetic covariance within populations between host plant preference and a trait affecting performance on different hosts (cryptic color pattern) varies in magnitude predictably among populations according to migration regimes. Specifically, genetic covariance within populations is high in heterogeneous habitats where migration between populations locally adapted to different host plants generates nonrandom associations (i.e., linkage disequilibrium) between alleles at color pattern and host preference loci. Conversely, genetic covariance is low in homogeneous habitats where a single host exists and migration between hosts does not occur. Our results show that habitat structure and patterns of migration can strongly affect the evolution and variability of genetic covariance within populations.     

Ice nucleation temperature of individual leaves in relation to population sizes of ice nucleation active bacteria and frost injury

Ice nucleation temperatures of individual leaves were determined by a tube nucleation test. With this assay, a direct quantitative relationship was obtained between the temperatures at which ice nucleation occurred on individual oat (Avena sativa L.) leaves and the population sizes of ice nucleation active (INA) bacteria present on those leaves. In the absence of INA bacteria, nucleation of supercooled growth-chamber grown oat leaves did not occur until temperatures were below approximately −5°C. Both nucleation temperature and population size of INA bacteria were determined on the same individual, field-grown oat leaves. Leaves with higher ice nucleation temperatures harbored larger populations of INA bacteria than did leaves with lower nucleation temperatures. Log₁₀ mean populations of INA bacteria per leaf were 5.14 and 3.51 for leaves with nucleation temperatures of −2.5°C and −3.0°C, respectively. Nucleation frequencies (the ratio of ice nuclei to viable cells) of INA bacteria on leaves were lognormally distributed. Strains from two very different collections of Pseudomonas syringae and one of Erwinia herbicola were cultured on nutrient glycerol agar and tested for nucleation frequency at −5°C. Nucleation frequencies of these bacterial strains were also lognormally distributed within each of the three sets. The tube nucleation test was used to determine the frequency with which individual leaves in an oat canopy harbored large populations of INA bacteria throughout the growing season. This test also predicted relative frost hazard to tomato (Lycopersicon esculentum Mill) plants.     

Flow cytometric analysis of experimental parameters for the immunofluorescent labeling of BrdUrd in various tumour cell lines

This report describes the results of the comparison of three different methods and three monoclonal antibodies to stain cells in suspension for incorporated bromodeoxyuridine and total DNA content. The procedures were tested in three different experimental tumour cell lines. The sensitivity of the different procedures was expressed as the ratio of the anti-BrdUrd fluorescence intensities of the S and G1 phase cells (FS/FG1 ratio). There were remarkable differences in sensitivity between the different procedures. With the heat denaturation the most favourable FS/FG1 ratio's were obtained but substantial cell loss occurred during this procedure which is a disadvantage for clinical application. With the pepsin digestion + acid denaturation procedure cell loss was negligible. The standard acid denaturation procedure was inferior to the other two methods. Using the pepsin digestion + acid denaturation procedure we examined the variations in sensitivity for the different monoclonal antibodies and cell lines and the influence of BrdUrd concentration, labelingtime and cell concentration. The binding characteristics for the various antibodies differed considerably in our hands. Only with the IU4 antibody we obtained FS/FG1 ratio's comparable with those described in the literature. No difference was observed between the cell lines. Variation in cell concentration between 1 x 10(4) to 1 x 10(6) ml nor BrdUrd concentration appeared to influence the sensitivity of the procedure. A labelingtime of 1 h or even 30 min seems to be more than sufficient for an optimal FS/FG1 ratio. Our results indicate that using the appropriate antibody and immunofluorescence BrdUrd can be detected by flow cytometry, after incorporation into the DNA of tumour cells under a wide range of culture conditions.(ABSTRACT TRUNCATED AT 250 WORDS)     

Effects of free-food deliveries and delays on choice under concurrent-chains schedules

Pigeons responded on a concurrent-chains schedule with two equal variable-interval (VI) schedules as initial links and delays to food of 3 and 12s as the two terminal links. In even-numbered sessions, no other reinforcement schedule was present, and all pigeons showed a strong preference for the response key that had the shorter, 3-s terminal-link delay. In odd-numbered sessions, the initial links were interrupted at random times by one of three different types of events. When the interruptions were immediate food deliveries, the response percentages increased on the key that had the 3-s delay. When the interruptions were 30-s delays followed by food, the response percentages remained approximately unchanged. When the interruptions were 30-s delays with no food, the response percentages decreased. The results were used to compare the predictions of different mathematical models of concurrent-chains performance. The results favored models that assume that preference is determined by the relative amount of advantage that is gained when a terminal link is entered.     

The within-host population dynamics of normal flora in the presence of an invading pathogen and antibiotic treatments

A mathematical competition model between normal flora and an invading pathogen was devised to allow analysis of bacterial infections in a host. The normal flora includes the various microorganisms that live on or within the host and act as a primary human immune system. Despite the important role of the normal flora, no mathematical study has been undertaken on models of the interaction between it and invading pathogens against a background of antibiotic treatment. To quantify key elements of bacterial behavior in a host, pairs of nonlinear differential equations were used to describe three categories of human health conditions, namely, healthy, latent infection, and active infection. In addition, a cutoff value was proposed to represent the minimum population level required for survival. The recovery of normal flora after antibiotic treatment was also included in the simulation because of its relation to human health recovery. The significance of each simulation parameter for the bacterial growth model was investigated. The devised simulation showed that bacterial proliferation rate, carrying capacity, initial population levels, and competition intensity have a significant effect on bacterial behavior. Consequently, a model was established to describe competition between normal flora and an infiltrating pathogen. Unlike other population models, the recovery process described by the devised model can describe the human health recovery mechanism.     

Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI

Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.     

Previously unreported "hemidesmosomal junctions" between folliculo-stellate cells and pituitary adenoma cells

Thirty-eight non-functioning pituitary adenomas were ultrastructurally investigated with particular attention to the Folliculo-Stellate (FS) cells. A large number of FS cells were found in four cases, one of which disclosed a new type of intercellular junction between FS cells and surrounding adenoma cells. These junctions were characterized by 1) the presence of plasmalemmal attachment plaques only in FS cells, 2) the cytoplasmic filaments assembling in parallel to the attachment plaques, 3) the parallel plasma membranes being separated by the intercellular amorphous material and 4) the intercellular space of approximately 25 nm width. They were similar to hemidesmosomes, but were quite different from hemidesmosome-like intercellular specializations which have been described in the normal meninges and human meningiomas. Accordingly, we designated these new junctions as "hemidesmosomal junctions" which appeared to be one of the ultrastructural features characterizing FS cells.     

Comparison of non-linear models to describe the lactation curves for milk yield and composition in buffaloes (Bubalus bubalis)

In order to describe the lactation curves of milk yield (MY) and composition in buffaloes, seven non-linear mathematical equations (Wood, Dhanoa, Sikka, Nelder, Brody, Dijkstra and Rook) were used. Data were 116 117 test-day records for MY, fat (FP) and protein (PP) percentages of milk from the first three lactations of buffaloes which were collected from 893 herds in the period from 1992 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly production records of dairy buffaloes using the NLIN and MODEL procedures in SAS and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination root means square error (RMSE), Durbin–Watson statistic and Akaike’s information criterion (AIC). The Dijkstra model provided the best fit of MY and PP of milk for the first three parities of buffaloes due to the lower values of RMSE and AIC than other models. For the first-parity buffaloes, Sikka and Brody models provided the best fit of FP, but for the second- and third-parity buffaloes, Sikka model and Brody equation provided the best fit of lactation curve for FP, respectively. The results of this study showed that the Wood and Dhanoa equations were able to estimate the time to the peak MY more accurately than the other equations. In addition, Nelder and Dijkstra equations were able to estimate the peak time at second and third parities more accurately than other equations, respectively. Brody function provided more accurate predictions of peak MY over the first three parities of buffaloes. There was generally a positive relationship between 305-day MY and persistency measures and also between peak yield and 305-day MY, calculated by different models, within each lactation in the current study. Overall, evaluation of the different equations used in the current study indicated the potential of the non-linear models for fitting monthly productive records of buffaloes.     

Induction of a new Lipoxygenase Form in Cucumber Leaves by Salicylic Acid or 2,6-Dichloroisonicotinic Acid*

Changes in lipoxygenase (LOX) protein pattern and/or activity were investigated in relation to acquired resistance of cucumber (Cucumis sativus L.) leaves against two powdery mildews, Sphaerotheca fuliginea (Schlecht) Salmon and Erysiphe cichoracearum DC et Merat. Acquired resistance was established by spraying leaves with salicylic acid (SA) or 2,6-dichloroisonicotinic acid (INA) and estimated in whole plants by infested leaf area compared to control plants. SA was more effective than INA. According to Western blots, untreated cucumber leaves contained a 97 kDa LOX form, which remained unchanged for up to 48 h after pathogen inoculation. Upon treatment with SA alone for 24 h or with INA plus pathogen, an additional 95 kDa LOX form appeared which had an isoelectric point in the alkaline range. For the induction of this form, a threshold concentration of 1 mM SA was required, higher SA concentrations did not change LOX-95 expression which remained similar between 24 h and 96 h but further increased upon mildew inoculation. Phloem exudates contained only the LOX-97 form, in intercellular washing fluid no LOX was detected. dichloroisonicotinic localization revealed LOX protein in the cytosol of the mesophyll cells without differences between the forms.     

The influence of anthropometric characteristics to the handgrip and pinch strength in 6-10-year old children

The aim of the study was to investigate the relationships between handgrip and pinch strength values with basic body (body height, body mass, BMI) and with specific hand anthropometric parameters (fingers spans, lengths and perimeters) in prepubertal children. Body height, body mass and five fingers spans, lengths and perimeters were measured in 461 6-10 year old Estonian children according to Visnapuu & Jürim?e (2007). BMI was calculated (kg/m2). The maximal handgrip strength of the right and left hand was measured with hand dynamometer. The right and left key and tip pinch were measured with a pinch gauge. Body height, as a rule, in combination with BMI, was the strongest predictor of handgrip strength, especially in older children (about 40-60 % of the total variance, R2 x 100). From the hand anthropometry, the most important span parameter was FS2 (see explanations in the methods), which explained about 10-50% (R2 x 100) of the total variance. In older groups, the FS1 and FS3 were added to the models. From the length parameters, the most important was IFL, which in younger groups together with MFL explained 10-30% of the total variance, and in older groups the addition of RFL increased the influence to 45% (R2 x 100). From the perimeters, the most important one was P2 which explained the variability of the handgrip strength in younger groups by 15-30% (R2 x 100) and in older groups together with P3 and P4 even 30-40%. In children, the basic anthropometric parameters (body height and BMI) contribute more to the prediction of handgrip strength than the specific anthropometric parameters. With increasing age the contribution of basic and specific hand anthropometry increases and the relation between anthropometry and handgrip strength is stronger in boys compared with girls. The relation of basic and hand anthropometry to the tip and key pinch strength is relatively low.     

Parameter stability of the functional-structural plant model GREENLAB as affected by variation within populations, among seasons and among growth stages

BACKGROUND AND AIMS: It is increasingly accepted that crop models, if they are to simulate genotype-specific behaviour accurately, should simulate the morphogenetic process generating plant architecture. A functional-structural plant model, GREENLAB, was previously presented and validated for maize. The model is based on a recursive mathematical process, with parameters whose values cannot be measured directly and need to be optimized statistically. This study aims at evaluating the stability of GREENLAB parameters in response to three types of phenotype variability: (1) among individuals from a common population; (2) among populations subjected to different environments (seasons); and (3) among different development stages of the same plants. METHODS: Five field experiments were conducted in the course of 4 years on irrigated fields near Beijing, China. Detailed observations were conducted throughout the seasons on the dimensions and fresh biomass of all above-ground plant organs for each metamer. Growth stage-specific target files were assembled from the data for GREENLAB parameter optimization. Optimization was conducted for specific developmental stages or the entire growth cycle, for individual plants (replicates), and for different seasons. Parameter stability was evaluated by comparing their CV with that of phenotype observation for the different sources of variability. A reduced data set was developed for easier model parameterization using one season, and validated for the four other seasons. KEY RESULTS AND CONCLUSIONS: The analysis of parameter stability among plants sharing the same environment and among populations grown in different environments indicated that the model explains some of the inter-seasonal variability of phenotype (parameters varied less than the phenotype itself), but not inter-plant variability (parameter and phenotype variability were similar). Parameter variability among developmental stages was small, indicating that parameter values were largely development-stage independent. The authors suggest that the high level of parameter stability observed in GREENLAB can be used to conduct comparisons among genotypes and, ultimately, genetic analyses.     

Allozyme variation in populations, full-sib families and selfed lines in Betula pendula Roth

Changes in genetic variability in populations (stand origins), full-sib (FS) families and three generations of selfed lines of Betula pendula were observed based on 15 allozyme loci. Growth vigour, measured as stem volume, and its relationship with heterozygosity was studied to determine the effect of inbreeding. Pooled FS families showed a higher percentage of polymorphic loci (P) and allelic numbers per locus (A) than those of natural populations, but no difference in heterozygosity. There was no difference in allozyme variability between fast-and slow-growing family groups, and heterozygosity was not correlated with stem volume among FS families. Allozyme variability was significantly decreased in advancing generations of selfing, and the further the selfing generation, the lower the heterozygosity and the slower the growth. Observed heterozygosity after advancing generations of inbreeding was increasingly higher than expected, indicating overdominance effects or, alternatively, selection against deleterious homozygotes.     

Inter Subject Variability and Reproducibility of Diffusion Tensor Imaging within and between Different Imaging Sessions

The aim of these studies was to provide reference data on intersubject variability and reproducibility of diffusion tensor imaging. Healthy volunteers underwent imaging on two occasions using the same 3T Siemens Verio magnetic resonance scanner. At each session two identical diffusion tensor sequences were obtained along with standard structural imaging. Fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity maps were created and regions of interest applied in normalised space. The baseline data from all 26 volunteers were used to calculate the intersubject variability, while within session and between session reproducibility were calculated from all the available data. The reproducibility of measurements were used to calculate the overall and within session 95% prediction interval for zero change. The within and between session reproducibility data were lower than the values for intersubject variability, and were different across the brain. The regional mean (range) coefficient of variation figures for within session reproducibility were 2.1 (0.9–5.5%), 1.2 (0.4–3.9%), 1.2 (0.4–3.8%) and 1.8 (0.4–4.3%) for fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity, and were lower than between session reproducibility measurements (2.4 (1.1–5.9%), 1.9 (0.7–5.7%), 1.7 (0.7–4.7%) and 2.4 (0.9–5.8%); p<0.001). The calculated overall and within session 95% prediction intervals for zero change were similar. This study provides additional reference data concerning intersubject variability and reproducibility of diffusion tensor imaging conducted within the same imaging session and different imaging sessions. These data can be utilised in interventional studies to quantify change within a single imaging session, or to assess the significance of change in longitudinal studies of brain injury and disease.     

Characterization of a three bacteria mixed culture in a chemostat: evaluation and application of a quantitative terminal-restriction fragment length polymorphism (T-RFLP) analysis for absolute and species specific cell enumeration

Growth dynamics of Pseudomonas aeruginosa, Burkholderia cepacia, and Staphylococcus aureus in a batch and chemostat, were investigated as a laboratory model system for persistent infections in cystic fibrosis. Most species-specific enumeration methods for mixed cultures are laborious or only qualitative, and therefore impede generation of quantitative data required for validation of mathematical models. Here, a quantitative T-RFLP method was evaluated and applied for specific and absolute cell number enumerations. The method was tested to be unbiased by quantitative sample composition and allowed reproducible enumerations of mixed cultures. For assay validation, samples of defined concentration containing one, two or three species were quantified. Logarithmically transformed absolute cell numbers of single-species dilutions were linear within a lower working range of 10(4)-10(6) cfu/mL (species-dependent) and an upper working range of 10(10) cfu/mL. Quantifications of single species (10(6)-10(10) cfu/mL) spiked with one or two other species agreed well with single species controls. Differences between slopes of first order linear regression of spiked and pure dilution series were insignificant. Coefficient of variation of defined mixed replicates was maximum 4.39%, of a three-species chemostat it was maximum 1.76%. T-RFLP monitoring of pure cultures in parallel shake flasks and of a three-species mixed chemostat gave very consistent results. Coexistence of at least two species after a time period equivalent to more than 33 volume exchanges was found. This result was not predicted from pure cultures clearly indicating the need for quantitative mixed culture experiments to better understand microbial growth dynamics and for mathematical model validation.     

Simultaneous biofeedback of heart rate and frontal EMG as a pretraining for the control of EEG theta activity

Following one base-line session, 20 normal subjects received four half hour sessions consisting of simultaneous feedback of heart rate and frontalis muscle (pretraining). Ten subjects received contingent (CF), the other ten noncontingent feedback (NCF). Subjects were asked to lower heart rate and frontal muscle tension (EMG). Heart rate within sessions decreased up to 19 bpm, with a mean of 4 bpm for the CF group. There was only a weak decrease over sessions, however, because of the strong habituation effect. The following events accompanied the heart rate decrease: (1) an increase of the variability of the heart rate, (2) a decrease of the variance of the EMG, (3) an increased correlation between heart rate slowing and EMG decrease, and (4) an increasing subjective experience of control of heart rate and EMG. After pretraining, subjects received eight sessions of auditory feedback of their frontal EEG theta activity (four sessions with CF and four sessions with NCF in balanced order). There was a weak increase of theta for the CF condition over sessions, but a decrease within the sessions. Pretraining on heart rate and frontal EMG control had no influence on the performance during theta training. It was hypothesized that control of heart rate slowing and theta control involve different mechanisms.     

Chemometrics models for overcoming high between subject variability: applications in clinical metabolic profiling studies

In human metabolic profiling studies, between-subject variability is often the dominant feature and can mask the potential classifications of clinical interest. Conventional models such as principal component analysis (PCA) are usually not effective in such situations and it is therefore highly desirable to find a suitable model which is able to discover the underlying pattern hidden behind the high between-subject variability. In this study we employed two clinical metabolomics data sets as the testing grounds, in which such variability had been observed, and we demonstrate that a proper choice of chemometrics model can help to overcome this issue of high between-subject variability. Two data sets were used to represent two different types of experiment designs. The first data set was obtained from a small-scale study investigating volatile organic compounds (VOCs) collected from chronic wounds using a skin patch device and analysed by thermal desorption-gas chromatography-mass spectrometry. Five patients were recruited and for each patient three sites sampled in triplicate: healthy skin, boundary of the lesion and top of the lesion, the aim was to discriminate these three types of samples based on their VOC profile. The second data set was from a much larger study involving 35 healthy subjects, 47 patients with chronic obstructive pulmonary disease and 33 with asthma. The VOCs in the breath of each subject were collected using a mask device and analysed again by GC–MS with the aim of discriminating the three types of subjects based on breath VOC profiles. Multilevel simultaneous component analysis, multilevel partial least squares for discriminant analysis, ANOVA-PCA, and a novel simplified ANOVA-PCA model—which we have named ANOVA-Mean Centre (ANOVA-MC)—were applied on these two data sets. Significantly improved results were obtained by using these models. We also present a novel validation procedure to verify statistically the results obtained from those models.