Factorial Discriminant Analysis Research Articles

Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging

A shortwave infrared (SWIR) hyperspectral imaging system with wavelength range between 1000 and 2500 nm was used to assess the potential to detect aflatoxin B1 (AFB1) contaminants on the surface of healthy maize kernels. Four different AFB1 solutions were prepared and deposited on kernels surface to achieve 10, 20, 100, and 500 ppb, respectively. A drop of 20% methanol was dipped on the surface of 30 healthy kernels in the same way to generate the control samples. Based on the standard normal variate (SNV) transformation spectra, principal components analysis (PCA) was used to reduce the dimensionality of the spectral data, and then stepwise factorial discriminant analysis (FDA) was performed on latent variables provided by the PCA's. Furthermore, beta coefficients of the first three of four discriminant factors were analyzed and key wavelengths, which can represent AFB1 and be used to differentiate different level of AFB1 were indentified. Furthermore, 150 independent samples were used as verification set to test the reproducibility of the proposed method. A minimum classification accuracy of 88% was achieved for the validation set and verification set. Results indicated that hyperspectral imaging technology, accompanied by the PCA-FDA method, can be used to detect AFB1 at concentrations as low as 10 ppb when applied directly on the maize surface.

Comparative performance of classification algorithms for the development of models of spatial distribution of landscape structures

This work aimed to evaluate whether different types of landscape structures (undulations, lynchets and undisturbed surfaces) can be discriminated by their morphometric attributes and the soil thickness. Three models based on the factorial discriminant analysis (FDA), the multinomial logistic regression (MLR) and the classification and regression trees (CART), respectively, were developed to classify different types of landscape structures. All these statistical techniques were performed using a training sample of 586 individuals over a 17ha area located in the south-western Parisian Basin. The models developed by the CART and FDA revealed that in addition to soil thickness, the morphometric attributes slope and profile curvature significantly influence the spatial distribution of landscape structures. In addition to the variables selected by CART and FDA models, MLR model included elevation. An external validation of the classification models based on a validation sample of 148 individuals, revealed an overall well classification by CART model of 85% while those achieved with MLR and FDA models were 72% and 77%, respectively. As the predictor variables are known at all the nodes of a regular grid covering the study area; the three models developed were then used to map the landscape structures all over the 17ha area. Resulting maps revealed a total disagreement between the three models for only 3% of the study area. For more than 50% of the study area the three models predicted a similar landscape structure. For the remaining surface, at least two of the three models predicted a similar landscape structure.

Saccharomyces cerevisiae mixed culture of blackberry (Rubus ulmifolius L.) juice: synergism in the aroma compounds production.

Blackberry (Rubus sp.) juice was fermented using four different strains of Saccharomyces cerevisiae (Vitilevure-CM4457, Enoferm-T306, ICV-K1, and Greroche Rhona-L3574) recognized because of their use in the wine industry. A medium alcoholic graduation spirit (<6°GL) with potential to be produced at an industrial scale was obtained. Alcoholic fermentations were performed at 28°C, 200 rpm, and noncontrolled pH. The synergistic effect on the aromatic compounds production during fermentation in mixed culture was compared with those obtained by monoculture and physic mixture of spirits produced in monoculture. The aromatic composition was determined by HS-SPME-GC. The differences in aromatic profile principally rely on the proportions in aromatic compounds and not on the number of those compounds. The multivariance analysis, principal component analysis (PCA), and factorial discriminant analysis (DFA) permit to demonstrate the synergism between the strains.

Identification of sulfur fumed Pinelliae Rhizoma using an electronic nose.

Background:Pinelliae Rhizoma is a commonly used Chinese herb which will change brown during the natural drying process. However, sulfur fumed Pinelliae Rhizoma will get a better appearance than naturally dried one. Sulfur fumed Pinelliae Rhizoma is potentially toxical due to sulfur dioxide and sulfites formed during the fuming procedures. The odor components in sulfur fumed Pinelliae Rhizoma is complex. At present, there is no analytical method available to determine sulfur fumed Pinelliae Rhizoma simply and rapidly. To ensure medication safety, it is highly desirable to have an effective and simple method to identify sulfur fumed Pinelliae Rhizoma.Materials and Methods:This paper presents a novel approach using an electronic nose based on metal oxide sensors to identify whether Pinelliae Rhizoma was fumed with sulfur, and to predict the fuming degree of Pinelliae Rhizoma. Multivariate statistical methods such as principal components analysis (PCA), discriminant factorial analysis (DFA) and partial least squares (PLS) were used for data analyzing and identification. The use of the electronic nose to discriminate between different fuming degrees Pinelliae Rhizoma and naturally dried Pinelliae Rhizoma was demonstrated.Results:The electronic nose was also successfully applied to identify unknown samples including sulfur fumed samples and naturally dried samples, high recognition value was obtained. Quantitative analysis of fuming degree of Pinelliae Rhizoma was also demonstrated. The method developed is simple and fast, which provides a new quality control method of Chinese herbs from the aspect of odor.Conclusion:It has shown that this electronic nose based metal oxide sensor is sensitive to sulfur and sulfides. We suggest that it can serve as a supportive method to detect residual sulfur and sulfides.

Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue

Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. To the best of our knowledge, this is the first attempt to demonstrate whether the combined e-nose and e-tongue technologies could be successfully applied to the detection of adulteration of argan oil.

Feasibility of the application of electronic nose technology to detect insect infestation in wheat

Wu, J., D.S. Jayas, Q. Zhang, N.D.G. White and R.K. York. 2013. Feasibility of the application of electronic nose technology to detect insect infestation in wheat. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 55: 3.1-3.9. An Alpha MOS FOX3000 electronic nose (e-nose) equipped with 12 metal oxide semiconductor (MOS) sensors was used to evaluate the presence of insects in wheat. Samples of Canada western red spring (CWRS) wheat (cv. AC Barrie) infested with rusty grain beetle (RGB), Cryptolestes ferrugineus (Stephens), and red flour beetle (RFB), Tribolium castaneum (Herbst), were placed in glass jars fitted for the collection of headspace volatiles. Different insect densities (0, 1, 2, 10 and 20 insects/kg) were tested for each insect species in combination with three moisture content levels for wheat (14%, 16%, and 18% wet mass basis). The headspace volatiles from insect-infested or non-infested wheat were sampled and injected into the e-nose. The response of e-nose sensors, in the form of a multi-dimensional matrix, was extracted and interpreted using AlphaSoft V8.0 software. The automated pattern recognition algorithms in the software (Principal Component Analysis, Discriminant Factorial Analysis, and Partial Least Square) were used to evaluate the samples.

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Discriminative Power of Chemically Sensitive Silicon Nanowire Field Effect Transistors to Volatile Organic Compounds

We report on the sensing of different polar and nonpolar volatile organic compounds (VOCs) in an atmosphere with background humidity (relative humidity: 40%), using molecularly modified silicon nanowire field effect transistors (SiNW FETs). In this endeavor, a systematic comparative analysis is performed with: (i) SiNW FETs that were functionalized with a series of molecules having different electron-withdrawing and electron-donating end groups; and (ii) SiNW FETs that are functionalized with a series of molecules having similar functional groups but different backbone lengths. The analysis of the sensing signals are focused on three main FET parameters: (i) changes in the threshold voltage, (ii) changes in the carrier mobility, and (iii) changes in the on-current, compared to the baseline values under vacuum. Using discriminant factor analysis, the performance of the molecularly modified SiNW FETs is further analyzed as sensors array. The combination of sensors having the best discriminative power between the various VOCs are identified and discussed in terms of their constituent surface modifications.

Clinical chemistry of farmed red deer (&amp;lt;i&amp;gt;Cervus elaphus&amp;lt;/i&amp;gt;) yearling hinds reared on grass or &amp;lt;i&amp;gt;papillonaceous&amp;lt;/i&amp;gt; pasture paddocks in Hungary

Abstract. Yearling red deer (Cervus elaphus) hinds of identical initial body weight were reared on a monocotyledonous grass (group 1) or on a papillonaceous plant pasture (group 2) for 212 days. At the end of the experiment (when deer were shot) blood was taken from ten animals of each group for serum biochemical analysis. Hinds of group 2 provided higher final body weight (90±3.5 vs. 101±6.6 kg) and higher daily body weight gain (105.7±10.7 vs. 153.8±26.8 g/day). Within serum nitrogenous compounds group 2 provided higher total protein concentrations, while from the lipids only serum triglyceride levels were higher in this group. Serum potassium was in both groups higher than the reference range with a superposed slight hyperkalaemia in group 2. Higher lactate dehydrogenase and alkaline phosphatase activities were found in group 2 and lower aspartate aminotransferase activity values. Inorganic phosphate concentration showed a significant difference (group 1 provides higher values). Results refer to an expressed venison growth as a result of the rich dietary protein supply of group 2. Findings were evaluated as well with discriminant factor analysis, outlining the relative importance of the single blood biochemical parameters in shaping the inter-group differences.

Failure mode classification of reinforced concrete column using Fisher method

In order to apply the performance-based seismic design, an engineer must first find out whether the column is expected to fail in shear before or after flexural yielding. According to column failure characteristics and failure mode of reinforced concrete column, the UW-PEER structure performance database was discussed and analyzed. In order to investigate the relevance of failure mode and factors such as longitudinal reinforcement ratio, transverse reinforcement ratio, hoop spacing to depth ratio, aspect ratio, shearing resistance demand to shear capacity ratio and axial load ratio, Fisher’s discriminant analysis (FDA) of the above factors was carried out. A discriminant function was developed to identify column failure mode. Results show that three factors, i.e., Vp/Vn, hoop spacing to depth ratio and aspect ratio have important influence on the failure mode. The failure mode has less to do with longitudinal reinforcement ratio, transverse reinforcement ratio and axial load ratio. Through using these three factors and the model proposed, over 85.6% of the original grouped cases were correctly classified. The value of coefficient of Vp/Vn is the largest, which means that discriminant equation is most sensitive to the shearing resistance demand to shear capacity ratio.

Use of Front-Face Fluorescence Spectroscopy to Differentiate Sheep Milks from Different Genotypes and Feeding Systems

The objective of the present study was to assess the potential of front-face fluorescence spectroscopy coupled with chemometric tools for the evaluation of the quality of milk samples according to the feeding system and genotype. Fifty (n = 50) ewe's milk samples were scanned after excitation set at 250, 290, 322, and 380 nm and emission set at 410 nm. Thirty out of the 50 samples composed the first trial and were obtained from two different genotypes (i.e., Comisana versus Sicilo-Sarde); the second trial was composed of 20 samples obtained from the Sicilo-Sarde genotype with two different feeding systems in pen (soybean versus scotch bean). Milk samples were divided into four groups named Sicilo-Sarde with pasture feeding (Spas), Comisana with pasture feeding (Cpas), Sicilo-Sarde feeding on scotch bean (Ssco), and Sicilo-Sarde feeding on soybean (Ssoy). The factorial discriminant analysis was applied to the: (i) four groups (i.e., Spas, Ssco, Ssoy, and Cpas) and (ii) three groups composed only of Sicilo-Sarde genotype (i.e., Spas, Ssco, and Ssoy). Considering the four groups, the best result was obtained with the excitation vitamin A spectra since correct classification amounting to 76% was observed. When the factorial discriminant analysis was performed with the three groups belonging to the Sicilo-Sarde genotype, the best result was obtained again with vitamin A spectra (i.e., emission and excitation spectra) since 88.6% of correct classification was observed. Concatenation technique applied to the five fluorescence spectra improved the rate of classification between the four groups since 44 out of 50 samples were correctly classified. No misclassification was observed between milk samples collected from ewes with pasture feeding from the pen feeding. It was concluded from the obtained results that fluorescence spectroscopy could be considered as a powerful tool for differentiating between raw milks according to both genotype and feeding system.

Phytoplankton distribution in the Nador lagoon (Morocco) and possible risks for harmful algal blooms

1 - Phytoplankton populations and environmental parameters were analysed seasonally in the lagoon of Nador (Morocco) over an annual cycle (2006-2007) at six stations. Seven phytoplankton Classes were observed: Diatomophyceae, Dinophyceae, Raphidophyceae, Prasinophyceae, Euglenophyceae, Dictyochophyceae and Cyanophyceae. Seven taxa were identified that may present a risk of inducing harmful blooms i.e. Alexandrium minutum, Pseudonitzschia delicatissima and P. fraudulenta, Prorocentrum minimum, Prorocentrum rhatymum, Dinophysis sacculus, Gymnodinium catenatum and Karenia mikimotoi. Physico-chemical analyses were carried out on water samples: Dissolved Organic Carbon (DOC) and nutrients (NH4 +, NO3-, PO4 3- and SiO2).2 - Favourable and unfavourable factors for bloom development were identified. The possible relations between environmental parameters and the occurrence of potentially harmful phytoplankton blooms were investigated through a Factorial Discriminant Analysis. The proliferation of potentially harmful phytoplankton was linked to the availability of various nutrients (N, P, Si). Prorocentrum minimum tolerated waters with high nitrate contents, while Alexandrium minutum tolerated nutrientpoor waters. The most frequently observed taxa, Pseudonitzschia species and Dinophysis sacculus, were adapted to variations of water quality. Excessive levels of nutrients appeared as inhibiting for proliferation of potentially harmful phytoplankton which disappeared for the benefit of a very dense bloom of Tetraselmis sp. (Prasinophyceae).3 - A risk of toxicity was observed during all samplings throughout the lagoon, it was particularly pronounced during the summer sampling in the northern continental part of the lagoon with proliferation of Pseudonitzschia species.

Inter-sport variability of muscle volume distribution identified by segmental bioelectrical impedance analysis in four ball sports

The aim of this study was to evaluate and quantify differences in muscle distribution in athletes of various ball sports using segmental bioelectrical impedance analysis (SBIA). Participants were 115 male collegiate athletes from four ball sports (baseball, soccer, tennis, and lacrosse). Percent body fat (%BF) and lean body mass were measured, and SBIA was used to measure segmental muscle volume (MV) in bilateral upper arms, forearms, thighs, and lower legs. We calculated the MV ratios of dominant to nondominant, proximal to distal, and upper to lower limbs. The measurements consisted of a total of 31 variables. Cluster and factor analyses were applied to identify redundant variables. The muscle distribution was significantly different among groups, but the %BF was not. The classification procedures of the discriminant analysis could correctly distinguish 84.3% of the athletes. These results suggest that collegiate ball game athletes have adapted their physique to their sport movements very well, and the SBIA, which is an affordable, noninvasive, easy-to-operate, and fast alternative method in the field, can distinguish ball game athletes according to their specific muscle distribution within a 5-minute measurement. The SBIA could be a useful, affordable, and fast tool for identifying talents for specific sports.

A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions

Background:Upper digestive endoscopy with biopsy and histopathological evaluation of the biopsy material is the standard method for diagnosing gastric cancer (GC). However, this procedure may not be widely available for screening in the developing world, whereas in developed countries endoscopy is frequently used without major clinical gain. There is a high demand for a simple and non-invasive test for selecting the individuals at increased risk that should undergo the endoscopic examination. Here, we studied the feasibility of a nanomaterial-based breath test for identifying GC among patients with gastric complaints.Methods:Alveolar exhaled breath samples from 130 patients with gastric complaints (37 GC/32 ulcers / 61 less severe conditions) that underwent endoscopy/biopsy were analyzed using nanomaterial-based sensors. Predictive models were built employing discriminant factor analysis (DFA) pattern recognition, and their stability against possible confounding factors (alcohol/tobacco consumption; Helicobacter pylori) was tested. Classification success was determined (i) using leave-one-out cross-validation and (ii) by randomly blinding 25% of the samples as a validation set. Complementary chemical analysis of the breath samples was performed using gas chromatography coupled with mass spectrometry.Results:Three DFA models were developed that achieved excellent discrimination between the subpopulations: (i) GC vs benign gastric conditions, among all the patients (89% sensitivity; 90% specificity); (ii) early stage GC (I and II) vs late stage (III and IV), among GC patients (89% sensitivity; 94% specificity); and (iii) ulcer vs less severe, among benign conditions (84% sensitivity; 87% specificity). The models were insensitive against the tested confounding factors. Chemical analysis found that five volatile organic compounds (2-propenenitrile, 2-butoxy-ethanol, furfural, 6-methyl-5-hepten-2-one and isoprene) were significantly elevated in patients with GC and/or peptic ulcer, as compared with less severe gastric conditions. The concentrations both in the room air and in the breath samples were in the single p.p.b.v range, except in the case of isoprene.Conclusion:The preliminary results of this pilot study could open a new and promising avenue to diagnose GC and distinguish it from other gastric diseases. It should be noted that the applied methods are complementary and the potential marker compounds identified by gas-chromatography/mass spectrometry are not necessarily responsible for the differences in the sensor responses. Although this pilot study does not allow drawing far-reaching conclusions, the encouraging preliminary results presented here have initiated a large multicentre clinical trial to confirm the observed patterns for GC and benign gastric conditions.

The spectrum of cognitive disorders in Parkinson's disease: A data‐driven approach

The objective of this study was to identify different cognitive phenotypes in Parkinson's disease (PD) using a data-driven approach. A model-based cluster analysis was conducted on the neuropsychological test results of 558 PD patients from 2 European movement disorder centers (Lille, n = 403; Maastricht, n = 155). The number of clusters was determined according to their clinical relevance as well as on the basis of 3 statistical criteria: the cubic cluster criterion, the pseudo F statistic, and the total squared correlation ratio (R(2)). A factorial discriminant analysis was performed to assess the quality of the cluster's separation. Descriptive variables were used to further characterize the clusters. A 5-cluster model was considered the clinically most relevant. The 5 clusters comprised: (1) cognitively intact patients (19.39%); (2) patients without cognitive deficits but with slight mental slowing (41.29%); (3) patients with slightly impaired overall cognitive efficiency and deficits in all cognitive domains except recognition memory (12.93%); (4) patients with severe mental slowing, impaired overall cognitive efficiency, and severe cognitive impairment in all domains, including memory (23.88%); and (5) patients with very severe impairment in all cognitive domains (2.51%). Cognitively intact patients were significantly younger and had received more years of formal education. Patients in the last 3 clusters had more severe motor symptoms, longer disease duration, and more axial signs. In the last cluster, most patients were demented. Our results confirm the heterogeneity of cognitive presentations in PD, ranging from cognitively intact patients with rather high levels of performance in each cognitive domain to very severely impaired patients.

Discrimination of traditional Chinese medicinal materials with different tastes based on electronic tongue

To investigate the use of the electronic tongue in the evaluation of traditional Chinese medicinal materials with sour, bitter, sweet or salty tastes, and assess the possible application of the sensor in the evaluation of different tastes of traditional Chinese medicinal materials. Aqueous extracts of 22 traditional Chinese medicinal materials were measured by the electronic tongue. The data collected with the tongue was evaluated for discrimination of the samples with multivariate statistical methods: principal component analysis (PCA) and discriminant factor analysis (DFA). The electronic tongue was capable of discriminating between samples with different taste modalities and could also distinguish different samples eliciting the same basic taste. Twenty-two traditional Chinese medicinal materials could be classified into five clusters based on PCA. These differences seem to derive from the different tastes. DFA was applied to construct a model to discriminate traditional Chinese medicinal materials with different tastes. And the samples yielded about 88.2% accuracy for cross-validation. We confirmed that the electronic tongue may provide an analytical procedure for classification of the samples with respect to tastes of the traditional Chinese medicinal materials.

1024 – Prospective analysis of STN DBS in parkinson's disease: motor and non-motor symptoms evolution link to electrodes localization

Even if evidence suggests that Deep Brain Stimulation (DBS) of the Subthalamic Nucleus (STN) provides a beneficial clinical effect in Parkinson's disease (PD) for up to 10 years there are also several potential risks among them psychiatrics complications. The aim of this study was to identify the patient-related, medicosurgical, and psychosocial factors that influence PD patient's evolution in STN DBS. Method We investigate, in 36 STN DBS PD patients, depression, anxiety and mania at baseline, 3-mounth post-surgery and 6-mounth post-surgery. At baseline, demographic data and clinical characteristics (duration of disease, PD medication, past psychiatric history were also collected. Scores at motor section of the unified Parkinson disease rating scale and PD medication were evaluated at baseline, 3-mounth post-surgery and 6-mounth post-surgery. A multivariate analysis and a factorial discriminant analysis (FDA) were considered to study differences between surgical outcomes considering an adjustment on disease duration. Results Thirty six patients with Parkinson's disease treated with STN-DBS were analyzed considering stimulating electrodes localization (‘in’, ‘frontier’, ‘out’ of STN). Concerning the pre-surgical parameters, no configuration was found. Considering only 3-mounth post-surgery parameters (electrodes localization, motor scores, psychiatric data, PD medication) patients differ according to surgical positioning: ‘frontier’/’out’ and ‘in’ but the best separation between the 3 groups of localization was obtain including pre-surgical post-surgery parameters. This segregation was maintained at 6-mounth postsurgery. Conclusions Our work provides preliminary answer to the question of impact of the exact electrode position on neuropsychological side effects of STN-DBS and enhances the importance of combining pychiatrico-neuro-surgical data.

A note on the special fillet fatty acid composition of the dwarf carp(cyprinus carpio carpio)living in thermal Lake Hévíz, Hungary

Fatty acid (FA) composition of the fillet and the intestinal content of dwarf common carp (Cyprinus carpio carpio) living in Lake Hévíz was determined in wintertime collected samples and results were compared to widespread literature data on carp. Fillet FA profile of the thermally adapted (28 °C) Hévíz dwarf carps differed from profiles originated from divergent culture and feeding conditions in the overall level of saturation. Fillet myristic acid proportions largely exceeded all literature data in spite of poor dietary supply. Fillet fatty acid results indicate the effects of thermal adaptation (high saturation level) and the correlative effects of feed components rich in omega-3 fatty acids, with special respect to docosahexaenoic acid. With the application of discriminant factor analysis the Hévíz sample was accurately differentiated from the literature data on carp fillet fatty acid profile, mostly based on C14:0, C18:1 n9, C18:2 n6, C20:1 n9 and C20:4 n6 FAs. In summary, fillet FA profile suggested thermal adaptation, location specificity and the ingestion of algal and bacterial material.

Detection of Alzheimer‘S and Parkinson‘S Disease From Exhaled Breath Using Nanomaterial-Based Sensors

To study the feasibility of a novel method in nanomedicine that is based on breath testing for identifying Alzheimer's disease (AD) and Parkinson's disease (PD), as representative examples of neurodegenerative conditions. Alveolar breath was collected from 57 volunteers (AD patients, PD patients and healthy controls) and analyzed using combinations of nanomaterial-based sensors (organically functionalized carbon nanotubes and gold nanoparticles). Discriminant factor analysis was applied to detect statistically significant differences between study groups and classification success was estimated using cross-validation. The pattern identification was supported by chemical analysis of the breath samples using gas chromatography combined with mass spectrometry. The combinations of sensors could clearly distinguish AD from healthy states, PD from healthy states, and AD from PD states, with a classification accuracy of 85, 78 and 84%, respectively. Gas chromatography combined with mass spectrometry analysis showed statistically significant differences in the average abundance of several volatile organic compounds in the breath of AD, PD and healthy subjects, thus supporting the breath prints observed with the sensors. The breath prints that were identified with combinations of nanomaterial-based sensors have future potential as cost-effective, fast and reliable biomarkers for AD and PD.

Pulp volatiles measured by an electronic nose are related to harvest season, TSS concentration and TSS/TA ratio among 39 peaches and nectarines

Fruit of 39 peach and nectarine cultivars were harvested at commercial maturity from an orchard between June 5 and September 9 in 2010. Total soluble solids (TSS) concentration ranged from 8.90% to 19.97%, titratable acidity (TA) concentration ranged from 0.20% to 1.12%, and TSS/TA ratio ranged from 9.36 to 92.80 in the tested pulp samples of 39 cultivars. Pulp volatiles of 39 cultivars at harvest were evaluated by a FOX 4000 electronic nose (e-nose) equipped with 18 metallic oxide sensors, which 13 sensors were positive and five sensors were negative in intensity values. Based on e-nose measurements of pulp volatiles of 39 cultivars at harvest, cultivars harvested in June, July, and both August and September could be differentiated, cultivars of low TSS concentration (<12%) were separated clearly from high ones (>16%), and cultivars of low TSS/TA ratio (<35) were separated clearly from high ones (>45) in the discriminant factor analysis (DFA) plot of 39 cultivars. 117 samples of three replicates for each cultivar were separated into three individual groups according to the classifications of harvest season, TSS concentration and TSS/TA ratio, respectively. Clearer separations were reached in these DFA plots of separated 117 samples than that of 39 cultivars based on pulp volatiles at harvest measured by the e-nose.