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The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers' expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an "ad hoc" designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours' algorithm and Partial Least Square-Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar's test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values ( p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours' model is considered the most convenient strategy to assess meat and fish freshness., Competing Interests: The authors declare no conflict of interest.

The aim of this work was to investigate the applicability of e-senses (electronic nose, electronic tongue and electronic eye) for the characterization of edible olive oils (extra virgin, olive and pomace) and for the assessment of extra virgin olive oil and olive oil quality decay during storage at different temperatures. In order to obtain a complete description of oil samples, physico-chemical analyses on quality and nutritional parameters were also performed. Data were processed by PCA and a targeted data processing flow-sheet has been applied to physico-chemical and e-senses dataset starting from data pre-processing introducing an innovative normalization method, called t0 centering. On e-senses data a powerful mid-level data fusion approach has been employed to extract relevant information from different analytical sources combining their individual contributions. On physico-chemical data, an alternative approach for grouping extra virgin olive oil and olive oil samples on the basis of their freshness was applied and two classes were identified: fresh and oxidized. A k-NN classification rule was developed to test the performance of e-senses to classify samples in the two classes of freshness and the average value of correctly classified samples was 94%. Results demonstrated that the combined application of e-senses and the innovative data processing strategy allows to characterize edible olive oils of different categories on the basis of their sensorial properties and also to follow the evolution during storage of extra-virgin olive oil and olive oil sensorial properties thus assessing the quality decay of oils., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The consumption of whole-grain food-including pasta-has been increasing steadily. In the case of whole-grain pasta, given the many different producers, it seems important to have some objective parameters to define its overall quality. In this study, commercial whole-grain pasta samples representative of the Italian market have been characterized from both molecular and electronic-senses (electronic nose and electronic tongue) standpoint in order to provide a survey of the properties of different commercial samples. Only 1 pasta product showed very low levels of heat damage markers (furosine and pyrraline), suggesting that this sample underwent to low temperature dry treatment. In all samples, the furosine content was directly correlated to protein structural indices, since protein structure compactness increased with increasing levels of heat damage markers. Electronic senses were able to discriminate among pasta samples according to the intensity of heat treatment during the drying step. Pasta sample with low furosine content was discriminated by umami taste and by sensors responding to aliphatic and inorganic compounds. Data obtained with this multidisciplinary approach are meant to provide hints for identifying useful indices for pasta quality. Practical Application As observed for semolina pasta, objective parameters based on heat-damage were best suited to define the overall quality of wholegrain pasta, almost independently of compositional differences among commercial samples. Drying treatments of different intensity also had an impact on instrumental sensory traits that may provide a reliable alternative to analytical determination of chemical markers of heat damage in all cases where there is a need for avoiding time-consuming procedures. [ABSTRACT FROM AUTHOR]

Abstract: A commercial electronic nose (PEN 2, Win Muster Airsense) was used to classify four peach cultivars and to assess the ripening stage during shelf-life. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguish among four diverse cultivars. PCA applied on the electronic nose data, collected during shelf-life from harvest until senescence, highlighted the ability of the instrument to assess the ripening stage of peaches. The loading analysis identified one sensor as the only relevant one in the discrimination of peaches on the basis of their shelf-life. The responses of the sensor were plotted against time and fitted with a sigmoid transition function allowing the definition of three different ripening stages (unripe, ripe, over-ripe) based on the trend of first and second time derivatives. Classification and regression tree (CART) analysis was applied to characterise peach samples into the three classes. The performance of the electronic nose was compared with the results of classical and non-destructive techniques such as ethylene measurement and colour evaluation, frequently used to assess the ripening stage of climacteric fruit. [Copyright &y& Elsevier]

Abstract: FT-NIR spectroscopy and an electronic nose were used to evaluate the Optimal Cooking Time for milled, parboiled and quick-cooking rice and compared with gelatinization time and the suggested cooking time reported on package labels. Rice spectral data collected in reflectance mode using an FT-NIR spectrometer were transformed into second derivative for chemometric analysis. This technique detected the modifications induced by the hydration process during cooking and related to specific molecular groups. A electronic nose/commercial, consisting of metal oxide semiconductor and metal oxide semiconductor field effect transistor sensors, was used to follow the evolution of rice aromas during cooking. The results obtained by the techniques confirmed the feasibility of both methods to follow the rice profile during cooking and to evaluate the optimal cooking time, rapidly and objectively. [Copyright &y& Elsevier]

The performance of two innovative packaging materials was investigated on two Sardinian extra-virgin olive oils (Nera di Gonnos and Bosana). In particular, a transparent plastic film loaded with a UV-blocker (packaging B) and a metallized material (packaging C) were compared each other and to brown-amber glass (packaging A). During accelerated shelf-life tests at 40 and 60 °C, the evolution of quality parameters (i.e., acidity, peroxide value, K270, and phenolic content) was monitored, together with the aromatic fingerprint evaluated by electronic nose. Packaging B resulted in the best-performing material in protecting oil from oxidation, due to its lower oxygen transmission rate (0.1 ± 0.02 cm3/m2 24 h) compared to packaging C (0.23 ± 0.04 cm3/m2 24 h). At the end of storage, phenolic reduction was on average 25% for packaging B and 58% for packaging C, and the aromatic fingerprint was better preserved in packaging B. In addition, other factors such as the sanitary status of the olives at harvesting and the storage temperature were demonstrated to have a significant role in the shelf life of packaged extra-virgin olive oil. [ABSTRACT FROM AUTHOR]

Background: Diagnostic methods for the early identification of ovarian cancer (OC) represent an unmet clinical need, as no reliable diagnostic tools are available. Here, we tested the feasibility of electronic nose (e-nose), composed of ten metal oxide semiconductor (MOS) sensors, as a diagnostic tool for OC detection. Methods: Women with suspected ovarian masses and healthy subjects had volatile organic compounds analysis of the exhaled breath using e-nose. Results: E-nose analysis was performed on breath samples collected from 251 women divided into three groups: 86 OC cases, 51 benign masses, and 114 controls. Data collected were analyzed by Principal Component Analysis (PCA) and K-Nearest Neighbors' algorithm (K-NN). A first 1-K-NN (cases vs. controls) model has been developed to discriminate between OC cases and controls; the model performance tested in the prediction gave 98% of sensitivity and 95% of specificity, when the strict class prediction was applied; a second 1-K-NN (cases vs. controls + benign) model was built by grouping the non-cancer groups (controls + benign), thus considering two classes, cases and controls + benign; the model performance in the prediction was of 89% for sensitivity and 86% for specificity when the strict class prediction was applied. Conclusions: Our preliminary results suggested the potential role of e-nose for the detection of OC. Further studies aiming to test the potential adoption of e-nose in the early diagnosis of OC are needed. [ABSTRACT FROM AUTHOR]

Abstract: A combined acoustic–mechanical technique was employed to simultaneously characterise in uniaxial compression mode the texture properties of commercial toasted sliced breads and laboratory-made ones that were made by modifying the amount of sucrose in the recipe. Also an Image Analysis technique was used to describe the morphology of the gas cells. The analysis of the flavour release was performed by means of a commercial electronic nose system. Advanced techniques of multivariate statistical analysis were employed to get the maximum information from the proposed instrumental techniques. Some selected mechanical parameters and flavour release responses are dominant in discriminating sliced bread samples and both express similar information. The Regularised Discriminant Analysis output for the acoustic, mechanical and flavour release data was: Non Error Rate Cross Validated equal to 80.1% in the cross-validation mode. The NERCV % index is an estimation of the predictive potential of the model that was validated according to the leave one out (LOO) method. The LOO procedure removes each sample from the data set, one at a time. The classification model is rebuilt and the class of the removed sample is predicted by using the obtained model. By combining these instrumental characterisation methods, the result of the texture/aroma description of these cellular solids was improved. [Copyright &y& Elsevier]

Abstract: An electronic nose and an electronic tongue, in combination with multivariate analysis, have been used to verify the geographical origin and the uniqueness of specific extra virgin olive oils. The olive oil samples belong to a small production, located in the lake of Garda (north of Italy) and distinguished with a European Protected Denomination of Origin trademark since 1998. In order to obtain a complete description of oil samples, free acidity, peroxide value, ultraviolet indices, and phenol content have been also determined. The dataset includes 36 Garda oils and 17 oils from other regions. Two classification models have been built by means of Counterpropagation Artificial Neural Networks in order to separate Garda and not-Garda oils, as follows: first, by using all the chemical variables and sensor signals; second, by using electronic tongue sensors; finally, by using four selected electronic nose sensors. All the models have been also tested with 19 commercial olive oil samples. Neural networks have provided very satisfactory results and have indicated the electronic nose as the most appropriate tool for the characterization of the analyzed oils. These results have suggested how electronic nose, in combination with neural networks, could represent a fast, cheap and functional method to classify and describe extra virgin olive oils from a circumscribed geographical area. [Copyright &y& Elsevier]