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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.

The potentials of Fourier transform (FT) near- (NIR) and mid-infrared (IR) spectroscopy, and electronic nose (e-nose) on varietal classification of Turkish olive oils were demonstrated. A total of 63 samples were analyzed, comprising Ayvalik, Memecik, and Erkence oils. Spectra were pretreated with standard normal variate and second derivative. Classification models were built with orthogonal partial least square-discriminant analysis (OPLS-DA), considering the single data sets and also the combined FT-NIR-IR spectra. OPLS-DA models were validated both by cross validation and external prediction. All the models gave good results, being the average correct classification percentages in prediction higher than 90% for spectroscopic data and equal to 82% for e-nose data. The combined FT-NIR-IR data set gave the best results in terms of coefficients of determination (0.95 and 0.67). Different e-nose sensors discriminated Ayvalik, Memecik, and Erkence oils, explaining their distinct aromatic profiles., Turkish Scholarship for International Students (YTB)

Evaluating the possibility of extending shelf life of rice germ (a by-product of rice milling process) by reducing water activity in combination with storage atmosphere packaging, without any heat treatment, is the aim of the present study. Samples at different water activities (0.55, 0.45 and 0.36) were packed in air, argon or under vacuum, and stored at 27 °C for 150 days. To the aim, a non-targeted approach was applied by means of an FT-NIR spectrometer in reflectance with a rotating sample holder and a portable electronic nose, equipped with 10 non-specific sensors. For understanding the impact of the factors under study on the rice germ shelf life, a modified mid-level data fusion approach was applied to enhance the information most correlated with time. Moreover, Principal Component Analysis was applied on fused data to follow samples evolution during storage and identify different clusters according to the storage conditions. The rice germ case study allowed to better understand the information captured by the non-specific sensors: a 2D correlation map was developed combining the e-nose data with the NIR spectral information, highlighting relationships among NIR absorption bands and classes of chemical compounds inducing e-nose responses. A data fusion approach highlighted the importance of water activity on rice germ storage, while no interesting differences were ascribable to storage atmosphere packaging systems. In terms of correlation, the sensors could be divided in two groups, negatively inter-correlated: sensors ascribable to aromatic compounds (WC) and correlated with the NIR band around 4800–4900 cm−1 (N–H bending of primary amides, typical for peptides coming from protein hydrolysis); broad-range response sensors (WS), linked with the NIR band at 5128 cm−1 (second overtone of C O stretching of esters).

Electronic senses were applied in order to evaluate the effect of brewing temperature on the sensorial properties of espresso coffees (ECs) produced by a bar machine of the latest generation able to work with constant, increasing and decreasing water temperature profiles. The obtained ECs were analyzed by e-nose, e-tongue and e-eye to depict their aroma and taste fingerprint and to evaluate the visual characteristics of foam. Physicochemical analyses were carried out to determine the extraction rate of typical EC components and to evaluate their antioxidant activity. The electronic devices coupled with multivariate statistical analysis demonstrated a good ability to discriminate and characterize coffee samples on the basis of their sensorial properties in relation to the brewing temperature. According to these results, electronic senses can be applied to assess the influence of the percolation parameters on the sensory attributes of ECs, thus resulting useful tools for the optimization of processing conditions.

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&rsquo, 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.

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.

In recent decades, there has been a substantial increase in the consumption of fruits and vegetables due to their nutritional properties since they are known as sources of vitamins, minerals, fiber, and antioxidants. Moreover, a substantial growth in fresh-cut fruits and vegetables has been noticed because of their ease to use; in fact changes in human life styles have led consumers to move towards ready-to-eat products. In this context, product quality must be preserved at each step of product handling, processing, and storage, and therefore rapid methods should be available to provide useful information in process management. In this review an overview of the applications of widely used non-destructive techniques, namely, electronic nose and visible/near infrared spectroscopy, for measuring quality of fruits and vegetables is presented. A brief description of spectroscopic and electronic devices and a selection of applications are provided. Future perspectives about the simplification/application of these non-destructive techniques are finally explored.

BACKGROUND The aim of this study is to investigate the feasibility of a ‘holistic’ approach, using near infrared (NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica. RESULTS Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively. CONCLUSION NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples. © 2014 Society of Chemical Industry

The objectives of the present study were to determine the effects of the brewing method on the amount of the major catechins, methylxanthines, total polyphenols and antioxidant capacity in green and black teas, and to correlate the chemical composition of tea infusions to their sensorial properties by the combined use of the electronic nose and tongue. For this purpose, tea infusions were prepared from 7 green teas and 6 black teas using different extraction times (3 - 5 and 10 min). The extraction of total polyphenols and catechins was increased by prolonging the infusion time from 3 - 5 to 10 min, while the caffeine amounts did not increase. The antioxidant activity of teas was well correlated with polyphenol content. Electronic tongue and electronic nose were able to discriminate green teas from black teas on the basis of their sensorial properties. Considering the taste, green teas were characterized by astringency and aftertaste-astringency and this sensation was increased by prolonging the infusion time; black teas were perceived as more bitter, sour, salty and the extraction time affected the astringent sensation. The aroma of green and black teas was discriminated by specific sensors and the increase of the extraction time produced more richly flavoured infusions. This work adds information about correlation between sensorial properties, antioxidant capacity and chemical composition of green and black teas.

Alcoholic fermentation is a complex biochemical process during which yeasts convert sugars to ethanol, carbon dioxide, and other metabolic byproducts that contribute to the chemical composition and sensorial properties of the fermented foodstuffs. Alcoholic fermentation is the basis for the manufacturing of alcoholic beverages such as wine and beer. Control of fermentation is generally considered as a prerequisite to determine the quality of the final product. In this context, fermentation monitoring is a growing need, which calls for fast, low-cost, and nondestructive methods providing real-time or online information in order to assure an effective control at all stages of the process. In this chapter, after a brief description of the main aspects of the alcoholic fermentation, an overview on the applicability of the electronic nose and the electronic tongue to the alcoholic fermentation monitoring has been presented and discussed.

Effective fermentation monitoring is a growing need due to the rapid pace of change in the wine industry, which calls for fast methods providing real time information in order to assure the quality of the final product. The objective of this work is to investigate the potential of non-destructive techniques associated with chemometric data analysis, to monitor time-related changes that occur during red wine fermentation. Eight micro-fermentation trials conducted in the Valtellina region (Northern Italy) during the 2009 vintage, were monitored by a FT-NIR and a FT-IR spectrometer and by an electronic nose and tongue. The spectroscopic technique was used to investigate molecular changes, while electronic nose and electronic tongue evaluated the evolution of the aroma and taste profile during the must-wine fermentation. Must-wine samples were also analysed by traditional chemical methods in order to determine sugars (glucose and fructose) consumption and alcohol (ethanol and glycerol) production. Principal Component Analysis was applied to spectral, electronic nose and electronic tongue data, as an exploratory tool, to uncover molecular, aroma and taste modifications during the fermentation process. Furthermore, the chemical data and the PC1 scores from spectral, electronic nose and electronic tongue data were modelled as a function of time to identify critical points during fermentation. The results showed that NIR and MIR spectroscopies are useful to investigate molecular changes involved in wine fermentation while electronic nose and electronic tongue can be applied to detect the evolution of taste and aroma profile. Moreover, as demonstrated through the modeling of NIR, MIR, electronic nose and electronic tongue data, these non destructive methods are suitable for the monitoring of must-wine fermentation giving crucial information about the quality of the final product in agreement with chemical parameters. Although in this study the measurements were carried out in off-line mode, in future these non destructive techniques could be valid and simple tools, able to provide in-time information about the fermentation process and to assure the quality of wine. (C) 2011 Elsevier B.V. All rights reserved.

Perilla frutescens is an annual plant widely used in therapeutics as well as in food preparations in Asian countries. Despite its interesting properties this plant is unknown to Western people. The aim of the present study was to describe the sensory characteristics of Perilla frutescens by means of sensory evaluation. Instrumental sensory devices (electronic nose and tongue) were used to discriminate different cultivars of this plant. The relationship between the measures obtained through human senses and instrumental analysis was also investigated. Results evidenced marked differences among cultivars tested. The Korean variety of Perilla was perceived as the least bitter and was associated to a high intensity of Cooling sensation. The crisp green-leaved Perilla was the most odourous sample being described by high intensity of Grassy and Floral odour and, accordingly, by electronic nose sensors, while the crisp red-leaved Perilla was perceived as the least astringent and pungent. Sensory diversity might be explained by the different chemical composition of Perilla chemotypes.

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.

In the present work, innovative analytical techniques, such as an amperometric electronic tongue and a commercial electronic nose were used, together with spectrophotometric methods, to predict sensorial descriptors of Italian red dry wines of different denominations of origin. Genetic Algorithms were employed to select variables and build predictive regression models. On the selected models, an accurate validation technique (the Bootstrap procedure) and a procedure for the detection of outliers (Williams plot) were applied. The results obtained demonstrate the possibility of using these innovative techniques in order to describe and predict a large part of the selected sensorial information. It was not possible to build an acceptable regression model for only one descriptor, sourness. The proposed analytical methods have the advantage of being rapid and objective; furthermore, the statistical methods applied could be considered a rational operative procedure for building regression models with real predictive capability. (c) 2005 Elsevier Ltd. All rights reserved.

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.

The shelf life of Crescenza, a traditional Italian soft cheese, was measured using classical analysis and a commercial electronic nose. Two lots of samples directly supplied by a manufacturer at the beginning of their commercial life were stored at 2 constant temperatures (8 and 15 degrees C) and analyzed until their respective expiration dates. Among the physicochemical parameters, pH, acidity, hue, and apparent yield rheological index appeared to be the best predictors of the quality decay. Changes in these indices were described with a sigmoidal transition function allowing definition of a loose and a severe shelf-life protocol, based on the trend of first and second time derivatives. A time range of 1 to 3 d at 15 degrees C and 4 to 8 d at 8 degrees C was accordingly assessed to maintain the freshness of Crescenza cheese. The quality decay of cheese aroma was evaluated by inspecting the headspace fingerprint of the same set of samples using the electronic nose. Sample classification through the aroma fingerprint confirmed the predicted shelf-life time ranges. A clear discrimination between "fresh," "aged," and "very aged" samples was obtained using principal components analysis, cluster analysis, and linear discriminant analysis statistical techniques. The predictive ability of the linear discriminant analysis classification model was confirmed by considering a new set of cheese samples purchased at the beginning of their commercial life from a local market and analyzed until their expiration date.

An electronic tongue based on amperometric detection in a flow injection system (FIA) and a commercial electronic nose have been used to characterize and classify four types of Barbera wines having different denominations of origin and produced in northern Italy in enclosed geographical areas. E-nose and e-tongue data were compared and elaborated together with those of chemical analysis and colour evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA), linear discriminant analysis (LDA) and classification and regression trees (CART) analysis. In the classification of Barbera wines the best results were obtained from LDA applied to electronic nose and electronic tongue data, this analysis giving 100% correct assignation and 98.1% correct prediction.

The aim of this study is to investigate the feasibility of a 'holistic' approach, using near infrared (NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica.Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively.NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples.

This study aimed at characterizing the sensory quality of Italian PDO dry-cured Parma, San Daniele and Toscano hams, applying a multi-disciplinary approach. Ham sensory profile as well as physico-chemical, aromatic, morphological and textural characteristics was investigated. There was a great difference between Toscano ham and Parma and San Daniele hams, which were more similar even though differentiated. Toscano ham showed higher scores for pork-meat odor, saltiness, dryness, fibrousness and hardness; accordingly, this ham was described by a high NaCl content and by high values of instrumental hardness, cohesiveness, gumminess and chewiness. Parma ham was characterized by a cured flavor, whereas San Daniele ham showed a wider fatty area and higher pH values. Parma and San Daniele hams were also described by higher values of sweetness, RGB color values and water activity. Sensory characteristics evaluated by trained assessors were correlated to instrumental measures, indicating that instrumental devices can be effectively applied for dry-cured ham characterization.

The aim of this work was to investigate the applicability of non-destructive techniques in monitoring freshness decay of fresh-cut Valerianella locusta L. during storage at different temperature. The sampling was performed for 15 days for Valerianella samples preserved at 4 and 10 °C, and for 7 days for samples stored at 20 °C. The quality decay of samples was evaluated by quality parameters (pH, water content, total phenols, chlorophyll a fluorescence) and by non-destructive systems (electronic nose and visible-near infrared spectroscopy). Cluster Analysis (CA) was performed on quality indices and four clusters were identified, namely "fresh", "acceptable", "spoiled" and "very spoiled". Principal Component Analysis (PCA) was applied on the electronic nose data in order to evaluate the feasibility of this technique as a rapid and non-destructive approach for monitoring the freshness of fresh-cut Valerianella during storage. Linear Discriminant Analysis (LDA) and PLS-discriminant analysis (PLS-DA) models were developed to test the performance of electronic nose and VIS-NIR, respectively, to classify samples in the four classes of freshness. The average value of samples correctly classified using LDA was 95.5% and the cross validation error rate was equal to 8.7%. The results obtained from PLS-DA models, in validation, gave a positive predictive value (PPV) of classification between 74% and 96%. Finally, predictive models were performed using Partial Least Squares (PLS) regression analysis between quality indices and VIS-NIR data. RPD values3 were obtained for water content and pH. Excellent results were obtained for total phenols with Rcv(2) and RPD equal to 0.89 and 3.19, and for chlorophyll a fluorescence with Rcv(2) and RPD equal to 0.92 and 3.22, respectively. Results demonstrated that electronic nose and VIS-NIR are complementary techniques able to support the conventional techniques in the shelf-life assessment of fresh-cut V. locusta L. providing information useful for a better management of the product along the distribution chain.

Publisher Summary It is possible to differentiate and classify extra virgin olive oil (EVOOs) from different geographical areas by using a commercial electronic nose and by applying multidimensional chemometric techniques. There is a growing emphasis and consensus that intelligent sensor arrays or electronic noses are most effective in the quality control of raw and manufactured products—for example, for determination of food freshness and maturity monitoring, shelf-life investigations, authenticity assessments of products and even microbial pathogen detection and environmental control. This application area is particularly important because the e-nose can be trained to recognize hazardous chemicals as well as odors. Furthermore, with respect to the human nose, the e-nose does not fatigue as easily, is less costly, and can travel easily into outer space. It also holds the promise of being much cheaper, smaller and easier to use and maintain than a mass spectrometer.

Publisher Summary Electronic nose and electronic tongue are particularly suitable for carrying out rapid and objective sensory measurements, which are important in food industries. They are the common electrochemical sensor systems responding to flavor/odor (volatiles) or taste (solubles), using an array of simple and non-specific sensors and a pattern recognition software system. The key principle involved in the electronic nose concept is the transfer of the total headspace of a sample to a sensor array that detects the presence of volatile compounds in the headspace and a pattern of signals is provided that are dependent on the selectivity and sensitivity of sensors and the characteristics of the volatile compounds in the headspace. Contrary to traditional analytical methods, the electrochemical sensor responses do not need and do not provide information on the nature of the compounds under investigation, but only on digital fingerprint of the typical food products. Results illustrated in this chapter, it is clear that the sensor responses of the electronic nose can be used to define the threshold of the shelf-life of Crescenza samples, honey and wine stored at different temperatures.