Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC-MS analysis of volatile compounds

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Comparative Characterization of Volatile Compounds of Ningxiang Pig, Duroc and Their Crosses (Duroc × Ningxiang) by Using SPME-GC-MS

With the aim to study the flavor characteristics of Ningxiang pigs (NX), Duroc (DC) pigs, and their crosses (Duroc × Ningxiang, DN), electronic nose and gas chromatography-mass spectrometry analysis were used to detect the volatile flavor substances in NX, DC, and DN (n = 34 pigs per population). A total of 120 volatile substances were detected in the three populations, of which 18 substances were common. Aldehydes were the main volatile substances in the three populations. Further analysis revealed that tetradecanal, 2-undecenal, and nonanal were the main aldehyde substances in the three kinds of pork, and the relative content of benzaldehyde in the three populations had significant differences. The flavor substances of DN were similar to that of NX and showed certain heterosis in flavor substances. These results provide a theoretical basis for the study of flavor substances of China local pig breeds and new ideas for pig breeding.

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

Application progress of intelligent flavor sensing system in the production process of fermented foods based on the flavor properties

Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms.

Physicochemical and antioxidant activity of fruit harvested from eight jujube (Ziziphus jujuba Mill.) cultivars at different development stages

Jujube is a crop highly resistant to drought and salinity, making it one of the main fruit trees in Xinjiang. The present study evaluated the changes in the physicochemical and antioxidant activities of jujube fruit of eight different cultivars from Xinjiang, China. The developmental stages were selected according to the days after full bloom and fruit peel colour during ripening; these stages included young (S1), fruit core-hardening (S2), green ripening (S3), half-red maturity (S4) and complete red. In present study, different cultivars of jujube fruit showed similar chemical profiles, but their amounts showed great variation. HZ had the highest content of sugars, and JY had the highest content of cAMP and cGMP, while relatively higher levels of ascorbic acid, catechin, epicatechin, rutin, proanthocyanidin and antioxidant activity were found in ‘FS’ than in other cultivars, indicating that ‘FS’ could be used as a potential natural antioxidant. Regarding the development stages of jujube fruit, the moisture, ascorbic acid, total polyphenol, catechin, epicatechin, proanthocyanidin and rutin contents decreased during the development of all jujube cultivars, while the fructose, glucose, sucrose, cAMP, and cGMP contents greatly increased. The antioxidant activity determined by DPPH and ABTS radical scavenging decreased as the fruits matured. Therefore, the results suggest that green jujube (S1) could be used for natural antioxidants (catechin, epicatechin, proanthocyanidin) and that the advanced ripening stage(S5) is the proper picking period for fresh fruit and commercial processing.

Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study

Cheese aroma is known to affect consumer preference. One of the methods to measure cheese aroma is the use of an electronic nose (e-nose), which has been used to classify cheese types, production areas, and cheese ages. However, few studies have directly compared the aroma intensity scores derived from sensory evaluations with the values of metal oxide semiconductor sensors that can easily measure the aroma intensity. This pilot study aimed to investigate the relationship between sensory evaluation scores and e-nose values with respect to cheese aroma. Five types of processed cheese (two types of normal processed cheese, one type containing aged cheese, and two types containing blue cheese), and one type of natural cheese were used as samples. The sensor values obtained using the electronic nose, which measured sample aroma non-destructively, and five sensory evaluation scores related to aroma (aroma intensity before intake, during mastication, and after swallowing; taste intensity during mastication; and remaining flavor after swallowing (lasting flavor)) determined by six panelists, were compared. The e-nose values of many of the tested cheese types were significantly different, whereas the sensory scores of the one or two types of processed cheese containing blue cheese and those of the natural cheese were significantly different. Significant correlations were observed between the means of e-nose values and the medians of aroma intensity scores derived from the sensory evaluation testing before intake, during mastication, and after swallowing. In particular, the aroma intensity score during mastication was found to have a linear relationship with the e-nose values (Pearson’s R = 0.983). In conclusion, the e-nose values correlated with the sensory scores with respect to cheese aroma intensity and could be helpful in predicting them.

Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques

Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively.

Determination of volatile organic compounds in Slovak bryndza cheese by the electronic nose and the headspace solid-phase microextraction gas chromatography-mass spectrometry

The aim of the present study was to describe volatile organic compounds of the traditional Slovak bryndza cheese determined by using an electronic nose (e-nose) and a gas chromatography mass spectrometry (GCMS) with head-space solid phase microextraction (HS-SPME). For the first time, e-nose based on the gas chromatography principle with a flame ionization detector was described to identify and quantify aroma active compounds of bryndza cheese from Slovakia. The e-nose detects aroma compounds of very small concentrations in real-time of a few minutes and identifies them by comparing Kovats´ retention indices with the NIST library. Bryndza cheese produced from unpasteurized ewe´s milk and from a mixture of raw ewe´s and pasteurized cow´s types of milk were collected from 2 different Slovak farms beginning in May through to September 2019. The flavour and aroma of bryndza cheese are apparently composed of compounds contained in milk and the products of fermentation of the substrate by bacteria and fungi. Regarding volatile organic compounds, 25 compounds were detected and identified by an electronic nose with a discriminant >0.900 with ethyl acetate, isopentyl acetate, 2-butanone, acetic acid, butanoic acid, and butane-2,3-dione confirmed by gas chromatography. We confirm the suitability of the electronic nose to be used for monitoring of bryndza cheese quality.

The Impact of Different Factors on the Quality and Volatile Organic Compounds Profile in "Bryndza" Cheese

The aim of this study was to evaluate the influence of different factors on the basic physicochemical and microbiological parameters, as well as volatile organic compounds of traditionally (farm) and industrially produced "bryndza" cheese. The samples were obtained from eight producers in different areas of Slovakia during the ewe's milk production season, from May to September. The physicochemical parameters set by the legislation were monitored by reference methods. The "bryndza" cheese microbiota was determined by using the plate cultivation method. There was analysis of volatile organic compounds carried out by electronic nose, as well as gas chromatography mass spectrometry. Seasonality and production technology (traditional and industrial ones) are the main factors that affect the standard quality of "bryndza" cheese. Lactic acid bacteria were dominated from bacterial microbiota, mostly presumptive lactococci, followed presumptive lactobacilli and enterococci. The numbers of coliform bacteria were higher in traditionally produced "bryndza" cheese than in industrially produced "bryndza" cheese. The presence of Dipodascus geotrichum was detected in all samples. There were key volatile organic compounds such as ethyl acetate, isoamyl acetate, 2-butanone, hexanoic acid, D-limonene, and 2,3-butanedione. The statistically significant differences were found among "bryndza" cheese samples and these differences were connected with the type of milk and dairies.

Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques

In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R² = 0.9399 for milk fat; R² = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.

Addition of selected starter/non-starter lactic acid bacterial inoculums to stabilise PDO Pecorino Siciliano cheese production

The present study was carried out to produce Protected Denomination of Origin (PDO) Pecorino Siciliano cheese with a multi-species lactic acid bacteria (LAB) culture, composed of starter and non-starter strains in order to reduce the microbiological variability of the products derived without LAB inoculums. To this end, cheese samples produced in six factories located in five provinces (Agrigento, Catania, Enna, Palermo and Trapani) of Sicily, and previously characterised for physicochemical, microbiological and sensory aspects, have been investigated in this work for bacterial microbiome, fatty acid (FA) composition as well as volatile organic compound (VOC) profiles. Analysis of the cheese microbiomes indicated that streptococci (30.62-77.18% relative abundance) and lactobacilli (on average 25.90% relative abundance) dominated the bacterial communities of control cheeses, produced without exogenous inoculums, whereas the cheeses produced with the selected multi-strain culture saw the dominance of lactococci (in the range 6.49-14.92% relative abundance), streptococci and lactobacilli. After the addition of the selected mixed culture, Shannon index increased in all cheeses, but only the cheeses produced with the selected LAB mixed culture in the factory 2 showed Gini-Simpson diversity index (0.79) closer to the reference value (0.94) for a perfect even community. FA composition, mainly represented by saturated FA (on average 69.60% and 69.39% in control cheeses and experimental cheeses, respectively), was not affected by adding LAB culture. The presence of polyunsaturated FA ranged between 7.93 and 8.03% of FA. VOC profiles were different only for the content of butanoic acid, registered for the experimental cheeses at higher concentrations (on average 662.54 mg/kg) than control cheeses (barely 11.96 mg/kg). This study validated addition of the ad hoc starter/non-starter culture for PDO Pecorino cheese production.

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs' Compliance with CFPR Guidelines

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors’ array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

Cheeses Made from Raw and Pasteurized Cow's Milk Analysed by an Electronic Nose and an Electronic Tongue

Cheese prepared from whole milk, raw and pasteurized, were analysed by an electronic nose based on piezoelectric quartz crystals and an electronic tongue based on potentiometric sensors, immediately after their preparation and along ripening (after 7 and 21 days). Whey was also analysed by the potentiometric electronic tongue. Results obtained by the electronic nose and tongue were found to be complementary, with the electronic nose being more sensitive to differences in the milk and the electronic tongue being more sensitive to milk pasteurization. Electronic tongue was able to distinguish cheeses made from raw and pasteurized milk, both analysing the whey or the curd, with correct classification rate of 96% and 84%, respectively. Besides, the electronic nose was more sensitive than the electronic tongue to the ripening process, with large differences between samples after 7 and 21 days, while the electronic tongue was only sensitive to the initial maturation stages, with large difference between freshly prepared cheese and with seven days of maturation.

Preparación, Caracterización y Aplicación de sensores de Pd-SnO como nariz electrónica en la clasificación de vinos peruanos y su comparación con técnicas cromatográficas

Se prepararon sensores basados en SnO dopados con paladio (0, 1, 3, 5 y 7%) mediante el método  de  impregnación húmeda.  Para  caracterizarlos,  se  usaron las técnicas  de espectroscopía infrarroja con transformada de Fourier (FTIR), adsorción y desorción de N2  (BET), difracción de rayos X (XRD), microscopía electrónica de barrido (SEM) y espectroscopia de energía dispersiva de rayos X (EDX). Los sensores se evaluaron con etanol para que conformaran, junto a dos sensores  comerciales,  una  nariz  electrónica (E-nose) que  lograse  detectar  componentes volátiles  del  aroma  en  vinos  peruanos. Los  resultados  fueron  interpretados  a través  del análisis  de  componentes principales  (PCA)  con  la  finalidad  de buscar  una  técnica  que complementase la información recolectada por la cromatografía de gases (GC) y la cromatografía líquida (HPLC), y justamente comparando los PCAs obtenidos del GC y el HPLC con los resultantes de la E-nose se encontró que estos últimos clasificaban mejor las muestras. Fue posible diferenciar tanto vinos de la misma uva como de distinta cepa. Además, se logró detectar vinos adulterados, lo cual contribuye a la industria vitivinícola en el control de su producción con la finalidad de mejorar la calidad de esta bebida para el consumidor.

Application of Electronic Nose Systems on Animal-Source Food

Electronic nose, which is designed to perceive artificially the odour-active molecules in a sample headspace, has seen an increased use in the food industry as a rapid and reliable tool for quality assessment, classification, and authentication of several food items. The use of chemometrics and pattern recognition methods, together with gas sensors, emerged to be a very powerful analytical approach. In this chapter, an overview of the recent achievements in the field of electronic nose applications on animal-source food is given. Moreover, the authors deal with the recent research trends to overcome the actual sensor shortcomings, including sensor fusion techniques and their applications to evaluate animal-source foods and novel electronic nose systems.

Design of a portable electronic nose for real-fake detection of liquors

Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g., using evaporation and thermal desorption), which makes the portable design even more difficult. To realize convenient and rapid detection of liquors, we designed a portable e-nose platform that consists of hardware and software systems. The hardware system contains an evaporation/sampling module, a reaction module, a control/data acquisition and analysis module, and a power module. The software system provides a user-friendly interface and can achieve automatic sampling and data processing. This e-nose platform has been applied to the real-fake recognition of Chinese liquors. Through parameter optimization of a one-class support vector machine classifier, the error rate of the negative samples is greatly reduced, and the overall recognition accuracy is improved. The results validated the feasibility of the designed portable e-nose platform.

Discrimination of American ginseng and Asian ginseng using electronic nose and gas chromatography-mass spectrometry coupled with chemometrics

BACKGROUND

American ginseng (Panax quinquefolius L.) and Asian ginseng (Panax ginseng Meyer) products, such as slices, have a similar appearance, but they have significantly different prices, leading to widespread adulteration in the commercial market. Their aroma characteristics are attracting increasing attention and are supposed to be effective and nondestructive markers to determine adulteration.

METHODS

The aroma characteristics of American and Asian ginseng were investigated using gas chromatography-mass spectrometry(GC-MS) and an electronic nose (E-nose). Their volatile organic compounds were separated, classified, compared, and analyzed with different pattern recognition.

RESULTS

The E-nose showed a good performance in grouping with a principle component analysis explaining 94.45% of variance. A total of 69 aroma components were identified by GC-MS, with 35.6% common components and 64.6% special ingredients between the two ginsengs. It was observed that the components and the number of terpenes and alcohols were markedly different, indicating possible reasons for their difference. The results of pattern recognition confirmed that the E-nose processing result is similar to that of GC-MS. The interrelation between aroma constituents and sensors indicated that special sensors were highly related to some terpenes and alcohols. Accordingly, the contents of selected constituents were accurately predicted by corresponding sensors with most R2 reaching 90%.

CONCLUSION

Combined with advanced chemometrics, the E-nose is capable of discriminating between American and Asian ginseng in both qualitative and quantitative angles, presenting an accurate, rapid, and nondestructive reference approach.

A Novel MOS Nanowire Gas Sensor Device (S3) and GC-MS-Based Approach for the Characterization of Grated Parmigiano Reggiano Cheese

To determine the originality of a typical Italian Parmigiano Reggiano cheese, it is crucial to define and characterize its quality, ripening period, and geographical origin. Different analytical techniques have been applied aimed at studying the organoleptic and characteristic volatile organic compounds (VOCs) profile of this cheese. However, most of the classical methods are time consuming and costly. The aim of this work was to illustrate a new simple, portable, fast, reliable, non-destructive, and economic sensor device S3 based on an array of six metal oxide semiconductor nanowire gas sensors to assess and discriminate the quality ranking of grated Parmigiano Reggiano cheese samples and to identify the VOC biomarkers using a headspace SPME-GC-MS. The device could clearly differentiate cheese samples varying in quality and ripening time when the results were analyzed by multivariate statistical analysis involving principal component analysis (PCA). Similarly, the volatile constituents of Parmigiano Reggiano identified were consistent with the compounds intimated in the literature. The obtained results show the applicability of an S3 device combined with SPME-GC-MS and sensory evaluation for a fast and high-sensitivity analysis of VOCs in Parmigiano Reggiano cheese and for the quality control of this class of cheese.

Novel method for the producing area identification of Zhongning Goji berries by electronic nose

High-quality Zhongning Goji berries (ZNG) are illegally adulterated in the market by adding non-ZNG (NZNG). An accurate, rapid, and effective approach for the producing area identification of ZNG is needed to protect the geographical indications of Goji berry products and to ensure fair trade. Samples from different regions were collected and their odors were detected by an electronic nose (E-nose). Principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were employed to build identification models. The E-nose models were further verified by gas chromatography-mass spectrometry (GC-MS). The identification rates of the PCA, CA, and LDA models were 91.0%, 98.9%, and 100%, respectively. The PCA and CA models presented good results, and the LDA model showed optimum performance. These conditions indicate the feasibility of using the E-nose technique for ZNG identification. GC-MS analysis revealed differences and similarities in total ion current chromatograms between ZNG and NZNG.

Comprehensive evaluation of direct injection mass spectrometry for the quantitative profiling of volatiles in food samples

Although qualitative strategies based on direct injection mass spectrometry (DIMS) have recently emerged as an alternative for the rapid classification of food samples, the potential of these approaches in quantitative tasks has scarcely been addressed to date. In this paper, the applicability of different multivariate regression procedures to data collected by DIMS from simulated mixtures has been evaluated. The most relevant factors affecting quantitation, such as random noise, the number of calibration samples, type of validation, mixture complexity and similarity of mass spectra, were also considered and comprehensively discussed. Based on the conclusions drawn from simulated data, and as an example of application, experimental mass spectral fingerprints collected by direct thermal desorption coupled to mass spectrometry were used for the quantitation of major volatiles in Thymus zygis subsp. zygis chemotypes. The results obtained, validated with the direct thermal desorption coupled to gas chromatography-mass spectrometry method here used as a reference, show the potential of DIMS approaches for the fast and precise quantitative profiling of volatiles in foods.This article is part of the themed issue 'Quantitative mass spectrometry'.

Influence of PDO Ragusano cheese biofilm microbiota on flavour compounds formation

The objectives of the present study were to characterize the biofilm microbiota of 11 different farms (from A to K), producing PDO Ragusano cheese, and to investigate on its ability to generate volatile organic compounds (VOCs) in milk samples inoculated with biofilm and incubated under Ragusano cheese making conditions. The biofilms were subjected to plate counting and PCR/T/DGGE analysis and the VOCs generated in incubated milk samples were evaluated through SmartNose, GC/O, and GC/MS. Streptococcus thermophilus was the dominant species both in biofilms and in incubated milks. Lactobacillus, Lactococcus, Enterococcus and Leuconostoc were also identified. Low levels of Pseudomonas spp. and yeasts counts were detected, whereas coliforms, Listeria monocytogenes and Salmonella spp., were never found. SmartNose and GC/O analyses were able to differentiate incubated milk samples on the basis of the odour compounds, highlighting that samples E and F overlapped and sample C was clearly separated from the others. These results complied with those acquired by GC/MS analysis, that detected in total 20 VOCs. Principal component analysis showed positive correlations (r > 0.6; P < 0.05) between some lactic acid bacteria (LAB) and VOCs: such as Enterococcus hirae with alcohols, Lactococcus lactis, Lactobacillus plantarum, Lactobacillus casei and Lactobacillus delbrueckii with aldehydes, and Lactobacillus fermentum, Lactobacillus helveticus and Lactobacillus hilgardii with ketones. This work demonstrates that biofilm represents an excellent source of LAB biodiversity, which contribute to generate VOCs during the production of PDO Ragusano cheese.

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.

Evaluation of extra virgin olive oil stability by artificial neural network

The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power.

Discrimination of chicken seasonings and beef seasonings using electronic nose and sensory evaluation

This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes.