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

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Leuconostoc performance in soy-based fermentations - Survival, acidification, sugar metabolism, and flavor comparisons

Leuconostoc spp. is often regarded as the flavor producer, responsible for the production of acetoin and diacetyl in dairy cheese. In this study, we investigate seven plant-derived Leuconostoc strains, covering four species, in their potential as a lyophilized starter culture for flavor production in fermented soy-based cheese alternatives. We show that the process of lyophilization of Leuconostoc can be feasible using a soy-based lyoprotectant, with survivability up to 63% during long term storage. Furthermore, the storage in this media improves the subsequent growth in a soy-based substrate in a strain specific manner. The utilization of individual raffinose family oligosaccharides was strain dependent, with Leuconostoc pseudomesenteroides NFICC99 being the best consumer. Furthermore, we show that all investigated strains were able to produce a range of volatile flavor compounds found in dairy cheese products, as well as remove certain dairy off-flavors from the soy-based substrate like hexanal and 2-pentylfuran. Also here, NFICC99 was strain producing most cheese-related volatile flavor compounds, followed by Leuconostoc mesenteroides NFICC319. These findings provide initial insights into the development of Leuconostoc as a potential starter culture for plant-based dairy alternatives, as well as a promising approach for generation of stable, lyophilized cultures.

Comparative Analysis of Acanthopanacis Cortex and Periplocae Cortex Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis

Chinese Herbal Medicines (CHMs) can be identified by experts according to their odors. However, the identification of these medicines is subjective and requires long-term experience. The samples of Acanthopanacis Cortex and Periplocae Cortex used were dried cortexes, which are often confused in the market due to their similar appearance, but their chemical composition and odor are different. The clinical use of the two herbs is different, but the phenomenon of being confused with each other often occurs. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two species for fast and robust discrimination, in order to provide a scientific basis for avoiding confusion and misuse in the process of production, circulation and clinical use. In this study, the odor and volatile components of these two medicinal materials were detected by the E-nose and by gas chromatography-mass spectrometry (GC-MS), respectively. An E-nose combined with pattern analysis methods such as principal component analysis (PCA) and partial least squares (PLS) was used to discriminate the cortex samples. The E-nose was used to determine the odors of the samples and enable rapid differentiation of Acanthopanacis Cortex and Periplocae Cortex. GC-MS was utilized to reveal the differences between the volatile constituents of Acanthopanacis Cortex and Periplocae Cortex. In all, 82 components including 9 co-contained components were extracted by chromatographic peak integration and matching, and 24 constituents could be used as chemical markers to distinguish these two species. The E-nose detection technology is able to discriminate between Acanthopanacis Cortex and Periplocae Cortex, with GC-MS providing support to determine the material basis of the E-nose sensors' response. The proposed method is rapid, simple, eco-friendly and can successfully differentiate these two medicinal materials by their odors. It can be applied to quality control links such as online detection, and also provide reference for the establishment of other rapid detection methods. The further development and utilization of this technology is conducive to the further supervision of the quality of CHMs and the healthy development of the industry.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors' responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production - a review

Controversies surrounding the name and how the electronics nose (e-nose) works have been at the center stage since the advent of the technology. Notwithstanding the controversies, the technology has gained popularity in the sensory analysis of dairy foods, because of its rapid results delivery on product aroma profile or pattern, which can be used to assess quality. This review critically evaluated the advances made in the application of the e-nose or artificial sensory system in the dairy industry, focusing on the evaluation of milk, yoghurt and cheese properties, and the trends and prospects of the technology. Most of the e-nose devices applied in the available scientific publications used sensors such as metal oxide semiconductor sensors (MOS), metal-oxide-semiconductor field-effect transistor (MOSFET), conducting polymers composites and quartz microbalance (QMB), and flame ionization detector FID, in a recent study. Though known for aroma sensing, the technology has been applied to evaluate the shelf life or microbial spoilage and to discriminate dairy products based on the volatile profile composition, as determined by the sensors. In most cases, the limitation of the technology is the inability of it to provide information on the nature of constituting compounds, except in gas chromatography and mass spectrometry-based e-nose systems.

Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain

Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of different types of tea to provide a competitive tool in terms of time and costs to recognize and enhance the quality of the product in the food chain. Analyzed samples are representative of the three major types of tea: black, green, and white. VOCs were studied in parallel with different technologies and methods: gas chromatography coupled with mass spectrometer and solid phase microextraction (SPME-GC-MS) and a device called small sensor system, (S3). S3 is made up of tailor-made metal oxide gas sensors, whose operating principle is based on the variation of sensor resistance based on volatiloma exposure. The data obtained were processed through multivariate statistics, showing the full file of the pre-established aim. From the results obtained, it is understood how supportive an innovative technology can be, remotely controllable supported by machine learning (IoF), aimed in the future at increasing food safety along the entire production chain, as an early warning system for possible microbiological or chemical contamination.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

Nanostructured MOS Sensor for the Detection, Follow up, and Threshold Pursuing of Campylobacter Jejuni Development in Milk Samples

Food poisoning is still the first cause of hospitalization worldwide and the most common microbial agent, Campylobacter jejuni, is the most commonly reported gastrointestinal disease in humans in the EU (European Union) as is reported by the European Union One Health 2018 Zoonoses Report styled by the EFSA (European Food Safety Authority) and ECDC (European Center for Disease Prevention and Control). One of the vehicles of transmission of this disease is milk. Nanostructured MOS (Metal Oxide Semiconductor) sensors have extensively demonstrated their ability to reveal the presence and follow the development of microbial species. The main objective of this work was to find a set up for the detection and development follow up of C. jejuni in milk samples. The work was structured in two different studies, the first one was a feasibility survey and the second one was to follow up the development of the bacteria inside milk samples. The obtained results of the first study demonstrate the ability of the sensor array to differentiate the contaminated samples from the control ones. Thanks to the second study, it has been possible to find the limit of microbial safety of the contaminated milk samples.

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R² ≥ 0.9998).

Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO

Extra virgin olive oil (EVOO) is characterized by its aroma and other sensory attributes. These are determined by the geographical origin of the oil, extraction process, place of cultivation, soil, tree varieties, and storage conditions. In the present work, an array of metal oxide gas sensors (called S3), in combination with the SPME-GC-MS technique, was applied to the discrimination of different types of olive oil (phase 1) and to the identification of four varieties of Garda PDO extra virgin olive oils coming from west and east shores of Lake Garda (phase 2). The chemical analysis method involving SPME-GC-MS provided a complete volatile component of the extra virgin olive oils that was used to relate to the S3 multisensory responses. Furthermore, principal component analysis (PCA) and k-Nearest Neighbors (k-NN) analysis were carried out on the set of data acquired from the sensor array to determine the best sensors for these tasks and to assess the capability of the system to identify various olive oil samples. k-NN classification rates were found to be 94.3% and 94.7% in the two phases, respectively. These first results are encouraging and show a good capability of the S3 instrument to distinguish different oil samples.

Innovative Sensor Approach to Follow Campylobacter jejuni Development

Campylobacter spp infection affects more than 200,000 people every year in Europe and in the last four years a trend shows an increase in campylobacteriosis. The main vehicle for transmission of the bacterium is contaminated food like meat, milk, fruit and vegetables. In this study, the aim was to find characteristic volatile organic compounds (VOCs) of C. jejuni in order to detect its presence with an array of metal oxide (MOX) gas sensors. Using a starting concentration of 10³ CFU/mL, VOCs were analyzed using Gas-Chromatography Mass-Spectrometry (GC-MS) with a Solid-Phase Micro Extraction (SPME) technique at the initial time (T0) and after 20 h (T20). It has been found that a Campylobacter sample at T20 is characterized by a higher number of alcohol compounds that the one at T0 and this is due to sugar fermentation. Sensor results showed the ability of the system to follow bacteria curve growth from T0 to T20 using Principal Component Analysis (PCA). In particular, this results in a decrease of ΔR/R₀ value over time. For this reason, MOX sensors are a promising technology for the development of a rapid and sensitive system for C. jejuni.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

This work reports the recent results achieved at the SENSOR Lab, Brescia (Italy) to address the selectivity of metal oxide based gas sensors. In particular, two main strategies are being developed for this purpose: (i) investigating different sensing mechanisms featuring different response spectra that may be potentially integrated in a single device; (ii) exploiting the electronic nose (EN) approach. The former has been addressed only recently and activities are mainly focused on determining the most suitable configuration and measurements to exploit the novel mechanism. Devices suitable to exploit optical (photoluminescence), magnetic (magneto-optical Kerr effect) and surface ionization in addition to the traditional chemiresistor device are here discussed together with the sensing performance measured so far. The electronic nose is a much more consolidated technology, and results are shown concerning its suitability to respond to industrial and societal needs in the fields of food quality control and detection of microbial activity in human sweat.