Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications

Assessment of fruity aroma intensity in olive oils from different Spanish regions using a portable electronic nose

BACKGROUND

The organoleptic profile of an olive oil is a fundamental quality parameter obtained by human sensory panels. In this work, a portable electronic nose was employed to predict the fruity aroma intensity of 199 olive oil samples from different Spanish regions and cultivar varieties ('Picual', 'Arbequina', and 'Cornicabra'), with special emphasis in testing the robustness of the predictions versus cultivar variety variability. The primary data given by the electronic nose were used to obtain two different feature vectors that were employed to fit ridge and lasso regressions models to two datasets: one consisting of all the samples and another just the cv. Picual samples.

RESULTS

The results obtained showed mean average error (MAE) values below 0.88 in all cases, with an MAE of 0.67 for the 'Picual' model. These MAE values and the similarities in the model parameters fitted for the different data folds are in agreement with the results obtained in previous studies.

CONCLUSION

The large number of samples analyzed and the results obtained show the robustness of the approach and the applicability of the methods. Also, the results suggest that better performance can be obtained when specific models are fitted for particular cultivars. Overall, the proposed methods are capable of providing useful information for a fast screening of the fruity aroma intensity of olive oils. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Formation of volatile compounds in salt-mediated naturally fermented cassava

Cassava is a starchy staple typically consumed in tropical countries; however, its high moisture content renders it susceptible to post-harvest deterioration. Fermentation has been used to improve shelf-life, functional properties, nutrient bioavailability, minimize toxic compounds, and alter aroma. In this study, the effect of added salt (5-25 %) on the pH, titratable acidity (TTA), and volatile compounds (VOCs) in cassava fermented was investigated. A sharp reduction in pH from 6.98 to 6.20 to 4.81-4.00 and concomitant increase in TTA (0.027-0.297 %) was observed in all the samples on day 2 except the 25 % added salt ferments. The 32 VOCs quantitated on day 50 by headspace solid-phase microextraction (HS-SPME) arrow coupled with gas chromatography-mass spectrometry (GC-MS) and classified as: alcohol (9), aldehydes (6), ketones (5), carboxylic acids (5), esters (3), nitriles (2), phenol (1) and hydrocarbon (1) were affected by the amount of added salt. PCA explained 68.50 % of the variance and cluster samples based on the similarities between the identified VOCs and showed that fermentation mediated by 15 % added salt presented a VOCs profile comparable to using 20 % of salt, with the former representing a lower cost. The addition of salt can be used to control acidification, adopted as an effective preservation technique, and mediate VOCs production during cassava fermentation.

Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry

Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.

Effect of microbial fermentation on functional traits and volatiloma profile of pâté olive cake

In this study, the pâté olive cake (POC), a by-product of the olive oil industry, was subjected to fermentation in a bioreactor using three microbial strains, Lactiplantibacillus plantarum, Wickerhamomyces anomalus and Candida boidinii, previously isolated from fermented table olive brines. Chemical, microbiological and molecular analyses were carried out at the beginning and at the end of fermentation. The lowest pH value (4.09) was reached after 10 days in sample inoculated with C. boidinii. Microbiological analyses exhibited the dominance of yeasts throughout the whole process (from 5.5 to 7.80 Log10 CFU/g), as confirmed by PCR-DGGE analysis. The microbial cultures affected both phenolic and volatile organic compound profiles. Moreover, the POC samples treated with different microbial strains were investigated for biological assays. The sample fermented with W. anomalus showed the greatest diffusion speed of transepithelial transport through Caco-2 cell, the highest inhibitory activity towards the tested cyclooxygenases and the highest antioxidant activity.

Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends

An electronic nose (e-nose) is a device designed to recognize and classify odors. The equipment is built around a series of sensors that detect the presence of odors, especially volatile organic compounds (VOCs), and generate an electric signal (voltage), known as e-nose data, which contains chemical information. In the food business, the use of e-noses for analyses and quality control of fruits and plantation crops has increased in recent years. Their use is particularly relevant due to the lack of non-invasive and inexpensive methods to detect VOCs in crops. However, the majority of reports in the literature involve commercial e-noses, with only a few studies addressing low-cost e-nose (LC-e-nose) devices or providing a data-oriented description to assist researchers in choosing their setup and appropriate statistical methods to analyze crop data. Therefore, the objective of this study is to discuss the hardware of the two most common e-nose sensors: electrochemical (EC) sensors and metal oxide sensors (MOSs), as well as a critical review of the literature reporting MOS-based low-cost e-nose devices used for investigating plantations and fruit crops, including the main features of such devices. Miniaturization of equipment from lab-scale to portable and convenient gear, allowing producers to take it into the field, as shown in many appraised systems, is one of the future advancements in this area. By utilizing the low-cost designs provided in this review, researchers can develop their own devices based on practical demands such as quality control and compare results with those reported in the literature. Overall, this review thoroughly discusses the applications of low-cost e-noses based on MOSs for fruits, tea, and coffee, as well as the key features of their equipment (i.e., advantages and disadvantages) based on their technical parameters (i.e., electronic and physical parts). As a final remark, LC-e-nose technology deserves significant attention as it has the potential to be a valuable quality control tool for emerging countries.

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.

Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis

Sourdough can improve bakery products' shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.

E-Nose Discrimination of Almond Oils Extracted from Roasted Kernels

Almonds contain around 50% fat with a health-promoting fatty acid profile that can be extracted by pressing to obtain high-quality oils. To improve oil sensory properties, the almonds can be subjected to roasting treatments before oil extraction. However, intense thermal treatments may cause the appearance of undesirable volatile compounds causing unpleasant aromas. Thus, oils from almonds subjected to different roasting treatments (30, 45, 60 and 90 min at 150 °C) were analyzed from sensory and the chemical points of view. In addition, an electronic device (E-nose) was used in order to evaluate its usefulness in discriminating samples according to their aromas. The almonds’ roasting treatments caused changes in the sensory properties, since defects such as a burned, dry smell or wood fragrance appeared when almonds were subjected to roasting treatments (>45 min). These data agree with the analysis of volatile compounds, which showed an increase in the content of aldehyde and aromatic groups in roasted almonds oils while alcohols and terpenes decreased. Partial least squares discriminant analysis and partial least squares obtained from the E-nose were able to classify samples (97.5% success) and quantify the burned defect of the oils (Rp2 of 0.88), showing that the E-nose can be an effective tool for classifying oils.

Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor

Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated with a MOS gas sensor reduced this problem and is a promising solution for applications requiring the selective detection of volatile compounds. Nevertheless, the interpretation of the sensor output signals, which take the form of complex, unique patterns, is difficult and requires advanced signal processing techniques. The study focuses on the development of a methodology to measure and process the output signal of a thermally modulated MOS gas sensor based on a B-spline curve and artificial neural networks (ANNs), which enable the quantitative analysis of volatile components (ethanol and acetone) coexisting in mixtures. B-spline approximation applied in the first stage allowed for the extraction of relevant information from the gas sensor output voltage and reduced the size of the measurement dataset while maintaining the most vital features contained in it. Then, the determined parameters of the curve were used as the input vector for the ANN model based on the multilayer perceptron structure. The results show great usefulness of the combination of B-spline and ANN modeling techniques to improve response selectivity of a thermally modulated MOS gas sensor.

Conversion of Carbon Dioxide into Chemical Vapor Deposited Graphene with Controllable Number of Layers via Hydrogen Plasma Pre-Treatment

In this work, we report the conversion of carbon dioxide (CO₂) gas into graphene on copper foil by using a thermal chemical vapor deposition (CVD) method assisted by hydrogen (H₂) plasma pre-treatment. The synthesized graphene has been characterized by Raman spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The results show the controllable number of layers (two to six layers) of high-quality graphene by adjusting H₂ plasma pre-treatment powers (100-400 W). The number of layers is reduced with increasing H₂ plasma pre-treatment powers due to the direct modification of metal catalyst surfaces. Bilayer graphene can be well grown with H₂ plasma pre-treatment powers of 400 W while few-layer graphene has been successfully formed under H₂ plasma pre-treatment powers ranging from 100 to 300 W. The formation mechanism is highlighted.