Quartz Crystal Microbalance (QCM) sensor arrays selection for olive oil sensory evaluation

Quantification of Enzymatic Biofilm Removal Using the Sauerbrey Equation: Application to the Case of Pseudomonas protegens

A methodology for the quantitative analysis of enzymatic removal of biofilms (BF) was developed, based on a quartz crystal microbalance (QCM) under stationary conditions. This was applied to the case of Pseudomonas protegens (PP) BFs, through a series of five enzymes, whose removal activity was screened using the presented methodology. The procedure is based on the following: when BFs can be modeled as rigid materials, QCM can be used as a balance under stationary conditions for determining the BFs mass reduction by enzymatic removal. For considering a BF as a rigid model, energy dissipation effects, associated with viscoelastic properties of the BF, must be negligible. Hence, a QCM system with detection of dissipation (referred to as QCM with dissipation) was used for evaluating the energy losses, which, in fact, resulted in negligible energy losses in the case of dehydrated PP BFs, validating the application of the Sauerbrey equation for the change of mass calculations. The stationary methodology reduces operating times and simplifies data analysis in comparison to dynamic approaches based on flow setups, which requires the incorporation of dissipation effects due to the liquid media. By carrying out QCM, glycosidase-type enzymes showed BF removal higher than 80% at enzyme concentration 50 ppm, reaching removal over 90% in the cases of amylase and cellulase/xylanase enzymes. The highest removal percentage produced a reduction from about 15 to 1 μg in the BF mass. Amylase enzyme was tested from below 50 to 1 ppm, reaching around 60% of removal at 1 ppm. The obtained results were supported by other instrumental techniques such as Raman spectroscopy, attenuated total reflection Fourier transform infrared spectroscopy, atomic force microscopy, high performance anion exchange chromatography, thermogravimetric analysis, and differential scanning calorimetry. The removal quantifications obtained with QCM were compared with those obtained by well-established screening techniques (UV-vis spectrophotometry using crystal violet and agar diffusion test). The proposed methodology expands the possibility of using a quartz microbalance to perform enzymatic activity screening.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Novel monkey-wrench-shaped microstrip patch sensor for food evaluation and analysis

BACKGROUND

Microwave sensor technology is considered to be a non-destructive and hygienic means for food evaluation and analysis. The rapid progress in microwave sensor technologies motivated us to present a novel monkey-wrench-shaped microstrip patch sensor for evaluating food quality. The proposed antenna is considered as a liquid sensor to detect adulteration in liquids by examining the relationship between concentration, shift in resonant frequency, and variation in reflection coefficient.

RESULTS

The sensor has a compact size of 17 × 14 mm² imprinted on an FR4 substrate of thickness 1.57 mm. This microwave system is proposed for monitoring overall milk quality using a microstrip sensor and has considerably good numerical sensitivity and accuracy (13.11% and 88.5% respectively), which makes the system attractive for detecting adulteration. Further, the Q-factor for the proposed sensor is 209 and has a standard deviation less than the difference between non-adulterated and adulterated values, giving resolution high enough to distinguish adulteration with an acceptable statistical accuracy. In this study, different milk categories (buffalo, goat, and cow milk) are considered as liquid samples to detect adulteration by demonstrating the variation in reflection coefficient due to change in dielectric properties for the following different cases: (i) adulteration of milk with water; (ii) adulteration of milk with synthetic milk powder; (iii) adulteration of milk with caustic soda; and (iv) adulteration of milk with vegetable oil. The referred variation in reflection coefficient and resonant frequency is due to a change in the dielectric properties of a liquid when a varying concentration of an adulterant/solute is added to the liquid sample.

CONCLUSION

The simulation and measured results show good agreement that validates the proposed sensor for food adulteration detection with high sensitivity. In terms of performance, the proposed sensor shows accuracy with high spatial resolution and reduced penetration depth to detect the adulteration in various milk samples (i.e. buffalo, goat, and cow milk). © 2021 Society of Chemical Industry.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array

We devised a low-cost mobile electronic nose (e-nose) system using a quartz crystal microbalance (QCM) sensor array functionalized with various polymer-based thin active films (i.e., polyacrylonitrile, poly(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate)). It works based on the gravimetric detection principle, where the additional mass of the adsorbed molecules on the polymer surface can induce QCM resonance frequency shifts. To collect and process the obtained sensing data sets, a multichannel data acquisition (DAQ) circuitry was developed and calibrated using a function generator resulting in a device frequency resolution of 0.5 Hz. Four prepared QCM sensors demonstrated various sensitivity levels with high reproducibility and consistency under exposure to seven different volatile organic compounds (VOCs). Moreover, two types of machine learning algorithms (i.e., linear discriminant analysis and support vector machine models) were employed to differentiate and classify those tested analytes, in which classification accuracies of up to 98 and 99% could be obtained, respectively. This high-performance e-nose system is expected to be used as a versatile sensing platform for performing reliable qualitative and quantitative analyses in complex gaseous mixtures containing numerous VOCs for early disease diagnosis and environmental quality monitoring.

Olive oil authentication: A comparative analysis of regulatory frameworks with especial emphasis on quality and authenticity indices, and recent analytical techniques developed for their assessment. A review

Over the last decades, olive oil quality and authenticity control has become an issue of great importance to consumers, suppliers, retailers, and regulators in both traditional and emerging olive oil producing countries, mainly due to the increasing worldwide popularity and the trade globalization of this product. Thus, in order to ensure olive oil authentication, various national and international laws and regulations have been adopted, although some of them are actually causing an enormous debate about the risk that they can represent for the harmonization of international olive oil trade standards. Within this context, this review was designed to provide a critical overview and comparative analysis of selected regulatory frameworks for olive oil authentication, with special emphasis on the quality and purity criteria considered by these regulation systems, their thresholds and the analytical methods employed for monitoring them. To complete the general overview, recent analytical advances to overcome drawbacks and limitations of the official methods to evaluate olive oil quality and to determine possible adulterations were reviewed. Furthermore, the latest trends on analytical approaches to assess the olive oil geographical and varietal origin traceability were also examined.

Electronic noses: Powerful tools in meat quality assessment

Main factors that are considered by consumers when choosing meat products are colour and aroma, of which the latter is a more reliable indicator of quality. However, a simple sensory evaluation of hedonistic qualities is often not sufficient to determine whether protein is past its shelf life, and consumption of spoiled meat can lead to serious health hazards. Some volatile compounds can be used as spoilage indicators, and so a device equipped with a sensor sensitive to particular odorants would prove useful. Unfortunately, no such single compound has yet been identified, as the changes taking place in a sample of meat during storage are contingent on numerous factors. On the other hand, a combination of volatile compounds may form a unique 'fingerprint' which can be analysed pattern recognition algorithms with an electronic nose. It can supplement established techniques of meat quality assessment by providing results that correlate well with hedonic perception in a short time and at a low cost.

Use of array of conducting polymers for differentiation of coconut oil products

An array of chemiresistors based on conducting polymers was assembled for the differentiation of coconut oil products. The chemiresistor sensors were fabricated through the potentiostatic electrodeposition of polyaniline (PANi), polypyrrole (PPy) and poly(3-methylthiophene) (P-3MTp) on the gap separating two planar gold electrodes set on a Teflon substrate. The change in electrical resistance of the sensors was measured and observed after exposing the array to the headspace of oil samples. The sensor response was found rapid, reversible and reproducible. Different signals were obtained for each coconut oil sample and pattern recognition techniques were employed for the analysis of the data. The developed system was able to distinguish virgin coconut oil (VCO) from refined, bleached & deodorised coconut oil (RBDCO), flavoured VCO, homemade VCO, and rancid VCO.

Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality

A wide variety of olive oil samples from different origins and olive types has been chemically analyzed as well as evaluated by trained sensory panelists. Six chemical parameters have been obtained for each sample (free fatty acids, peroxide value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) and linked to their quality using an artificial neural network-based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality. Two different methods were defined to assess the statistical performance of the model (a K-fold cross-validation (K = 6) and three different blind tests), and both of them showed around a 95-96% correct classification rate. These results support that a relationship between the chemical and the sensory analyses exists and that the mathematical tool can potentially be implemented into a device that could be employed for various useful applications.

Advances in artificial olfaction: sensors and applications

The artificial olfaction, based on electronic systems (electronic noses), includes three basic functions that operate on an odorant: a sample handler, an array of gas sensors, and a signal-processing method. The response of these artificial systems can be the identity of the odorant, an estimate concentration of the odorant, or characteristic properties of the odour as might be perceived by a human. These electronic noses are bio inspired instruments that mimic the sense of smell. The complexity of most odorants makes characterisation difficult with conventional analysis techniques, such as gas chromatography. Sensory analysis by a panel of experts is a costly process since it requires trained people who can work for only relatively short periods of time. The electronic noses are easy to build, provide short analysis times, in real time and on-line, and show high sensitivity and selectivity to the tested odorants. These systems are non-destructive techniques used to characterise odorants in diverse applications linked with the quality of life such as: control of foods, environmental quality, citizen security or clinical diagnostics. However, there is much research still to be done especially with regard to new materials and sensors technology, data processing, interpretation and validation of results. This work examines the main features of modern electronic noses and their most important applications in the environmental, and security fields. The above mentioned main components of an electronic nose (sample handling system, more advanced materials and methods for sensing, and data processing system) are described. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in the different applications are also mentioned.