Determination of three quality parameters in vegetable oils using potentiometric e-tongue

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Application of lime peel oil composite nanoemulsion to prevent toxigenic fungi in nuts

Food byproduct oils may have antimicrobial impacts when used in coating and preservation. Nuts are known to suffer from toxigenic fungi and their related mycotoxins. The present study utilized lime oil emulsion to minimize fungal infection and reduce aflatoxin B1 (AFB1). Besides, it evaluated lime oil's impact on nuts' protection against oxidation and deterioration during storage. Lime oil was extracted using hydrodistillation, and gas chromatography (GC-MS) evaluated volatile constituents. Oil was loaded into a composite emulsion of whey protein, Arabic gum, gelatin, and carboxymethyl cellulose. The antimicrobial and antifungal properties of the nut-coating emulsion were evaluated. A simulated Aspergillus flavus infection experiment evaluated composite resistance for fungal infection and AFB1 production. Oxidation and acidity changes in nuts oil composition were evaluated by proximate analysis, fatty acid composition, and induction period. The oil majority was recorded for terpenes and monoterpenes, including limonene (44.69 ± 2.11%). The emulsion was characterized by zeta potential (-21.16 ± 1.28 mV), stability (99.61 ± 0.02%), and polydispersity index (0.41 ± 0.05). Antimicrobial properties recorded a high antibacterial inhibition zone (up to 28.37 ± 0.11 mm) and anti-mycotoxigenic fungi (up to 37.61 ± 0.24 mm). For the simulated experiment, fungal growth reduction ranged between 78.02% for filmed-peanut and 84.5% for filmed-almond, while AFB1 was not detected in filmed hazelnut and almond. During the one-year storage of samples, there was a slight change in nut oil composition and oxidation progress in filmed nuts, while there was a significant change in non-filmed nuts. The result recommended lime-composite as an edible nut coating that prevents aflatoxigenic contamination, oxidation changes, and improved shelf life.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

Quantification of phosphatides in sunflower oils using a potentiometric e-tongue

Consisting of two fatty acyl groups, phospholipids are a vital part of vegetable oils and the source of essential fatty acids. Moreover, phospholipids influence oxidative and flavor stability and color evolution of vegetable oils, and their quantification has a significant role in the quality assessment of oils. In this study, we proposed a new highly efficient, affordable, environmentally friendly, and simple approach for the evaluation of phospholipid concentrations based on potentiometric multisensor systems coupled with chemometric data processing. Support vector machines, partial least squares, and multiple linear regressions were used to predict phosphatide concentrations based on potentiometric multisensor system responses. Application of multivariate regression tools yielded the following root mean square errors of prediction: 0.005 mg/100 g of oil in the range 0.0-59.4 mg/100 g for refined oils; 0.008 mg/100 g in the range 0.0-100 mg/100 g for low phosphatide oils and 0.24 mg/100 g in the range 100-2270 mg/100 g for high phosphatide oils. This approach can be considered as a rapid and straightforward method to quantify the phosphatides in sunflower oils.

E-Senses, Panel Tests and Wearable Sensors: A Teamwork for Food Quality Assessment and Prediction of Consumer’s Choices

At present, food quality is of utmost importance, not only to comply with commercial regulations, but also to meet the expectations of consumers; this aspect includes sensory features capable of triggering emotions through the citizen’s perception. To date, key parameters for food quality assessment have been sought through analytical methods alone or in combination with a panel test, but the evaluation of panelists’ reactions via psychophysiological markers is now becoming increasingly popular. As such, the present review investigates recent applications of traditional and novel methods to the specific field. These include electronic senses (e-nose, e-tongue, and e-eye), sensory analysis, and wearables for emotion recognition. Given the advantages and limitations highlighted throughout the review for each approach (both traditional and innovative ones), it was possible to conclude that a synergy between traditional and innovative approaches could be the best way to optimally manage the trade-off between the accuracy of the information and feasibility of the investigation. This evidence could help in better planning future investigations in the field of food sciences, providing more reliable, objective, and unbiased results, but it also has important implications in the field of neuromarketing related to edible compounds.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Evaluation of Olive Oil Quality with Electrochemical Sensors and Biosensors: A Review

Electrochemical sensors, sensor arrays and biosensors, alongside chemometric instruments, have progressed remarkably of late, being used on a wide scale in the qualitative and quantitative evaluation of olive oil. Olive oil is a natural product of significant importance, since it is a rich source of bioactive compounds with nutritional and therapeutic properties, and its quality is important both for consumers and for distributors. This review aims at analysing the progress reported in the literature regarding the use of devices based on electrochemical (bio)sensors to evaluate the bioactive compounds in olive oil. The main advantages and limitations of these approaches on construction technique, analysed compounds, calculus models, as well as results obtained, are discussed in view of estimation of future progress related to achieving a portable, practical and rapid miniature device for analysing the quality of virgin olive oil (VOO) at different stages in the manufacturing process.

Discrimination of Camellia seed oils extracted by supercritical CO2 using electronic tongue technology

Analytical method which combines electronic tongue technique and chemometrics analysis is developed to discriminate oil types and predict oil quality. All the studied Camellia oil samples from pressing, n-hexane extraction and supercritical CO₂ extraction (SCCE), were successfully identified by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Furthermore, multi factor linear regression model (MLRM) was established to predict oil quality, which are indicated by acid value (AV) and peroxide value (POV). The practical potential of e-tongue for the discrimination and assessment of Camellia oils has shown promising application in the characterization of Camellia oils in the oil quality evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-021-00973-1.

Advanced techniques in edible oil authentication: A systematic review and critical analysis

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

Analytical methods for determining the peroxide value of edible oils: A mini-review

Edible oils are prone to oxidation during processing and storage that may negatively affect the oil quality and human health. Determining the peroxide value (PV) of edible oils is essential because PV is one of the most typically used quality parameters to monitor lipid oxidation and control oil quality. Many approaches have been developed to determine the PV of oils. Among them, iodometric titration is the commonly used method for PV determination. Considering the limitations related to titrimetric methods, such as time and environmental concerns, several instrumental techniques have been considered as reliable alternatives. The advantages and limitations of classical titration and instrumental methods are summarized in this review. The prospects and reformative aspects for the future applications of these approaches in PV determination are also discussed.

Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration

The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty application in the meat industry. This study aimed to determine the optimal dilution level of meat extract for e-tongue evaluations and also to develop three standardized meat extraction methods. For practicality, the developed methods were applied to detect low levels of meat adulteration using beef and pork mixtures and turkey and chicken mixtures as case studies. Dilution factor of 1% w/v of liquid meat extract was determined to be the optimum for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w chicken in turkey and pork in beef with linear discriminant analysis accuracies (LDA) of 78.13% (recognition) and 64.73% (validation). Even higher LDA accuracies of 89.62% (recognition) and 68.77% (validation) were achieved for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w of pork in beef. Partial least square models could predict both sets of meat mixtures with good accuracies. Extraction by cooking was the best method for discriminating meat mixtures and can be applied for meat quality evaluations with the e-tongue.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Silicon-Based Optoelectronic Tongue for Label-Free and Nonspecific Recognition of Vegetable Oils

A special electronic tongue system based on photoelectric measurements on Si-Si/SiN X sensitive structures is reported. The sensing approach is based on measuring of minority carrier lifetime in silicon-based substrates using microwave-detected photoconductance decay. This inexpensive and environmentally friendly combinatorial electronic sensing platform is able to create characteristic electronic fingerprints of liquids, detect, and recognize them. In particular, an application of the optoelectronic tongue for recognition of vegetable oils and their mixtures is described.

Application of an electronic tongue as a single-run tool for olive oils' physicochemical and sensory simultaneous assessment

Olive oil is highly appreciated due to its nutritional and organoleptic characteristics. However, a huge compositional variation is observed between olive oils, requiring the use of diverse analytical techniques for its classification including titration, spectrophotometry and chromatography, as well as sensory analysis. Chemical analysis is usually time-consuming, expensive and require skilled technicians, while the sensorial ones are dependent upon individual subjective evaluations, even if performed by trained panellists. This work evaluated and demonstrated the feasibility of using a potentiometric electronic tongue, comprising non-specific lipid polymeric and cross-sensitive sensor membranes, coupled with chemometric tools based on different sub-sets of sensors (from 11 to 14 sensors), to predict key quality parameters of olive oils based on single-run assays. The multivariate linear models established for 23 centenarian olive trees from different cultivars allowed predicting peroxide value, oxidative stability, total phenols and tocopherols contents, CIELAB scale parameters (L*, a* and b* values), as well as 11 gustatory-retronasal positive attributes (green, sweet, bitter, pungent, tomato and tomato leaves, apple, banana, cabbage, fresh herbs and dry fruits) with satisfactory accuracy (0.90 ± 0.07 ≤ R² ≤ 0.98 ± 0.02 for the repeated K-fold-CV procedure, which ensured that 25% of the data was used for internal-validation purposes). The electronic tongue device had an accuracy statistically similar to that achieved with standard analytical techniques, pointing out the versatility of the device for the fast and simultaneous chemical and sensory analysis of olive oil.

Electrochemical Sensor-Based Devices for Assessing Bioactive Compounds in Olive Oils: A Brief Review

Electrochemical bioinspired sensor devices combined with chemometric tools have experienced great advances in the last years, being extensively used for food qualitative and quantitative evaluation, namely for olive oil analysis. Olive oil plays a key role in the Mediterranean diet, possessing unique and recognized nutritional and health properties as well as highly appreciated organoleptic characteristics. These positive attributes are mainly due to olive oil richness in bioactive compounds such as phenolic compounds. In addition, these compounds enhance their overall sensory quality, being mainly responsible for the usual olive oil pungency and bitterness. This review aims to compile and discuss the main research advances reported in the literature regarding the use of electrochemical sensor based-devices for assessing bioactive compounds in olive oil. The main advantages and limitations of these fast, accurate, bioinspired voltammetric, potentiometric and/or amperometric sensor green-approaches will be addressed, aiming to establish the future challenges for becoming a practical quality analytical tool for industrial and commercial applications.