Monitoring of quality and storage time of unsealed pasteurized milk by voltammetric electronic tongue

Analysis of milk adulteration by means of a potentiometric electronic tongue

The adulteration of milk presents significant challenges in the food industry, promoting the need for efficient detection methods. This study introduces a potentiometric electronic tongue for rapid and accurate milk adulteration detection. Utilizing polymeric membranes integrated with various additives, the electronic tongue distinguishes between different milk types and detects common adulterants. Experimental results demonstrate its effectiveness in discriminating raw, pasteurized, and medicated cow milk, as well as goat milk. Moreover, it successfully identifies adulterants like water and bovine milk in goat milk samples. Chemometric analyses, including Principal Component Analysis and Partial Least Squares regression, correlate sensor responses with traditional milk parameters such as fat, protein, and lactose content with up to a 0.97 R2 on the validation step. Strong correlations validate the electronic tongue's potential for rapid milk quality assessment. This innovative approach offers a cost-effective, reliable solution for detecting milk adulteration in contrast with current techniques that require numerous, time consuming experiments.

Multidimensional calibration spaces in Staphylococcus Aureus detection using chitosan-based genosensors and electronic tongue

Mastitis diagnosis can be made by detecting Staphylococcus aureus (S. aureus), which requires high sensitivity and selectivity. Here, we report on microfluidic genosensors and electronic tongues to detect S. aureus DNA using impedance spectroscopy with data analysis employing visual analytics and machine learning techniques. The genosensors were made with layer-by-layer films containing either 10 bilayers of chitosan/chondroitin sulfate or 8 bilayers of chitosan/sericin functionalized with an active layer of cpDNA S. aureus. The specific interactions leading to hybridization in these genosensors allowed for a low limit of detection of 5.90 × 10-19 mol/L. The electronic tongue had four sensing units made with 6-bilayer chitosan/chondroitin sulfate films, 10-bilayer chitosan/chondroitin sulfate, 8-bilayer chitosan/sericin, and 8-bilayer chitosan/gold nanoparticles modified with sericin. Despite the absence of specific interactions, various concentrations of DNA S. aureus could be distinguished when the impedance data were plotted using a dimensionality reduction technique. Selectivity of S. aureus DNA was confirmed using multidimensional calibration spaces, based on machine learning, with accuracy up to 89 % for the genosensors and 66 % for the electronic tongue. Hence, with these computational methods one may opt for the more expensive genosensors or the simpler and cheaper electronic tongue, depending on the sensitivity level required to diagnose mastitis.

Microbiological Risk Assessment of Ready-to-Eat Leafy Green Salads via a Novel Electrochemical Sensor

Nowadays, the growing interest in a healthy lifestyle, to compensate for modern stressful habits, has led to an increased demand for wholesome products with quick preparation times. Fresh and ready-to-eat leafy green vegetables are generally perceived as salutary and safe, although they have been recognized as a source of food poisoning outbreaks worldwide. The reason is that these products retain much of their indigenous microflora after minimal industrial processing, and are expected to be consumed without any additional treatment by consumers. Microbiological safety requires a systematic approach that encompasses all aspects of production, processing and distribution. Nevertheless, the most common laboratory techniques used for the detection of pathogens are expensive, time consuming, need laboratory professionals and are not able to provide prompt results, required to undertake effective corrective actions. In this context, the solution proposed in this work is a novel electrochemical sensing system, able to provide real-time information on microbiological risk, which is also potentially embeddable in an industrial production line. The results showed the sensor ability to detect leafy green salad bacterial contaminations with adequate sensibility, even at a low concentration.

Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine)

In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.

Analysis of Milk Using a Portable Potentiometric Electronic Tongue Based on Five Polymeric Membrane Sensors

A portable potentiometric electronic tongue (PE-tongue) was developed and applied to evaluate the quality of milk with different fat content (skimmed, semi-skimmed, and whole) and with different nutritional content (classic, calcium-enriched, lactose-free, folic acid–enriched, and enriched in sterols of vegetal origin). The system consisted of a simplified array of five sensors based on PVC membranes, coupled to a data logger. The five sensors were selected from a larger set of 20 sensors by applying the genetic algorithm (GA) to the responses to compounds usually found in milk including salts (KCl, CaCl₂, and NaCl), sugars (lactose, glucose, and galactose), and organic acids (citric acid and lactic acid). Principal component analysis (PCA) and support vector machine (SVM) results indicated that the PE-tongue consisting of a five-electrode array could successfully discriminate and classify milk samples according to their nutritional content. The PE-tongue provided similar discrimination capability to that of a more complex system formed by a 20-sensor array. SVM regression models were used to predict the physicochemical parameters classically used in milk quality control (acidity, density, %proteins, %lactose, and %fat). The prediction results were excellent and similar to those obtained with a much more complex array consisting of 20 sensors. Moreover, the SVM method confirmed that spoilage of unsealed milk could be correctly identified with the simplified system and the increase in acidity could be accurately predicted. The results obtained demonstrate the possibility of using the simplified PE-tongue to predict milk quality and provide information on the chemical composition of milk using a simple and portable system.

A Potentiometric Electronic Tongue as a Discrimination Tool of Water-Food Indicator/Contamination Bacteria

Microorganism assessment plays a key role in food quality and safety control but conventional techniques are costly and/or time consuming. Alternatively, electronic tongues (E-tongues) can fulfill this critical task. Thus, a potentiometric lab-made E-tongue (40 lipid sensor membranes) was used to differentiate four common food contamination bacteria, including two Gram positive (Enterococcus faecalis, Staphylococcus aureus) and two Gram negative (Escherichia coli, Pseudomonas aeruginosa). Principal component analysis and a linear discriminant analysis-simulated annealing algorithm (LDA-SA) showed that the potentiometric signal profiles acquired during the analysis of aqueous solutions containing known amounts of each studied bacteria allowed a satisfactory differentiation of the four bacterial strains. An E-tongue-LDA-SA model (12 non-redundant sensors) correctly classified 98 ± 5% of the samples (repeated K-fold-CV), the satisfactory performance of which can be attributed to the capability of the lipid membranes to establish electrostatic interactions/hydrogen bonds with hydroxyl, amine and/or carbonyl groups, which are comprised in the bacteria outer membranes. Furthermore, multiple linear regression models, based on selected subsets of E-tongue sensors (12–15 sensors), also allowed quantifying the bacteria contents in aqueous solutions (0.993 ± 0.011 ≤ R2 ≤ 0.998 ± 0.005, for repeated K-fold-CV). In conclusion, the E-tongue could be of great value as a preliminary food quality and safety diagnosis tool.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Cellulose/chitosan pH-responsive indicator incorporated with carrot anthocyanins for intelligent food packaging

In this study, the possible use of anthocyanins of black carrot (ABC) as a chemo-responsive dye to fabricate a colorimetric pH indicator in a cellulose-chitosan matrix was investigated in order to the monitoring of spoilage in pasteurized milk. Cellulose paper was impregnated with a chitosan solution prepared by a sol-gel method containing ABC (total anthocyanins content of 10 mg/100 mL) and characterized. The swelling and water solubility increased by incorporation of ABC into the chitosan-cellulose film. The colorimetric pH indicator showed an obvious color variation from pink to khaki at different pH values (pH 2-11). Stability tests revealed that the indicator had acceptable color stability during one-month storage at 20 °C. The results also confirmed the immobilization of ABC into the matrix of the polymeric indicator with no significant effect on the chemical and super-molecular structure of the samples. In food trial, fresh pasteurized milk was entirely discerned through a perceptible color change from blue to violet rose color after 48 h storage at 20 °C, which was comfortably observable by the naked eye. The results proved that the fabricated indicator could be used as food grade biomaterials to monitor freshness/spoilage of milk.

Rapid methods and sensors for milk quality monitoring and spoilage detection

Monitoring of food quality, in particular, milk quality, is critical in order to maintain food safety and human health. To guarantee quality and safety of milk products and at the same time deliver those as soon as possible, rapid analysis methods as well as sensitive, reliable, cost-effective, easy-to-use devices and systems for process control and milk spoilage detection are needed. In this paper, we review different rapid methods, sensors and commercial systems for milk spoilage and microorganism detection. The main focus lies on chemical sensors and biosensors for detection/monitoring of the well-known indicators associated with bacterial growth and milk spoilage such as changes in pH value, conductivity/impedance, adenosine triphosphate level, concentration of dissolved oxygen and produced CO₂. These sensors offer several advantages, like high sensitivity, fast response time, minimal sample preparation, miniaturization and ability for real-time monitoring of milk spoilage. In addition, electronic-nose- and electronic-tongue systems for the detection of characteristic volatile and non-volatile compounds related to microbial growth and milk spoilage are described. Finally, wireless sensors and color indicators for intelligent packaging are discussed.

Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films

A nanostructured electrochemical bi-sensor system for the analysis of milks has been developed using the layer-by-layer technique. The non-enzymatic sensor [CHI+IL/CuPcS]₂, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPcS) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity, and chitosan (CHI), that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPcS]₂-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV⁻vis, and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal component analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using a PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components, such as fats, proteins, and acidity, can also be obtained. The method developed is simple, and the short response time permits its use in assaying milk samples online.

Electronic Tongue-A Tool for All Tastes?

Electronic tongue systems are traditionally used to analyse: food products, water samples and taste masking technologies for pharmaceuticals. In principle, their applications are almost limitless, as they are able to almost completely reduce the impact of interferents and can be applied to distinguish samples of extreme complexity as for example broths from different stages of fermentation. Nevertheless, their applications outside the three principal sample types are, in comparison, rather scarce. In this review, we would like to take a closer look on what are real capabilities of electronic tongue systems, what can be achieved using mixed sensor arrays and by introduction of biosensors or molecularly imprinted polymers in the matrix. We will discuss future directions both in the sense of applications as well as system development in the ever-growing trend of low cost analysis.

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage day's discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.