1
|
Americo da Silva T, Acuña Caldeira Juncá M, Braunger ML, Riul A, Fernandes Barbin D. Application of a microfluidic electronic tongue based on impedance spectroscopy for coconut water analysis. Food Res Int 2024; 187:114353. [PMID: 38763640 DOI: 10.1016/j.foodres.2024.114353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/21/2024]
Abstract
The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.
Collapse
Affiliation(s)
- Tatiana Americo da Silva
- Department of Food Engineering, School of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas, 13083-862, São Paulo, Brazil
| | - Marina Acuña Caldeira Juncá
- Department of Food Engineering, School of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas, 13083-862, São Paulo, Brazil
| | - Maria Luisa Braunger
- Department of Applied Physics, "Gleb Wataghin" Institute of Physics, University of Campinas (UNICAMP), Rua Bertrand Russell, 599-749, Cidade Universitária, Campinas, 13083-865, São Paulo, Brazil; Centre for Education, Research and Innovation in Energy Environment do IMT Nord Europe, France
| | - Antonio Riul
- Department of Applied Physics, "Gleb Wataghin" Institute of Physics, University of Campinas (UNICAMP), Rua Bertrand Russell, 599-749, Cidade Universitária, Campinas, 13083-865, São Paulo, Brazil.
| | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas, 13083-862, São Paulo, Brazil.
| |
Collapse
|
2
|
Ye J, Fan M, Zhang X, Liang Q, Zhang Y, Zhao X, Lin CT, Zhang D. A novel biomimetic electrochemical taste-biosensor based on conformational changes of the taste receptor. Biosens Bioelectron 2024; 249:116001. [PMID: 38199084 DOI: 10.1016/j.bios.2024.116001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/06/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024]
Abstract
Taste sensor, a useful tool which could detect and identify thousands of different chemical substances in liquid environments, has attracted continuous concern from beverage and foodstuff industry and its consumers. Although many taste sensing methods have been extensively developed, the assessment of tastant content remains challenging due to the limitations of sensor selectivity and sensitivity. Here we present a novel biomimetic electrochemical taste-biosensor based on bioactive sensing elements and immune amplification with nanomaterials carrier to address above concerns, while taking sweet taste perception as a model. The proposed biosensor based on ligand binding domain (T1R2 VFT) of human sweet taste receptor protein showed human mimicking character and initiated the application of immune recognition in gustation biosensor, which can precisely and sensitively distinguish sweet substances against other related gustation substances with detection limit of 5.1 pM, far less than that of taste sensors without immune amplification whose detection limit was 0.48 nM. The performance test demonstrated the biosensor has the capacity of monitoring the response of sweet substances in real food environments, which is crucial in practical. This biomimetic electrochemical taste-biosensor can work as a new screening platform for newly developed tastants and disclose sweet perception mechanism.
Collapse
Affiliation(s)
- Jing Ye
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Minzhi Fan
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Xiaoyu Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Qi Liang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China; College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Yunshan Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China
| | - Xiaoyu Zhao
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China; College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Cheng-Te Lin
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Diming Zhang
- Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou, 311121, China.
| |
Collapse
|
3
|
Liu J, Xu Y, Liu S, Yu S, Yu Z, Low SS. Application and Progress of Chemometrics in Voltammetric Biosensing. BIOSENSORS 2022; 12:bios12070494. [PMID: 35884297 PMCID: PMC9313226 DOI: 10.3390/bios12070494] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/03/2022] [Accepted: 07/06/2022] [Indexed: 12/14/2022]
Abstract
The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.
Collapse
Affiliation(s)
- Jingjing Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (Y.X.); (S.L.); (S.Y.)
- Correspondence: (J.L.); (S.S.L.)
| | - Yifei Xu
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (Y.X.); (S.L.); (S.Y.)
| | - Shikun Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (Y.X.); (S.L.); (S.Y.)
| | - Shixin Yu
- College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; (Y.X.); (S.L.); (S.Y.)
| | - Zhirun Yu
- College of Law, The Australian National University, Canberra 2600, Australia;
| | - Sze Shin Low
- Research Centre of Life Science and HealthCare, China Beacons Institute, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China
- Correspondence: (J.L.); (S.S.L.)
| |
Collapse
|
4
|
Voltammetric sensing using an array of modified SPCE coupled with machine learning strategies for the improved identification of opioids in presence of cutting agents. J Electroanal Chem (Lausanne) 2021. [DOI: 10.1016/j.jelechem.2021.115770] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
5
|
|
6
|
Sempionatto JR, Montiel VRV, Vargas E, Teymourian H, Wang J. Wearable and Mobile Sensors for Personalized Nutrition. ACS Sens 2021; 6:1745-1760. [PMID: 34008960 DOI: 10.1021/acssensors.1c00553] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
While wearable and mobile chemical sensors have experienced tremendous growth over the past decade, their potential for tracking and guiding nutrition has emerged only over the past three years. Currently, guidelines from doctors and dietitians represent the most common approach for maintaining optimal nutrition status. However, such recommendations rely on population averages and do not take into account individual variability in responding to nutrients. Precision nutrition has recently emerged to address the large heterogeneity in individuals' responses to diet, by tailoring nutrition based on the specific requirements of each person. It aims at preventing and managing diseases by formulating personalized dietary interventions to individuals on the basis of their metabolic profile, background, and environmental exposure. Recent advances in digital nutrition technology, including calories-counting mobile apps and wearable motion tracking devices, lack the ability of monitoring nutrition at the molecular level. The realization of effective precision nutrition requires synergy from different sensor modalities in order to make timely reliable predictions and efficient feedback. This work reviews key opportunities and challenges toward the successful realization of effective wearable and mobile nutrition monitoring platforms. Non-invasive wearable and mobile electrochemical sensors, capable of monitoring temporal chemical variations upon the intake of food and supplements, are excellent candidates to bridge the gap between digital and biochemical analyses for a successful personalized nutrition approach. By providing timely (previously unavailable) dietary information, such wearable and mobile sensors offer the guidance necessary for supporting dietary behavior change toward a managed nutritional balance. Coupling of the rapidly emerging wearable chemical sensing devices-generating enormous dynamic analytical data-with efficient data-fusion and data-mining methods that identify patterns and make predictions is expected to revolutionize dietary decision-making toward effective precision nutrition.
Collapse
Affiliation(s)
- Juliane R. Sempionatto
- Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, United States
| | | | - Eva Vargas
- Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, United States
| | - Hazhir Teymourian
- Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, United States
| | - Joseph Wang
- Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, United States
| |
Collapse
|
7
|
Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics. SENSORS 2020; 20:s20174798. [PMID: 32854411 PMCID: PMC7506631 DOI: 10.3390/s20174798] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/20/2020] [Accepted: 08/21/2020] [Indexed: 11/17/2022]
Abstract
Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.
Collapse
|
8
|
Kovacs Z, Szöllősi D, Zaukuu JLZ, Bodor Z, Vitális F, Aouadi B, Zsom-Muha V, Gillay Z. Factors Influencing the Long-Term Stability of Electronic Tongue and Application of Improved Drift Correction Methods. BIOSENSORS-BASEL 2020; 10:bios10070074. [PMID: 32645901 PMCID: PMC7400105 DOI: 10.3390/bios10070074] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/03/2020] [Indexed: 11/16/2022]
Abstract
Temperature, memory effect, and cross-contamination are suspected to contribute to drift in electronic tongue (e-tongue) sensors, therefore drift corrections are required. This paper aimed to assess the disturbing effects on the sensor signals during measurement with an Alpha Astree e-tongue and to develop drift correction techniques. Apple juice samples were measured at different temperatures. pH change of apple juice samples was measured to assess cross-contamination. Different sequential orders of model solutions and apple juice samples were applied to evaluate the memory effect. Model solutions corresponding to basic tastes and commercial apple juice samples were measured for six consecutive weeks to model drift of the sensor signals. Result showed that temperature, cross-contamination, and memory effect influenced the sensor signals. Three drift correction methods: additive drift correction based on all samples, additive drift correction based on reference samples, and multi sensor linear correction, were developed and compared to the component correction in literature through linear discriminant analysis (LDA). LDA analysis showed all the four methods were effective in reducing sensor drift in long-term measurements but the additive correction relative to the whole sample set gave the best results. The results could be explored for long-term measurements with the e-tongue.
Collapse
Affiliation(s)
- Zoltan Kovacs
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
- Correspondence:
| | - Dániel Szöllősi
- Institute of Pharmacology, Medical University of Vienna, 1090 Vienna, Austria;
| | - John-Lewis Zinia Zaukuu
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
| | - Zsanett Bodor
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
| | - Flóra Vitális
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
| | - Balkis Aouadi
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
| | - Viktória Zsom-Muha
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
| | - Zoltan Gillay
- Department of Physics and Control, Faculty of Food Science, Szent István University, Somlói út 14–16, H-1118 Budapest, Hungary; (J.-L.Z.Z.); (Z.B.); (F.V.); (B.A.); (V.Z.-M.); (Z.G.)
| |
Collapse
|