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Poeta E, Liboà A, Mistrali S, Núñez-Carmona E, Sberveglieri V. Nanotechnology and E-Sensing for Food Chain Quality and Safety. SENSORS (BASEL, SWITZERLAND) 2023; 23:8429. [PMID: 37896524 PMCID: PMC10610592 DOI: 10.3390/s23208429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/02/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023]
Abstract
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.
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Affiliation(s)
- Elisabetta Poeta
- Department of Life Sciences, University of Modena and Reggio Emilia, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy
| | - Aris Liboà
- Department of Chemistry, Life Science and Environmental Sustainability, University of Parma, Parco Area delle Scienze, 11/a, 43124 Parma, PR, Italy;
| | - Simone Mistrali
- Nano Sensor System srl (NASYS), Via Alfonso Catalani, 9, 42124 Reggio Emilia, RE, Italy;
| | - Estefanía Núñez-Carmona
- National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy;
| | - Veronica Sberveglieri
- Nano Sensor System srl (NASYS), Via Alfonso Catalani, 9, 42124 Reggio Emilia, RE, Italy;
- National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy;
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The Effect of Data Fusion on Improving the Accuracy of Olive Oil Quality Measurement. Food Chem X 2023; 18:100622. [DOI: 10.1016/j.fochx.2023.100622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/08/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
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Tian H, Chen B, Lou X, Yu H, Yuan H, Huang J, Chen C. Rapid detection of acid neutralizers adulteration in raw milk using FGC E-nose and chemometrics. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01403-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Vanarse A, Osseiran A, Rassau A, van der Made P. Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts. SENSORS (BASEL, SWITZERLAND) 2022; 22:440. [PMID: 35062402 PMCID: PMC8778084 DOI: 10.3390/s22020440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/30/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.
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Affiliation(s)
- Anup Vanarse
- Brainchip Research Institute, Perth 6000, Australia; (A.O.); (P.v.d.M.)
| | - Adam Osseiran
- Brainchip Research Institute, Perth 6000, Australia; (A.O.); (P.v.d.M.)
| | - Alexander Rassau
- School of Engineering, Edith Cowan University, Joondalup 6027, Australia;
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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal Chim Acta 2021; 1161:338403. [DOI: 10.1016/j.aca.2021.338403] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023]
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Farahmand E, Razavi SH, Mohtasebi SS. Investigating effective variables to produce desirable aroma in sourdough using e‐nose and sensory panel. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Elham Farahmand
- Bioprocess Engineering Laboratory (BPEL), Department of Food Science, Engineering and Technology, Faculty of Agricultural Engineering and Technology University of Tehran Karaj Iran
| | - Seyed Hadi Razavi
- Bioprocess Engineering Laboratory (BPEL), Department of Food Science, Engineering and Technology, Faculty of Agricultural Engineering and Technology University of Tehran Karaj Iran
| | - Seyed Saeid Mohtasebi
- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology University of Tehran Karaj Iran
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Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2020.11.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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A Review on the Application of Chemometrics and Machine Learning Algorithms to Evaluate Beer Authentication. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01864-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Karami H, Rasekh M, Mirzaee‐Ghaleh E. Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.14696] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hamed Karami
- Department of Biosystems Engineering University of Mohaghegh Ardabili Ardabil Iran
| | - Mansour Rasekh
- Department of Biosystems Engineering University of Mohaghegh Ardabili Ardabil Iran
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Karami H, Rasekh M, Mirzaee-Ghaleh E. Qualitative analysis of edible oil oxidation using an olfactory machine. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2020. [DOI: 10.1007/s11694-020-00506-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Nouri B, Mohtasebi SS, Rafiee S. Quality detection of pomegranate fruit infected with fungal disease. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2020. [DOI: 10.1080/10942912.2019.1705851] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Behzad Nouri
- Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Seyed Saeid Mohtasebi
- Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Shahin Rafiee
- Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
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Nouri B, Mohtasebi SS, Jahanbakhshi A. Application of an olfactory system to detect and distinguish bitter chocolates with different percentages of cocoa. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13248] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Behzad Nouri
- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and TechnologyUniversity of Tehran Karaj Iran
| | - Seyed Saeid Mohtasebi
- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and TechnologyUniversity of Tehran Karaj Iran
| | - Ahmad Jahanbakhshi
- Department of Biosystems EngineeringUniversity of Mohaghegh Ardabili Ardabil Iran
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Voss HGJ, Mendes Júnior JJA, Farinelli ME, Stevan SL. A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose. SENSORS 2019; 19:s19112646. [PMID: 31212701 PMCID: PMC6603620 DOI: 10.3390/s19112646] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 02/16/2019] [Accepted: 02/25/2019] [Indexed: 02/06/2023]
Abstract
Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages.
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Affiliation(s)
- Henike Guilherme Jordan Voss
- Graduate Program in Applied Computing (PPGCA), State University of Ponta Grossa (UEPG), Ponta Grossa (PR) 84030-900, Brazil.
| | - José Jair Alves Mendes Júnior
- Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology of Parana (UTFPR), Curitiba (PR) 80230-901, Brazil.
| | - Murilo Eduardo Farinelli
- Graduate Program in Chemical Engineering, Federal University of Technology of Parana (UTFPR), Ponta Grossa (PR) 84016-210, Brazil.
| | - Sergio Luiz Stevan
- Graduate Program in Applied Computing (PPGCA), State University of Ponta Grossa (UEPG), Ponta Grossa (PR) 84030-900, Brazil.
- Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology of Parana (UTFPR), Ponta Grossa (PR) 84016-210, Brazil.
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Ocvirk M, Mlinarič NK, Košir IJ. Comparison of sensory and chemical evaluation of lager beer aroma by gas chromatography and gas chromatography/mass spectrometry. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2018; 98:3627-3635. [PMID: 29250799 DOI: 10.1002/jsfa.8840] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 12/11/2017] [Accepted: 12/11/2017] [Indexed: 05/28/2023]
Abstract
BACKGROUND Although the evaluation of beer is conducted by sensory experts, we cannot neglect the influence of human factors and subjectivity. This problem could be solved by the chemical analysis of the volatile part of beer aroma and, from this, we can build a database for the construction of a model that classifies samples in a comparable manner to sensory assessment. RESULTS Twenty-two batches of the same beer brand were assessed by sensory evaluation and described chemically in terms of the contents of alcohols and esters (n = 9), hop essential oil compounds (n = 15) using gas chromatography (GC) and other aroma volatiles (n = 33) as analysed by head space solid-phase microextraction (SPME)-GC/mass spectrometry. The best match of 91% with respect to sample classification on the basis of chemical analyses to sensory scores was achieved with a dataset of results from headspace-SPME and analyses of higher alcohols and esters by regularized discriminant analysis. CONCLUSION The results of the present study show that deviations in beer aroma are not a consequence of a permanent repeatable error in brewing process, nor are they influenced by raw materials, but, instead, they are a consequence of alcoholic fermentation. Sensory analysis could be replaced with chemical/statistical analysis on an appropriate data set and for a distinct beer brand. The good results achieved confirm our approach; however, for different beer brands or types, this method should be optimised. © 2017 Society of Chemical Industry.
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Affiliation(s)
- Miha Ocvirk
- Institute of Hop Research and Brewing, Žalec, Slovenia
| | | | - Iztok J Košir
- Institute of Hop Research and Brewing, Žalec, Slovenia
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Tohidi M, Ghasemi-Varnamkhasti M, Ghafarinia V, Bonyadian M, Mohtasebi SS. Development of a metal oxide semiconductor-based artificial nose as a fast, reliable and non-expensive analytical technique for aroma profiling of milk adulteration. Int Dairy J 2018. [DOI: 10.1016/j.idairyj.2017.09.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1075-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Assessment of high pressure processed mandarin juice in the headspace by using electronic nose and chemometric analysis. INNOV FOOD SCI EMERG 2017. [DOI: 10.1016/j.ifset.2017.05.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Kiani S, Minaei S, Ghasemi-Varnamkhasti M, Ayyari M. An original approach for the quantitative characterization of saffron aroma strength using electronic nose. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1306709] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Sajad Kiani
- Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
| | - Saeid Minaei
- Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
| | | | - Mahdi Ayyari
- Horticultural Science Department, Tarbiat Modares University, Tehran, Iran
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Ghasemi-Varnamkhasti M, Lozano J. Electronic nose as an innovative measurement system for the quality assurance and control of bakery products: A review. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.eaef.2016.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose. SENSORS 2016; 16:s16050636. [PMID: 27153069 PMCID: PMC4883327 DOI: 10.3390/s16050636] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 04/22/2016] [Accepted: 04/27/2016] [Indexed: 11/21/2022]
Abstract
Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.
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Prediction of banana quality indices from color features using support vector regression. Talanta 2015; 148:54-61. [PMID: 26653423 DOI: 10.1016/j.talanta.2015.10.073] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Revised: 10/16/2015] [Accepted: 10/24/2015] [Indexed: 11/24/2022]
Abstract
Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acidity and firmness as the output. Experimental results provided an improvement in predictive accuracy as compared with those obtained by using artificial neural network (ANN).
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