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Bose A, Bhattacharyya N, Bhattacharjee P. A SMART methodology for assessment of hexanal in potato crisps using electronic nose technology: sensor screening by scalar machine learning classifier method. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2024; 61:150-160. [PMID: 38192713 PMCID: PMC10771541 DOI: 10.1007/s13197-023-05831-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 07/18/2023] [Accepted: 08/26/2023] [Indexed: 01/10/2024]
Abstract
There is a pertinent need to develop a rapid and accurate methodology for the detection of the onset and the progression of rancidity in the most popular savory product worldwide, viz. fried potato crisps for food safety and health concerns. Rancidity in the fried crisps-one set prepared using C18:2-lean deodorized virgin coconut oil under modified deep frying conditions (140 °C, 5 min),-and another set deep fried (170 °C, 3 min) in C18:2-rich oil (simulating commercial frying conditions) was determined by 'rancidity indices' generated (using Mahalanobis distance) from the data obtained by MO-based electronic nose analysis of hexanal (in Likens-Nickerson extract of volatiles from potato crisps), the most prominent rancidity marker, using screened sensors calibrated with standard hexanal, and classified using support vector machine. This also allowed unambiguous discrimination of the two sets of potato fries. The correlation of hexanal contents with the said indices yielded robust regression models which could accurately predict rancidity status of the crisps, forgoing GC-FID analysis of rancidity marker in the same. The 'SMART' models developed would allow rapid-cum-accurate detection of the onset and progression of rancidity in fried potato crisps on an industrial scale, forgoing the need to conduct biochemical analyses. Supplementary Information The online version contains supplementary material available at 10.1007/s13197-023-05831-y.
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Affiliation(s)
- Anupama Bose
- Department of Food Technology and Biochemical Engineering, Jadavpur University, Kolkata, 700 032 India
| | - Nabarun Bhattacharyya
- Centre for Development of Advanced Computing (C-DAC), E-2/1 Block-GP, Sector-V, Salt Lake, Kolkata, 700091 India
| | - Paramita Bhattacharjee
- Department of Food Technology and Biochemical Engineering, Jadavpur University, Kolkata, 700 032 India
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2
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Panseri S, Borgonovo F, Guarino M, Chiesa L, Piana ML, Rizzi R, Mortarino M. Monitoring Volatile Organic Compounds and Aroma Profile of Robinia pseudoacacia L. Honey at Different Storage Temperatures during Shelf Life. Foods 2023; 12:3105. [PMID: 37628103 PMCID: PMC10453254 DOI: 10.3390/foods12163105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/27/2023] Open
Abstract
Bee honey has different volatile organic compound profiles that depend on the botanical origin and the state of conservation and which are mainly responsible for its specific aroma. During honey storage, the profile of these molecules and other indicators, such as 5-hydroxymethylfurfural and the diastatic index, can change depending on temperature and time. This study analyzed the variations that these parameters in acacia honey stored at three different temperatures for a total period of 550 days, using gas chromatography coupled with mass spectrometry and an electronic nose equipped with 10 different sensors. The results confirm that the composition of acacia honey varies over time due to both the reduction in the concentration of volatile molecules (e.g., formic acid, a natural acaricide) and the increase in compounds resulting from heat-dependent degradations (e.g., 5-hydroxymethylfurfural). This study supports the usefulness of the electronic nose for the early detection of aromatic alterations in honey subjected to high-temperature storage.
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Affiliation(s)
- Sara Panseri
- Department of Veterinary Medicine and Animal Sciences, Università degli Studi di Milano, Via dell’Università 6, 26900 Lodi, Italy; (S.P.); (L.C.); (R.R.)
| | - Federica Borgonovo
- Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy; (F.B.); (M.G.)
| | - Marcella Guarino
- Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy; (F.B.); (M.G.)
| | - Luca Chiesa
- Department of Veterinary Medicine and Animal Sciences, Università degli Studi di Milano, Via dell’Università 6, 26900 Lodi, Italy; (S.P.); (L.C.); (R.R.)
| | - Maria Lucia Piana
- Piana Ricerca e Consulenza S.r.l., Via Umbria 41, Castel San Pietro Terme, 40024 Bologna, Italy;
| | - Rita Rizzi
- Department of Veterinary Medicine and Animal Sciences, Università degli Studi di Milano, Via dell’Università 6, 26900 Lodi, Italy; (S.P.); (L.C.); (R.R.)
| | - Michele Mortarino
- Department of Veterinary Medicine and Animal Sciences, Università degli Studi di Milano, Via dell’Università 6, 26900 Lodi, Italy; (S.P.); (L.C.); (R.R.)
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Osmólska E, Stoma M, Starek-Wójcicka A. Juice Quality Evaluation with Multisensor Systems-A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4824. [PMID: 37430738 DOI: 10.3390/s23104824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry.
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Affiliation(s)
- Emilia Osmólska
- Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
| | - Monika Stoma
- Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
| | - Agnieszka Starek-Wójcicka
- Department of Biological Bases of Food and Feed Technologies, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
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Dash KK, Sharma M, Tiwari A. Heat and mass transfer modeling and quality changes during deep fat frying: A comprehensive review. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.13999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Kshirod K. Dash
- Department of Food Processing Technology Ghani Khan Choudhury Institute of Engineering and Technology (GKCIET) Malda West Bengal India
| | - Maanas Sharma
- Department of Food Engineering and Technology Tezpur University Tezpur Assam India
| | - Ajita Tiwari
- Department of Agricultural Engineering Assam University Silchar Assam India
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Deb M, Routray W, Dash KK. Assessment of quality change with frying temperature and sensory analysis using Fuzzy logic of hydrocolloids fortified flour‐based multilayered snack. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- M. Deb
- Department of Food Engineering and Technology Tezpur University Tezpur Assam India
| | - Winny Routray
- Department of Food Process Engineering National Institute of Technology Rourkela India
| | - Kshirod K. Dash
- Department of Food Processing Technology Ghani Khan Choudhury Institute of Engineering & Technology Malda West Bengal India
- Department of Food Engineering and Technology Tezpur University Tezpur Assam India
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Karami H, Rasekh M, Mirzaee‐Ghaleh E. Identification of olfactory characteristics of edible oil during storage period using metal oxide semiconductor sensor signals and ANN methods. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15749] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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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7
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A deep feature mining method of electronic nose sensor data for identifying beer olfactory information. J FOOD ENG 2019. [DOI: 10.1016/j.jfoodeng.2019.07.023] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhu L, Jia H, Chen Y, Wang Q, Li M, Huang D, Bai Y. A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System. SENSORS 2019; 19:s19153417. [PMID: 31382683 PMCID: PMC6696477 DOI: 10.3390/s19153417] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/01/2019] [Accepted: 08/02/2019] [Indexed: 11/16/2022]
Abstract
Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.
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Affiliation(s)
- Longtu Zhu
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Honglei Jia
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Yibing Chen
- Jilin Province Soil and Fertilizer Station, Changchun 130031, China
| | - Qi Wang
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Mingwei Li
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Dongyan Huang
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
| | - Yunlong Bai
- College of Information, Jilin Agricultural University, Changchun 130118, China
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Srivastava S, Mishra G, Mishra HN. Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain. Food Chem 2019; 283:604-610. [PMID: 30722918 DOI: 10.1016/j.foodchem.2019.01.076] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 01/06/2019] [Accepted: 01/06/2019] [Indexed: 10/27/2022]
Abstract
Fuzzy controller artmap based algorithms via E-nose selective metal oxides sensor (MOS) data was applied for classification of S. oryzae infestation in rice grains. The screened defuzzified data of selective sensors was further applied to detect S. oryzae infested rice with PCA and MLR techniques. Reliability of data was cross validated with reference methods of protein and uric acid content. Out of 18 MOS, 6 sensors namely P30/2, P30/1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity change. Defuzzified score of 62.17 for P30/2 and 59.33 for P30/1 MOS further confirmed validity studies of E-nose sensor response with reference methods. The PCA plots were able to classify up to 84.75% of rice with variable degree of S. oryzae infestation. The MLR values of predicted versus reference values of protein and uric acid content were found to be fitting with R2 of 0.972, 0.997 and RMSE values of 2.08, 1.05.
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Affiliation(s)
- Shubhangi Srivastava
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India.
| | - Gayatri Mishra
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
| | - Hari Niwas Mishra
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
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10
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Ghasemi-Varnamkhasti M, Apetrei C, Lozano J, Anyogu A. Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.07.018] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Abstract
AbstractSoft rot is a severe bacterial disease of potatoes, and soft rot infection can cause significant economic losses during the storage period of potatoes. In this study, potato soft rot was selected as the research object, and a type of potato tuber soft rot disease early detection method based on the electronic nose technology was proposed. An optimized bionic electronic nose gas chamber and a scientific and reasonable sampling device were designed to detect a change in volatile substances of the infected soft rot disease of potato tuber. The infection of soft rot disease in potato tuber samples was detected and identified by using the RBF NN algorithm and SVM algorithm. The results revealed that the proposed bionic electronic nose system can be utilized for early detection of potato tuber soft rot disease. Through comparison and analysis, the recognition rate using the SVM algorithm reached up to 89.7%, and the results were superior to the RBF NN algorithm.
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12
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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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13
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Frying disposal time of sunflower oil using hybrid electronic nose-fuzzy logic approach. Lebensm Wiss Technol 2017. [DOI: 10.1016/j.lwt.2017.01.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Giovanelli G, Torri L, Sinelli N, Buratti S. Comparative study of physico-chemical and sensory characteristics of French fries prepared from frozen potatoes using different cooking systems. Eur Food Res Technol 2017. [DOI: 10.1007/s00217-017-2870-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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15
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Qiu S, Wang J. The prediction of food additives in the fruit juice based on electronic nose with chemometrics. Food Chem 2017; 230:208-214. [PMID: 28407902 DOI: 10.1016/j.foodchem.2017.03.011] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 02/08/2017] [Accepted: 03/03/2017] [Indexed: 12/01/2022]
Abstract
Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R2s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring.
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Affiliation(s)
- Shanshan Qiu
- Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China; College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China
| | - Jun Wang
- Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China.
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Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis. Food Chem 2016; 221:379-385. [PMID: 27979217 DOI: 10.1016/j.foodchem.2016.10.089] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 09/30/2016] [Accepted: 10/20/2016] [Indexed: 11/22/2022]
Abstract
An electronic nose (e-nose), having 18 metal oxide semiconductor (MOS) sensors, guided determination of frying disposal time of sunflower oil is reported. The ranking and screening of MOS sensors, specific for volatile organic compounds, was performed using fuzzy logic. A correlation was examined between rancidity indices of fried oil (total polar compounds (TPC), and triglyceride dimers-polymers (TGDP), among others) and e-nose based odor index. Fuzzy logic screened 6 MOS sensors (LY2/G, LY2/AA, LY2/GH, LY2/gCT1, T30/1, and P30/1) to deconvolute the rancid fried oils using hierarchical clustering on principal component space. A good relationship was noted between rancidity indices and odor index (R2>0.85). Based on maximum discard limits of rancidity indices (25% TPC and 10% TGDP), the frying disposal time of 15.2h (TPC) vs. 15.8h (e-nose) and 15.5h (TGDP) vs. 16.3h (e-nose) was determined. The demonstrated methodology holds a potential extension to different fried oils and products.
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Lotfivand N, Abdolzadeh V, Hamidon MN. Artificial olfactory system with fault-tolerant sensor array. ISA TRANSACTIONS 2016; 63:425-435. [PMID: 27038885 DOI: 10.1016/j.isatra.2016.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 01/21/2016] [Accepted: 03/13/2016] [Indexed: 06/05/2023]
Abstract
Numerous applications of artificial olfaction resulting from research in many branches of sciences have caused considerable interest in the enhancement of these systems. In this paper, we offer an architecture which is suitable for critical applications, such as medical diagnosis, where reliability and precision are deemed important. The proposed architecture is able to tolerate failures in the sensors of the array. In this study, the discriminating ability of the proposed architecture in detecting complex odors, as well as the performance of the proposed architecture in encountering sensor failure, were investigated and compared with the generic architecture. The results demonstrated that by applying the proposed architecture in the artificial olfactory system, the performance of system in the healthy mode was identical to the classic structure. However, in the faulty situation, the proposed architecture implied high identification ability of odor samples, while the generic architecture showed very poor performance in the same situation. Based on the results, it was possible to achieve high odor identification through the developed artificial olfactory system using the proposed architecture.
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Affiliation(s)
- Nasser Lotfivand
- Department of Electronic Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
| | - Vida Abdolzadeh
- Young Researchers and Elite club, Tabriz Branch, Islamic Azad University,Tabriz, Iran.
| | - Mohd Nizar Hamidon
- Institute of Advanced Technology, University Putra Malaysia,43400 Serdang, Selangor, Malaysia.
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Ghosh PK, Chatterjee S, Bhattacharjee P, Bhattacharyya N. Removal of Rancid-Acid Odor of Expeller-Pressed Virgin Coconut Oil by Gamma Irradiation: Evaluation by Sensory and Electronic Nose Technology. FOOD BIOPROCESS TECH 2016. [DOI: 10.1007/s11947-016-1752-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Early detection of Zygosaccharomyces rouxii--spawned spoilage in apple juice by electronic nose combined with chemometrics. Int J Food Microbiol 2015; 217:68-78. [PMID: 26490651 DOI: 10.1016/j.ijfoodmicro.2015.10.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 09/21/2015] [Accepted: 10/11/2015] [Indexed: 11/22/2022]
Abstract
Spoilage spawned by Zygosaccharomyces rouxii can cause sensory defect in apple juice, which could hardly be perceived in the early stage and therefore would lead to the serious economic loss. Thus, it is essential to detect the contamination in early stage to avoid costly waste of products or recalls. In this work the performance of an electronic nose (e-nose) coupled with chemometric analysis was evaluated for diagnosis of the contamination in apple juice, using test panel evaluation as reference. The feasibility of using e-nose responses to predict the spoilage level of apple juice was also evaluated. Coupled with linear discriminant analysis (LDA), detection of the contamination was achieved after 12h, corresponding to the cell concentration of less than 2.0 log 10 CFU/mL, the level at which the test panelists could not yet identify the contamination, indicating that the signals of e-nose could be utilized as early indicators for the onset of contamination. Loading analysis indicated that sensors 2, 6, 7 and 8 were the most important in the detection of Z. rouxii-contaminated apple juice. Moreover, Z. rouxii counts in unknown samples could be well predicted by the established models using partial least squares (PLS) algorithm with high correlation coefficient (R) of 0.98 (Z. rouxii strain ATCC 2623 and ATCC 8383) and 0.97 (Z. rouxii strain B-WHX-12-53). Based on these results, e-nose appears to be promising for rapid analysis of the odor in apple juice during processing or on the shelf to realize the early detection of potential contamination caused by Z. rouxii strains.
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Messina V, Sancho A, Walsöe de Reca N. Monitoring odour of heated extra-virgin olive oils from Arbequina and Manzanilla cultivars using an electronic nose. EUR J LIPID SCI TECH 2015. [DOI: 10.1002/ejlt.201400651] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Valeria Messina
- (CINSO) - (CITEDEF); UNIDEF (Strategic I & D for Defense)-CONICET-MINDEF (Ministry of Defense); Buenos Aires Argentina
- The National Council for Scientific and Technical Research (CONICET); Buenos Aires Argentina
| | - Ana Sancho
- Food Technology Institute; INTA (B1708WAB); Moron Buenos Aires Argentina
| | - Noemi Walsöe de Reca
- (CINSO) - (CITEDEF); UNIDEF (Strategic I & D for Defense)-CONICET-MINDEF (Ministry of Defense); Buenos Aires Argentina
- The National Council for Scientific and Technical Research (CONICET); Buenos Aires Argentina
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Survey on Operational Perishables Quality Control and Logistics. LECTURE NOTES IN COMPUTER SCIENCE 2015. [DOI: 10.1007/978-3-319-24264-4_28] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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