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Roveda AC, Dias BC, Passini LN, Manzani D, Petruci JFDS. Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging. Mikrochim Acta 2024; 191:354. [PMID: 38809328 DOI: 10.1007/s00604-024-06426-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/10/2024] [Indexed: 05/30/2024]
Abstract
A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.
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Affiliation(s)
- Antonio Carlos Roveda
- São Carlos Institute of Chemistry - IQSC, University of São Paulo - USP, São Carlos, SP, Brazil
- Institute of Geosciences and Exact Sciences, São Paulo State University - UNESP, Rio Claro, SP, Brazil
| | | | - Luan N Passini
- São Carlos Institute of Chemistry - IQSC, University of São Paulo - USP, São Carlos, SP, Brazil
| | - Danilo Manzani
- São Carlos Institute of Chemistry - IQSC, University of São Paulo - USP, São Carlos, SP, Brazil
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Mehdizadeh SA, Noshad M, Chaharlangi M, Ampatzidis Y. Development of an Innovative Optoelectronic Nose for Detecting Adulteration in Quince Seed Oil. Foods 2023; 12:4350. [PMID: 38231827 DOI: 10.3390/foods12234350] [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: 11/01/2023] [Revised: 11/27/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024] Open
Abstract
In this study, an innovative odor imaging system capable of detecting adulteration in quince seed edible oils mixed with sunflower oil and sesame oil based on their volatile organic compound (VOC) profiles was developed. The system comprises a colorimetric sensor array (CSA), a data acquisition unit, and a machine learning algorithm for identifying adulterants. The CSA was created using a method that involves applying a mixture of six different pH indicators (methyl violet, chlorophenol red, Nile blue, methyl orange, alizarin, cresol red) onto a Thin Layer Chromatography (TLC) silica gel plate. Subsequently, difference maps were generated by subtracting the "initial" image from the "final" image, with the resulting color changes being converted into digital data, which were then further analyzed using Principal Component Analysis (PCA). Following this, a Support Vector Machine was employed to scrutinize quince seed oil that had been adulterated with varying proportions of sunflower oil and sesame oil. The classifier was progressively supplied with an increasing number of principal components (PCs), starting from one and incrementally increasing up to five. Each time, the classifier was optimized to determine the hyperparameters utilizing a random search algorithm. With one to five PCs, the classification error accounted for a range of 37.18% to 1.29%. According to the results, this novel system is simple, cost-effective, and has potential applications in food quality control and consumer protection.
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Affiliation(s)
- Saman Abdanan Mehdizadeh
- Department of Mechanics of Biosystems Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
| | - Mohammad Noshad
- Department of Food Science & Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
| | - Mahsa Chaharlangi
- Central Laboratory, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
| | - Yiannis Ampatzidis
- Southwest Florida Research and Education Center, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
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Liu X, Huo D, Li J, Ma Y, Liu H, Luo H, Zhang S, Luo X, Hou C. Pattern-recognizing-assisted detection of mildewed wheat by Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array. Food Chem 2023; 415:135525. [PMID: 36870207 DOI: 10.1016/j.foodchem.2023.135525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 01/09/2023] [Accepted: 01/17/2023] [Indexed: 01/22/2023]
Abstract
In order to timely discriminate wheat with different mildew rates, a Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array was designed. Using array points to capture volatile gases of wheat with different mildew rates, and output RGB values. The correlation between ΔR/ΔG/ΔB values and odor components was established. The ΔG values of array points 2' and 3' showed the best correlation with mildew rate, with R2 of 0.9816 and 0.9642. The ΔR value of 3 and the ΔG value of 2 correlate well with the mildew rate, with R2 of 0.9625 and 0.9502, respectively. Then, the ΔRGB values are subjected to pattern recognition processing, and LDA achieves 100% correct discrimination for all samples, or divides high and low mildew areas. This method provides an odor-based monitoring tool for fast, visual and nondestructive evaluation of food safety and quality through visualization of odors produced by different mildew rates.
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Affiliation(s)
- Xiaofang Liu
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Bioengineering College of Chongqing University, Chongqing 400044, PR China
| | - Danqun Huo
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Bioengineering College of Chongqing University, Chongqing 400044, PR China; Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province, College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, PR China
| | - Jiawei Li
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Bioengineering College of Chongqing University, Chongqing 400044, PR China; Chongqing University Three Gorges Hospital, Chongqing 404000,PR China
| | - Yi Ma
- Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province, College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, PR China
| | - Huan Liu
- Chongqing Institute for Food and Drug Control, Chongqing 401121, PR China
| | - Huibo Luo
- Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province, College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, PR China
| | - Suyi Zhang
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Bioengineering College of Chongqing University, Chongqing 400044, PR China; National Engineering Research Center of Solid-State Brewing, Luzhou Laojiao Group Co. Ltd., Luzhou 646000, PR China.
| | - Xiaogang Luo
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
| | - Changjun Hou
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
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Aghili NS, Rasekh M, Karami H, Edriss O, Wilson AD, Ramos J. Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil. SENSORS (BASEL, SWITZERLAND) 2023; 23:6294. [PMID: 37514589 PMCID: PMC10383484 DOI: 10.3390/s23146294] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/02/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.
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Affiliation(s)
- Nadia Sadat Aghili
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Mansour Rasekh
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Hamed Karami
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq
| | - Omid Edriss
- Department of Computer, Rafsanjan Branch, Islamic Azad University, Rafsanjan 77181-84483, Iran
| | - Alphus Dan Wilson
- Southern Hardwoods Laboratory, Pathology Department, Center for Forest Health & Disturbance, Forest Genetics & Ecosystems Biology, Southern Research Station, USDA Forest Service, 432 Stoneville Road, Stoneville, MS 38776-0227, USA
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA
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Fernandes GM, Barreto DN, Batista AD, da Silveira Petruci JF. A fully integrated 3D printed platform for sulfite determination in beverages via gas diffusion membrane extraction and digital video treatment. Food Chem 2023; 406:135094. [PMID: 36470085 DOI: 10.1016/j.foodchem.2022.135094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/18/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022]
Abstract
In this study, we have described a miniaturized, simple, and low-cost device for sulfite determination in beverages by coupling Gas Diffusion Microextraction to paper-based analytical devices. The color change of an acid-base indicator - promoted by the generated gaseous SO2 - impregnated onto the paper surface was monitored in the function of time by video recording using a smartphone. The analytical information was related to the Hue, Saturation, Value (HSV) color space extracted from the video file. The complete analytical platform was built using a 3D printer, allowing the easy fabrication of a low-cost tailored device. Under optimized conditions, a linear relation from 5 to 90 mg L-1 was obtained using 30 µL of the reagent, 1 mL of sample, and 10 min of analysis. The relative standard deviation and the limit of detection were 2.2 % and 1.6 mg L-1, respectively. The method was successfully employed in several beverages, such as juices, soda, and coconut water.
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Affiliation(s)
| | - Diandra Nunes Barreto
- Federal University of Uberlândia (UFU), Institute of Chemistry, Uberlândia, MG, Brazil
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Gomes JS, de Sousa RMF, Petruci JFDS. Paper-based colorimetric sensor array for the rapid and on-site discrimination of green tea samples based on the flavonoid composition. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:2471-2478. [PMID: 35687068 DOI: 10.1039/d2ay00590e] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Green tea is a worldwide appreciated food product with Chinese production estimated to reach over 3m tons in 2027 and with many valuable health effects. The development of analytical methods to discriminate among green tea samples is induced by economic benefits and to avoid deliberate origin mislabeling and adulteration. In this study, we present a paper-based colorimetric sensor array comprised of six ordinary reagents tailored for the discrimination of green tea extracts of different brands according to differences in the composition of flavonoids. The colorimetric array was rationally designed based on indicators that differentially react with a variety of flavonoids via specific functional groups. 4 μL of each reagent was impregnated onto the paper surface followed by the addition of the green tea extract. After 1 minute, digital images were acquired using a smartphone and the color changes were employed to build differential maps with a unique fingerprint for each green tea sample. Moreover, principal component analysis (PCA) and hierarchical component analysis (HCA) were employed to successfully discriminate among the samples, enabling the origin and adulteration identification of the samples. Therefore, this study provides a simple, effective, low-cost, and portable method for quick discrimination and quality control of green tea samples.
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Affiliation(s)
- Jéssica Santos Gomes
- Institute of Chemistry, Federal University of Uberlândia, 38408-902, Uberlandia, MG, Brazil.
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A Portable Battery-Operated Sensor System for Simple and Rapid Assessment of Virgin Olive Oil Quality Grade. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10030102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Virgin olive oil quality is assessed by chemical as well as sensory analysis. Two of the most important parameters that define the quality of virgin olive oils are the free acidity and the peroxide index. These chemical parameters are usually determined by manual titration procedures that must be carried out in a laboratory by trained personnel. In this paper, a portable sensor system to support the quality grade assessment of virgin olive oil is presented. The system is battery operated and characterized by small dimensions, light weight and quick measurement response (about 30 s). The working principle is based on the measurement of the electrical conductance of an emulsion between a chemical reagent and the olive oil sample. Two different chemical reagents have been investigated: (1) a hydro-alcoholic solution (HAS), made of 60% ethanol and 40% distilled water; (2) 100% distilled water (DW). Tests have been carried out on a set of 40 olive oil samples. The results have shown how, for most of the fresh virgin olive oil samples (31 samples out of 40), the free acidity can be estimated with good accuracy from the electrical conductance of the emulsion using HAS as the reagent. In the case of the full set of samples, the emulsion electrical conductance, using HAS as the reagent, is a function of both the sample free acidity as well as the compounds produced by oil oxidation, and a compensation method based on the measured electrical conductance, using DW as the reagent, has been introduced to improve the accuracy in the estimated free acidity. Tests have also been carried out on the full set of samples, using a k-nearest neighbors algorithm, to demonstrate the feasibility of olive oil classification according to the quality grade. The results have shown how measurements carried out using only the HAS reagent provide better classification accuracy than measurements carried out using both the HAS and DW reagents. The proposed system can be a low-cost alternative to standard laboratory analyses to evaluate the quality grade of virgin olive oil.
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Eriotou E, Karabagias IK, Maina S, Koulougliotis D, Kopsahelis N. Geographical origin discrimination of "Ntopia" olive oil cultivar from Ionian islands using volatile compounds analysis and computational statistics. Eur Food Res Technol 2021; 247:3083-3098. [PMID: 34566491 PMCID: PMC8450699 DOI: 10.1007/s00217-021-03863-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/02/2021] [Accepted: 09/04/2021] [Indexed: 12/01/2022]
Abstract
The aim of the present study was to characterize the aroma profile of olive oil of the "Ntopia" (local) cultivar from the Ionian islands (Zakynthos, Kefalonia, Leukada, and Kerkyra) (Greece), and investigate whether specific volatile compounds could be considered as indicators of olive oil geographical origin, using computational statistics. In this context, 137 olive oil samples were subjected to headspace solid phase microextraction coupled to gas chromatography/mass spectrometry using the internal standard method. Computational statistics on the semi-quantitative data of olive oil samples, as rapid machine learning algorithms, showed that specific volatile compounds could be used as indicators of geographical origin of olive oil of the "Ntopia" cultivar, among the four main Ionian islands. Volatile compounds such as ethanol, pentanal, 2,4-dimethylheptane, 3,7-dimethyl-1,3,6-octatriene (E), 2,5-dimethylnonane, 1-hexanol, 6-methyl-5-hepten-2-one, octanal, dl-Limonene, acetic acid hexyl ester and dodecane could aid to the geographical origin discrimination of "Ntopia" olive oil cultivar when two (Zakynthos and Kefalonia) or four (Zakynthos, Kefalonia, Leukada and Kerkyra) Ionian islands are subjected to statistical analysis. The discrimination rate using the cross-validation method was 100% and 85.7%, respectively. These results were further evaluated using training and holdout partitions, during which a comparable classification rate was obtained. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00217-021-03863-2.
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Affiliation(s)
- Effimia Eriotou
- Department of Food Science and Technology, Ionian University, 28100 Argostoli, Kefalonia Greece
| | - Ioannis K. Karabagias
- Laboratory of Food Chemistry, Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece
- Department of Food Science and Technology, School of Agricultural Sciences, University of Patras, Charilaou Trikoupi 2, 30100 Agrinio, Greece
| | - Sofia Maina
- Department of Food Science and Technology, Ionian University, 28100 Argostoli, Kefalonia Greece
- Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Dionysios Koulougliotis
- Department of Environment, Ionian University, M. Minotou-Giannopoulou, 29100 Zakynthos, Greece
| | - Nikolaos Kopsahelis
- Department of Food Science and Technology, Ionian University, 28100 Argostoli, Kefalonia Greece
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