1
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Wang W, Zhu A, Wei H, Yu L. A novel method for vegetable and fruit classification based on using diffusion maps and machine learning. Curr Res Food Sci 2024; 8:100737. [PMID: 38681525 PMCID: PMC11046067 DOI: 10.1016/j.crfs.2024.100737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/10/2024] [Accepted: 04/12/2024] [Indexed: 05/01/2024] Open
Abstract
Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.
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Affiliation(s)
- Wenbo Wang
- School of Management, Shenyang University of Technology, 110870, Shenyang, China
| | - Aimin Zhu
- School of Management, Shenyang University of Technology, 110870, Shenyang, China
| | - Hongjiang Wei
- School of Management, Shenyang University of Technology, 110870, Shenyang, China
| | - Lijuan Yu
- School of Management, Shenyang University of Technology, 110870, Shenyang, China
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2
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Yuan Y, Chen X. Vegetable and fruit freshness detection based on deep features and principal component analysis. Curr Res Food Sci 2023; 8:100656. [PMID: 38188650 PMCID: PMC10767316 DOI: 10.1016/j.crfs.2023.100656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Vegetable and fruit freshness detecting can ensure that consumers get vegetables and fruits with good taste and rich nutrition, improve the health level of diet, and ensure that the agricultural and food industries provide high-quality products to meet consumer needs and increase sales and market share. At present, the freshness detection of vegetables and fruits mainly relies on manual observation and judgment, which has the problems of subjectivity and low accuracy, and it is difficult to meet the needs of large-scale, high-efficiency, and rapid detection. Although some studies have shown that large-scale detection of vegetable and fruit freshness can be carried out based on artificially extracted features, there is still the problem of poor adaptability of artificially extracted features, which leads to low efficiency of freshness detection. To solve this problem, this paper proposes a novel method for detecting the freshness of vegetables and fruits more objectively, accurately and efficiently using deep features extracted by pre-trained deep learning models of different architectures. First, resized images of vegetables and fruits are fed into a pre-trained deep learning model for deep feature extraction. Then, the deep features are fused and the fused deep features are dimensionally reduced to a representative low-dimensional feature space by principal component analysis. Finally, vegetable and fruit freshness are detected by three machine learning methods. The experimental results show that combining the deep features extracted by the three architecture pre-trained deep learning models GoogLeNet, DenseNet-201 and ResNeXt-101 combined with PCA dimensionality reduction processing has achieved the highest accuracy rate of 96.98% for vegetable and fruit freshness detection. This research concluded that the proposed method is promising to improve the efficiency of freshness detection of vegetables and fruits.
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Affiliation(s)
- Yue Yuan
- School of Information Engineering, Shenyang University, Shenyang, 110042, China
| | - Xianlong Chen
- Liaoning Provincial Public Security Department, Shenyang, 110000, China
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3
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Vinicius da Silva Ferreira M, Barbosa JL, Kamruzzaman M, Barbin DF. Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:6120-6138. [PMID: 37937362 DOI: 10.1039/d3ay01192e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
An electronic nose (e-nose) is a device designed to recognize and classify odors. The equipment is built around a series of sensors that detect the presence of odors, especially volatile organic compounds (VOCs), and generate an electric signal (voltage), known as e-nose data, which contains chemical information. In the food business, the use of e-noses for analyses and quality control of fruits and plantation crops has increased in recent years. Their use is particularly relevant due to the lack of non-invasive and inexpensive methods to detect VOCs in crops. However, the majority of reports in the literature involve commercial e-noses, with only a few studies addressing low-cost e-nose (LC-e-nose) devices or providing a data-oriented description to assist researchers in choosing their setup and appropriate statistical methods to analyze crop data. Therefore, the objective of this study is to discuss the hardware of the two most common e-nose sensors: electrochemical (EC) sensors and metal oxide sensors (MOSs), as well as a critical review of the literature reporting MOS-based low-cost e-nose devices used for investigating plantations and fruit crops, including the main features of such devices. Miniaturization of equipment from lab-scale to portable and convenient gear, allowing producers to take it into the field, as shown in many appraised systems, is one of the future advancements in this area. By utilizing the low-cost designs provided in this review, researchers can develop their own devices based on practical demands such as quality control and compare results with those reported in the literature. Overall, this review thoroughly discusses the applications of low-cost e-noses based on MOSs for fruits, tea, and coffee, as well as the key features of their equipment (i.e., advantages and disadvantages) based on their technical parameters (i.e., electronic and physical parts). As a final remark, LC-e-nose technology deserves significant attention as it has the potential to be a valuable quality control tool for emerging countries.
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Affiliation(s)
- Marcus Vinicius da Silva Ferreira
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jose Lucena Barbosa
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
| | - Mohammed Kamruzzaman
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Douglas Fernandes Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
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4
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Bakhshipour A. A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques. Food Sci Nutr 2023; 11:6116-6132. [PMID: 37823103 PMCID: PMC10563735 DOI: 10.1002/fsn3.3548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 10/13/2023] Open
Abstract
A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.
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Affiliation(s)
- Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural SciencesUniversity of GuilanRashtIran
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5
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Gan Z, Zhou Q, Zheng C, Wang J. Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis. Biosens Bioelectron 2023; 237:115540. [PMID: 37523812 DOI: 10.1016/j.bios.2023.115540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/09/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023]
Abstract
Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.
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Affiliation(s)
- Ziyu Gan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Qin'an Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Chengyu Zheng
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Jun Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
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6
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Qin Y, Jia W, Sun X, LV H. Development of electronic nose for detection of micro-mechanical damages in strawberries. Front Nutr 2023; 10:1222988. [PMID: 37588052 PMCID: PMC10425553 DOI: 10.3389/fnut.2023.1222988] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/03/2023] [Indexed: 08/18/2023] Open
Abstract
A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries.
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Affiliation(s)
- Yingdong Qin
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Computer and Information Engineering, Beijing University of Agriculture, Beijing, China
| | - Wenshen Jia
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Department of Risk Assessment Lab for Agro-products (Beijing), Ministry of Agriculture and Rural Affairs, Beijing, China
- Key Laboratory of Urban Agriculture (North China), Ministry of Agriculture and Rural Affairs, Beijing, China
- Lu'an Branch, Anhui Institute of Innovation for Industrial Technology, Lu'an, China
| | - Xu Sun
- School of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, Liaoning, China
| | - Haolin LV
- College of Computer and Information, China Three Gorges University, Yichang, China
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7
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Han S, He K, An J, Qiao M, Ke R, Wang X, Xu Y, Tang X. Detection of Specific Volatile Organic Compounds in Tribolium castaneum (Herbst) by Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry. Foods 2023; 12:2484. [PMID: 37444222 DOI: 10.3390/foods12132484] [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: 05/24/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
The red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), is a major storage pest that could lead to a wide range of damage. Its secretions have a significant impact on the quality of stored grain and food, leading to serious food safety problems such as grain spoilage and food carcinogenesis. This study investigates new detection techniques for grain storage pests to improve grain insect detection in China. The primary volatile organic chemicals (VOCs) in these secretions are identified using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The specific VOCs that are unique to T. castaneum are selected as criteria for determining the presence of T. castaneum in the granary. To obtain more specific VOCs, experiments were designed for the analysis of T. castaneum samples under different extraction times, two types of SPME fibers and two GC-MS devices of different manufacturers. The experimental results showed that 12 VOCs were detected at relatively high levels, seven of which were common and which were not detected in other grains and grain insects. The seven compounds are 1-pentadecene, 2-methyl-p-benzoquinone, 2-ethyl-p-benzoquinone, 1-hexadecene, cis-9-tetradecen-1-ol, m-cresol and paeonol. These seven compounds can be used as volatile markers to identify the presence of T. castaneum, which could serve as a research foundation for the creation of new techniques for T. castaneum monitoring.
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Affiliation(s)
- Shaoyun Han
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Ke He
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Jing An
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Mengmeng Qiao
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Runhui Ke
- Sinolight Inspection& Certification Co., Ltd., Beijing 100083, China
| | - Xiao Wang
- Sinolight Inspection& Certification Co., Ltd., Beijing 100083, China
| | - Yang Xu
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Xiuying Tang
- College of Engineering, China Agricultural University, Beijing 100083, China
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8
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Lee SH, Kim HY. Effect of Seawater Curing Agent on the Flavor Profile of Dry-Cured Bacon Determined by Sensory Evaluation, Electronic Nose, and Fatty Composition Analysis. Foods 2023; 12:foods12101974. [PMID: 37238794 DOI: 10.3390/foods12101974] [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: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
The purpose of this study was to check the applicability of seawater as a natural curing agent by analyzing the difference it causes in the flavor of dry-aged bacon. Pork belly was cured for seven days, and dried and aged for twenty-one days. The curing methods included the following: wet curing with salt in water, dry curing with sea salt, brine curing with brine solution, and bittern curing with bittern solution. The seawater-treated groups showed a lower volatile basic nitrogen value than the sea-salt-treated groups (p < 0.05); dry curing showed a higher thiobarbituric acid reactive substance value than other treatments (p < 0.05). Methyl- and butane- volatile compounds and polyunsaturated fatty acids such as g-linolenic and eicosapentaenoic were the highest in the bittern-cured group, lending it superior results compared to those of the control and other treatments in sensory flavor analyses (cheesy and milky). Therefore, bittern is considered to have significant potential as a food-curing agent.
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Affiliation(s)
- Sol-Hee Lee
- Department of Animal Resources Science, Kongju National University, Yesan-Gun 32439, ChungNam-Do, Republic of Korea
| | - Hack-Youn Kim
- Department of Animal Resources Science, Kongju National University, Yesan-Gun 32439, ChungNam-Do, Republic of Korea
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9
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Li Y, Yang K, He Z, Liu Z, Lu J, Zhao D, Zheng J, Qian MC. Can Electronic Nose Replace Human Nose?-An Investigation of E-Nose Sensor Responses to Volatile Compounds in Alcoholic Beverages. ACS OMEGA 2023; 8:16356-16363. [PMID: 37179643 PMCID: PMC10173318 DOI: 10.1021/acsomega.3c01140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023]
Abstract
Electronic nose (E-nose) technology is frequently attempted to simulate the human olfactory system to recognize complex odors. Metal oxide semiconductors (MOSs) are E-noses' most popular sensor materials. However, these sensor responses to different scents were poorly understood. This study investigated the characteristic responses of sensors to volatile compounds in a MOS-based E-nose platform, using baijiu as an evaluation system. The results showed that the sensor array had distinctive responses for different volatile compounds, and the response intensities varied depending on the sensors and the volatile compounds. Some sensors had dose-response relationships in a specific concentration range. Among all the volatiles investigated in this study, fatty acid esters had the greatest contribution to the overall sensor response of baijiu. Different aroma types of Chinese baijiu and different brands of strong aroma-type baijiu were successfully classified using the E-nose. This study provided an understanding of detailed MOS sensor response with volatile compounds, which could be further applied to improve the E-nose technology and its practical application in food and beverages.
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Affiliation(s)
- Yuzhu Li
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Kangzhuo Yang
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Zhanglan He
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Zhipeng Liu
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Jialing Lu
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Dong Zhao
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Jia Zheng
- Flavor
Science Innovation Center, Technology Research Center, Wuliangye Yibin Co., Ltd., Yibin, Sichuan 644007, China
- Solid-State
Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin, Sichuan 644007, China
- Key
Laboratory of Wuliangye-Flavor Liquor Solid-State Fermentation, China
National Light Industry, Yibin, Sichuan 644007, China
| | - Michael C. Qian
- Department
of Food Science and Technology, Oregon State
University, Corvallis, Oregon 97331, United States
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10
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Imamura G, Minami K, Yoshikawa G. Repetitive Direct Comparison Method for Odor Sensing. BIOSENSORS 2023; 13:368. [PMID: 36979580 PMCID: PMC10046632 DOI: 10.3390/bios13030368] [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: 01/31/2023] [Revised: 03/02/2023] [Accepted: 03/08/2023] [Indexed: 06/18/2023]
Abstract
Olfactory sensors are one of the most anticipated applications of gas sensors. To distinguish odors-complex mixtures of gas species, it is necessary to extract sensor responses originating from the target odors. However, the responses of gas sensors tend to be affected by interfering gases with much higher concentrations than target odor molecules. To realize practical applications of olfactory sensors, extracting minute sensor responses of odors from major interfering gases is required. In this study, we propose a repetitive direct comparison (rDC) method, which can highlight the difference in odors by alternately injecting the two target odors into a gas sensor. We verified the feasibility of the rDC method on chocolates with two different flavors by using a sensor system based on membrane-type surface stress sensors (MSS). The odors of the chocolates were measured by the rDC method, and the signal-to-noise ratios (S/N) of the measurements were evaluated. The results showed that the rDC method achieved improved S/N compared to a typical measurement. The result also indicates that sensing signals could be enhanced for a specific combination of receptor materials of MSS and target odors.
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Affiliation(s)
- Gaku Imamura
- International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, Japan
- Graduate School of Information Science and Technology, Osaka University, 1-2 Yamadaoka, Suita 565-0871, Japan
| | - Kosuke Minami
- Research Center for Functional Materials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, Japan
| | - Genki Yoshikawa
- Research Center for Functional Materials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, Japan
- Materials Science and Engineering, Graduate School of Pure and Applied Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8571, Japan
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11
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Anisimov D, Abramov AA, Gaidarzhi VP, Kaplun DS, Agina EV, Ponomarenko SA. Food Freshness Measurements and Product Distinguishing by a Portable Electronic Nose Based on Organic Field-Effect Transistors. ACS OMEGA 2023; 8:4649-4654. [PMID: 36777610 PMCID: PMC9909782 DOI: 10.1021/acsomega.2c06386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/11/2023] [Indexed: 05/14/2023]
Abstract
Determination of food freshness, which is the most ancient role of the human sense of smell, is still a challenge for compact and inexpensive electronic nose devices. Fast, sensitive, and reusable sensors are long-awaited in the food industry to replace slow, labor-intensive, and expensive bacteriological methods. In this work, we present microbiological verification of a novel approach to food quality monitoring and spoilage detection using an electronic nose based on organic field-effect transistors (OFETs) and its application for distinguishing products. The compact device presented is able to detect spoilage-related gases as early as at the 4 × 104 CFU g-1 bacteria count level, which is 2 orders of magnitude below the safe consumption threshold. Cross-selective sensor array based on OFETs with metalloporphyrin receptors were made on a single substrate using solution processing leading to a low production cost. Moreover, machine learning methods applied to the sensor array response allowed us to compare spoilage profiles and separate them by the type of food: pork, chicken, fish, or milk. The approach presented can be used to monitor food spoilage and distinguish different products with an affordable and portable device.
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Affiliation(s)
- Daniil
S. Anisimov
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Anton A. Abramov
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Victoria P. Gaidarzhi
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Darya S. Kaplun
- The
Federal Research Centre “Fundamentals of Biotechnology”
of the Russian Academy of Sciences, Moscow 119071, Russia
| | - Elena V. Agina
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Sergey A. Ponomarenko
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
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12
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Monitoring Botrytis cinerea Infection in Kiwifruit Using Electronic Nose and Machine Learning Techniques. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02967-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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13
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Zhang J, Xia J, Zhang Q, Yang N, Li G, Zhang F. Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:4690-4702. [PMID: 36353817 DOI: 10.1039/d2ay01478e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.
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Affiliation(s)
- Jixiong Zhang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, 100193, China.
- National Observation and Research Station of Agriculture Green Development, Quzhou, 057250, China
| | - Jingjing Xia
- Institute of Materia Medica, Xinjiang University, Urumqi, 830017, China
| | | | - Nei Yang
- Nucteh Company Limited, Beijing, 100084, China.
| | - Guangqin Li
- Nucteh Company Limited, Beijing, 100084, China.
| | - Fusuo Zhang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, 100193, China.
- National Observation and Research Station of Agriculture Green Development, Quzhou, 057250, China
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14
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Abstract
Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.
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15
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Li X, Wang B, Yi C, Gong W. Gas sensing technology for meat quality assessment: A review. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xinxing Li
- Beijing Laboratory of Food Quality and Safety China Agricultural University Beijing China
- Nanchang Institute of Technology Nanchang China
| | - Biao Wang
- Beijing Laboratory of Food Quality and Safety China Agricultural University Beijing China
| | - Chen Yi
- Changchun Urban Planning & Research Center Changchun China
| | - Weiwei Gong
- China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute Beijing China
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16
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Wang H, Xiao H, Wu Y, Zhou F, Hua C, Ba L, Shamim S, Zhang W. Characterization of volatile compounds and microstructure in different tissues of ‘Eureka’ lemon ( Citrus limon). INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2022. [DOI: 10.1080/10942912.2022.2046600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Haiou Wang
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Research Institute for Agricultural Mechanization, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - HongWei Xiao
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Yulong Wu
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - Feng Zhou
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - Chun Hua
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - Long Ba
- State Laboratory of Bioelectronics, School of Biomedical Engineering and Department of Physics, Southeast University, Nanjing, PR China
| | - Sara Shamim
- State Laboratory of Bioelectronics, School of Biomedical Engineering and Department of Physics, Southeast University, Nanjing, PR China
| | - Wei Zhang
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
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17
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Electronic nose for detection of food adulteration: a review. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2022; 59:846-858. [PMID: 35185196 PMCID: PMC8814237 DOI: 10.1007/s13197-021-05057-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 10/21/2022]
Abstract
The food products may attract unscrupulous vendors to dilute it with inexpensive alternative food sources to achieve more profit. The risk of high value food adulteration with cheaper substitutes has reached an alarming stage in recent years. Commonly available detection methods for food adulteration are costly, time consuming and requires high degree of technical expertise. However, a rapid and suitable detection method for possible adulterant is being evolved to tackle the aforesaid issues. In recent years, electronic nose (e-nose) system is being evolved for falsification detection of food products with reliable and rapid way. E-nose has the ability to artificially perceive aroma and distinguish them. The use of chemometric analysis together with gas sensor arrays have shown to be a significant procedure for quality monitoring in food. E-nose techniques with numerous provisions are reliable and favourable for food industry in food fraud detection. In the present review, the contributions of gas sensor based e-nose system are discussed extensively with a view to ascertain the adulteration of food products.
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18
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Non-intrusive prediction of fruit spoilage and storage time via detecting volatiles in sealed packaging using laser spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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19
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Mohammad-Razdari A, Rousseau D, Bakhshipour A, Taylor S, Poveda J, Kiani H. Recent advances in E-monitoring of plant diseases. Biosens Bioelectron 2022; 201:113953. [PMID: 34998118 DOI: 10.1016/j.bios.2021.113953] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 02/09/2023]
Abstract
Infectious plant diseases are caused by pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, phytoplasma, and nematodes. Plant diseases have a significant effect on the plant quality and yield and they can destroy the entire plant if they are not controlled in time. To minimize disease-related losses, it is essential to identify and control pathogens in the early stages. Plant disease control is thus a fundamental challenge both for global food security and sustainable agriculture. Conventional methods for plant diseases control have given place to electronic control (E-monitoring) due to their lack of portability, being time consuming, need for a specialized user, etc. E-monitoring using electronic nose (e-nose), biosensors, wearable sensors, and 'electronic eyes' has attracted increasing attention in recent years. Detection, identification, and quantification of pathogens based on electronic sensors (E-sensors) are both convenient and practical and may be used in combination with conventional methods. This paper discusses recent advances made in E-sensors as component parts in combination with wearable sensors, in electronic sensing systems to control and detect viruses, bacteria, pathogens and fungi. In addition, future challenges using sensors to manage plant diseases are investigated.
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Affiliation(s)
- Ayat Mohammad-Razdari
- Department of Mechanical Engineering of Biosystems, Shahrekord University, 8818634141, Shahrekord, Iran.
| | - David Rousseau
- Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAe IRHS, Université d'Angers, France
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Stephen Taylor
- Mass Spectrometry and Instrumentation Group, Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK.
| | - Jorge Poveda
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra (UPNA), Campus Arrosadía, Pamplona, Spain
| | - Hassan Kiani
- Department of Biosystems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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20
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Müller-Maatsch J, van Ruth SM. Handheld Devices for Food Authentication and Their Applications: A Review. Foods 2021; 10:2901. [PMID: 34945454 PMCID: PMC8700508 DOI: 10.3390/foods10122901] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/18/2021] [Accepted: 11/21/2021] [Indexed: 12/18/2022] Open
Abstract
This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.
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Affiliation(s)
- Judith Müller-Maatsch
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 EV Wageningen, The Netherlands;
| | - Saskia M. van Ruth
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 EV Wageningen, The Netherlands;
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
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21
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Tang Y, Xu K, Zhao B, Zhang M, Gong C, Wan H, Wang Y, Yang Z. A novel electronic nose for the detection and classification of pesticide residue on apples. RSC Adv 2021; 11:20874-20883. [PMID: 35479381 PMCID: PMC9034013 DOI: 10.1039/d1ra03069h] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/04/2021] [Indexed: 12/28/2022] Open
Abstract
Excessive pesticide residues are a serious problem faced by food regulatory authorities, suppliers, and consumers. To assist with this challenge, this work aimed to develop a method of detecting and classifying pesticide residue on fruit samples using an electronic nose, through the application of three different data-recognition algorithms. The apple samples carried various concentrations of two known pesticides, namely cypermethrin and chlorpyrifos. Data collection was performed using a PEN3 electronic nose equipped with 10 metal oxide semiconductor (MOS) sensors. In order to classify and analyze these pesticide residues on the apple samples, principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) results were combined with sensor output responses to realize MOS sensor array data visualization. The results indicated that all three data-recognition algorithms accurately identified the pesticide residues in the apple samples, with the PCA algorithm exhibiting the best classification and discrimination ability. Consequently, this work has shown that the MOS electronic nose, in combination with data-recognition algorithms, can provide support for the rapid and non-destructive identification of pesticide residues in fruits and can provide an effective tool for the detection of pesticide residues in agricultural products. The MOS electronic nose in combination with data-recognition algorithms can provide an effective tool for the detection of pesticide residues in agricultural products.![]()
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Affiliation(s)
- Yong Tang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Kunli Xu
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Bo Zhao
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Meichao Zhang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China.,Bureau of Science, Technology, Agriculture and Livestock MaoXian, Aba Qiang and Tibetan Autonomous Prefecture Sichuan 623200 China
| | - Chenhui Gong
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Hailun Wan
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Yuanhui Wang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Zepeng Yang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
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22
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Fully integrated ultra-sensitive electronic nose based on organic field-effect transistors. Sci Rep 2021; 11:10683. [PMID: 34021171 PMCID: PMC8140082 DOI: 10.1038/s41598-021-88569-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/12/2021] [Indexed: 12/21/2022] Open
Abstract
Modern solid-state gas sensors approaching ppb-level limit of detection open new perspectives for process control, environmental monitoring and exhaled breath analysis. Organic field-effect transistors (OFETs) are especially promising for gas sensing due to their outstanding sensitivities, low cost and small power consumption. However, they suffer of poor selectivity, requiring development of cross-selective arrays to distinguish analytes, and environmental instability, especially in humid air. Here we present the first fully integrated OFET-based electronic nose with the whole sensor array located on a single substrate. It features down to 30 ppb limit of detection provided by monolayer thick active layers and operates in air with up to 95% relative humidity. By means of principal component analysis, it is able to discriminate toxic air pollutants and monitor meat product freshness. The approach presented paves the way for developing affordable air sensing networks for the Internet of Things.
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23
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Valcárcel M, Ibáñez G, Martí R, Beltrán J, Cebolla-Cornejo J, Roselló S. Optimization of electronic nose drift correction applied to tomato volatile profiling. Anal Bioanal Chem 2021; 413:3893-3907. [PMID: 33893513 DOI: 10.1007/s00216-021-03340-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/24/2021] [Accepted: 04/12/2021] [Indexed: 11/30/2022]
Abstract
E-noses can be routinely used to evaluate the volatile profile of tomato samples once the sensor drift and standardization issues are adequately solved. Short-term drift can be corrected using a strategy based on a multiplicative drift correction procedure coupled with a PLS adaptation of the component correction. It must be performed specifically for each sequence, using all sequence signals data. With this procedure, a drastic reduction of sensor signal %RSD can be obtained, ranging between 91.5 and 99.7% for long sequences and between 75.7 and 98.8% for short sequences. On the other hand, long-term drift can be fixed up using a synthetic reference standard mix (with a representation of main aroma volatiles of the species) to be included in each sequence that would enable sequence standardization. With this integral strategy, a high number of samples can be analyzed in different sequences, with a 94.4% success in the aggrupation of the same materials in PLS-DA two-dimensional graphical representations. Using this graphical interface, e-noses can be used to developed expandable maps of volatile profile similitudes, which will be useful to select the materials that most resemble breeding objectives or to analyze which preharvest and postharvest procedures have a lower impact on the volatile profile, avoiding the costs and sample limitations of gas chromatography.
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Affiliation(s)
- Mercedes Valcárcel
- Joint Research Unit UJI-UPV - Improvement of Agri-Food Quality, COMAV, Universitat Politècnica de València, Cno. de Vera s/n, 46022, València, Spain
| | - Ginés Ibáñez
- Joint Research Unit UJI-UPV - Improvement of Agri-Food Quality, Agricultural Sciences and Natural Environment Department, Universitat Jaume I, Avda. Sos Baynat s/n, 12071, Castelló de la Plana, Spain
| | - Raúl Martí
- Joint Research Unit UJI-UPV - Improvement of Agri-Food Quality, COMAV, Universitat Politècnica de València, Cno. de Vera s/n, 46022, València, Spain
| | - Joaquim Beltrán
- Research Institute for Pesticides and Water (IUPA), Universitat Jaume I, Avda. Sos Baynat s/n, 12071, Castelló de la Plana, Spain
| | - Jaime Cebolla-Cornejo
- Joint Research Unit UJI-UPV - Improvement of Agri-Food Quality, COMAV, Universitat Politècnica de València, Cno. de Vera s/n, 46022, València, Spain
| | - Salvador Roselló
- Joint Research Unit UJI-UPV - Improvement of Agri-Food Quality, Agricultural Sciences and Natural Environment Department, Universitat Jaume I, Avda. Sos Baynat s/n, 12071, Castelló de la Plana, Spain.
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24
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Aouadi B, Zaukuu JLZ, Vitális F, Bodor Z, Fehér O, Gillay Z, Bazar G, Kovacs Z. Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5479. [PMID: 32987908 PMCID: PMC7583984 DOI: 10.3390/s20195479] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/15/2020] [Accepted: 09/21/2020] [Indexed: 01/28/2023]
Abstract
Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.
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Affiliation(s)
- Balkis Aouadi
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - John-Lewis Zinia Zaukuu
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Flora Vitális
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Zsanett Bodor
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Orsolya Fehér
- Institute of Agribusiness, Faculty of Economics and Social Sciences, Szent István University, H-2100 Gödöllő, Hungary;
| | - Zoltan Gillay
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - George Bazar
- Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, H-7400 Kaposvár, Hungary;
- ADEXGO Kft., H-8230 Balatonfüred, Hungary
| | - Zoltan Kovacs
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
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25
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Amkor A, El Barbri N. A measurement prototype based on gas sensors for detection of pesticide residues in edible mint. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2020. [DOI: 10.1007/s11694-020-00617-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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26
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Jiang H, Liu T, He P, Ding Y, Chen Q. Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features. Food Chem 2020; 338:127828. [PMID: 32822904 DOI: 10.1016/j.foodchem.2020.127828] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/05/2020] [Accepted: 08/10/2020] [Indexed: 01/09/2023]
Abstract
The fatty acid content of flour is an important indicator for determining the deterioration of flour. We propose a novel rapid measurement method for fatty acid content during flour storage based on a self-designed color-sensitive gas sensor array. First, a color-sensitive gas sensor array was prepared to capture the odor changes during flour storage. The sensor features were then optimized using genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO). Finally, back propagation neural network (BPNN) models were established to measure the fatty acid content during flour storage. Experimental results showed that the optimization effects of the three algorithms improved in the following order: GA < ACO < PSO, for the sensor features optimization. In the validation set, the determination coefficient of the best PSO-BPNN model was 0.9837. The overall results demonstrate that the models established on the optimized features can realize rapid measurements of fatty acid content during flour storage.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Tong Liu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Peihuan He
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yuhan Ding
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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27
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Adedeji AA, Ekramirad N, Rady A, Hamidisepehr A, Donohue KD, Villanueva RT, Parrish CA, Li M. Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review. Foods 2020; 9:E927. [PMID: 32674380 PMCID: PMC7404779 DOI: 10.3390/foods9070927] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/04/2020] [Accepted: 07/06/2020] [Indexed: 01/06/2023] Open
Abstract
In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.
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Affiliation(s)
- Akinbode A. Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
| | - Nader Ekramirad
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
| | - Ahmed Rady
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
- Department of Biosystems and Agricultural Engineering, Alexandria University, Alexandria 21526, Egypt
| | - Ali Hamidisepehr
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
| | - Kevin D. Donohue
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA; (K.D.D.); (C.A.P.)
| | - Raul T. Villanueva
- Department of Entomology, University of Kentucky, Princeton, KY 42445-0469, USA;
| | - Chadwick A. Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA; (K.D.D.); (C.A.P.)
| | - Mengxing Li
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
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Jiang W, Gao D. Five Typical Stenches Detection Using an Electronic Nose. SENSORS 2020; 20:s20092514. [PMID: 32365549 PMCID: PMC7248900 DOI: 10.3390/s20092514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/03/2020] [Accepted: 04/07/2020] [Indexed: 02/06/2023]
Abstract
This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.
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Innamorato V, Longobardi F, Cervellieri S, Cefola M, Pace B, Capotorto I, Gallo V, Rizzuti A, Logrieco AF, Lippolis V. Quality evaluation of table grapes during storage by using 1H NMR, LC-HRMS, MS-eNose and multivariate statistical analysis. Food Chem 2020; 315:126247. [PMID: 32006866 DOI: 10.1016/j.foodchem.2020.126247] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 01/14/2020] [Accepted: 01/16/2020] [Indexed: 12/17/2022]
Abstract
Three non-targeted methods, i.e. 1H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified.
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Affiliation(s)
- Valentina Innamorato
- Dipartimento di Chimica, Università di Bari "Aldo Moro", Via Orabona 4, 70126 Bari, Italy; Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), Via Amendola 122/O, 70126 Bari, Italy
| | - Francesco Longobardi
- Dipartimento di Chimica, Università di Bari "Aldo Moro", Via Orabona 4, 70126 Bari, Italy.
| | - Salvatore Cervellieri
- Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), Via Amendola 122/O, 70126 Bari, Italy
| | - Maria Cefola
- Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Bernardo Pace
- Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Imperatrice Capotorto
- Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Vito Gallo
- Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, via Orabona 4, Bari I-70125, Italy
| | - Antonino Rizzuti
- Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, via Orabona 4, Bari I-70125, Italy
| | - Antonio F Logrieco
- Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), Via Amendola 122/O, 70126 Bari, Italy
| | - Vincenzo Lippolis
- Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), Via Amendola 122/O, 70126 Bari, Italy
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Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery. SENSORS 2019; 20:s20010050. [PMID: 31861804 PMCID: PMC6983139 DOI: 10.3390/s20010050] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/15/2019] [Accepted: 12/18/2019] [Indexed: 11/17/2022]
Abstract
Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.
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