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Li X, Li M, Li J, Gao Y, Liu C, Hao G. Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:1516-1535. [PMID: 38184781 PMCID: PMC11123445 DOI: 10.1111/pbi.14283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/09/2023] [Accepted: 12/21/2023] [Indexed: 01/08/2024]
Abstract
Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.
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Affiliation(s)
- Xiao‐Hong Li
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine ChemicalsGuizhou UniversityGuiyangChina
| | - Meng‐Zhao Li
- National Key Laboratory of Green Pesticide, College of ChemistryCentral China Normal UniversityWuhanChina
| | - Jing‐Yi Li
- National Key Laboratory of Green Pesticide, College of ChemistryCentral China Normal UniversityWuhanChina
| | - Yang‐Yang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine ChemicalsGuizhou UniversityGuiyangChina
| | - Chun‐Rong Liu
- National Key Laboratory of Green Pesticide, College of ChemistryCentral China Normal UniversityWuhanChina
| | - Ge‐Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine ChemicalsGuizhou UniversityGuiyangChina
- National Key Laboratory of Green Pesticide, College of ChemistryCentral China Normal UniversityWuhanChina
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2
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Wang D, Zhang M, Jiang Q, Mujumdar AS. Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application. Foods 2024; 13:1662. [PMID: 38890891 PMCID: PMC11171494 DOI: 10.3390/foods13111662] [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: 03/27/2024] [Revised: 05/02/2024] [Accepted: 05/24/2024] [Indexed: 06/20/2024] Open
Abstract
The quality of fresh foods tends to deteriorate rapidly during harvesting, storage, and transportation. Intelligent detection equipment is designed to monitor and ensure product quality in the supply chain, measure appropriate food quality parameters in real time, and thus minimize quality degradation and potential financial losses. Through various available tracking devices, consumers can obtain actionable information about fresh food products. This paper reviews the recent progress in intelligent detection equipment for sensing the quality deterioration of fresh foods, including computer vision equipment, electronic nose, smart colorimetric films, hyperspectral imaging (HSI), near-infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), ultrasonic non-destructive testing, and intelligent tracing equipment. These devices offer the advantages of high speed, non-destructive operation, precision, and high sensitivity.
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Affiliation(s)
- Dianyuan Wang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; (D.W.); (Q.J.)
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi 214122, China
| | - Min Zhang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; (D.W.); (Q.J.)
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi 214122, China
| | - Qiyong Jiang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; (D.W.); (Q.J.)
| | - Arun S. Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Ste. Anne decBellevue, QC H9X 3V9, Canada;
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3
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Zhang Y, Zareef M, Rong Y, Lin H, Chen Q, Ouyang Q. Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets. Food Chem 2024; 439:138172. [PMID: 38091785 DOI: 10.1016/j.foodchem.2023.138172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/07/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.
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Affiliation(s)
- Yuxin Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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4
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Modesti M, Alfieri G, Chieffo C, Mencarelli F, Vannini A, Catalani A, Chilosi G, Bellincontro A. Destructive and non-destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high-quality wines. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:2314-2325. [PMID: 37950679 DOI: 10.1002/jsfa.13120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 11/13/2023]
Abstract
BACKGROUND Botrytis cinerea (Bc) is the causative agent of gray mold disease in wine grape bunches. Under particular climatic and edaphic conditions, typical of some wine regions, the grapes infected by this fungus can develop noble rot, the basic phenomenon for the production of sweet botrytized wines or some high-quality dry wines, such as Amarone. The possibility of early detection of noble rot on plants and at postharvest is an interesting option for managing botrytized wines. RESULTS The present work aimed at early detection of noble rot and monitoring its development, at postharvest, on Trebbiano wine grapes by means of destructive and non-destructive analytical approaches (e.g., electronic nose and near-infrared spectroscopy). The development of Bc led to substantial modifications in grape composition, including dehydration, biosynthesis, and accumulation of different compounds due to Bc metabolism, grape stress responses, or both. However, these modifications are appreciable, notably at advanced stages of infection. Consequently, a specific focus was to monitor the infection in the first 72 h post inoculation for testing, potentially through non-destructive technologies, and to identify the real early stages of Bc development. CONCLUSION The destructive chemical analyses performed over the 16 monitored days confirmed what is widely reported in the literature regarding the metabolic/compositional changes that occur following the development of Bc. Moreover, non-destructive technologies allowed us to identify the evolution of Bc, even at early stages of its presence. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Margherita Modesti
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Gianmarco Alfieri
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Chiara Chieffo
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Fabio Mencarelli
- Department of Agriculture Food and Environment (DAFE), University of Pisa, Pisa, Italy
| | - Andrea Vannini
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Alessia Catalani
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Gabriele Chilosi
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Andrea Bellincontro
- Department for Innovation in Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
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Cascos G, Montero-Fernández I, Marcía-Fuentes JA, Aleman RS, Ruiz-Canales A, Martín-Vertedor D. Electronic Prediction of Chemical Contaminants in Aroma of Brewed Roasted Coffee and Quantification of Acrylamide Levels. Foods 2024; 13:768. [PMID: 38472880 DOI: 10.3390/foods13050768] [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: 01/23/2024] [Revised: 02/17/2024] [Accepted: 02/25/2024] [Indexed: 03/14/2024] Open
Abstract
The aim of this research was to apply an electronic device as indirect predictive technology to evaluate toxic chemical compounds in roasted espresso coffee. Fresh coffee beans were subjected to different thermal treatments and analyzed to determine volatile organic compounds, content of acrylamide and 5-hydroxymethylfurfural, sensory characteristics and electronic nose data. In total, 70 different volatile compounds were detected and grouped into 15 chemical families. The greatest percentage of these compounds were furans, pyrazines, pyridines and aldehydes. The positive aroma detected had the intensity of coffee odor and a roasted aroma, whereas the negative aroma was related to a burnt smell. A linear relationship between the toxic substances and the sensory defect was established. A high sensory defect implied a lower content of acrylamide and a higher content of 5-hydroxymethylfurfural. Finally, electronic signals were also correlated with the sensory defect. This relationship allowed us to predict the presence of these contaminants in the roasted coffee beverage with an indirect method by using this electronic device. Thus, this device may be useful to indirectly evaluate the chemical contaminants in coffee beverages according to their sensory characteristics.
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Affiliation(s)
- Gema Cascos
- Technological Institute of Food and Agriculture (CICYTEX-INTAEX), Junta of Extremadura, Avda. Adolfo Suárez, s/n, 06007 Badajoz, Spain
| | - Ismael Montero-Fernández
- Department of Chemical Engineering and Physical Chemistry, Area of Chemical Engineering, Faculty of Sciences, University of Extremadura, Avda. de Elvas, s/n, 06006 Badajoz, Spain
| | | | - Ricardo S Aleman
- School of Nutrition and Food Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA
| | - Antonio Ruiz-Canales
- Engineering Department, Polytechnic High School of Orihuela, Miguel Hernández University of Elche, 03312 Orihuela, Spain
| | - Daniel Martín-Vertedor
- Technological Institute of Food and Agriculture (CICYTEX-INTAEX), Junta of Extremadura, Avda. Adolfo Suárez, s/n, 06007 Badajoz, Spain
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6
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Wang Y, Zhu Q, Liu S, Jiao L, Dong D. Rapid Determination of Different Ripening Stages of Occidental Pears ( Pyrus communis L.) by Volatile Organic Compounds Using Proton-Transfer-Reaction Mass Spectrometry (PTR-MS). Foods 2024; 13:620. [PMID: 38397597 PMCID: PMC10887963 DOI: 10.3390/foods13040620] [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: 01/09/2024] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Determination of Occidental pear (Pyrus communis) ripening is difficult because the appearance of Occidental pears does not change significantly during the ripening process. Occidental pears at different ripening stages release different volatile organic compounds (VOCs), which can be used to determine fruit ripeness non-destructively and rapidly. In this study, VOCs were detected using proton-transfer-reaction mass spectrometry (PTR-MS). Notably, data were acquired within 1 min. Occidental pears harvested at five separate times were divided into three ripening stages: unripe, ripe, and overripe. The results showed that the composition of VOCs differed depending on the ripening stage. In particular, the concentrations of esters and terpenes significantly increased during the overripe stage. Three ripening stages were clearly discriminated by heatmap clustering and principal component analysis (PCA). This study provided a rapid and non-destructive method to evaluate the ripening stages of Occidental pears. The result can help fruit farmers to decide the optimum harvest time and hence reduce their economic losses.
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Affiliation(s)
- Yuanmo Wang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.W.); (Q.Z.); (D.D.)
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.W.); (Q.Z.); (D.D.)
| | - Songzhong Liu
- Institute of Forestry & Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Leizi Jiao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.W.); (Q.Z.); (D.D.)
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Daming Dong
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.W.); (Q.Z.); (D.D.)
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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7
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Madhubhashini MN, Liyanage CP, Alahakoon AU, Liyanage RP. Current applications and future trends of artificial senses in fish freshness determination: A review. J Food Sci 2024; 89:33-50. [PMID: 38051021 DOI: 10.1111/1750-3841.16865] [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: 07/20/2023] [Revised: 10/16/2023] [Accepted: 11/16/2023] [Indexed: 12/07/2023]
Abstract
Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e-nose), and electronic tongue (e-tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e-tongue determines the freshness of fish in a completely destructive way. There are developed e-noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large-scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses.
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Affiliation(s)
- M Nerandi Madhubhashini
- Department of Information and Communication Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Chamara P Liyanage
- Department of Information and Communication Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Amali U Alahakoon
- Department of Biosystems Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Rumesh Prasanga Liyanage
- Department of Biosystems Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
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8
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Tang M. Analysis and evaluate of agricultural resources using data analytic methods. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:627-649. [PMID: 38303437 DOI: 10.3934/mbe.2024027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
In the agricultural sector, farmers and agribusiness are confronted with a multitude of complex choices every day. These selections are influenced by multiple variables that significantly affect their outcomes. The primary source of revenue for a good deal of individuals is derived from the agricultural sector. The provision of precise and punctual predictions on crop yields has significant importance in facilitating informed investment choices and shaping agricultural policies. One of the challenges encountered is the presence of old or incomplete data about the accessibility of resources. This represents a significant obstacle in accurately ascertaining the present state of affairs. The process of evaluating becomes complex as a result of the diverse range of soil conditions and climatic factors. This research introduces a novel approach called Enhanced Gravitational Search Optimized based Gated Recurrent Unit (EGSO-GRU) for the purpose of calculating crop production. The dataset was first gathered and pre-processed using a normalization method. Enhanced independent component analyses (EICA) have been employed for the purpose of extracting features. To determine the suggest method achievement with regard to accuracy (95.89%), specificity (92.4%), MSE (0.071), RMSE (0.210) and MAE (0.199). The proposed method achieved greater crop prediction accuracy, outperforming the majority of the existing models. The necessity of this progress is vital to the successful operation of crops. The concept signifies a technological advancement aimed at optimizing agricultural resources, hence fostering enhanced productivity and long-term sustainability within the farming industry.
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Affiliation(s)
- Min Tang
- School of Marxism, Xi'an Jiaotong University, Xi'an 710049, China
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9
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [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: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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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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11
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Das J, Mishra HN. A comprehensive review of the spoilage of shrimp and advances in various indicators/sensors for shrimp spoilage monitoring. Food Res Int 2023; 173:113270. [PMID: 37803582 DOI: 10.1016/j.foodres.2023.113270] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/09/2023] [Accepted: 07/11/2023] [Indexed: 10/08/2023]
Abstract
Shrimp is a popular internationally traded shellfish due to its unique taste, texture, and nutritional value. Shrimp is highly perishable because it has enough free amino acids, high moisture levels, non-nitrogenous compounds used for microbial growth, and melanosis. Shrimp spoilage after death is caused by various reasons, like autolysis (endogenous proteinases actions during shrimp storage), growth of spoilage microorganisms, ATP degradation, melanin formation, and lipid peroxidation. A microbial byproduct, total volatile basic nitrogen, is one of the major reasons for the generation of foul odors from shrimp spoilage. Shrimp freshness monitoring is crucial for market sellers and exporters. Traditional methods for estimating shrimp freshness are expensive and inaccessible to the general public. Sensors are rapid, sensitive, selective, and portable food toxins' detection tools, devoid of expensive instruments, skilled people, sample pretreatment, and a long detection time. This review addresses shrimp spoilage causes. The mechanisms of different stages of shrimp spoilage after death, like rigor mortis, dissolution of rigor mortis, autolysis, and microbial spoilage mechanisms, are discussed. This review highlights the last five years' advances in shrimp freshness detection sensors and indicators like colorimetric pH indicators, fluorescence sensors, electronic noses, and biosensors, their working principles, and their sensitivities. Commercially available indicators and sensors for shrimp spoilage monitoring are also discussed. A review highlighting the applications of the different sensors and indicators for monitoring shrimp freshness is unavailable to date. Challenges and future perspectives in this field are explained at the end.
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Affiliation(s)
- Joyati Das
- Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
| | - Hari Niwas Mishra
- Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal 721302, India
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12
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Deng G, Li J, Liu H, Wang Y. Volatile compounds and aroma characteristics of mushrooms: a review. Crit Rev Food Sci Nutr 2023:1-18. [PMID: 37788142 DOI: 10.1080/10408398.2023.2261133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Mushrooms are popular due to their rich medicinal and nutritional value. Of the many characteristics of mushrooms, aroma has received extensive attention and research as a key determinant of consumer preference. This paper reviews the production, role and contribution of common volatile compounds (VCs) in wild and cultivated mushrooms, and explores the methods used to characterize them and the factors influencing aroma. To date, more than 347 common VCs have been identified in mushrooms, such as aldehydes, ketones, alcohols and sulfur-containing compounds. Extraction and identification of VCs is a critical step and combining multiple analytical methods is an effective strategy in mushroom aroma studies. In addition, the VCs and the aroma of mushrooms are affected by a variety of factors such as genetics, growing conditions, and processing methods. However, the mechanism of influence is unknown. Further studies on the production mechanisms of VCs, their contribution to aroma, and the factors influencing their formation need to be determined in order to fully elucidate aroma and flavor of mushrooms.
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Affiliation(s)
- Guangmei Deng
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Jieqing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
| | - Honggao Liu
- Yunnan Key Laboratory of Gastrodia and Fungi Symbiotic Biology, Zhaotong University, Zhaotong, Yunnan, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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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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14
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Buśko M, Gracka A, Jeleń H, Szablewska KS, Przybylska-Balcerek A, Szwajkowska-Michałek L, Góral T. The Effect of Organic and Conventional Cultivation Systems on the Profile of Volatile Organic Compounds in Winter Wheat Grain, Including Susceptibility to Fusarium Head Blight. Metabolites 2023; 13:1045. [PMID: 37887370 PMCID: PMC10609054 DOI: 10.3390/metabo13101045] [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: 09/04/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
The grain of 30 winter wheat cultivars differing in terms of their resistance to FHB (Fusarium head blight) was tested. The cultivars were grown in four variants of field trials established in a split-plot design: control without fungicides, chemical control of FHB with fungicides after Fusarium inoculation, Fusarium head inoculation, and organic cultivation. The profile of volatile compounds in grain samples was determined by mean headspace-solid phase microextraction and analyzed by gas chromatography time-of-flight mass spectroscopy. The identified volatile profile comprised 146 compounds belonging to 14 chemical groups. The lowest abundance of volatile organic compounds (VOCs) was found for the organic cultivation variant. The performed discriminant analysis facilitated the complete separation of grain for individual experimental variants based on the number of VOCs decreasing from 116 through 62, 37 down to 14. The grain from organic farming was characterized by a significantly different VOCs profile than the grain from the other variants of the experiment. The compounds 1-methylcycloheptanol, 2-heptanone, 2(3H)-furanone, and 5-hexyldihydro-2(3H)-furanone showed statistically significant differences between all four experimental variants.
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Affiliation(s)
- Maciej Buśko
- Department of Chemistry, Poznań University of Life Sciences, 60-625 Poznań, Poland; (M.B.); (K.S.S.); (L.S.-M.)
| | - Anna Gracka
- Food Volatilomics and Sensomics Group, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, 60-624 Poznań, Poland; (A.G.); (H.J.)
| | - Henryk Jeleń
- Food Volatilomics and Sensomics Group, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, 60-624 Poznań, Poland; (A.G.); (H.J.)
| | - Kinga Stuper Szablewska
- Department of Chemistry, Poznań University of Life Sciences, 60-625 Poznań, Poland; (M.B.); (K.S.S.); (L.S.-M.)
| | - Anna Przybylska-Balcerek
- Department of Chemistry, Poznań University of Life Sciences, 60-625 Poznań, Poland; (M.B.); (K.S.S.); (L.S.-M.)
| | - Lidia Szwajkowska-Michałek
- Department of Chemistry, Poznań University of Life Sciences, 60-625 Poznań, Poland; (M.B.); (K.S.S.); (L.S.-M.)
| | - Tomasz Góral
- Plant Breeding and Acclimatization Institute-National Research Institute, 05-870 Radzików, Poland;
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15
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Ferreira I, Dias T, Mouazen AM, Cruz C. Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle. Foods 2023; 12:3527. [PMID: 37835181 PMCID: PMC10572273 DOI: 10.3390/foods12193527] [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: 08/13/2023] [Revised: 09/04/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Terfezia arenaria is a desert truffle native to the Mediterranean Basin region, highly appreciated for its nutritional and aromatic properties. Despite the increasing interest in this desert truffle, T. arenaria is not listed as an edible truffle authorized for trade in the European Union. Therefore, our objective was to showcase T. arenaria's nutritional and chemical composition and volatile profile. The nutritional analysis showed that T. arenaria is a good source of carbohydrates (67%), proteins (14%), and dietary fibre (10%), resulting in a Nutri-Score A. The truffle's volatile profile was dominated by eight-carbon volatile compounds, with 1-octen-3-ol being the most abundant (64%), and 29 compounds were reported for the first time for T. arenaria. T. arenaria's nutritional and chemical compositions were similar to those of four commercial mushroom and truffle species, while the aromatic profile was not. An electronic nose corroborated that T. arenaria's aromatic profile differs from that of the other four tested mushroom and truffle species. Our data showed that T. arenaria is a valuable food resource with a unique aroma and an analogous composition to meat, which makes it an ideal source for plant-based meat products. Our findings could help promote a sustainable future exploitation of T. arenaria and ensure the quality and authenticity of this delicacy.
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Affiliation(s)
- Inês Ferreira
- cE3c—Centre for Ecology, Evolution and Environmental Changes & CHANGE, Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Bloco C2, 1749-016 Lisboa, Portugal; (I.F.); (C.C.)
| | - Teresa Dias
- cE3c—Centre for Ecology, Evolution and Environmental Changes & CHANGE, Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Bloco C2, 1749-016 Lisboa, Portugal; (I.F.); (C.C.)
| | - Abdul M. Mouazen
- Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium;
| | - Cristina Cruz
- cE3c—Centre for Ecology, Evolution and Environmental Changes & CHANGE, Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Bloco C2, 1749-016 Lisboa, Portugal; (I.F.); (C.C.)
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16
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Yin L, Jayan H, Cai J, El-Seedi HR, Guo Z, Zou X. Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics. Foods 2023; 12:2968. [PMID: 37569237 PMCID: PMC10419230 DOI: 10.3390/foods12152968] [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: 06/24/2023] [Revised: 07/28/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.
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Affiliation(s)
- Limei Yin
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Heera Jayan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Jianrong Cai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Hesham R. El-Seedi
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, Biology Medical Center, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
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17
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Faraco Filho RL, Oliveira Barino F, Calderano J, Valle Alvarenga ÍF, Campos D, Dos Santos AB. In-fiber Mach-Zehnder interferometer as a promising tool for optical nose and odor prediction during the fermentation process. OPTICS LETTERS 2023; 48:3905-3908. [PMID: 37527079 DOI: 10.1364/ol.486742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/21/2023] [Indexed: 08/03/2023]
Abstract
In this paper, we present an in-fiber Mach-Zehnder interferometer (MZI) applied to coffee bean fermentation monitoring. Two MZIs, based on a combination of a fiber taper cascaded by a micro-tapered long-period fiber grating, were installed in a fermentation barrel to monitor the liquids and gases released during the fermentation process. During this process, a variety of odors arise due to the yeast activity and their classification is important to decide when to stop the fermentation process. In this work, we show that the in-fiber MZIs are good candidates for optical noses in this scenario.
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18
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Abouelenein D, Acquaticci L, Alessandroni L, Borsetta G, Caprioli G, Mannozzi C, Marconi R, Piatti D, Santanatoglia A, Sagratini G, Vittori S, Mustafa AM. Volatile Profile of Strawberry Fruits and Influence of Different Drying Methods on Their Aroma and Flavor: A Review. Molecules 2023; 28:5810. [PMID: 37570780 PMCID: PMC10420878 DOI: 10.3390/molecules28155810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/20/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Strawberries are the most popular berry fruit in the world, due to their distinctive aroma, flavor, and known health properties. Because volatile substances play a large role in strawberry flavor, even little alterations can have a big impact on how the fruit tastes. Strawberries are thought to have a complex aroma. Fresh strawberry fruits contain more than 360 volatile compounds, including esters, furans, terpenes, alcohols, aldehydes, ketones, and sulfur compounds. Despite having far lower concentrations than esters, terpenoids, furanones, and sulfur compounds, all have a considerable impact on how people perceive the aroma of strawberries. With a focus on the active aroma components and the many analytical methods used to identify them, including gas chromatography, electronic nose sensing, and proton-transfer- reaction mass spectrometry, the present review's aim was to provide a summary of the relevant literature. Additionally, strawberry fruits are frequently dried to create a powder in order to increase their shelf life. Consequently, the impact of various drying techniques on strawberries' volatile profile was investigated in the current review. This review can be considered a good reference for research concerning the aroma profile of strawberries. It helps to better understand the complex aroma and flavor of strawberries and provides a guide for the effects of drying processing.
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Affiliation(s)
- Doaa Abouelenein
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
- Department of Pharmacognosy, Faculty of Pharmacy, Zagazig University, Zagazig 44519, Egypt
| | - Laura Acquaticci
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Laura Alessandroni
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Germana Borsetta
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Giovanni Caprioli
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Cinzia Mannozzi
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Riccardo Marconi
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Diletta Piatti
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Agnese Santanatoglia
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Gianni Sagratini
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Sauro Vittori
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
| | - Ahmed M. Mustafa
- CHemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy; (D.A.); (L.A.); (L.A.); (G.B.); (G.C.); (C.M.); (R.M.); (D.P.); (A.S.); (G.S.); (A.M.M.)
- Department of Pharmacognosy, Faculty of Pharmacy, Zagazig University, Zagazig 44519, Egypt
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19
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Xing Z, Zogona D, Wu T, Pan S, Xu X. Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food. Food Chem 2023; 415:135650. [PMID: 36868065 DOI: 10.1016/j.foodchem.2023.135650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/27/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023]
Abstract
Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.
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Affiliation(s)
- Zheng Xing
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China; Shenzhen Institute of Nutrition and Health, Shenzhen, Guangdong 518038, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture,Genome Analysis Laboratory of the Ministry of Agriculture,Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518038, China
| | - Daniel Zogona
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China
| | - Ting Wu
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China
| | - Siyi Pan
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China
| | - Xiaoyun Xu
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China; Shenzhen Institute of Nutrition and Health, Shenzhen, Guangdong 518038, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture,Genome Analysis Laboratory of the Ministry of Agriculture,Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518038, China.
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20
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Nadimi M, Hawley E, Liu J, Hildebrand K, Sopiwnyk E, Paliwal J. Enhancing traceability of wheat quality through the supply chain. Compr Rev Food Sci Food Saf 2023; 22:2495-2522. [PMID: 37078119 DOI: 10.1111/1541-4337.13150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/02/2023] [Accepted: 03/16/2023] [Indexed: 04/21/2023]
Abstract
With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.
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Affiliation(s)
- Mohammad Nadimi
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | - Jing Liu
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | | | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
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21
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Tian H, Xiong J, Chen S, Yu H, Chen C, Huang J, Yuan H, Lou X. Rapid identification of adulteration in raw bovine milk with soymilk by electronic nose and headspace-gas chromatography ion-mobility spectrometry. Food Chem X 2023; 18:100696. [PMID: 37187488 PMCID: PMC10176159 DOI: 10.1016/j.fochx.2023.100696] [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: 01/27/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
The adulteration of soymilk (SM) into raw bovine milk (RM) to gain profit without declaration could cause a health risk. In this study, electronic nose (E-nose) and headspace-gas chromatography ion-mobility spectrometry (HS-GC-IMS) were applied to establish a rapid and effective method to identify adulteration in RM with SM. The obtained data from HS-GC-IMS and E-nose can distinguish the adulterated samples with SM by principal component analysis. Furthermore, a quantitative model of partial least squares was established. The detection limits of E-nose and HS-GC-IMS quantitative models were 1.53% and 1.43%, the root mean square errors of prediction were 0.7390 and 0.5621, the determination coefficients of prediction were 0.9940 and 0.9958, and the relative percentage difference were 10.02 and 13.27, respectively, indicating quantitative regression and good prediction performances of SM adulteration levels in RM were achieved. This research can provide scientific information on the rapid, non-destructive and effective adulteration detection for RM.
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22
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Singh S, Gaur S. Development of rapid and non-destructive electric nose (E-nose) system for shelf life evaluation of different edible seeds. Food Chem 2023; 426:136562. [PMID: 37311301 DOI: 10.1016/j.foodchem.2023.136562] [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: 03/22/2023] [Revised: 05/24/2023] [Accepted: 06/05/2023] [Indexed: 06/15/2023]
Abstract
Shelf life estimation is crucial in ensuring the quality of food products. However, traditional methods are time-consuming and inefficient. Therefore, there is an urgent need for simple, efficient and rapid techniques for quality assessments. An electronic nose (E-nose) serves as a solution by rapidly and accurately detecting release of volatile organic compounds (VOCs) during food deterioration. This study aims to develop Arduino-Uno R3 microprocessor based E-nose, equipped with MQ4, MQ5, MQ9 and MQ135 sensors for evaluating shelf life of different edible seeds over the storage period of 150 days. Sensor values were recorded, revealing a significant increase (p-value ≤ 0.05) in MQ5 sensor readings for Nigella seeds from 349 to 480. Sensor values were positively correlated with physical, microbiological and Fourier-transform infrared (FTIR) spectroscopy parameters. Maximum peak shifts were observed from 3000 cm-1 to 2800 cm-1 and 1500 cm-1 to 1000 cm-1 wavenumbers. Hence, this study provides successful E-nose system to determine shelf life of seeds.
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Affiliation(s)
- Shubhi Singh
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida, India
| | - Smriti Gaur
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida, India.
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23
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Xie C, Zhou W. A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques. Foods 2023; 12:foods12112266. [PMID: 37297510 DOI: 10.3390/foods12112266] [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: 03/13/2023] [Revised: 05/13/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023] Open
Abstract
Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.
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Affiliation(s)
- Chuanqi Xie
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, The Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Weidong Zhou
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, The Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
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Damdam AN, Ozay LO, Ozcan CK, Alzahrani A, Helabi R, Salama KN. IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection. Foods 2023; 12:foods12112227. [PMID: 37297471 DOI: 10.3390/foods12112227] [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: 04/18/2023] [Revised: 05/23/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Food spoilage is a major concern in the food industry, especially for highly perishable foods such as beef. In this paper, we present a versatile Internet of Things (IoT)-enabled electronic nose system to monitor food quality by evaluating the concentrations of volatile organic compounds (VOCs). The IoT system consists mainly of an electronic nose, temperature/humidity sensors, and an ESP32-S3 microcontroller to send the sensors' data to the server. The electronic nose consists of a carbon dioxide gas sensor, an ammonia gas sensor, and an ethylene gas sensor. This paper's primary focus is to use the system for identifying beef spoilage. Hence, the system performance was examined on four beef samples stored at different temperatures: two at 4 °C and two at 21 °C. Microbial population quantifications of aerobic bacteria, Lactic Acid Bacteria (LAB), and Pseudomonas spp., in addition to pH measurements, were conducted to evaluate the beef quality during a period of 7 days to identify the VOCs concentrations that are associated with raw beef spoilage. The spoilage concentrations that were identified using the carbon dioxide, ammonia, and ethylene sensors were 552 ppm-4751 ppm, 6 ppm-8 ppm, and 18.4 ppm-21.1 ppm, respectively, as determined using a 500 mL gas sensing chamber. Statistical analysis was conducted to correlate the bacterial growth with the VOCs production, where it was found that aerobic bacteria and Pseudomonas spp. are responsible for most of the VOCs production in raw beef.
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Affiliation(s)
- Asrar Nabil Damdam
- Sensors Lab, Advanced Membranes and Porous Materials Center, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Levent Osman Ozay
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Cagri Kaan Ozcan
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Ashwaq Alzahrani
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Raghad Helabi
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Kahled Nabil Salama
- Sensors Lab, Advanced Membranes and Porous Materials Center, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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25
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Sun D, Zhou C, Hu J, Li L, Ye H. Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning. Food Chem 2023; 408:135166. [PMID: 36521293 DOI: 10.1016/j.foodchem.2022.135166] [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: 08/31/2022] [Revised: 11/24/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022]
Abstract
Off-flavors can have significant impacts on the quality of salmonid products. This study investigated the possibility of comprehensive off-flavor profiling considering both olfactory and taste sensory perspectives by combining near-infrared hyperspectral imaging (NIR-HSI) and machine/deep learning. Four feature extraction algorithms were employed for the extraction and interpretation of spectral fingerprint information regarding off-flavor-related compounds. Classification models, including the partial least squares discriminant analysis, least-squares support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were constructed using the full wavelengths and selected spectral features for the identification of off-flavor salmonids. The 1DCNN achieved the highest discrimination accuracy with full and selected wavelengths (i.e., 91.11 and 86.39 %, respectively). Furthermore, the prediction and visualization of off-flavor-related compounds were achieved with acceptable performances (R2 > 0.6) for practical applications. These results indicate the potential of NIR-HSI for the off-flavor profiling of salmonid muscle samples for producers and researchers.
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Affiliation(s)
- Dawei Sun
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, PR China.
| | - Chengquan Zhou
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, PR China.
| | - Jun Hu
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, PR China.
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, PR China.
| | - Hongbao Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, PR China.
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26
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Kumar A, Castro M, Feller JF. Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages. SENSORS (BASEL, SWITZERLAND) 2023; 23:4017. [PMID: 37112358 PMCID: PMC10141392 DOI: 10.3390/s23084017] [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: 03/10/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.
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27
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Zhang J, Pan L, Tu K. Aroma in freshly squeezed strawberry juice during cold storage detected by E-nose, HS–SPME–GC–MS and GC-IMS. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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28
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Portable beef-freshness detection platform based on colorimetric sensor array technology and bionic algorithms for total volatile basic nitrogen (TVB-N) determination. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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29
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Lin Y, Ma J, Sun DW, Cheng JH, Wang Q. A pH-Responsive colourimetric sensor array based on machine learning for real-time monitoring of beef freshness. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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30
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Cheli F, Ottoboni M, Fumagalli F, Mazzoleni S, Ferrari L, Pinotti L. E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology? Toxins (Basel) 2023; 15:146. [PMID: 36828460 PMCID: PMC9958648 DOI: 10.3390/toxins15020146] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/17/2023] [Accepted: 02/04/2023] [Indexed: 02/16/2023] Open
Abstract
Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.
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Affiliation(s)
- Federica Cheli
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
- CRC I-WE (Coordinating Research Centre: Innovation for Well-Being and Environment), University of Milan, 20100 Milan, Italy
| | - Matteo Ottoboni
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Francesca Fumagalli
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Sharon Mazzoleni
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Luca Ferrari
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Luciano Pinotti
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
- CRC I-WE (Coordinating Research Centre: Innovation for Well-Being and Environment), University of Milan, 20100 Milan, Italy
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31
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Flexible sensing enabled agri-food cold chain quality control: A review of mechanism analysis, emerging applications, and system integration. Trends Food Sci Technol 2023. [DOI: 10.1016/j.tifs.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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32
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Adnouni M, Jiang L, Zhang X, Zhang L, Pathare PB, Roskilly A. Computational modelling for decarbonised drying of agricultural products: Sustainable processes, energy efficiency, and quality improvement. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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33
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Sun P, Shi Y, Shi Y. Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose. SENSORS (BASEL, SWITZERLAND) 2023; 23:1524. [PMID: 36772572 PMCID: PMC9919135 DOI: 10.3390/s23031524] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/18/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.
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Affiliation(s)
- Pengjiao Sun
- The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
- Electronics and Communication Engineering School, Jilin Technology College of Electronic Information, Jilin 132021, China
| | - Yunbo Shi
- The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
- Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
- National Experimental Teaching Demonstration Center for Measurement and Control Technology and Instrumentation, Harbin University of Science and Technology, Harbin 150080, China
| | - Yeping Shi
- The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
- Electronics and Communication Engineering School, Jilin Technology College of Electronic Information, Jilin 132021, China
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34
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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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35
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Weng K, Song L, Bao Q, Cao Z, Zhang Y, Zhang Y, Chen G, Xu Q. Comparative Characterization of Key Volatile Compounds in Slow- and Fast-Growing Duck Raw Meat Based on Widely Targeted Metabolomics. Foods 2022; 11:foods11243975. [PMID: 36553717 PMCID: PMC9778640 DOI: 10.3390/foods11243975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
The volatile aroma compounds in raw duck meat strongly affect consumers' purchase decisions and they vary among breeds with different growth rates. In this study, slow-growing (SG) Liancheng White and fast-growing (FG) Cherry Valley ducks were selected, and their volatile compounds were characterized using electric nose and gas chromatography-mass spectrometry. Furthermore, a widely targeted metabolomics approach was used to investigate the metabolites associated with volatile compounds. The results showed that hexanal, nonanal, octanal, heptanal, and 2-pentylfuran were abundantly present in duck meat, regardless of the breed. The higher nonanal and octanal rates contributed to the fatty and fruity aroma in SG meat than FG meat, while FG meat had a mushroom note resulting from higher octenol. Furthermore, widely targeted metabolomics showed a lower carnitine content in SG meat, which might promote lipid deposition to produce more octanal and nonanal. Higher sugar and amino acid contents led to a meaty aroma, whereas more trimethylamine N-oxide may generate a fishy note in SG meat. Taken together, this study characterized the raw duck meat aroma and provided the basic mechanism of the formation of the key volatile compound.
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Affiliation(s)
- Kaiqi Weng
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
| | - Lina Song
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
| | - Qiang Bao
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
| | - Zhengfeng Cao
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
| | - Yu Zhang
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
| | - Yang Zhang
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
| | - Guohong Chen
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
| | - Qi Xu
- Key Laboratory for Evaluation and Utilization of Poultry Genetic Resources of Ministry of Agriculture and Rural Affairs, Yangzhou University, Yangzhou 225009, China
- Correspondence: ; Tel.: +86-0514-8799-7206
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36
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Bassey AP, Boateng EF, Zhu Z, Zhou T, Nasiru MM, Guo Y, Dou H, Ye K, Li C, Zhou G. Volatilome evaluation of modified atmosphere packaged chilled and super-chilled pork loins using electronic nose and HS-GC-IMS integration. Food Packag Shelf Life 2022. [DOI: 10.1016/j.fpsl.2022.100953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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37
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Li X, Cheng X, Yang J, Wang X, Lü X. Unraveling the difference in physicochemical properties, sensory, and volatile profiles of dry chili sauce and traditional fresh dry chili sauce fermented by Lactobacillus plantarum PC8 using electronic nose and HS-SPME-GC-MS. FOOD BIOSCI 2022. [DOI: 10.1016/j.fbio.2022.102057] [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]
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38
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Gao G, Zhang X, Yan Z, Cheng Y, Li H, Xu G. Monitoring Volatile Organic Compounds in Different Pear Cultivars during Storage Using HS-SPME with GC-MS. Foods 2022; 11:foods11233778. [PMID: 36496586 PMCID: PMC9735802 DOI: 10.3390/foods11233778] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/16/2022] [Accepted: 11/19/2022] [Indexed: 11/25/2022] Open
Abstract
Aroma, which plays an essential role in food perception and acceptability, depends on various mixture of volatile organic compounds (VOCs). Meanwhile, as a field of metabolomics, VOC analysis is highly important for aroma improvement and discrimination purposes. In this work, VOCs in pear fruits were determined via headspace solid-phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) to study variations among different cultivars and storage stages. In 12 cultivars of pear fruits, a total of 121 VOCs were quantified, including 40 esters, 32 alcohols, 16 aldehydes, 13 alkenes, 11 ketones, 4 acids, and 5 other compounds. The types and amounts of VOCs in different cultivars varied dramatically, which were in the range of 13-71 and 3.63-55.65 mg/kg FW (fresh weight), respectively. The Dr. Guyot cultivar showed the highest level of VOCs, both in type and amount. After 21 days storage at 4 °C, total concentration of VOCs increased from initial levels of 50.76 to 101.33 mg/kg FW. Storage at 20 °C made a larger contribution to production for VOCs than that at 4 °C, resulting in the maximum content of VOCs (117.96 mg/kg FW) in fruit after 14 days storage at 4 °C plus 7 days at 20 °C. During storage, the content of esters showed a gradual increase, while the content of alcohols and aldehydes decreased. Based on the results presented, related alcohols were recognized as the intermediates of conversion from aldehydes to esters.
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Affiliation(s)
- Guanwei Gao
- Ministry of Agriculture and Rural Affairs, Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, China
- Laboratory of Quality & Safety Risk Assessment for Fruit, Xingcheng 125100, China
| | - Xinnan Zhang
- Ministry of Agriculture and Rural Affairs, Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, China
- Key Laboratory of Germplasm Resources Utilization of Horticultural Crops, Ministry of Agriculture and Rural Afffairs, Xingcheng 125100, China
| | - Zhen Yan
- Ministry of Agriculture and Rural Affairs, Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, China
- Laboratory of Quality & Safety Risk Assessment for Fruit, Xingcheng 125100, China
| | - Yang Cheng
- Ministry of Agriculture and Rural Affairs, Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, China
- Laboratory of Quality & Safety Risk Assessment for Fruit, Xingcheng 125100, China
| | - Haifei Li
- Ministry of Agriculture and Rural Affairs, Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, China
- Laboratory of Quality & Safety Risk Assessment for Fruit, Xingcheng 125100, China
| | - Guofeng Xu
- Ministry of Agriculture and Rural Affairs, Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, China
- Laboratory of Quality & Safety Risk Assessment for Fruit, Xingcheng 125100, China
- Correspondence:
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39
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Zhang H, Han Y. A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm. SENSORS (BASEL, SWITZERLAND) 2022; 22:8977. [PMID: 36433576 PMCID: PMC9698078 DOI: 10.3390/s22228977] [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: 09/30/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 06/16/2023]
Abstract
To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.
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40
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Hassoun A, Jagtap S, Trollman H, Garcia-Garcia G, Abdullah NA, Goksen G, Bader F, Ozogul F, Barba FJ, Cropotova J, Munekata PE, Lorenzo JM. Food processing 4.0: Current and future developments spurred by the fourth industrial revolution. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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41
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Li Y, Sun J, Huang L, Liu S, Wang S, Zhang D, Zhu M, Wang J. Nanozyme-encoded luminescent detection for food safety analysis: An overview of mechanisms and recent applications. Compr Rev Food Sci Food Saf 2022; 21:5077-5108. [PMID: 36200572 DOI: 10.1111/1541-4337.13055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 08/25/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
With the rapid growth in global food production, delivery, and consumption, reformative food analytical techniques are required to satisfy the monitoring requirements of speed and high sensitivity. Nanozyme-encoded luminescent detections (NLDs) integrating nanozyme-based rapid detections with luminescent output signals have emerged as powerful methods for food safety monitoring, not only because of their preeminent performance in analysis, such as rapid, facile, low background signal, and ultrasensitive, but also due to their strong attractiveness for future sensing research. However, the lack of a full understanding of the fundamentals of NLDs for food safety detection technologies limits their further application. In this review, a systematic overview of the mechanisms of NLDs and their applications in the food industry is summarized, which covers the nanozyme-mimicking types and their luminescent signal generation mechanisms, as well as their applications in monitoring common foodborne contaminants. As demonstrated by previous studies, NLDs are bridging the gap to practical-oriented food analytical technologies and various opportunities to improve their food analytical performance to be considered in the future are proposed.
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Affiliation(s)
- Yuechun Li
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Jing Sun
- Qinghai Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
| | - Lunjie Huang
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
| | - Sijie Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Shaochi Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Daohong Zhang
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
| | - Mingqiang Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
| | - Jianlong Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling, China
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42
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Pulluri KK, Kumar VN. Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose. SENSORS (BASEL, SWITZERLAND) 2022; 22:7789. [PMID: 36298140 PMCID: PMC9609363 DOI: 10.3390/s22207789] [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: 08/31/2022] [Revised: 09/23/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Food adulteration is the most serious problem found in the food industry as it harms people's healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.
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Lin Y, Ma J, Wang Q, Sun DW. Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Crit Rev Food Sci Nutr 2022; 63:1649-1669. [PMID: 36222697 DOI: 10.1080/10408398.2022.2131725] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510641, China
| | - Qijun Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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Wu Y, Zhang J, Hu X, Huang X, Zhang X, Zou X, Shi J. A visible colorimetric sensor array based on chemo-responsive dyes and chemometric algorithms for real-time potato quality monitoring systems. Food Chem 2022; 405:134717. [DOI: 10.1016/j.foodchem.2022.134717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/20/2022] [Accepted: 10/20/2022] [Indexed: 11/28/2022]
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Wu Z, Ye X, Bian F, Yu G, Gao G, Ou J, Wang Y, Li Y, Du X. Determination of the geographical origin of Tetrastigma hemsleyanum Diels & Gilg using an electronic nose technique with multiple algorithms. Heliyon 2022; 8:e10801. [PMID: 36203902 PMCID: PMC9529587 DOI: 10.1016/j.heliyon.2022.e10801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/08/2022] [Accepted: 09/23/2022] [Indexed: 11/15/2022] Open
Abstract
Tetrastigma hemsleyanum Diels & Gilg, an herbal medicinal plant, is planted widely in bamboo forests in southern China to promote economic benefits. Volatile compounds (VOCs) of T. hemsleyanum from different geographical regions are difficult to identify in field forests. In this study, VOCs from leaf samples of different geographical origins were analyzed using an electronic nose with 10 different sensors. Principal component analysis (PCA), partial least-squares regression (PLS), hierarchical cluster analysis (HCA), and radial basis function (RBF) neural networks were used to determine differences among different local samples. The results demonstrated that PCA achieved an accurate discrimination percentage of 91.31% for different samples and HCA separated the samples into different groups. The RBF neural network was successfully applied to predict samples with no specified localities. T. hemsleyanum samples from geographically close regions tended to group together, whereas those from distant geographical regions showed obvious differences. These results indicate that an electronic nose is an effective tool for detecting VOCs and discriminating the geographical origins of T. hemsleyanum. This study provides insights for further studies on the fast detection of VOCs from plants and effect of forests and plant herbal medicines on improving air quality.
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Chen L, Ning F, Zhao L, Ming H, Zhang J, Yu W, Yi S, Luo L. Quality assessment of royal jelly based on physicochemical properties and flavor profiles using HS-SPME-GC/MS combined with electronic nose and electronic tongue analyses. Food Chem 2022; 403:134392. [DOI: 10.1016/j.foodchem.2022.134392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022]
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Andre RS, Mercante LA, Facure MHM, Sanfelice RC, Fugikawa-Santos L, Swager TM, Correa DS. Recent Progress in Amine Gas Sensors for Food Quality Monitoring: Novel Architectures for Sensing Materials and Systems. ACS Sens 2022; 7:2104-2131. [PMID: 35914109 DOI: 10.1021/acssensors.2c00639] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The increasing demand for food production has necessitated the development of sensitive and reliable methods of analysis, which allow for the optimization of storage and distribution while ensuring food safety. Methods to quantify and monitor volatile and biogenic amines are key to minimizing the waste of high-protein foods and to enable the safe consumption of fresh products. Novel materials and device designs have allowed the development of portable and reliable sensors that make use of different transduction methods for amine detection and food quality monitoring. Herein, we review the past decade's advances in volatile amine sensors for food quality monitoring. First, the role of volatile and biogenic amines as a food-quality index is presented. Moreover, a comprehensive overview of the distinct amine gas sensors is provided according to the transduction method, operation strategies, and distinct materials (e.g., metal oxide semiconductors, conjugated polymers, carbon nanotubes, graphene and its derivatives, transition metal dichalcogenides, metal organic frameworks, MXenes, quantum dots, and dyes, among others) employed in each case. These include chemoresistive, fluorometric, colorimetric, and microgravimetric sensors. Emphasis is also given to sensor arrays that record the food quality fingerprints and wireless devices that operate as radiofrequency identification (RFID) tags. Finally, challenges and future opportunities on the development of new amine sensors are presented aiming to encourage further research and technological development of reliable, integrated, and remotely accessible devices for food-quality monitoring.
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Affiliation(s)
- Rafaela S Andre
- Nanotechnology National Laboratory for Agriculture (LNNA), Embrapa Instrumentação, 13560-970, Sao Carlos, São Paulo, Brazil
| | - Luiza A Mercante
- Institute of Chemistry, Federal University of Bahia (UFBA), 40170-280, Salvador, Bahia, Brazil
| | - Murilo H M Facure
- Nanotechnology National Laboratory for Agriculture (LNNA), Embrapa Instrumentação, 13560-970, Sao Carlos, São Paulo, Brazil.,PPGQ, Department of Chemistry, Center for Exact Sciences and Technology, Federal University of Sao Carlos (UFSCar), 13565-905, Sao Carlos, São Paulo, Brazil
| | - Rafaela C Sanfelice
- Science and Technology Institute, Federal University of Alfenas, 37715-400, Poços de Caldas, Minas Gerais, Brazil
| | - Lucas Fugikawa-Santos
- São Paulo State University - UNESP, Institute of Geosciences and Exact Sciences, 13506-700, Rio Claro, São Paulo, Brazil
| | - Timothy M Swager
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Daniel S Correa
- Nanotechnology National Laboratory for Agriculture (LNNA), Embrapa Instrumentação, 13560-970, Sao Carlos, São Paulo, Brazil.,PPGQ, Department of Chemistry, Center for Exact Sciences and Technology, Federal University of Sao Carlos (UFSCar), 13565-905, Sao Carlos, São Paulo, Brazil
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48
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Kim C, Lee KK, Kang MS, Shin DM, Oh JW, Lee CS, Han DW. Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review. Biomater Res 2022; 26:40. [PMID: 35986395 PMCID: PMC9392354 DOI: 10.1186/s40824-022-00287-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/13/2022] [Indexed: 11/19/2022] Open
Abstract
Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.
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Roy M, Doddappa M, Yadav BK, Jaganmohan R, Sinija VR, Manickam L, Sarvanan S. Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:4097-4108. [PMID: 34997578 DOI: 10.1002/jsfa.11759] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/15/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA). RESULTS The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods. CONCLUSION The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Mrinmoy Roy
- Planning and Monitoring Cell, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Manoj Doddappa
- Planning and Monitoring Cell, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Binod K Yadav
- Liaison Office, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Rangarajan Jaganmohan
- Department of Food Product Development, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Vadakkepulppara Rn Sinija
- Food Processing Business Incubation Centre, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Loganathan Manickam
- Department of Academics and Human Resource Development, Indian Institute of Food Processing Technology, Thanjavur, India
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Hassoun A, Alhaj Abdullah N, Aït-Kaddour A, Ghellam M, Beşir A, Zannou O, Önal B, Aadil RM, Lorenzo JM, Mousavi Khaneghah A, Regenstein JM. Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Crit Rev Food Sci Nutr 2022; 64:873-889. [PMID: 35950635 DOI: 10.1080/10408398.2022.2110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | | | - Mohamed Ghellam
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Ayşegül Beşir
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Oscar Zannou
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Begüm Önal
- Gourmet International Ltd, Izmir, Turkey
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Jose M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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