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Sonwal S, Gupta VK, Shukla S, Umapathi R, Ghoreishian SM, Han S, Bajpai VK, Cho Y, Huh YS. Panoramic view of artificial fruit ripening agents sensing technologies and the exigency of developing smart, rapid, and portable detection devices: A review. Adv Colloid Interface Sci 2024; 331:103199. [PMID: 38909548 DOI: 10.1016/j.cis.2024.103199] [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: 12/22/2023] [Revised: 04/22/2024] [Accepted: 05/18/2024] [Indexed: 06/25/2024]
Abstract
Recently, the availability of point-of-care sensor systems has led to the rapid development of smart and portable devices for the detection of hazardous analytes. The rapid flow of artificially ripened fruits into the market is associated with an elevated risk to human life, agriculture, and the ecosystem due to the use of artificial fruit ripening agents (AFRAs). Accordingly, there is a need for the development of "Point-of-care Sensors" to detect AFRAs due to several advantages, such as simple operation, promising detection mechanism, higher selectivity and sensitivity, compact, and portable. Traditional detection approaches are time-consuming and inappropriate for on-the-spot analyses. Presented comprehensive review aimed to reveal how such technology has systematically evolved over time (through conventional, advanced, and portable smart techniques) detection detect AFRA, till date. Moreover, focuses and highlights a framework of initiatives undertaken for technological advancements in the development of smart the portable detection techniques (kits) for the onsite detection of AFRAs in fruits with in-depth discussion over sensing mechanism and analytical performance of the sensing technology. Notably, colorimetric detection methods have the greatest potential for real-time monitoring of AFRA and its residues because they are easy to assemble, have a high level of selectivity and sensitivity, and can be read by the human eye independently. This study sought to differentiate between traditional credible strategies by presenting new prospects, perceptions, and challenges related to portable devices. This review provides systematic framework of advances in portable field recognition strategies for the on-spot AFRA detection in fruits and critical information for development of new paper-based portable sensors for fruit diagnostic sectors.
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Affiliation(s)
- Sonam Sonwal
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | - Vivek Kumar Gupta
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | - Shruti Shukla
- Department of Nanotechnology, North-Eastern Hill University (NEHU), East Khasi Hills, Shillong, Meghalaya 793022, India
| | - Reddicherla Umapathi
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | | | - Soobin Han
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | - Vivek Kumar Bajpai
- Department of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea
| | - Youngjin Cho
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of korea.
| | - Yun Suk Huh
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea.
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2
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Trebar M, Žalik A, Vidrih R. Assessment of 'Golden Delicious' Apples Using an Electronic Nose and Machine Learning to Determine Ripening Stages. Foods 2024; 13:2530. [PMID: 39200457 PMCID: PMC11353998 DOI: 10.3390/foods13162530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/29/2024] [Accepted: 08/08/2024] [Indexed: 09/02/2024] Open
Abstract
Consumers often face a lack of information regarding the quality of apples available in supermarkets. General appearance factors, such as color, mechanical damage, or microbial attack, influence consumer decisions on whether to purchase or reject the apples. Recently, devices known as electronic noses provide an easy-to-use and non-destructive assessment of ripening stages based on Volatile Organic Compounds (VOCs) emitted by the fruit. In this study, the 'Golden Delicious' apples, stored and monitored at the ambient temperature, were analyzed in the years 2022 and 2023 to collect data from four Metal Oxide Semiconductor (MOS) sensors (MQ3, MQ135, MQ136, and MQ138). Three ripening stages (less ripe, ripe, and overripe) were identified using Principal Component Analysis (PCA) and the K-means clustering approach from various datasets based on sensor measurements in four experiments. After applying the K-Nearest Neighbors (KNN) model, the results showed successful classification of apples for specific datasets, achieving an accuracy higher than 75%. For the dataset with measurements from all experiments, an impressive accuracy of 100% was achieved on specific test sets and on the evaluation set from new, completely independent experiments. Additionally, correlation and PCA analysis showed that choosing two or three sensors can provide equally successful results. Overall, the e-nose results highlight the importance of analyzing data from several experiments performed over a longer period after the harvest of apples. There are similarities and differences in investigated VOC parameters (ethylene, esters, alcohols, and aldehydes) for less or more mature apples analyzed during autumn or spring, which can improve the determination of the ripening stage with higher predicting success for apples investigated in the spring.
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Affiliation(s)
- Mira Trebar
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
| | - Anamarie Žalik
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia; (A.Ž.); (R.V.)
| | - Rajko Vidrih
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia; (A.Ž.); (R.V.)
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Qiao J, Su G, Yuan L, Wu L, Weng X, Liu S, Feng Y, Jiang D, Chen Y, Ma Y. Effect of swelling agent treatment on grape fruit quality and the application of electronic nose identification detection. FRONTIERS IN PLANT SCIENCE 2024; 14:1292335. [PMID: 38298605 PMCID: PMC10828016 DOI: 10.3389/fpls.2023.1292335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024]
Abstract
The swelling agent is a plant growth regulator that alters the composition and content of nutrients and volatile gases in the fruit. To identify whether grape fruit had been treated with swelling agent, the odor information and quality indexes of grape berries treated with different concentrations of swelling agent were examined by using electronic nose technology and traditional methods. The contents of soluble sugars, soluble solids, soluble proteins and vitamin C were significantly increased in N-(2-chloro-4-pyridyl)-N'-phenylurea (CPPU) treated fruit. The contents of hexanal, (E)-2-hexenal, and nonanal aldehydes decreased significantly. Similarly, the levels of phenyl ethanol, 1-octanol, ethanol, and ethyl acetate alcohols and esters also decreased noticeably. Additionally, the levels of damascenone, linalool, and geraniol ketones and terpenoids decreased. However, the contents of benzaldehyde, D-limonene, acetic acid and hexanoic acid increased. In addition, the electrical signals generated by the electronic nose (e-nose) were analyzed by linear discriminant analysis (LDA), support vector machine (SVM) and random forest (RF). The average recognition rate of SVM was 94.4%. The results showed that electronic nose technology can be used to detect whether grapes have been treated with swelling agent, and it is an economical and efficient detection method.
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Affiliation(s)
- Jianlei Qiao
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Guoqiang Su
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Liang Yuan
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Lin Wu
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Xiaohui Weng
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China
- Weihai Institute for Bionics, Jilin University, Weihai, China
| | - Shuang Liu
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Yucai Feng
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Dan Jiang
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Yuxuan Chen
- College of Horticulture, Jilin Agricultural University, Changchun, China
| | - Yuan Ma
- College of Horticulture, Jilin Agricultural University, Changchun, China
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Asadi M, Ghasemnezhad M, Bakhshipour A, Olfati JA, Mirjalili MH. Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems. BMC PLANT BIOLOGY 2024; 24:13. [PMID: 38163882 PMCID: PMC10759769 DOI: 10.1186/s12870-023-04661-6] [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: 05/27/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.
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Affiliation(s)
- Mojdeh Asadi
- Department of Horticultural Sciences, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Mahmood Ghasemnezhad
- Department of Horticultural Sciences, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Jamal-Ali Olfati
- Department of Horticultural Sciences, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Mohammad Hossein Mirjalili
- Department of Agriculture, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran
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Yuan Y, Chen X. Vegetable and fruit freshness detection based on deep features and principal component analysis. Curr Res Food Sci 2023; 8:100656. [PMID: 38188650 PMCID: PMC10767316 DOI: 10.1016/j.crfs.2023.100656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Vegetable and fruit freshness detecting can ensure that consumers get vegetables and fruits with good taste and rich nutrition, improve the health level of diet, and ensure that the agricultural and food industries provide high-quality products to meet consumer needs and increase sales and market share. At present, the freshness detection of vegetables and fruits mainly relies on manual observation and judgment, which has the problems of subjectivity and low accuracy, and it is difficult to meet the needs of large-scale, high-efficiency, and rapid detection. Although some studies have shown that large-scale detection of vegetable and fruit freshness can be carried out based on artificially extracted features, there is still the problem of poor adaptability of artificially extracted features, which leads to low efficiency of freshness detection. To solve this problem, this paper proposes a novel method for detecting the freshness of vegetables and fruits more objectively, accurately and efficiently using deep features extracted by pre-trained deep learning models of different architectures. First, resized images of vegetables and fruits are fed into a pre-trained deep learning model for deep feature extraction. Then, the deep features are fused and the fused deep features are dimensionally reduced to a representative low-dimensional feature space by principal component analysis. Finally, vegetable and fruit freshness are detected by three machine learning methods. The experimental results show that combining the deep features extracted by the three architecture pre-trained deep learning models GoogLeNet, DenseNet-201 and ResNeXt-101 combined with PCA dimensionality reduction processing has achieved the highest accuracy rate of 96.98% for vegetable and fruit freshness detection. This research concluded that the proposed method is promising to improve the efficiency of freshness detection of vegetables and fruits.
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Affiliation(s)
- Yue Yuan
- School of Information Engineering, Shenyang University, Shenyang, 110042, China
| | - Xianlong Chen
- Liaoning Provincial Public Security Department, Shenyang, 110000, China
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Sun M, Ma J, Cai Z, Yan J, Ma R, Yu M, Xie Y, Shen Z. Sensory Determination of Peach and Nectarine Germplasms with Instrumental Analysis. Foods 2023; 12:4444. [PMID: 38137247 PMCID: PMC10743018 DOI: 10.3390/foods12244444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/30/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
The flavour and mouthfeel of peaches are crucial qualities of peach germplasm resources that significantly influence consumer preferences. In this study, we utilized 212 peach germplasm resources from the Nanjing Peach Resource Repository, National Fruit Germplasm facility, Jiangsu Academy of Agricultural Sciences as materials for sensory analysis, electronic nose analysis, and composition analysis via high-performance liquid chromatography (HPLC). In the sensory analysis, we divided 212 peach germplasms into three clusters based on hierarchical cluster analysis (d = 5). No.27, No.151, and No.46 emerged as the most representative of these clusters. The electronic nose was used to conduct an evaluation of the aroma profiles of the 212 peach germplasms, revealing that the primary distinguishing factors of peach aroma can be attributed to three sensors: W1S (methane), W1W (terpenes and organosulfur compounds), and W5S (hydrocarbons and aromatic compounds). The primary differences in the aromatic substances were characterized by sensors W2W (aromatic compounds, sulphur, and chlorine compounds) and W1C (aromatic benzene). The HPLC analysis indicated that the persistence of peach sensory characteristics was positively correlated with acids and sourness and negatively correlated with sweetness and the ratio of sugar to acids. The overall impression of the 212 peach germplasms revealed a negative correlation with acids, while a positive correlation was observed between the overall impression and the ratio of sugar to acids. Therefore, this study substantially contributes to the preliminary screening of the analysed specific characteristics of peach germplasms such as No.27, No.46, No.151, and No.211. These selections may provide valuable information for the potential creation of superior germplasm resources.
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Affiliation(s)
- Meng Sun
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China
| | - Julin Ma
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
| | - Zhixiang Cai
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China
| | - Juan Yan
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China
| | - Ruijuan Ma
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China
| | - Mingliang Yu
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China
| | - Yinfeng Xie
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
| | - Zhijun Shen
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, China; (M.S.)
- Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Street, Nanjing 210014, 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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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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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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10
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Palumbo M, Attolico G, Capozzi V, Cozzolino R, Corvino A, de Chiara MLV, Pace B, Pelosi S, Ricci I, Romaniello R, Cefola M. Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview. Foods 2022; 11:3925. [PMID: 36496732 PMCID: PMC9737221 DOI: 10.3390/foods11233925] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/23/2022] [Accepted: 11/28/2022] [Indexed: 12/09/2022] Open
Abstract
Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.
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Affiliation(s)
- Michela Palumbo
- Department of Science of Agriculture, Food and Environment, University of Foggia, Via Napoli, 25, 71122 Foggia, Italy
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Giovanni Attolico
- Institute on Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council of Italy (CNR), Via G. Amendola, 122/O, 70126 Bari, Italy
| | - Vittorio Capozzi
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Rosaria Cozzolino
- Institute of Food Science, National Research Council (CNR), Via Roma 64, 83100 Avellino, Italy
| | - Antonia Corvino
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Maria Lucia Valeria de Chiara
- Department of Science of Agriculture, Food and Environment, University of Foggia, Via Napoli, 25, 71122 Foggia, Italy
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Bernardo Pace
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Sergio Pelosi
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Ilde Ricci
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
| | - Roberto Romaniello
- Department of Science of Agriculture, Food and Environment, University of Foggia, Via Napoli, 25, 71122 Foggia, Italy
| | - Maria Cefola
- Institute of Sciences of Food Production, National Research Council of Italy (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
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