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Li L, Jia X, Fan K. Recent advance in nondestructive imaging technology for detecting quality of fruits and vegetables: a review. Crit Rev Food Sci Nutr 2024:1-19. [PMID: 39291966 DOI: 10.1080/10408398.2024.2404639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
As an integral part of daily dietary intake, the market demand for fruits and vegetables is continuously growing. However, traditional methods for assessing the quality of fruits and vegetables are prone to subjective influences, destructive to samples, and fail to comprehensively reflect internal quality, thereby resulting in various shortcomings in ensuring food safety and quality control. Over the past few decades, imaging technologies have rapidly evolved and been widely employed in nondestructive detection of fruit and vegetable quality. This paper offers a thorough overview of recent advancements in nondestructive imaging technologies for assessing the quality of fruits and vegetables, including hyperspectral imaging (HSI), fluorescence imaging (FI), magnetic resonance imaging (MRI), thermal imaging (TI), terahertz imaging, X-ray imaging (XRI), ultrasonic imaging, and microwave imaging (MWI). The principles and applications of these imaging techniques in nondestructive testing are summarized. The challenges and future trends of these technologies are discussed.
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Affiliation(s)
- Lijing Li
- College of Life Science, Yangtze University, Jingzhou, Hubei, China
| | - Xiwu Jia
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, Hubei, China
| | - Kai Fan
- College of Life Science, Yangtze University, Jingzhou, Hubei, China
- Institute of Food Science and Technology, Yangtze University, Jingzhou, Hubei, China
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2
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Zheng Y, Luo X, Gao Y, Sun Z, Huang K, Gao W, Xu H, Xie L. Lycopene detection in cherry tomatoes with feature enhancement and data fusion. Food Chem 2024; 463:141183. [PMID: 39278075 DOI: 10.1016/j.foodchem.2024.141183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 08/15/2024] [Accepted: 09/05/2024] [Indexed: 09/17/2024]
Abstract
Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.
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Affiliation(s)
- Yuanhao Zheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, PR China
| | - Xuan Luo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Yuan Gao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, PR China; Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Zhizhong Sun
- College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, PR China
| | - Kang Huang
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
| | - Weilu Gao
- Department of Electrical and Computer Engineering, The University of Utah, 201 Presidents' Cir, Salt Lake City, UT 84112, USA
| | - Huirong Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, PR China; Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Lijuan Xie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, PR China.
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3
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Ryu J, Kim S, Lim Y, Ohn JH, Kim SW, Cho JH, Park HS, Lee J, Kim ES, Kim NH, Song JE, Kim SH, Suh EC, Mukhtorov D, Park JH, Kim SK, Kim HW. Sodium Intake Estimation in Hospital Patients Using AI-Based Imaging: Prospective Pilot Study. JMIR Form Res 2024; 8:e48690. [PMID: 38363594 PMCID: PMC10907947 DOI: 10.2196/48690] [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: 05/08/2023] [Revised: 12/31/2023] [Accepted: 01/02/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Measurement of sodium intake in hospitalized patients is critical for their care. In this study, artificial intelligence (AI)-based imaging was performed to determine sodium intake in these patients. OBJECTIVE The applicability of a diet management system was evaluated using AI-based imaging to assess the sodium content of diets prescribed for hospitalized patients. METHODS Based on the information on the already investigated nutrients and quantity of food, consumed sodium was analyzed through photographs obtained before and after a meal. We used a hybrid model that first leveraged the capabilities of the You Only Look Once, version 4 (YOLOv4) architecture for the detection of food and dish areas in images. Following this initial detection, 2 distinct approaches were adopted for further classification: a custom ResNet-101 model and a hyperspectral imaging-based technique. These methodologies focused on accurate classification and estimation of the food quantity and sodium amount, respectively. The 24-hour urine sodium (UNa) value was measured as a reference for evaluating the sodium intake. RESULTS Results were analyzed using complete data from 25 participants out of the total 54 enrolled individuals. The median sodium intake calculated by the AI algorithm (AI-Na) was determined to be 2022.7 mg per day/person (adjusted by administered fluids). A significant correlation was observed between AI-Na and 24-hour UNa, while there was a notable disparity between them. A regression analysis, considering patient characteristics (eg, gender, age, renal function, the use of diuretics, and administered fluids) yielded a formula accounting for the interaction between AI-Na and 24-hour UNa. Consequently, it was concluded that AI-Na holds clinical significance in estimating salt intake for hospitalized patients using images without the need for 24-hour UNa measurements. The degree of correlation between AI-Na and 24-hour UNa was found to vary depending on the use of diuretics. CONCLUSIONS This study highlights the potential of AI-based imaging for determining sodium intake in hospitalized patients.
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Affiliation(s)
- Jiwon Ryu
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sejoong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Yejee Lim
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Jung Hun Ohn
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sun-Wook Kim
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Jae Ho Cho
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Hee Sun Park
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Jongchan Lee
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Eun Sun Kim
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Nak-Hyun Kim
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Ji Eun Song
- Department of Nursing, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Su Hwan Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eui-Chang Suh
- Department of Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | - Jung Hyun Park
- LOAPI-Healthcare, AItheNutrigene, Seongnam-si, Republic of Korea
| | - Sung Kweon Kim
- LOAPI-Healthcare, AItheNutrigene, Seongnam-si, Republic of Korea
| | - Hye Won Kim
- Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
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4
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Li P, Zheng J, Li P, Long H, Li M, Gao L. Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8. SENSORS (BASEL, SWITZERLAND) 2023; 23:6701. [PMID: 37571485 PMCID: PMC10422388 DOI: 10.3390/s23156701] [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: 06/28/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023]
Abstract
The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.
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Affiliation(s)
| | | | | | | | | | - Lihong Gao
- Chongqing Academy of Agricultural Sciences, Chongqing 401329, China
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5
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Duckena L, Alksnis R, Erdberga I, Alsina I, Dubova L, Duma M. Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy. Foods 2023; 12:foods12101990. [PMID: 37238808 DOI: 10.3390/foods12101990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R2 = 0.86, R2 = 0.84, R2 = 0.82, and R2= 0.73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively.
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Affiliation(s)
- Lilija Duckena
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Reinis Alksnis
- Department of Mathematics, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ieva Erdberga
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ina Alsina
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Laila Dubova
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Mara Duma
- Department of Chemistry, Faculty of Food Technology, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
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6
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Grabska J, Beć KB, Ueno N, Huck CW. Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review. Foods 2023; 12:foods12101946. [PMID: 37238763 DOI: 10.3390/foods12101946] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.
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Affiliation(s)
- Justyna Grabska
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Krzysztof B Beć
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Nami Ueno
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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7
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Fan M, Rakotondrabe TF, Chen G, Guo M. Advances in microbial analysis: based on volatile organic compounds of microorganisms in food. Food Chem 2023; 418:135950. [PMID: 36989642 DOI: 10.1016/j.foodchem.2023.135950] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/30/2022] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
In recent years, microbial volatile organic compounds (mVOCs) produced by microbial metabolism have attracted more and more attention because they can be used to detect food early contamination and flaws. So far, many analytical methods have been reported for the determination of mVOCs in food, but few integrated review articles discussing these methods are published. Consequently, mVOCs as indicators of food microbiological contamination and their generation mechanism including carbohydrate, amino acid, and fatty acid metabolism are introduced. Meanwhile, a detailed summary of the mVOCs sampling methods such as headspace, purge trap, solid phase microextraction, and needle trap is presented, and a systematic and critical review of the analytical methods (ion mobility spectrometry, electronic nose, biosensor, and so on) of mVOCs and their application in the detection of food microbial contamination is highlighted. Finally, the future concepts that can help improve the detection of food mVOCs are prospected.
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Affiliation(s)
- Minxia Fan
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China.
| | - Tojofaniry Fabien Rakotondrabe
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Guilin Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Mingquan Guo
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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8
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Ghanei Ghooshkhaneh N, Mollazade K. Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review. FOOD BIOPROCESS TECH 2023. [DOI: 10.1007/s11947-023-03005-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
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9
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Innovative non-destructive technologies for quality monitoring of pineapples: Recent advances and applications. Trends Food Sci Technol 2023. [DOI: 10.1016/j.tifs.2023.02.005] [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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10
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Ye W, Xu W, Yan T, Yan J, Gao P, Zhang C. Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review. Foods 2022; 12:foods12010132. [PMID: 36613348 PMCID: PMC9818947 DOI: 10.3390/foods12010132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/06/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
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Affiliation(s)
- Weixin Ye
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Wei Xu
- College of Agriculture, Shihezi University, Shihezi 832003, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Jingkun Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
- Correspondence: (P.G.); (C.Z.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
- Correspondence: (P.G.); (C.Z.)
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11
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Li X, Wang D, Ma F, Yu L, Mao J, Zhang W, Jiang J, Zhang L, Li P. Rapid detection of sesame oil multiple adulteration using a portable Raman spectrometer. Food Chem 2022; 405:134884. [DOI: 10.1016/j.foodchem.2022.134884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/14/2022]
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12
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Tang T, Zhang M, Mujumdar AS. Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology. Compr Rev Food Sci Food Saf 2022; 21:5171-5198. [PMID: 36156851 DOI: 10.1111/1541-4337.13039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 01/28/2023]
Abstract
Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.
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Affiliation(s)
- Tiantian Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, Jiangsu, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Montreal, Quebec, Canada
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13
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Chen Q, Qian J, Yang H, Wu W. Sustainable food cold chain logistics: From microenvironmental monitoring to global impact. Compr Rev Food Sci Food Saf 2022; 21:4189-4209. [PMID: 35904269 DOI: 10.1111/1541-4337.13014] [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: 03/11/2022] [Revised: 06/02/2022] [Accepted: 07/05/2022] [Indexed: 01/28/2023]
Abstract
Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon. Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.
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Affiliation(s)
- Qian Chen
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jianping Qian
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Han Yang
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenbin Wu
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
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14
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Preparation of Methylcellulose Film-Based CO2 Indicator for Monitoring the Ripeness Quality of Mango Fruit cv. Nam Dok Mai Si Thong. Polymers (Basel) 2022; 14:polym14173616. [PMID: 36080690 PMCID: PMC9460386 DOI: 10.3390/polym14173616] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/24/2022] Open
Abstract
Day-to-day advancements in food science and technology have increased. Indicators, especially biopolymer-incorporated organic dye indicators, are useful for monitoring the ripeness quality of agricultural fruit products. In this investigation, methylcellulose films—containing pH dye-based indicators that change color depending on the carbon dioxide (CO2) levels—were prepared. The level of CO2 on the inside of the packaging container indicated the ripeness of the fruit. Changes in the CO2 level, caused by the ripeness metabolite during storage, altered the pH. The methylcellulose-based film contained pH-sensitive dyes (bromothymol blue and methyl red), which responded (through visible color change) to CO2 levels produced by ripeness metabolites formed during respiration. The indicator solution and indicator label were monitored for their response to CO2. In addition, a kinetic approach was used to correlate the response of the indicator label to the changes in mango ripeness. Color changes (the total color difference of a mixed pH dye-based indicator), correlated well with the CO2 levels in mango fruit. In the ‘Nam Dok Mai Si Thong’ mango fruit model, the indicator response correlated with respiration patterns in real-time monitoring of ripeness at various constant temperatures. Based on the storage test, the indicator labels exhibited color changes from blue, through light bright green, to yellow, when exposed to CO2 during storage time, confirming the minimal, half-ripe, and fully-ripe levels of mango fruit, respectively. The firmness and titratable acidity (TA) of the fruit decreased from 44.54 to 2.01 N, and 2.84 to 0.21%, respectively, whereas the soluble solid contents (SSC) increased from 10.70 to 18.26% when the fruit ripened. Overall, we believe that the application of prepared methylcellulose-based CO2 indicator film can be helpful in monitoring the ripeness stage, or quality of, mango and other fruits, with the naked eye, in the food packaging system.
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15
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Manzoor MF, Hussain A, Naumovski N, Ranjha MMAN, Ahmad N, Karrar E, Xu B, Ibrahim SA. A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products. Front Nutr 2022; 9:901342. [PMID: 35928834 PMCID: PMC9343702 DOI: 10.3389/fnut.2022.901342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/31/2022] [Indexed: 01/10/2023] Open
Abstract
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
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Affiliation(s)
| | - Abid Hussain
- Department of Agriculture and Food Technology, Faculty of Life Science, Karakoram International University, Gilgit-Baltistan, Pakistan
| | - Nenad Naumovski
- School of Rehabilitation and Exercise Science, Faculty of Health, University of Canberra, Canberra, ACT, Australia
- Functional Foods and Nutrition Research (FFNR) Laboratory, University of Canberra, Bruce, ACT, Australia
| | | | - Nazir Ahmad
- Department of Nutritional Sciences, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Emad Karrar
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- *Correspondence: Bin Xu
| | - Salam A. Ibrahim
- Food Microbiology and Biotechnology Laboratory, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
- Salam A. Ibrahim
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16
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Mohd Ali M, Hashim N, Abd Aziz S, Lasekan O. Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms. AGRICULTURE 2022; 12:1013. [DOI: 10.3390/agriculture12071013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars, i.e., MD2, Morris, and Josapine, which were subjected to different storage temperatures, i.e., 5, 10, and 25 °C and a relative humidity of 85% to 90%. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among the different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100%. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars, which could enhance the grading and sorting processes of the fruit.
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17
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Zhang J, Wu C, Yuan R, Huang JA, Yang X. Gap controlled self-assembly Au@Ag@Au NPs for SERS assay of thiram. Food Chem 2022; 390:133164. [PMID: 35551030 DOI: 10.1016/j.foodchem.2022.133164] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/23/2022] [Accepted: 05/03/2022] [Indexed: 12/21/2022]
Abstract
Thiram (TRM), one dithiocarbamate fungicide, is hazardous due to its ever-growing threat to our production and living. In order to detecting TRM more sensitively, a subtle surface-enhanced Raman scattering (SERS) substrate was reported to achieve TRM detection based on oil-water biphasic self-assembly interface of multi-interstitial Au@Ag@Au NPs crosslinking with 4,4' -Diamino-p-Terphenyl (DATP). This Au@Ag@Au@DATP array shows a noteworthy enhanced Raman signal and stability by controlling the inter-particle spacing of Au@Ag@Au NPs, which overcomes problems of traditional randomly self-assembly methods without cross linker. The Au@Ag@Au@DATP array attained the limit of detection (LOD) of 7.56 × 10-3 ppb for TRM. In addition, this work gives a new approach for controlling gap of SERS hot spot, which have distinct potential in rapid assessment and identification of pesticides on foods.
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Affiliation(s)
- Jiale Zhang
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, PR China
| | - Caijun Wu
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, PR China
| | - Ruo Yuan
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, PR China
| | - Jian-An Huang
- Faculty of Medicine, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 5A, 90220 Oulu, Finland
| | - Xia Yang
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, PR China.
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18
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Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This paper presents a rule-based methodology for dynamic viewpoint selection for maturity classification of red and yellow sweet peppers. The method makes an online decision to capture an additional next-best viewpoint based on an economic analysis that considers potential misclassification and robot operational costs. The next-best viewpoint is selected based on color variations on the pepper. Peppers were classified into mature and immature using a random forest classifier based on principle components of various color features derived from an RGB-D camera. The method first attempts to classify maturity based on a single viewpoint. An additional viewpoint is acquired and added to the point cloud only when it is deemed profitable. The methodology was evaluated using leave-one-out cross-validation on datasets of 69 red and 70 yellow sweet peppers from three different maturity stages. Classification accuracy was increased by 6% and 5% using dynamic viewpoint selection along with 52% and 12% decrease in economic costs for red and yellow peppers, respectively, compared to using a single viewpoint. Sensitivity analyses were performed for misclassification and robot operational costs.
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19
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Hebling E Tavares JP, da Silva Medeiros ML, Barbin DF. Near-infrared techniques for fraud detection in dairy products: A review. J Food Sci 2022; 87:1943-1960. [PMID: 35362099 DOI: 10.1111/1750-3841.16143] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/09/2022] [Accepted: 03/16/2022] [Indexed: 01/14/2023]
Abstract
The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.
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Affiliation(s)
| | | | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, Brazil
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20
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Manzoor MF, Hussain A, Tazeddinova D, Abylgazinova A, Xu B. Assessing the Nutritional-Value-Based Therapeutic Potentials and Non-Destructive Approaches for Mulberry Fruit Assessment: An Overview. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6531483. [PMID: 35371246 PMCID: PMC8970939 DOI: 10.1155/2022/6531483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/15/2022] [Indexed: 01/22/2023]
Abstract
Among different fruits, mulberry is the most highlighted natural gift in its superior nutritional and bioactive composition, indispensable for continuing a healthy life. It also acts as a hepatoprotective immunostimulator and improves vision, anti-microbial, anti-cancer agent, anti-stress activity, atherosclerosis, neuroprotective functions, and anti-obesity action. The mulberry fruits also help reduce neurological disorders and mental illness. The main reason for that is the therapeutic potentials present in the nutritional components of the mulberry fruit. The available methods for assessing mulberry fruits are mainly chromatographic based, which are destructive and possess many limitations. However, recently some non-invasive techniques, including chlorophyll fluorescence, image processing, and hyperspectral imaging, were employed to detect various mulberry fruit attributes. The present review attempts to collect and explore available information regarding the nutritional and medicinal importance of mulberry fruit. Besides, non-destructive methods established for the fruit are also elaborated. This work helps encourage many more research works to dug out more hidden information about the essential nutrition of mulberry that can be helpful to resolve many mental-illness-related issues.
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Affiliation(s)
| | - Abid Hussain
- Department of Agriculture and Food Technology, Karakoram International University, Gilgit, Pakistan
| | - Diana Tazeddinova
- Department of Technology and Catering Organization, South Ural State University, Chelyabinsk, Russia
- Higher School of Technologies of Food and Processing Productions, Zhangir Khan West Kazakhstan Agrarian Technical University, Uralsk, Kazakhstan
| | - Aizhan Abylgazinova
- Higher School of Technologies of Food and Processing Productions, Zhangir Khan West Kazakhstan Agrarian Technical University, Uralsk, Kazakhstan
- Scientific-Production Center of Livestock and Veterinary Medicine, Nur-Sultan, Kazakhstan
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
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21
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Reddy P, Guthridge KM, Panozzo J, Ludlow EJ, Spangenberg GC, Rochfort SJ. Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview. SENSORS 2022; 22:s22051981. [PMID: 35271127 PMCID: PMC8914962 DOI: 10.3390/s22051981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 11/30/2022]
Abstract
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.
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Affiliation(s)
- Priyanka Reddy
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - Kathryn M. Guthridge
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - Joe Panozzo
- Agriculture Victoria Research, 110 Natimuk Road, Horsham, VIC 3400, Australia;
- Centre for Agriculture Innovation, University of Melbourne, Parkville, VIC 3010, Australia
| | - Emma J. Ludlow
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - German C. Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Simone J. Rochfort
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
- Correspondence:
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22
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Edge Detection Aided Geometrical Shape Analysis of Indian Gooseberry (Phyllanthus emblica) for Freshness Classification. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-021-02206-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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23
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Nondestructive Methods for the Quality Assessment of Fruits and Vegetables Considering Their Physical and Biological Variability. FOOD ENGINEERING REVIEWS 2022. [DOI: 10.1007/s12393-021-09300-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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24
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Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach. SENSORS 2022; 22:s22020414. [PMID: 35062374 PMCID: PMC8780071 DOI: 10.3390/s22020414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/16/2021] [Accepted: 12/26/2021] [Indexed: 11/16/2022]
Abstract
A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good, obtaining global information of its interior. While deep learning (DL) methods have outperformed state-of-the-art benchmarks in various applications, the reason for lacking adaptation of DL algorithms such as convolutional neural networks (CNNs) can be traced back to its high data inefficiency and the absence of annotated data. Active learning is a framework that has been heavily used in machine learning when the labelled instances are scarce or cumbersome to obtain. This is specifically of interest when the DL algorithm is highly uncertain about the label of an instance. By allowing the human-in-the-loop for guidance, a continuous improvement of the DL algorithm based on a sample efficient manner can be obtained. This paper seeks to study the applicability of active learning when grading 'Galia' muskmelons based on its shelf life. We propose k-Determinantal Point Processes (k-DPP), which is a purely diversity-based method that allows to take influence on the exploration within the feature space based on the chosen subset k. While getting coequal results to uncertainty-based approaches when k is large, we simultaneously obtain a better exploration of the data distribution. While the implementation based on eigendecomposition takes up a runtime of O(n3), this can further be reduced to O(n·poly(k)) based on rejection sampling. We suggest the use of diversity-based acquisition when only a few labelled samples are available, allowing for better exploration while counteracting the disadvantage of missing the training objective in uncertainty-based methods following a greedy fashion.
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25
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Mohd Ali M, Hashim N. Non-destructive methods for detection of food quality. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00003-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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26
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MA X, LUO H, ZHANG F, GAO F. A bibliometric and visual analysis of fruit quality detection research. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.72322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Xueting MA
- Tarim University, China; Tarim University, China
| | - Huaping LUO
- Tarim University, China; Tarim University, China
| | - Fei ZHANG
- Tarim University, China; Tarim University, China
| | - Feng GAO
- Tarim University, China; Tarim University, China
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27
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Kumar R, Paul V, Pandey R, Sahoo RN, Gupta VK. Reflectance based non-destructive determination of colour and ripeness of tomato fruits. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2022; 28:275-288. [PMID: 35221583 PMCID: PMC8847509 DOI: 10.1007/s12298-022-01126-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 06/14/2023]
Abstract
UNLABELLED The preference and quality of tomato fruit are primarily determined by its apparent colour and appearance. Non-destructive and rapid methods for assessment of tomato colour and ripeness are therefore of immense significance. This study was conducted to identify reflectance-based indices and to develop models for the non-destructive determination of colour and ripeness (maturity) of tomato fruits. Tomato fruits of two varieties and two hybrids, representing different ripening stages were investigated. Fruits were either harvested directly from the plants or they were picked up from the lots stored at 25 °C. Reflectance from individual fruit was recorded in a spectrum ranging from 350 to 2500 nm. These fruits at different ripening stages were ranked on a relative ripening score (0.0-8.5). Obtained data (reflectance and ripening score) were subjected to chemometric analysis. In total, six models were developed. The first-best model was based on the index R521 (reflectance at wavelength 521 nm) i.e., y (colour/ripeness) = - 2.456 ln (x) - 1.093 where x is R521. This model had a root mean standard error of prediction (RMSEP) ≥ 0.86 and biasness = - 0.09. The second-best model y = 2.582 ln (x) - 0.805 was based on the index R546 (x) and had RMSEP ≥ 0.89 and biasness = 0.10. Models could bifurcate tomatoes into basic ripening stages and also red and beyond red tomato fruits from other stages across the varieties/hybrids and ripening conditions [for plant harvested (fresh) and stored (aged) fruits]. Findings will prove useful in developing simple and thereby cost-effective tools for rapid screening/sorting of tomato fruits based on their colour or ripeness not only for basic research (phenotyping) but also for the purpose of processing, value-addition, and pharmaceutical usages. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12298-022-01126-2.
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Affiliation(s)
- Rajeev Kumar
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
- Present Address: Division of Vegetable Production, ICAR-Indian Institute of Vegetable Research (IIVR), Varanasi, Uttar Pradesh 221 305 India
| | - Vijay Paul
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
| | - Rakesh Pandey
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
| | - R. N. Sahoo
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
| | - V. K. Gupta
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, Delhi 110012 India
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28
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Sen M. Food Chemistry: Role of Additives, Preservatives, and Adulteration. Food Chem 2021. [DOI: 10.1002/9781119792130.ch1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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29
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Gopalakrishnan K, Sharma A, Emanuel N, Prabhakar PK, Kumar R. Sensors for Non‐Destructive Quality Evaluation of Food. Food Chem 2021. [DOI: 10.1002/9781119792130.ch13] [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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30
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Sarkar T, Mukherjee A, Chatterjee K, Shariati MA, Rebezov M, Rodionova S, Smirnov D, Dominguez R, Lorenzo JM. Comparative Analysis of Statistical and Supervised Learning Models for Freshness Assessment of Oyster Mushrooms. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02161-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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31
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Khodabakhshian R, Baghbani R. Classification of bananas during ripening using peel roughness analysis—An application of atomic force microscopy to food process. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
| | - Reza Baghbani
- Department of Agricultural Engineering Technical and Vocational University (TVU) Tehran Iran
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32
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A quick look to the use of time domain nuclear magnetic resonance relaxometry and magnetic resonance imaging for food quality applications. Curr Opin Food Sci 2021. [DOI: 10.1016/j.cofs.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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33
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Manzoor MF, Hussain A, Sameen A, Sahar A, Khan S, Siddique R, Aadil RM, Xu B. Novel extraction, rapid assessment and bioavailability improvement of quercetin: A review. ULTRASONICS SONOCHEMISTRY 2021; 78:105686. [PMID: 34358980 PMCID: PMC8350193 DOI: 10.1016/j.ultsonch.2021.105686] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 05/12/2023]
Abstract
Quercetin (QUR) have got the attention of scientific society frequently due to their wide range of potential applications. QUR has been the focal point for research in various fields, especially in food development. But, the QUR is highly unstable and can be interrupted by using conventional assessment methods. Therefore, researchers are focusing on novel extraction and non-invasive tools for the non-destructive assessment of QUR. The current review elaborates the different novel extraction (ultrasound-assisted extraction, microwave-assisted extraction, supercritical fluid extraction, and enzyme-assisted extraction) and non-destructive assessment techniques (fluorescence spectroscopy, terahertz spectroscopy, near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, and surface-enhanced Raman spectroscopy) for the extraction and identification of QUR in agricultural products. The novel extraction approaches facilitate shorter extraction time, involve less organic solvent, and are environmentally friendly. While the non-destructive techniques are non-interruptive, label-free, reliable, accurate, and environmental friendly. The non-invasive spectroscopic and imaging methods are suitable for the sensitive detection of bioactive compounds than conventional techniques. QUR has potential therapeutic properties such as anti-obesity, anti-diabetes, antiallergic, antineoplastic agent, neuroprotector, antimicrobial, and antioxidant activities. Besides, due to the low bioavailability of QUR innovative drug delivery strategies (QUR loaded gel, QUR polymeric micelle, QUR nanoparticles, glucan-QUR conjugate, and QUR loaded mucoadhesive nanoemulsions) have been proposed to improve its bioavailability and providing novel therapeutic approaches.
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Affiliation(s)
- Muhammad Faisal Manzoor
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu Province 212013, China; Riphah College of Rehabilitation and Allied Health Sciences, Riphah International University, Faisalabad 38000, Pakistan
| | - Abid Hussain
- Department of Agriculture and Food Technology, Karakoram International University Gilgit, Pakistan
| | - Aysha Sameen
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Amna Sahar
- Department of Food Engineering, University of Agriculture, Faisalabad 38000, Pakistan
| | - Sipper Khan
- University of Hohenheim, Institute of Agricultural Engineering, Tropics and Subtropics Group, Garbenstrasse 9, 70593 Stuttgart, Germany
| | - Rabia Siddique
- Department of Chemistry, Government College University Faisalabad, 38000, Pakistan
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu Province 212013, China.
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34
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The Advantage of Multispectral Images in Fruit Quality Control for Extra Virgin Olive Oil Production. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02099-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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35
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Ahmed Z, Faisal Manzoor M, Hussain A, Hanif M, Zia-Ud-Din, Zeng XA. Study the impact of ultra-sonication and pulsed electric field on the quality of wheat plantlet juice through FTIR and SERS. ULTRASONICS SONOCHEMISTRY 2021; 76:105648. [PMID: 34182313 PMCID: PMC8250445 DOI: 10.1016/j.ultsonch.2021.105648] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 08/01/2023]
Abstract
Pulsed electric field (PEF) and Ultrasound (US) are commonly used in food processing. We investigated the combined impact of pulsed electric field (PEF) and ultrasound (US) on the wheat plantlet juice. When compared with the individual treatments, the highest values of total phenolics, total flavonoids, chlorophyll, ORAC assay, and DPPH activities were obtained using the combined (US + PEF) methods. The US + PEF significantly decreased the peroxidase and polyphenol oxidase activities from 0.87 to 0.27 Abs min-1 and 0.031-0.016 Abs min-1. Also, the synergistic application significantly lowered the yeast and mold (3.92 to 2.11 log CFU/mL), E. coli/Coliform (1.95 to 0.96 log CFU/mL), and aerobics (4.41 to 2.01 log CFU/mL). Furthermore, Fourier Transform Infrared (FT-IR) and surface-enhanced Raman spectroscopy (SERS) was used to analyzing juice quality. Gold nanoparticles (AuNPs) were used as the SERS substrates, which provided stronger Raman peaks for the samples treated with US + PEF methods. The FT-IR analysis showed significant enhancement of the nutritional molecules. The enhanced quality of wheat plantlet juice combined with lower yeast and mold suggests the suitability of integrated methods for further research and applications.
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Affiliation(s)
- Zahoor Ahmed
- School of Food Science and Engineering, South China University of Technology, Guangzhou, PR China; Overseas Expertise Introduction Center for Discipline Innovation of Food Nutrition and Human Health (111 Center), Guangzhou, PR China
| | - Muhammad Faisal Manzoor
- School of Food Science and Engineering, South China University of Technology, Guangzhou, PR China; Overseas Expertise Introduction Center for Discipline Innovation of Food Nutrition and Human Health (111 Center), Guangzhou, PR China
| | - Abid Hussain
- School of Food Science and Engineering, South China University of Technology, Guangzhou, PR China; Department of Agriculture and Food Science, Karakorum International University, Gilgit, Pakistan
| | - Muddasir Hanif
- School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510640, PR China
| | - Zia-Ud-Din
- Department of Human Nutrition, The University of Agriculture, Peshawar, Pakistan
| | - Xin-An Zeng
- School of Food Science and Engineering, South China University of Technology, Guangzhou, PR China; Overseas Expertise Introduction Center for Discipline Innovation of Food Nutrition and Human Health (111 Center), Guangzhou, PR China.
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36
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Assessment and Feasibility Study of Lemon Ripening Using X-ray Image of Information Visualization. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital radiography (DR) is a mature technology and has been broadly used in medical diagnosis. Currently, it’s also used for fruit quality inspection in the market. This purpose of the study is to conduct non-destructive experiments for visual comparisons of digital radiography images, further construct visualized grayscale image analysis technology, and analyze the changes in lemon quality and ripening using quantitative statistical methods. The materials used for the experiments were three lemons of different ripening. A general medical X-ray DR system for was used in this study for 2D digital radiography. The medical X-ray DR images were created based on the Digital Imaging and Communications in Medicine (DICOM) standard. Photometric interpretation of monochrome was applied to create multi-layered grayscale images. Then quantitative analyses and comparisons were performed with image matrix structures and grayscale pixel values in the tissues using visualization techniques and statistical methods. After layer segmentation on the radiological images, the correlations between the lemon structures and tissue changes were assessed by using the Kruskal–Wallis test. The results showed that the p values for lemon, fiber, and pulp were all under 0.05, while the peel layer did not exhibit significant change. The pulp layer is the best region for statistical analyses to determine the lemon ripening. In conclusion, this study can provide a solid reference for future quality classification in the agricultural market. The research findings can be referenced for developing computing techniques applied to agricultural inspection, expanding the scope of application of the medical DR technology.
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Gupta AK, Pathak U, Tongbram T, Medhi M, Terdwongworakul A, Magwaza LS, Mditshwa A, Chen T, Mishra P. Emerging approaches to determine maturity of citrus fruit. Crit Rev Food Sci Nutr 2021; 62:5245-5266. [PMID: 33583257 DOI: 10.1080/10408398.2021.1883547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Owing to their health-boosting properties and other appreciable properties, citrus fruit is widely consumed and commercialized worldwide. Destination markets around the world vary in their fruit quality requirements and are also highly influenced by climatic conditions, agronomical and postharvest practices. Hence, harvesting decisions are arduous. Maturity indices in citrus fruit are highly variable and dependent on the species and varieties, growing regions, and destination markets. For decades, determination of the maturity of citrus fruit and predicting the near time of harvesting was a challenge for producers, researchers, and food safety agencies. Thus, the current review provides a correlation between maturity and internal components and an overview of techniques of maturity determination for citrus fruits. Also, stress has been given to the destructive and nondestructive methods to determine the maturity level of different citrus species. The techniques presented in this review portray continuous productiveness as an excellent quality assessment, particularly as ripening and maturity analysis tools for citrus fruits. Traditional techniques are time-consuming, laborious, costly, destructive, and tedious. Thus, these nondestructive techniques hold great potential to replace conventional procedures.
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Affiliation(s)
- Arun Kumar Gupta
- Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India
| | - Urbi Pathak
- Department of Food Science, ISA Lille, Lille, France
| | - Thoithoi Tongbram
- Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India
| | - Manisha Medhi
- Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India.,Department of Food Processing and Quality Management, Pub Kamrup College, Kamrup, Assam, India
| | | | - Lembe Samukelo Magwaza
- Discipline of Crop and Horticultural Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg, South Africa
| | - Asanda Mditshwa
- Discipline of Crop and Horticultural Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg, South Africa
| | - Tao Chen
- Department of Chemical and Process Engineering, University of Surrey, Guildford, UK
| | - Poonam Mishra
- Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India
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38
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He Y, Xiao Q, Bai X, Zhou L, Liu F, Zhang C. Recent progress of nondestructive techniques for fruits damage inspection: a review. Crit Rev Food Sci Nutr 2021; 62:5476-5494. [PMID: 33583246 DOI: 10.1080/10408398.2021.1885342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
In the process of growing, harvesting, and storage, fruits are vulnerable to mechanical damage, microbial infections, and other types of damage, which not only reduce the quality of fruits, increase the risk of fungal infections, in turn greatly affect food safety, but also sharply reduce economic benefits. Hence, it is essential to identify damaged fruits in time. Rapid and nondestructive detection of fruits damage is in great demand. In this paper, the latest research progresses on the detection of fruits damage by nondestructive techniques, including visible/near-infrared spectroscopy, chlorophyll fluorescence techniques, computer vision, multispectral and hyperspectral imaging, structured-illumination reflectance imaging, laser-induced backscattering imaging, optical coherence tomography, nuclear magnetic resonance and imaging, X-ray imaging, electronic nose, thermography, and acoustic methods, are summarized. We briefly introduce the principles of these techniques, summarize their applicability. The challenges and future trends are also proposed to provide beneficial reference for future researches and real-world applications.
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Affiliation(s)
- Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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39
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Hussain A, Pu H, Hu B, Sun DW. Au@Ag-TGANPs based SERS for facile screening of thiabendazole and ferbam in liquid milk. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118908. [PMID: 32949944 DOI: 10.1016/j.saa.2020.118908] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/21/2020] [Accepted: 08/29/2020] [Indexed: 06/11/2023]
Abstract
Surface-enhanced Raman spectroscopy based on thioglycolic acid (TGA) functionalized silver-coated gold nanoparticles (Au@Ag-TGANPs) was developed for the facile screening of thiabendazole (TBZ) and ferbam (0.025-10 ppm) in liquid milk for the first time. Results showed that silver-coated gold nanoparticles (Au@AgNPs) with a core size of 32 nm and a shell thickness of 5 nm was successfully modified with 3 nm TGA. The sensitive Au@Ag-TGANPs could enhance TBZ and ferbam signals by factors of 6.4 × 104 and 9.8 × 104, respectively, and achieved the detection of TBZ and ferbam with limits of detection of 0.12 and 0.003 ppm, R2 of 0.988 and 0.9821, percent recoveries of 88-103% and of 87.2-103.5%, and relative standard deviations of 4.1-9.2% and 3.5-8.3%, respectively. The current simple and green method could thus be used to detect other unsafe chemicals in future studies.
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Affiliation(s)
- Abid Hussain
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Bingxue Hu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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, South China University of Technology, Guangzhou Higher Education Mega Center, 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, Belfield, Dublin 4, Ireland.
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40
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Su Z, Zhang C, Yan T, Zhu J, Zeng Y, Lu X, Gao P, Feng L, He L, Fan L. Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches. FRONTIERS IN PLANT SCIENCE 2021; 12:736334. [PMID: 34567050 PMCID: PMC8462090 DOI: 10.3389/fpls.2021.736334] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 08/11/2021] [Indexed: 05/08/2023]
Abstract
Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R 2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.
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Affiliation(s)
- Zhenzhu Su
- Institute of Biotechnology, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecology Agriculture, Shihezi University, Shihezi, China
| | - Jianan Zhu
- Institute of Biotechnology, Zhejiang University, Hangzhou, China
| | - Yulan Zeng
- Institute of Biotechnology, Zhejiang University, Hangzhou, China
| | - Xuanjun Lu
- Institute of Biotechnology, Zhejiang University, Hangzhou, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecology Agriculture, Shihezi University, Shihezi, China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
- *Correspondence: Lei Feng
| | - Linhai He
- Hangzhou Liangzhu Linhai Vegetable and Fruit Professional Cooperative, Hangzhou, China
| | - Lihui Fan
- Hangzhou Liangzhu Linhai Vegetable and Fruit Professional Cooperative, Hangzhou, China
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41
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Mohd Ali M, Hashim N, Aziz SA, Lasekan O. Emerging non-destructive thermal imaging technique coupled with chemometrics on quality and safety inspection in food and agriculture. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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42
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Harel B, Parmet Y, Edan Y. Maturity classification of sweet peppers using image datasets acquired in different times. COMPUT IND 2020. [DOI: 10.1016/j.compind.2020.103274] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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43
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Jaramillo-Acevedo CA, Choque-Valderrama WE, Guerrero-Álvarez GE, Meneses-Escobar CA. Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2020. [DOI: 10.1515/ijfe-2019-0161] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractProper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.
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Affiliation(s)
- César Augusto Jaramillo-Acevedo
- Universidad Tecnológica de Pereira, Facultad de Ingenierías, Ingeniería de Sistemas y Computación, Grupo de Investigación de Inteligencia Artificial GIA, Carrera 27 #10-02, Pereira, Risaralda, Colombia
| | - William Enrique Choque-Valderrama
- Universidad Tecnológica de Pereira, Facultad de Ingenierías, Ingeniería de Sistemas y Computación, Grupo de Investigación de Inteligencia Artificial GIA, Carrera 27 #10-02, Pereira, Risaralda, Colombia
| | - Gloria Edith Guerrero-Álvarez
- Universidad Tecnológica de Pereira, Facultad de Tecnología, Escuela de Química, Grupo de Investigación Oleoquímica, Carrera 27 #10-02, Pereira, Risaralda, Colombia
| | - Carlos Augusto Meneses-Escobar
- Universidad Tecnológica de Pereira, Facultad de Ingenierías, Ingeniería de Sistemas y Computación, Grupo de Investigación de Inteligencia Artificial GIA, Carrera 27 #10-02, Pereira, Risaralda, Colombia
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44
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Hussain N, Pu H, Hussain A, Sun DW. Rapid detection of ziram residues in apple and pear fruits by SERS based on octanethiol functionalized bimetallic core-shell nanoparticles. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 236:118357. [PMID: 32375074 DOI: 10.1016/j.saa.2020.118357] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/03/2020] [Accepted: 04/08/2020] [Indexed: 06/11/2023]
Abstract
Existing approaches for the screening of unsafe materials in food matrices are time-consuming, tiresome and destructive in nature. Therefore, in the current study, a surface-enhanced Raman spectroscopy (SERS) method based on octanethiol-functionalized core-shell nanoparticles (Oct/Au@AgNPs) was established for rapid detection of ziram in apple and pear fruits. The morphology of substrate was evaluated using high-resolution TEM images and superimposed HAADF-STEM-EDS elemental mapping images, which confirmed that Au@AgNPs having gold (Au) core size of 28 nm in diameter and silver (Ag) shell of 5.5 nm in thickness were successfully grafted with octanethiol. The SERS method with the sensitive nanoparticles could detect ziram of up to 0.015 and 0.016 ppm in apple and pear with high coefficients of determination (R2) of 0.9987 and 0.9993, respectively. Furthermore, satisfactory recoveries (80-106%) were also accomplished for the fungicide in real samples. This work demonstrated that the functionalized silver-coated gold nanoparticles were easy to prepare and could be used as sensitive SERS platforms for monitoring of other agrochemicals in foods.
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Affiliation(s)
- Nisar Hussain
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Abid Hussain
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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, South China University of Technology, Guangzhou Higher Education Mega Center, 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, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
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Adedeji AA, Ekramirad N, Rady A, Hamidisepehr A, Donohue KD, Villanueva RT, Parrish CA, Li M. Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review. Foods 2020; 9:E927. [PMID: 32674380 PMCID: PMC7404779 DOI: 10.3390/foods9070927] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/04/2020] [Accepted: 07/06/2020] [Indexed: 01/06/2023] Open
Abstract
In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.
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Affiliation(s)
- Akinbode A. Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
| | - Nader Ekramirad
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
| | - Ahmed Rady
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
- Department of Biosystems and Agricultural Engineering, Alexandria University, Alexandria 21526, Egypt
| | - Ali Hamidisepehr
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
| | - Kevin D. Donohue
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA; (K.D.D.); (C.A.P.)
| | - Raul T. Villanueva
- Department of Entomology, University of Kentucky, Princeton, KY 42445-0469, USA;
| | - Chadwick A. Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA; (K.D.D.); (C.A.P.)
| | - Mengxing Li
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA; (N.E.); (A.R.); (A.H.); (M.L.)
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46
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Harel B, van Essen R, Parmet Y, Edan Y. Viewpoint Analysis for Maturity Classification of Sweet Peppers. SENSORS 2020; 20:s20133783. [PMID: 32640557 PMCID: PMC7374390 DOI: 10.3390/s20133783] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/01/2020] [Accepted: 07/01/2020] [Indexed: 11/24/2022]
Abstract
The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.
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Affiliation(s)
- Ben Harel
- Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel; (R.v.E.); (Y.P.); (Y.E.)
- Correspondence:
| | - Rick van Essen
- Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel; (R.v.E.); (Y.P.); (Y.E.)
- Farm Technology Group, Wageningen University and Research, 6700 AA Wageningen, The Netherlands
| | - Yisrael Parmet
- Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel; (R.v.E.); (Y.P.); (Y.E.)
| | - Yael Edan
- Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel; (R.v.E.); (Y.P.); (Y.E.)
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Hussain A, Sun DW, Pu H. Bimetallic core shelled nanoparticles (Au@AgNPs) for rapid detection of thiram and dicyandiamide contaminants in liquid milk using SERS. Food Chem 2020; 317:126429. [DOI: 10.1016/j.foodchem.2020.126429] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 07/30/2019] [Accepted: 02/17/2020] [Indexed: 01/03/2023]
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Exploring the chemical composition, emerging applications, potential uses, and health benefits of durian: A review. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107189] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Ezhilan M, Nesakumar N, Babu KJ, Srinandan CS, Rayappan JBB. A Multiple Approach Combined with Portable Electronic Nose for Assessment of Post-harvest Sapota Contamination by Foodborne Pathogens. FOOD BIOPROCESS TECH 2020. [DOI: 10.1007/s11947-020-02473-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Stuart MB, Stanger LR, Hobbs MJ, Pering TD, Thio D, McGonigle AJ, Willmott JR. Low-Cost Hyperspectral Imaging System: Design and Testing for Laboratory-Based Environmental Applications. SENSORS 2020; 20:s20113293. [PMID: 32527066 PMCID: PMC7308922 DOI: 10.3390/s20113293] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 06/02/2020] [Accepted: 06/08/2020] [Indexed: 02/03/2023]
Abstract
The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.
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Affiliation(s)
- Mary B. Stuart
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK; (M.B.S.); (L.R.S.); (M.J.H.)
| | - Leigh R. Stanger
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK; (M.B.S.); (L.R.S.); (M.J.H.)
| | - Matthew J. Hobbs
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK; (M.B.S.); (L.R.S.); (M.J.H.)
| | - Tom D. Pering
- Department of Geography, University of Sheffield, Sheffield S10 2TN, UK; (T.D.P.); (A.J.S.M.)
| | - Daniel Thio
- Nunnery Lane Dental Practice, York YO23 1AH, UK;
| | - Andrew J.S. McGonigle
- Department of Geography, University of Sheffield, Sheffield S10 2TN, UK; (T.D.P.); (A.J.S.M.)
- School of Geosciences, University of Sydney, Sydney, NSW 2006, Australia
- Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Jon R. Willmott
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK; (M.B.S.); (L.R.S.); (M.J.H.)
- Correspondence:
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