1
|
Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I, Cho BK. Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1240361. [PMID: 37662162 PMCID: PMC10471194 DOI: 10.3389/fpls.2023.1240361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
Collapse
Affiliation(s)
- Umuhoza Aline
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | - Tanima Bhattacharya
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
| |
Collapse
|
2
|
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: 0] [Impact Index Per Article: 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.
Collapse
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
| |
Collapse
|
3
|
Ding R, Yu L, Wang C, Zhong S, Gu R. Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review. Crit Rev Anal Chem 2023:1-18. [PMID: 36966435 DOI: 10.1080/10408347.2023.2189477] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2023]
Abstract
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.
Collapse
Affiliation(s)
- Rong Ding
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lianhui Yu
- Chengdu Pushi Pharmaceutical Technology Co., Ltd, Chengdu, China
| | - Chenghui Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shihong Zhong
- School of Pharmacy, Southwest Minzu University, Chengdu, China
| | - Rui Gu
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| |
Collapse
|
4
|
Taghinezhad E, Szumny A, Figiel A. The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process. Molecules 2023; 28:molecules28072930. [PMID: 37049695 PMCID: PMC10096048 DOI: 10.3390/molecules28072930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 03/29/2023] Open
Abstract
Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.
Collapse
Affiliation(s)
- Ebrahim Taghinezhad
- Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
- Department of Food Chemistry and Biocatalysis, Wroclaw University of Environmental and Life Science, CK Norwida 25, 50-375 Wrocław, Poland
- Correspondence:
| | - Antoni Szumny
- Department of Food Chemistry and Biocatalysis, Wroclaw University of Environmental and Life Science, CK Norwida 25, 50-375 Wrocław, Poland
| | - Adam Figiel
- Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, Chełmońskiego 37a, 51-630 Wrocław, Poland
| |
Collapse
|
5
|
The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
6
|
Hassoun A, Jagtap S, Garcia-Garcia G, Trollman H, Pateiro M, Lorenzo JM, Trif M, Rusu AV, Aadil RM, Šimat V, Cropotova J, Câmara JS. Food quality 4.0: From traditional approaches to digitalized automated analysis. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Cai Z, Huang W, Wang Q, Li J. Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models. FRONTIERS IN PLANT SCIENCE 2022; 13:952942. [PMID: 36035725 PMCID: PMC9399745 DOI: 10.3389/fpls.2022.952942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
Citrus fruits are susceptible to fungal infection after harvest. To reduce the economic loss, it is necessary to reject the infected citrus fruit before storage and transportation. However, the infected area in the early stage of decay is almost invisible on the fruit surface, so the detection of early decayed citrus is very challenging. In this study, a structured-illumination reflectance imaging (SIRI) system combined with a visible light-emitting diode (LED) lamp and a monochrome camera was developed to detect early fungal infection in oranges. Under sinusoidal modulation illumination with spatial frequencies of 0.05, 0.15, and 0.25 cycles mm-1, three-phase-shifted images with phase offsets of - 2π/3, 0, and 2π/3 were acquired for each spatial frequency. The direct component (DC) and alternating component (AC) images were then recovered by image demodulation using a three-phase-shifting approach. Compared with the DC image, the decayed area can be clearly identified in the AC image and RT image (AC/DC). The optimal spatial frequency was determined by analyzing the AC image and pixel intensity distribution. Based on the texture features extracted from DC, AC, and RT images, four kinds of classification models including partial least square discriminant analysis (PLS-DA), support vector machine (SVM), least squares-support vector machine (LS-SVM), and k-nearest neighbor (KNN) were established to detect the infected oranges, respectively. Model optimization was also performed by extracting important texture features. Compared to all models, the PLS-DA model developed based on eight texture features of RT images achieved the optimal classification accuracy of 96.4%. This study showed for the first time that the proposed SIRI system combined with appropriate texture features and classification model can realize the early detection of decayed oranges.
Collapse
Affiliation(s)
- Zhonglei Cai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiangbo Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| |
Collapse
|
9
|
Langenkämper D, Mogstad AA, Hansen IM, Baussant T, Bergsagel Ø, Nilssen I, Frost TK, Nattkemper TW. Exploring time series of hyperspectral images for cold water coral stress response analysis. PLoS One 2022; 17:e0272408. [PMID: 35939502 PMCID: PMC9359567 DOI: 10.1371/journal.pone.0272408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/19/2022] [Indexed: 11/29/2022] Open
Abstract
Hyperspectral imaging (HSI) is a promising technology for environmental monitoring with a lot of undeveloped potential due to the high dimensionality and complexity of the data. If temporal effects are studied, such as in a monitoring context, the analysis becomes more challenging as time is added to the dimensions of space (image coordinates) and wavelengths. We conducted a series of laboratory experiments to investigate the impact of different stressor exposure patterns on the spectrum of the cold water coral Desmophyllum pertusum. 65 coral samples were divided into 12 groups, each group being exposed to different types and levels of particles. Hyperspectral images of the coral samples were collected at four time points from prior to exposure to 6 weeks after exposure. To investigate the relationships between the corals’ spectral signatures and controlled experimental parameters, a new software tool for interactive visual exploration was developed and applied, the HypIX (Hyperspectral Image eXplorer) web tool. HypIX combines principles from exploratory data analysis, information visualization and machine learning-based dimension reduction. This combination enables users to select regions of interest (ROI) in all dimensions (2D space, time point and spectrum) for a flexible integrated inspection. We propose two HypIX workflows to find relationships in time series of hyperspectral datasets, namely morphology-based filtering workflow and embedded driven response analysis workflow. With these HypIX workflows three users identified different temporal and spatial patterns in the spectrum of corals exposed to different particle stressor conditions. Corals exposed to particles tended to have a larger change rate than control corals, which was evident as a shifted spectrum. The responses, however, were not uniform for coral samples undergoing the same exposure treatments, indicating individual tolerance levels. We also observed a good inter-observer agreement between the three HyPIX users, indicating that the proposed workflow can be applied to obtain reproducible HSI analysis results.
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Shiddiq M, Herman H, Arief DS, Fitra E, Husein IR, Ningsih SA. Wavelength selection of multispectral imaging for oil palm fresh fruit ripeness classification. APPLIED OPTICS 2022; 61:5289-5298. [PMID: 36256213 DOI: 10.1364/ao.450384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/13/2022] [Indexed: 06/16/2023]
Abstract
Multispectral imaging has been recently proposed for high-speed sorting and grading machine vision of fruits. It is a prospective method applied in yet traditional sorting and grading of oil palm fresh fruit bunches (FFB). The ripeness of oil palm FFBs determines the quality of crude palm oil (CPO). Implementation of multispectral imaging for the task needs wavelength selection from hyperspectral datasets. This study aimed to obtain the optimum wavelengths and use them for oil palm FFB classification based on three ripeness levels. We have selected eight optimum wavelengths using principal component analysis (PCA) regression which represented the ripeness levels.
Collapse
|
12
|
Xiang Y, Chen Q, Su Z, Zhang L, Chen Z, Zhou G, Yao Z, Xuan Q, Cheng Y. Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation. FRONTIERS IN PLANT SCIENCE 2022; 13:860656. [PMID: 35586212 PMCID: PMC9108868 DOI: 10.3389/fpls.2022.860656] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.
Collapse
Affiliation(s)
- Yun Xiang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Qijun Chen
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Zhongjing Su
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Lu Zhang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Zuohui Chen
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Guozhi Zhou
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Zhuping Yao
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Qi Xuan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Yuan Cheng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| |
Collapse
|
13
|
Si W, Xiong J, Huang Y, Jiang X, Hu D. Quality Assessment of Fruits and Vegetables Based on Spatially Resolved Spectroscopy: A Review. Foods 2022; 11:foods11091198. [PMID: 35563921 PMCID: PMC9104625 DOI: 10.3390/foods11091198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/11/2022] [Accepted: 04/19/2022] [Indexed: 01/15/2023] Open
Abstract
Damage occurs easily and is difficult to find inside fruits and vegetables during transportation or storage, which not only brings losses to fruit and vegetable distributors, but also reduces the satisfaction of consumers. Spatially resolved spectroscopy (SRS) is able to detect the quality attributes of fruits and vegetables at different depths, which is of great significance to the quality classification and defect detection of horticultural products. This paper is aimed at reviewing the applications of spatially resolved spectroscopy for measuring the quality attributes of fruits and vegetables in detail. The principle of light transfer in biological tissues, diffusion approximation theory and methodologies are introduced, and different configuration designs for spatially resolved spectroscopy are compared and analyzed. Besides, spatially resolved spectroscopy applications based on two aspects for assessing the quality of fruits and vegetables are summarized. Finally, the problems encountered in previous studies are discussed, and future development trends are presented. It can be concluded that spatially resolved spectroscopy demonstrates great application potential in the field of fruit and vegetable quality attribute evaluation. However, due to the limitation of equipment configurations and data processing speed, the application of spatially resolved spectroscopy in real-time online detection is still a challenge.
Collapse
Affiliation(s)
- Wan Si
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (W.S.); (J.X.); (X.J.)
| | - Jie Xiong
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (W.S.); (J.X.); (X.J.)
| | - Yuping Huang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (W.S.); (J.X.); (X.J.)
- Correspondence:
| | - Xuesong Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (W.S.); (J.X.); (X.J.)
| | - Dong Hu
- College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China;
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Md Saleh R, Kulig B, Arefi A, Hensel O, Sturm B. Prediction of total carotenoids, color and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rosalizan Md Saleh
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Industrial Crops Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang, Selangor Malaysia
| | - Boris Kulig
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Arman Arefi
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Oliver Hensel
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Barbara Sturm
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Leibniz Institute for Agricultural Engineering and Bioeconomy(ATB) Max‐Eyth‐Allee 100 14469 Potsdam Germany
- Humboldt Universität zu Berlin Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences 10115 Berlin Germany
| |
Collapse
|
16
|
Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030997] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.
Collapse
|
17
|
Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection. Foods 2021; 11:foods11010008. [PMID: 35010134 PMCID: PMC8750721 DOI: 10.3390/foods11010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022] Open
Abstract
Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.
Collapse
|
18
|
Kusumiyati, Hadiwijaya Y, Putri IE, Munawar AA. Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy. Heliyon 2021; 7:e07677. [PMID: 34401571 PMCID: PMC8353486 DOI: 10.1016/j.heliyon.2021.e07677] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 07/26/2021] [Indexed: 11/26/2022] Open
Abstract
Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.
Collapse
Affiliation(s)
- Kusumiyati
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Yuda Hadiwijaya
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Ine Elisa Putri
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Agus Arip Munawar
- Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Indonesia
| |
Collapse
|
19
|
Lu Y, Lu R. Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging. Foods 2021; 10:1094. [PMID: 34069201 PMCID: PMC8156177 DOI: 10.3390/foods10051094] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/05/2021] [Accepted: 05/13/2021] [Indexed: 12/02/2022] Open
Abstract
Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers.
Collapse
Affiliation(s)
- Yuzhen Lu
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Renfu Lu
- United States Department of Agriculture Agricultural Research Service, East Lansing, MI 48824, USA;
| |
Collapse
|
20
|
Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues. PHOTONICS 2021. [DOI: 10.3390/photonics8050162] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.
Collapse
|
21
|
Identification of Common Skin Defects and Classification of Early Decayed Citrus Using Hyperspectral Imaging Technique. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01960-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
22
|
Yu G, Ma B, Chen J, Li X, Li Y, Li C. Nondestructive identification of pesticide residues on the Hami melon surface using deep feature fusion by Vis/
NIR
spectroscopy and
1D‐CNN. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13602] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Guowei Yu
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
| | - Benxue Ma
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs Shihezi China
| | - Jincheng Chen
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
- Mechanical Equipment Research Institute, Xinjiang Academy of Agricultural and Reclamation Science Shihezi China
| | - Xiaozhan Li
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
| | - Yujie Li
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
| | - Cong Li
- College of Mechanical and Electrical Engineering Shihezi University Shihezi China
| |
Collapse
|
23
|
Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning. SENSORS 2020; 20:s20195484. [PMID: 32992678 PMCID: PMC7583884 DOI: 10.3390/s20195484] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 02/06/2023]
Abstract
Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.
Collapse
|
24
|
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.
Collapse
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.)
| |
Collapse
|
25
|
Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10082851] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.
Collapse
|
26
|
Das Choudhury S, Samal A, Awada T. Leveraging Image Analysis for High-Throughput Plant Phenotyping. FRONTIERS IN PLANT SCIENCE 2019; 10:508. [PMID: 31068958 PMCID: PMC6491831 DOI: 10.3389/fpls.2019.00508] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 04/02/2019] [Indexed: 05/18/2023]
Abstract
The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.
Collapse
Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Ashok Samal
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
| |
Collapse
|
27
|
A New Insight into Biospeckle Activity in Apple Tissues. SENSORS 2019; 19:s19030497. [PMID: 30691034 PMCID: PMC6387188 DOI: 10.3390/s19030497] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 01/21/2019] [Accepted: 01/22/2019] [Indexed: 11/16/2022]
Abstract
The monitoring and characterization of agricultural products before harvest or during ripening, storage, and shelf life has recently been increasingly explored in the literature. The analysis of biospeckle activity has potential for the determination of the optimal harvest window, the monitoring of the fruit ripening process, and the detection of diseases and bruising. In this technique, the specimen is illuminated with coherent light and speckle intensity fluctuations are analyzed using diverse methodologies. Prior work shows that biospeckle activity is strongly correlated to physiological indexes conventionally used to evaluate fruit texture and composition. Here, we scrupulously investigate the biospeckle activity of Gala apple fruits during postharvest stages. We simulate realistic conditions for shelf-life monitoring, namely an unknown history of the fruit and storage in an uncontrolled atmosphere. Scattering spot images are acquired with multiple exposure times using a simple optical setup. The contrast, reflecting biospeckle activity, is computed after eliminating inhomogeneous zones. The results show, for the first time, speckle activity at short time scales. The retrieved correlations between speckle parameters and the ratio of apples’ firmness to their soluble solids content reveal significant links despite the unknown fruit’s origin, harvest date, and storage history.
Collapse
|
28
|
Hussain A, Pu H, Sun DW. Measurements of lycopene contents in fruit: A review of recent developments in conventional and novel techniques. Crit Rev Food Sci Nutr 2018; 59:758-769. [DOI: 10.1080/10408398.2018.1518896] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Abid Hussain
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, PR China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, PR China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, PR China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Centre, Guangzhou, China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
| |
Collapse
|
29
|
Xia Y, Huang W, Fan S, Li J, Chen L. Effect of fruit moving speed on online prediction of soluble solids content of apple using Vis/NIR diffuse transmission. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12915] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Yu Xia
- College of Mechanical and Electronic Engineering, Northwest A&F University; Yangling Shaanxi China
- Beijing Research Center of Intelligent Equipment for Agriculture; Beijing China
- National Research Center of Intelligent Equipment for Agriculture; Beijing China
- Key Laboratory of Agri-Informatics, Ministry of Agriculture; Beijing China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture; Beijing China
| | - Wenqian Huang
- Beijing Research Center of Intelligent Equipment for Agriculture; Beijing China
- National Research Center of Intelligent Equipment for Agriculture; Beijing China
- Key Laboratory of Agri-Informatics, Ministry of Agriculture; Beijing China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture; Beijing China
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture; Beijing China
- National Research Center of Intelligent Equipment for Agriculture; Beijing China
- Key Laboratory of Agri-Informatics, Ministry of Agriculture; Beijing China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture; Beijing China
| | - Jiangbo Li
- Beijing Research Center of Intelligent Equipment for Agriculture; Beijing China
- National Research Center of Intelligent Equipment for Agriculture; Beijing China
- Key Laboratory of Agri-Informatics, Ministry of Agriculture; Beijing China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture; Beijing China
| | - Liping Chen
- College of Mechanical and Electronic Engineering, Northwest A&F University; Yangling Shaanxi China
- Beijing Research Center of Intelligent Equipment for Agriculture; Beijing China
- National Research Center of Intelligent Equipment for Agriculture; Beijing China
- Key Laboratory of Agri-Informatics, Ministry of Agriculture; Beijing China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture; Beijing China
| |
Collapse
|
30
|
Time-Domain Functional Diffuse Optical Tomography System Based on Fiber-Free Silicon Photomultipliers. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7121235] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
31
|
Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging. J FOOD QUALITY 2017. [DOI: 10.1155/2017/1023498] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of (T670+T760-T640)/(T670+T760+T640) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index ((T670+T760-T640)/(T670+T760+T640)). Different indexes were used as the input of linear discriminate analysis (LDA) and of k-nearest neighbor (k-NN) algorithm to identify the maturity, and it was found that k-NN with (T670+T760-T640)/(T670+T760+T640) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.
Collapse
|