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Xia Y, Liu W, Meng J, Hu J, Liu W, Kang J, Luo B, Zhang H, Tang W. Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review. FRONTIERS IN PLANT SCIENCE 2024; 14:1324881. [PMID: 38269139 PMCID: PMC10805836 DOI: 10.3389/fpls.2023.1324881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024]
Abstract
Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper.
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Affiliation(s)
- Yu Xia
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Wenxi Liu
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jingwu Meng
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Jinghao Hu
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Wenbo Liu
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Jie Kang
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Bin Luo
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Han Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wei Tang
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
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Peng W, Ren Z, Wu J, Xiong C, Liu L, Sun B, Liang G, Zhou M. Qualitative and Quantitative Assessments of Apple Quality Using Vis Spectroscopy Combined with Improved Particle-Swarm-Optimized Neural Networks. Foods 2023; 12:1991. [PMID: 37238810 PMCID: PMC10217276 DOI: 10.3390/foods12101991] [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: 04/04/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Exploring a cost-effective and high-accuracy optical detection method is of great significance in promoting fruit quality evaluation and grading sales. Apples are one of the most widely economic fruits, and a qualitative and quantitative assessment of apple quality based on soluble solid content (SSC) was investigated via visible (Vis) spectroscopy in this study. Six pretreatment methods and principal component analysis (PCA) were utilized to enhance the collected spectra. The qualitative assessment of apple SSC was performed using a back-propagation neural network (BPNN) combined with second-order derivative (SD) and Savitzky-Golay (SG) smoothing. The SD-SG-PCA-BPNN model's classification accuracy was 87.88%. To improve accuracy and convergence speed, a dynamic learning rate nonlinear decay (DLRND) strategy was coupled with the model. After that, particle swarm optimization (PSO) was employed to optimize the model. The classification accuracy was 100% for testing apples via the SD-SG-PCA-PSO-BPNN model combined with a Gaussian DLRND strategy. Then, quantitative assessments of apple SSC values were performed. The correlation coefficient (r) and root-square-mean error for prediction (RMSEP) in testing apples were 0.998 and 0.112 °Brix, surpassing a commercial fructose meter. The results demonstrate that Vis spectroscopy combined with the proposed synthetic model has significant value in qualitative and quantitative assessments of apple quality.
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Affiliation(s)
- Wenping Peng
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Zhong Ren
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- Key Laboratory of Optic-Electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Junli Wu
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Chengxin Xiong
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Longjuan Liu
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Bingheng Sun
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Gaoqiang Liang
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Mingbin Zhou
- Key Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
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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: 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.
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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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4
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Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Lazim SSR, Mat Nawi N, Bejo SK, Mohamed Shariff AR, Abdullah N. Prediction and classification of soluble solid contents to determine the maturity level of watermelon using visible and shortwave near infrared spectroscopy. INTERNATIONAL FOOD RESEARCH JOURNAL 2022. [DOI: 10.47836/ifrj.29.6.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The present work investigated the potential application of a portable and low-cost spectroscopic technique to predict the soluble solid content (SSC) for determining the maturity level of watermelons. A total of 63 watermelon samples were used in the present work, representing three different maturity levels: unmatured, matured, and over-matured. Before spectral acquisition, each watermelon sample was cut into half, producing 126 fruit portions. Visible shortwave near infrared (VSNIR) spectrometer was used to record the spectral data from the skin surface of each portion. The SSC of each portion was measured using a digital refractometer. Partial least square (PLS) regression method was used to establish both calibration and prediction models to predict the SSC values from the watermelon samples. Support vector machine (SVM) classifier was used to categorise spectral data into the respective maturity levels. Results showed that the coefficient of determination (R2) values for calibration models of unmatured, matured, and over-matured were 0.65, 0.81, and 0.78, respectively. For the prediction model, the R2 values for unmatured, matured, and over-matured were 0.60, 0.74, and 0.76, respectively. The SVM yielded good classification accuracy of 85%. The present work demonstrated that the proposed spectroscopic method could be applied to predict and classify the maturity level of watermelons based on their skin condition.
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Ma X, Luo H, Liao J, Zhao J. The knowledge domain and emerging trends in apple detection based on NIRS: A scientometric analysis with CiteSpace (1989-2021). Food Sci Nutr 2022; 10:4091-4102. [PMID: 36514752 PMCID: PMC9731563 DOI: 10.1002/fsn3.3010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/15/2022] [Accepted: 07/16/2022] [Indexed: 12/16/2022] Open
Abstract
In this paper, 317 literature in the Web of Science (WoS) related to research on apple by near-infrared spectroscopy (NIRS) were drawn on the knowledge map of the number of literature, the co-occurrence network of authors and institutions, the co-occurrence and clustering of keywords based on CiteSpace. And a related analysis was carried out. Combined with the results of visual analysis and related literature, the research hotspots were sorted out and discussed. This paper provides a certain reference for relevant researchers to study in this field and provides a new method for macroscopically grasping the current status of apple quality detection research, which helps new researchers to quickly integrate into this field and obtain more valuable scientific information.
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Affiliation(s)
- Xueting Ma
- Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous RegionTarim UniversityAlar 843300China,College of Mechanical and Electrical EngineeringTarim UniversityChina
| | - Huaping Luo
- Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous RegionTarim UniversityAlar 843300China,College of Mechanical and Electrical EngineeringTarim UniversityChina
| | - Jiean Liao
- Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous RegionTarim UniversityAlar 843300China,College of Mechanical and Electrical EngineeringTarim UniversityChina
| | - Jinfei Zhao
- Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous RegionTarim UniversityAlar 843300China,College of Mechanical and Electrical EngineeringTarim UniversityChina
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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.
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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;
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Zhang M, Shen M, Li H, Zhang B, Zhang Z, Quan P, Ren X, Xing L, Zhao J. Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120598. [PMID: 34802937 DOI: 10.1016/j.saa.2021.120598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/25/2021] [Accepted: 11/06/2021] [Indexed: 06/13/2023]
Abstract
In this study, the effect of maturity variation on the prediction of the soluble solids content (SSC) and firmness of apples was determined using visible and near-infrared spectroscopy. In 2018, 520 apples from six ripening stages were collected. The single maturity model and multi-maturity model of SSC and firmness were established using partial least-squares regression. Apples at the same and different maturity stages were used to verify the developed model. Whereas the single maturity model was affected by maturity variation, the multi-maturity model could accurately predict the SSC and firmness of apples at different maturity stages. The multi-maturity model developed based on six maturity calibration sets had the best predictive performance. The root mean square error of prediction (RMSEP) of SSC and firmness was 0.614-0.802 °Brix and 0.402-0.650 kg/cm2, respectively. The long-term performance of the optimal multi-maturity model was evaluated using validation sets. The predictive performance was decreased and the RMSEP increased when the model was used to predict the SSC and firmness of apples in different seasons. The predictive performance of the model was improved after slope/bias (S/B) correction, and the RMSEP of SSC and firmness decreased to 0.405-0.587°Brix and 0.518-0.628 kg/cm2 respectively. Overall, the multi-maturity model eliminated the effect of maturity variation, and the multi-maturity model coupled with S/B correction permitted the rapid and accurate detection of the SSC and firmness of apples at different maturity stages and in different seasons.
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Affiliation(s)
- Mengsheng Zhang
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Maosheng Shen
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Hao Li
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Bo Zhang
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Zhongxiong Zhang
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Pengkun Quan
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China
| | - Xiaolin Ren
- Northwest A&F University, College of Horticulture, Yangling, Shaanxi 712100, China
| | - Libo Xing
- Northwest A&F University, College of Horticulture, Yangling, Shaanxi 712100, China
| | - Juan Zhao
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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9
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Karatas N. Morphological, sensory and biochemical characteristics of summer apple genotypes. BRAZ J BIOL 2021; 82:e234780. [PMID: 33825755 DOI: 10.1590/1519-6984.234780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/27/2020] [Indexed: 11/22/2022] Open
Abstract
Summer apples are one of the most important plant community in Artvin province located Northeastern part of Turkey. In present study 22 local apple genotypes were characterized by phenological, morphological, biochemical and sensory properties. Harvest date was the main phenological data. Morphological measurements included fruit weight, fruit shape, fruit ground color, fruit over color, fruit over color coverage and fruit firmness, respectively. Sensory measurements were as juiciness and aroma and biochemical characteristics included organic acids, SSC (Soluble Solid Content), vitamin C, total phenolic content and antioxidant capacity. Genotypes exhibited variable harvest dates ranging from 11 July to 13 August and cv. Summered harvested 30 July 2017. The majority of genotypes were harvested before cv. Summered. Fruit weight were also quite variable among genotypes which found to be between 89 g and 132 g, and most of the genotypes had bigger fruits than cv. Summered. Pink, red, yellow and green fruit skin color was evident and main fruit shape were determined as round, conic and oblate among genotypes. ART08-9, ART08-4, ART08-21 and ART08-22 had distinct bigger fruits and ART08-1, ART08-2, ART08-5, ART08-12 and ART08-17 had higher total phenolic content and antioxidant capacity. The results of the study showed significant differences for most of the phenological, morphological, sensory and biochemical characteristics. Thus, the phonological, morphological, sensory and biochemical characteristics of summer apple genotypes were distinguishable and these results suggest that phonological, morphological, sensory and biochemical differences of the summer apple genotypes can be attributed to differences in genetic background of genotypes which placed different groups by PCoA analysis.
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Affiliation(s)
- N Karatas
- Ataturk University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Erzurum, Turkey
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10
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Wang F, Zhao C, Yang G. Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods. Foods 2020; 9:E1778. [PMID: 33266189 PMCID: PMC7761122 DOI: 10.3390/foods9121778] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 11/25/2020] [Accepted: 11/26/2020] [Indexed: 11/22/2022] Open
Abstract
Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible-near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650-1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.
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Affiliation(s)
- Fan Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (F.W.); (G.Y.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097, China
| | - Chunjiang Zhao
- Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (F.W.); (G.Y.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097, China
| | - Guijun Yang
- Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; (F.W.); (G.Y.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097, China
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11
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Quantitative analysis of fat and protein concentrations of milk based on fibre-optic evaluation of back scattering intensity. Int Dairy J 2020. [DOI: 10.1016/j.idairyj.2020.104743] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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12
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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165399] [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
Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.
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Lashgari M, Imanmehr A, Tavakoli H. Fusion of acoustic sensing and deep learning techniques for apple mealiness detection. Journal of Food Science and Technology 2020; 57:2233-2240. [PMID: 32431349 DOI: 10.1007/s13197-020-04259-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 09/22/2019] [Accepted: 01/16/2020] [Indexed: 10/25/2022]
Abstract
Mealiness in apple fruit can occur during storage or because of harvesting in an inappropriate time; it degrades the quality of the fruit and has a considerable role in the fruit industry. In this paper, a novel non-destructive approach for detection of mealiness in Red Delicious apple using acoustic and deep learning techniques was proposed. A confined compression test was performed to assign labels of mealy and non-mealy to the apple samples. The criteria for the assignment were hardness and juiciness of the samples. For the acoustic measurements, a plastic ball pendulum was used as the impact device, and a microphone was installed near the sample to record the impact response. The recorded acoustic signals were converted to images. Two famous pre-trained convolutional neural networks, AlexNet and VGGNet were fine-tuned and employed as classifiers. According to the result obtained, the accuracy of AlexNet and VGGNet for classifying the apples to the two categories of mealy and non-mealy apples was 91.11% and 86.94%, respectively. In addition, the training and classification speed of AlexNet was higher. The results indicated that the suggested method provides an effective and promising tool for assessment of mealiness in apple fruit non-destructively and inexpensively.
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Affiliation(s)
- Majid Lashgari
- Department of Mechanical Engineering of Biosystems, Arak University, Arak, 38156-8-8349 Iran
| | - Abdullah Imanmehr
- Department of Mechanical Engineering of Biosystems, Arak University, Arak, 38156-8-8349 Iran
| | - Hamed Tavakoli
- Department of Mechanical Engineering of Biosystems, Arak University, Arak, 38156-8-8349 Iran
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14
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Baranyai L. Laser induced diffuse reflectance imaging – Monte Carlo simulation of backscattering measured on the surface. MethodsX 2020; 7:100958. [PMID: 32637331 PMCID: PMC7327276 DOI: 10.1016/j.mex.2020.100958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 06/06/2020] [Indexed: 11/18/2022] Open
Abstract
The Monte Carlo simulation algorithm of photon trajectory computation is implemented in object oriented R code. Diffuse reflectance, also called backscattering, is modeled in semi-infinite homogeneous media. Spatial photon flux leaving the surface of the media is collected. The profile of intensity along radii relative to the incident point is used to simulate measurement of computer vision systems. Four optical parameters of the media are used: absorption coefficient, scattering coefficient, anisotropy factor and refractive index. Five parameters are used to describe configuration of the vision system: number of photons, radius of circular light beam, limiting energy level of photons, radius of observed area, spatial resolution of the vision system.The incident angle of the light beam is included in the photon launch procedure. Initial direction is typically assumed to be normal with x,y,z coordinates of 0,0,1. In the proposed modification, initial move vector is calculated based on the incident angle and refractive index of the media. Additionally, elliptic distortion of the circular light beam on the surface is calculated based on the incident angle. Photon flux leaving media through the surface is corrected with Lambertian method to measure intensity captured by an imaging device in normal position. The software implementing the method is written in R language, the R code is available as standard package.
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Qiao S, Tian Y, Gu W, He K, Yao P, Song S, Wang J, Wang H, Zhang F. Research on simultaneous detection of SSC and FI of blueberry based on hyperspectral imaging combined MS-SPA. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.eaef.2019.11.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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16
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Shao Y, Xuan G, Hu Z, Gao Z, Liu L. Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis. PLoS One 2019; 14:e0222633. [PMID: 31532801 PMCID: PMC6750588 DOI: 10.1371/journal.pone.0222633] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 09/04/2019] [Indexed: 11/18/2022] Open
Abstract
Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry's competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350-2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.
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Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an, Shandong, China
- Nanjing Research Institute For Agricultural Mechanization, Ministry of Agriculture, Nanjing, Jiangsu, China
| | - Guantao Xuan
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an, Shandong, China
- College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, Missouri, United States of America
| | - Zhichao Hu
- Nanjing Research Institute For Agricultural Mechanization, Ministry of Agriculture, Nanjing, Jiangsu, China
| | - Zongmei Gao
- Department of Biological Systems Engineering, Washington State University, Pullman, Washington, United States of America
| | - Lei Liu
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an, Shandong, China
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Influences of Detection Position and Double Detection Regions on Determining Soluble Solids Content (SSC) for Apples Using On-line Visible/Near-Infrared (Vis/NIR) Spectroscopy. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01530-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Xu X, Xu H, Xie L, Ying Y. Effect of measurement position on prediction of apple soluble solids content (SSC) by an on-line near-infrared (NIR) system. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9964-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Zhang B, Gu B, Tian G, Zhou J, Huang J, Xiong Y. Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: A technical review. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.09.018] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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21
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Vasighi-Shojae H, Gholami-Parashkouhi M, Mohammadzamani D, Soheili A. Ultrasonic based determination of apple quality as a nondestructive technology. SENSING AND BIO-SENSING RESEARCH 2018. [DOI: 10.1016/j.sbsr.2018.09.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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22
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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
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Mechatronic components in apple sorting machines with computer vision. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9728-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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24
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Zhang L, Zhang B, Zhou J, Gu B, Tian G. Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2017; 2017:2525147. [PMID: 29123938 PMCID: PMC5662809 DOI: 10.1155/2017/2525147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Revised: 08/18/2017] [Accepted: 09/07/2017] [Indexed: 06/01/2023]
Abstract
Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.
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Affiliation(s)
- Lin Zhang
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
| | - Baohua Zhang
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
| | - Jun Zhou
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
| | - Baoxing Gu
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
| | - Guangzhao Tian
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
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Kim H, Rajwa B, Bhunia AK, Robinson JP, Bae E. Development of a multispectral light-scatter sensor for bacterial colonies. JOURNAL OF BIOPHOTONICS 2017; 10:634-644. [PMID: 27412151 DOI: 10.1002/jbio.201500338] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2015] [Revised: 05/16/2016] [Accepted: 06/01/2016] [Indexed: 06/06/2023]
Abstract
We report a multispectral elastic-light-scatter instrument that can simultaneously detect three-wavelength scatter patterns and associated optical densities from individual bacterial colonies, overcoming the limits of the single-wavelength predecessor. Absorption measurements on liquid bacterial samples revealed that the spectroscopic information can indeed contribute to sample differentiability. New optical components, including a pellicle beam splitter and an optical cage system, were utilized for robust acquisition of multispectral images. Four different genera and seven shiga toxin producing E. coli serovars were analyzed; the acquired images showed differences in scattering characteristics among the tested organisms. In addition, colony-based spectral optical-density information was also collected. The optical model, which was developed using diffraction theory, correctly predicted wavelength-related differences in scatter patterns, and was matched with the experimental results. Scatter-pattern classification was performed using pseudo-Zernike (GPZ) polynomials/moments by combining the features collected at all three wavelengths and selecting the best features via a random-forest method. The data demonstrate that the selected features provide better classification rates than the same number of features from any single wavelength. Three wavelength-merged scatter pattern from E. coli.
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Affiliation(s)
- Huisung Kim
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Bartek Rajwa
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Arun K Bhunia
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN 47907, USA
| | - J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Euiwon Bae
- Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
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Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7020189] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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27
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Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of ‘Valencia’ orange (Citrus sinensis) and ‘Star Ruby’ grapefruit (Citrus x paradisi Macfad). J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2016.08.015] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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28
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Wu X, Wu B, Sun J, Yang N. Classification of Apple Varieties Using Near Infrared Reflectance Spectroscopy and Fuzzy Discriminant C-Means Clustering Model. J FOOD PROCESS ENG 2016. [DOI: 10.1111/jfpe.12355] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang People's Republic of China
- Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry; Jiangsu University; Zhenjiang 212013 People's Republic of China
| | - Bin Wu
- Department of Information Engineering; ChuZhou Vocational Technology College; Chuzhou People's Republic of China
| | - Jun Sun
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang People's Republic of China
- Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry; Jiangsu University; Zhenjiang 212013 People's Republic of China
| | - Ning Yang
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang People's Republic of China
- Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry; Jiangsu University; Zhenjiang 212013 People's Republic of China
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29
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Fan S, Guo Z, Zhang B, Huang W, Zhao C. Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0313-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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30
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Measuring Agarwood Formation Ratio Quantitatively by Fluorescence Spectral Imaging Technique. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2015; 2015:205089. [PMID: 26089935 PMCID: PMC4451979 DOI: 10.1155/2015/205089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Revised: 10/13/2014] [Accepted: 10/13/2014] [Indexed: 11/17/2022]
Abstract
Agarwood is a kind of important and precious traditional Chinese medicine. With the decreasing of natural agarwood, artificial cultivation has become more and more important in recent years. Quantifying the formation of agarwood is an essential work which could provide information for guiding cultivation and controlling quality. But people only can judge the amount of agarwood qualitatively by experience before. Fluorescence multispectral imaging method is presented to measure the agarwood quantitatively in this paper. A spectral cube from 450 nm to 800 nm was captured under the 365 nm excitation sources. The nonagarwood, agarwood, and rotten wood in the same sample were distinguished based on analyzing the spectral cube. Then the area ratio of agarwood to the whole sample was worked out, which is the quantitative information of agarwood area percentage. To our knowledge, this is the first time that the formation of agarwood was quantified accurately and nondestructively.
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31
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Fan S, Huang W, Guo Z, Zhang B, Zhao C. Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-014-0079-1] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Zhang L, Xu H, Gu M. Use of signal to noise ratio and area change rate of spectra to evaluate the Visible/NIR spectral system for fruit internal quality detection. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2014.04.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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