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Shao Y, Ji S, Shi Y, Xuan G, Jia H, Guan X, Chen L. Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 319:124538. [PMID: 38833885 DOI: 10.1016/j.saa.2024.124538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 05/14/2024] [Accepted: 05/25/2024] [Indexed: 06/06/2024]
Abstract
Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes' growth stage and color coordinates, providing great significance for designing a tomato quality grading system.
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Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Shengheng Ji
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Yukang Shi
- \Shandong Industrial Technician College, Weifang 261000, China
| | - Guantao Xuan
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
| | - Huijie Jia
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
| | - Xianlu Guan
- College of Engineering, South China Agricultural University and Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
| | - Long Chen
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
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2
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Liu D, Zhang H, Lv F, Tao Y, Zhu L. Combining transfer learning and hyperspectral imaging to identify bruises of pears across different bruise types. J Food Sci 2024; 89:2597-2610. [PMID: 38558325 DOI: 10.1111/1750-3841.17050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/05/2024] [Accepted: 03/10/2024] [Indexed: 04/04/2024]
Abstract
Mechanical bruise is one of the most crucial factors affecting the quality of pears, which has a huge influence on postharvest transportation, storage, and sale of pears. To rapidly detect early bruises of pears across different bruise types, hyperspectral imaging technology coupled with transfer learning methods was performed in this study. Two transfer learning methods, that is, transfer component analysis (TCA) and manifold embedded distribution alignment (MEDA), were applied for two tasks (impact bruise → crush bruise, crush bruise → impact bruise). Supporting vector machine (SVM) was set as a baseline to conduct analysis and comparison of the transferability of the models. The result showed that, for task 1 (impact bruise → crush bruise), MEDA and TCA-SVM model achieved a classification accuracy of 93.33% and 91.11% in target domain, individually. For task 2 (crush bruise →impact bruise), MEDA and TCA-SVM model achieved an accuracy of 88.89% and 85.19% in target domain, respectively. Both the two models improved the accuracy compared with SVM models (84.44% for task 1; 77.04% for task 2). Overall, the results indicated that transfer learning approaches could perform pear bruise detection across different bruise types. Hyperspectral imaging in combination with transfer learning methods is a promising possibility for the efficient and cost-saving field detection of fruit bruises among different bruise types. PRACTICAL APPLICATION: The production and export of pears are faced with problems of mechanical damage due to vibration, collision, impact, and other factors, which cause chemical changes in color, odor, and taste. Sometimes the bruise was too slight to be ignored which would infect with other fruits in the future. In this study, we used hyperspectral imaging combined with transfer learning method could detect these slight bruises caused by different factors. Distinguishing different types of damage can provide a reference for quick judgment of the process causing damage and take prompt measures to reduce economic losses.
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Affiliation(s)
- Dayang Liu
- College of Computer and Control Engineering,Northeast Forestry University, Harbin, Heilongjiang, China
| | - Huiting Zhang
- College of Computer and Control Engineering,Northeast Forestry University, Harbin, Heilongjiang, China
| | - Feng Lv
- College of Computer and Control Engineering,Northeast Forestry University, Harbin, Heilongjiang, China
| | - Yanrong Tao
- State Grid Siping Power Supply Company, Siping, China
| | - Liangkuan Zhu
- College of Computer and Control Engineering,Northeast Forestry University, Harbin, Heilongjiang, China
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3
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Mao Y, Li H, Wang Y, Fan K, Song Y, Han X, Zhang J, Ding S, Song D, Wang H, Ding Z. Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging. Foods 2022; 11:foods11162537. [PMID: 36010536 PMCID: PMC9407140 DOI: 10.3390/foods11162537] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/02/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.
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Affiliation(s)
- Yilin Mao
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - He Li
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Yu Wang
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Kai Fan
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Yujie Song
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Xiao Han
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Jie Zhang
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Shibo Ding
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
| | - Dapeng Song
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
| | - Hui Wang
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
| | - Zhaotang Ding
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China
- Correspondence:
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Ren Y, Lin X, Lei T, Sun DW. Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes. Crit Rev Food Sci Nutr 2021; 62:4267-4293. [PMID: 34275402 DOI: 10.1080/10408398.2021.1947773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.
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Affiliation(s)
- Yuqiao Ren
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Xiaohui Lin
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
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Özdoğan G, Lin X, Sun DW. Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.044] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Chen G, Zhang X, Wu Z, Su J, Cai G. An efficient tea quality classification algorithm based on near infrared spectroscopy and random Forest. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Guikun Chen
- Computer Engineering College Jimei University Xiamen China
| | | | - Zebiao Wu
- Computer Engineering College Jimei University Xiamen China
| | - Jinhe Su
- Computer Engineering College Jimei University Xiamen China
| | - Guorong Cai
- Computer Engineering College Jimei University Xiamen China
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Lin X, Sun DW. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.06.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang YJ, Li LQ, Shen SS, Liu Y, Ning JM, Zhang ZZ. Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:3803-3811. [PMID: 32201954 DOI: 10.1002/jsfa.10393] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/25/2020] [Accepted: 03/21/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea-leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea-leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea-leaf quality. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Lu-Qing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Shan-Shan Shen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jing-Ming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zheng-Zhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Chen C, Chen J, Yuan Z, Liao E, Xia W, Wang H, Xiong YL. Effect of the wheat starch/wheat protein ratio in a batter on fat absorption and quality attributes of fried battered and breaded fish nuggets. J Food Sci 2020; 85:2098-2104. [PMID: 32572976 DOI: 10.1111/1750-3841.15303] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 04/26/2020] [Accepted: 04/28/2020] [Indexed: 11/26/2022]
Abstract
Battered and breaded fish nuggets (BBFNs) were prepared by treating fish with a batter composed of wheat starch (WS) and wheat protein (WP) blends (at the ratios of 15:1, 13:1, 11:1, 9:1, and 7:1, w/w), frying at 170 °C (40 s) followed by 190 °C (30 s). Fried BBFNs were evaluated for moisture and fat contents, color, shrinkage, acrylamide content, and fat distribution. Results showed that moisture content and brightness (L* value from colorimetry) increased with a decrease of WS/WP ratio to 11:1 w/w, then decreased as WS/WP ratio further decreased, while fat content, fat distribution level, and shrinkage of fried BBFNs presented opposite results. However, there was a slight influence of WS/WP ratio on yellowness (b* value), redness (a* value), and acrylamide content of fried BBFNs. Among WS/WP ratios, fried BBFNs with 11:1 w/w have the highest moisture content (16.43%) and the lowest fat content (23.39%), fat distribution level, shrinkage (10.72%), and acrylamide content (57 mg/kg), while a crust with golden-yellow color was observed. This study demonstrates that moisture evaporation and fat absorption were significantly influenced by WS/WP ratio in the batter (P < 0.05), with the most effective results in quality attributes improvement of fried BBFNs. PRACTICAL APPLICATION: This study clearly showed that the fat content and quality attributes of fried BBFNs were significantly affected by WS/WP ratio in the batter (P < 0.05). The inhibition of fat absorption and improvement of shrinkage and color in fried BBFNs was the most effective for a 11:1 w/w WS/WP ratio.
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Affiliation(s)
- Chaofan Chen
- The College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Jiwang Chen
- The College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China.,Key Laboratory for Deep Processing of Major Grain and Oil, Ministry of Education and Hubei Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan, China
| | - Zijun Yuan
- The College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China
| | - E Liao
- The College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China.,Key Laboratory for Deep Processing of Major Grain and Oil, Ministry of Education and Hubei Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan, China
| | - Wenshui Xia
- The College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China.,School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Haibin Wang
- The College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China.,Key Laboratory for Deep Processing of Major Grain and Oil, Ministry of Education and Hubei Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan, China
| | - Youling L Xiong
- The Department of Animal & Food Sciences, University of Kentucky, Lexington, KY
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Ni C, Liu H, Liu Q, Sun Y, Pan L, Fisk ID, Liu Y. Rapid and nondestructive monitoring for the quality of Jinhua dry‐cured ham using hyperspectral imaging and chromometer. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13443] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Chendie Ni
- College of Food Science and TechnologyShanghai Ocean University Shanghai China
| | - Hai Liu
- College of Food Science and TechnologyShanghai Ocean University Shanghai China
| | - Qiang Liu
- College of Food Science and TechnologyNanjing Agricultural University Nanjing China
| | - Ye Sun
- College of Food Science and TechnologyNanjing Agricultural University Nanjing China
| | - Leiqing Pan
- College of Food Science and TechnologyNanjing Agricultural University Nanjing China
| | - Ian Denis Fisk
- Division of Food SciencesUniversity of Nottingham Loughborough UK
| | - Yuan Liu
- Department of Food Science & TechnologyShanghai Jiao Tong University Shanghai China
- Shanghai Engineering Research Center of Food Safety Shanghai China
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Application of Computational Intelligence in Describing the Drying Kinetics of Persimmon Fruit (Diospyros kaki) During Vacuum and Hot Air Drying Process. Processes (Basel) 2020. [DOI: 10.3390/pr8050544] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.
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High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems. CHEMENGINEERING 2020. [DOI: 10.3390/chemengineering4010008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In modern fruit processing technology, non-destructive quality measuring techniques are sought for determining and controlling changes in the optical, structural, and chemical properties of the products. In this context, changes inside the product can be measured during processing. Especially for industrial use, fast, precise, but robust methods are particularly important to obtain high-quality products. In this work, a newly developed multi-spectral imaging system was implemented and adapted for drying processes. Further it was investigated if the system could be used to link changes in the surface spectral reflectance during mango drying with changes in moisture content and contents of chemical components. This was achieved by recovering the spectral reflectance from multi-spectral image data and comparing the spectral changes with changes of the total soluble solids (TSS), pH-value and the relative moisture content xwb of the products. In a first step, the camera was modified to be used in drying, then the changes in the spectra and quality criteria during mango drying were measured. For this, mango slices were dried at air temperatures of 40–80 °C and relative air humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectral imaging system. The quality criteria were then predicted from spectral data. It could be shown that the newly developed multi-spectral imaging system can be used for quality control in fruit drying. There are strong indications as well, that it can be employed for the prediction of chemical quality criteria of mangoes during drying. This way, quality changes can be monitored inline during the process using only one single measuring device.
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Li Y, Sun Y, Jiang J, Liu J. Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu. PLANT METHODS 2019; 15:73. [PMID: 31333757 PMCID: PMC6621968 DOI: 10.1186/s13007-019-0458-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/03/2019] [Indexed: 05/25/2023]
Abstract
BACKGROUND Reflectance spectroscopy, like IR, VIS-NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. But less studies have been made on the application of NIR reflectance spectroscopy to plant leaf color and pigments analysis and the possibility of using it for genetic breeding selection. Here, we examine the ability of NIR reflectance spectroscopy to determine the plant leaf color and chlorophyll content in Sassafras tzumu leaves and use the prediction results for genetic selection. Fresh and living tree leaves were used for NIR spectra collection, leaf color parameters (a*, b* and L*) and chlorophyll content were measured with standard analytical methods, partial least squares regression (PLSR) were used for model construction, the coefficient of determination (R2) [cross-validation ( R CV 2 ) and validation ( R V 2 )] and root mean square error (RMSE) [cross-validation (RMSECV) and validation (RMSEV)] were used for model performance evaluation, significant Multivariate Correlation algorithm was applied for model improvement, to find out the most important region related to the leaf color parameters and chlorophyll model, which have been simulated 100 times for accuracy estimation. RESULTS Leaf color parameters (a*, b* and L*) and chlorophyll content were well predicted by NIR reflectance spectroscopy on fresh leaves in vivo. The mean R CV 2 and RMSECV of a*, b*, L* and chlorophyll content were (0.82, 4.43), (0.63, 3.72), (0.61, 2.35) and (0.86, 0.13%) respectively. Three most important NIR regions, including 1087, 1215 and 2219 nm, which were highly related to a*, b*, L* and chlorophyll content were found. NIR reflectance spectra technology can be successfully used for genetic breeding program. High heritability of a*, b*, L* and chlorophyll content (h 2 = 0.77, 0.89, 0.78, 0.81 respectively) were estimated. Several families with bright red color or bright yellow color were selected. CONCLUSIONS NIR spectroscopy is promising for the rapid prediction of leaf color and chlorophyll content of living fresh leaves. It has the ability to simultaneously measure multiple plant leaf traits, potentially allowing for quick and economic prediction in situ.
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Affiliation(s)
- Yanjie Li
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province China
| | - Yang Sun
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province China
| | - Jingmin Jiang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province China
| | - Jun Liu
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province China
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Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol 2019; 10:197-220. [DOI: 10.1146/annurev-food-032818-121155] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.
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Affiliation(s)
- Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland;,
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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15
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Rapid prediction of yellow tea free amino acids with hyperspectral images. PLoS One 2019; 14:e0210084. [PMID: 30785888 PMCID: PMC6382264 DOI: 10.1371/journal.pone.0210084] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 12/16/2018] [Indexed: 01/19/2023] Open
Abstract
Free amino acids are an important indicator of the freshness of yellow tea. This study investigated a novel procedure for predicting the free amino acid (FAA) concentration of yellow tea. It was developed based on the combined spectral and textural features from hyperspectral images. For the purposes of exploration and comparison, hyperspectral images of yellow tea (150 samples) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing. To reduce the dimension of spectral data, five feature wavelengths were extracted using the successive projections algorithm (SPA). Five textural features (angular second moment, entropy, contrast, correlation, and homogeneity) were extracted as textural variables from the characteristic grayscale images of the five characteristic wavelengths using the gray-level co-occurrence matrix (GLCM). The FAA content prediction model with different variables was established by a genetic algorithm-support vector regression (GA-SVR) algorithm. The results showed that better prediction results were obtained by combining the feature wavelengths and textural variables. Compared with other data, this prediction result was still very satisfactory in the GA-SVR model, indicating that data fusion was an effective way to enhance hyperspectral imaging ability for the determination of free amino acid values in yellow tea.
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16
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Applications of Non-destructive Technologies for Agricultural and Food Products Quality Inspection. SENSORS 2019; 19:s19040846. [PMID: 30781709 PMCID: PMC6413199 DOI: 10.3390/s19040846] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/19/2019] [Accepted: 01/19/2019] [Indexed: 01/15/2023]
Abstract
The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation.
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17
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Dong CW, Zhu HK, Zhao JW, Jiang YW, Yuan HB, Chen QS. Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools. J Zhejiang Univ Sci B 2018; 18:544-548. [PMID: 28585431 DOI: 10.1631/jzus.b1600423] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).
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Affiliation(s)
- Chun-Wang Dong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.,Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Hong-Kai Zhu
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jie-Wen Zhao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yong-Wen Jiang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.,Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Hai-Bo Yuan
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Quan-Sheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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18
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Liu Y, Pu H, Sun DW. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.08.013] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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19
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Xie C, Chu B, He Y. Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging. Food Chem 2017; 245:132-140. [PMID: 29287354 DOI: 10.1016/j.foodchem.2017.10.079] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 09/08/2017] [Accepted: 10/13/2017] [Indexed: 01/28/2023]
Abstract
This study investigated the feasibility of using hyperspectral imaging for determining banana color (L∗, a∗ and b∗) and firmness as well as classifying ripe and unripe samples. The hyperspectral images at wavelengths 380-1023nm were acquired. Partial least squares (PLS) models were built to predict color and firmness. Two-wavelength combination method λi-λjλi+λj,λi2-λj2λi2+λj2,λiλjandλi-λj was used to identify the effective wavelengths. Based on the selected wavelengths, PLS models obtained good results with the coefficient of determination in prediction (Rp2) of 0.795 for L∗, 0.972 for a∗, 0.773 for b∗ and 0.760 for firmness. The corresponding residual predictive deviation (RPD) values were 2.234, 6.098, 2.119 and 2.062, respectively. The classification results of ripe and unripe samples were excellent in two different principal components spaces (PC1+PC2 and PC1+PC3). It indicated hyperspectral imaging can be used to non-destructively determine banana color and firmness as well as classify ripe and unripe samples.
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Affiliation(s)
- Chuanqi Xie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Bingquan Chu
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
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20
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Li L, Xie S, Zhu F, Ning J, Chen Q, Zhang Z. Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: A method of fabrication. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1354021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Shimeng Xie
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Fengyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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21
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Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics. Sci Rep 2016; 6:30313. [PMID: 27468701 PMCID: PMC4965860 DOI: 10.1038/srep30313] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 07/04/2016] [Indexed: 12/30/2022] Open
Abstract
This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea.
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22
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Xie C, He Y. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves. SENSORS 2016; 16:s16050676. [PMID: 27187387 PMCID: PMC4883367 DOI: 10.3390/s16050676] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 04/30/2016] [Accepted: 05/05/2016] [Indexed: 11/16/2022]
Abstract
This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.
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Affiliation(s)
- Chuanqi Xie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
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23
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Olson MR, Graham E, Hamad S, Uchupalanun P, Ramanathan N, Schauer JJ. Quantification of elemental and organic carbon in atmospheric particulate matter using color space sensing-hue, saturation, and value (HSV) coordinates. THE SCIENCE OF THE TOTAL ENVIRONMENT 2016; 548-549:252-259. [PMID: 26802353 DOI: 10.1016/j.scitotenv.2016.01.032] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Revised: 01/06/2016] [Accepted: 01/07/2016] [Indexed: 05/22/2023]
Abstract
A fast and cost effective application of color sensing was used to quantify color coordinates of atmospheric particulate matter collected on filters to quantify elemental and organic carbon (EC/OC) loading. This is a unique and novel approach for estimating OC composition. The method used a colorimeter and digital photography to obtain XYZ color space values and mathematically transformed them to HSV cylindrical-coordinates; a quantification method was applied to estimate the NIOSH and IMPROVE (TOR) EC/OC loadings from a set of globally diverse PM samples. When applied to 315 samples collected at three US EPA Chemical Speciation Network (CSN) sampling sites, the HSV model proved to be a robust method for EC measurement with an R(2)=0.917 for predicted versus measured loading results and a CV(RMSE)=16.1%. The OC quantified from the same sample filters had an R(2)=0.671 and a CV(RMSE)=24.8% between the predicted and measured results. The method was applied to NIOSH EC/OC results from a set of samples from rural China, Bagdad, and the San Joaquin Valley, CA, and the EC and OC CV(RMSE) were 30.8% and 49.3%, respectively. Additionally, the method was applied to samples with color quantified by a digital photographic image (DPI) with EC results showing good agreement with a CV(RMSE) of 22.6%. OC concentrations were not captured as accurately with the DPI method, with a CV(RMSE) of 77.5%. The method's low analytical cost makes it a valuable tool for estimating EC/OC exposure in developing regions and for large scale monitoring campaigns.
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Affiliation(s)
- Michael R Olson
- University of Wisconsin-Madison, Department of Civil and Environmental Engineering, WSEL 660 North Park Street, Madison, WI 53706, USA
| | - Eric Graham
- Nexleaf Analytics, 2356 Pelham Ave., Los Angeles, CA 90064, USA; Central Washington University, Department of Biology, Ellensburg, WA 98926, USA
| | - Samera Hamad
- University of Wisconsin-Madison, Department of Civil and Environmental Engineering, WSEL 660 North Park Street, Madison, WI 53706, USA
| | - Pajean Uchupalanun
- University of Wisconsin-Madison, Department of Civil and Environmental Engineering, WSEL 660 North Park Street, Madison, WI 53706, USA
| | | | - James J Schauer
- University of Wisconsin-Madison, Department of Civil and Environmental Engineering, WSEL 660 North Park Street, Madison, WI 53706, USA.
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24
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Xie C, He Y. External characteristic determination of eggs and cracked eggs identification using spectral signature. Sci Rep 2016; 6:21130. [PMID: 26882990 PMCID: PMC4756659 DOI: 10.1038/srep21130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 01/19/2016] [Indexed: 11/09/2022] Open
Abstract
This study was carried out to use hyperspectral imaging technique for determining color (L*, a* and b*) and eggshell strength and identifying cracked chicken eggs. Partial least squares (PLS) models based on full and selected wavelengths suggested by regression coefficient (RC) method were established to predict the four parameters, respectively. Partial least squares-discriminant analysis (PLS-DA) and RC-partial least squares-discriminant analysis (RC-PLS-DA) models were applied to identify cracked eggs. PLS models performed well with the correlation coefficient (rp) of 0.788 for L*, 0.810 for a*, 0.766 for b* and 0.835 for eggshell strength. RC-PLS models also obtained the rp of 0.771 for L*, 0.806 for a*, 0.767 for b* and 0.841 for eggshell strength. The classification results were 97.06% in PLS-DA model and 88.24% in RC-PLS-DA model. It demonstrated that hyperspectral imaging technique has the potential to be used to detect color and eggshell strength values and identify cracked chicken eggs.
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Affiliation(s)
- Chuanqi Xie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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