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Wei X, Liu S, Xie C, Fang W, Deng C, Wen Z, Ye D, Jie D. Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning. Front Plant Sci 2023; 14:1260625. [PMID: 38126009 PMCID: PMC10731295 DOI: 10.3389/fpls.2023.1260625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023]
Abstract
In the production of edible fungi, the use of degraded strains in cultivation incurs significant economic losses. Based on micro-hyperspectral imaging and machine learning, this study proposes an early, nondestructive method for detecting different degradation degrees of Pleurotus geesteranus strains. In this study, an undegraded strain and three different degradation-level strains were used. During the mycelium growth, 600 micro-hyperspectral images were obtained. Based on the average transmittance spectra of the region of interest (ROI) in the range of 400-1000 nm and images at feature bands, feature spectra and images were extracted using the successive projections algorithm (SPA) and the deep residual network (ResNet50), respectively. Different feature input combinations were utilized to establish support vector machine (SVM) classification models. Based on the results, the spectra-input-based model performed better than the image-input-based model, and feature extraction improved the classification results for both models. The feature-fusion-based SPA+ResNet50-SVM model was the best; the accuracy rate of the test set was up to 90.8%, which was better than the accuracy rates of SPA-SVM (83.3%) and ResNet50-SVM (80.8%). This study proposes a nondestructive method to detect the degradation of Pleurotus geesteranus strains, which could further inspire new methods for the phenotypic identification of edible fungi.
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Affiliation(s)
- Xuan Wei
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Shiyang Liu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Chuangyuan Xie
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Wei Fang
- College of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Chanjuan Deng
- College of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Zhiqiang Wen
- College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - Dengfei Jie
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
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2
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Liu S, Dong F, Hao J, Qiao L, Guo J, Wang S, Luo R, Lv Y, Cui J. Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton. Spectrochim Acta A Mol Biomol Spectrosc 2023; 291:122342. [PMID: 36682252 DOI: 10.1016/j.saa.2023.122342] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/17/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
The antioxidant enzymes play the crucial role in inhibiting mutton spoilage. In this study, visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with entropy weight method (EWM) was developed for the first time to evaluate the antioxidant properties of Tan mutton. The comprehensive index of antioxidant enzymes (AECI) consisting of peroxidase (49.34%), catalase (37.97%) and superoxidase (12.69%) was constructed by the EWM. Partial least squares regression, least squares support vector machine and artificial neural networks (ANN) were developed based on characteristic wavelengths extracted by successful projections algorithm, uninformative variable selection, iteratively retains informative variables (IRIV), regression coefficient and competitive adaptive reweighted sampling (CARS). The textural features (TF) were extracted by the gray level co-occurrence matrix and fused with the spectral data to establish models. Visualization of the changes in antioxidant enzyme activity was constructed from the optimal model. In addition, two-dimensional correlation spectra (2D-COS) with AECI as a perturbation variable was used to identify spectral features, revealing chemical bond changes order under the characteristic peaks at 612-799-473-708-559 nm. The results showed that the IRIV-CARS-TF-ANN model performed the best, with prediction set coefficient of determination (RP2) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the RP2 of full band, IRIV and IRIV-CARS, respectively. It was suggested that fusion data of HSI may effectively predict the activity of antioxidant enzymes in Tan mutton.
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Affiliation(s)
- Sijia Liu
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Fujia Dong
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jie Hao
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Lu Qiao
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jianhong Guo
- School of Chemical & Biological Engineering, Yinchuan University of Energy, Yinchuan 750021, China
| | - Songlei Wang
- School of Food & Wine, Ningxia University, Yinchuan 750021, China.
| | - Ruiming Luo
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
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Cheng J, Sun J, Yao K, Xu M, Wang S, Fu L. Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves. J Sci Food Agric 2023; 103:2690-2699. [PMID: 36479694 DOI: 10.1002/jsfa.12376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/21/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves. RESULTS Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg-1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. CONCLUSION The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Simin Wang
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
| | - Lvhui Fu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China
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4
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Aheto JH, Huang X, Wang C, Tian X, Yi R, Yuena W. Fabrication and evaluation of chitosan modified filter paper for chlorpyrifos detection in wheat by surface-enhanced Raman spectroscopy. J Sci Food Agric 2022; 102:7323-7330. [PMID: 35767555 DOI: 10.1002/jsfa.12098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 04/10/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Chlorpyrifos is a commonly used organophosphorus pesticide in agriculture. However, its neurotoxicity poses a huge threat to human health. In the present study, a chitosan-modified filter paper-based surface enhanced Raman scattering active substrate (Ch/AgNPs/paper) was fabricated and used to detect trace amounts of chlorpyrifos in 120 treated wheat samples. RESULTS Results showed that the Ch/AgNPs/paper substrate could be used to enhance the chlorpyrifos spectral fingerprint only up to a concentration of 0.000558 mg L-1 . Following Raman spectra acquisition, three pre-processing methods, including Savitzky-Golay (Savitsky-Golay filter with a second order polynomial) smoothing with first derivative and second derivative and normalization, were used to reduce baseline variation and increase resolutions of spectral peak features of the original spectra dataset. Then, prediction models based on partial least squares were established for detecting chlorpyrifos pesticide residue in wheat. The partial least squares model with normalization yielded optimal result, with a correlation coefficient of 0.9764, root mean square error of prediction of 1.22 mg L-1 in the prediction, and relative analysis deviation of 4.12. Five unknown samples were prepared to verify the accuracy of the prediction model. The predicted recoveries were calculated to be between 97.25% and 119.38% with an absolute t value of 0.598. The value of a t-test shows that the prediction model is accurate and reliable. CONCLUSION The present study demonstrates that the proposed method can achieve rapid detection of chlorpyrifos in wheat. © 2022 Society of Chemical Industry.
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Affiliation(s)
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Chengquan Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Xiaoyu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Ren Yi
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- Suzhou Polytechnic Institute of Agriculture, School of Smart Agriculture, Suzhou, China
| | - Wang Yuena
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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Tang T, Zhang M, Mujumdar AS. Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology. Compr Rev Food Sci Food Saf 2022; 21:5171-5198. [PMID: 36156851 DOI: 10.1111/1541-4337.13039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 01/28/2023]
Abstract
Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.
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Affiliation(s)
- Tiantian Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, Jiangsu, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Montreal, Quebec, Canada
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Liu H, Liu H, Li J, Wang Y. Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation. Crit Rev Anal Chem 2022:1-24. [PMID: 36154534 DOI: 10.1080/10408347.2022.2124839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.
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Affiliation(s)
- Hong Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Honggao Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Zhaotong University, Zhaotong, China
| | - Jieqing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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7
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Arjun AD, Chakraborty SK, Mahanti NK, Kotwaliwale N. Non-destructive assessment of quality parameters of white button mushrooms (Agaricus bisporus) using image processing techniques. J Food Sci Technol 2022; 59:2047-59. [PMID: 35531410 DOI: 10.1007/s13197-021-05219-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 03/29/2021] [Accepted: 07/25/2021] [Indexed: 10/20/2022]
Abstract
Considering that appearance of white button mushroom (WBM) as the trigger for registering its quality, this study was aimed at analyzing the visual cues by the application of image processing tools. While L-a-b colour space and skewness was used for estimating chromatic and morphological characteristics; onset of discolouration of WBM was predicted by hyperspectral image analysis. Undamaged (UD) and damaged (D) mushrooms were stored under refrigerated conditions (3-5 °C and 90% Rh). RGB and hyperspectral images were acquired on alternate storage days 1, 3, 5, 7 and 9. Weight loss, texture and moisture content of stored mushrooms were also recorded during the storage period. Colour changes in stored UD and D were found to be in b (21.55) and a (2399) value, respectively. Browning index in D was 83-212% higher than UD mushrooms across the storage period. Weight and firmness losses in D were higher by 65.9 and 31.4%, respectively than UD. Morphological characteristic in terms of aspect ratio and roundness were not found to vary significantly over the storage period for both UD and D mushrooms. Chemometrics revealed that multiplicative scatter correction was the best pre-processing tool and that onset on discolouration is conspicuous in the spectral region of 520-800 nm. k-NN fared better than PLS-DA for correct classification (100%) of UD and D mushrooms.
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8
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An T, Yu S, Huang W, Li G, Tian X, Fan S, Dong C, Zhao C. Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging. Spectrochim Acta A Mol Biomol Spectrosc 2022; 269:120791. [PMID: 34968835 DOI: 10.1016/j.saa.2021.120791] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 12/13/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
The rapid and non-destructive detection of moisture in withering leaves is an unsolved problem because the leaves are stacked together and have random orientation. To address this issue, this study aimed to establish more robust and accurate models. The performance of front side, back side and multi-region models were compared, and the front side model showed the worst transferability. Therefore, five effective wavelength (EW) selection algorithms were combined with a successive projection algorithm (SPA) to select EWs. It was found that the shuffled frog leaping algorithm (SFLA) combined with SPA was the best method for the front side model for moisture analyses. Based on the selected EWs, the extreme learning machine (ELM) became the model with the best self-verification result. Subsequently, moisture distribution maps of withering leaves were successfully generated. Considering the processing demand of withering leaves, local region models developed based on partial least squares and the SFLA-SPA method were applied to predict the moisture of withering leaves in the local and stacked region. The results showed that the RPD, Rcv and Rp values were above 1.6, 0.870 and 0.897, respectively. These results provide a useful reference for the non-destructive detection of moisture in withering leaves.
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Affiliation(s)
- Ting An
- College of Engineering and Technology, Southwest University, Chongqing 400715, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Siyao Yu
- College of Mechanical and Electrical Engineering Shihezi University, Shihezi 832000, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.
| | - Chunjiang Zhao
- College of Engineering and Technology, Southwest University, Chongqing 400715, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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Pandiselvam R, Mahanti NK, Manikantan M, Kothakota A, Chakraborty SK, Ramesh S, Beegum PS. Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108588] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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10
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Tian S, Xu H. Nondestructive Methods for the Quality Assessment of Fruits and Vegetables Considering Their Physical and Biological Variability. Food Eng Rev. [DOI: 10.1007/s12393-021-09300-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Nazir A, AlDhaheri M, Mudgil P, Marpu P, Kamal-Eldin A. Hyperspectral imaging based kinetic approach to assess quality deterioration in fresh mushrooms (Agaricus bisporus) during postharvest storage. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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12
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Yu R, Zhu X, Bai X, Tian Z, Jiang Y, Yang G. Inversion reflectance by apple tree canopy ground and unmanned aerial vehicle integrated remote sensing data. J Plant Res 2021; 134:729-736. [PMID: 33590370 DOI: 10.1007/s10265-020-01249-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 12/24/2020] [Indexed: 06/12/2023]
Abstract
To obtain accurate spatially continuous reflectance from Unmanned Aerial Vehicle (UAV) remote sensing, UAV data needs to be integrated with the data on the ground. Here, we tested accuracy of two methods to inverse reflectance, Ground-UAV-Linear Spectral Mixture Model (G-UAV-LSMM) and Minimum Noise Fraction-Pixel Purity Index-Linear Spectral Mixture Model (MNF-PPI-LSMM). At wavelengths of 550, 660, 735 and 790 nm, which were obtained by UAV multispectral observations, we calculated the canopy abundance based on the two methods to acquire the inversion reflectance. The correlation of the inversion and measured reflectance values was stronger in G-UAV-LSMM than MNF-PPI-LSMM. We conclude that G-UAV-LSMM is the better model to obtain the canopy inversion reflectance.
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Affiliation(s)
- Ruiyang Yu
- College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China
| | - Xicun Zhu
- College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China.
- National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China.
| | - Xueyuan Bai
- College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China
| | - Zhongyu Tian
- College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China
| | - Yuanmao Jiang
- College of Horticulture Science and Engineering, National Apple Engineering and Technology Research Center, Shandong Agricultural University, Tai'an, 271018, China
| | - Guijun Yang
- National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
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Subramaniam S, Jiao S, Zhang Z, Jing P. Impact of post-harvest processing or thermal dehydration on physiochemical, nutritional and sensory quality of shiitake mushrooms. Compr Rev Food Sci Food Saf 2021; 20:2560-2595. [PMID: 33786992 DOI: 10.1111/1541-4337.12738] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 02/07/2021] [Accepted: 02/11/2021] [Indexed: 12/20/2022]
Abstract
Shiitake mushrooms are one of the most popular and highly consumed mushrooms worldwide both in fresh and dry forms. However, it rapidly starts losing its quality immediately after harvest which necessitates processing and/or proper storage before being distributed. However, the processes used for preserving other mushrooms (e.g., Agaricus) become unviable for shiitake due to its uniqueness (higher respiration rate, varied biochemicals, growth, etc.) which demands individual studies on shiitake. This review starts by listing the factors and their interdependence leading to a quality decline in shiitake after harvest. Understanding well about these factors, numerous post-harvest operations preserve shiitake as fresh form for a shorter period and as dried forms for a longer shelf-life. These processes also affect the intrinsic quality and nutrients of shiitake. This review comprehensively summarizes and discusses the effects of chemical processing (washing, fumigation, coating, and ozone), modified atmosphere packaging (including irradiation) on the quality of fresh shiitake while discussing their efficiency in extending their shelf-life by inhibiting microbial spoilage and deterioration in quality including texture, appearance, nutrients, and favor. It also reviews the impact of thermal dehydration on the quality of dried shiitake mushrooms, especially the acquired unique textural, nutritional, and aromatic properties along with their merits and limitations. Since shiitake are preferred to be low-cost consumer products, the applicability of freeze-drying and sophisticated novel methodologies, which prove to be expensive and/or complex, are discussed. The review also outlines the challenges and proposes the subsequent future directives, which either retains/enhances the desirable quality in shiitake mushrooms.
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Affiliation(s)
- Shankar Subramaniam
- Shanghai Food Safety and Engineering Technology Research Center, Key Laboratory of Urban Agriculture, Ministry of Agriculture, Bor S. Luh Food Safety Research Centre, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Shunshan Jiao
- Shanghai Food Safety and Engineering Technology Research Center, Key Laboratory of Urban Agriculture, Ministry of Agriculture, Bor S. Luh Food Safety Research Centre, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhentao Zhang
- Technical Institute of Physics and Chemistry, CAS, Beijing, China
| | - Pu Jing
- Shanghai Food Safety and Engineering Technology Research Center, Key Laboratory of Urban Agriculture, Ministry of Agriculture, Bor S. Luh Food Safety Research Centre, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
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14
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Chakraborty SK, Mahanti NK, Mansuri SM, Tripathi MK, Kotwaliwale N, Jayas DS. Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis-NIR (400-1000 nm) hyperspectral imaging. J Food Sci Technol 2021; 58:437-450. [PMID: 33568838 PMCID: PMC7847924 DOI: 10.1007/s13197-020-04552-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 11/26/2022]
Abstract
Aflatoxin-B1 contamination in maize is a major food safety issue across the world. Conventional detection technique of toxins requires highly skilled technicians and is time-consuming. Application of appropriate chemometrics along with hyperspectral imaging (HSI) can identify aflatoxin-B1 infected maize kernels. Present study was undertaken to classify 240 maize kernels inoculated with six different concentrations (25, 40, 70, 200, 300 and 500 ppb) of aflatoxin-B1 by using Vis-NIR HSI. The reflectance spectral data were pre-processed (multiplicative scatter correction (MSC), standard normal variate (SNV), Savitsky-Golay smoothing and their combinations) and classified using partial least square discriminant analysis (PLS-DA) and k-nearest neighbour (k-NN). PLS model was also developed to predict the concentration of aflatoxin-B1in naturally contaminated maize kernels inoculated with Aspergillus flavus. The potential wavelength (508 nm) was selected based on principal component analysis (PCA) loadings to distinguish between sterile and infected maize kernels. PCA score plots revealed a distinct separation of low contaminated samples (25, 40 and 70 ppb) from highly contaminated samples (200, 300 and 500 ppb) without any overlapping of data. The maximum classification accuracy of 94.7% was obtained using PLS-DA with SNV pre-processed data. Across all the combinations of pre-processing and classification models, the best efficiency (98.2%) was exhibited by k-NN model with raw data. The developed PLS model depicted good prediction accuracy ( R CV 2 = 0.820, SECV = 79.425, RPDCV = 2.382) during Venetian-blinds cross-validation. The results of pixel-wise classification (k-NN) and concentration distribution maps (PLS with raw spectra) were quite close to the result obtained by reference method (HPLC analysis) of aflatoxin-B1 detection.
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Affiliation(s)
- Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Naveen Kumar Mahanti
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Shekh Mukhtar Mansuri
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Manoj Kumar Tripathi
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Nachiket Kotwaliwale
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal, MP 462038 India
| | - Digvir Singh Jayas
- Department of Bio Systems Engineering, University of Manitoba, Winnipeg, Canada
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Mahanti NK, Chakraborty SK, Kotwaliwale N, Vishwakarma AK. Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy. J Food Sci 2020; 85:3653-3662. [PMID: 32888324 DOI: 10.1111/1750-3841.15420] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/24/2020] [Accepted: 07/22/2020] [Indexed: 11/29/2022]
Abstract
The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications.
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Affiliation(s)
- Naveen Kumar Mahanti
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Nachiket Kotwaliwale
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Anand Kumar Vishwakarma
- Department of Soil Chemistry and Fertility, ICAR-Indian Institute of Soil Science, Bhopal, India
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Yuan D, Jiang J, Qiao X, Qi X, Wang W. An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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17
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Aheto JH, Huang X, Tian X, Ren Y, Bonah E, Alenyorege EA, Lv R, Dai C. Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13225] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Joshua H. Aheto
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xingyi Huang
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xiaoyu Tian
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Yi Ren
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Suzhou Polytechnic Institute of Agriculture; Suzhou China
| | - Ernest Bonah
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Laboratory Services Department; Food and Drugs Authority; Accra Ghana
| | - Evans A. Alenyorege
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Faculty of Agriculture; University for Development Studies; Tamale Ghana
| | - Riqin Lv
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Biological Science and Food Engineering; Chuzhou University; No. 1528 Fengle Avenue, Yu District, Zhangzhou City China
| | - Chunxia Dai
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang Jiangsu China
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18
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Lin X, Sun DW. Research advances in browning of button mushroom (Agaricus bisporus): Affecting factors and controlling methods. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.05.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Pan TT, Sun DW, Paliwal J, Pu H, Wei Q. New Method for Accurate Determination of Polyphenol Oxidase Activity Based on Reduction in SERS Intensity of Catechol. J Agric Food Chem 2018; 66:11180-11187. [PMID: 30209938 DOI: 10.1021/acs.jafc.8b03985] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Rapid and accurate measurement of polyphenol oxidase (PPO) activity is important in the food industry as PPOs play a vital role in catalyzing enzymatic reactions. The aim of this study was to develop surface-enhanced Raman scattering (SERS) approach for accurate determination of PPO activity in fruit and vegetables using the reduction in SERS intensity of catechol in reaction medium. Within a certain catechol concentration, when a purified PPO solution was analyzed, the reduction in SERS intensity (Δ I) was linear to PPO activity ( Ec) in a wide range of 500-50 000 U/L, and a linear regression equation of log Δ I/Δ t = 0.6223 log Ec + 0.8072, with a correlation coefficient of 0.9689 and a limit of detection of 224.65 U/L, was obtained. The method was used for detecting PPO activity in apple and potato samples, and the results were compared with those obtained from colorimetric assay, which demonstrated that the proposed method could be successfully used for detecting PPO activity in food samples.
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Affiliation(s)
- Ting-Tiao Pan
- 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 , Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods , Guangzhou Higher Education Mega Center, Guangzhou 510006 , China
- Department of Biosystems Engineering , University of Manitoba , E2-376, EITC, 75A Chancellor's Circle , Winnipeg , R3T 2N2 Manitoba , Canada
| | - 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 , 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 (FRCFT), Agriculture and Food Science Centre , University College Dublin , National University of Ireland, Belfield, Dublin 4 , Ireland
| | - Jitendra Paliwal
- Department of Biosystems Engineering , University of Manitoba , E2-376, EITC, 75A Chancellor's Circle , Winnipeg , R3T 2N2 Manitoba , Canada
| | - 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 , 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 , Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods , Guangzhou Higher Education Mega Center, Guangzhou 510006 , China
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20
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Xu Y, Chen Q, Liu Y, Sun X, Huang Q, Ouyang Q, Zhao J. A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork. Korean J Food Sci Anim Resour 2018; 38:362-375. [PMID: 29805285 PMCID: PMC5960833 DOI: 10.5851/kosfa.2018.38.2.362] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 03/15/2018] [Accepted: 03/16/2018] [Indexed: 12/20/2022] Open
Abstract
This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.
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Affiliation(s)
- Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.,State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 210036, China
| | - Yan Liu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Xin Sun
- Animal Science Department, North Dakota State University, Fargo, United States
| | - Qiping Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jiewen Zhao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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21
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Zhu H, Chu B, Fan Y, Tao X, Yin W, He Y. Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models. Sci Rep 2017; 7:7845. [PMID: 28798306 DOI: 10.1038/s41598-017-08509-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 07/11/2017] [Indexed: 11/08/2022] Open
Abstract
We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre = 0.9812, RPD = 5.17) and SSC (R pre = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.
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22
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Blasco J, Munera S, Aleixos N, Cubero S, Molto E. Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest. Measurement, Modeling and Automation in Advanced Food Processing 2017; 161:71-91. [DOI: 10.1007/10_2016_51] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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23
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Cheng W, Sun DW, Pu H, Liu Y. Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat. Lebensm Wiss Technol 2016. [DOI: 10.1016/j.lwt.2016.05.003] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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24
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Sun J, Ma B, Dong J, Zhu R, Zhang R, Jiang W. Detection of internal qualities of hami melons using hyperspectral imaging technology based on variable selection algorithms. J FOOD PROCESS ENG 2016. [DOI: 10.1111/jfpe.12496] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jingtao Sun
- College of Food Science and Engineering; Shihezi University; Shihezi 832000 China
| | - Benxue Ma
- College of Mechanical and Electrical Engineering; Shihezi University; Shihezi 832000 China
| | - Juan Dong
- College of Food Science and Engineering; Shihezi University; Shihezi 832000 China
| | - Rongguang Zhu
- College of Mechanical and Electrical Engineering; Shihezi University; Shihezi 832000 China
| | - Ruoyu Zhang
- College of Mechanical and Electrical Engineering; Shihezi University; Shihezi 832000 China
| | - Wei Jiang
- College of Mechanical and Electrical Engineering; Shihezi University; Shihezi 832000 China
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25
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Pan TT, Sun DW, Cheng JH, Pu H. Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation. Compr Rev Food Sci Food Saf 2016; 15:529-541. [DOI: 10.1111/1541-4337.12191] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Revised: 12/12/2015] [Accepted: 12/16/2015] [Indexed: 01/06/2023]
Affiliation(s)
- Ting-Tiao Pan
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Hongbin Pu
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
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26
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Khulal U, Zhao J, Hu W, Chen Q. Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem 2015; 197 Pt B:1191-9. [PMID: 26675857 DOI: 10.1016/j.foodchem.2015.11.084] [Citation(s) in RCA: 110] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 10/13/2015] [Accepted: 11/16/2015] [Indexed: 11/29/2022]
Abstract
Hyperspectral imaging (HSI) system has been used to assess the chicken quality in this work. Principle component analysis (PCA) and Ant Colony Optimization (ACO) were comparatively used for data dimension reduction. First, we selected 5 dominant wavelength images from chicken hypercube using PCA and ACO. Then, 6 textural variables based on statistical moments were extracted from each dominant wavelength image, thus totaling to 30 variables. Next, we selected the classic back propagation artificial neural network (BPANN) algorithm for modeling. Experimental results showed the performance of ACO-BPANN model is superior to that of PCA-BPANN model, and the optimum ACO-BPANN model was achieved with RMSEP=6.3834 mg/100g and R=0.7542 in the prediction set. Our work implies that HSI integrating spectral and spatial information has a high potential in quantifying TVB-N content of chicken in rapid and non-destructive manner, and ACO has superiority in dimension reduction of hypercube.
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Affiliation(s)
- Urmila Khulal
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiewen Zhao
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Weiwei Hu
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Zhang C, Liu F, Kong W, He Y. Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves. Sensors (Basel) 2015; 15:16576-88. [PMID: 26184198 DOI: 10.3390/s150716576] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 06/27/2015] [Accepted: 07/03/2015] [Indexed: 11/16/2022]
Abstract
Visible and near-infrared hyperspectral imaging covering spectral range of 380–1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500–900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (rp) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with rp of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves.
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28
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Yang YC, Sun DW, Wang NN, Xie A. Real-time evaluation of polyphenol oxidase (PPO) activity in lychee pericarp based on weighted combination of spectral data and image features as determined by fuzzy neural network. Talanta 2015; 139:198-207. [DOI: 10.1016/j.talanta.2015.02.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 02/02/2015] [Accepted: 02/06/2015] [Indexed: 10/23/2022]
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29
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Kim N, Kim WY. Measurement of polyphenol oxidase activity using optical waveguide lightmode spectroscopy-based immunosensor. Food Chem 2015; 169:211-7. [PMID: 25236218 DOI: 10.1016/j.foodchem.2014.07.130] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Revised: 07/08/2014] [Accepted: 07/29/2014] [Indexed: 11/19/2022]
Abstract
Polyphenol oxidase (PPO) is an important quality index during food processing involving heat-treatment and sensitive determination of PPO activity has been a critical concern in the food industry. In this study, a new measurement of PPO activity exploiting an optical waveguide lightmode spectroscopy-based immunosensor is presented using a polyclonal anti-PPO antibody that was immobilized in situ to the surface of a 3-aminopropyltriethoxysilane-treated optical grating coupler activated with glutaraldehyde. When analysed with a purified PPO fraction from potato tubers, a linear relationship was found between PPO activities of 0.0005607-560.7U/mL and the sensor responses obtained. The sensor was applicable to measurement of PPO activity in real samples that were prepared from potato tubers, grapes and Kimchi cabbage, and the analytical results were compared with those obtained by a conventional colorimetric assay measuring PPO activity. When tested for long-term stability, the sensor was reusable up to 10th day after preparation.
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Affiliation(s)
- Namsoo Kim
- Research Group of Convergence Technology, Korea Food Research Institute, Seongnam 463-746, Republic of Korea.
| | - Woo-Yeon Kim
- Department of Biotechnology, Chung-Ang University, Ansung 456-756, Republic of Korea
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Pu YY, Feng YZ, Sun DW. Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. Compr Rev Food Sci Food Saf 2015; 14:176-188. [PMID: 33401804 DOI: 10.1111/1541-4337.12123] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Accepted: 10/13/2014] [Indexed: 11/30/2022]
Abstract
Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.
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Affiliation(s)
- Yuan-Yuan Pu
- Food Refrigeration and Computerized Food Technology (FRCRT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland
| | - Yao-Ze Feng
- Food Refrigeration and Computerized Food Technology (FRCRT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCRT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland
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Huang H, Liu L, Ngadi MO. Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors (Basel) 2014; 14:7248-76. [PMID: 24759119 DOI: 10.3390/s140407248] [Citation(s) in RCA: 125] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2013] [Revised: 04/07/2014] [Accepted: 04/08/2014] [Indexed: 11/16/2022]
Abstract
Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed.
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Zhang R, Ying Y, Rao X, Li J. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. J Sci Food Agric 2012; 92:2397-2408. [PMID: 22522423 DOI: 10.1002/jsfa.5702] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2011] [Revised: 01/04/2012] [Accepted: 03/10/2012] [Indexed: 05/31/2023]
Abstract
Hyperspectral fluorescence imaging (HSFI) is potentially useful for assessing food and agricultural products, because it combines the merits of both hyperspectral imaging and fluorescence spectroscopy. This paper provides an introduction to HSFI: the principle and components of HSFI, calibration and image processing are described. In addition, recent advances in the application of HSFI to food and agricultural product assessment are reviewed, such as contaminant detection, constituent analysis and quality evaluation. Finally, current limitations and likely future development trends are discussed.
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Affiliation(s)
- Ruoyu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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Manley M, Mcgoverin CM, Engelbrecht P, Geladi P. Influence of grain topography on near infrared hyperspectral images. Talanta 2012; 89:223-30. [DOI: 10.1016/j.talanta.2011.11.086] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2011] [Revised: 10/19/2011] [Accepted: 11/27/2011] [Indexed: 11/20/2022]
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Affiliation(s)
- Jerome Workman
- Unity Scientific LLC, 117 Old State Rd., Brookfield, Connecticut 06804, and United States National University, 11255 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Barry Lavine
- Department of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, United States
| | - Ray Chrisman
- Atodyne Technologies, L.L.C., 4699 Pontiac Trail, Ann Arbor, Michigan 48105, United States
| | - Mel Koch
- Center for Process Analytical Chemistry (CPAC), University of Washington, Seattle, Washington 98195-1700, United States
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Duan Z, Xing Z, Shao Y, Zhao X. Effect of electron beam irradiation on postharvest quality and selected enzyme activities of the white button mushroom, Agaricus bisporus. J Agric Food Chem 2010; 58:9617-9621. [PMID: 20698564 DOI: 10.1021/jf101852e] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Agaricus bisporus fruit bodies were exposed to different doses (1, 2, 3, and 4 kGy) of electron beam irradiation (EBI), and various physiological changes associated with postharvest deterioration, as well as selected enzymes considered to play a role in senescence, were monitored over a subsequent 16-day storage period at 4 degrees C and 75-85% relative humidity. EBI retarded postharvest mushroom softening and overall increases in malondialdehyde levels were more pronounced in controls compared with those of the irradiated samples. After 10 days of storage, polyphenoloxidase activity in samples irradiated with 1-4 kGy doses was significantly (P<0.05) lower compared to that in control samples. Superoxide dismutase activity generally declined throughout the postharvest storage period in both irradiated and control samples, but no clear correlation between enzyme activity and EBI dosage was evident. Catalase activity decreased more slowly and to a lesser extent in fruit bodies exposed to 1 kGy compared with that in the controls and the other irradiated samples.
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Affiliation(s)
- Zhanfeng Duan
- Institute for Agri-food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201106, PR China
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