1
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Park Y, Noda I, Jung YM. Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS). APPLIED SPECTROSCOPY 2024:37028241255393. [PMID: 38872353 DOI: 10.1177/00037028241255393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.
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Affiliation(s)
- Yeonju Park
- Department of Chemistry, Institute for Molecular Science and Fusion Technology, and Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon, Korea
| | - Isao Noda
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware, USA
| | - Young Mee Jung
- Department of Chemistry, Institute for Molecular Science and Fusion Technology, and Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon, Korea
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2
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Park Y, Noda I, Jung YM. Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS). APPLIED SPECTROSCOPY 2024:37028241256397. [PMID: 38835153 DOI: 10.1177/00037028241256397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.
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Affiliation(s)
- Yeonju Park
- Department of Chemistry, Institute for Molecular Science and Fusion Technology, and Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon, Korea
| | - Isao Noda
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware, USA
| | - Young Mee Jung
- Department of Chemistry, Institute for Molecular Science and Fusion Technology, and Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon, Korea
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3
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Li K, Zhang X, Zhang J, Du B, Song X, Wang G, Li Q, Zhang Y, Liu F, Zhang Z. Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy. ACS OMEGA 2024; 9:16138-16146. [PMID: 38617685 PMCID: PMC11007685 DOI: 10.1021/acsomega.3c09994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/08/2024] [Accepted: 03/18/2024] [Indexed: 04/16/2024]
Abstract
Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.
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Affiliation(s)
- Ke Li
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Xin Zhang
- College
of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Jing Zhang
- College
of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Biao Du
- Beijing
Yixingyuan Petrochemical Technology Co., Ltd., Beijing 101301, China
| | - Xiaoping Song
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Guixuan Wang
- Beijing
Yixingyuan Petrochemical Technology Co., Ltd., Beijing 101301, China
| | - Qi Li
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Yinglan Zhang
- Leibniz
Institut für Polymerforschung Dresden e.V., Hohe Straße 6, Dresden 01069, Germany
- Institut
für Werkstoffwissenschaft, Technische
Universität Dresden, Dresden 01062, Germany
| | - Fan Liu
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Zhengdong Zhang
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
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4
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Li B, Xiao D, Xie H, Huang J, Yan Z. Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm. ACS OMEGA 2023; 8:35232-35241. [PMID: 37780011 PMCID: PMC10536090 DOI: 10.1021/acsomega.3c04999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/25/2023] [Indexed: 10/03/2023]
Abstract
As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.
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Affiliation(s)
- Boyan Li
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Dong Xiao
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Hongfei Xie
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Jie Huang
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Zelin Yan
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
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5
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Liu H, Liu H, Li J, Wang Y. Rapid and Accurate Authentication of Porcini Mushroom Species Using Fourier Transform Near-Infrared Spectra Combined with Machine Learning and Chemometrics. ACS OMEGA 2023; 8:19663-19673. [PMID: 37305306 PMCID: PMC10249093 DOI: 10.1021/acsomega.3c01229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/12/2023] [Indexed: 06/13/2023]
Abstract
Porcini mushrooms have high nutritional value and great potential, but different species are easily confused, so it is essential to identify them rapidly and precisely. The diversity of nutrients in stipe and cap will lead to differences in spectral information. In this research, Fourier transform near-infrared (FT-NIR) spectral information about imparity species of porcini mushroom stipe and cap was collected and combined into four data matrices. FT-NIR spectra of four data sets were combined with chemometric methods and machine learning for accurate evaluation and identification of different porcini mushroom species. From the results: (1) improved visualization level of t-distributed stochastic neighbor embedding (t-SNE) results after the second derivative preprocessing compared with raw spectra; (2) after using multiple pretreatment combinations to process the four data matrices, the model accuracies based on support vector machine and partial least-square discriminant analysis (PLS-DA) under the best preprocessing method were 98.73-99.04% and 98.73-99.68%, respectively; (3) by comparing the modeling results of FT-NIR spectra with different data matrices, it was found that the PLS-DA model based on low-level data fusion has the highest accuracy (99.68%), but residual neural network (ResNet) model based on the stipe, cap, and average spectral data matrix worked better (100% accuracy). The above results suggest that distinct models should be selected for dissimilar spectral data matrices of porcini mushrooms. Additionally, FT-NIR spectra have the advantages of being nondevastate and fast; this method is expected to be a promising analytical tool in food safety control.
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Affiliation(s)
- Hong Liu
- College
of Agronomy and Biotechnology, Yunnan Agricultural
University, Kunming 650201, China
- Medicinal
Plants Research Institute, Yunnan Academy
of Agricultural Sciences, Kunming 650200, China
| | - Honggao Liu
- Yunnan
Key Laboratory of Gastrodia and Fungi Symbiotic Biology, Zhaotong University, Zhaotong 657000, Yunnan, China
| | - Jieqing Li
- College
of Agronomy and Biotechnology, Yunnan Agricultural
University, Kunming 650201, China
| | - Yuanzhong Wang
- Medicinal
Plants Research Institute, Yunnan Academy
of Agricultural Sciences, Kunming 650200, China
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6
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Huo J, Zhang M, Wang D, S Mujumdar A, Bhandari B, Zhang L. New preservation and detection technologies for edible mushrooms: A review. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3230-3248. [PMID: 36700618 DOI: 10.1002/jsfa.12472] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/11/2022] [Accepted: 01/26/2023] [Indexed: 06/17/2023]
Abstract
Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Jingyi Huo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, China
| | - Dayuan Wang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald College, McGill University, Quebec, Canada
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia
| | - Lujun Zhang
- R&D Center, Shandong Qihe Biotechnology Co., Ltd, Zibo, China
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7
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Xia R, Hou Z, Xu H, Li Y, Sun Y, Wang Y, Zhu J, Wang Z, Pan S, Xin G. Emerging technologies for preservation and quality evaluation of postharvest edible mushrooms: A review. Crit Rev Food Sci Nutr 2023:1-19. [PMID: 37083462 DOI: 10.1080/10408398.2023.2200482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Edible mushrooms are the highly demanded foods of which production and consumption have been steadily increasing globally. Owing to the quality loss and short shelf-life in harvested mushrooms, it is necessary for the implementation of effective preservation and intelligent evaluation technologies to alleviate this issue. The aim of this review was to analyze the development and innovation thematic lines, topics, and trends by bibliometric analysis and review of the literature methods. The challenges faced in researching these topics were proposed and the mechanisms of quality loss in mushrooms during storage were updated. This review summarized the effects of chemical processing (antioxidants, ozone, and coatings), physical treatments (non-thermal plasma, packaging and latent thermal storage) and other emerging application on the quality of fresh mushrooms while discussing the efficiency in extending the shelf-life. It also discussed the emerging evaluation techniques based on the various chemometric methods and computer vision system in monitoring the freshness and predicting the shelf-life of mushrooms which have been developed. Preservation technology optimization and dynamic quality evaluation are vital for achieving mushroom quality control. This review can provide a comprehensive research reference for reducing mushroom quality loss and extending shelf-life, along with optimizing efficiency of storage and transportation operations.
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Affiliation(s)
- Rongrong Xia
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Zhenshan Hou
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Heran Xu
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Yunting Li
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Yong Sun
- Beijing Academy of Food Sciences, Beijing, China
| | - Yafei Wang
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Jiayi Zhu
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Zijian Wang
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Song Pan
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Guang Xin
- College of Food Science, Shenyang Agricultural University, Shenyang, China
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8
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Chen J, Liu H, Li T, Wang Y. Edibility and species discrimination of wild bolete mushrooms using FT-NIR spectroscopy combined with DD-SIMCA and RF models. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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9
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Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of Boletus tomentipes. Food Res Int 2023; 167:112679. [PMID: 37087255 DOI: 10.1016/j.foodres.2023.112679] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.
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10
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A rapid identification based on FT-NIR spectroscopies and machine learning for drying temperatures of Amomum tsao-ko. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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11
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A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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12
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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] [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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13
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A rapid and effective method for species identification of edible boletes: FT-NIR spectroscopy combined with ResNet. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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14
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Xiao D, Yan Z, Li J, Fu Y, Li Z, Li B. Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis. ACS OMEGA 2022; 7:23919-23928. [PMID: 35847264 PMCID: PMC9280928 DOI: 10.1021/acsomega.2c02665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.
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Affiliation(s)
- Dong Xiao
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Zelin Yan
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Jian Li
- Technical
Service Parlor, Unit 31434 of the Chinese
People’s Liberation Army, Shenyang 110000, China
| | - Yanhua Fu
- School
of JangHo Architecture, Northeastern University, Shenyang 110819, China
| | - Zhenni Li
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Boyan Li
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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15
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Jin X, Xiao ZY, Xiao DX, Dong A, Nie QX, Wang YN, Wang LF. Quantitative inversion model of protein and fat content in milk based on hyperspectral techniques. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2022.105467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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16
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Chen X, Li J, Liu H, Wang Y. A fast multi-source information fusion strategy based on deep learning for species identification of boletes. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121137. [PMID: 35290943 DOI: 10.1016/j.saa.2022.121137] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/24/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Wild mushroom market is an important economic source of Yunnan province in China, and its wild mushroom resources are also valuable wealth in the world. This work will put forward a method of species identification and optimize the method in order to maintain the market order and protect the economic benefits of wild mushrooms. Here we establish deep learning (DL) models based on the two-dimensional correlation spectroscopy (2DCOS) images of near-infrared spectroscopy from boletes, and optimize the identification effect of the model. The results show that synchronous 2DCOS is the best method to establish DL model, and when the learning rate was 0.01, the epochs were 40, using stipes and caps data, the identification effect would be further improved. This method retains the complete information of the samples and can provide a fast and noninvasive method for identifying boletes species for market regulators.
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Affiliation(s)
- Xiong Chen
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Jieqing Li
- College of Resources and Environmental, Yunnan Agricultural University, Kunming 650201, China
| | - Honggao Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; Zhaotong University, Zhaotong 657000, China.
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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17
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Rachineni K, Rao Kakita VM, Awasthi NP, Shirke VS, Hosur RV, Chandra Shukla S. Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification. Curr Res Food Sci 2022; 5:272-277. [PMID: 35141528 PMCID: PMC8816647 DOI: 10.1016/j.crfs.2022.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/25/2021] [Accepted: 01/09/2022] [Indexed: 02/01/2023] Open
Abstract
Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes. NMR helps to identify the fingerprints of honey chemical constituents. Combined NMR and ML tools can determine the type of adulteration in honey. Supervised classification schemes, Logistic regression, DNN, and LGBM are utilized. Corn, brown rice, and jaggery adulterations are discriminated in honey.
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Affiliation(s)
- Kavitha Rachineni
- Export Inspection Agency – Mumbai, E-3, Industrial Area (MIDC), Andheri East, Mumbai, 400 093, India
- Corresponding author.
| | - Veera Mohana Rao Kakita
- UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Kalina Campus, Santacruz, Mumbai, 400 098, India
| | - Neeraj Praphulla Awasthi
- Export Inspection Agency – Mumbai, E-3, Industrial Area (MIDC), Andheri East, Mumbai, 400 093, India
| | - Vrushali Siddesh Shirke
- Export Inspection Agency – Mumbai, E-3, Industrial Area (MIDC), Andheri East, Mumbai, 400 093, India
| | - Ramakrishna V. Hosur
- UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Kalina Campus, Santacruz, Mumbai, 400 098, India
| | - Satish Chandra Shukla
- Export Inspection Agency- Chennai (Head Office), 6th Floor CMDA Tower-II, No: 1 Gandhi Irwin Road, Egmore, Chennai, 600008, India
- Corresponding author.
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Dong JE, Zhang J, Li T, Wang YZ. The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy. Front Microbiol 2021; 12:771428. [PMID: 34899656 PMCID: PMC8656461 DOI: 10.3389/fmicb.2021.771428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/19/2021] [Indexed: 11/29/2022] Open
Abstract
Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.
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Affiliation(s)
- Jian-E Dong
- College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China
| | - Ji Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Tao Li
- College of Chemistry, Biological and Environment, Yuxi Normal University, Yuxi, China
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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