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He S, Long W, Hai C, Chen H, Tang C, Rong X, Yang J, Fu H. Rapid identification of traditional Chinese medicines (Lonicerae japonicae flos and Lonicerae flos) and their origins using excitation-emission matrix fluorescence spectroscopy coupled with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123639. [PMID: 37979539 DOI: 10.1016/j.saa.2023.123639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/06/2023] [Accepted: 11/09/2023] [Indexed: 11/20/2023]
Abstract
Lonicerae japonicae flos (LJF) and Lonicerae flos (LF) are important traditional Chinese medicine with various effects and prescription compatibility. The accurate identification of LJF and LF and their geographical origin are of great significance to the quality control and correct medication. In this work, a simple, rapid and efficient strategy for identification of Lonicerae japonicae flos and Lonicerae flos and their geographical origin was proposed by combining excitation-emission matrix fluorescence (EEMF) with chemometrics. Excitation-emission matrix fluorescence (EEMF) spectra of LJF and LF samples were characterized by parallel factor analysis (PARAFAC) to acquire chemically meaningful information. Classification models were built using three chemometric methods, including partial least squares-discrimination analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA) and random forest (RF). These models were utilized to identify LJF and LF and their geographical origin. Results revealed that PCA-LDA model gained the optimal performance with 100% classification accuracy for distinguishing between LJF and LJF from different geographical origin. Therefore, the proposed strategy could be a competitive alternative for fast and accurate differentiation of LJF and LF and their geographical origin.
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Affiliation(s)
- Song He
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Chengying Hai
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Chuanjie Tang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Ximeng Rong
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China.
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Hong Y, Birse N, Quinn B, Li Y, Jia W, McCarron P, Wu D, da Silva GR, Vanhaecke L, van Ruth S, Elliott CT. Data fusion and multivariate analysis for food authenticity analysis. Nat Commun 2023; 14:3309. [PMID: 37291121 PMCID: PMC10250487 DOI: 10.1038/s41467-023-38382-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/27/2023] [Indexed: 06/10/2023] Open
Abstract
A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Brian Quinn
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Philip McCarron
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Gonçalo Rosas da Silva
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Lynn Vanhaecke
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
- Laboratory of Integrative Metabolomics, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, The Netherlands
- School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom.
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani, 12120, Thailand.
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Chu X, Zhang K, Wei H, Ma Z, Fu H, Miao P, Jiang H, Liu H. A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae. FRONTIERS IN PLANT SCIENCE 2023; 14:1180203. [PMID: 37332705 PMCID: PMC10272841 DOI: 10.3389/fpls.2023.1180203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 05/09/2023] [Indexed: 06/20/2023]
Abstract
Introduction Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. Methods This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. Results The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. Discussion These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.
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Affiliation(s)
- Xuan Chu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Kun Zhang
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongyu Wei
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiyu Ma
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Han Fu
- College of Engineering, South China Agricultural University, Guangzhou, China
| | - Pu Miao
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongzhe Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Hongli Liu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
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Park E, Kim YS, Omari MK, Suh HK, Faqeerzada MA, Kim MS, Baek I, Cho BK. High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng ( Panax ginseng Meyer) Using a Hyperspectral Reflectance Image. SENSORS 2021; 21:s21165634. [PMID: 34451076 PMCID: PMC8402434 DOI: 10.3390/s21165634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/15/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022]
Abstract
Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.
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Affiliation(s)
- Eunsoo Park
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Yun-Soo Kim
- R&D Headquarters, Korea Ginseng Corporation, Daejeon 34128, Korea;
| | - Mohammad Kamran Omari
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Hyun-Kwon Suh
- Department of Life Resources Industry, Dong-A University, Busan 49315, Korea;
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
- Department of Smart Agriculture System, Chungnam National University, Daejeon 34134, Korea
- Correspondence:
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Monitoring Thermal and Non-Thermal Treatments during Processing of Muscle Foods: A Comprehensive Review of Recent Technological Advances. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196802] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Muscle food products play a vital role in human nutrition due to their sensory quality and high nutritional value. One well-known challenge of such products is the high perishability and limited shelf life unless suitable preservation or processing techniques are applied. Thermal processing is one of the well-established treatments that has been most commonly used in order to prepare food and ensure its safety. However, the application of inappropriate or severe thermal treatments may lead to undesirable changes in the sensory and nutritional quality of heat-processed products, and especially so for foods that are sensitive to thermal treatments, such as fish and meat and their products. In recent years, novel thermal treatments (e.g., ohmic heating, microwave) and non-thermal processing (e.g., high pressure, cold plasma) have emerged and proved to cause less damage to the quality of treated products than do conventional techniques. Several traditional assessment approaches have been extensively applied in order to evaluate and monitor changes in quality resulting from the use of thermal and non-thermal processing methods. Recent advances, nonetheless, have shown tremendous potential of various emerging analytical methods. Among these, spectroscopic techniques have received considerable attention due to many favorable features compared to conventional analysis methods. This review paper will provide an updated overview of both processing (thermal and non-thermal) and analytical techniques (traditional methods and spectroscopic ones). The opportunities and limitations will be discussed and possible directions for future research studies and applications will be suggested.
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Zhu H, Gowen A, Feng H, Yu K, Xu JL. Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products. SENSORS 2020; 20:s20185322. [PMID: 32957597 PMCID: PMC7570506 DOI: 10.3390/s20185322] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/05/2020] [Accepted: 09/14/2020] [Indexed: 11/25/2022]
Abstract
Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.
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Affiliation(s)
- Hongyan Zhu
- College of Electronic Engineering, Guangxi Normal University, Guilin 541004, China;
| | - Aoife Gowen
- UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland;
| | - Hailin Feng
- School of Information Engineering, Zhejiang Agricultural and Forestry University, Hangzhou 310000, China;
| | - Keping Yu
- Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan;
| | - Jun-Li Xu
- UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland;
- Correspondence:
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7
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Gao R, Chen C, Wang H, Chen C, Yan Z, Han H, Chen F, Wu Y, Wang Z, Zhou Y, Si R, Lv X. Classification of multicategory edible fungi based on the infrared spectra of caps and stalks. PLoS One 2020; 15:e0238149. [PMID: 32833991 PMCID: PMC7444812 DOI: 10.1371/journal.pone.0238149] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 08/10/2020] [Indexed: 11/18/2022] Open
Abstract
As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.
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Affiliation(s)
- Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
- * E-mail: (CC); (XL)
| | - Hang Wang
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Ziwei Yan
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Huijie Han
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, China
| | - Fangfang Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Yan Wu
- Quality of Products Supervision and Inspection Institute, Urumqi, Xinjiang, China
| | - Zhiao Wang
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Yuxiu Zhou
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Rumeng Si
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
- * E-mail: (CC); (XL)
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Hassoun A, Heia K, Lindberg SK, Nilsen H. Performance of Fluorescence and Diffuse Reflectance Hyperspectral Imaging for Characterization of Lutefisk: A Traditional Norwegian Fish Dish. Molecules 2020; 25:molecules25051191. [PMID: 32155769 PMCID: PMC7179441 DOI: 10.3390/molecules25051191] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 02/26/2020] [Accepted: 03/04/2020] [Indexed: 11/16/2022] Open
Abstract
Lutefisk is a traditional Norwegian fish dish made from dried fish, such as cod or other whitefish. In Norway and other Nordic countries, lutefisk is considered among the most popular dishes served during Christmas or other festive occasions. However, to date, little attention has been paid to this product, and available research on the quality, processing, and chemistry of lutefisk is still limited. The quality of this very delicate product, with a high pH value, depends on many factors, such as the initial quality of raw materials (stockfish), the quantity of lye used during the preparation process of lutefisk, and time during soaking in the lye and water, among others, making it challenging to both optimize processing and monitor the quality of lutefisk. In this study, four commercially available lutefisk brands (labelled as A, B, C, and D) were characterized using two online spectroscopic techniques, namely fluorescence and diffuse reflectance hyperspectral imaging, implemented on conveyor belts to mimic industrial applications. The samples were also analyzed by the use of an offline laboratory instrument based on visible/near infrared diffuse reflectance spectroscopy. Three traditional measurements, including texture, water content, and pH, were also conducted on the same samples. Supervised classification PLS-DA models were built with each dataset and relationships between the spectroscopic measurements and the traditional data were investigated using canonical correlations. The spectroscopic methods, especially fluorescence spectroscopy, demonstrated high performance for the discrimination between samples of the different brands, with high correlations between the spectral and traditional measurements. Although more validations of the results of this study are still required, these preliminary findings suggest that the destructive, laborious, and time-consuming traditional techniques can be replaced by rapid and nondestructive online measurements based on hyperspectral imaging used in fluorescence or diffuse reflectance mode.
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Xiao W, Yang J, Fang H, Zhuang J, Ku Y. Development of online classification system for construction waste based on industrial camera and hyperspectral camera. PLoS One 2019; 14:e0208706. [PMID: 30650081 PMCID: PMC6334961 DOI: 10.1371/journal.pone.0208706] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 11/21/2018] [Indexed: 12/02/2022] Open
Abstract
Construction waste is a serious problem that should be addressed to protect environment and save resources, some of which have a high recovery value. To efficiently recover construction waste, an online classification system is developed using an industrial near-infrared hyperspectral camera. This system uses the industrial camera to capture a region of interest and a hyperspectral camera to obtain the spectral information about objects corresponding to the region of interest. The spectral information is then used to build classification models based on extreme learning machine and resemblance discriminant analysis. To further improve this system, an online particle swarm optimization extreme learning machine is developed. The results indicate that if a near-infrared hyperspectral camera is used in conjunction with an industrial camera, construction waste can be efficiently classified. Therefore, extreme learning machine and resemblance discriminant analysis can be used to classify construction waste. Particle swarm optimization can be used to further enhance the proposed system.
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Affiliation(s)
- Wen Xiao
- Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China
| | - Jianhong Yang
- Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China
- * E-mail:
| | - Huaiying Fang
- Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China
| | - Jiangteng Zhuang
- Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China
| | - Yuedong Ku
- Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China
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Rodríguez-Pérez R, Fernández L, Marco S. Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study. Anal Bioanal Chem 2018; 410:5981-5992. [PMID: 29959482 DOI: 10.1007/s00216-018-1217-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/13/2018] [Accepted: 06/21/2018] [Indexed: 01/29/2023]
Abstract
Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.
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Affiliation(s)
- Raquel Rodríguez-Pérez
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia, The Barcelona Institute for Science and Technology, Baldiri Reixac 4-8, 08028, Barcelona, Spain
| | - Luis Fernández
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia, The Barcelona Institute for Science and Technology, Baldiri Reixac 4-8, 08028, Barcelona, Spain.,Department of Electronics and Biomedical Engineering, University of Barcelona, Martí i Franqués 1, 08028, Barcelona, Spain
| | - Santiago Marco
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia, The Barcelona Institute for Science and Technology, Baldiri Reixac 4-8, 08028, Barcelona, Spain. .,Department of Electronics and Biomedical Engineering, University of Barcelona, Martí i Franqués 1, 08028, Barcelona, Spain.
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11
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Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves. SENSORS 2018; 18:s18072077. [PMID: 29958467 PMCID: PMC6068755 DOI: 10.3390/s18072077] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 06/16/2018] [Accepted: 06/26/2018] [Indexed: 11/17/2022]
Abstract
Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400⁻1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400⁻1000 nm (visible range) and 900⁻1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way.
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12
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Liu Y, Pu H, Sun DW. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.08.013] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study. Comput Biol Med 2017; 88:60-71. [PMID: 28700901 DOI: 10.1016/j.compbiomed.2017.06.018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/16/2017] [Accepted: 06/17/2017] [Indexed: 10/19/2022]
Abstract
With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations.
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14
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Xiong Z, Sun DW, Pu H, Gao W, Dai Q. Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review. Crit Rev Food Sci Nutr 2017; 57:755-768. [PMID: 25975703 DOI: 10.1080/10408398.2014.954282] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
With improvement in people's living standards, many people nowadays pay more attention to quality and safety of meat. However, traditional methods for meat quality and safety detection and evaluation, such as manual inspection, mechanical methods, and chemical methods, are tedious, time-consuming, and destructive, which cannot meet the requirements of modern meat industry. Therefore, seeking out rapid, non-destructive, and accurate inspection techniques is important for the meat industry. In recent years, a number of novel and noninvasive imaging techniques, such as optical imaging, ultrasound imaging, tomographic imaging, thermal imaging, and odor imaging, have emerged and shown great potential in quality and safety assessment. In this paper, a detailed overview of advanced applications of these emerging imaging techniques for quality and safety assessment of different types of meat (pork, beef, lamb, chicken, and fish) is presented. In addition, advantages and disadvantages of each imaging technique are also summarized. Finally, future trends for these emerging imaging techniques are discussed, including integration of multiple imaging techniques, cost reduction, and developing powerful image-processing algorithms.
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Affiliation(s)
- Zhenjie Xiong
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
| | - Da-Wen Sun
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China.,c Food Refrigeration and Computerised Food Technology , Agriculture and Food Science Centre, University College Dublin, National University of Ireland , Belfield , Dublin , Ireland
| | - Hongbin Pu
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
| | - Wenhong Gao
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
| | - Qiong Dai
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
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15
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Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis. SENSORS 2016; 16:s16101735. [PMID: 27775556 PMCID: PMC5087520 DOI: 10.3390/s16101735] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 09/29/2016] [Accepted: 10/13/2016] [Indexed: 12/03/2022]
Abstract
A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0–6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead’s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R2 of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.
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16
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Cheng JH, Nicolai B, Sun DW. Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. Meat Sci 2016; 123:182-191. [PMID: 27750085 DOI: 10.1016/j.meatsci.2016.09.017] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 09/26/2016] [Accepted: 09/29/2016] [Indexed: 12/20/2022]
Abstract
Muscle foods are very important for a well-balanced daily diet. Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods.
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Affiliation(s)
- Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Bart Nicolai
- MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, 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.
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17
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Chen TH, Zhu YP, Wang P, Han MY, Wei R, Xu XL, Zhou GH. The use of the impedance measurements to distinguish between fresh and frozen–thawed chicken breast muscle. Meat Sci 2016; 116:151-7. [DOI: 10.1016/j.meatsci.2016.02.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 11/26/2015] [Accepted: 02/01/2016] [Indexed: 10/22/2022]
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18
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Ivorra E, Sánchez AJ, Verdú S, Barat JM, Grau R. Shelf life prediction of expired vacuum-packed chilled smoked salmon based on a KNN tissue segmentation method using hyperspectral images. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2016.01.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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19
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“Turn-off” fluorescent data array sensor based on double quantum dots coupled with chemometrics for highly sensitive and selective detection of multicomponent pesticides. Anal Chim Acta 2016; 916:84-91. [DOI: 10.1016/j.aca.2016.02.021] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Revised: 02/16/2016] [Accepted: 02/17/2016] [Indexed: 01/14/2023]
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20
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Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen–thawed fish muscle. Food Chem 2016; 197:855-63. [DOI: 10.1016/j.foodchem.2015.11.019] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 10/07/2015] [Accepted: 11/04/2015] [Indexed: 01/08/2023]
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21
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Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP. Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer. PLoS One 2015; 10:e0143197. [PMID: 26600316 PMCID: PMC4658183 DOI: 10.1371/journal.pone.0143197] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 11/02/2015] [Indexed: 12/05/2022] Open
Abstract
The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network’s modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.
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Affiliation(s)
- Salvador Gutiérrez
- Instituto de las Ciencias de la Vid y del Vino, University of La Rioja, CSIC, Gobierno de La Rioja, Ctra. De Burgos Km. 6, 26007, Logroño, Spain
| | - Javier Tardaguila
- Instituto de las Ciencias de la Vid y del Vino, University of La Rioja, CSIC, Gobierno de La Rioja, Ctra. De Burgos Km. 6, 26007, Logroño, Spain
| | - Juan Fernández-Novales
- Instituto de las Ciencias de la Vid y del Vino, University of La Rioja, CSIC, Gobierno de La Rioja, Ctra. De Burgos Km. 6, 26007, Logroño, Spain
| | - María P. Diago
- Instituto de las Ciencias de la Vid y del Vino, University of La Rioja, CSIC, Gobierno de La Rioja, Ctra. De Burgos Km. 6, 26007, Logroño, Spain
- * E-mail:
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22
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Cheng JH, Sun DW, Pu HB, Chen X, Liu Y, Zhang H, Li JL. Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2015.03.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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23
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Girón J, Gil-Sánchez L, García-Breijo E, Jesús Pagán M, Barat JM, Grau R. Development of potentiometric equipment for the identification of altered dry-cured hams: A preliminary study. Meat Sci 2015; 106:1-5. [DOI: 10.1016/j.meatsci.2015.03.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 02/01/2015] [Accepted: 03/05/2015] [Indexed: 11/29/2022]
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24
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Study of high strength wheat flours considering their physicochemical and rheological characterisation as well as fermentation capacity using SW-NIR imaging. J Cereal Sci 2015. [DOI: 10.1016/j.jcs.2014.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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25
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Kamruzzaman M, Makino Y, Oshita S. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Anal Chim Acta 2015; 853:19-29. [DOI: 10.1016/j.aca.2014.08.043] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 07/22/2014] [Accepted: 08/20/2014] [Indexed: 10/24/2022]
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26
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Wang W, Heitschmidt GW, Windham WR, Feldner P, Ni X, Chu X. Feasibility of Detecting Aflatoxin B1on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging. J Food Sci 2014; 80:M116-22. [DOI: 10.1111/1750-3841.12728] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2013] [Accepted: 10/29/2014] [Indexed: 11/28/2022]
Affiliation(s)
- Wei Wang
- College of Engineering, China Agricultural Univ; No. 17 Tsinghua East Rd. Beijing 100083 China
| | - Gerald W. Heitschmidt
- Quality & Safety Assessment Research Unit; Richard B. Russell Research Center; USDA-ARS, 950 College Station Rd. Athens GA 30605 U.S.A
| | - William R. Windham
- Quality & Safety Assessment Research Unit; Richard B. Russell Research Center; USDA-ARS, 950 College Station Rd. Athens GA 30605 U.S.A
| | - Peggy Feldner
- Quality & Safety Assessment Research Unit; Richard B. Russell Research Center; USDA-ARS, 950 College Station Rd. Athens GA 30605 U.S.A
| | - Xinzhi Ni
- Crop Genetics and Breeding Research Unit-USDA-ARS; 2747 Davis Road Tifton GA 31793 U.S.A
| | - Xuan Chu
- College of Engineering, China Agricultural Univ; No. 17 Tsinghua East Rd. Beijing 100083 China
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27
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Classification of unaltered and altered dry-cured ham by impedance spectroscopy: A preliminary study. Meat Sci 2014; 98:695-700. [DOI: 10.1016/j.meatsci.2014.05.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Revised: 05/07/2014] [Accepted: 05/08/2014] [Indexed: 11/17/2022]
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28
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Cheng JH, Sun DW. Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: Current research and potential applications. Trends Food Sci Technol 2014. [DOI: 10.1016/j.tifs.2014.03.006] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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29
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Recent developments in hyperspectral imaging for assessment of food quality and safety. SENSORS 2014; 14:7248-76. [PMID: 24759119 PMCID: PMC4029639 DOI: 10.3390/s140407248] [Citation(s) in RCA: 125] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [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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30
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Cheng JH, Qu JH, Sun DW, Zeng XA. Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage. Food Res Int 2014. [DOI: 10.1016/j.foodres.2013.12.009] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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31
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Khojastehnazhand M, Khoshtaghaza MH, Mojaradi B, Rezaei M, Goodarzi M, Saeys W. Comparison of Visible–Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Res Int 2014. [DOI: 10.1016/j.foodres.2013.12.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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32
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Cheng JH, Sun DW, Zeng XA, Pu HB. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. INNOV FOOD SCI EMERG 2014. [DOI: 10.1016/j.ifset.2013.10.013] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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33
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Wu D, Sun DW. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications. INNOV FOOD SCI EMERG 2013. [DOI: 10.1016/j.ifset.2013.04.016] [Citation(s) in RCA: 225] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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