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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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2
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Ragupathy S, Thirugnanasambandam A, Henry T, Vinayagam V, Sneha R, Newmaster SG. Flower Species Ingredient Verification Using Orthogonal Molecular Methods. Foods 2024; 13:1862. [PMID: 38928803 PMCID: PMC11203286 DOI: 10.3390/foods13121862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/04/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence.
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Affiliation(s)
- Subramanyam Ragupathy
- Natural Health Product Research Alliance, College of Biological Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (A.T.); (T.H.); (V.V.); (S.G.N.)
| | - Arunachalam Thirugnanasambandam
- Natural Health Product Research Alliance, College of Biological Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (A.T.); (T.H.); (V.V.); (S.G.N.)
| | - Thomas Henry
- Natural Health Product Research Alliance, College of Biological Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (A.T.); (T.H.); (V.V.); (S.G.N.)
| | - Varathan Vinayagam
- Natural Health Product Research Alliance, College of Biological Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (A.T.); (T.H.); (V.V.); (S.G.N.)
| | - Ragupathy Sneha
- College of Medicine, American University of Antigua, Jobberwock Beach Road, Coolidge P.O. Box W1451, Antigua;
| | - Steven G. Newmaster
- Natural Health Product Research Alliance, College of Biological Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (A.T.); (T.H.); (V.V.); (S.G.N.)
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3
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [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: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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4
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Hategan AR, David M, Berghian-Grosan C, Magdas DA. Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies. Food Chem X 2023; 20:100902. [PMID: 38144738 PMCID: PMC10739978 DOI: 10.1016/j.fochx.2023.100902] [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: 02/20/2023] [Revised: 09/07/2023] [Accepted: 09/23/2023] [Indexed: 12/26/2023] Open
Abstract
The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1st derivative, 2nd derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin.
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Affiliation(s)
- Ariana Raluca Hategan
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania
- Faculty of Physics, Babeș-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania
| | - Maria David
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania
- Faculty of Physics, Babeș-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania
| | - Camelia Berghian-Grosan
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania
| | - Dana Alina Magdas
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania
- Faculty of Physics, Babeș-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania
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5
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Smaoui S, Tarapoulouzi M, Agriopoulou S, D'Amore T, Varzakas T. Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics. Foods 2023; 12:4254. [PMID: 38231684 DOI: 10.3390/foods12234254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 01/19/2024] Open
Abstract
Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.
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Affiliation(s)
- Slim Smaoui
- Laboratory of Microbial, Enzymatic Biotechnology, and Biomolecules (LBMEB), Center of Biotechnology of Sfax, University of Sfax-Tunisia, Sfax 3029, Tunisia
| | - Maria Tarapoulouzi
- Department of Chemistry, Faculty of Pure and Applied Science, University of Cyprus, P.O. Box 20537, Nicosia CY-1678, Cyprus
| | - Sofia Agriopoulou
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
| | - Teresa D'Amore
- IRCCS CROB, Centro di Riferimento Oncologico della Basilicata, 85028 Rionero in Vulture, Italy
| | - Theodoros Varzakas
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
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Ahmed MW, Hossainy SJ, Khaliduzzaman A, Emmert JL, Kamruzzaman M. Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review. Compr Rev Food Sci Food Saf 2023; 22:4378-4403. [PMID: 37602873 DOI: 10.1111/1541-4337.13227] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/20/2023] [Accepted: 07/28/2023] [Indexed: 08/22/2023]
Abstract
The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.
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Affiliation(s)
- Md Wadud Ahmed
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Sahir Junaid Hossainy
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Alin Khaliduzzaman
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
| | - Jason Lee Emmert
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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7
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Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
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Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
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8
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Wu LY, Liu FM, Weng SS, Lin WC. EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method. Foods 2023; 12:foods12112118. [PMID: 37297360 DOI: 10.3390/foods12112118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan's border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the "detection rate of unqualified cases" and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, Fβ was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of "pre-launch (2019) random sampling inspection" and "post-launch (2020-2022) model prediction sampling inspection". For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.
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Affiliation(s)
- Li-Ya Wu
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
| | - Fang-Ming Liu
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
| | - Sung-Shun Weng
- Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Wen-Chou Lin
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
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Massei A, Falco N, Fissore D. Use of machine learning tools and NIR spectra to estimate residual moisture in freeze-dried products. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 293:122485. [PMID: 36801736 DOI: 10.1016/j.saa.2023.122485] [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: 09/15/2022] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
Residual Moisture (RM) in freeze-dried products is one of the most important critical quality attributes (CQAs) to monitor, since it affects the stability of the active pharmaceutical ingredient (API). The standard experimental method adopted for the measurements of RM is the Karl-Fischer (KF) titration, that is a destructive and time-consuming technique. Therefore, Near-Infrared (NIR) spectroscopy was widely investigated in the last decades as an alternative tool to quantify the RM. In the present paper, a novel method was developed based on NIR spectroscopy combined with machine learning tools for the prediction of RM in freeze-dried products. Two different types of models were used: a linear regression model and a neural network based one. The architecture of the neural network was chosen so as to optimize the prediction of the residual moisture, by minimizing the root mean square error with the dataset used in the learning step. Moreover, the parity plots and the absolute error plots were reported, allowing a visual evaluation of the results. Different factors were considered when developing the model, namely the range of wavelengths considered, the shape of the spectra and the type of model. The possibility of developing the model using a smaller dataset, obtained with just one product, that could be then applied to a wider range of products was investigated, as well as the performance of a model developed for a dataset encompassing several products. Different formulations were analyzed: the main part of the dataset was characterized by a different percentage of sucrose in solution (3%, 6% and 9% specifically); a smaller part was made up of sucrose-arginine mixtures at different percentages and only one formulation was characterized by another excipient, the trehalose. The product-specific model for the 6% sucrose mixture was found consistent for the prediction of RM in other sucrose containing mixtures and in the one containing trehalose, while failed for the dataset with higher percentage of arginine. Therefore, a global model was developed by including a certain percentage of all the available dataset in the calibration phase. Results presented and discussed in this paper demonstrate the higher accuracy and robustness of the machine learning based model with respect to the linear models.
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Affiliation(s)
- Ambra Massei
- Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129 Torino, Italy; Global Pharmaceutical Development Department, Merck Serono SpA, via Luigi Einaudi 11, 00012 Guidonia Montecelio (Roma), Italy
| | - Nunzia Falco
- Global Pharmaceutical Development Department, Merck Serono SpA, via Luigi Einaudi 11, 00012 Guidonia Montecelio (Roma), Italy
| | - Davide Fissore
- Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129 Torino, Italy.
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10
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Iida D, Kokawa M, Kitamura Y. Estimation of Apple Mealiness by Means of Laser Scattering Measurement. FOOD BIOPROCESS TECH 2023. [DOI: 10.1007/s11947-023-03068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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11
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Amin J, Selwal A, Sabha A. SaffNet: an ensemble-based approach for saffron adulteration prediction using statistical image features. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-21. [PMID: 37362696 PMCID: PMC9989557 DOI: 10.1007/s11042-023-14934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 09/23/2022] [Accepted: 02/22/2023] [Indexed: 06/28/2023]
Abstract
Saffron is one of the costlier spices that are cultivated in specific regions of the world. Due to its restricted accessibility and more popularity, eventually saffron adulteration is one of the concerning issues in the recent times. It becomes difficult for human vision to discriminate between real and adulterated saffron samples. With the emergence of visual computing and data-driven algorithms, the saffron adulteration prediction systems (SAPS) are designed to predict the original and adulterated saffron samples. However, the majority of the techniques exhibit promising performance but the problem of generalization capabilities (unseen - samples) and scarcity of the saffron databases are still open research challenges. In this work, to overcome these issues, we propose a novel ensemble-based saffron prediction model (SaffNet) using statistical image features for the detection of contamination in the Kashmiri saffron. As data-driven approaches mainly rely on the representative samples, but to the best of our knowledge the standard benchmark datasets for Kashmiri saffron is not available. Therefore, we have created our novel Saffron dataset (Saff-Kash) collected afresh from different parts of Kashmir valley that includes the samples of both the authentic and adulterated saffron classes. The primary aim of the work is to anticipate the adulteration in saffron samples. Thereafter, these images are pre-processed and the dataset is prepared for the proposed SaffNet model. The SaffNet architecture designed using gradient boosting ensemble evaluated on Saff-Kash outperforms the outcomes of individual classifiers i.e., Support vector machine (SVM), decision tree, and K-Nearest neighbor (KNN) with an overall accuracy of 98%. Moreover, the execution time taken by the SaffNet model for training the SVM classifier is 8.56 milliseconds whereas for gradient boosting classifier it is 7.7 milliseconds.
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Affiliation(s)
- Junaid Amin
- Department of Computer Science and Information Technology, Central University of Jammu, Samba 181143, J&K, Jammu, India
| | - Arvind Selwal
- Department of Computer Science and Information Technology, Central University of Jammu, Samba 181143, J&K, Jammu, India
| | - Ambreen Sabha
- Department of Computer Science and Information Technology, Central University of Jammu, Samba 181143, J&K, Jammu, India
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12
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Estimation of Acetic Acid Concentration from Biogas Samples Using Machine Learning. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING 2023. [DOI: 10.1155/2023/2871769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
In a biogas plant, the acetic acid concentration is a major component of the substrate as it determines the pH value, and this pH value correlates with the volume of biogas produced. Since it requires specialized laboratory equipment, the concentration of acetic acid in a biogas substrate cannot be measured on-line. The project aims to use NIR sensors and machine learning algorithms to estimate the acetic acid concentration in a biogas substrate based on the measured intensities of the substrate. As a result of this project, it was possible to determine whether the acetic acid concentration in a biogas substrate is higher or lower than 2 g/l using machine learning models.
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13
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Pang B, Bowker B, Xue CH, Chang YG, Zhang J, Gao L, Zhuang H. Evaluation of visible spectroscopy and low-field nuclear magnetic resonance techniques for screening the presence of defects in broiler breast fillets. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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14
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A comprehensive overview of emerging techniques and chemometrics for authenticity and traceability of animal-derived food. Food Chem 2023; 402:134216. [DOI: 10.1016/j.foodchem.2022.134216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 08/21/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022]
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15
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Feng Y, Chen C, Liu S, Dong B, Yu Y, Chen C, Lv X. A novel technology of structural distance feature of Raman spectra and convolutional neural network for alcohol dependence diagnosis. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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16
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LI M, AHETO JH, RASHED MMA, HAN F. Tracing models for checking beef adulterated with pig blood by Fourier transform near-infrared paired with linear and nonlinear chemometrics. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.104622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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17
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Ni J, Zhao Y, Zhou Z, Zhao L, Han Z. Condiment recognition using convolutional neural networks with attention mechanism. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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18
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Wu S, Wang L, Zhou G, Liu C, Ji Z, Li Z, Li W. Strategies for the content determination of capsaicin and the identification of adulterated pepper powder using a hand-held near-infrared spectrometer. Food Res Int 2023; 163:112192. [PMID: 36596130 DOI: 10.1016/j.foodres.2022.112192] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
Abstract
To achieve the goals of rapid content determination of capsaicin and adulteration detection of pepper powder. The method based on the hand-held near-infrared spectrometer combined with ensemble preprocessing was proposed. DoE-based ensemble preprocessing technique was utilized to develop the partial least squares regression models of red pepper [Capsicum annuum L. var. conoides (Mill.) Irish] powders. The performance of final models was evaluated using coefficient of determination (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Model development using selective ensemble preprocessing gave the best prediction of capsaicin in Yanjiao pepper powder (R2 = 0.9800, RPD = 7.090, RMSEP = 0.00689) and Tianying pepper powder (R2 = 0.8935, RPD = 3.017, RMSEP = 0.06154). Moreover, the potential of grey wolf optimizer-support vector machine (GWO-SVM) to detect adulterated pepper powder was investigated. The samples were composed of two authentic products and three different adulterants with different adulteration levels. The results showed that the classification accuracy of GWO-SVM model for Yanjiao peppers was over 90 %, which realized the adulteration detection of Yanjiao pepper. And GWO-SVM showed better performance in detecting adulterated Tianying pepper compared to hierarchical cluster analysis, orthogonal partial least squares discriminant analysis and random forest. In summary, the quality control strategy established in this paper can provide a solution for the adulteration detection and quality evaluation of pepper powder in a rapid and on-site way.
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Affiliation(s)
- Sijun Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Long Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Guoming Zhou
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Chao Liu
- Shandong wisdom instrument Co., Ltd., Jinan 250000, China
| | - Zhongrui Ji
- Shandong wisdom instrument Co., Ltd., Jinan 250000, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
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19
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Influence of measurement procedure on the use of a handheld NIR spectrophotometer. Food Res Int 2022; 161:111836. [DOI: 10.1016/j.foodres.2022.111836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/19/2022] [Accepted: 08/21/2022] [Indexed: 11/20/2022]
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20
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Han F, Ming L, Aheto JH, Rashed MMA, Zhang X, Huang X. Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics. Front Nutr 2022; 9:935099. [PMID: 36386895 PMCID: PMC9643882 DOI: 10.3389/fnut.2022.935099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/20/2022] [Indexed: 09/14/2023] Open
Abstract
This work aims to investigate a feasible and practical technique for the authentication of edible animal blood food (EABF) using Fourier transform near-infrared (FT-NIR) coupled with fast chemometrics. A total of 540 samples were used, including raw duck blood tofu (DBT), cow blood-based gel (CBG), pig blood-based gel (PBG), and DBT binary and ternary adulterated with CBG and PBG. The protein, fat, total sugar, and 16 kinds of amino acids were measured to validate the difference in basic organic matters among EABFs according to species. Fisher linear discriminate analysis (Fisher LDA) and extreme learning machine (ELM) were implemented comparatively to identify the adulterated EABF. To predict adulteration levels, four extreme learning machine regression (ELMR) models were constructed and optimized. Results showed that, by analyzing 27 crucial spectral variables, the ELM model provides higher accuracy of 93.89% than Fisher LDA for the independent samples. All the correlation coefficients of the optimized ELMR models' training and prediction sets were better than 0.94, the root mean square errors were all less than 3.5%, and the residual prediction deviation and the range error ratios were all higher than 4.0 and 12.0, respectively. In conclusion, the FT-NIR paired with ELM have great potential in authenticating the EABF. This work presents amino acids content in EABFs for the first time and built tracing models for rapid authentication of DBT, which can be used to manage the EABF market, thereby preventing illegal adulteration and unfair competition.
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Affiliation(s)
- Fangkai Han
- School of Biological and Food Engineering, Suzhou University, Suzhou, Anhui, China
| | - Li Ming
- School of Biological and Food Engineering, Suzhou University, Suzhou, Anhui, China
| | - Joshua H. Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Marwan M. A. Rashed
- School of Biological and Food Engineering, Suzhou University, Suzhou, Anhui, China
| | - Xiaorui Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
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21
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Dashti A, Weesepoel Y, Müller-Maatsch J, Parastar H, Kobarfard F, Daraei B, Yazdanpanah H. Assessment of meat authenticity using portable Fourier transform infrared spectroscopy combined with multivariate classification techniques. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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22
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Varrà MO, Ghidini S, Fabrile MP, Ianieri A, Zanardi E. Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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23
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Beć KB, Grabska J, Huck CW. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022; 11:foods11101465. [PMID: 35627034 PMCID: PMC9140213 DOI: 10.3390/foods11101465] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 01/27/2023] Open
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
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24
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Magdas D, David M, Berghian-Grosan C. Fruit spirits fingerprint pointed out through artificial intelligence and FT-Raman spectroscopy. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108630] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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25
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Sá M, Ferrer-Ledo N, Gao F, Bertinetto CG, Jansen J, Crespo JG, Wijffels RH, Barbosa M, Galinha CF. Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry. Microb Biotechnol 2022; 15:1824-1838. [PMID: 35175653 PMCID: PMC9151345 DOI: 10.1111/1751-7915.14013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 11/27/2022] Open
Abstract
Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real-time process control of a microalgae production factory still require further development. In this mini-review, non-destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up-and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.
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Affiliation(s)
- Marta Sá
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands.,Stichting imec Nederland - OnePlanet Research Center, Wageningen, 6708WH, The Netherlands
| | - Narcis Ferrer-Ledo
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands
| | - Fengzheng Gao
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands
| | - Carlo G Bertinetto
- Institute for Molecules and Materials (Analytical Chemistry), Radboud University, Nijmegen, The Netherlands
| | - Jeroen Jansen
- Institute for Molecules and Materials (Analytical Chemistry), Radboud University, Nijmegen, The Netherlands
| | - João G Crespo
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, FCT NOVA, Caparica, 2829-516, Portugal
| | - Rene H Wijffels
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands.,Faculty of Biosciences and Aquaculture, Nord University, Bodø, N-8049, Norway
| | - Maria Barbosa
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands
| | - Claudia F Galinha
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, FCT NOVA, Caparica, 2829-516, Portugal
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26
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Xu K, Yi Y, Deng J, Wang Y, Zhao B, Sun Q, Gong C, Yang Z, Wan H, He R, Wu X, Yao B, Zhang M, Tang Y. Evaluation of the freshness of rainbow trout ( Oncorhynchus mykiss) fillets by the NIR, E-nose and SPME-GC-MS. RSC Adv 2022; 12:11591-11603. [PMID: 35425088 PMCID: PMC9006240 DOI: 10.1039/d2ra00038e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/30/2022] [Indexed: 11/21/2022] Open
Abstract
A comparison study on the freshness of rainbow trout (Oncorhynchus mykiss) fillets in the course of their sale was performed using near-infrared spectroscopy (NIRS), solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), and the electronic nose (E-nose) technique. Quantitative analysis of the volatile salt nitrogen (TVB-N) of rainbow trout fillets with different freshness using NIR combined with the partial least squares (PLS) method revealed that the predicted values of TVB-N of the samples were significantly correlated with the true values (P < 0.01). SPME-GC-MS combined with E-nose analysis demonstrated that there were significant differences in the volatile flavor components of rainbow trout fillets at different freshness, and E-nose combined with principal component analysis (PCA) and linear discriminant analysis (LDA) could achieve rapid and non-destructive freshness ranking of rainbow trout fillets based on volatile flavor characteristics. Consequently, the NIRS and E-nose non-destructive testing techniques are capable of acting as rapid screening tools for detecting the freshness of rainbow trout fillets during their sale. Successful evaluation of rainbow trout (Oncorhynchus mykiss) fillet freshness using the NIR and E-nose non-destructive techniques combined with SPME-GC-MS.![]()
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Affiliation(s)
- Kunli Xu
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Yuwen Yi
- Cuisine Science Key Laboratory of Sichuan Province, University of Sichuan Tourism, Chengdu, Sichuan 610100, China
| | - Jing Deng
- Cuisine Science Key Laboratory of Sichuan Province, University of Sichuan Tourism, Chengdu, Sichuan 610100, China
| | - Yuanhui Wang
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Bo Zhao
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Qianran Sun
- Chengdu Esun Modern Technology Co., Ltd., Chengdu, Sichuan 610041, China
| | - Chenhui Gong
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Zepeng Yang
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Hailun Wan
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Ruiyan He
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Xinyu Wu
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Bo Yao
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Meichao Zhang
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
| | - Yong Tang
- School of Food and Biological Engineering, University of Xihua, Chengdu, Sichuan 610039, China
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27
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Dashti A, Müller-Maatsch J, Weesepoel Y, Parastar H, Kobarfard F, Daraei B, AliAbadi MHS, Yazdanpanah H. The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification. Foods 2021; 11:71. [PMID: 35010197 PMCID: PMC8750306 DOI: 10.3390/foods11010071] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022] Open
Abstract
Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400-1000 nm) and a handheld NIR (900-1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95-100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.
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Affiliation(s)
- Abolfazl Dashti
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran
| | - Judith Müller-Maatsch
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands; (J.M.-M.); (Y.W.)
| | - Yannick Weesepoel
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands; (J.M.-M.); (Y.W.)
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran P.O. Box 11155-9516, Iran;
| | - Farzad Kobarfard
- Department of Medicinal Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran;
| | - Bahram Daraei
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
| | | | - Hassan Yazdanpanah
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran; (A.D.); (B.D.)
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14155-6153, Iran
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28
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Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.11.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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29
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Müller-Maatsch J, van Ruth SM. Handheld Devices for Food Authentication and Their Applications: A Review. Foods 2021; 10:2901. [PMID: 34945454 PMCID: PMC8700508 DOI: 10.3390/foods10122901] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/18/2021] [Accepted: 11/21/2021] [Indexed: 12/18/2022] Open
Abstract
This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.
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Affiliation(s)
- Judith Müller-Maatsch
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 EV Wageningen, The Netherlands;
| | - Saskia M. van Ruth
- Wageningen Food Safety Research, Wageningen University and Research, P.O. Box 230, 6700 EV Wageningen, The Netherlands;
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
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30
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Abstract
Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.
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31
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Yang B, Chen C, Chen F, Chen C, Tang J, Gao R, Lv X. Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 260:119956. [PMID: 34049008 DOI: 10.1016/j.saa.2021.119956] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/17/2021] [Accepted: 05/08/2021] [Indexed: 06/12/2023]
Abstract
Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.
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Affiliation(s)
- Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Fangfang Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Jun Tang
- Centre for Physical and Chemical Analysis, Xinjiang University, Urumqi 830046, China
| | - Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China.
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32
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Li A, Li C, Gao M, Yang S, Liu R, Chen W, Xu K. Beef Cut Classification Using Multispectral Imaging and Machine Learning Method. Front Nutr 2021; 8:755007. [PMID: 34746211 PMCID: PMC8564009 DOI: 10.3389/fnut.2021.755007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/17/2021] [Indexed: 01/23/2023] Open
Abstract
Classification of beef cuts is important for the food industry and authentication purposes. Traditional analytical methods are time constraints and incompatible with the modern food industry. Taking advantage of its rapidness and being nondestructive, multispectral imaging (MSI) has been widely applied to obtain a precise characterization of food and agriculture products. This study aims at developing a beef cut classification model using MSI and machine learning classifiers. Beef samples are imaged with a snapshot multi-spectroscopic camera within a range of 500-800 nm. In order to find a more accurate classification model, single- and multiple-modality feature sets are used to develop an accurate classification model with different machine learning-based classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) algorithms. The results demonstrate that the optimized LDA classifier achieved a prediction accuracy of over 90% with multiple modality feature fusion. By combining machine learning and feature fusion, the other classification models also achieved a satisfying accuracy. Furthermore, this study demonstrates the potential of machine learning and feature fusion method for meat classification by using multiple spectral imaging in future agricultural applications.
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Affiliation(s)
- Ang Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Chenxi Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Moyang Gao
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Si Yang
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Rong Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Wenliang Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Kexin Xu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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Kaczmarek A, Muzolf-Panek M. Predictive Modeling of Changes in TBARS in the Intramuscular Lipid Fraction of Raw Ground Beef Enriched with Plant Extracts. Antioxidants (Basel) 2021; 10:736. [PMID: 34066946 PMCID: PMC8148524 DOI: 10.3390/antiox10050736] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/27/2021] [Accepted: 05/05/2021] [Indexed: 12/20/2022] Open
Abstract
The aim of the study was to develop and compare the predictive models of lipid oxidation in minced raw beef meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary and thyme) expressed as value changes of TBARS (thiobarbituric acid reactive substances) in various time/temperature conditions. Meat samples were stored at the temperatures of 4, 8, 12, 16 and 20 °C. The value changes of TBARS in samples stored at 12 °C were used as the external validation dataset. Lipid oxidation increased significantly with storage time and temperature. The rate of this increase varied depending on the addition of the plant extract and was the most pronounced in the control sample. The dependence of lipid oxidation on temperature was adequately modeled by the Arrhenius and log-logistic equation with high average R2 coefficients (≥0.98) calculated for all extracts. Kinetic models and artificial neural networks (ANNs) were used to build the predictive models. The obtained result demonstrates that both kinetic Arrhenius (R2 = 0.972) and log-logistic (R2 = 0.938) models as well as ANN (R2 = 0.935) models can predict changes in TBARS in raw ground beef meat during storage.
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Affiliation(s)
- Anna Kaczmarek
- Department of Food Quality and Safety Management, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-637 Poznań, Poland;
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34
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McVey C, Gordon U, Haughey SA, Elliott CT. Assessment of the Analytical Performance of Three Near-Infrared Spectroscopy Instruments (Benchtop, Handheld and Portable) through the Investigation of Coriander Seed Authenticity. Foods 2021; 10:956. [PMID: 33925477 PMCID: PMC8145574 DOI: 10.3390/foods10050956] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/14/2021] [Accepted: 04/23/2021] [Indexed: 11/16/2022] Open
Abstract
The performance of three near-infrared spectroscopy (NIRS) instruments was compared through the investigation of coriander seed authenticity. The Thermo Fisher iS50 NIRS benchtop instrument, the portable Ocean Insights Flame-NIR and the Consumer Physics handheld SCiO device were assessed in conjunction with chemometric modelling in order to determine their predictive capabilities and use as quantitative tools through regression analysis. Two hundred authentic coriander seed samples and ninety adulterated samples were analysed on each device. Prediction models were developed and validated using SIMCA 15 chemometric software. All instruments correctly predicted 100% of the adulterated samples. The best models resulted in correct predictions of 100%, 98.5% and 95.6% for authentic coriander samples using spectra from the iS50, Flame-NIR and SCiO, respectively. The development of regression models highlighted the limitations of the Flame-NIR and SCiO for quantitative analysis, compared to the iS50. However, the results indicate their use as screening tools for on-site analysis of food, at various stages of the food supply chain.
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Affiliation(s)
| | | | - Simon A. Haughey
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, Northern Ireland, UK; (C.M.); (U.G.); (C.T.E.)
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35
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Ren G, Wang Y, Ning J, Zhang Z. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2135-2142. [PMID: 32981110 DOI: 10.1002/jsfa.10836] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/28/2020] [Accepted: 09/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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36
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Khoubnasabjafari M, Mogaddam MRA, Rahimpour E, Soleymani J, Saei AA, Jouyban A. Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications. Crit Rev Anal Chem 2021; 52:1461-1487. [PMID: 33691552 DOI: 10.1080/10408347.2021.1889961] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.
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Affiliation(s)
- Maryam Khoubnasabjafari
- Tuberculosis and Lung Diseases Research Center and Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.,Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohamad Reza Afshar Mogaddam
- Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elaheh Rahimpour
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jafar Soleymani
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Liver and Gastrointestinal Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Amir Ata Saei
- Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry I, Karolinska Institutet, Stockholm, Sweden
| | - Abolghasem Jouyban
- Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Beć KB, Grabska J, Huck CW. Principles and Applications of Miniaturized Near-Infrared (NIR) Spectrometers. Chemistry 2021; 27:1514-1532. [PMID: 32820844 PMCID: PMC7894516 DOI: 10.1002/chem.202002838] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/20/2020] [Indexed: 12/16/2022]
Abstract
This review article focuses on the principles and applications of miniaturized near-infrared (NIR) spectrometers. This technology and its applicability has advanced considerably over the last few years and revolutionized several fields of application. What is particularly remarkable is that the applications have a distinctly diverse nature, ranging from agriculture and the food sector, through to materials science, industry and environmental studies. Unlike a rather uniform design of a mature benchtop FTNIR spectrometer, miniaturized instruments employ diverse technological solutions, which have an impact on their operational characteristics. Continuous progress leads to new instruments appearing on the market. The current focus in analytical NIR spectroscopy is on the evaluation of the devices and associated methods, and to systematic characterization of their performance profiles.
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Affiliation(s)
- Krzysztof B. Beć
- Institute of Analytical Chemistry and RadiochemistryCCB-Center for Chemistry and BiomedicineLeopold-Franzens UniversityInnrain 80/826020InnsbruckAustria
| | - Justyna Grabska
- Institute of Analytical Chemistry and RadiochemistryCCB-Center for Chemistry and BiomedicineLeopold-Franzens UniversityInnrain 80/826020InnsbruckAustria
| | - Christian W. Huck
- Institute of Analytical Chemistry and RadiochemistryCCB-Center for Chemistry and BiomedicineLeopold-Franzens UniversityInnrain 80/826020InnsbruckAustria
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Amirvaresi A, Nikounezhad N, Amirahmadi M, Daraei B, Parastar H. Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection. Food Chem 2020; 344:128647. [PMID: 33229154 DOI: 10.1016/j.foodchem.2020.128647] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/11/2020] [Accepted: 11/11/2020] [Indexed: 02/07/2023]
Abstract
In this work, the potential of near-infrared (NIR) and mid-infrared (MIR) spectroscopy along with chemometrics was investigated for authentication and adulteration detection of Iranian saffron samples. First, authentication of one-hundred saffron samples was examined by principal component analysis (PCA). The results showed the NIR spectroscopy can better predict the origin of samples than the MIR. Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect four common plant-derived adulterants (i.e., saffron style, calendula, safflower, and rubia). In all cases, PLS-DA classification figures of merit in terms of sensitivity, specificity, error rate and accuracy were satisfactory for both NIR and MIR datasets. The built models were then successfully validated using test set and also commercial samples. Finally, partial least squares regression (PLSR) was used to estimate the amount of adulteration. In this case, only NIR showed a good performance with regression coefficients (R2) in range of 0.95-0.99.
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Affiliation(s)
- Arian Amirvaresi
- Department of Chemistry, Sharif University of Technology, Tehran, Iran
| | - Nastaran Nikounezhad
- Food and Drug Laboratory Research Center, Food and Drug Organization, Tehran, Iran
| | - Maryam Amirahmadi
- Food and Drug Laboratory Research Center, Food and Drug Organization, Tehran, Iran
| | - Bahram Daraei
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Science, Tehran, Iran
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran, Iran.
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39
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Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device. Foods 2020; 9:foods9091294. [PMID: 32938016 PMCID: PMC7555696 DOI: 10.3390/foods9091294] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/09/2020] [Accepted: 09/11/2020] [Indexed: 12/29/2022] Open
Abstract
This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950-1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square-discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.
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Hassoun A, Måge I, Schmidt WF, Temiz HT, Li L, Kim HY, Nilsen H, Biancolillo A, Aït-Kaddour A, Sikorski M, Sikorska E, Grassi S, Cozzolino D. Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years. Foods 2020; 9:E1069. [PMID: 32781687 PMCID: PMC7466239 DOI: 10.3390/foods9081069] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/01/2020] [Indexed: 12/27/2022] Open
Abstract
Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.
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Affiliation(s)
- Abdo Hassoun
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Ingrid Måge
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Walter F. Schmidt
- United States Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705-2325, USA;
| | - Havva Tümay Temiz
- Department of Food Engineering, Bingol University, 12000 Bingol, Turkey;
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China;
| | - Hae-Yeong Kim
- Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea;
| | - Heidi Nilsen
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, 67100 Via Vetoio, Coppito, L’Aquila, Italy;
| | | | - Marek Sikorski
- Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland;
| | - Ewa Sikorska
- Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland;
| | - Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via Celoria, 2, 20133 Milano, Italy;
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, 39 Kessels Rd, Coopers Plains, QLD 4108, Australia;
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Qi J, Li Y, Zhang C, Wang C, Wang J, Guo W, Wang S. Geographic origin discrimination of pork from different Chinese regions using mineral elements analysis assisted by machine learning techniques. Food Chem 2020; 337:127779. [PMID: 32795859 DOI: 10.1016/j.foodchem.2020.127779] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 08/01/2020] [Accepted: 08/03/2020] [Indexed: 01/17/2023]
Abstract
Porkis thelargest-producedandmost-consumedmeat intheworld, and the food market globalization has increased public attention to food origin. Therefore, advanced techniques are required to determine the geographical origin of pork. This study investigated the prospects of using fingerprint analysis of mineral elements and machine learning to facilitate the traceability of pork origin in China. The results showed that each of seven regions had a characteristic element content profile. To improve the performance of the origin traceability model, popular machine learning techniques in food authenticity were introduced. This resulted in a high-performance origin tracing model. Comparing various machine learning algorithms, the feedforward neural network achieved superior performance with an overall accuracy of 95.71% and area under the curve close to one. Thus, this study proves that mineral elements analysis assisted by machine learning can be applied to distinguish pork samples within a country.
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Affiliation(s)
- Jing Qi
- China Meat Research Center, Beijing 100068, China
| | - Yingying Li
- China Meat Research Center, Beijing 100068, China
| | - Chen Zhang
- China Meat Research Center, Beijing 100068, China
| | - Cheng Wang
- China Meat Research Center, Beijing 100068, China
| | | | - Wenping Guo
- China Meat Research Center, Beijing 100068, China
| | - Shouwei Wang
- China Meat Research Center, Beijing 100068, China.
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van Kollenburg G, Weesepoel Y, Parastar H, van den Doel A, Buydens L, Jansen J. Dataset of the application of handheld NIR and machine learning for chicken fillet authenticity study. Data Brief 2020; 29:105357. [PMID: 32195297 PMCID: PMC7078282 DOI: 10.1016/j.dib.2020.105357] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 02/20/2020] [Accepted: 02/24/2020] [Indexed: 12/04/2022] Open
Abstract
Diffuse reflectance near-infrared (NIR) data (908-1676 nm) of chicken breast fillets was recorded in a non-destructive way using a portable miniaturised NIR spectrometer. The NIR data was used to discriminate between fresh and thawed breast fillets and to determine the birds' growth conditions. NIR data was recorded of 153 commercial supermarket chicken fillet samples by applying the NIR device equipped with the standard issue collar on the samples in three different ways: (i) directly on the meat (ii) through the top foil of the package (i.e. with an air pocket between the foil and the breast fillet), and (iii) through the top foil with the packaging turned bottom up (i.e. no air pocket between the foil and the breast fillet). In order to generate thawed samples, the fresh samples were frozen and subsequently thawed. The freshness of the fillets was checked using β-hydroxyacyl-CoA-dehydrogenase of 13% of the sample set. Five NIR spectra were collected per measurement mode from each sample resulting in 4590 raw NIR spectra. Multivariate statistics was applied and the interpretation of these calculations can be found in Parastar et al. [1]. The NIR data has a reuse potential for follow-up studies of chicken breast fillet authentication using a similar brand NIR device or to serve as calibration transfer data.
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Affiliation(s)
- Geert van Kollenburg
- Radboud University, Department of Analytical Chemistry/ Chemometrics, Institute for Molecules and Materials (IMM), Heyendaalseweg 135, 6525 AJ Nijmegen, the Netherlands
| | - Yannick Weesepoel
- Wageningen Food Safety Research (WFSR), Wageningen University & Research, Wageningen, the Netherlands
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran, Iran
| | - André van den Doel
- Radboud University, Department of Analytical Chemistry/ Chemometrics, Institute for Molecules and Materials (IMM), Heyendaalseweg 135, 6525 AJ Nijmegen, the Netherlands
| | - Lutgarde Buydens
- Radboud University, Department of Analytical Chemistry/ Chemometrics, Institute for Molecules and Materials (IMM), Heyendaalseweg 135, 6525 AJ Nijmegen, the Netherlands
| | - Jeroen Jansen
- Radboud University, Department of Analytical Chemistry/ Chemometrics, Institute for Molecules and Materials (IMM), Heyendaalseweg 135, 6525 AJ Nijmegen, the Netherlands
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