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Foli LP, Hespanhol MC, Cruz KAML, Pasquini C. Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124297. [PMID: 38640625 DOI: 10.1016/j.saa.2024.124297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.
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Affiliation(s)
- Letícia P Foli
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Maria C Hespanhol
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Kaíque A M L Cruz
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Celio Pasquini
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Rua Monteiro Lobato, 290, Campinas, SP 13083-862, Brazil.
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2
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Thantar S, Mihailova A, Islam MD, Maxwell F, Hamed I, Vlachou C, Kelly SD. Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics. Talanta 2024; 273:125910. [PMID: 38492284 DOI: 10.1016/j.talanta.2024.125910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/06/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.
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Affiliation(s)
- Saw Thantar
- Department of Nuclear Technology, Kyaukse Technological University, Kyaukse, Myanmar
| | - Alina Mihailova
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria.
| | - Marivil D Islam
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Florence Maxwell
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Islam Hamed
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Christina Vlachou
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Simon D Kelly
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
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Rocamora-Rivera B, Arroyo-Manzanares N, Viñas P. Detection of Adulterated Oregano Samples Using Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry Analysis. Foods 2024; 13:516. [PMID: 38397493 PMCID: PMC10888469 DOI: 10.3390/foods13040516] [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: 01/10/2024] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Oregano is often adulterated for economic reasons. This fraud mainly consists of adding other species with lower commercial value, such as olive leaves. To ensure the authenticity of oregano, an analytical method based on the analysis of the volatile organic compound (VOC) profile obtained by headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) was developed and validated. Samples of ecological Mediterranean oregano adulterated with different percentages of two types of olive leaves (cornicabra and manzanilla) were studied using a non-targeted analysis. Moreover, a total of 30 VOCs were identified in the analyzed samples, and 24 compounds could be quantified using calibration curves based on Boltzmann's equation. A chemometric model based on orthogonal partial least squares discriminant analysis (OPLS-DA) was used to detect the adulterated oregano samples, obtaining a 100% validation success rate, and partial least squares (PLS) analysis was used to quantify the percentage of adulterant. Finally, the proposed methodology was applied to 15 commercial oregano samples, resulting in two of them being classified as adulterated with 31 and 43% of olive leaves, respectively.
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Affiliation(s)
| | - Natalia Arroyo-Manzanares
- Department of Analytical Chemistry, Faculty of Chemistry, University of Murcia, 30100 Murcia, Spain; (B.R.-R.); (P.V.)
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Cozzolino D, Sanal P, Schreuder J, Williams PJ, Assadi Soumeh E, Dekkers MH, Anderson M, Boisen S, Hoffman LC. Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System. Foods 2024; 13:212. [PMID: 38254513 PMCID: PMC10814904 DOI: 10.3390/foods13020212] [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: 12/01/2023] [Revised: 12/21/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R2CV) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R2CV of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R2CV of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R2CV of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness.
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Affiliation(s)
- Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia;
| | - Pooja Sanal
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; (P.S.); (E.A.S.)
| | - Jana Schreuder
- Food Science Department, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (J.S.); (P.J.W.)
| | - Paul James Williams
- Food Science Department, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (J.S.); (P.J.W.)
| | - Elham Assadi Soumeh
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; (P.S.); (E.A.S.)
| | - Milou Helene Dekkers
- Queensland Animal Science Precinct (QASP), The University of Queensland, Gatton Campus, St. Lucia, Brisbane, QLD 4072, Australia; (M.H.D.); (M.A.); (S.B.)
| | - Molly Anderson
- Queensland Animal Science Precinct (QASP), The University of Queensland, Gatton Campus, St. Lucia, Brisbane, QLD 4072, Australia; (M.H.D.); (M.A.); (S.B.)
| | - Sheree Boisen
- Queensland Animal Science Precinct (QASP), The University of Queensland, Gatton Campus, St. Lucia, Brisbane, QLD 4072, Australia; (M.H.D.); (M.A.); (S.B.)
| | - Louwrens Christiaan Hoffman
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia;
- Food Science Department, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (J.S.); (P.J.W.)
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Biswas A, Hazra SK, Chaudhari SR. Detection of barley malt syrup as an adulterant in honey by 1H NMR profile. Food Chem 2023; 429:136842. [PMID: 37454619 DOI: 10.1016/j.foodchem.2023.136842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/21/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Currently, Barley Malt Syrup (BMS) is one of the forms of growing adulteration in honey. However, there have been no reports regarding its identification by NMR. In this aspect, we proposed a 1H NMR profiling method to discriminate between authentic and honey adulterated with BMS. The authenticated honey samples were artificially adulterated with varying percentages of BMS. It was found that a marker peak primarily falling around the 5.40 ppm region exhibited discrimination between pure and adulterated samples. Furthermore, NMR data of the samples were analyzed using statistical models. The findings demonstrate that NMR sugar profiles region, when combined with PCA analysis, can effectively detect varying degrees of adulteration. Despite qualitative nature of the outcomes, spiking studies have revealed that approach can reliably identify sugar addition at levels as low as 5-10%. Overall, NMR-based approach proves to be effective in detecting BMS as an adulterant in honey.
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Affiliation(s)
- Anisha Biswas
- Department of Plantation Products, Spice and Flavor Technology, CSIR-Central Food Technological Research Institute, Mysore, Karnataka 570020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sudipta Kumar Hazra
- Department of Plantation Products, Spice and Flavor Technology, CSIR-Central Food Technological Research Institute, Mysore, Karnataka 570020, India
| | - Sachin R Chaudhari
- Department of Plantation Products, Spice and Flavor Technology, CSIR-Central Food Technological Research Institute, Mysore, Karnataka 570020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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Zhang M, Li Q, Nie L, Hai P, Zhang W, Caiji W, Liang W, Zhang H, Zang H. Nondestructive rapid identification of wild Cordyceps sinensis with portable instrument. PHYTOCHEMICAL ANALYSIS : PCA 2023. [PMID: 38035800 DOI: 10.1002/pca.3310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023]
Abstract
INTRODUCTION Cordyceps sinensis (CS) is a precious medicinal fungus. Wild CS (WCS) and artificial CS (ACS) are destroyed for their identification using traditional methods, which are time consuming and labor-intensive. Therefore, it is crucial to establish a nondestructive identification method to rapidly screen WCS. OBJECTIVE The aim of this study was to provide technical support for rapid screening of CS and evaluation of its quality. The applicability of the model was improved through model transfer. METHODS In this study, continuous wavelet transform was used to analyze the differences in moisture content and active components between WCS and ACS from the perspective of characteristic molecular groups. A portable instrument and a laboratory benchtop instrument were used to determine CS spectra. Partial least squares discrimination analysis was conducted for the identification of WCS and ACS while preserving the original shape of CS. Moreover, improved principal component analysis was utilized to transfer the model between the two types of near-infrared spectroscopy (NIRS) instruments. RESULTS The results demonstrated that three peaks, at 1443, 1941, and 2183 nm, were characteristic absorption peaks. The model based on NIRS could initially provide rapid differentiation between WCS and ACS. At the same time, the accuracy of the external test set was further improved to over 95% through forward transfer. CONCLUSION Therefore, this method could be used for rapid screening of WCS and provides technical support for the nondestructive identification of CS and initial assessment of CS quality.
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Affiliation(s)
- Mengqi Zhang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qin Li
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lei Nie
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ping Hai
- Qinghai Key Laboratory of Modernization of Chinese and Tibetan Medicine, Key Laboratory of Chinese and Tibetan Medicine Quality Control of National Medical Products Administration, Qinghai Institute for Drug Control, Xining, China
| | - Wei Zhang
- Qinghai Key Laboratory of Modernization of Chinese and Tibetan Medicine, Key Laboratory of Chinese and Tibetan Medicine Quality Control of National Medical Products Administration, Qinghai Institute for Drug Control, Xining, China
| | - Wangmao Caiji
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wenyan Liang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui Zhang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hengchang Zang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Glycoengineering Research Center, Shandong University, Jinan, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, China
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Kashani Zadeh H, Hardy M, Sueker M, Li Y, Tzouchas A, MacKinnon N, Bearman G, Haughey SA, Akhbardeh A, Baek I, Hwang C, Qin J, Tabb AM, Hellberg RS, Ismail S, Reza H, Vasefi F, Kim M, Tavakolian K, Elliott CT. Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115149. [PMID: 37299875 DOI: 10.3390/s23115149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.
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Affiliation(s)
| | - Mike Hardy
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | - Mitchell Sueker
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA
| | - Yicong Li
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | | | | | | | - Simon A Haughey
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | | | - Insuck Baek
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Chansong Hwang
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Jianwei Qin
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Amanda M Tabb
- Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Rosalee S Hellberg
- Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Shereen Ismail
- School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
| | - Hassan Reza
- School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
| | | | - Moon Kim
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, Khong Luang 12120, Thailand
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Guo B, Zou Z, Huang Z, Wang Q, Qin J, Guo Y, Pan S, Wei J, Guo H, Zhu D, Su Z. A simple and green method for simultaneously determining the geographical origin and glycogen content of oysters using ATR–FTIR and chemometrics. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Teye E, Amuah CLY, Yeh TS, Nyorkeh R. Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2023; 2023:3364720. [PMID: 36760654 PMCID: PMC9904916 DOI: 10.1155/2023/3364720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.
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Affiliation(s)
- Ernest Teye
- Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Charles L. Y. Amuah
- Department of Physics, Laser and Fibre Optics Centre, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Tai-Sheng Yeh
- Department of Food Science and Nutrition, Meiho University, Neipu Township, Taiwan
| | - Regina Nyorkeh
- Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
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10
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Chen J, Liu S, Yin L, Cao H, Xi G, Zhang Z, Liu J, Luo R, Han L, Yin Y, Guo J. Non-destructive preservation state estimation of waterlogged archaeological wooden artifacts. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121840. [PMID: 36115308 DOI: 10.1016/j.saa.2022.121840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Non-destructive preservation state estimation is an essential prerequisite for the preservation and conservation of waterlogged archaeological wooden artifacts. Herein, Near Infrared (NIR) spectroscopy coupled with orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to assess sixty-four waterlogged archaeological woods collected from seven excavation sites in the period range of 2900 BCE-1912 CE, aiming at developing a non-destructive, accurate and rapid preservation state estimation methodology. The role of non-decayed recent wood of relevant species on preservation state estimation was studied in prior, showing the use of non-decayed recent wood could not improve the predictive ability. Besides, the high variability in terms of chemical structure between archaeological softwoods and archaeological hardwoods did affect the preservation state estimation. Thus, a simple OPLS-DA model of non-destructively distinguishing archaeological hardwoods from softwoods, R2Xcum of 0.659, R2Ycum of 0.836 and Q2cum of 0.763, was established to avoid and overcome destructive approach for wood identification. Then, the well-defined three grouped separations of slightly-decayed, moderately-decayed and severely-decayed waterlogged archaeological woods were revealed in OPLS-DA models, providing R2Xcum of 0.793, R2Ycum of 0.738, Q2cum of 0.680, and R2Xcum of 0.780, R2Ycum of 0.901, Q2cum of 0.870, for waterlogged archaeological hardwoods and waterlogged archaeological softwoods respectively. Potential predictive wood spectral bands were screened and tentatively identified as hydroxyls of crystalline cellulose, acetyl groups of hemicelluloses, C-H bands of lignin, which guaranteed the elimination of non-structural compounds, such as water and inorganic components interference. Furthermore, the developed NIR methodology was validated by an extensively used destructive method consisting of anatomical characteristics, maximum water content and basic density analyses. The results indicated that NIR coupled to chemometrics could non-destructively and accurately predict the preservation states of waterlogged archaeological wooden artifacts and avoid the interference of water and inorganic deposits.
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Affiliation(s)
- Jiabao Chen
- Research Institute of Wood Industry, Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China; Wood Collection of Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China
| | - Shoujia Liu
- Research Institute of Wood Industry, Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China; Wood Collection of Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China
| | - Lijuan Yin
- Research Institute of Wood Industry, Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China; Wood Collection of Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China
| | - Huimin Cao
- Research Institute of Wood Industry, Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China
| | - Guanglan Xi
- National Center of Archaeology, Heping Road No. 21, Beijing 100031, China; Institute of Cultural Heritage and History of Science and Technology, University of Science and Technology Beijing, Xueyuan Road No.30, Beijing 100083, China
| | - Zhiguo Zhang
- National Center of Archaeology, Heping Road No. 21, Beijing 100031, China
| | - Jian'an Liu
- Zhejiang Provincial Institute of Cultural Relics and Archaeology, Jiaogong Road No.71, Hangzhou 310012, Zhejiang, China
| | - Rupeng Luo
- Zhejiang Provincial Institute of Cultural Relics and Archaeology, Jiaogong Road No.71, Hangzhou 310012, Zhejiang, China
| | - Liuyang Han
- Institute of Cultural Heritage and History of Science and Technology, University of Science and Technology Beijing, Xueyuan Road No.30, Beijing 100083, China
| | - Yafang Yin
- Research Institute of Wood Industry, Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China; Wood Collection of Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China
| | - Juan Guo
- Research Institute of Wood Industry, Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China; Wood Collection of Chinese Academy of Forestry, Dongxiaofu No.1, Beijing 100091, China.
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11
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An Easy-to-Use and Cheap Analytical Approach Based on NIR and Chemometrics for Tomato and Sweet Pepper Authentication by Non-volatile Profile. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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12
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Tan A, Wang Y, Zhao Y, Wang B, Li X, Wang AX. Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 283:121759. [PMID: 35985223 DOI: 10.1016/j.saa.2022.121759] [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: 05/01/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
This study proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples. We tested its effectiveness on two datasets of manure and polyglutamic acid (γ-PGA) solution, respectively. First, the optimal primary Bi-LSTM networks for cattle manure and the first batch of γ-PGA were developed by ablation experiments and both proved to outperform one-dimensional convolutional neural network (1D-CNN), Partial Least Square (PLS) and Extreme Learning Machine (ELM) models. Then, two types of transfer learning approaches were carried out to determine model transferability to non-homologous samples. For poultry manure and the second batch of γ-PGA, the obtained predicting results verified that the second approach of fine-tuning Bi-LSTM layers and re-training FC layers transcended the first approach of fixing Bi-LSTM layers and only re-training FC layers by reducing the RMSEPtarget of 23.4275% and 50.7343%, respectively. Finally, comparisons with fine-tuning 1D-CNN and other traditional model transfer methods further identified the superiority of the proposed methodology with exceeding accuracy and smaller variation, which decreased RMSEPtarget of poultry manure and the second batch of γ-PGA of 7.2832% and 48.1256%, 67.1117% and 80.6924% when compared to that acquired by fine-tuning 1D-CNN, Tradaboost-ELM and CCA-PLS which were the best of five traditional methods, respectively. The study demonstrates the potential of the Fine-tuning-Bi-LSTM enabled NIR technology to be used as a simple, cost effective and reliable detection tool for a wide range of applications under various new scenarios.
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Affiliation(s)
- Ailing Tan
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Yunxin Wang
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China.
| | - Yong Zhao
- School of Electrical Engineering, Yanshan University, The Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Qinhuangdao 066004, China
| | - Bolin Wang
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Xiaohang Li
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Alan X Wang
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76706, USA
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13
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Authenticity analysis of oregano: development, validation and fitness for use of several food fingerprinting techniques. Food Res Int 2022; 162:111962. [DOI: 10.1016/j.foodres.2022.111962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/18/2022]
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14
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Thanavanich C, Phuangsaijai N, Thiraphatchotiphum C, Theanjumpol P, Kittiwachana S. Instant quantification of sugars in milk tablets using near-infrared spectroscopy and chemometric tools. Sci Rep 2022; 12:18802. [PMID: 36335160 PMCID: PMC9637167 DOI: 10.1038/s41598-022-23537-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Milk tablets are a popular dairy product in many Asian countries. This research aimed to develop an instant and rapid method for determining sucrose and lactose contents in milk tablets using near-infrared (NIR) spectroscopy. For the quantitative analysis, a training set composed of laboratory-scale milk samples was generated based on a central composite design (CCD) and used to establish partial least squares (PLS) regression for the predictions of sucrose and lactose contents resulting in R2 values of 0.9749 and 0.9987 with the corresponding root mean square error of calibration (RMSEC) values of 1.69 and 0.35. However, the physical difference between the laboratory-scale powder and the final product milk tablet samples resulted in spectral deviations that dramatically affected the predictive performance of the PLS models. Therefore, calibration transfer methods called direct standardization (DS) and piecewise direct standardization (PDS) were used to adjust the NIR spectra from the real milk tablet samples before the quantitative prediction. Using high-performance liquid chromatography (HPLC) as a reference method, the developed NIR-chemometric model could be used to instantly predict the sugar contents in real milk tablets by producing root mean square error of prediction (RMSEP) values for sucrose and lactose of 5.04 and 4.22 with Q2 values of 0.7973 and 0.9411, respectively, after the PDS transformation.
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Affiliation(s)
- Chanat Thanavanich
- grid.7132.70000 0000 9039 7662Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200 Thailand
| | - Nutthatida Phuangsaijai
- grid.7132.70000 0000 9039 7662Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200 Thailand
| | - Chanidapha Thiraphatchotiphum
- grid.7132.70000 0000 9039 7662Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200 Thailand
| | - Parichat Theanjumpol
- grid.7132.70000 0000 9039 7662Postharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200 Thailand ,Postharvest Technology Innovation Center, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10400 Thailand
| | - Sila Kittiwachana
- grid.7132.70000 0000 9039 7662Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200 Thailand
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15
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Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis. Foods 2022; 11:foods11162401. [PMID: 36010401 PMCID: PMC9407431 DOI: 10.3390/foods11162401] [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: 06/22/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 11/25/2022] Open
Abstract
Edible bird’s nests (EBNs) are vulnerable to adulteration due to their huge demand for traditional medicine and high market price. Presently, there are pressing needs to explore field-deployable rapid screening techniques to detect adulteration of EBNs. The objective of this study is to explore the feasibility of using a handheld near-infrared (VIS/SW-NIR) spectroscopic device for the determination of EBN authenticity against the benchmark performance of a benchtop mid-infrared (MIR) spectrometer. Forty-nine authentic EBNs from the different states in Malaysia and 13 different adulterants (five types) were obtained and used to simulate the adulteration of EBNs at 1, 5 and 10% adulteration by mass (a total of 15 adulterated samples). The VIS/SW-NIR and MIR spectra collated were subsequently processed, modelled and classified using multi-class discriminant analysis. The VIS/SW-NIR results showed 100% correct classification for the collagen and nutrient agar classes in authenticity classification, while for the other classes, the lowest correct classification rate was 96.3%. For MIR analysis, only the karaya gum class had 100% correct classification whilst for the other four classes, the lowest rate of correct classification was at 94.4%. In conclusion, the combination of spectroscopic analysis with chemometrics can be a powerful screening tool to detect EBN adulteration.
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16
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Hoffman LC, Ni D, Dayananda B, Abdul Ghafar N, Cozzolino D. Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy. SENSORS 2022; 22:s22134988. [PMID: 35808484 PMCID: PMC9269732 DOI: 10.3390/s22134988] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/29/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022]
Abstract
Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.
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Affiliation(s)
- Louwrens Christiaan Hoffman
- Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (L.C.H.); (D.N.)
| | - Dongdong Ni
- Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (L.C.H.); (D.N.)
| | - Buddhi Dayananda
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (B.D.); (N.A.G.)
| | - N Abdul Ghafar
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (B.D.); (N.A.G.)
| | - Daniel Cozzolino
- Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (L.C.H.); (D.N.)
- Correspondence:
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17
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Assi S, Arafat B, Abbas I, Evans K. Evaluation of portable near-infrared spectroscopy for authentication of mRNA based COVID-19 vaccines. PLoS One 2022; 17:e0267214. [PMID: 35507562 PMCID: PMC9067670 DOI: 10.1371/journal.pone.0267214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/04/2022] [Indexed: 11/30/2022] Open
Abstract
Since its identification in 2019, Covid-19 has spread to become a global pandemic. Until now, vaccination in its different forms proves to be the most effective measure to control the outbreak and lower the burden of the disease on healthcare systems. This arena has become a prime target to criminal networks that spread counterfeit Covid-19 vaccines across the supply chain mainly for profit. Counterfeit vaccines provide false sense of security to individuals, heightens the risk of exposure and outbreak of the virus, and increase the risk of harm linked to Covid-19 infection. Moreover, the increase in counterfeit vaccines feeds hesitancy towards vaccination and erodes the trust in mass immunisation programmes. It is therefore of paramount importance to work on rapid and reliable methods for vaccine authentication. Subsequently this work utilised a portable and non-destructive near infrared (NIR) spectroscopic method for authentication of Covid-19 vaccines. A total of 405 Covid-19 vaccines samples, alongside their main constituents, were measured as received through glass vials. Spectral quality and bands were inspected by considering the raw spectra of the vaccines. Authentication was explored by applying principal component analysis (PCA) to the multiplicative scatter correction-first derivative spectra. The results showed that NIR spectra of the vaccine featured mainly bands corresponding to the mRNA active ingredient. Fewer bands corresponded to the excipients and protein spectra. The vaccines NIR spectra were strongly absorbing with maximum absorbances up to 2.7 absorbance units and that differentiated them from samples containing normal saline only (constituent reported for counterfeit Covid-19 vaccines). Clustering based on PCA offered optimal authentication of Covid-19 vaccines when applied over the range of 9000–4000 cm-1These findings shed light on the potential of using NIR for analysing Covid-19 vaccines and presents a rapid and effective initial technique for Covid-19 vaccine authentication.
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Affiliation(s)
- Sulaf Assi
- Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
- * E-mail:
| | - Basel Arafat
- Faculty of Health, Education, Medicine and Social Care, Chelmsford, United Kingdom
| | - Ismail Abbas
- Faculty of Science, Lebanese University, Beirut, Lebanon
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18
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Nagy MM, Wang S, Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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19
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Tirado-Kulieva VA, Hernández-Martínez E, Suomela JP. Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04023-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractThe constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.
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20
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Gunning Y, Taous F, El Ghali T, Gibbon JD, Wilson E, Brignall RM, Kemsley EK. Mitigating instrument effects in 60 MHz 1H NMR spectroscopy for authenticity screening of edible oils. Food Chem 2022; 370:131333. [PMID: 34788960 DOI: 10.1016/j.foodchem.2021.131333] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/01/2021] [Accepted: 10/03/2021] [Indexed: 11/04/2022]
Abstract
Low field (60 MHz) 1H NMR spectroscopy was used to analyse a large (n = 410) collection of edible oils, including olive and argan, in an authenticity screening scenario. Experimental work was carried out on multiple spectrometers at two different laboratories, aiming to explore multivariate model stability and transfer between instruments. Three modelling methods were employed: Partial Least Squares Discriminant Analysis, Random Forests, and a One Class Classification approach. Clear inter-instrument differences were observed between replicated data collections, sufficient to compromise effective transfer of models based on raw data between instruments. As mitigations to this issue, various data pre-treatments were investigated: Piecewise Direct Standardisation, Standard Normal Variates, and Rank Transformation. Datasets comprised both phase corrected and magnitude spectra, and it was found that that the latter spectral form may offer some advantages in the context of pattern recognition and classification modelling, particularly when used in combination with the Rank Transformation pre-treatment.
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Affiliation(s)
- Yvonne Gunning
- Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich NR4 7UQ, UK
| | - Fouad Taous
- Centre National de l'Energie des Sciences et des Techniques Nucléaires (CNESTEN) Rabat, Morocco
| | - Tibari El Ghali
- Centre National de l'Energie des Sciences et des Techniques Nucléaires (CNESTEN) Rabat, Morocco
| | | | - E Wilson
- Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich NR4 7UQ, UK
| | | | - E Kate Kemsley
- Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich NR4 7UQ, UK.
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21
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Brasil YL, Cruz-Tirado J, Barbin DF. Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108418] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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22
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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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23
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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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24
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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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25
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On-line monitoring of egg freshness using a portable NIR spectrometer in tandem with machine learning. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110643] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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26
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Shi X, Chen Q, Liu S, Wang J, Peng D, Kong L. Combining targeted metabolite analyses and transcriptomics to reveal the specific chemical composition and associated genes in the incompatible soybean variety PI437654 infected with soybean cyst nematode HG1.2.3.5.7. BMC PLANT BIOLOGY 2021; 21:217. [PMID: 33990182 PMCID: PMC8120846 DOI: 10.1186/s12870-021-02998-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/30/2021] [Indexed: 05/04/2023]
Abstract
BACKGROUND Soybean cyst nematode, Heterodera glycines, is one of the most devastating pathogens of soybean and causes severe annual yield losses worldwide. Different soybean varieties exhibit different responses to H. glycines infection at various levels, such as the genomic, transcriptional, proteomic and metabolomic levels. However, there have not yet been any reports of the differential responses of incompatible and compatible soybean varieties infected with H. glycines based on combined metabolomic and transcriptomic analyses. RESULTS In this study, the incompatible soybean variety PI437654 and three compatible soybean varieties, Williams 82, Zhonghuang 13 and Hefeng 47, were used to clarify the differences in metabolites and transcriptomics before and after the infection with HG1.2.3.5.7. A local metabolite-calibrated database was used to identify potentially differential metabolites, and the differences in metabolites and metabolic pathways were compared between the incompatible and compatible soybean varieties after inoculation with HG1.2.3.5.7. In total, 37 differential metabolites and 20 KEGG metabolic pathways were identified, which were divided into three categories: metabolites/pathways overlapped in the incompatible and compatible soybeans, and metabolites/pathways specific to either the incompatible or compatible soybean varieties. Twelve differential metabolites were found to be involved in predicted KEGG metabolite pathways. Moreover, 14 specific differential metabolites (such as significantly up-regulated nicotine and down-regulated D-aspartic acid) and their associated KEGG pathways (such as the tropane, piperidine and pyridine alkaloid biosynthesis, alanine, aspartate and glutamate metabolism, sphingolipid metabolism and arginine biosynthesis) were significantly altered and abundantly enriched in the incompatible soybean variety PI437654, and likely played pivotal roles in defending against HG1.2.3.5.7 infection. Three key metabolites (N-acetyltranexamic acid, nicotine and D,L-tryptophan) found to be significantly up-regulated in the incompatible soybean variety PI437654 infected by HG1.2.3.5.7 were classified into two types and used for combined analyses with the transcriptomic expression profiling. Associated genes were predicted, along with the likely corresponding biological processes, cellular components, molecular functions and pathways. CONCLUSIONS Our results not only identified potential novel metabolites and associated genes involved in the incompatible response of PI437654 to soybean cyst nematode HG1.2.3.5.7, but also provided new insights into the interactions between soybeans and soybean cyst nematodes.
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Affiliation(s)
- Xue Shi
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qiansi Chen
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
| | - Shiming Liu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiajun Wang
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Deliang Peng
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Lingan Kong
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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Mallet A, Pérémé M, Awhangbo L, Charnier C, Roger JM, Steyer JP, Latrille É, Bendoula R. Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations. WASTE MANAGEMENT (NEW YORK, N.Y.) 2021; 126:664-673. [PMID: 33872975 DOI: 10.1016/j.wasman.2021.03.045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 06/12/2023]
Abstract
Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH4).gTS-1 compared to 60 NmL(CH4).gTS-1 for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste.
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Affiliation(s)
- Alexandre Mallet
- INRAE, Univ Montpellier, LBE, 102 Av des Etangs, Narbonne F-11100, France; INRAE, UMR ITAP, Montpellier University, Montpellier, France; Bioentech, F-11100 Narbonne, France; ChemHouse Research Group, Montpellier, France.
| | - Margaud Pérémé
- INRAE, Univ Montpellier, LBE, 102 Av des Etangs, Narbonne F-11100, France; ChemHouse Research Group, Montpellier, France
| | - Lorraine Awhangbo
- INRAE, Univ Montpellier, LBE, 102 Av des Etangs, Narbonne F-11100, France; ChemHouse Research Group, Montpellier, France
| | | | - Jean-Michel Roger
- INRAE, UMR ITAP, Montpellier University, Montpellier, France; ChemHouse Research Group, Montpellier, France
| | | | - Éric Latrille
- INRAE, Univ Montpellier, LBE, 102 Av des Etangs, Narbonne F-11100, France; ChemHouse Research Group, Montpellier, France
| | - Ryad Bendoula
- INRAE, UMR ITAP, Montpellier University, Montpellier, France
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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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