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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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Jia W, Qin Y, Zhao C. Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach. Food Chem 2024; 439:138123. [PMID: 38064835 DOI: 10.1016/j.foodchem.2023.138123] [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: 08/28/2023] [Revised: 11/18/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
Individual detection techniques cannot guarantee accurate and reliable results when combatting the presence of adulterated lamb meat in the market. Here, we propose an approach combining the electronic nose and near-infrared spectroscopy fusion data with machine learning methods to effectively detect adulterated lamb meat (mixed with duck meat). To comprehensively analyse the data from both techniques, the F1-score-based Model Reliability Estimation (F1-score-MRE) data fusion method was introduced. The obtained results demonstrate the superiority of the F1-score-MRE method, achieving an accuracy rate of 98.58% (F1-score: 0.9855) in detecting adulterated lamb meat. This surpasses the performance of the traditional data fusion and feature concatenation methods. Furthermore, the F1-score-MRE data fusion method exhibited enhanced stability and accuracy compared with the single electronic nose and near-infrared data processed by the self-adaptive BPNN model (accuracy: 94.36%, 93.66%; F1-score: 0.9435, 0.9368). This study offers a promising solution to address concerns regarding adulterated lamb meat.
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Affiliation(s)
- Wenshen Jia
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Department of Risk Assessment Lab for Agro-products (Beijing), Ministry of Agriculture and Rural Affairs, Beijing 100097, China; Key Laboratory of Urban Agriculture (North China), Ministry of Agriculture and Rural Affairs, Beijing 100097, China; Lu'an Branch, Anhui Institute of Innovation for Industrial Technology, Lu'an 237100, China.
| | - Yingdong Qin
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Changtong Zhao
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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3
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Deconinck E, Lievens S, Canfyn M, Van Campenhout P, Debehault L, Gremaux L, Balcaen M. Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration. Molecules 2024; 29:1116. [PMID: 38474628 DOI: 10.3390/molecules29051116] [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: 01/24/2024] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.
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Affiliation(s)
- Eric Deconinck
- Sciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
| | - Sybrien Lievens
- Sciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
- VUB, Faculty of Sciences and Bio-Engineering, Department Chemistry, Analytical, Environmental and Geo-Chemistry, Pleinlaan 2, B-1050 Brussels, Belgium
| | - Michael Canfyn
- Sciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
| | - Peter Van Campenhout
- Sciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
| | - Loic Debehault
- Sciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
| | - Lies Gremaux
- Sciensano, Scientific Direction Epidemiology and Public Health, Service Lifestyle and Chronic Diseases, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
| | - Margot Balcaen
- Sciensano, Scientific Direction Epidemiology and Public Health, Service Lifestyle and Chronic Diseases, J. Wytsmanstraat 14, B-1050 Brussels, Belgium
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4
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Cozzolino D, Wu W, Zhang S, Beya M, van Jaarsveld PF, Hoffman LC. The ability of a portable near infrared instrument to evaluate the shelf-life of fresh and thawed goat muscles. Food Res Int 2024; 180:114047. [PMID: 38395546 DOI: 10.1016/j.foodres.2024.114047] [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: 11/21/2023] [Revised: 01/15/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024]
Abstract
The objective of this study was to evaluate the use of a portable near infrared (NIR) instrument to monitor the shelf-life of four goat muscles [longissimus thoracis et lumborum (LTL), semimembranosus (SM), semitendinosus (ST) and biceps femoris (BF)] stored for up to 8 days (4 °C). The NIR spectra of the muscle samples were collected at day 0, and after 1, 4 and 8 days of storage using a MicroNIR instrument (900-1600 nm). The coefficient of determination in cross-validation (R2) and the standard error in cross validation (SECV) obtained for the prediction of days of storage ranged between 0.76 and 0.86, where the SECV ranged from 0.32 to 0.41. The best statistics in cross-validation were obtained for the prediction of days of storage in the BF samples, followed by the ST and LTL muscles. Differences in the PLS loadings for the cross-validation models were observed due to the interactions between the different muscle samples and days of storage. Overall, these results showed the potential of NIR spectroscopy to identify the time of storage in four different goat muscles. Similar data and techniques could be used to predict the remaining shelf life of meat derived from different species under storage. This information can then be used as a tool to predict and guarantee the safety of meat samples to the consumer along the meat supply and value chains.
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Affiliation(s)
- D Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia.
| | - W Wu
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - S Zhang
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - M Beya
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - P F van Jaarsveld
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - L C Hoffman
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
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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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Kim J, Kurniawan H, Faqeerzada MA, Kim G, Lee H, Kim MS, Baek I, Cho BK. Proximate Content Monitoring of Black Soldier Fly Larval ( Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging. Food Sci Anim Resour 2023; 43:1150-1169. [PMID: 37969323 PMCID: PMC10636226 DOI: 10.5851/kosfa.2023.e33] [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: 05/17/2023] [Revised: 06/26/2023] [Accepted: 07/02/2023] [Indexed: 11/17/2023] Open
Abstract
Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.
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Affiliation(s)
- Juntae Kim
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
| | - Hary Kurniawan
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
| | - Geonwoo Kim
- Department of Bio-Industrial Machinery
Engineering, College of Agriculture and Life Science, Gyeongsang National
University, Jinju 52828, Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering,
College of Agriculture, Life & Environment Science, Chungbuk National
University, Cheongju 28644, Korea
| | - Moon Sung Kim
- Environmental Microbial and Food Safety
Laboratory, Agricultural Research Service, United States Department of
Agriculture, Beltsville, MD 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety
Laboratory, Agricultural Research Service, United States Department of
Agriculture, Beltsville, MD 20705, USA
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery
Engineering, College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
- Department of Smart Agriculture Systems,
College of Agricultural and Life Science, Chungnam National
University, Daejeon 34134, Korea
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int 2023; 172:113105. [PMID: 37689840 DOI: 10.1016/j.foodres.2023.113105] [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: 04/12/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 09/11/2023]
Abstract
The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., México 64849, Mexico.
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8
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Boukria O, Boudalia S, Bhat ZF, Hassoun A, Aït-Kaddour A. Evaluation of the adulteration of camel milk by non-camel milk using multispectral image, fluorescence and infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 300:122932. [PMID: 37270971 DOI: 10.1016/j.saa.2023.122932] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/24/2023] [Accepted: 05/27/2023] [Indexed: 06/06/2023]
Abstract
In the present study, the focus was to evaluate the potential of three spectroscopic techniques (Middle Infrared -MIR-, fluorescence, and multispectral imaging -MSI-) to check the level of adulteration in camel milk with goat, cow, and ewe milks. Camel milk was adulterated with goat, ewe, and cow milks, respectively, at 6 different levels viz. 0.5, 1, 2, 5, 10, and 15%. After preprocessing the data with standard normal variate (SNV), multiplicative scattering correction (MSC), and normalization (area under spectrum = 1), partial least squares regression (PLSR) and partial least squares discriminant analysis (PLSDA) were used to predict the adulteration level and their belonging group, respectively. The PLSR and PLSDA models, validated using external data, highlighted that fluorescence spectroscopy was the most accurate technique giving a Rp2 ranging between 0.63 and 0.96 and an accuracy ranging between 67 and 83%. However, no technique has allowed the construction of robust PLSR and PLSDA models for the simultaneous prediction of contamination of camel milk by the three milks.
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Affiliation(s)
- Oumayma Boukria
- Applied Organic Chemistry Laboratory, Sciences and Techniques Faculty, Sidi Mohamed Ben Abedallah University, BP 2202 route d'Immouzer, Fès, Morocco
| | - Sofiane Boudalia
- Laboratoire de Biologie, Département d'Écologie et Génie de l'Environnement, Faculté des Sciences de la Nature et de la Vie & Sciences de la Terre et l'Univers, Université 8 Mai 1945 Guelma, BP 401, Guelma 24000, Algeria
| | - Zuhaib F Bhat
- Division of Livestock Products Technology, SKUAST-J, India
| | - Abdo Hassoun
- Université Littoral Côte d'Opale, UMRt 1158 BioEcoAgro, USC ANSES, INRAe, Université Artois, Université Lille, Université Picardie Jules Verne, Université Liège, Junia, F-62200 Boulogne-sur-Mer, France
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