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Alotaibi RF, AlTilasi HH, Al-Mutairi AM, Alharbi HS. Chromatographic and spectroscopic methods for the detection of cocoa butter in cocoa and its derivatives: A review. Heliyon 2024; 10:e31467. [PMID: 38882372 PMCID: PMC11176802 DOI: 10.1016/j.heliyon.2024.e31467] [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: 01/28/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 06/18/2024] Open
Abstract
Currently, there is fierce competition in the cocoa industry to develop products that possess distinctive sensory characteristics and flavours. This is because cocoa and its derivatives provide numerous health and functional advantages, which is essential to their economics. The fatty acid and triglyceride composition of cocoa determines its quality. This review emphasises the necessity of developing precise, adaptable analytical techniques to identify and quantify cocoa butter in cocoa and its derived products, from cocoa beans to chocolate bars. Key chromatographic and spectroscopic techniques play crucial roles in understanding the fundamental principles underlying the production of cocoa with desirable flavours. This significantly impacts the sustainability, traceability, and authenticity of cocoa products while also supporting the battle against adulteration.
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Affiliation(s)
- Razan F. Alotaibi
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Hissah H. AlTilasi
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Adibah M. Al-Mutairi
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Hibah S. Alharbi
- Saudi Food and Drug Authority, Riyadh, 0112038222, Kingdom of Saudi Arabia
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2
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Li Y, Chen B, Ye S, Wu Q, Zhu L, Ding Y. Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:587-600. [PMID: 38648105 DOI: 10.1080/19440049.2024.2341104] [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: 01/28/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.
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Affiliation(s)
- Yaxin Li
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Baoguo Chen
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Shuhong Ye
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Qi Wu
- China National Institute of Standardization, Beijing, China
| | - Lin Zhu
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Yan Ding
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
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3
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Hernández-Fernández J, Martinez-Trespalacios J, Marquez E. Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging. Foods 2024; 13:1200. [PMID: 38672873 PMCID: PMC11049462 DOI: 10.3390/foods13081200] [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: 10/06/2023] [Revised: 11/21/2023] [Accepted: 11/25/2023] [Indexed: 04/28/2024] Open
Abstract
Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN).
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Affiliation(s)
- Joaquín Hernández-Fernández
- Chemistry Program, Department of Natural and Exact Sciences, San Pablo Campus, University of Cartagena, Cartagena 130015, Colombia
- Department of Natural and Exact Sciences, Universidad de la Costa, Barranquilla 080002, Colombia
- Chemical Engineering Program, School of Engineering, Universidad Tecnológica de Bolivar, Parque Industrial y Tecnológico Carlos Vélez Pombo, Km 1 Vía Turbaco, Turbaco 130001, Colombia;
| | - Jose Martinez-Trespalacios
- Chemical Engineering Program, School of Engineering, Universidad Tecnológica de Bolivar, Parque Industrial y Tecnológico Carlos Vélez Pombo, Km 1 Vía Turbaco, Turbaco 130001, Colombia;
- Facultad de Arquitectura e Ingeniería, Institución Universitaria Mayor de Cartagena, Cartagena 130015, Colombia
| | - Edgar Marquez
- Grupo de Investigaciones en Química Y Biología, Departamento de Química Y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Barranquilla 081007, Colombia
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4
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Wei N, Sun Y, Jiang T, Gao Q. Direct object detection with snapshot multispectral compressed imaging in a short-wave infrared band. OPTICS LETTERS 2024; 49:1941-1944. [PMID: 38621046 DOI: 10.1364/ol.517284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/12/2024] [Indexed: 04/17/2024]
Abstract
Snapshot multispectral imaging (SMSI) has attracted much attention in recent years for its compact structure and superior performance. High-level image analysis based on SMSI, such as object classification and recognition, usually takes the image reconstruction as the first step, which hinders its application in many important real-time scenarios. Here we demonstrate the first, to our knowledge, reconstruction-free strategy for object detection with SMSI in the short-wave infrared (SWIR) band. The implementation of our SMSI is based on a modified 4f system which modulates the light with a random phase mask, and the distinctive point spread function in each narrowband endows the system with spectrum resolving ability. A deep learning network with a CenterNet structure is trained to detect a small object by constructing a dataset with the PSF of our SMSI system and the sky images as background. Our results indicate that a small object with a spectral feature can be detected directly with the compressed image output by our SMSI system. This work paves the way toward the use of SMSI to detect a multispectral object in practical applications.
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Meza-Márquez OG, Rodríguez-Híjar AR, Gallardo-Velázquez T, Osorio-Revilla G, Ramos-Monroy OA. The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics. Foods 2024; 13:1157. [PMID: 38672830 PMCID: PMC11049648 DOI: 10.3390/foods13081157] [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: 03/21/2024] [Revised: 04/06/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.
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Affiliation(s)
- Ofelia Gabriela Meza-Márquez
- Departamento de Ingeniería Bioquímica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Zacatenco, Av. Wilfrido Massieu S/N, Esq. Cda. Miguel Stampa, Col. Unidad Profesional Adolfo López Mateos, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México C.P. 07738, Mexico; (A.R.R.-H.); (G.O.-R.); (O.A.R.-M.)
| | - Andrés Ricardo Rodríguez-Híjar
- Departamento de Ingeniería Bioquímica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Zacatenco, Av. Wilfrido Massieu S/N, Esq. Cda. Miguel Stampa, Col. Unidad Profesional Adolfo López Mateos, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México C.P. 07738, Mexico; (A.R.R.-H.); (G.O.-R.); (O.A.R.-M.)
| | - Tzayhri Gallardo-Velázquez
- Departamento de Biofísica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Santo Tomás, Prolongación de Carpio y Plan de Ayala S/N, Col. Santo Tomás, Alcaldía Miguel Hidalgo, Ciudad de México C.P. 11340, Mexico;
| | - Guillermo Osorio-Revilla
- Departamento de Ingeniería Bioquímica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Zacatenco, Av. Wilfrido Massieu S/N, Esq. Cda. Miguel Stampa, Col. Unidad Profesional Adolfo López Mateos, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México C.P. 07738, Mexico; (A.R.R.-H.); (G.O.-R.); (O.A.R.-M.)
| | - Oswaldo Arturo Ramos-Monroy
- Departamento de Ingeniería Bioquímica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Zacatenco, Av. Wilfrido Massieu S/N, Esq. Cda. Miguel Stampa, Col. Unidad Profesional Adolfo López Mateos, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México C.P. 07738, Mexico; (A.R.R.-H.); (G.O.-R.); (O.A.R.-M.)
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Salehi A, Shariatifar N, Jahed-Khaniki G, Sadighara P, Hozoori M. Simple and rapid determination of tartrazine in fake saffron using the metal organic framework (Fe SA MOF@CNF) by HPLC/PDA. Sci Rep 2024; 14:8217. [PMID: 38589481 PMCID: PMC11002026 DOI: 10.1038/s41598-024-58825-x] [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: 12/22/2023] [Accepted: 04/03/2024] [Indexed: 04/10/2024] Open
Abstract
The present study of a novel metal-organic framework containing Fe single atoms doped on electrospun carbon nanofibers (Fe SA-MOF@CNF) based on dispersive micro solid phase extraction (D-μ-SPE) using HPLC-PDA for detection tartrazine in fake saffron samples was designed. The Fe SA-MOF@CNF sorbent was extensively characterized through various techniques including N2 adsorption-desorption isotherms, X-ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The specific area of surface of the sorbent was 577.384 m2/g. The study variables were optimized via the central composite design (CCD), which included a sorbent mass of 15 mg, a contact time of 6 min, a pH of 7.56, and a tartrazine concentration of 300 ng/ml. Under the optimum condition, the calibration curve of this method was linear in the range of 5-1000 ng/mL, with a correlation coefficient of 0.992. The LOD and LOQ values were ranged 0.38-0.74 and 1.34-2.42 ng/ml, respectively. This approach revealed significant improvements, including high extraction recovery (98.64), recovery rates (98.43-102.72%), and accuracy (RSDs < 0.75 to 3.6%). the enrichment factors were obtained in the range of 80.6-86.4 with preconcentration factor of 22.3. Consequently, the D-μ-SPE method based on synthesized Fe SA-MOF@CNF could be recommended as a sustainable sorbent for detecting tartrazine in saffron samples.
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Affiliation(s)
- Ali Salehi
- Department of Environmental Health, Food Safety Division, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Saffron Institute University of Torbat Heydarieh, Torbat Heydarieh, Iran
| | - Nabi Shariatifar
- Department of Environmental Health, Food Safety Division, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Drug Design and Development Research Center, The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran.
| | - Gholamreza Jahed-Khaniki
- Department of Environmental Health, Food Safety Division, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Parisa Sadighara
- Department of Environmental Health, Food Safety Division, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hozoori
- Department of Family and Community Medicine, Qom University of Medical Sciences, Qom, Iran
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Ma H, Guo J, Liu G, Xie D, Zhang B, Li X, Zhang Q, Cao Q, Li X, Ma F, Li Y, Wan G, Li Y, Wu D, Ma P, Guo M, Yin J. Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review. Crit Rev Food Sci Nutr 2024:1-23. [PMID: 38523442 DOI: 10.1080/10408398.2024.2329956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.
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Affiliation(s)
- Haiyang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Jiajun Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guishan Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Delang Xie
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Bingbing Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaojun Li
- School of Electronic and Electrical Engineering, Ningxia University, Yinchuan, China
| | - Qian Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Qingqing Cao
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaoxue Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Fang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Yang Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guoling Wan
- College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Yan Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Di Wu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Ping Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Mei Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Junjie Yin
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
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Wu B, Tang W, Zhou J, Jia H, Shen H, Qi Z. Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades. Heliyon 2024; 10:e27732. [PMID: 38486786 PMCID: PMC10938135 DOI: 10.1016/j.heliyon.2024.e27732] [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: 08/06/2023] [Revised: 12/10/2023] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Mee tea, one of the major types of green tea in China, is often used for export because of its elegant appearance, high fragrance and strong taste. However, the quality of tea differs greatly due to the difference in raw material selection and production technology level. In order to accurately and quickly differentiate different grades of Mee tea, fuzzy fast pseudoinverse linear discriminant analysis (FFPLDA) was proposed based on fast pseudoinverse linear discriminant analysis (FPLDA) for extracting discriminant information from near-infrared (NIR) spectra. Firstly, NIR spectra of Mee tea samples were acquired, and then they were preprocessed by multiplicative scatter correlation (MSC). Secondly, the compression of data was achieved by principal component analysis (PCA). Thirdly, linear discriminant analysis (LDA), FPLDA, FFPLDA and fuzzy Foley-Sammon transformation (FFST) were respectively performed to retrieve discriminant information from NIR data. Finally, the K-nearest neighbor (KNN) was utilized to classify Mee tea grades. In this study, experimental results showed that the accuracy of FFPLDA was higher than that of LDA, FFST and FPLDA. Therefore, NIR spectroscopy coupled with FFPLDA and KNN has a good effect in discrimination of Mee tea grades and also a great application potential.
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Affiliation(s)
- Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China
| | - Wenbo Tang
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Jin Zhou
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Hongwen Jia
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China
| | - Hualei Shen
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Zuxuan Qi
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
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Alagappan S, Ma S, Nastasi JR, Hoffman LC, Cozzolino D. Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix. SENSORS (BASEL, SWITZERLAND) 2024; 24:924. [PMID: 38339641 PMCID: PMC10857114 DOI: 10.3390/s24030924] [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: 01/03/2024] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.
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Affiliation(s)
- Shanmugam Alagappan
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia; (S.A.); (S.M.); (J.R.N.)
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia;
| | - Siyu Ma
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia; (S.A.); (S.M.); (J.R.N.)
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia;
| | - Joseph Robert Nastasi
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia; (S.A.); (S.M.); (J.R.N.)
| | - Louwrens C. Hoffman
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia;
| | - Daniel Cozzolino
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia Campus, Brisbane, QLD 4072, Australia;
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Ma X, Guo X, Lin B, Wang H, Dong Q, Huang S, Li L, Zang H. Detection and analysis of hyaluronic acid raw materials from different sources by NIR and aquaphotomics. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:537-550. [PMID: 38180114 DOI: 10.1039/d3ay01963b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Hyaluronic acid (HA), a polysaccharide, is widely used for its essential physiological functions. Although the structures of low molecular weight HA produced by both acid and enzyme degradation methods are extremely similar, there are still differences due to the different degradation principles. There is currently no clear way to distinguish between HA prepared by acidolysis and enzymatic hydrolysis. Based on near-infrared (NIR) spectroscopy and aquaphotomics technology, a method for distinguishing HA raw materials and their mixtures from different sources was proposed, and HA with different mixed ratios was accurately quantified. First, NIR spectra of the HA samples were collected. The spectra were then preprocessed to improve the spectral resolution. Spectral information was extracted based on wavelet transform and principal component analysis, resulting in a final selection of 12 characteristic wavelengths containing classification information. The discriminative and quantitative models were then constructed using the 12 wavelengths. The discriminative model achieved a 100% identification rate for HA from different sources. The correlation coefficient of calibration (Rc), validation (Rp), external test (Rt), root mean square error of cross validation (RMSECV), calibration (RMSEC), validation (RMSEP), and external test (RMSET) of the mixed proportion quantitative model were 0.9876, 0.9876, 0.9898, 0.0546, 0.0433, 0.0440, and 0.0347, respectively. In this study, the problem of structural similarity and non-identifiability of HA produced by acidolysis and enzymatic hydrolysis was addressed, and quality monitoring of HA feedstock in HA circulating links was achieved. This is the first time to achieve accurate quantification of solid mixtures using the aquaphotomics method.
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Affiliation(s)
- Xiaobo Ma
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Xueping Guo
- Bloomage Biotechnol Corp Ltd, Jinan 250012, PR China
| | - Boran Lin
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Haowei Wang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Qin Dong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Siling Huang
- Bloomage Biotechnol Corp Ltd, Jinan 250012, PR China
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, 250012, Shandong, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, 250012, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, 250012, Shandong, China
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11
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Neng J, Wang J, Wang Y, Zhang Y, Chen P. Trace analysis of food by surface-enhanced Raman spectroscopy combined with molecular imprinting technology: Principle, application, challenges, and prospects. Food Chem 2023; 429:136883. [PMID: 37506657 DOI: 10.1016/j.foodchem.2023.136883] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a rapid detection method with high sensitivity and simple pretreatment, but can be affected by interference from matrix components. By incorporating molecularly imprinted polymers (MIPs) that recognize specific targets, MIP-SERS sensors effectively overcome the interference of complex matrices and offer improved stability and sensitivity. This review provides a comprehensive understanding of the applications of MIP-SERS sensors for the detection of trace toxic substances in food. The underlying mechanism and development of SERS technology and the principle and classification of MIPs technology are discussed. Furthermore, the types of MIP-SERS sensors are introduced, with their advantages and disadvantages systematically illustrated. Recent advances in MIP-SERS technology for the detection of mycotoxins, additives, prohibited dyes, pesticides, veterinary drug residues, and other hazardous substances in food are highlighted. Finally, this review discusses the challenges associated with MIP-SERS technology and proposes future development prospects.
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Affiliation(s)
- Jing Neng
- College of Food Science and Engineering, Zhejiang University of Technology, Deqing 313299, China.
| | - Jiana Wang
- College of Food Science and Engineering, Zhejiang University of Technology, Deqing 313299, China.
| | - Yan Wang
- College of Food Science and Engineering, Zhejiang University of Technology, Deqing 313299, China.
| | - Yilong Zhang
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310027, China.
| | - Peng Chen
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310027, China.
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12
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Haji A, Desalegn K, Hassen H. Selected food items adulteration, their impacts on public health, and detection methods: A review. Food Sci Nutr 2023; 11:7534-7545. [PMID: 38107123 PMCID: PMC10724644 DOI: 10.1002/fsn3.3732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 12/19/2023] Open
Abstract
Every living thing requires food to survive. Clean, fresh, and healthy foods are important to human health. Today, food is affected by various counterfeits. Adulteration of food is the intentional deterioration of the quality of food offered for sale by either the addition or substitution of an inferior substance or by the omission of a valuable ingredient. Economically motivated adulteration is the intentional adulteration of food for financial gain, and has enormous public health implications, making it an important issue in food science. Almost every food, including milk and dairy products, fats and oils, fruits and vegetables, grain foods, coffee, tea, honey, etc., is susceptible to adulteration. It is difficult to find food that is free from adulteration. Consumption of adulterated food contributes to numerous diseases in society, ranging from mild to life threatening. Therefore, detection of adulteration in food is essential to ensure the safety of the food we consume. To provide consumers with food that is free of adulterants, various detection methods such as physical, chemical, biochemical, and molecular techniques are used to identify adulterants in food. This review aims to provide up-to-date information on food adulteration, its impact on health, and the analytical techniques used to detect adulteration in food.
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Affiliation(s)
- Abdulmajid Haji
- Department of Post‐Harvest ManagementCollege of Agriculture and Veterinary Medicine, Jimma UniversityJimmaEthiopia
| | - Kasahun Desalegn
- Department of Post‐Harvest ManagementCollege of Agriculture and Veterinary Medicine, Jimma UniversityJimmaEthiopia
| | - Hayat Hassen
- Department of Post‐Harvest ManagementCollege of Agriculture and Veterinary Medicine, Jimma UniversityJimmaEthiopia
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13
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Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I, Cho BK. Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1240361. [PMID: 37662162 PMCID: PMC10471194 DOI: 10.3389/fpls.2023.1240361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
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Affiliation(s)
- Umuhoza Aline
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | - Tanima Bhattacharya
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
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14
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Kadac-Czapska K, Trzebiatowska PJ, Knez E, Zaleska-Medynska A, Grembecka M. Microplastics in food - a critical approach to definition, sample preparation, and characterisation. Food Chem 2023; 418:135985. [PMID: 36989641 DOI: 10.1016/j.foodchem.2023.135985] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 03/29/2023]
Abstract
The ubiquity of microplastics (MPs) is a more and more frequently brought up topic. The fact that such particles are present in food raises particular concern. Information regarding the described contamination is incoherent and difficult to interpret. Problems appear already at the level of the definition of MPs. This paper will discuss ways of explaining the concept of MPs and methods used for its analysis. Isolation of characterised particles is usually performed using filtration, etching and/or density separation. Spectroscopic techniques are commonly applied for analysis, whereas visual evaluation of the particles is possible thanks to microscopic analysis. Basic information about the sample can be obtained by the combination of Fourier Transform Infrared spectroscopy or Raman spectroscopy and microscopy or using the thermal method combined with spectroscopy or chromatography. The unification of the research methodology will allow a credible assessment of the influence of this pollution coming from food on health.
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15
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Kharbach M, Alaoui Mansouri M, Taabouz M, Yu H. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches. Foods 2023; 12:2753. [PMID: 37509845 PMCID: PMC10379817 DOI: 10.3390/foods12142753] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/30/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
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Affiliation(s)
- Mourad Kharbach
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
- Department of Computer Sciences, University of Helsinki, 00560 Helsinki, Finland
| | - Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, 90014 Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Mohammed Taabouz
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Rabat BP 6203, Morocco
| | - Huiwen Yu
- Shenzhen Hospital, Southern Medical University, Shenzhen 518005, China
- Chemometrics group, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
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16
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Xu N, Xu H, Chen S, Hu H, Xu Z, Feng H, Li Q, Jiang T, Chen Y. Snapshot hyperspectral imaging based on equalization designed DOE. OPTICS EXPRESS 2023; 31:20489-20504. [PMID: 37381443 DOI: 10.1364/oe.493498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/22/2023] [Indexed: 06/30/2023]
Abstract
Hyperspectral imaging attempts to determine distinctive information in spatial and spectral domain of a target. Over the past few years, hyperspectral imaging systems have developed towards lighter and faster. In phase-coded hyperspectral imaging systems, a better coding aperture design can improve the spectral accuracy relatively. Using wave optics, we post an equalization designed phase-coded aperture to achieve desired equalization point spread functions (PSFs) which provides richer features for subsequent image reconstruction. During the reconstruction of images, our raised hyperspectral reconstruction network, CAFormer, achieves better results than the state-of-the-art networks with less computation by substituting self-attention with channel-attention. Our work revolves around the equalization design of the phase-coded aperture and optimizes the imaging process from three aspects: hardware design, reconstruction algorithm, and PSF calibration. Our work is putting snapshot compact hyperspectral technology closer to a practical application.
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17
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Damdam AN, Ozay LO, Ozcan CK, Alzahrani A, Helabi R, Salama KN. IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection. Foods 2023; 12:foods12112227. [PMID: 37297471 DOI: 10.3390/foods12112227] [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: 04/18/2023] [Revised: 05/23/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Food spoilage is a major concern in the food industry, especially for highly perishable foods such as beef. In this paper, we present a versatile Internet of Things (IoT)-enabled electronic nose system to monitor food quality by evaluating the concentrations of volatile organic compounds (VOCs). The IoT system consists mainly of an electronic nose, temperature/humidity sensors, and an ESP32-S3 microcontroller to send the sensors' data to the server. The electronic nose consists of a carbon dioxide gas sensor, an ammonia gas sensor, and an ethylene gas sensor. This paper's primary focus is to use the system for identifying beef spoilage. Hence, the system performance was examined on four beef samples stored at different temperatures: two at 4 °C and two at 21 °C. Microbial population quantifications of aerobic bacteria, Lactic Acid Bacteria (LAB), and Pseudomonas spp., in addition to pH measurements, were conducted to evaluate the beef quality during a period of 7 days to identify the VOCs concentrations that are associated with raw beef spoilage. The spoilage concentrations that were identified using the carbon dioxide, ammonia, and ethylene sensors were 552 ppm-4751 ppm, 6 ppm-8 ppm, and 18.4 ppm-21.1 ppm, respectively, as determined using a 500 mL gas sensing chamber. Statistical analysis was conducted to correlate the bacterial growth with the VOCs production, where it was found that aerobic bacteria and Pseudomonas spp. are responsible for most of the VOCs production in raw beef.
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Affiliation(s)
- Asrar Nabil Damdam
- Sensors Lab, Advanced Membranes and Porous Materials Center, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Levent Osman Ozay
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Cagri Kaan Ozcan
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Ashwaq Alzahrani
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Raghad Helabi
- Uvera Lab, Research and Development Department, Uvera Inc., Thuwal 23955-6900, Saudi Arabia
| | - Kahled Nabil Salama
- Sensors Lab, Advanced Membranes and Porous Materials Center, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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18
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Huo J, Zhang M, Wang D, S Mujumdar A, Bhandari B, Zhang L. New preservation and detection technologies for edible mushrooms: A review. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3230-3248. [PMID: 36700618 DOI: 10.1002/jsfa.12472] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/11/2022] [Accepted: 01/26/2023] [Indexed: 06/17/2023]
Abstract
Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Jingyi Huo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, China
| | - Dayuan Wang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald College, McGill University, Quebec, Canada
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia
| | - Lujun Zhang
- R&D Center, Shandong Qihe Biotechnology Co., Ltd, Zibo, China
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19
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Cebi N, Bekiroglu H, Erarslan A, Rodriguez-Saona L. Rapid Sensing: Hand-Held and Portable FTIR Applications for On-Site Food Quality Control from Farm to Fork. Molecules 2023; 28:molecules28093727. [PMID: 37175136 PMCID: PMC10179800 DOI: 10.3390/molecules28093727] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/11/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Today, one of the world's biggest problems is the assurance of food integrity from farm to fork. Economically motivated food adulteration and food authenticity problems are increasing daily with considerable health and economic effects. Early detection and prevention of food integrity-related problems could be provided by the application of effective on-site food analysis technologies. FTIR spectroscopy coupled with chemometrics can be used for the rapid quality control of a wide variety of food products with fast, high-throughput, accurate and nondestructive analysis advantages. In particular, hand-held and portable FTIR instruments have the potential to surveil food quality and food safety in various critical segments of the food supply chain. In this review, we explore the abilities of hand-held and portable FTIR spectrometers combined with multivariate statistics to conduct a quality evaluation of various food products in terms of food adulteration and authenticity issues. An examination of the literature showed that comparable results were obtained based on detection limits, correlation coefficient (R2) values, standard error values and discrimination power by using both portable/hand-held FTIR spectrometers and benchtop FTIR spectrometers. In conclusion, this review highlights the potential usefulness of portable and hand-held FTIR spectrometers combined with chemometrics for maintaining the food quality through the presentation of various applications that may shed light for on-site food control at any point of the food supply chain.
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Affiliation(s)
- Nur Cebi
- Food Engineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey
| | - Hatice Bekiroglu
- Food Engineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey
| | - Azime Erarslan
- Bioengineering Department, Chemical-Metallurgical Faculty, Yıldız Technical University, 34210 Istanbul, Turkey
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, 100 Parker Food Science and Technology 2015 Fyffe Road, Columbus, OH 43210, USA
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20
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Wu Q, Oliveira MM, Achata EM, Kamruzzaman M. Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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21
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Chen Y, Wu H, Liu Y, Wang Y, Lu C, Li T, Wei Y, Ning J. Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3093-3101. [PMID: 36418909 DOI: 10.1002/jsfa.12350] [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: 08/09/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Yuyu Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Huiting Wu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Chengye Lu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yuming Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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22
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Chen R, Liu F, Zhang C, Wang W, Yang R, Zhao Y, Peng J, Kong W, Huang J. Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1128300. [PMID: 37025139 PMCID: PMC10072231 DOI: 10.3389/fpls.2023.1128300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
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Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| | - Jing Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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23
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Liu Y, Zuo M, Wang K, Jiao L, Yang G, Yang C, Zhao X, Dong D. Rapid identification of artificial fragrant rice based on volatile organic compounds: From PTR-MS to FTIR. Food Chem 2023; 418:135952. [PMID: 36940544 DOI: 10.1016/j.foodchem.2023.135952] [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: 01/10/2023] [Revised: 02/28/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
The volatile organic compounds (VOCs) released from foods can reflect their internal properties. Artificial fragrant rice (AFR) is a fraudulent food product in which the flavor of low-quality rice is artificially enhanced by addition of essence. In this study, proton-transfer reaction mass spectrometry, long optical path gas phase FTIR spectroscopy and fiber optic evanescent wave were used to analyze the characteristic mass-charge ratios signal and infrared fingerprint signal of four essence which may be used to make AFR, and the prepared AFR samples with different essence levels (0.001 %-0.3 %) were used to verify the detection performance of the detection methods. The results show that the three detection methods effectively identified AFR containing the minimum recommended dose of essence (≥0.1 %, w/w). The above detection methods can provide detection results in real time without complex sample pretreatment and provide options as rapid screening methods for food regulatory authorities to identify AFR.
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Affiliation(s)
- Yachao Liu
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Min Zuo
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
| | - Ke Wang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Leizi Jiao
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Guiyan Yang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Chongshan Yang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xiande Zhao
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Daming Dong
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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Czaja TP, Vickovic D, Pedersen SJ, Hougaard AB, Ahrné L. Spectroscopic characterisation of acidified milk powders. Int Dairy J 2023. [DOI: 10.1016/j.idairyj.2023.105664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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25
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Castro RC, Ribeiro DSM, Santos JLM, Nunes C, Reis S, N M J Páscoa R. Chemometric-assisted surface-enhanced Raman spectroscopy for metformin determination using gold nanoparticles as substrate. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:122118. [PMID: 36401918 DOI: 10.1016/j.saa.2022.122118] [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: 09/06/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
A fast, simple, and reliable method for determination of metformin was developed by coupling surface-enhanced Raman spectroscopy (SERS) with chemometric methods. This relayed on the utilization of a portable Raman spectrometer and of citrate stabilized gold nanoparticles (AuNPs) as substrate, to carry out the measurement of SERS scattering signals, thus assuring improved sensitivity. The obtained datasets were analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Upon optimization of the PLS model, in terms of latent variables, spectral region and pre-processing techniques, RMSECV and R2CV values of 0.42 mg/L and 0.94, respectively, were obtained. The optimized PLS regression model was further validated with the projection of commercial pharmaceutical samples, providing good results in terms of R2P (0.97), RE (4.54 %) and analytical sensitivity (2.13 mg/L).
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Affiliation(s)
- Rafael C Castro
- LAQV, REQUIMTE, Department of Chemical Sciences, Laboratory of Applied Chemistry, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira n ° 228, 4050-313 Porto, Portugal
| | - David S M Ribeiro
- LAQV, REQUIMTE, Department of Chemical Sciences, Laboratory of Applied Chemistry, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira n ° 228, 4050-313 Porto, Portugal.
| | - João L M Santos
- LAQV, REQUIMTE, Department of Chemical Sciences, Laboratory of Applied Chemistry, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira n ° 228, 4050-313 Porto, Portugal.
| | - Cláudia Nunes
- LAQV, REQUIMTE, Department of Chemical Sciences, Laboratory of Applied Chemistry, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira n ° 228, 4050-313 Porto, Portugal
| | - Salette Reis
- LAQV, REQUIMTE, Department of Chemical Sciences, Laboratory of Applied Chemistry, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira n ° 228, 4050-313 Porto, Portugal
| | - Ricardo N M J Páscoa
- LAQV, REQUIMTE, Department of Chemical Sciences, Laboratory of Applied Chemistry, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira n ° 228, 4050-313 Porto, Portugal.
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El Maouardi M, Kharbach M, Cherrah Y, De Braekeleer K, Bouklouze A, Vander Heyden Y. Quality Control and Authentication of Argan Oils: Application of Advanced Analytical Techniques. Molecules 2023; 28:molecules28041818. [PMID: 36838806 PMCID: PMC9966767 DOI: 10.3390/molecules28041818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
In addition to the nutritional and therapeutic benefits, Argan oil is praised for its unique bio-ecological and botanic interest. It has been used for centuries to treat cardiovascular issues, diabetes, and skin infections, as well as for its anti-inflammatory and antiproliferative properties. Argan oil is widely commercialized as a result of these characteristics. However, falsifiers deliberately blend Argan oil with cheaper vegetable oils to make economic profits. This reduces the quality and might result in health issues for consumers. Analytical techniques that are rapid, precise, and accurate are employed to monitor its quality, safety, and authenticity. This review provides a comprehensive overview of studies on the quality assessment of Moroccan Argan oil using both untargeted and targeted approaches. To extract relevant information on quality and adulteration, the analytical data are coupled with chemometric techniques.
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Affiliation(s)
- Meryeme El Maouardi
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat 10100, Morocco
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
| | - Mourad Kharbach
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Yahya Cherrah
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat 10100, Morocco
| | - Kris De Braekeleer
- Pharmacognosy, Bioanalysis & Drug Discovery Unit, Faculty of Pharmacy, University Libre Brussels, 1050 Brussels, Belgium
| | - Abdelaziz Bouklouze
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat 10100, Morocco
| | - Yvan Vander Heyden
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
- Correspondence:
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Ren X, Wang H, Chen J, Xu W, He Q, Wang H, Zhan F, Chen S, Chen L. Emerging 2D Copper-Based Materials for Energy Storage and Conversion: A Review and Perspective. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2204121. [PMID: 36526607 DOI: 10.1002/smll.202204121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/23/2022] [Indexed: 06/17/2023]
Abstract
2D materials have shown great potential as electrode materials that determine the performance of a range of electrochemical energy technologies. Among these, 2D copper-based materials, such as Cu-O, Cu-S, Cu-Se, Cu-N, and Cu-P, have attracted tremendous research interest, because of the combination of remarkable properties, such as low cost, excellent chemical stability, facile fabrication, and significant electrochemical properties. Herein, the recent advances in the emerging 2D copper-based materials are summarized. A brief summary of the crystal structures and synthetic methods is started, and innovative strategies for improving electrochemical performances of 2D copper-based materials are described in detail through defect engineering, heterostructure construction, and surface functionalization. Furthermore, their state-of-the-art applications in electrochemical energy storage including supercapacitors (SCs), alkali (Li, Na, and K)-ion batteries, multivalent metal (Mg and Al)-ion batteries, and hybrid Mg/Li-ion batteries are described. In addition, the electrocatalysis applications of 2D copper-based materials in metal-air batteries, water-splitting, and CO2 reduction reaction (CO2 RR) are also discussed. This review also discusses the charge storage mechanisms of 2D copper-based materials by various advanced characterization techniques. The review with a perspective of the current challenges and research outlook of such 2D copper-based materials for high-performance energy storage and conversion applications is concluded.
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Affiliation(s)
- Xuehua Ren
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Haoyu Wang
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Jun Chen
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Weili Xu
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Qingqing He
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Huayu Wang
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Feiyang Zhan
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Shaowei Chen
- Department of Chemistry and Biochemistry, University of California, 1156 High Street, Santa Cruz, CA, 95060, USA
| | - Lingyun Chen
- Department of Applied Chemistry, School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, 401331, P. R. China
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Xia Q, Huang Z, Zhang P, Bu H, Bao L, Chen D. Nontargeted detection and recognition of adulterants in milk powder using Raman imaging and neural networks. Analyst 2023; 148:412-421. [PMID: 36541331 DOI: 10.1039/d2an01540d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Raman imaging technology combined with targeted chemometrics can play a vital role in the rapid detection of milk powder adulteration, which threatens the lives of infants and other people. However, these methods always suffer from a narrow detection range. Nontargeted methods show a broader detection range but cannot recognize adulterants. Here, a novel nontargeted chemometric method, named as the adversarial discrimination neural network (ADNN), is proposed to detect and recognize adulterants simultaneously. The method comprises building a tight boundary in the feature space of Raman images to discriminate milk powder samples from the majority of adulterated cases. Then a first-order partial derivative of the ADNN is calculated to recognize different adulterants through a local approximation strategy. A validation set containing samples adulterated with various adulterants at concentrations ranging from 0.3% to 1.5% w/w was provided to challenge the proposed method. The validated detection accuracy of the proposed method for authentic and adulterated samples was 99.9% and 99.7% and the adulterants were recognized correctly. The ADNN-Raman represents a novel nontargeted and end-to-end tool for detecting and recognizing adulterants in milk powder simultaneously, providing new insights into nontargeted chemometric analysis.
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Affiliation(s)
- Qi Xia
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Zhixuan Huang
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Pengfei Zhang
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Hanping Bu
- Nestlé Food Safety Institute of China, Nestlé R & D (China) Ltd, Beijing 100016, China
| | - Lei Bao
- Nestlé Food Safety Institute of China, Nestlé R & D (China) Ltd, Beijing 100016, China
| | - Da Chen
- Tianjin Engineering Research Center of Civil Aviation Energy Environment and Green Development, Civil Aviation University of China, Tianjin, 300300, China.
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Ordoudi SA, Strani L, Cocchi M. Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28010337. [PMID: 36615530 PMCID: PMC9822006 DOI: 10.3390/molecules28010337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/23/2022] [Accepted: 12/23/2022] [Indexed: 01/04/2023]
Abstract
Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.
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Affiliation(s)
- Stella A. Ordoudi
- Laboratory of Food Chemistry and Technology, School of Chemistry, Aristotle University of Thessaloniki (AUTh), GR-54124 Thessaloniki, Greece
- Correspondence:
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia (UNIMORE), Via Campi 103, 41125 Modena, Italy
| | - Marina Cocchi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia (UNIMORE), Via Campi 103, 41125 Modena, Italy
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Xu Y, Zhang J, Wang Y. Recent trends of multi-source and non-destructive information for quality authentication of herbs and spices. Food Chem 2023; 398:133939. [DOI: 10.1016/j.foodchem.2022.133939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/19/2022] [Accepted: 08/10/2022] [Indexed: 11/15/2022]
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Detection of sodium hydrosulfite adulteration in wheat flour by FT-MIR spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01763-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Jiang H, Yuan W, Ru Y, Chen Q, Wang J, Zhou H. Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 282:121689. [PMID: 35914356 DOI: 10.1016/j.saa.2022.121689] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 07/14/2022] [Accepted: 07/26/2022] [Indexed: 05/10/2023]
Abstract
Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.
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Affiliation(s)
- Hongzhe Jiang
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
| | - Weidong Yuan
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Yu Ru
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Qing Chen
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Jinpeng Wang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hongping Zhou
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
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Rady A, Watson N. Detection and quantification of peanut contamination in garlic powder using NIR sensors and machine learning. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Jahani R, van Ruth S, Weesepoel Y, Alewijn M, Kobarfard F, Faizi M, Shojaee AliAbadi MH, Mahboubi A, Nasiri A, Yazdanpanah H. Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2022; 21:e128372. [PMID: 36942059 PMCID: PMC10024328 DOI: 10.5812/ijpr-128372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/21/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022]
Abstract
Background Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains. Methods This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study. Results Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and 1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively. Conclusions As a proof of concept, NIRS technology coupled with appropriate multivariate classification models enables fast detection of citric acid-adulterated lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.
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Affiliation(s)
- Reza Jahani
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saskia van Ruth
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
- Food Quality and Design Group, Wageningen University and Research, Wageningen, The Netherlands
- School of Biological Sciences, Queen’s University Belfast, Belfast, Northern Ireland, UK
| | - Yannick Weesepoel
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Martin Alewijn
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Farzad Kobarfard
- Department of Medicinal Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Faizi
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Arash Mahboubi
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Pharmaceutics, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Nasiri
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Yazdanpanah
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Corresponding Author: Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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35
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Edible vegetable oils from oil crops: Preparation, refining, authenticity identification and application. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Ma P, Zhang Z, Jia X, Peng X, Zhang Z, Tarwa K, Wei CI, Liu F, Wang Q. Neural network in food analytics. Crit Rev Food Sci Nutr 2022; 64:4059-4077. [PMID: 36322538 DOI: 10.1080/10408398.2022.2139217] [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] [Indexed: 06/16/2023]
Abstract
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
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Affiliation(s)
- Peihua Ma
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Zhikun Zhang
- CISPA Helmholtz Center for Information Security, Saarbrucken, Germany
| | - Xiaoxue Jia
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Xiaoke Peng
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Zhi Zhang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Kevin Tarwa
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Cheng-I Wei
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Fuguo Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Qin Wang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
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Martins MS, Nascimento MH, Barbosa LL, Campos LC, Singh MN, Martin FL, Romão W, Filgueiras PR, Barauna VG. Detection and quantification using ATR-FTIR spectroscopy of whey protein concentrate adulteration with wheat flour. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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38
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Jin G, Xu Y, Cui C, Zhu Y, Zong J, Cai H, Ning J, Wei C, Hou R. Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:6123-6130. [PMID: 35474316 DOI: 10.1002/jsfa.11964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/24/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy. RESULTS First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Yifan Xu
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Chuanjian Cui
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Yuanyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Jianfa Zong
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Huimei Cai
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Chaoling Wei
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Ruyan Hou
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
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He Y, Zeng W, Zhao Y, Zhu X, Wan H, Zhang M, Li Z. Rapid detection of adulteration of goat milk and goat infant formulas using near-infrared spectroscopy fingerprints. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2022.105536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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40
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Discrimination of raw and sulfur-fumigated ginseng based on Fourier transform infrared spectroscopy coupled with chemometrics. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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41
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Santos YJS, Malegori C, Colnago LA, Vanin FM. Application on infrared spectroscopy for the analysis of total phenolic compounds in fruits. Crit Rev Food Sci Nutr 2022; 64:2906-2916. [PMID: 36178354 DOI: 10.1080/10408398.2022.2128036] [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] [Indexed: 11/03/2022]
Abstract
Recent studies have demonstrated the metabolic benefits of phenolic compounds on human health. However, traditional analytical methods used for quantification of total phenolic compounds are time-consuming, laborious, require a high volume of reagents, mostly toxic substances, and involve several steps that can result in systematic and instrumental errors. Spectroscopic techniques have been used as alternatives to these methods for the determination of bioactive compounds directly in the food matrix by minimal sample preparation, without using toxic reagents. Therefore, this overview presents the advantages of nondestructive methods focusing on infrared spectroscopy (IR), for the quantification of total phenolic compounds in fruits. In addition, the main difficulties in applying these spectroscopic techniques are presented, as well as a comparison between the quantification of total phenolic compounds by traditional and IR methods. This review concludes by focusing on model building, highlighting that IR data are mainly processed using the partial least-squares (PLS) regression method to predict total phenolic content. The development of portable and inexpensive IR instruments, combined with multivariate data processing, could give to the consumers a straightforward technology to evaluate the total phenolic content of fruits prior to purchase.
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Affiliation(s)
- Y J S Santos
- Food Engineering Department, University of São Paulo, Faculty of Animal Science and Food Engineering (USP/FZEA), Pirassununga, SP, Brazil
| | - C Malegori
- Department of Pharmacy (DIFAR), University of Genova, Genova, Italy
| | - L A Colnago
- Brazilian Corporation for Agricultural Research - Embrapa Instrumentation, São Carlos, SP, Brazil
| | - F M Vanin
- Food Engineering Department, University of São Paulo, Faculty of Animal Science and Food Engineering (USP/FZEA), Pirassununga, SP, Brazil
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42
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Species Identification of Bovine Bone Marrow from Nonbovine Products Using Multiplex PCR Technology. J FOOD QUALITY 2022. [DOI: 10.1155/2022/3905536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Bovine bone marrow is traditionally regarded as a highly nutritious food that has been widely used as a medicinal and health food for several decades in China. A large number of adulterated and counterfeit bone marrows from pigs and donkeys have been used in place of bovine bone marrow in commercial products, which are almost identical morphologically between species. Therefore, we explored the feasibility of multiplex PCR technology to differentiate bovine bone marrows from different domestic animals. Three pairs of specific primers for bovine, pig, and donkey were designed according to the conserved sequence in mitochondrial cytochrome b. A modified method was used to extract the genomic DNA from common domestic animals’ bone marrows. The optimal reaction conditions for triple PCR were optimized. A three-fold PCR detection assay was successfully established to identify three species of bovine, pig, and donkey. Three primers have good specificity and high sensitivity. Additionally, the assay sensitivity test confirmed that the extracted DNA concentration was the lowest in bovine bone marrow at 10°pg/μL. The assay also showed 100% specificity. Rapid authentication of bovine bone marrow and differentiation from nonbovine products can be achieved using an improved SDS alkali denaturation method and species-specific PCR assay. Both species-specific PCR methods described in this study can be potentially applied for the quality evaluation of functional food and drug resources.
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Abstract
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
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Shao Y, Wang Y, Zhu D, Xiong X, Tian Z, Balakin AV, Shkurinov AP, Xu D, Wu Y, Peng Y, Zhu Y. Measuring heavy metal ions in water using nature existed microalgae as medium based on terahertz technology. JOURNAL OF HAZARDOUS MATERIALS 2022; 435:129028. [PMID: 35525009 DOI: 10.1016/j.jhazmat.2022.129028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 06/14/2023]
Abstract
Heavy metal pollution in water seriously affects human health. The disadvantages of traditional metal ion detection methods involve long and cumbersome chemical pretreatment in the early stage, and large volume of samples. In this study, microalgae were used as the medium, and terahertz spectroscopy technology was employed to collect the changes of material components in it, so as to deduce the types and concentrations of heavy metal pollution in water. Through the partial least square(PLS), we establish the prediction model of heavy metal concentration, and the results show that the best detection time for Pb2+ is 6 h and Ni2+ is 18 h. The principal component analysis(PCA) shows that β-carotene is the most affected substance. Afterward we collect five real surface waters in East China and verify that the judgment accuracy of Pb2+ and Ni2+ are 100% and 93.2% respectively. The results indicate that the time is shorter than the traditional pretreatment time from more than 20-6 h, the sample volume is reduced from 50 mL to 10 mL, the detection accuracy is improved from 10 ng/mL to 1 ng/mL. In a word, we provide a new fast and real-time method for biological monitoring of heavy metal pollution in water.
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Affiliation(s)
- Yongni Shao
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China; Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
| | - Yutian Wang
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China
| | - Di Zhu
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China
| | - Xin Xiong
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China
| | - Zhengan Tian
- Shanghai International Travel Healthcare Center, Shanghai Customs District, 200335, China
| | - Alexey V Balakin
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China; Department of Physics and International Laser Center, Lomonosov Moscow State University, Leninskie Gory 1, Moscow 19991, Russia
| | - Alexander P Shkurinov
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China; Department of Physics and International Laser Center, Lomonosov Moscow State University, Leninskie Gory 1, Moscow 19991, Russia
| | - Duo Xu
- Department of Culture, Media & Creative Industries, Faculty of Arts & Humanities, King's College Lonson, WC2R 2LS London, United Kingdom
| | - Yimei Wu
- College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yan Peng
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China; Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China.
| | - Yiming Zhu
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, 516 JunGong Road, Shanghai 200093, China; Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China.
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45
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Sequential decision fusion pipeline for the high-throughput species recognition of medicinal caterpillar fungus by using ATR-FTIR. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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46
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Zhu J, Li H, Rao Z, Ji H. Identification of slightly sprouted wheat kernels using hyperspectral imaging technology and different deep convolutional neural networks. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Bian X, Wu D, Zhang K, Liu P, Shi H, Tan X, Wang Z. Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants. BIOSENSORS 2022; 12:bios12080586. [PMID: 36004982 PMCID: PMC9406014 DOI: 10.3390/bios12080586] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022]
Abstract
The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; (D.W.); (K.Z.); (P.L.); (X.T.); (Z.W.)
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co., Ltd., Binzhou 256500, China;
- Correspondence:
| | - Deyun Wu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; (D.W.); (K.Z.); (P.L.); (X.T.); (Z.W.)
| | - Kui Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; (D.W.); (K.Z.); (P.L.); (X.T.); (Z.W.)
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; (D.W.); (K.Z.); (P.L.); (X.T.); (Z.W.)
| | - Huibing Shi
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co., Ltd., Binzhou 256500, China;
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; (D.W.); (K.Z.); (P.L.); (X.T.); (Z.W.)
| | - Zhigang Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; (D.W.); (K.Z.); (P.L.); (X.T.); (Z.W.)
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Innovative Application of SERS in Food Quality and Safety: A Brief Review of Recent Trends. Foods 2022; 11:foods11142097. [PMID: 35885344 PMCID: PMC9322305 DOI: 10.3390/foods11142097] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 02/06/2023] Open
Abstract
Innovative application of surface-enhanced Raman scattering (SERS) for rapid and nondestructive analyses has been gaining increasing attention for food safety and quality. SERS is based on inelastic scattering enhancement from molecules located near nanostructured metallic surfaces and has many advantages, including ultrasensitive detection and simple protocols. Current SERS-based quality analysis contains composition and structural information that can be used to establish an electronic file of the food samples for subsequent reference and traceability. SERS is a promising technique for the detection of chemical, biological, and harmful metal contaminants, as well as for food poisoning, and allergen identification using label-free or label-based methods, based on metals and semiconductors as substrates. Recognition elements, including immunosensors, aptasensors, or molecularly imprinted polymers, can be linked to SERS tags to specifically identify targeted contaminants and perform authenticity analysis. Herein, we highlight recent studies on SERS-based quality and safety analysis for different foods categories spanning the whole food chain, ‘from farm to table’ and processing, genetically modified food, and novel foods. Moreover, SERS detection is a potential tool that ensures food safety in an easy, rapid, reliable, and nondestructive manner during the COVID-19 pandemic.
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50
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Faith Ndlovu P, Samukelo Magwaza L, Zeray Tesfay S, Ramaesele Mphahlele R. Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review. Food Res Int 2022; 157:111198. [DOI: 10.1016/j.foodres.2022.111198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 03/25/2022] [Accepted: 03/27/2022] [Indexed: 01/17/2023]
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