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Yu K, Wu H, Xiong H, Wang G, Wei X, Liang X, Chen R, Zhang Y, Zhang K, Wang Z. Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association. APPLIED SPECTROSCOPY 2024; 78:605-615. [PMID: 38404185 DOI: 10.1177/00037028241231994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43- ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32- ν1- PO43- ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.
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Affiliation(s)
- Kai Yu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Hao Wu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Hongli Xiong
- Department of Forensic Medicine, Faculty of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Gongji Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Xin Wei
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Xinggong Liang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Run Chen
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | | | - Kai Zhang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, China
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Liang Y, Tian J, Hu X, Huang Y, He K, Xie L, Yang H, Huang D, Zhou Y, Xia Y. Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques. J Food Sci 2024; 89:3540-3553. [PMID: 38720570 DOI: 10.1111/1750-3841.17102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/21/2024] [Accepted: 04/12/2024] [Indexed: 06/14/2024]
Abstract
Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with anR P 2 $\ R_P^2$ of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with anR P 2 $R_P^2$ of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.
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Affiliation(s)
- Yan Liang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - Yuexiang Huang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Kangling He
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Liangliang Xie
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Haili Yang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Dan Huang
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - Yifei Zhou
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Yuanyuan Xia
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
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3
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Li L, Dong S, Cao S, Chen Y, Shen J, Li M, Cui Q, Zhang Y, Huang C, Dai Q, Ning J. E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea. Food Chem X 2024; 21:101124. [PMID: 38298355 PMCID: PMC10828643 DOI: 10.1016/j.fochx.2024.101124] [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: 06/28/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.
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Affiliation(s)
| | | | - Shuci Cao
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Yurong Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Jingfei Shen
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Qingqing Cui
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Ying Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Chuxuan Huang
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Qianying Dai
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Provincial Laboratory, Hefei 230036 Anhui, People's Republic of China
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4
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Benedetto A, Robotti E, Belay MH, Ghignone A, Fabbris A, Goggi E, Cerruti S, Manfredi M, Barberis E, Peletto S, Arillo A, Giaccio N, Masini MA, Brandi J, Cecconi D, Marengo E, Brizio P. Multi-Omics Approaches for Freshness Estimation and Detection of Illicit Conservation Treatments in Sea Bass ( Dicentrarchus Labrax): Data Fusion Applications. Int J Mol Sci 2024; 25:1509. [PMID: 38338789 PMCID: PMC10855268 DOI: 10.3390/ijms25031509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024] Open
Abstract
Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.
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Affiliation(s)
- Alessandro Benedetto
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Masho Hilawie Belay
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
- Department of Chemistry, Mekelle University, Mekelle P.O. Box 231, Ethiopia
| | - Arianna Ghignone
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Alessia Fabbris
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Eleonora Goggi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Simone Cerruti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Marcello Manfredi
- Department of Translational Medicine, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy;
| | - Elettra Barberis
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Simone Peletto
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Alessandra Arillo
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Nunzia Giaccio
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Maria Angela Masini
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Jessica Brandi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Daniela Cecconi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Paola Brizio
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
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5
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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Kang MJ, Kim KR, Kim K, Morrill AG, Jung C, Sun S, Lee DH, Suh JH, Sung J. Metabolomic analysis reveals linkage between chemical composition and sensory quality of different floral honey samples. Food Res Int 2023; 173:113454. [PMID: 37803778 DOI: 10.1016/j.foodres.2023.113454] [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: 04/26/2023] [Revised: 09/05/2023] [Accepted: 09/10/2023] [Indexed: 10/08/2023]
Abstract
Honey has a distinct flavor characterized by various volatiles and non-volatiles from diverse origins. In this study, metabolomics combined with sensory analysis was performed to identify relationships between chemical profile and sensory quality of honey. Targeted metabolomic analysis was conducted to determine volatile and non-volatile profiles of seven different honey. Volatile profile was analyzed using headspace solid-phase microextraction (HS-SPME) coupled to GC - MS. LC - MS/MS, HPLC - UV, and HPLC-RI were employed to analyze flavonoids, organic acids, and sugars, respectively. Authentic standards were utilized for confirmation of metabolites. Sensory evaluation included quantitative descriptive analysis and consumer acceptance test. The results showed that sucrose (sweetness) was responsible for a positive hedonic perception, while organic acids and flavonoids (sourness, astringency, bitterness) negatively affected consumer acceptance. Volatiles with floral notes (e.g. decyl formate) were preferred, but others with off-flavors (e.g. 2-methylbenzofuran) were not preferred by consumers. Flavor familiarity was strongly correlated with the consumer acceptance of honey, indicating that the balance between volatiles and non-volatiles is significant for honey flavor quality. This work demonstrates the role of key flavor compounds in honey quality, and may be applicable to the quality control of honey.
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Affiliation(s)
- Min Jeong Kang
- Department of Food Science and Technology, College of Agricultural and Environmental Sciences, University of Georgia, 100 Cedar Street, Athens, GA 30602, USA
| | - Keup-Rae Kim
- Department of Food Science and Biotechnology, Andong National University, Andong, Republic of Korea; Agriculture Science and Technology Research Institute, Andong National University, Andong, Republic of Korea
| | - Keono Kim
- Department of Food Science and Biotechnology, Andong National University, Andong, Republic of Korea; Agriculture Science and Technology Research Institute, Andong National University, Andong, Republic of Korea
| | - Aria G Morrill
- Department of Food Science and Technology, College of Agricultural and Environmental Sciences, University of Georgia, 100 Cedar Street, Athens, GA 30602, USA
| | - Chuleui Jung
- Department of Plant Medicals, Andong National University, Andong, Republic of Korea; Agriculture Science and Technology Research Institute, Andong National University, Andong, Republic of Korea
| | - Sukjun Sun
- Department of Plant Medicals, Andong National University, Andong, Republic of Korea; Agriculture Science and Technology Research Institute, Andong National University, Andong, Republic of Korea
| | - Dong-Hee Lee
- Industry-Academy Cooperation Foundation, Andong National University, Andong, Republic of Korea
| | - Joon Hyuk Suh
- Department of Food Science and Technology, College of Agricultural and Environmental Sciences, University of Georgia, 100 Cedar Street, Athens, GA 30602, USA.
| | - Jeehye Sung
- Department of Food Science and Biotechnology, Andong National University, Andong, Republic of Korea; Agriculture Science and Technology Research Institute, Andong National University, Andong, Republic of Korea.
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Xu P, Fu L, Xu K, Sun W, Tan Q, Zhang Y, Zha X, Yang R. Investigation into maize seed disease identification based on deep learning and multi-source spectral information fusion techniques. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
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8
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Li X, Cai M, Li M, Wei X, Liu Z, Wang J, Jia K, Han Y. Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109416] [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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9
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Nichani K, Uhlig S, Stoyke M, Kemmlein S, Ulberth F, Haase I, Döring M, Walch SG, Gowik P. Essential terminology and considerations for validation of non-targeted methods. Food Chem X 2022; 17:100538. [PMID: 36845497 PMCID: PMC9943841 DOI: 10.1016/j.fochx.2022.100538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/16/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
Through their suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack." Instead, they exploit all the constituents of the haystack. This new type of analytical method is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing need to be propagated for the benefit of those associated with academic research, commercial development, or official control. This paper addresses frequently asked questions regarding terminology in connection with NTMs. The widespread development and adoption of these methods also necessitate the need to develop innovative approaches for NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap for approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions therefor.
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Affiliation(s)
- Kapil Nichani
- QuoData GmbH, Prellerstr. 14, 01309 Dresden, Germany,Institute of Nutritional Sciences, University of Potsdam, Arthur-Scheunert Allee 114-116, 14558 Nuthetal, Germany,Corresponding authors at: QuoData GmbH, Prellerstr. 14, 01309 Dresden, Germany (K. Nichani).
| | - Steffen Uhlig
- QuoData GmbH, Fabeckstr. 43, 14195 Berlin, Germany,Corresponding authors at: QuoData GmbH, Prellerstr. 14, 01309 Dresden, Germany (K. Nichani).
| | - Manfred Stoyke
- Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Diedersdorfer Weg 1, 12277 Berlin, Germany
| | - Sabine Kemmlein
- Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Diedersdorfer Weg 1, 12277 Berlin, Germany
| | - Franz Ulberth
- European Commission, Joint Research Centre, Retieseweg 111, 2440 Geel, Belgium
| | - Ilka Haase
- Max Rubner-Institut (MRI) - Bundesforschungsinstitut für Ernährung und Lebensmittel, Nationales Referenzzentrum für authentische Lebensmittel, E-C-Baumannstr. 20, 95236 Kulmbach, Germany
| | - Maik Döring
- Max Rubner-Institut (MRI) - Bundesforschungsinstitut für Ernährung und Lebensmittel, Nationales Referenzzentrum für authentische Lebensmittel, E-C-Baumannstr. 20, 95236 Kulmbach, Germany
| | - Stephan G Walch
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weißenburger Str. 3, 76187 Karlsruhe, Germany
| | - Petra Gowik
- Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Diedersdorfer Weg 1, 12277 Berlin, Germany
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Volatile fingerprinting by solid-phase microextraction mass spectrometry for rapid classification of honey botanical source. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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11
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Cui ZY, Liu CL, Li DD, Wang YZ, Xu FR. Anticoagulant activity analysis and origin identification of Panax notoginseng using HPLC and ATR-FTIR spectroscopy. PHYTOCHEMICAL ANALYSIS : PCA 2022; 33:971-981. [PMID: 35715878 DOI: 10.1002/pca.3152] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
INTRODUCTION Panax notoginseng is one of the traditional precious and bulk-traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported. OBJECTIVES We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method. MATERIALS AND METHODS Pharmacological experiments, HPLC, and ATR-FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted. RESULTS The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside Rg1 had the greatest influence on the anticoagulant effect. The results of spectroscopy combined with chemometrics show that the variable selection method could extract a small number of variables containing valid information and improve the performance of the model. The variable importance in projection has the best ability to identify the origins of P. notoginseng; the accuracy of the training set and the test set was 0.975 and 0.984, respectively. CONCLUSION This method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins.
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Affiliation(s)
- Zhi-Ying Cui
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
| | - Chun-Lu Liu
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Yunnan, Kunming, China
| | - Dan-Dan Li
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Yunnan, Kunming, China
| | - Fu-Rong Xu
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
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Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review. Molecules 2022; 27:molecules27154846. [PMID: 35956791 PMCID: PMC9369811 DOI: 10.3390/molecules27154846] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.
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Dai Y, Dai Z, Guo G, Wang B. Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2101060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Yuanfeng Dai
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Zuoxiao Dai
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Guangzhi Guo
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Boran Wang
- School of Microelectronics, Fudan University, Shanghai, China
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14
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Wang HP, Chen P, Dai JW, Liu D, Li JY, Xu YP, Chu XL. Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116648] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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15
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Prayitno YA, Emmawati A, Prabowo S, Candra KP, Rahmadi A. AUTENTIKASI CEPAT MADU HUTAN KALIMATAN TIMUR DENGAN ATR-FTIR SPEKTROSKOPI KOMBINASI ANALISIS KEMOMETRIKA. JURNAL TEKNOLOGI DAN INDUSTRI PANGAN 2021. [DOI: 10.6066/jtip.2021.32.2.181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Honey adulteration is mostly conducted by the addition of sucrose. In this study, the authentication of honey was conducted using ATR-FTIR and chemometrics. Pure honey samples (MA) were collected from nine regions in East Kalimantan. The ATR-FTIR spectra of these samples were then compared to sucrose-adulterated honey (MS), which were prepared in the sucrose concentration from 2.5 to 50% (v / v).The data analysis was performed using chemometrics techniques: 1) Principle Component Analysis (PCA) method, 2) classification with Discriminant Analysis (DA), and 3) regression with (PCR) and (PLS). As a result, PCA was able to visualize the differences between MS and MA. DA analysis was able to distinguish MS and MA at wave numbers from 1200 to 800 cm-1 with 92.5% performance index. Quantitative calibration models of the sucrose-adulterated honey could be obtained from PLS and PCR, while the best calibration model was obtained with the PLS method from the 2nd derivative spectra. In summary, sucrose-adulterated honey from East Kalimantan can be authenticated using ATR-FTIR method in combination with chemometric analysis.
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16
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Corona P, Frangipane MT, Moscetti R, Lo Feudo G, Castellotti T, Massantini R. Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data. Foods 2021; 10:2575. [PMID: 34828856 PMCID: PMC8618948 DOI: 10.3390/foods10112575] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/29/2022] Open
Abstract
The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.
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Affiliation(s)
- Piermaria Corona
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
- CREA Research Centre for Forestry and Wood, 52100 Arezzo, Italy
| | - Maria Teresa Frangipane
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
| | - Roberto Moscetti
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
| | - Gabriella Lo Feudo
- CREA Research Centre for Olive, Fruit and Citrus Crops, 87036 Rende, Italy;
| | - Tatiana Castellotti
- CREA Research Centre for Agricultural Policies and Bioeconomy, 87036 Rende, Italy;
| | - Riccardo Massantini
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
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Azcarate SM, Ríos-Reina R, Amigo JM, Goicoechea HC. Data handling in data fusion: Methodologies and applications. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116355] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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18
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Pauliuc D, Ciursă P, Ropciuc S, Dranca F, Oroian M. Physicochemical parameters prediction and authentication of different monofloral honeys based on FTIR spectra. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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19
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Sotiropoulou NS, Xagoraris M, Revelou PK, Kaparakou E, Kanakis C, Pappas C, Tarantilis P. The Use of SPME-GC-MS IR and Raman Techniques for Botanical and Geographical Authentication and Detection of Adulteration of Honey. Foods 2021; 10:foods10071671. [PMID: 34359541 PMCID: PMC8303172 DOI: 10.3390/foods10071671] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of this review is to describe the chromatographic, spectrometric, and spectroscopic techniques applied to honey for the determination of botanical and geographical origin and detection of adulteration. Based on the volatile profile of honey and using Solid Phase microextraction-Gas chromatography-Mass spectrometry (SPME-GC-MS) analytical technique, botanical and geographical characterization of honey can be successfully determined. In addition, the use of vibrational spectroscopic techniques, in particular, infrared (IR) and Raman spectroscopy, are discussed as a tool for the detection of honey adulteration and verification of its botanical and geographical origin. Manipulation of the obtained data regarding all the above-mentioned techniques was performed using chemometric analysis. This article reviews the literature between 2007 and 2020.
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20
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Feng L, Wu B, Zhu S, He Y, Zhang C. Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins. Front Nutr 2021; 8:680357. [PMID: 34222304 PMCID: PMC8247466 DOI: 10.3389/fnut.2021.680357] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/25/2021] [Indexed: 01/25/2023] Open
Abstract
Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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21
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Robert C, Jessep W, Sutton JJ, Hicks TM, Loeffen M, Farouk M, Ward JF, Bain WE, Craigie CR, Fraser-Miller SJ, Gordon KC. Evaluating low- mid- and high-level fusion strategies for combining Raman and infrared spectroscopy for quality assessment of red meat. Food Chem 2021; 361:130154. [PMID: 34077882 DOI: 10.1016/j.foodchem.2021.130154] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 05/11/2021] [Accepted: 05/15/2021] [Indexed: 10/21/2022]
Abstract
The implementation of Raman and infrared spectroscopy with three data fusion strategies to predict pH and % IMF content of red meat was investigated. Raman and FTIR systems were utilized to assess quality parameters of intact red meat. Quantitative models were built using PLS, with model performances assessed with respect to the determination coefficient (R2), root mean square error and normalized root mean square error (NRMSEP). Results obtained on validation against an independent test set show that the high-level fusion strategy had the best performance in predicting the observed pH; with RP2 and NRMSEP values of 0.73 and 12.9% respectively, whereas low-level fusion strategy showed promise in predicting % IMF (NRMSEP = 8.5%). The fusion of data from more than one technique at low and high level resulted in improvement in the model performances; highlighting the possibility of information enhancement.
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Affiliation(s)
- Chima Robert
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - William Jessep
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Joshua J Sutton
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Talia M Hicks
- AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North 4410, New Zealand
| | - Mark Loeffen
- Delytics Ltd., Waikato Innovation Centre, Hamilton East, Hamilton 3216, New Zealand
| | - Mustafa Farouk
- AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton 3240, New Zealand
| | - James F Ward
- AgResearch, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand
| | - Wendy E Bain
- AgResearch, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand
| | - Cameron R Craigie
- AgResearch, Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | - Sara J Fraser-Miller
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
| | - Keith C Gordon
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
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Yue J, Fan J, Li Y, Ren H. Rapid authentication of mono-floral honeys by capillary zone electrophoresis. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00914-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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23
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Wang J, Chen Q, Belwal T, Lin X, Luo Z. Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy. Compr Rev Food Sci Food Saf 2021; 20:2476-2507. [DOI: 10.1111/1541-4337.12741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/08/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Jingjing Wang
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang People's Republic of China
| | - Tarun Belwal
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
- Ningbo Research Institute Zhejiang University Ningbo People's Republic of China
- Fuli Institute of Food Science Hangzhou People's Republic of China
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24
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Maléchaux A, Garcia R, Le Dréau Y, Pires A, Dupuy N, Cabrita MJ. Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:4177-4190. [PMID: 33819028 DOI: 10.1021/acs.jafc.0c06594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, 1H and 13C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using 1H and 13C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.
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Affiliation(s)
- Astrid Maléchaux
- Aix Marseille Université, Avignon Université, CNRS, IRD, IMBE, 13013 Marseille, France
| | - Raquel Garcia
- Mediterranean Institute for Agriculture, Environment and Development (MED), Departamento de Fitotecnia, Escola de Ciências e Tecnologia, Universidade de Évora, Pólo da Mitra, Apartado 94, 7006-554 Évora, Portugal
| | - Yveline Le Dréau
- Aix Marseille Université, Avignon Université, CNRS, IRD, IMBE, 13013 Marseille, France
| | - Arona Pires
- Centro de Química de Évora, Universidade de Évora, Colégio Luis António Verney, 7000 Évora, Portugal
| | - Nathalie Dupuy
- Aix Marseille Université, Avignon Université, CNRS, IRD, IMBE, 13013 Marseille, France
| | - Maria Joao Cabrita
- Mediterranean Institute for Agriculture, Environment and Development (MED), Departamento de Fitotecnia, Escola de Ciências e Tecnologia, Universidade de Évora, Pólo da Mitra, Apartado 94, 7006-554 Évora, Portugal
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Wang Z, Ren P, Wu Y, He Q. Recent advances in analytical techniques for the detection of adulteration and authenticity of bee products - A review. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2021; 38:533-549. [PMID: 33705260 DOI: 10.1080/19440049.2020.1871081] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Bee products have been considered as functional foods for a long time in China because of their wide range of biological activity. China has the largest number of bee colonies and the highest production of bee products in the world. Major bee products include honey, royal jelly, propolis and bee pollen. In recent years, consumption of bee products in China has been increasing due to an increased public awareness of their nutritional and health benefits. With the development of the Chinese economy and the improvement of people's living standards, high-end and gift-oriented products have become more popular and bee products are one of the options. However, the production of bee products cannot increase rapidly in short term and this is a driver for substantial economic-motivated adulteration. This is compounded by globalisation of supply chains which has also resulted in a rise in bee products fraud. These illicit products are eroding market prices and consumer trust, causing significant damage to the beekeeping industry. In order to provide information or solutions for regulators and consumers, in this article, we review he characteristics of bee products in China and the current situation regarding adulteration and authenticity of bee products. Moreover, advances in analytical techniques for detection of adulteration and authenticity of bee products including sensory techniques, DNA methods, isotope ratio mass spectrometry, spectroscopic techniques and mass spectrometry are reviewed. Finally, the applications and limitations of analytical methods in authentication are critically assessed. Suggestions are also put forward for the future management of China's bee products industry.
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Affiliation(s)
- Ziying Wang
- Department of Food Science and Engineering, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Pingping Ren
- Applied, Industrial and Clinical Division, Bruker Biospin GmbH, Rheinstetten, Germany
| | - Yongning Wu
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, China
| | - Qinghua He
- Department of Food Science and Engineering, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
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On the rapid and non-destructive approach for barbiturates, benzodiazepines, and phenothiazines determination and differentiation using spectral combination analysis and chemometric methods. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105853] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing. Foods 2021; 10:foods10020317. [PMID: 33546316 PMCID: PMC7913484 DOI: 10.3390/foods10020317] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/28/2021] [Accepted: 01/30/2021] [Indexed: 11/16/2022] Open
Abstract
There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.
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Wang X, Esquerre C, Downey G, Henihan L, O'Callaghan D, O'Donnell C. Development of chemometric models using Vis-NIR and Raman spectral data fusion for assessment of infant formula storage temperature and time. INNOV FOOD SCI EMERG 2021. [DOI: 10.1016/j.ifset.2020.102551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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29
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Damiani T, Alonso-Salces RM, Aubone I, Baeten V, Arnould Q, Dall’Asta C, Fuselli SR, Fernández Pierna JA. Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation. Foods 2020; 9:E1450. [PMID: 33066066 PMCID: PMC7601766 DOI: 10.3390/foods9101450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/08/2020] [Accepted: 10/10/2020] [Indexed: 11/26/2022] Open
Abstract
In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a "year-by-year" validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.
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Affiliation(s)
- Tito Damiani
- Department of Food and Drugs, University of Parma, Viale delle Scienze 17/A, 43124 Parma, Italy;
| | - Rosa M. Alonso-Salces
- Grupo de Investigación Microbiología Aplicada, Centro de Investigación en Abejas Sociales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Dean Funes B7602AYL, Mar del Plata 3350, Argentina; (R.M.A.-S.); (I.A.); (S.R.F.)
- Departamento de Biología, CONICET, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Funes 3350, Mar del Plata 7600, Argentina
| | - Inés Aubone
- Grupo de Investigación Microbiología Aplicada, Centro de Investigación en Abejas Sociales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Dean Funes B7602AYL, Mar del Plata 3350, Argentina; (R.M.A.-S.); (I.A.); (S.R.F.)
| | - Vincent Baeten
- Quality and Authentication of Products Unit, Knowledge and Valorization of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Chée de Namur, 24, 5030 Gembloux, Belgium; (V.B.); (Q.A.); (J.A.F.P.)
| | - Quentin Arnould
- Quality and Authentication of Products Unit, Knowledge and Valorization of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Chée de Namur, 24, 5030 Gembloux, Belgium; (V.B.); (Q.A.); (J.A.F.P.)
| | - Chiara Dall’Asta
- Department of Food and Drugs, University of Parma, Viale delle Scienze 17/A, 43124 Parma, Italy;
| | - Sandra R. Fuselli
- Grupo de Investigación Microbiología Aplicada, Centro de Investigación en Abejas Sociales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Dean Funes B7602AYL, Mar del Plata 3350, Argentina; (R.M.A.-S.); (I.A.); (S.R.F.)
- Comisión de Investigaciones Científicas (CIC), La Plata, Argentina Camino General Belgrano 526, La Plata 1900, Argentina
| | - Juan Antonio Fernández Pierna
- Quality and Authentication of Products Unit, Knowledge and Valorization of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Chée de Namur, 24, 5030 Gembloux, Belgium; (V.B.); (Q.A.); (J.A.F.P.)
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Aouadi B, Zaukuu JLZ, Vitális F, Bodor Z, Fehér O, Gillay Z, Bazar G, Kovacs Z. Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5479. [PMID: 32987908 PMCID: PMC7583984 DOI: 10.3390/s20195479] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/15/2020] [Accepted: 09/21/2020] [Indexed: 01/28/2023]
Abstract
Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.
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Affiliation(s)
- Balkis Aouadi
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - John-Lewis Zinia Zaukuu
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Flora Vitális
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Zsanett Bodor
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Orsolya Fehér
- Institute of Agribusiness, Faculty of Economics and Social Sciences, Szent István University, H-2100 Gödöllő, Hungary;
| | - Zoltan Gillay
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - George Bazar
- Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, H-7400 Kaposvár, Hungary;
- ADEXGO Kft., H-8230 Balatonfüred, Hungary
| | - Zoltan Kovacs
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
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31
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Control chart and data fusion for varietal origin discrimination: Application to olive oil. Talanta 2020; 217:121115. [DOI: 10.1016/j.talanta.2020.121115] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/27/2020] [Accepted: 04/30/2020] [Indexed: 01/08/2023]
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Ríos-Reina R, Azcarate SM, Camiña JM, Goicoechea HC. Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines. Anal Chim Acta 2020; 1126:52-62. [PMID: 32736724 DOI: 10.1016/j.aca.2020.06.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 11/28/2022]
Abstract
Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.
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Affiliation(s)
- Rocío Ríos-Reina
- Área de Nutrición y Bromatología, Fac. Farmacia, Univ. Sevilla, C/P. García González No. 2, E-41012, Sevilla, Spain
| | - Silvana M Azcarate
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa-CONICET, Instituto de Ciencias de La Tierra y Ambientales de La Pampa (INCITAP), Av. Uruguay 151, 6300, Santa Rosa, La Pampa, Argentina.
| | - José M Camiña
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa-CONICET, Instituto de Ciencias de La Tierra y Ambientales de La Pampa (INCITAP), Av. Uruguay 151, 6300, Santa Rosa, La Pampa, Argentina
| | - Héctor C Goicoechea
- Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional Del Litoral-CONICET, Ciudad Universitaria, Santa Fe, S3000ZAA, Argentina
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Adamchuk L, Sukhenko V, Akulonok O, Bilotserkivets T, Vyshniak V, Lisohurska D, Lisohurska O, Slobodyanyuk N, Shanina O, Galyasnyj I. Methods for determining the botanical origin of honey. POTRAVINARSTVO 2020. [DOI: 10.5219/1386] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The demand for monofloral, original, and special (functional) kinds of honey, or those with geographical indication, is forecast. At the same time, there is a need to improve the methods for determining the botanical and geographical origin of honey. The purpose of the research was to select and apply a variety of techniques for identifying the botanical origin of honey for its correspondence to acacia species. Samples of honey from the Kyiv, Odesa, and Dnipro regions extracted in the spring and summer period were used in the research. Organoleptic, physicochemical, NMR spectrometry, and advanced melissopalynology methods were applied. The tests were carried out at the laboratories of the Department of Certification and Standardization of Agricultural Products, NULES, Ukraine; the Ukrainian Laboratory of Quality and Safety of Agricultural Products; and the Bruker BioSpin GmbH company (Germany). According to the research results, the requirements for acacia honey were met by the organoleptic method for samples B1 and B2; by the physicochemical method for A0 and A2; by NMR spectroscopy for not a single sample, all being assessed as polyfloral; and by pollen analysis for B1 and B2. The conducted studies confirm the need for a comprehensive approach to the identification of the botanical origin of honey for its conformity to acacia species. There is a need to review the physicochemical indicators for the compliance of honey with the acacia species obtained in Ukraine. After all, even the modern NMR spectrometry technique indicated that the specially fabricated sample that did not contain acacia pollen grains was acacia honey. Identification of the botanical origin of monofloral honey, in particular acacia, should be carried out in the following sequence: pollen analysis (by dominant pollen grains), safety (presence of antibiotics, pesticides), physicochemical parameters according to international requirements, organoleptic parameters.
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Ascenzi P, Bettinelli M, Boffi A, Botta M, De Simone G, Luchinat C, Marengo E, Mei H, Aime S. Rare earth elements (REE) in biology and medicine. RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI 2020. [DOI: 10.1007/s12210-020-00930-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
AbstractThis survey reports on topics that were presented at the workshop on “Challenges with Rare Earth Elements. The Periodic Table at work for new Science & Technology” hold at the Academia dei Lincei in November 2019. The herein reported materials refer to presentations dealing with studies and applications of rare earth elements (REE) in several areas of Biology and Medicine. All together they show the tremendous impact REE have in relevant fields of living systems and highlight, on one hand, the still existing knowledge gap for an in-depth understanding of their function in natural systems as well as the very important role they already have in providing innovative scientific and technological solutions in a number of bio-medical areas and in fields related to the assessment of the origin of food and on their manufacturing processes. On the basis of the to-date achievements one expects that new initiatives will bring, in a not too far future, to a dramatic increase of our understanding of the REE involvement in living organisms as well as a ramp-up in the exploitation of the peculiar properties of REE for the design of novel applications in diagnostic procedures and in the set-up of powerful medical devices. This scenario calls the governmental authorities for new responsibilities to guarantee a continuous availability of REE to industry and research labs together with providing support to activities devoted to their recovery/recycling.
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Huang F, Song H, Guo L, Guang P, Yang X, Li L, Zhao H, Yang M. Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 235:118297. [PMID: 32248033 DOI: 10.1016/j.saa.2020.118297] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/16/2020] [Accepted: 03/23/2020] [Indexed: 06/11/2023]
Abstract
The aim of this study is to find a fast, accurate, and effective method for the detection of adulteration in honey circulating in the market. Near-infrared spectroscopy and mid-infrared spectroscopy data on natural honey and syrup-adulterated honey were integrated in the experiment. A method for identifying natural honey and syrup-adulterated honey was established by incorporating these data into a Support Vector Machine (SVM). In this experiment, 112 natural pure honey samples of 20 common honey types from 10 provinces in China were collected, and 112 adulterated honey samples with different percentages of syrup (10, 20, 30, 40, 50, and 60%) were prepared using six types of syrup commonly used in industry. The total number of samples was 224. The near- and mid-infrared spectral data were obtained for all samples. The raw spectra were pre-processed by First Derivative (FD) transform, Second Derivative (SD) transform, Multiple Scattering Correction (MSC), and Standard Normal Variate Transformation (SNVT). The above-corrected data underwent low-level and intermediate-level data fusion. In the last step, Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed as the optimization algorithms to find the optimal penalty parameter c and the optimal kernel parameter g for the SVM, and to establish the best SVM-based detection model for natural honey and syrup-adulterated honey. The results reveal that, compared to low-level data fusion, intermediate-level data fusion significantly improves the detection model. With the latter, the accuracy, sensitivity and specificity of the optimal SVM model all reach 100%, which makes it ideal for the identification of natural honey and syrup-adulterated honey.
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Affiliation(s)
- Furong Huang
- Opto-electronic Department of Jinan University, Guangzhou 510632, China
| | - Han Song
- Opto-electronic Department of Jinan University, Guangzhou 510632, China
| | - Liu Guo
- Opto-electronic Department of Jinan University, Guangzhou 510632, China
| | - Peiwen Guang
- Opto-electronic Department of Jinan University, Guangzhou 510632, China
| | - Xinhao Yang
- Opto-electronic Department of Jinan University, Guangzhou 510632, China
| | - Liqun Li
- GuangDong Institute of Applied Biological Resources, Guangzhou 510636, China
| | - Hongxia Zhao
- GuangDong Institute of Applied Biological Resources, Guangzhou 510636, China.
| | - Maoxun Yang
- Zhuhai Da Hengqin Science and Technology Development Co., Ltd, Zhuhai 519000, China.
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Gamela RR, Costa VC, Sperança MA, Pereira-Filho ER. Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples. Food Res Int 2020; 132:109037. [DOI: 10.1016/j.foodres.2020.109037] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/23/2020] [Accepted: 01/25/2020] [Indexed: 12/23/2022]
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37
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Fast Classification of Geographical Origins of Honey Based on Laser-Induced Breakdown Spectroscopy and Multivariate Analysis. SENSORS 2020; 20:s20071878. [PMID: 32231046 PMCID: PMC7181300 DOI: 10.3390/s20071878] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 02/23/2020] [Accepted: 03/26/2020] [Indexed: 11/17/2022]
Abstract
Traceability of honey is highly required by consumers and food administration with the consideration of food safety and quality. In this study, a technique named laser-induced breakdown spectroscopy (LIBS) was used to fast trace geographical origins of acacia honey and multi-floral honey. LIBS emissions from elements of Mg, Ca, Na, and K had significant differences among different geographical origins. The clusters of honey from different geographical origins were visualized with principal component analysis. In addition, support vector machine (SVM) and linear discrimination analysis (LDA) were used to quantitively classify the origins. The results indicated that SVM performed better than LDA, and the discriminant results of multi-floral honey were better than acacia honey. The accuracy and mean average precision for multi-floral honey were 99.7% and 99.7%, respectively. This study provided a fast approach for geographical origin classification, and might be helpful for food traceability.
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38
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Front-Face Fluorescence of Honey of Different Botanic Origin: A Case Study from Tuscany (Italy). APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051776] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Honey is a natural pure food produced by honeybees from the nectar of various plants, and its chemical composition includes carbohydrates, water, and some minor compounds, which are very important for honey quality and authentication. Most of honey’s minor components are related to the botanic origin, climate, and geographic diversity. In this work, we report an original case study on monofloral honey samples of twelve different botanic origins produced in Tuscany (Italy) based on the ‘semi-quantitative’ analysis of emission, excitation, and synchronous front-face fluorescence spectra. This is the first front-face fluorescence study of Italian honey samples and, to our knowledge, the first fluorescence investigation of honey from inula (Inula viscosa (L.) Aiton), marruca (Paliurus spina-christi Mill.), lavender (Lavandula L. 1753), sulla (Hedysarum coronarium L.), arbutus (or strawberry tree) (Arbutus unedo L., 1753), and alfalfa (Medicago sativa L.) plants. Results obtained from fluorescence spectroscopy are discussed in terms of characteristic spectral emission profiles typical of honey of different botanic origins. Moreover, the spectral analysis based on the decomposition of the front-face fluorescence (FFF) spectra in terms of single main fluorophores’ components is here proposed to identify several minor compounds, such as amino acids, phenolic acids, vitamins, and other fluorescent bioactive molecules.
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39
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Jiang Y, Ge H, Zhang Y. Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning. Food Chem 2020; 307:125533. [DOI: 10.1016/j.foodchem.2019.125533] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/02/2019] [Accepted: 09/12/2019] [Indexed: 12/01/2022]
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40
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Feng L, Wu B, Zhu S, Wang J, Su Z, Liu F, He Y, Zhang C. Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods. FRONTIERS IN PLANT SCIENCE 2020; 11:577063. [PMID: 33240295 PMCID: PMC7683421 DOI: 10.3389/fpls.2020.577063] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 10/06/2020] [Indexed: 05/03/2023]
Abstract
Rice diseases are major threats to rice yield and quality. Rapid and accurate detection of rice diseases is of great importance for precise disease prevention and treatment. Various spectroscopic techniques have been used to detect plant diseases. To rapidly and accurately detect three different rice diseases [leaf blight (Xanthomonas oryzae pv. Oryzae), rice blast (Pyricularia oryzae), and rice sheath blight (Rhizoctonia solani)], three spectroscopic techniques were applied, including visible/near-infrared hyperspectral imaging (HSI) spectra, mid-infrared spectroscopy (MIR), and laser-induced breakdown spectroscopy (LIBS). Three different levels of data fusion (raw data fusion, feature fusion, and decision fusion) fusing three different types of spectral features were adopted to categorize the diseases of rice. Principal component analysis (PCA) and autoencoder (AE) were used to extract features. Identification models based on each technique and different fusion levels were built using support vector machine (SVM), logistic regression (LR), and convolution neural network (CNN) models. Models based on HSI performed better than those based on MIR and LIBS, with the accuracy over 93% for the test set based on PCA features of HSI spectra. The performance of rice disease identification varied with different levels of fusion. The results showed that feature fusion and decision fusion could enhance identification performance. The overall results illustrated that the three techniques could be used to identify rice diseases, and data fusion strategies have great potential to be used for rice disease detection.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Junmin Wang
- Institute of Crop Science and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Zhenzhu Su
- State Key Laboratory for Rice Biology, Institute of Biotechnology, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
- *Correspondence: Chu Zhang,
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41
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Sun L, Liu F, You G, Feng T, Wang M, Liu Y, Ren X, Deng Y. A comparative analysis of Aconiti Lateralis Radix and processed products using UHPLC-Q-TOF-MS combined with multivariate chemometrics strategies. J LIQ CHROMATOGR R T 2019. [DOI: 10.1080/10826076.2019.1659150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Lili Sun
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Fan Liu
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Guangjiao You
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Tao Feng
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Meng Wang
- Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yanan Liu
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiaoliang Ren
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yanru Deng
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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42
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A holistic approach to authenticate organic sweet oranges (Citrus Sinensis L. cv Osbeck) using different techniques and data fusion. Food Control 2019. [DOI: 10.1016/j.foodcont.2019.04.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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43
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Pei YF, Zuo ZT, Zhang QZ, Wang YZ. Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis. Molecules 2019; 24:molecules24142559. [PMID: 31337084 PMCID: PMC6680555 DOI: 10.3390/molecules24142559] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 07/08/2019] [Accepted: 07/11/2019] [Indexed: 02/07/2023] Open
Abstract
Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.
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Affiliation(s)
- Yi-Fei Pei
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
| | - Zhi-Tian Zuo
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Qing-Zhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China.
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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Schwolow S, Gerhardt N, Rohn S, Weller P. Data fusion of GC-IMS data and FT-MIR spectra for the authentication of olive oils and honeys—is it worth to go the extra mile? Anal Bioanal Chem 2019; 411:6005-6019. [DOI: 10.1007/s00216-019-01978-w] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 11/28/2022]
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Huang Z, Liu L, Li G, Li H, Ye D, Li X. Nondestructive Determination of Diastase Activity of Honey Based on Visible and Near-Infrared Spectroscopy. Molecules 2019; 24:molecules24071244. [PMID: 30934979 PMCID: PMC6480106 DOI: 10.3390/molecules24071244] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Revised: 03/27/2019] [Accepted: 03/27/2019] [Indexed: 11/16/2022] Open
Abstract
The activities of enzymes are the basis of evaluating the quality of honey. Beekeepers usually use concentrators to process natural honey into concentrated honey by concentrating it under high temperatures. Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature. The objective of this work is to study the kinetic mechanism of the temperature effect on diastase activity and to develop a nondestructive approach for quick determination of the diastase activity of honey through a heating process based on visible and near-infrared (Vis/NIR) spectroscopy. A total of 110 samples, including three species of botanical origin, were used for this study. To explore the kinetic mechanism of diastase activity under high temperatures, the honey of three kinds of botanical origins were processed with thermal treatment to obtain a variety of diastase activity. Diastase activity represented with diastase number (DN) was measured according to the national standard method. The results showed that the diastase activity decreased with the increase of temperature and heating time, and the sensitivity of acacia and longan to temperature was higher than linen. The optimum temperature for production and processing is 60 °C. Unsupervised clustering analysis was adopted to detect spectral characteristics of these honeys, indicating that different botanical origins of honeys can be distinguished in principal component spaces. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) algorithms were applied to develop quantitative relationships between Vis/NIR spectroscopy and diastase activity. The best result was obtained through Gaussian filter smoothing-standard normal variate (GF-SNV) pretreatment and the LS-SVM model, known as GF-SNV-LS-SVM, with a determination coefficient (R²) of prediction of 0.8872, and root mean square error (RMSE) of prediction of 0.2129. The overall results of this paper showed that the diastase activity of honey can be determined quickly and non-destructively with Vis/NIR spectral methods, which can be used to detect DN in the process of honey production and processing, and to maximize the nutrient content of honey.
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Affiliation(s)
- Zhenxiong Huang
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
- Fujian Engineering Research Center for Modern Agricultural Equipment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Lang Liu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
- Fujian Engineering Research Center for Modern Agricultural Equipment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Guojian Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
- Fujian Engineering Research Center for Modern Agricultural Equipment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Hong Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
- Fujian Engineering Research Center for Modern Agricultural Equipment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
- Fujian Engineering Research Center for Modern Agricultural Equipment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
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Hu W, Wan L, Jian Y, Ren C, Jin K, Su X, Bai X, Haick H, Yao M, Wu W. Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing. ADVANCED MATERIALS TECHNOLOGIES 2018:1800488. [DOI: 10.1002/admt.201800488] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Affiliation(s)
- Wenwen Hu
- School of Aerospace Science and TechnologyXidian University Shaanxi 710126 P. R. China
| | - Liangtian Wan
- The Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceSchool of SoftwareDalian University of Technology Dalian 116620 China
| | - Yingying Jian
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Cong Ren
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Ke Jin
- School of Aerospace Science and TechnologyXidian University Shaanxi 710126 P. R. China
| | - Xinghua Su
- School of Materials Science and EngineeringChang'an University Xi'an 710061 China
| | - Xiaoxia Bai
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Hossam Haick
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Mingshui Yao
- Fujian Institute of Research on the Structure of MatterChinese Academy of Sciences Fuzhou 350002 P. R. China
| | - Weiwei Wu
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
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