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Hoon Yun B, Yu HY, Kim H, Myoung S, Yeo N, Choi J, Sook Chun H, Kim H, Ahn S. Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks. Food Chem 2024; 439:138082. [PMID: 38070234 DOI: 10.1016/j.foodchem.2023.138082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 01/10/2024]
Abstract
This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis - linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.
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Affiliation(s)
- Byung Hoon Yun
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hyo-Yeon Yu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hyeongmin Kim
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Sangki Myoung
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Neulhwi Yeo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Jongwon Choi
- Department of Advanced Imaging, Chung-Ang University, Seoul 06974, South Korea.
| | - Hyang Sook Chun
- Department of Food Science & Technology, Chung-Ang University, Anseong 17546, South Korea.
| | - Hyeonjin Kim
- Department of Medical Sciences, Seoul National University, Seoul 03080, South Korea; Department of Radiology, Seoul National University Hospital, Seoul 03080, South Korea.
| | - Sangdoo Ahn
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
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2
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Khatun MA, Yoshimura J, Yoshida M, Suzuki Y, Huque R, Kelly SD, Munshi MK. Isotopic characteristics (δ 13C, δ 15N, and δ 18O) of honey from Bangladesh retail markets: Investigating sugar manipulation, botanical and geographical authentication. Food Chem 2024; 435:137612. [PMID: 37801765 DOI: 10.1016/j.foodchem.2023.137612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/02/2023] [Accepted: 09/26/2023] [Indexed: 10/08/2023]
Abstract
The study analyzed stable isotope composition (carbon, nitrogen, oxygen) of Bangladesh origin monofloral and multifloral honey for the first time to identify the C-4plant sugar adulteration, botanical and geographical differentiation. The C-4 sugar content (11.90 to 86.61%) using carbon isotope values of whole honey and protein extractidentified18% adulterated honeys.Whereas 82% honey have been detected as authentic with the δ13C ranges from -24.86 to -29.26‰ and the C-4 sugar values ranges from -6.75 to 5.67%. The chemometric approach using carbon and nitrogen isotopesvalues revealed discrimination among floral types of Bangladeshi authentic honeys indicating the influence of botanical source on isotopic composition. Canonical discriminant analysis (CDA) of C and N isotopes showed geographic distinction among Bangladeshi and overseas origin honeys. It is notable that the black seed honeys were correctly classified with the CDA analysis representing this type as unique Bangladesh brand as New Zealand manuka honey.
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Affiliation(s)
- Mst Afifa Khatun
- Food Safety and Quality Analysis Division, Institute of Food and Radiation Biology, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh.
| | - Junya Yoshimura
- Department of Food Science and Technology, Nippon Veterinary and Life Science University, Tokyo, Japan.
| | - Mitsuru Yoshida
- Department of Food Science and Technology, Nippon Veterinary and Life Science University, Tokyo, Japan.
| | - Yaeko Suzuki
- Advanced Analysis Center, National Agriculture and Food Research Organization, Tsukuba, Japan.
| | - Roksana Huque
- Food Safety and Quality Analysis Division, Institute of Food and Radiation Biology, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh.
| | - Simon D Kelly
- Food Safety & Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency, Vienna International Centre, Wagramer Strasse 5, P.O. Box 100, 1400 Vienna, Austria.
| | - M Kamruzzaman Munshi
- Food Safety and Quality Analysis Division, Institute of Food and Radiation Biology, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh.
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3
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Li R, Liu Y, Xia Z, Wang Q, Liu X, Gong Z. Discriminating geographical origins and determining active substances of water caltrop shells through near-infrared spectroscopy and chemometrics. Spectrochim Acta A Mol Biomol Spectrosc 2023; 303:123198. [PMID: 37531683 DOI: 10.1016/j.saa.2023.123198] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/28/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the geographical origins of the water caltrop shells from different regions of China. Two active substances, the total phenolic content (TPC) and total flavonoid content (TFC) in the water caltrop shells were determined through the technique as well. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was adopted to build the geographical discriminant model. Quantitative analysis models of TPC and TFC were built using partial least squares (PLS) regression. 1st derivative and randomization test (RT) methods were used to optimize the quantitative analysis models. It was found that the geographical discriminant model can correctly recognize the water caltrop shells from different regions of China with a total accuracy of 93.33%. The values of TPC and TFC obtained by the optimized models and the standard method are close. The coefficient of determination (R2) and the ratio of prediction to deviation for the two substances were 0.91, 0.89 and 3.02, 3.02, respectively. The results demonstrated the feasibility of NIRS combined with chemometric methods for the geographical discrimination of water caltrop shells and the quantitative analysis of TPC and TFC in water caltrop shells.
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Affiliation(s)
- Rui Li
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
| | - Yan Liu
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Hubei Key Laboratory for Processing and Transformation of Agricultural Products (Wuhan Polytechnic University), College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Center of Food Safety, Hubei Key Research Base of Humanities and Social Science, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China.
| | - Zhenzhen Xia
- Institute of Agricultural Quality Standards and Testing Technology Research, Hubei Academy of Agricultural Science, Wuhan 430064, PR China
| | - Qiao Wang
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
| | - Xin Liu
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
| | - Zhiyong Gong
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
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Du QY, He M, Gao X, Yu X, Zhang JN, Shi J, Zhang F, Lu YY, Wang HQ, Yu YJ, Zhang X. Geographical discrimination of Flos Trollii by GC-MS and UHPLC-HRMS-based untargeted metabolomics combined with chemometrics. J Pharm Biomed Anal 2023; 234:115550. [PMID: 37429118 DOI: 10.1016/j.jpba.2023.115550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 06/21/2023] [Accepted: 06/25/2023] [Indexed: 07/12/2023]
Abstract
For centuries, Flos Trollii has been consumed as functional tea and a folk medicine in China's north and northwest zones. The quality of Flos Trollii highly depends on the producing zones. Unfortunately, few studies have been reported on the geographical discrimination of Flos Trollii. This work comprehensively investigated Flos Trollii compounds with an integration strategy combining gas chromatography-mass spectrometry (GC-MS) and ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) with chemometrics to explore the differences between Flos Trollii obtained from various origins of China. About 71 volatile and 22 involatile markers were identified with GC-MS and UHPLC-HRMS, respectively. Geographical discrimination models were synthetically investigated based on the identified markers. The results indicated that the UHPLC-HRMS coupled with the fisher discrimination model provided the best prediction capability (>97%). This study provides a new solution for Flos Trollii discrimination.
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Affiliation(s)
- Qing-Yu Du
- School of Pharmacy, Ningxia Medical University, Yinchuan, China
| | - Min He
- School of Pharmacy, Ningxia Medical University, Yinchuan, China
| | - Xin Gao
- School of Pharmacy, Ningxia Medical University, Yinchuan, China
| | - Xin Yu
- School of Pharmacy, Ningxia Medical University, Yinchuan, China
| | - Jia-Ni Zhang
- School of Pharmacy, Ningxia Medical University, Yinchuan, China
| | - Jie Shi
- School of Pharmacy, Ningxia Medical University, Yinchuan, China
| | - Fang Zhang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - You-Yuan Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China
| | - Han-Qing Wang
- School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China
| | - Yong-Jie Yu
- School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China.
| | - Xia Zhang
- School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China.
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Hur SH, Kim S, Kim H, Jeong S, Chung H, Kim YK, Kim HJ. Geographical discrimination of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS). Curr Res Food Sci 2023; 6:100532. [PMID: 37377492 PMCID: PMC10290993 DOI: 10.1016/j.crfs.2023.100532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/09/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
This study presents a method for discriminating the geographical origin of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS) and multivariate analysis, such as orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap analysis, and canonical discriminant analysis (CDA). Herein, 102 samples were analyzed for the content of 33 elements using optimized conditions of 200 Hz (repetition rate), 50 μm (spot size), and 90% (energy). Significant differences in count per second (cps) values of the elements were observed between domestic and imported peppers, with variations of up to 5.66 times (133Cs). The OPLS-DA model accuracy achieved an R2 of 0.811 and a Q2 of 0.733 for distinguishing dried chili peppers of different geographical origins. The variable importance in projection (VIP) and s-plot identified elements 10 and 3 as key to the OPLS-DA model, and in the heatmap, six elements were estimated to be significant in discriminating between domestic and imported samples. Furthermore, CDA showed a high accuracy of 99.02%. This method can ensure food safety for consumers, and accurately determine the geographic origin of agricultural products.
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Affiliation(s)
- Suel Hye Hur
- National Agricultural Products Quality Management Service, Gimcheon, 39660, Republic of Korea
| | - Seyeon Kim
- National Agricultural Products Quality Management Service, Gimcheon, 39660, Republic of Korea
| | - Hyoyoung Kim
- National Agricultural Products Quality Management Service, Gimcheon, 39660, Republic of Korea
| | - Seongsoo Jeong
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul, 04763, Republic of Korea
| | - Hoeil Chung
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul, 04763, Republic of Korea
| | - Yong-Kyoung Kim
- National Agricultural Products Quality Management Service, Gimcheon, 39660, Republic of Korea
| | - Ho Jin Kim
- National Agricultural Products Quality Management Service, Gimcheon, 39660, Republic of Korea
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Fang S, Huang WJ, Wei Y, Tao M, Hu X, Li T, Kalkhajeh YK, Deng WW, Ning J. Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics. J Sci Food Agric 2019; 99:6937-6943. [PMID: 31414496 DOI: 10.1002/jsfa.9982] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/05/2019] [Accepted: 08/07/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds. RESULTS Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples. CONCLUSION The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Shimao Fang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Wen-Jing Huang
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Yuming Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Meng Tao
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Xin Hu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Yusef K Kalkhajeh
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
| | - Wei-Wei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
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Drivelos SA, Higgins K, Kalivas JH, Haroutounian SA, Georgiou CA. Data fusion for food authentication. Combining rare earth elements and trace metals to discriminate "Fava Santorinis" from other yellow split peas using chemometric tools. Food Chem 2014; 165:316-22. [PMID: 25038681 DOI: 10.1016/j.foodchem.2014.03.083] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2013] [Revised: 03/13/2014] [Accepted: 03/15/2014] [Indexed: 11/24/2022]
Abstract
"Fava Santorinis", is a protected designation of origin (PDO) yellow split pea species growing only in the island of Santorini in Greece. Due to its nutritional quality and taste, it has gained a high monetary value. Thus, it is prone to adulteration with other yellow split peas. In order to discriminate "Fava Santorinis" from other yellow split peas, four classification methods utilising rare earth elements (REEs) measured through inductively coupled plasma-mass spectrometry (ICP-MS) are studied. The four classification processes are orthogonal projection analysis (OPA), Mahalanobis distance (MD), partial least squares discriminant analysis (PLS-DA) and k nearest neighbours (KNN). Since it is known that trace elements are often useful to determine geographical origin of food products, we further quantitated for trace elements using ICP-MS. Presented in this paper are results using the four classification processes based on the fusion of the REEs data with the trace element data. Overall, the OPA method was found to perform best with up to 100% accuracy using the fused data.
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Affiliation(s)
- Spiros A Drivelos
- Chemistry Laboratory, Agricultural University of Athens, 75 Iera Odos, 118 55 Athens, Greece
| | - Kevin Higgins
- Department of Chemistry, Idaho State University, 921 South 8th Avenue, STOP 8023, ID, USA
| | - John H Kalivas
- Department of Chemistry, Idaho State University, 921 South 8th Avenue, STOP 8023, ID, USA
| | - Serkos A Haroutounian
- Chemistry Laboratory, Agricultural University of Athens, 75 Iera Odos, 118 55 Athens, Greece
| | - Constantinos A Georgiou
- Chemistry Laboratory, Agricultural University of Athens, 75 Iera Odos, 118 55 Athens, Greece.
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