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Li C, Sheng M, Zhang M, Rogers KM, Nie J, Shao S, Xiao J, Yuan Y. Similarity recognition approach to identify zero-added MSG soy sauce using stable isotopes and amino acid profiles. Food Chem 2024; 461:140859. [PMID: 39163723 DOI: 10.1016/j.foodchem.2024.140859] [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: 06/17/2024] [Revised: 08/09/2024] [Accepted: 08/11/2024] [Indexed: 08/22/2024]
Abstract
Seasonings such as naturally fermented soy sauce without added monosodium glutamate (MSG), are currently a growth market in China. However, fraudulent and mislabeled zero-added MSG soy sauce may cause a risk of excessive MSG intake, increasing food safety issues for consumers. This study investigates stable carbon and nitrogen isotopes and 16 amino acids in typical Chinese in-market soy sauces and uses a similarity method to establish criteria to authenticate MSG addition claims. Results reveal most zero-added MSG soy sauces had lower δ13C values (-25.2 ‰ to -17.7 ‰) and glutamic acid concentrations (8.97 mg mL-1 to 34.76 mg mL-1), and higher δ15N values (-0.27 ‰ +0.95 ‰) and other amino acid concentrations than added-MSG labeled samples. A combined approach, using isotopes, amino acids, similarity coefficients and uncertainty values, was evaluated to rapidly and accurately identify zero-added MSG soy sauces from MSG containing counterparts.
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Affiliation(s)
- Chunlin Li
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China
| | - Meiling Sheng
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Menglin Zhang
- Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo, Vigo 36310, Spain; Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China
| | - Karyne M Rogers
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China; National Isotope Centre, GNS Science, 30 Gracefield Road, Lower Hutt 5040, New Zealand
| | - Jing Nie
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China
| | - Shengzhi Shao
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China
| | - Jianbo Xiao
- Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo, Vigo 36310, Spain.
| | - Yuwei Yuan
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China.
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Effects of Novel Preparation Technology on Flavor of Vegetable-Soy Sauce Compound Condiment. Foods 2023; 12:foods12061263. [PMID: 36981189 PMCID: PMC10048277 DOI: 10.3390/foods12061263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023] Open
Abstract
Vegetables contain important bioactive substances which have unique tastes and aromas and provide beneficial effects to human health. In this study, multiflavor blended soy sauce was prepared with the juice of eight kinds of vegetables, dried shrimp boiled stock, and six kinds of commercial soy sauce as raw materials, and thermal ultrasound was used as the sterilization method. The effects of adding different formulas of vegetable and seafood stock on the basic physical and chemical parameters, nutrition, antioxidant activity, flavor, and taste of soy sauce were investigated. The results showed that the basic physicochemical indices such as pH, total acid, color, soluble solids, and amino acid nitrogen of the product with a ratio of soy sauce to vegetable-seafood stock of 1:0.5 (v/v) could meet the production standards of soy sauce, and its flavor, taste, and sensory scores were relatively good, with the highest likeability (overall acceptability). The mixed soy sauce with a ratio of 1:2 (v/v) had higher vegetable and seafood flavors, and different vegetable flavors (celery, carrot, and onion) were more obvious, but its nutritional index was relatively low. Multiflavor vegetable-soy sauce can be used for quick cooking by chefs of catering enterprises, and may be used as a seasoning bag for prefabricated dishes and convenient foods, attracting increasing attention from manufacturers and consumers.
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Lin XW, Li FL, Wang S, Xie J, Pan QN, Wang P, Xu CH. A Novel Method Based on Multi-Molecular Infrared (MM-IR) AlexNet for Rapid Detection of Trace Harmful Substances in Flour. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02964-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Simultaneous detection of mixed foodborne pathogens by multi-molecular infrared spectroscopy identification system. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Metabolomics mechanism of traditional soy sauce associated with fermentation time. FOOD SCIENCE AND HUMAN WELLNESS 2022. [DOI: 10.1016/j.fshw.2021.11.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. SENSORS 2021; 21:s21227620. [PMID: 34833693 PMCID: PMC8619411 DOI: 10.3390/s21227620] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 02/07/2023]
Abstract
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
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Zhang X, Li Y, Tao Y, Wang Y, Xu C, Lu Y. A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin. Food Chem 2021; 337:127986. [PMID: 32920269 DOI: 10.1016/j.foodchem.2020.127986] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 08/30/2020] [Accepted: 08/31/2020] [Indexed: 10/23/2022]
Abstract
We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. Through training and learning with parvalbumin IR spectra from 16 fish species, IRN, support vector machine (SVM), and random forest (RF) models were successfully established and compared. The IRN model extracted highly representative features from the IR spectra, leading to high accuracy in recognizing parvalbumin (up to 97.3%) in a variety of seafood matrices. The proposed infrared spectroscopic IRN (IR-IRN) method was rapid (~20 min, cf. ELISA ~4 h) and required minimal expert knowledge for application. Thus, it could be extended for large-scale field screening and identification of parvalbumin or other potential allergens in complex food matrices.
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Affiliation(s)
- Xiaopeng Zhang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yaru Li
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China
| | - Yan Tao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yang Wang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300112, China
| | - Changhua Xu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China.
| | - Ying Lu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China.
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Zhang L, Yin M, Zheng Y, Xu CH, Tao NP, Wu X, Wang X. Brackish water improves the taste quality in meat of adult male Eriocheir sinensis during the postharvest temporary rearing. Food Chem 2020; 343:128409. [PMID: 33218856 DOI: 10.1016/j.foodchem.2020.128409] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 02/02/2023]
Abstract
We investigated the effect of temporary rearing in brackish water on the taste quality in meat of crab cooked. The main salinity-responsive factors included 5'-nucleotides and free amino acids (FAAs) in crab meat that were identified using tri-step infrared spectroscopy. Compared to the fresh water group, the contents of 5'-adenosine monophosphate and 5'-inosine monophosphate in the brackish water group significantly increased in the 2nd week and decreased in the 6th week, respectively. The contribution ratio of umami FAAs increased from 8.1 to 13.5% in the 4th week in the brackish water group, showing maximum value of equivalent umami concentration. Moreover, Ca2+ and Cl- contents significantly increased in the 4th and 6th weeks, respectively (P < 0.05). Infrared spectroscopy was an effective method to identify the taste components. With respect to the taste quality, four weeks were determined as the best period for temporary rearing of the crab in brackish water.
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Affiliation(s)
- Long Zhang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China; Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China; Shanghai Collaborative Innovation Centre for Aquatic Animal Genetics and Breeding, Shanghai Ocean University, Shanghai 201306, China; National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China
| | - Mingyu Yin
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China
| | - Yao Zheng
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China
| | - Chang-Hua Xu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China
| | - Ning-Ping Tao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China
| | - Xugan Wu
- Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China; Shanghai Collaborative Innovation Centre for Aquatic Animal Genetics and Breeding, Shanghai Ocean University, Shanghai 201306, China; National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China.
| | - Xichang Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China.
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Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics. Sci Rep 2019; 9:8256. [PMID: 31164667 PMCID: PMC6547656 DOI: 10.1038/s41598-019-44521-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 05/14/2019] [Indexed: 11/12/2022] Open
Abstract
Mid-infrared (MIR) and near-infrared (NIR) spectroscopy combined with chemometrics were explored to classify Cabernet Sauvignon wines from different countries (Australia, Chile and China). Commercial wines (n = 540) were scanned in transmission mode using MIR and NIR, and their characteristic fingerprint bands were extracted at 1750-1000 cm−1 and 4555-4353 cm−1. Through the identification system of Tri-step infrared spectroscopy, the correlation between macroscopic chemical fingerprints and geographical regions was explored more deeply. Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra were used to visualize or discriminate differences between samples and to realize geographical origin traceability of Cabernet Sauvignon wines. Through “external test set (n = 157)” validation, SIMCA models correctly classified 97%, 97% and 92% of Australian, Chilean and Chinese Cabernet Sauvignon wines, while the DA models correctly classified 86%, 85% and 77%, respectively. Based on unique digital fingerprints of spectroscopy (FT-MIR and FT-NIR) associated with chemometrics, geographical origin traceability was achieved in a more comprehensive, effective and rapid manner. The developed database models based on IR fingerprint spectroscopy with chemometrics could provide scientific basis and reference for geographical origin traceability of Cabernet Sauvignon wines (Australia, Chile and China).
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Hou SW, Wei W, Wang Y, Gan JH, Lu Y, Tao NP, Wang XC, Liu Y, Xu CH. Integrated recognition and quantitative detection of starch in surimi by infrared spectroscopy and spectroscopic imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 215:1-8. [PMID: 30818215 DOI: 10.1016/j.saa.2019.02.080] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 01/11/2019] [Accepted: 02/17/2019] [Indexed: 06/09/2023]
Abstract
Surimi products have become increasingly-consumed food with prominent characteristics of high nutrition and convenience and its supply falls short of demand. However, due to exhausted fishery resource in recent years, surimi adulteration, such as addition of plant proteins, starch and other animal origin meat, is becoming serious, so recognition of these exogenous substances has become an urgent issue. In this study, Fourier transform infrared spectroscopy (FT-IR) combined with infrared spectroscopic imaging could distinguish heterogeneity in surimi qualitatively and quantitatively and obtain integral chemical images so that spatial distribution of each component in surimi could be visually displayed, thus a rapid recognition method and a prediction model were developed. The different starch contents in surimi had been primarily identified through intensity change of infrared absorption peaks at 1045cm-1 and 988cm-1, specifically with peak shifts to 1041cm-1 and to 992cm-1, respectively. In infrared imaging analysis, principal components (PCs) were separated and one key PC was confirmed as starch by characteristic peaks comparison at 1147cm-1, 1075cm-1, 997cm-1 and 930cm-1. Meanwhile, an established statistic model could predict starch content in surimi correctly with a reliable correlation coefficient (R=0.9856) and root mean square error of prediction (RMSEP=5.64). Therefore, FT-IR combined with infrared spectroscopic imaging could be applicable to integrally recognize and quantitatively detect starch in surimi.
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Affiliation(s)
- Shi-Wei Hou
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Wei Wei
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yang Wang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300112, China
| | - Jian-Hong Gan
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Ying Lu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Ning-Ping Tao
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Xi-Chang Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yuan Liu
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Chang-Hua Xu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Shanghai 201306, China.
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