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Hu L, Zhu Y, Zhong C, Cai Q, Zhang H, Zhang X, Yao Q, Hang Y, Ge Y, Hu Y. Discrimination of three commercial tuna species through species-specific peptides: From high-resolution mass spectrometry discovery to MRM validation. Food Res Int 2024; 187:114462. [PMID: 38763689 DOI: 10.1016/j.foodres.2024.114462] [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: 03/18/2024] [Revised: 04/28/2024] [Accepted: 05/01/2024] [Indexed: 05/21/2024]
Abstract
The risk of tuna adulteration is high driven by economic benefits. The authenticity of tuna is required to protect both consumers and tuna stocks. Given this, the study is designed to identify species-specific peptides for distinguishing three commercial tropical tuna species. The peptides derived from trypsin digestion were separated and detected using ultrahigh-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS) in data-dependent acquisition (DDA) mode. Venn analysis showed that there were differences in peptide composition among the three tested tuna species. The biological specificity screening through the National Center for Biotechnology Information's Basic Local Alignment Search Tool (NCBI BLAST) revealed that 93 peptides could serve as potential species-specific peptides. Finally, the detection specificity of species-specific peptides of raw meats and processed products was carried out by multiple reaction monitoring (MRM) mode based on a Q-Trap mass spectrometer. The results showed that three, one and two peptides of Katsuwonus pelamis, Thunnus obesus and Thunnus albacores, respectively could serve as species-specific peptides.
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Affiliation(s)
- Lingping Hu
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang 314006, China; College of Food Science and Engineering, Hainan Tropical Ocean University, Yazhou Bay Innovation Institute, Marine Food Engineering Technology Research Center of Hainan Province, Collaborative Innovation Center of Marine Food Deep Processing, Sanya 572022, China.
| | - Yin Zhu
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang 314006, China.
| | - Chao Zhong
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang 314006, China.
| | - Qiang Cai
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang 314006, China.
| | - Hongwei Zhang
- Food and Agricultural Products Testing Agency, Technology Center of Qingdao Customs District, Qingdao, Shandong Province 266002, China.
| | - Xiaomei Zhang
- Food and Agricultural Products Testing Agency, Technology Center of Qingdao Customs District, Qingdao, Shandong Province 266002, China.
| | - Qian Yao
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, Sichuan Industrial Institute of Antibiotics, School of Pharmacy, Chengdu University, Chengdu 610106, China.
| | - Yuyu Hang
- College of Food Science and Engineering, Hainan Tropical Ocean University, Yazhou Bay Innovation Institute, Marine Food Engineering Technology Research Center of Hainan Province, Collaborative Innovation Center of Marine Food Deep Processing, Sanya 572022, China.
| | - Yingliang Ge
- College of Food Science and Engineering, Hainan Tropical Ocean University, Yazhou Bay Innovation Institute, Marine Food Engineering Technology Research Center of Hainan Province, Collaborative Innovation Center of Marine Food Deep Processing, Sanya 572022, China.
| | - Yaqin Hu
- College of Food Science and Engineering, Hainan Tropical Ocean University, Yazhou Bay Innovation Institute, Marine Food Engineering Technology Research Center of Hainan Province, Collaborative Innovation Center of Marine Food Deep Processing, Sanya 572022, China.
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Li C, Wang Y. Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives. Crit Rev Anal Chem 2022:1-20. [PMID: 36409298 DOI: 10.1080/10408347.2022.2148204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.
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Affiliation(s)
- Chaoping Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Hu L, Zhang H, Hu Z, Chin Y, Li G, Huang J, Zhang X, Jiang B, Hu Y. Differentiation of three commercial tuna species through Q-Exactive Orbitrap mass spectrometry based lipidomics and chemometrics. Food Res Int 2022; 158:111509. [DOI: 10.1016/j.foodres.2022.111509] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/22/2022] [Accepted: 06/10/2022] [Indexed: 11/26/2022]
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4
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Hu L, Zhang H, Hu Z, Chin Y, Zhang X, Chen J, Hu Y. Comparative proteomics analysis of three commercial tuna species through SWATH-MS based mass spectrometry and chemometrics. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Impact of the Covering Vegetable Oil on the Sensory Profile of Canned Tuna of Katsuwonus pelamis Species and Tuna’s Taste Evaluation Using an Electronic Tongue. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The impact of the covering vegetable oil (sunflower oil, refined olive oil and extra virgin olive oil, EVOO) on the physicochemical and sensory profiles of canned tuna (Katsuwonus pelamis species) was evaluated, using analytical techniques and a sensory panel. The results showed that canned tuna covered with EVOO possesses a higher content of total phenols and an enhanced antioxidant capacity. This covering medium also increased the appreciated redness-yellowness color of the canned tuna, which showed a higher chromatic and intense color. Olfactory and kinesthetic sensations were significantly dependent on the type of oil used as covering medium. Tuna succulence and adhesiveness were promoted by the use of EVOO, which also contributed to decreasing the tuna-related aroma sensations. The tuna sensory data could be successfully used to identify the type of vegetable oil used. Moreover, a potentiometric electronic tongue allowed discriminating between the canned tuna samples according to the vegetable oil used (mean sensitivity of 96 ± 8%; repeated K-fold cross-validation) and the fruity intensity of the EVOO (mean sensitivity of 100%; repeated K-fold cross-validation). Thus, the taste sensor device could be a practical tool to verify the authenticity of the declared covering medium in canned tuna and to perceive the differences in consumers’ taste.
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Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy. Foods 2021; 10:foods10102309. [PMID: 34681358 PMCID: PMC8535081 DOI: 10.3390/foods10102309] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/26/2022] Open
Abstract
A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (Rp2 = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (Rp2 = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (Rp2 = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.
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Liu Q, Ma C, Wei K, Tu K, Pan L. Quantitative determination of sugar profiles in peach fruit during storage by an integrating sphere system. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103843] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Zaroual H, Chénè C, El Hadrami EM, Karoui R. Application of new emerging techniques in combination with classical methods for the determination of the quality and authenticity of olive oil: a review. Crit Rev Food Sci Nutr 2021; 62:4526-4549. [DOI: 10.1080/10408398.2021.1876624] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Hicham Zaroual
- Université d'Artois, UMRT 1158 BioEcoAgro, ICV-Institut Charles VIOLLETTE, Lens, France
- Sidi Mohamed Ben Abdellah University, Applied Organic Chemistry Laboratory, Fez, Morocco
| | | | - El Mestafa El Hadrami
- Sidi Mohamed Ben Abdellah University, Applied Organic Chemistry Laboratory, Fez, Morocco
| | - Romdhane Karoui
- Université d'Artois, UMRT 1158 BioEcoAgro, ICV-Institut Charles VIOLLETTE, Lens, France
- INRA, USC 1281,Lille, France
- Yncréa, Lille, France
- University of the Littoral Opal Coast (ULCO), Boulogne sur Mer, France
- University of Lille, Lille, France
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