Establishment of Detection Model of Water-injected Meat Based on Low Field Nuclear Magnetic Resonance Transverse Relaxation Spectroscopy

XU Jianmei; YANG Fang; LIN Qing; WANG Jiahui; AI Zhujun

doi:10.13386/j.issn1002-0306.2020070304

Volume 42 Issue 11

May 2021

Turn off MathJax

Article Contents

Abstract

References

Science and Technology of Food Industry > 2021 > 42(11): 226-232. > DOI: 10.13386/j.issn1002-0306.2020070304

XU Jianmei, YANG Fang, LIN Qing, et al. Establishment of Detection Model of Water-injected Meat Based on Low Field Nuclear Magnetic Resonance Transverse Relaxation Spectroscopy[J]. Science and Technology of Food Industry, 2021, 42(11): 226−232. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020070304.

Citation:

PDF (1236 KB)

Establishment of Detection Model of Water-injected Meat Based on Low Field Nuclear Magnetic Resonance Transverse Relaxation Spectroscopy

1.
Modern Education Technology Center, Hainan Medical University, Haikou 571199, China
2.
School of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
3.
School of Tropical & Laboratory, Hainan Medical University, Haikou 571199, China

More Information

Received Date: July 26, 2020
Available Online: April 07, 2021

Graphical Abstract

Abstract

Abstract

The longissimus dorsi in pigs was prepared into the meat samples with water injection ratio of 0%, 2%, 6%, 10% and 14% by direct injection. The NMR signals generated by the meat samples were measured by LF-NMR technique. The transverse relaxation spectrum parameters obtained by inversion were taken as independent variables, combined with discriminant analysis and partial least square regression (PLSR) analysis, the models for detecting water-injected meat were developed, and multiple possibilities for PLSR modeling were attempted. It was showed that the developed discriminant model was validated with calibration set and leave-one-out cross, the total accuracy of the classification of water-injected meat was 89.4% and 88.2%, respectively. By combining the lever value with the student residual, the abnormal data was distinguished and deleted, and the prediction performance of the PLSR model established by using the variable projection importance analysis method to select out six transverse relaxation spectrum parameters as independent variables was optimal, the determination coefficient (R_c²) and standard error (SEV) from calibration set were 0.9603 and 1.0033%, the determination coefficient(R_cv²) and standard error (SECV) from cross validation were 0.9508 and 1.1169%, the determination coefficient(R_p²) and standard error (SEP) from prediction set were 0.9518 and 1.1280%, respectively. The best estimate of the confidence interval capable of predicting the percentage of water injection in unknown samples was about 2.256% at 95% confidence probability. The results showed that the DA model and PLSR model based on LF-NMR transverse relaxation spectrum could be used for qualitative and quantitative detection of water-injected meat.
- low-field nuclear magnetic resonance,
- discriminant analysis,
- partial least square regression,
- water-injected meat

FullText(HTML)

References (30)

References

[1]	李欣南, 关一夫. 掺假肉检验技术发展现状[J]. 食品研究与开发,2016,37(5):189−193. doi: 10.3969/j.issn.1005-6521.2016.05.044
[2]	程灵豪, 焦永亮. 白肌肉、注水肉、注胶肉的鉴别检验方法[J]. 中国动物检疫,2015,32(4):28−31. doi: 10.3969/j.issn.1005-944X.2015.04.009
[3]	邓咏梅. 关于我国猪肉水分限量的研究[J]. 肉类工业,2016(11):24−27. doi: 10.3969/j.issn.1008-5467.2016.11.008
[4]	杨志敏, 丁武, 张瑶. 应用近红外技术快速鉴别原料肉注水的研究[J]. 食品研究与开发,2012,33(5):118−120, 128. doi: 10.3969/j.issn.1005-6521.2012.05.034
[5]	孟一, 张玉华, 许丽丹, 等. 近红外光谱技术对猪肉注水、注胶的快速检测[J]. 食品科学,2014,35(8):299−303. doi: 10.7506/spkx1002-6630-201408060
[6]	唐鸣, 田潇瑜, 王旭, 等. 基于近红外特征波段的注水肉识别模型研究[J]. 农业机械学报,2018,49(S1):440−446. doi: 10.6041/j.issn.1000-1298.2018.S0.060
[7]	於海明, 徐佳琪, 刘浩鲁, 等. 基于高光谱和频谱特征的注水肉识别方法[J]. 农业机械学报,2019,50(11):367−372, 366. doi: 10.6041/j.issn.1000-1298.2019.11.041
[8]	Kamruzzaman M, Makino Y, Oshita S, et al. Assessment of visible near-infrared hyperspectral imaging as a tool for detection of horsemeat adulteration in minced beef[J]. Food and Bioprocess Technology,2015,8(5):1054−1062. doi: 10.1007/s11947-015-1470-7
[9]	阮榕生. 核磁共振技术在食品和生物体系中的应用[M]. 北京: 中国轻工出版社, 2009: 77−83.
[10]	Kirtil E, Mecit H, Oztop. ¹H Nuclear magnetic resonance relaxometry and magnetic resonance imaging and applications in food science and processing[J]. Food Engineering Reviews,2016,8(1):1−22. doi: 10.1007/s12393-015-9118-y
[11]	Wang H, Wang R, Song Y, et al. A fast and non-destructive LF-NMR and MRI method to discriminate adulterated shrimp[J]. Journal of Food Measurement and Characterization,2018,12(2):1340−1349. doi: 10.1007/s11694-018-9748-x
[12]	崔智勇, 丁杰, 徐艳, 等. 基于LF-NMR技术下3种猪肉水分含量预测模型的建立与比较[J]. 食品工业科技,2020,41(5):215−220, 226.
[13]	渠琛玲, 汪紫薇, 王雪珂, 等. 基于低场核磁共振的热风干燥过程花生仁含水率预测模型[J]. 农业工程学报,2019,35(12):290−296. doi: 10.11975/j.issn.1002-6819.2019.12.035
[14]	Bertram H C, Rasmussen M, Busk H, et al. Changes in porcine muscle water characteristics during growth-an in vitro low-field NMR relaxation study[J]. Journal of Magnetic Resonance,2002,157(2):267−276. doi: 10.1006/jmre.2002.2600
[15]	王胜威, 母应春, 赵旭, 等. 基于LF-NMR弛豫特性对注水、注胶羊肉辨别研究[J]. 食品工业,2015(6):184−188.
[16]	王欣, 王志永, 陈利华, 等. 注水肉糜的低场核磁弛豫特性及判别分析[J]. 现代食品科技,2016(5):79−84.
[17]	汪冬华. 多元统计分析与SPSS应用(第2版)[M]. 上海: 华东理工大学出版社, 2018: 173−483.
[18]	Monroy M, Prasher S, Ngadi M O, et al. Pork meat quality classification using Visible/Near-Infrared spectroscopic data[J]. Biosystems Engineering,2010,107 (3):271−276. doi: 10.1016/j.biosystemseng.2010.09.006
[19]	Wold S, Sjöström M, Eriksson L. PLS-regression: A basic tool of chemometrics[J]. Chemometrics and Intelligent Laboratory Systems,2001,58(2):109−130. doi: 10.1016/S0169-7439(01)00155-1
[20]	Liu J, Cao Y, Wang Q, et al. Rapid and non-destructive identification of water-injected beef samples using multispectral imaging analysis[J]. Food Chemistry,2016,190:938−943. doi: 10.1016/j.foodchem.2015.06.056
[21]	ElMasry G, Sun D W, Allen P. Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging[J]. Journal of Food Engineering,2013,117 (2):235−246. doi: 10.1016/j.jfoodeng.2013.02.016
[22]	Hazlewood C F, Chang D C, Nichols B L, et al. Nuclear magnetic resonance transverse relaxation times of water protons in skeletal muscle[J]. Biophysical Journal,1974,14(8):583−606. doi: 10.1016/S0006-3495(74)85937-0
[23]	Shaarani S M, Nott K P, Hall L D. Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat[J]. Meat Science,2006,72(3):398−403. doi: 10.1016/j.meatsci.2005.07.017
[24]	Fjelkner-Modig S, Tornberg E. Water distribution in porcine M. longissimus dorsi in relation to sensory properties[J]. Meat Science,1986,17(3):213−231. doi: 10.1016/0309-1740(86)90005-7
[25]	谭明乾, 林竹一, 李晨阳, 等. 基于低场核磁共振技术的小鼠体成分无损分析方法开发[J]. 分析科学学报,2018,34(4):24−31.
[26]	Kelly J F D, Downey G, Fouratier V. Initial study of honey adulteration by sugar solutions using midinfrared (MIR) spectroscopy and chemometrics[J]. Journal of Agricultural and Food Chemistry,2004,52(1):33−9. doi: 10.1021/jf034985q
[27]	闵顺耕, 李宁, 张明祥. 近红外光谱分析中异常值的判别与定量模型优化[J]. 光谱学与光谱分析,2004(10):1205−1209. doi: 10.3321/j.issn:1000-0593.2004.10.014
[28]	Nicola B M, Beullens K, Bobelyn E, et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review[J]. Postharvest Biology & Technology,2007,46(2):99−118.
[29]	Samuel D, Park B, Sohn M, et al. Visible-near-infrared spectroscopy to predict water-holding capacity in normal and pale broiler breast meat[J]. Poultry Science,2011,90 (4):914−921. doi: 10.3382/ps.2010-01116
[30]	张恒喜. 小样本多元数据分析方法及应用[M]. 西安: 西北工业大学出版社, 2002.

[1]	LIU Xueqing, LÜ Xinran, BAI Fengling, Li Jianrong. Biofilm Formation, Antimicrobial Resistance and Control of Vibrio Based on Quorum Sensing: A Review[J]. Science and Technology of Food Industry, 2023, 44(2): 445-452. DOI: 10.13386/j.issn1002-0306.2022020156
[2]	HAN Xiangpeng, CHEN Qingying, ZHANG Xingguo, HE Shuang, ZHONG Qingping. Inhibitory Effects of Enterococcus faecalis Z096 on Biofilm and Quorum Sensing of Vibrio parahaemolyticus[J]. Science and Technology of Food Industry, 2022, 43(19): 167-176. DOI: 10.13386/j.issn1002-0306.2021120291
[3]	SHANGGUAN Wendan, CHEN Ke, CHEN Qingying, ZHANG Xingguo, ZHONG Qingping. Quenching Effects of Lactobacillus pentosus DF9 on Quorum Sensing of Vibrio parahaemolyticus[J]. Science and Technology of Food Industry, 2022, 43(6): 126-132. DOI: 10.13386/j.issn1002-0306.2021070267
[4]	YU Shuchi, XING Wenjun, FENG Zilan, LIANG Yuqing, YANG Bo, XU Xiaoxiong, PEI Zhisheng. Inhibition of Kuding Tea Polyphenols against the Spoilage Characteristics of Pseudomonas fluorescens Based on Quorum Sensing[J]. Science and Technology of Food Industry, 2021, 42(21): 148-157. DOI: 10.13386/j.issn1002-0306.2021020127
[5]	SHAN Ke, GUO Quan-you, LI Bao-guo. Effects of NaCl and pH on Carbon Source Metabolism Ability of Proteus vulgaris and Hafnia alvei[J]. Science and Technology of Food Industry, 2019, 40(10): 168-173. DOI: 10.13386/j.issn1002-0306.2019.10.028
[6]	ZHANG Yu-jiao, LI Li-ping, CAO Xiao-juan, CHEN Zhong-jun, HU Wei-dong. Formation and Interaction of Multi-species Biofilm Formed by Foodborne Bacteria[J]. Science and Technology of Food Industry, 2019, 40(4): 312-316. DOI: 10.13386/j.issn1002-0306.2019.04.052
[7]	GUO Mu-han, LIAO Jian-meng, SUN Li-jun, WANG Ya-ling, FANG Zhi-jia, LIU Ying, XU De-feng. Detecting of quorum sensing signal molecules N-acyl-homoserine lactones during the biofilm formation of V.parahaemolyticus by high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)[J]. Science and Technology of Food Industry, 2018, 39(12): 261-266. DOI: 10.13386/j.issn1002-0306.2018.12.046
[8]	WU Hui-ying, HAN Xue, ZHANG Li-juan, WANG Yue. Quorum sensing system and its role in regulation of biofilm formation in lactic acid bacteria[J]. Science and Technology of Food Industry, 2018, 39(1): 318-322. DOI: 10.13386/j.issn1002-0306.2018.01.058
[9]	ZHANG Teng, TIAN Jian-jun, LI Zi-yuan, JIA Yuan-bo, HE Yin-feng. Reseach of the relationship between AI-2 /LuxS quorum sensing system and biofilm formation in Lactobacillus plantarum HE-1[J]. Science and Technology of Food Industry, 2014, (05): 130-132. DOI: 10.13386/j.issn1002-0306.2014.05.082
[10]	Research progress in specific spoilage organisms and quorum sensing in chilled meat[J]. Science and Technology of Food Industry, 2012, (22): 395-399. DOI: 10.13386/j.issn1002-0306.2012.22.091

Supplements (0)

Cited By

Cited by

Periodical cited type(5)

1.	李瑛. 植物材料包装与植物纹样的融合探究. 分子植物育种. 2024(03): 962-966 .
2.	杨明，孟勇，彭超，李美群，彭凌云，艾文胜. 粽叶用箬竹丰产培育关键技术. 世界竹藤通讯. 2024(04): 46-49 .
3.	汤晓，马明. 4种粽叶多酚提取物对草鱼片的保鲜效果. 食品研究与开发. 2023(05): 73-80 .
4.	温红瑞，贾英健，李晓双，史新茹，宋晓晴，刘婷婷，陈宝江. 槲皮万寿菊素抗氧化性的研究. 饲料工业. 2022(05): 51-54 .
5.	汤晓，马明. 四种常见粽叶提取物的抑菌活性与稳定性. 浙江农业学报. 2022(06): 1287-1296 .

Other cited types(3)

Get Citation

PDF

XML

Article Metrics

Article views (231) PDF downloads (17) Cited by(8)

Science and Technology of Food Industry

Establishment of Detection Model of Water-injected Meat Based on Low Field Nuclear Magnetic Resonance Transverse Relaxation Spectroscopy