Making Roasted Mutton Colourimetric Card Based on Machine Vision Technology

WANG Bo; HU Xiaoyan; YU Fangzhu; LIU Dengyong

doi:10.13386/j.issn1002-0306.2021070346

Volume 43 Issue 3

Jan. 2022

Turn off MathJax

Article Contents

Abstract

References

Science and Technology of Food Industry > 2022 > 43(3): 10-17. > DOI: 10.13386/j.issn1002-0306.2021070346

WANG Bo, HU Xiaoyan, YU Fangzhu, et al. Making Roasted Mutton Colourimetric Card Based on Machine Vision Technology[J]. Science and Technology of Food Industry, 2022, 43(3): 10−17. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021070346.

Citation:

PDF (3218 KB)

Making Roasted Mutton Colourimetric Card Based on Machine Vision Technology

WANG Bo^1,,
HU Xiaoyan^1,,
YU Fangzhu¹,
LIU Dengyong^{1, 2, ,}

1.
National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, College of Food Science and Technology, Bohai University, Jinzhou 121013, China
2.
Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, Nanjing 210095, China

More Information

Received Date: July 27, 2021
Available Online: December 06, 2021

Graphical Abstract

Abstract

Abstract

In order to establish a standardized method that can quickly and nondestructively identify the color changes in the process of mutton roasting, this study combined three algorithms (mean value algorithm, K-means algorithm and K-means+image noise reduction algorithm) based on machine vision technology to make the color recognition colourimetric card and carried out online monitoring of the color of roasted mutton. The results showed that the colorimetric cards made by the three algorithms could show the color changes in the process of mutton roasting. In order to clarify the accuracy of the three colorimetric cards, K-medoids algorithm combined with sensory experiment was used to verify the accuracy of color recognition of the colorimetric cards. The verification results of colourimetric card recognition accuracy using K-medoids algorithm showed that the accuracy of mean algorithm was 85.60%, that of K-means algorithm was 95.70%, and that of K-means algorithm+image noise reduction algorithm was 93.40%. The verification results of sensory experiments showed that the recognition accuracy of mean algorithm, K-means algorithm and K-means algorithm+image noise reduction algorithm were 67.32%, 73.71% and 68.74% respectively, the comparison showed that the color recognition accuracy of colourimetric card made by K-means algorithm was the highest for roasted mutton. The study proved that the colorimetric card can be used as the color evaluation criterion and provides guidance for barbecue meat processing. It had a good application prospect.
- roasted mutton,
- colorimetric card,
- machine vision,
- color,
- image identification

FullText(HTML)

References (33)

References

[1]	周光宏. 肉品加工学[M]. 北京: 中国农业出版社, 2008: 234−238. ZHOU G H. Meat processing[M]. Beijing: China Agricultural Press, 2008: 234−238.
[2]	张德权, 惠腾, 王振宇. 我国肉品加工科技现状及趋势[J]. 肉类研究,2020,34(1):1−8. [ZHANG D Q, HUI T, WANG Z Y. Current situation and futre trend of meat processing technology in china[J]. Meat Science,2020,34(1):1−8. doi: 10.7506/rlyj1001-8123-20191029-256
[3]	王丽, 李雪, 李亮, 等. 天然提取物在传统肉制品加工中的应用研究进展[J]. 食品安全质量检测学报,2020,11(8):2558−2563. [WANG L, LI X, LI L, et al. Research progress on the application of natural extracts in the processing of traditional meat products[J]. Journal of Food Safety & Quality,2020,11(8):2558−2563.
[4]	ZHUANG H, BOWKER B. Effect of marination on lightness of broiler breast fillets varies with raw meat color attributes[J]. LWT-Food Science and Technology,2016,69:233−235. doi: 10.1016/j.lwt.2016.01.054
[5]	RAMIREZ R, CAVA R. The crossbreeding of different Duroc lines with the Iberian pig affects colour and oxidative stability of meat during storage[J]. Meat Science,2007,77(3):339−347. doi: 10.1016/j.meatsci.2007.04.002
[6]	汤夕瑶, 张旭飞, 任惠峰, 等. 冷藏过程中凡纳滨对虾颜色变化与鲜度指标间的相关性研究[J/OL]. 食品与发酵工业: 1−13 [2021-08-26]. TANG X Y, ZHANG X F, REN H F, et al. Study on the correlation between the color change and freshness indexes of Litopenaeus vannamei during cold storage [J/OL]. Food and Fermentation Industries: 1−13[2021-08-26]. https://doi.org/10.13995/j.cnki.11-1802/ts.027896.
[7]	杨兆甜, 李方巍, 王震昊, 等. 食品颜色评价及在食品工业中的应用[J/OL]. 食品工业科技: 1−10 [2021-08-26]. YANG Z T, LI F W, REN Z H, et al. Food color evaluation and application in food industry [J/OL]. Science and Technology of Food Industry: 1−10 [2021-08-26]. https://doi.org/10.13386/j.issn1002-0306.2020110045.
[8]	杨晟, 张巧, 廖汝娥. 比色卡法快速测定环境水体中氮磷的研究[J]. 广东化工,2018,45(22):111−113. [YANG S, ZHANG Q, LIAO R E. Study on rapid of nitrogen and phorus in aquatic water using colorimetic card method[J]. Guangdong Chemical Industry,2018,45(22):111−113. doi: 10.3969/j.issn.1007-1865.2018.22.052
[9]	GIUSEPPE V, JÖRN S, FRAUKE S, et al. Evaluation of new colorimetric vitex 2 yeast identification card by use of different source media[J]. Journal of Clinical Microbiology,2008,46(11):3784−3787. doi: 10.1128/JCM.01318-08
[10]	王亭. 基于机器视觉的面饼检测开发系统[D]. 成都: 电子科技大学, 2020. WANG T. Development of cake defect system based on machine vision[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[11]	KERRY J P, LEDWARD D, KERRY J P, et al. Improving the sensory and nutritional quality of fresh meat, J. P. Kerry, D. Ledward (Eds.). Woodhead Publishing and CRC Press[J]. Meat Science,2009,83(2):337. doi: 10.1016/j.meatsci.2009.05.005
[12]	贾磊, 陈俊超. 机器视觉的水产食品外观品质检测系统[J]. 食品工业,2021,42(5):266−268. [JIA L, CHEN J C. Mechine vision inspection for appearance quality of aquaic food[J]. The Food Industry,2021,42(5):266−268.
[13]	KE L T, LIN J L, LI M Y, et al. Selection for high quality pepper seeds by machine vision and classifiers[J]. Journal of Integrative Agriculture,2018,17(9):1999−2006. doi: 10.1016/S2095-3119(18)62031-3
[14]	DAUGAARD S R B, ADLER-NISSEN J, CARSTENSEN J M. New vision technology for multidimensional quality monitoring of continuous frying of meat[J]. Food Control,2010,21(5):626−632. doi: 10.1016/j.foodcont.2009.09.007
[15]	AMIN TAHERI-GARAVAND, SOODABEH F, MAHMOUD O, et al. Meat quality evaluation based on computer vision technique: A review[J]. Meat science,2019,156:183−195. doi: 10.1016/j.meatsci.2019.06.002
[16]	SUN X, YOUNG J, LIU J H, et al. Prediction identify of pork loin quality using online computer vision system and artificial intelligence model[J]. Meat Science,2018,140:72−77. doi: 10.1016/j.meatsci.2018.03.005
[17]	NYALALA I, OKINDA C, KUNJIE C, et al. Weight and volume estimation of poultry and products based on computer vision systems: A review[J]. Poultry Science,2021,100(5):101072. doi: 10.1016/j.psj.2021.101072
[18]	胡煌, 吕飞, 丁玉庭. 肉制品的呈色机理和色泽评定研究进展[J]. 肉类研究,2016,30(12):48−53. [HU H, LV F, DING Y T. Progress in coloration mechanism and color assessment of meat products[J]. Meat Research,2016,30(12):48−53.
[19]	TOMASEVIC I, TOMOVIC , MILOVANOVIC B, et al. Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties[J]. Meat Science,2019,148:5−22. doi: 10.1016/j.meatsci.2018.09.015
[20]	郭爱玲, 马世榜, 刘伟, 等. 基于机器视觉技术的牛肉大理石花纹自动检测系统[J]. 南阳师范学院学报,2017,16(6):13−16. [GUO A L, MA S B, LIU W, et al. Automatic detection system of beef marbling based on machine vision technology[J]. Journal of Nanyang Normal University,2017,16(6):13−16. doi: 10.3969/j.issn.1671-6132.2017.06.004
[21]	王巧华, 王彩云, 马美湖. 基于机器视觉的鸭蛋新鲜度检测[J]. 中国食品学报,2017,17(8):268−274. [WANG Q H, WANG C Y, MA M H. Duck eggs’ freshness detection based on machine vision technology[J]. Journal of Chinese Institute of Food Science and Technology,2017,17(8):268−274.
[22]	陆逢贵. 基于机器视觉技术的糖熏鸡腿颜色识别研究[D]. 锦州: 渤海大学, 2020 LU F G. Research on color recognition of sugar-smoked chicken thighs based on machine vision technology [D]. Jinzhou: Bohai University, 2020.
[23]	刘登勇, 陆逢贵, 王博, 等. 一种用于评价熏鸡品质的比色卡及应用: 中国, CN201910924755.3[P]. 2020-01-03. LIU D Y, LU F G, WANG B, et al. A color card and application for evaluating the quality of smoked chicken: China, CN201910924755.3[P]. 2020-01-03.
[24]	KOUHI ESFAHANI R, SHAHBAZI F, AKBARZADEH M. Three phase classification of an uninterrupted traffic flow: A K-means clustering study[J]. Transportmetrica B,2019,7(1):546−558.
[25]	BARCA J C, RUMANTIR G. A modified K-means algorithm for noise reduction in optical motion capture data[C]. Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on, 2007.
[26]	LIU D, LI J. Safety monitoring data classification method based on wireless rough network of neighborhood rough sets[J]. Safety Science,2019,118:103−108. doi: 10.1016/j.ssci.2019.05.004
[27]	HASHEMZADEH M, ZADEMEHDI A. Fire detection for video surveillance applications using ICA K-medoids-based color model and efficient spatio-temporal visual features[J]. Expert Systems with Applications,2019,130(Sep.):60−78.
[28]	中华人民共和国国家质量监督检验检疫总局中国国家标准化管理委员会. GB/T16291.1-2012. 感官分析选拔、培训与管理评价员一般导则[S]. 北京: 中国标准出版社, 2012. General Administration of Quality Supervision, Inspection and Quarantine o of the People's Republic of China China National standardizing committee. GB/T16291.1-2012. General guidelines for sensory analysis selection, training and management of evaluators[S]. Beijing: Standards Press of China, 2012.
[29]	A N LEPPER-BLILIE E P. Consumer evaluation of palatability characteristics of a beef value-added cut compared to common retail cuts[J]. Meat Science,2014,96(1):419−422. doi: 10.1016/j.meatsci.2013.08.002
[30]	徐静, 宋健, 武杰, 等. 响应面法优化柠檬酸嫩化鸭肉效果的研究[J]. 安徽科技学院学报,2019,33(3):60−65. [XU J, SONG J, WU J, et al. Response surface methodology for tendering duck meat by citric acid[J]. Journal of Anhui Science and Technology University,2019,33(3):60−65.
[31]	孙甜甜, 赵镭, 钟葵, 等. 响应面法优化低盐麻辣火锅底料配方工艺研究[J]. 中国调味品,2021,46(1):128−134. [SUN T T, ZHAO L, ZHONG K, et al. Study on optimizing the foemuia of low-salt spicy hotpot seasonings by response surface method[J]. China Condiment,2021,46(1):128−134. doi: 10.3969/j.issn.1000-9973.2021.01.027
[32]	黄旭, 凌志刚, 李绣心. 融合判别式深度特征学习的图像识别算法[J]. 中国图象图形学报,2018,23(4):510−518. [HUAGN X, LING Z G, LI X X. Discriminative deep feature learning method by fusing linear discriminant analysis for image recognition[J]. Journal of Image and Graphics,2018,23(4):510−518.
[33]	ZHANG D, SHI K, GUO Z, et al. A class of elliptic systems with discontinuous variable exponents and L 1 data for image denoising[J]. Nonlinear Analysis: Real World Applications,2019,50:448−468. doi: 10.1016/j.nonrwa.2019.05.012

Supplements (0)

Cited By

Cited by

Periodical cited type(12)

1.	赵星，张嘉楠，张一鸣，金欣欣，苏俏，宋亚辉，李玉荣，王瑾. 花生籽仁蔗糖含量近红外光谱快速测定方法研究. 中国油料作物学报. 2025(01): 226-233 .
2.	魏松丽，张丽霞，孙强，芦鑫，靳淑秀，孙晓静，金璐，游静，黄纪念. 真空干燥花生油体的条件优化及性质表征. 河南工业大学学报(自然科学版). 2024(01): 8-16 .
3.	张亚靖，陈复生，王颖颖，刘晨，郑乾坤，殷丽君. 油脂体的提取方法及其在食品中应用的研究进展. 中国油脂. 2024(08): 131-136 .
4.	单子明，彭郁，秦琛强，傅娆，李茉，倪元颖，温馨. 植物油脂体提取及稳定性评价研究进展. 食品科学. 2024(19): 19251-19262 .
5.	忠梦，刘白宁，华威，王锋，荣瑞芬，段玉权. 不同包装核桃仁氧化机制分析. 食品科学. 2024(20): 65-73 .
6.	尹国友，杨卓凡，曾姣，张莹莹，孙婕，王召. 大豆油体包埋韭菜籽油微胶囊工艺优化及其稳定性评价. 食品科技. 2024(11): 267-275 .
7.	李天赐，陈毅保，刘昆仑，陈复生，杨趁仙，段晓杰，朱婷伟. 界面蛋白对水酶法提取植物油脂过程中乳状液稳定性影响的研究进展. 食品科学. 2023(17): 188-195 .
8.	王广婕，赵焕宇，苏成成，韦旋，吴梦果，单迪，黄萍，马佳歌，侯俊财，姜瞻梅. 油脂体的组成、结构及氧化稳定性研究进展. 食品科学. 2023(21): 293-302 .
9.	官梦姝，冯雪，刘月，朱秀清，姜瞻梅，江连洲，侯俊财. 3种天然酚类物质对大豆油脂体稳定性及体外消化性的影响. 食品科学. 2022(03): 10-18 .
10.	秦晓鹏，黄沙沙，聂成镇，禹晓，邓乾春，相启森，朱莹莹. 微波处理对萌动亚麻籽酚类化合物油相迁移的影响. 食品科学技术学报. 2022(03): 124-136 .
11.	汪锦，应瑞峰，王耀松，黄梅桂. 超声-水酶法对高品质薄壳山核桃油释放的影响. 食品与发酵工业. 2022(18): 177-182 .
12.	刘子豪，梅雅欣，彭郁，傅娆，秦琛强，倪元颖，温馨. 外源蛋白对大豆油脂体稳定性的影响. 食品科学. 2022(22): 1-9 .