Research on vinegar brand traceability based on near infrared spectrum technology combined with support vector machine

Authors

LIU Jing, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
GUAN Xiao, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
YI Cuiping, School of Chemistry and Biology Engineering, Changsha University of Science and Technology, Changsha, Hunan 410114, China

Abstract

Presented a fast and non-destructive method for the discrimination of vinegar brands by near-infrared spectroscopy technology. One hundred and fifty-two representative samples of vinegar including Bao Ning, East Lake, Heng Shun, Zhenjiang were collected from market. Multiplicative Scatter Correction (MSC) was used to handle the original near infrared spectrum (NIR) data and Principal Component Analysis (PCA) was used to process the spectral data after pretreatment according to the accumulative contribution rate of principal components to select principal components. Support Vector Machine (SVM) was then applied to build the brand traceability model with proper kernel function. Particle Swarm Optimization was applied to optimize the parameters of the model. The experiments indicated that the method combing near infrared spectroscopy with support vector machine could classify the vinegar brand with 100% accuracy.

Publication Date

1-28-2016

First Page

38

Last Page

40,50

DOI

10.13652/j.issn.1003-5788.2016.01.009

Recommended Citation

Jing, LIU; Xiao, GUAN; and Cuiping, YI (2016) "Research on vinegar brand traceability based on near infrared spectrum technology combined with support vector machine," Food and Machinery: Vol. 32: Iss. 1, Article 9.
DOI: 10.13652/j.issn.1003-5788.2016.01.009
Available at: https://www.ifoodmm.cn/journal/vol32/iss1/9

References

[1] 王莉, 刘飞, 何勇. 应用可见近红外光谱技术进行食用醋品牌和PH值的快速检测[J]. 光谱学与光谱分析, 2008, 28(4): 813-816.
[2] 徐广通, 袁洪福, 陆婉珍. 现代近红外光谱技术及应用进展[J]. 光谱学与光谱分析, 2000, 20(2): 134-142.
[3] Ken Watanabe, Shawn D Mansfield, Stavros Avramidis. Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber[J]. Journal of Wood Science, 2011, 57(4): 288-294.
[4] Justin J Skowno, Jonathan Saul Karpelowsky. Near-infrared spectroscopy for monitoring renal transplant perfusion[J]. Pediatric Nephrology, 2014, 29(11): 2 241-2 242.
[5] Ana Henriques, Paulo Cruz, Jorge Martins, et al. Determination of melamine content in amino resins by near-infrared spectroscopy[J]. Wood Science and Technology, 2013, 47(5): 939-940.
[6] Gastaldi D, Canonico F,Irico S, et al. Near-infrared spectroscopy investigation on the hydration degree of a cement paste[J]. Journal of Materials Science, 2010, 45(12): 3 169-3 174
[7] Liu Fei, He Yong, Wang Li. Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration[J]. Food and Bioprocess Technology, 2011, 4(8): 1 311-1 340.
[8] 宋海燕, 秦刚, 刘海芹. 基于近红外光谱技术的瓶装醋定性检测[J]. 光谱学与光谱分析, 2012, 32(6): 1 547-1 549.
[9] 赵强, 张工力, 陈星旦. 多元散射校正对近红外光谱分析定标模型的影响[J]. 光学精密工程, 2005, 13(1): 53-58.
[10] 李武, 胡冰, 王明伟. 基于主成分分析和支持向量机的太赫兹光谱冰片鉴别[J]. 光谱学与光谱分析, 2014, 34(12): 3 235-3 240.
[11] Huang Hong-zhong, Wang Hai-kun, Li Yan-feng. Support vector machine based estimation of remaining useful life: current research status and future trends[J]. Journal of Mechanical Science and Technology, 2015, 29(1): 151-153.
[12] 李玉军, 汤晓君, 刘君华. 粒子群优化算法在混合气体红外光谱定量分析中的应用[J]. 光谱学与光谱分析, 2009, 29(5): 1 276-1 277.