Quality Grading of Bacon Based on Multi-Data Fusion Technology

doi:10.7506/spkx1002-6630-201402042

Abstract

Abstract:

In recent years, problems with the quality of Chinese bacon such as acid value and peroxide value exceeding
the standards, fading, oil exudation and sticky feeling to the touch have received growing attention. A fast, accurate and
practical detection technology was developed to evaluate Chinese bacon quality. According to the relevant national standards
as well as the results of acid value, peroxide value and total bacterial number in bacon samples measured by biochemical
methods as the input of self-organizing feature map, the bacon samples were divided into four categories: safe to eat,
edible, not recommended to eat and inedible. The acid value and peroxide value of bacon were detected using near infrared
spectroscopy. The root mean square error prediction (RMSEP) results after the selection were 41% and 57% of those before
selecting wavelengths by the genetic algorithms. The selected number of wavelength was 1/3 of the total number of the
whole wavelength. Plaque area information was obtained by microscopic imaging technology, which has greatly improved
many problems with traditional testing methods for the determination of total bacterial numbers, such as complex operation,
subjectivity and time consuming. Finally, a quick discriminant model for grading the edibility of Chinese bacon was
established using the support vector machine approach based on the near-infrared spectral data and microscopic image data.

Key words: near infrared spectroscopy, multi-data fusion, support vector machine, Chinese bacon

CLC Number:

WANG Xin-kun, GUO Pei-yuan, LIN Yan. Quality Grading of Bacon Based on Multi-Data Fusion Technology[J]. FOOD SCIENCE, 2014, 35(2): 217-221.

References

[1]周光宏.畜产品加工学[M].北京:中国农业出版社,2003.126-129:112-123

[2]董寅初.现代肉类科技对中国传统风味腊肉品的影响[J].肉类研究,1999,(1):3-6.

[3]孙通徐惠荣应义斌. 近红外光谱分析技术在农产品/食品品质在线无损检测中的应用研究进展[J]. 光谱学与光谱分析 2009, 29(1)

[4]赵丽丽,张录达,宋忠祥等. 近红外光谱定量检测腌腊肉制品品质的研究[J].光谱学与光谱分析, 2007,27(1): 46-49.

[5]GB/2730-2005 腌腊肉制品卫生标准[S].

[6]GB/5009．37．2003食用植物油卫生标准分析方法[S].

[7] GB 47892-2010 食品安全国家标准食品微生物学检验: 菌落总数测定[S]

[8]徐云，吴静珠，王一鸣等．基于近红外光谱的未知类别样品聚类方法［Ｊ］．农业工程学报，2011, 27(8): 345-349

[9]A.M. Mouazen, R.Karoui, J.D.baerdemacker et al. Classification of soils into different moisture content levels based on VIS-NIR spectral[C].Proceeding of the 2006 ASABE Annual International Meeting,2006.9-12

[10]郭培源,陈岩,薛红,等.猪肉新鲜度智能检测装置:中国, 101144780A[P]. 2008-03-19.

[11]于瑞雪,郭培源.基于光电显微技术的猪肉新鲜度智能检测[J].北京工商大学学报:自然科学版, 2007,25(4): 34-38.

[12]陈冰, 李山, 吴宝海等.基于尺寸关联和偏最小二乘回归的多工序质量分析与预测[J].计算机集成制造系统, 2009, 15(2):389-398.

[13]Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing[M]. Electronic Industry Press.2005,254-284.

[14]王加华,韩东海.基于遗传算法的苹果近红外光谱分析[J]. 光谱与光谱学分析, 2008,28(10):2308-2311.

[15]毕松,郭培源.基于细菌菌斑变化的猪肉新鲜度检测方法研究[J]. 农机化研究, 2009,31(5):67-71.

[16]王树文, 闫成鑫, 张天序, 赵广州. 数学形态在图像处理中的应用[J]. 计算机工程与应用. 2004,40(32):90-92.

[17] Kingsbury N. Image processing with complex wavelets[J].Philos. Trans. R. soc. LondonA, Math. Phy. Sci., 1999, 357:2543-2560.

[18]陈纯.计算机图像处理技术与算法[M].北京:清华大学出版社, 2003.

[19] S.D.Shcakelford. Color image analysis technique for measuring of meat[J]. Meat Science, 2007,65(3):125~138.

[20]Kleynen O, Leemans V, Destain M F. Development of a multi-spectral vision system for the detection of defects on apples[J]. Journal of Food Engineering, 2005, 69(1): 41-49.

[21]史峰, 王小川, 郁磊等. MATLAB神经网络30个案例分析[M].北京航空航天大学出版社, 2011

[22]李静,查明,郭元玲,陈勇. 光谱成像信息的数据融合技术在存储表征中的应用[J].光谱学与光谱分析, 2011,31(10): 2639-2642.

[23]张学工.光雨. 统计学理论与支持向量机[J]. 自动化学报. 2000,26(1):32-42