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| 基于近红外光谱技术的蜂蜜掺假识别 | |
| 引用本文： | 屠振华,朱大洲,籍保平,陈红茜,庆兆珅.基于近红外光谱技术的蜂蜜掺假识别[J].农业工程学报,2011,27(11):382-387. |
| 作者姓名： | 屠振华 朱大洲 籍保平 陈红茜 庆兆珅 |
| 作者单位： | 1. 中国农业大学食品科学与营养工程学院,北京100083;(国家)食品行业生产力促进中心,北京100062 2. 国家农业信息化工程技术研究中心,北京,100097 3. 中国农业大学食品科学与营养工程学院,北京,100083 |
| 基金项目： | 国家“十一五”重大科技专项（2006BAD05A06-Z1）；国家自然科学基金（30901127） |
| 摘 要： | 为了实现蜂蜜掺假的快速识别，应用近红外光谱结合模式识别方法对蜂蜜掺假现象进行了识别分析。该研究收集了中国不同品种、不同地域的典型天然蜂蜜样品，根据目前市场上常见的蜂蜜掺假手段，掺假物质及相对含量情况配制了掺假蜂蜜样品，利用傅立叶近红外光谱仪采集其透反射近红外光谱，分别采用偏最小二乘判别分析（PLS-DA），独立软模式法（SIMCA），误差反向传播神经网络（BP-ANN）和最小二乘支持向量机（LS-SVM）等模式识别方法，进行蜂蜜掺假识别研究。研究结果表明：利用这4种方法在蜂蜜中掺入果葡糖浆和果葡糖水的情况下均能很好地识别出掺假蜂蜜样品，其中对于掺入果葡糖浆的掺假情况，校正集的正确判别率均达到95%以上，验证集的正确判别率均达到87%以上，对于掺入果葡糖水的掺假蜂蜜校正集的正确判别率均达到93%以上，验证集的正确判别率均达到84%以上。通过比较4种不同的识别算法，发现采用LS-SVM时，对两种掺假情况下校正集和验证集的正确判别率均达到了100%，表明基于近红外光谱的蜂蜜掺假快速准确识别是可行的。 |
| 关 键 词： | 红外光谱，算法，模式识别，蜂蜜，掺假识别 |
| 收稿时间： | 2011/1/25 0:00:00 |
| 修稿时间： | 9/8/2011 12:00:00 AM |
Adulteration detection of honey based on near-infrared spectroscopy |
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| Tu Zhenhu,Zhu Dazhou,Ji Baoping,Chen Hongqian and Qing Zhaoshen.Adulteration detection of honey based on near-infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(11):382-387. | |
| Authors: | Tu Zhenhu Zhu Dazhou Ji Baoping Chen Hongqian and Qing Zhaoshen |
| Abstract: | Near infrared spectroscopy combined with pattern recognition methods was used to discriminate the unadulterated and adulterated honey samples. Various crude honey samples from different area in China were collected, and the adulterated honey were prepared according to typical adulteration method, adulteration substance and construction in the market. FT-NIR spectrometer was used to measure the trans-reflectance spectra of honey. The differentiation models for adulteration of honey were constructed by four kinds of pattern recognition methods, including partial least squares discriminate analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), error back propagation network (BP-ANN), least-squares support vector machine (LS-SVM). The results showed that four methods could all correctly differentiate honey samples that were adulterated with high fructose syrup and fructose-plus-glucose solutions. For the adulteration of high fructose syrup, the classification accuracy of calibration set was above 95%, and the classification accuracy of prediction set was above 87%. For the adulteration of fructose-plus-glucose solutions, the classification accuracy of both calibration set was above 93%, and the classification accuracy of prediction set was above 84%. Compared with the four kinds of models, it was found that LS-SVM had the best results, the classification accuracy for both calibration set and prediction set were 100% for two kinds of adulteration. The present study indicated that the fast and accurate differentiation of the adulteration of honey by NIR spectra was feasible. |
| Keywords: | infrared spectroscopy algorithms pattern recognition honey adulteration detection |
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