光谱分析的葡萄酒掺水鉴别方法
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摘要
葡萄酒市场的迅猛发展,大量的中国优质葡萄酒也一直受假冒葡萄酒的侵害。假冒劣质葡萄酒的存在不仅影响中国优质葡萄酒的品牌,也会对人体产生一定的伤害。葡萄酒中掺水掺伪是制造假酒的最常见的手段,因此,对葡萄酒掺水掺伪的检测方法的研究也越来越受到国内外学者的重视。相比于传统的感官鉴定法、理化指标分析检验方法,具有快速、高效、无需破坏样本、非接触性等独特优势的可见-近红外光谱分析技术,更加适合于葡萄酒品质的快速检测。为了快速、准确的检测葡萄酒掺水问题,基于可见-近红外光谱构建了一种反映葡萄酒掺水程度的光谱吸收深度指数(DI),并设计构建了基于DI指数的葡萄酒掺水量的反演估算模型。首先采用长城解百纳葡萄酒(CC)、张裕解百纳葡萄酒(ZY)和西奥葡萄酒(XA)三种葡萄酒配制葡萄酒样本,分别提取相同量的葡萄酒作为实验对象,掺入比例为0%(未掺水的纯葡萄酒),4%,7.7%,11.1%,14.3%,17.2%的蒸馏水,获取样本共18份;另外对长城葡萄酒加大掺水比例,分别掺入比例为0%,20%,40%,60%,80%,90%的蒸馏水,获取样本数为6份,共获得24份掺有不同比例蒸馏水的葡萄酒样本。然后利用PSR-3500便携式地物光谱仪采集葡萄酒样本光谱数据,并对葡萄酒样本的原始光谱数据进行S-G滤波、特征波段选择、包络线去除等特征增强预处理;通过分析预处理后的葡萄酒样本的可见-近红外光谱特征,选取能反映葡萄酒掺水程度的837nm处稳定的吸收特性,构建了葡萄酒掺水的光谱吸收深度指数(DI)。为了提高光谱吸收深度指数DI的稳健性,DI指数中光谱反射率的值均采用837nm附近微小邻域均值进行计算。最后采用二次多项式拟合方法,给出了基于DI指数的葡萄酒掺水量的反演估算模型。选用长城解百纳葡萄酒在837nm处微小邻域内光谱吸收深度指数DI值,同时选择长城葡萄酒样本中的七个样本作为模型预测集,另外4个样本作为测试集,对该葡萄酒掺水量的反演估算模型进行验证分析。实验结果表明,采用二次多项式拟合方法,该模型结果的精度R平方高达0.999 2,且该模型的估算值与真实值的平均相对误差为0.042 5,表明了基于DI指数所构建的反演估算模型不仅可以判定待鉴别葡萄酒是否掺水并且可以定量分析葡萄酒的掺水量。光谱吸收深度指数DI构建简单,且能够反映不同品牌的葡萄酒的掺水稀释程度。研究结果可为低成本、手持式简易的葡萄酒光谱检测设备的设计与研发提供科学依据,进一步促进可见-近红外光谱分析在葡萄酒品质无损检测及相关领域的应用推广。
With the rapid development of wine market,a large number of Chinese high quality wine has been affected by inferior wine.The existence of fake inferior wine not only affects quality wine brand in China,will also do a certain harm to human body.Water adulteration in wine is the most common means of making fakes,therefore,study of wine water adulteration detection method has attracted more attention from the researchers both at home and abroad.Compared to traditional sensory assay methodor physical and chemical testing methods operated in laboratory,visible/near infrared spectral analysis technology is more suitable for rapid detection of wine quality with thequickness,high efficiency,non-destruction and non-contactfeatures.In order to detect the wine water blending problem rapidly and accurately,based on the visible/near infrared spectral analysis technology,this paper constructed a spectral absorption Depth Index(DI)to reflect the water degree blended in wine,and gave the wine mixing water inversion model based on DI Index to estimate the water content.First,this paper chosethree kinds of wine including the Changcheng cabernet wine(CC),Zhangyu cabernet wine(ZY)and Xiaocabernet wine(XA)to create 18 wine samples with 0% pure wine(no water),4%,7.7%,11.1%,7.7%and 17.2% of distilled water in the three kinds of wine respectively,and to create other 6wine samples with 0%,20%,40%,60%,80%,and 90% of distilled water in Changcheng wine.So there were totally 24 wine samples with different ratios of distilled water.Then,the wine spectral data were sampled using the PSR-3500 portable features spectrometer.After the preprocessing of the S-G filtering,special wavelength choosing,and continuum removing of the original spectral data,the visible/near infrared spectral features of wine samples were analyzed,anda spectral absorption depth Index(DI)of wine with distilled water was constructed using the stable spectral absorption property at 837 nm.In order to improve the robustness of DI index,the mean value of the spectral reflectance values near 837 nm small neighborhood was adopted.Finally,the wine mixing water inversion model based on DI index was created using the quadratic polynomial fitting method.To validate the inversion estimate model of the wine with water,the DI index of Changcheng cabernet wine was used,and seven samples were chosen as the prediction set,and the other four samples were chosen as test set in the experiment.Experimental results showed that the precision of Rsquare value of the model is up to 0.999 2with the quadratic polynomial fitting method,and the average relative error between the estimates of the model and the real value is 0.042 5.Experiments showed that the inversion estimated model based on DI index can not only identify whether the wine blended with water,but also make a quantitative analysis of the water content in wine.DI index was simple,and the DI index can reflect the water degree of different brands of wine.This study may provide a scientific basis for the design and development of low-cost and handheld portable spectrometers for wine detection,further promoting visible/near infrared spectral analysis technology in the quality detection of wine or other relative field.
With the rapid development of wine market,a large number of Chinese high quality wine has been affected by inferior wine.The existence of fake inferior wine not only affects quality wine brand in China,will also do a certain harm to human body.Water adulteration in wine is the most common means of making fakes,therefore,study of wine water adulteration detection method has attracted more attention from the researchers both at home and abroad.Compared to traditional sensory assay methodor physical and chemical testing methods operated in laboratory,visible/near infrared spectral analysis technology is more suitable for rapid detection of wine quality with thequickness,high efficiency,non-destruction and non-contactfeatures.In order to detect the wine water blending problem rapidly and accurately,based on the visible/near infrared spectral analysis technology,this paper constructed a spectral absorption Depth Index(DI)to reflect the water degree blended in wine,and gave the wine mixing water inversion model based on DI Index to estimate the water content.First,this paper chosethree kinds of wine including the Changcheng cabernet wine(CC),Zhangyu cabernet wine(ZY)and Xiaocabernet wine(XA)to create 18 wine samples with 0% pure wine(no water),4%,7.7%,11.1%,7.7%and 17.2% of distilled water in the three kinds of wine respectively,and to create other 6wine samples with 0%,20%,40%,60%,80%,and 90% of distilled water in Changcheng wine.So there were totally 24 wine samples with different ratios of distilled water.Then,the wine spectral data were sampled using the PSR-3500 portable features spectrometer.After the preprocessing of the S-G filtering,special wavelength choosing,and continuum removing of the original spectral data,the visible/near infrared spectral features of wine samples were analyzed,anda spectral absorption depth Index(DI)of wine with distilled water was constructed using the stable spectral absorption property at 837 nm.In order to improve the robustness of DI index,the mean value of the spectral reflectance values near 837 nm small neighborhood was adopted.Finally,the wine mixing water inversion model based on DI index was created using the quadratic polynomial fitting method.To validate the inversion estimate model of the wine with water,the DI index of Changcheng cabernet wine was used,and seven samples were chosen as the prediction set,and the other four samples were chosen as test set in the experiment.Experimental results showed that the precision of Rsquare value of the model is up to 0.999 2with the quadratic polynomial fitting method,and the average relative error between the estimates of the model and the real value is 0.042 5.Experiments showed that the inversion estimated model based on DI index can not only identify whether the wine blended with water,but also make a quantitative analysis of the water content in wine.DI index was simple,and the DI index can reflect the water degree of different brands of wine.This study may provide a scientific basis for the design and development of low-cost and handheld portable spectrometers for wine detection,further promoting visible/near infrared spectral analysis technology in the quality detection of wine or other relative field.
引文
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[3] LI Guang,LI Qi,JIN Li-qiong,et al(李广,李琪,金丽琼,等).China Brewing(中国酿造),2013,32(11):132.
[4] LONG Fei,DING Yi(龙飞,丁怡).Liquor-Making Science&Technology(酿酒科技),2014,3:97.
[5] Zhong Y F,Ma A L,Ong Y S,et al.Applied Soft Computing,2018,64:75.
[6] Zhang Y L,Chen J B,Lei Y,et al.Journal of Molecular Structure,2010,974(1-3):144.
[7] Luis Aleixandre-Tudo,Nieuwoudt Helene.Talanta,2018,176:526.
[8] Luis Aleixandre-Tudo,Nieuwoudt Helene,Olivieri Alejandro,et al.Food Control,2018,85:11.
[9] Zhao J,Zhong Y,Jia T,et al.ISPRS Journal of Photogrammetry and Remote Sensing,2017,135(1):31.
[10] Luis Aleixandre-Tudo,Buice Astrid,Nieuwoudt Helene,et al.Journal of Agricultral and Food Chemistry,2017,65(20):4009.
[11] LI Meng-hua,LI Jing-ming,LI Jun-hui(李梦华,李景明,李军会).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2015,35(6):1551.
[12] JIA Liu-jun,ZHANG Hai-hong,WANG Jian,et al(贾柳君,张海红,王健,等).Food and Fermentation Industries(食品与发酵工业),2017,(2):191.
[13] CHEN Li-dan,ZHAO Yan-ru(陈立旦,赵艳茹).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2014,30(8):168.
[14] WANG Xiao-yan,WANG Xi-chang,LIU Yuan,et al(王小燕,王锡昌,刘源,等).Food Science(食品科学).2011,32(1):265.
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