蜂蜜品质近红外光谱评价技术研究
|
摘要
随着人们生活水平的提高及保健意识的加强,来自于纯天然的蜂蜜因富含复杂的营养物质和保健功效成分而深受消费者青睐,销售量也呈逐年上升趋势。然而,近年来我国蜂蜜品质状况不容乐观,尤其是品种良莠不齐,标识不清,掺假蜜频频出现,以假乱真,以次充好的现象越来越普遍。因此,如何科学检测和有效监控蜂蜜质量仍是我国目前蜂业发展中亟待解决的关键技术问题。
本文以蜂蜜为研究对象,利用傅立叶近红外光谱分析技术和多种化学计量学分析方法,开展蜂蜜品种鉴别、组分定量和真伪鉴别技术研究,并建立相应的数学模型,通过比较分析,综合评价了我国蜂蜜质量,得出的主要研究结果如下:
1、通过优化仪器采集条件和参数,最终确定了光纤透反射模式、扫描分辨率(8 cm~(-1))、扫描次数(32次)、采集温度(40℃)、光程等光谱采集最优化组合。
2、探讨了蜂蜜品种、采集温度、蜂蜜产地等不同因素对蜂蜜近红外光谱响应特性的影响。在大部分波段范围内,不同品种蜂蜜光谱存在显著差异,不同采集温度影响光谱特性,不同产地来源的蜂蜜光谱亦有所差别。
3、采用不同的光谱预处理方法,建立了不同波段范围内的马氏距离判别分析(MD-DA)、判别偏最小二乘法(DPLS)和人工神经网络(ANN)等三种蜂蜜品种识别模型,对比分析了这三种模型的识别效果。研究结果表明,MD-DA结合一阶导数和卷积平滑处理光谱所建模型最优,其校正集和预测集样品识别率分别为87.4 %和85.3 %。DPLS结合一阶导数光谱在10000～4200 cm~(-1)波段建立的鉴别模型校正集和预测集样品识别率分别为70.8 %和70.7 %。在隐含层数为10、学习速率为0.4条件下建立的ANN模型校正集和预测集样品识别率分别为90.9 %和89.3 %。三者最优模型中非线性模型ANN的识别效果好于其他两种。
4、在11800～4100 cm~(-1)光谱范围内,分别建立了混合品种蜂蜜的还原糖、葡萄糖、果糖、水分、蔗糖、麦芽糖、淀粉酶、酸度、羟甲基糖醛9个指标的偏最小二乘法(PLS)、多元线性回归(MLR)模型。其中PLS模型的各指标RPD值分别为2.44、1.85、2.32、1.52、1.24、1.40、1.87、1.46和1.21,蔗糖、麦芽糖、淀粉酶、酸度、羟甲基糖醛的光谱经过导数处理后的PLS模型性能显著提高。各指标的MLR模型RPD值分别为2.26、1.86、2.32、1.50、1.69、1.07、1.27、1.70和1.49,与PLS模型的预测能力相当。还原糖、葡萄糖、果糖、水分模型预测精度均好于其他指标。
5、针对含量较高的还原糖、葡萄糖、果糖,采用BiPLS、SiPLS分别对近红外信息区间进行优化并建立模型,各指标对应的BiPLS模型的相关系数分别为0.913、0.903、0.881,RMSEP分别为1.92、1.73、1.15,RPD值分别为2.06、2.01、2.28。SiPLS模型的相关系数分别为0.916、0.921、0.902,RMSEP分别为1.77、1.73、1.16,RPD值分别为2.24、2.01、2.26,与BiPLS优化模型相比,SiPLS模型的预测精度与之相当。用ANN建立了蜂蜜还原糖、葡萄糖、果糖的定量非线性模型,相关系数分别为0.916、0.926、0.921,RMSEP分别为1.53、1.37、1.13,RPD值分别为2.59、2.53和2.32。相比线性模型,非线性模型的预测性能有所提高。
6、采用近红外透反射光谱结合距离匹配法(DM)、MD-DA、DPLS三种化学计量学方法,分别建立纯蜂蜜和掺C-4植物糖假蜂蜜的判别模型。研究结果表明,DM结合标准正态化(SNV)和一阶导数光谱预处理建立的模型总判别率为87.1 %,纯蜂蜜判别率为75 %,掺假蜜判别率为98.6 %;MD-DA结合原始光谱建立的鉴别模型总判别率为90.0 %,纯蜂蜜、掺假蜜判别率分别为100 %、81.1 %;DPLS结合一阶导数,中心化和13点平滑处理所建立的模型总判别率、纯蜂蜜、掺假蜜判别率分别为89.1 %、97.7 %、82 %。三者总判别率没有明显差异。
With the improvement of people's living standards and enhanced awareness of health, honey, as a kind of health product with high nutritional value, is very popular for consumers and the sales are growing continuously. However, the quality of honey is not satisfied in recent years, especially in the floral origin, identification and adulteration. Therefore, it is one of most important issues in the development of apiculture to identify and regulate the honey product quality and more effective techniques are required.
In the study, Fourier transform near infrared (FT-NIR) spectroscopy together with various chemometrics methods was applied to determine honey quality such as floral origin, components and authenticity. And the corresponding math models were also developed in order to evaluate quality of the honey in China.
The major results and conclusions are summaried as follows:
1、The spectra collection parameters on FT-NIR were analyzed. Through the comparison analysis, the optimized parameters were chosen including fiber optic diffuse transmission, 8 cm~(-1) of scan resolution, 32 of scan numbers, collection temperature(40℃)and optic lengths.
2、The influence of NIR spectra was analyzed based on different factors, i.e., floral origin, temperature and geographic origin. The results showed that there were significant differences between the spectra of different honey types in most of the wave bands, the spectra characteristics were obviously influenced by different temperatures and there was difference among spectra of honey samples from different geographic origins.
3、Three models for discrimination of botanical origin of honey were developed by Mahalanobis distance -discriminant analysis (MD-DA), Discriminant partial least squares analysis(DPLS)and Artificial neural net-work(ANN)together with different wavelength ranges and different spectra preprocessing methods, and the performance of the models was compared. The results showed that the best discrimination models for honey types developed by MD-DA together first derivative and S-G smoothing, with correct classification of the calibration and validation sets were 87.4 % and 85.3 %, respectively. DPLS together with first derivative and 10000-4200 cm~(-1) wavelenths gave the best classification results. The percentage of correctly classified were 70.8 % and 70.7 % for the calibration and validation sets, respectively. The ANN models with 10 hidden layers and 0.4 learning rate were developed. And those for model developed by ANN were 90.9 % and 89.3 %, respectively. The performance of nolinearANN model was better than the other two models.
4、The models for reducing sugar, glucose, fructose, moisture, sucrose, maltose, diastase activity, acidity and hydroxymethyl furfural were established by partial least squares (PLS) and multiple linear regression (MLR) in 11800-4100 cm~(-1), respectively. The results demonstrated that the ratio of prediction to deviation (RPD) of the model developed by PLS for the components were 2.44, 1.85, 2.32, 1.52, 1.24, 1.40, 1.87, 1.46 and 1.21, respectively. And those for model developed by MLR were 2.26, 1.86, 2.32, 1.50, 1.69, 1.07, 1.27, 1.70 and 1.49, respectively. The prediction accuracy of models for reducing sugar, glucose, fructose and water are better than other indicators.
5、The performances of models for determination of reducing sugar, glucose and fructose developed by BiPLS, SiPLS together with optimization conditions were compared. For the BiPLS models of the three indictors, the R were 0.913, 0.903 and 0.881, the RMSEP were 1.92, 1.73 and 1.15 and the RPD were 2.06, 2.01 and 2.28, respectively. Those for the SiPLS models, the R were 0.916, 0.921 and 0.902, the RMSEP were 1.77, 1.73 and 1.16 and the RPD were 2.24, 2.01 and 2.26, respectively. The models for those indicators were also developed by ANN ,with the R of 0.916, 0.926 and 0.921, the RMSEP of 1.53, 1.37 and 1.13 ,and the RPD of 2.59, 2.53 and 2.32, respectively. Compared the resluts of the two models, the prediction performance of non-linear models was better than linear models.
6、The use of fiber optic transreflectance near infrared spectroscopy (NIR) in combination with DM, MD-DA and DPLS chemometric techniques has been investigated to discriminate the authenticity of honey. The identification models were constructed to classify the pure honey and the adulterated honey samples with C4 plant sugar by DM, MD-DA and DPLS, respectively. The results showed that the discrimination model developed by DM together with SNV+1D was best. The total correct classifications of the model was 87.1 % and the correct identification rate of pure honey and adulterated were 75 % and 98.6 %, respectively. The discrimination model of MD-DA together with raw spectra gave the correct classificatoin of 90.0 %, with 100 % and 81.1 % of samples correctly classified for pure and adulterated honey, respectively. DPLS together with first derivative,mean center and 13 smoothing gave the correct classification of 89.1 %. Adulteration honey samples were correctly classified (97.7 %) and pure honey achieve a correct classification of 82 %.
本文以蜂蜜为研究对象,利用傅立叶近红外光谱分析技术和多种化学计量学分析方法,开展蜂蜜品种鉴别、组分定量和真伪鉴别技术研究,并建立相应的数学模型,通过比较分析,综合评价了我国蜂蜜质量,得出的主要研究结果如下:
1、通过优化仪器采集条件和参数,最终确定了光纤透反射模式、扫描分辨率(8 cm~(-1))、扫描次数(32次)、采集温度(40℃)、光程等光谱采集最优化组合。
2、探讨了蜂蜜品种、采集温度、蜂蜜产地等不同因素对蜂蜜近红外光谱响应特性的影响。在大部分波段范围内,不同品种蜂蜜光谱存在显著差异,不同采集温度影响光谱特性,不同产地来源的蜂蜜光谱亦有所差别。
3、采用不同的光谱预处理方法,建立了不同波段范围内的马氏距离判别分析(MD-DA)、判别偏最小二乘法(DPLS)和人工神经网络(ANN)等三种蜂蜜品种识别模型,对比分析了这三种模型的识别效果。研究结果表明,MD-DA结合一阶导数和卷积平滑处理光谱所建模型最优,其校正集和预测集样品识别率分别为87.4 %和85.3 %。DPLS结合一阶导数光谱在10000～4200 cm~(-1)波段建立的鉴别模型校正集和预测集样品识别率分别为70.8 %和70.7 %。在隐含层数为10、学习速率为0.4条件下建立的ANN模型校正集和预测集样品识别率分别为90.9 %和89.3 %。三者最优模型中非线性模型ANN的识别效果好于其他两种。
4、在11800～4100 cm~(-1)光谱范围内,分别建立了混合品种蜂蜜的还原糖、葡萄糖、果糖、水分、蔗糖、麦芽糖、淀粉酶、酸度、羟甲基糖醛9个指标的偏最小二乘法(PLS)、多元线性回归(MLR)模型。其中PLS模型的各指标RPD值分别为2.44、1.85、2.32、1.52、1.24、1.40、1.87、1.46和1.21,蔗糖、麦芽糖、淀粉酶、酸度、羟甲基糖醛的光谱经过导数处理后的PLS模型性能显著提高。各指标的MLR模型RPD值分别为2.26、1.86、2.32、1.50、1.69、1.07、1.27、1.70和1.49,与PLS模型的预测能力相当。还原糖、葡萄糖、果糖、水分模型预测精度均好于其他指标。
5、针对含量较高的还原糖、葡萄糖、果糖,采用BiPLS、SiPLS分别对近红外信息区间进行优化并建立模型,各指标对应的BiPLS模型的相关系数分别为0.913、0.903、0.881,RMSEP分别为1.92、1.73、1.15,RPD值分别为2.06、2.01、2.28。SiPLS模型的相关系数分别为0.916、0.921、0.902,RMSEP分别为1.77、1.73、1.16,RPD值分别为2.24、2.01、2.26,与BiPLS优化模型相比,SiPLS模型的预测精度与之相当。用ANN建立了蜂蜜还原糖、葡萄糖、果糖的定量非线性模型,相关系数分别为0.916、0.926、0.921,RMSEP分别为1.53、1.37、1.13,RPD值分别为2.59、2.53和2.32。相比线性模型,非线性模型的预测性能有所提高。
6、采用近红外透反射光谱结合距离匹配法(DM)、MD-DA、DPLS三种化学计量学方法,分别建立纯蜂蜜和掺C-4植物糖假蜂蜜的判别模型。研究结果表明,DM结合标准正态化(SNV)和一阶导数光谱预处理建立的模型总判别率为87.1 %,纯蜂蜜判别率为75 %,掺假蜜判别率为98.6 %;MD-DA结合原始光谱建立的鉴别模型总判别率为90.0 %,纯蜂蜜、掺假蜜判别率分别为100 %、81.1 %;DPLS结合一阶导数,中心化和13点平滑处理所建立的模型总判别率、纯蜂蜜、掺假蜜判别率分别为89.1 %、97.7 %、82 %。三者总判别率没有明显差异。
With the improvement of people's living standards and enhanced awareness of health, honey, as a kind of health product with high nutritional value, is very popular for consumers and the sales are growing continuously. However, the quality of honey is not satisfied in recent years, especially in the floral origin, identification and adulteration. Therefore, it is one of most important issues in the development of apiculture to identify and regulate the honey product quality and more effective techniques are required.
In the study, Fourier transform near infrared (FT-NIR) spectroscopy together with various chemometrics methods was applied to determine honey quality such as floral origin, components and authenticity. And the corresponding math models were also developed in order to evaluate quality of the honey in China.
The major results and conclusions are summaried as follows:
1、The spectra collection parameters on FT-NIR were analyzed. Through the comparison analysis, the optimized parameters were chosen including fiber optic diffuse transmission, 8 cm~(-1) of scan resolution, 32 of scan numbers, collection temperature(40℃)and optic lengths.
2、The influence of NIR spectra was analyzed based on different factors, i.e., floral origin, temperature and geographic origin. The results showed that there were significant differences between the spectra of different honey types in most of the wave bands, the spectra characteristics were obviously influenced by different temperatures and there was difference among spectra of honey samples from different geographic origins.
3、Three models for discrimination of botanical origin of honey were developed by Mahalanobis distance -discriminant analysis (MD-DA), Discriminant partial least squares analysis(DPLS)and Artificial neural net-work(ANN)together with different wavelength ranges and different spectra preprocessing methods, and the performance of the models was compared. The results showed that the best discrimination models for honey types developed by MD-DA together first derivative and S-G smoothing, with correct classification of the calibration and validation sets were 87.4 % and 85.3 %, respectively. DPLS together with first derivative and 10000-4200 cm~(-1) wavelenths gave the best classification results. The percentage of correctly classified were 70.8 % and 70.7 % for the calibration and validation sets, respectively. The ANN models with 10 hidden layers and 0.4 learning rate were developed. And those for model developed by ANN were 90.9 % and 89.3 %, respectively. The performance of nolinearANN model was better than the other two models.
4、The models for reducing sugar, glucose, fructose, moisture, sucrose, maltose, diastase activity, acidity and hydroxymethyl furfural were established by partial least squares (PLS) and multiple linear regression (MLR) in 11800-4100 cm~(-1), respectively. The results demonstrated that the ratio of prediction to deviation (RPD) of the model developed by PLS for the components were 2.44, 1.85, 2.32, 1.52, 1.24, 1.40, 1.87, 1.46 and 1.21, respectively. And those for model developed by MLR were 2.26, 1.86, 2.32, 1.50, 1.69, 1.07, 1.27, 1.70 and 1.49, respectively. The prediction accuracy of models for reducing sugar, glucose, fructose and water are better than other indicators.
5、The performances of models for determination of reducing sugar, glucose and fructose developed by BiPLS, SiPLS together with optimization conditions were compared. For the BiPLS models of the three indictors, the R were 0.913, 0.903 and 0.881, the RMSEP were 1.92, 1.73 and 1.15 and the RPD were 2.06, 2.01 and 2.28, respectively. Those for the SiPLS models, the R were 0.916, 0.921 and 0.902, the RMSEP were 1.77, 1.73 and 1.16 and the RPD were 2.24, 2.01 and 2.26, respectively. The models for those indicators were also developed by ANN ,with the R of 0.916, 0.926 and 0.921, the RMSEP of 1.53, 1.37 and 1.13 ,and the RPD of 2.59, 2.53 and 2.32, respectively. Compared the resluts of the two models, the prediction performance of non-linear models was better than linear models.
6、The use of fiber optic transreflectance near infrared spectroscopy (NIR) in combination with DM, MD-DA and DPLS chemometric techniques has been investigated to discriminate the authenticity of honey. The identification models were constructed to classify the pure honey and the adulterated honey samples with C4 plant sugar by DM, MD-DA and DPLS, respectively. The results showed that the discrimination model developed by DM together with SNV+1D was best. The total correct classifications of the model was 87.1 % and the correct identification rate of pure honey and adulterated were 75 % and 98.6 %, respectively. The discrimination model of MD-DA together with raw spectra gave the correct classificatoin of 90.0 %, with 100 % and 81.1 % of samples correctly classified for pure and adulterated honey, respectively. DPLS together with first derivative,mean center and 13 smoothing gave the correct classification of 89.1 %. Adulteration honey samples were correctly classified (97.7 %) and pure honey achieve a correct classification of 82 %.
引文
1.曹炜,尉亚辉主编.蜂产品保健原理与加工技术.北京:化学工业出版社,2002.
2.岑海燕,鲍一丹,何勇.基于光谱技术的杨梅汁品种快速鉴别方法的研究.光谱学与光谱分析,2007, 27 (3): 503~506.
3.陈建,陈晓,李伟,王加华,韩东海.基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究.光谱学与光谱分析,2008,28(8):1806~1809.
4.褚小立,袁洪福,陆婉珍.近红外光谱分析中光谱预处理及波长选择方法进展与应用.化学进展,2004,16 (4):528~542.
5.丛爽.面向Matlab工具箱的神经网络理论与应用.合肥:中国科学技术大学出版社,2003.
6.陈兰珍,芮玉奎,赵静,叶志华,薛晓锋,王鹏.应用ICP-MS测定不同种类蜂蜜中微量元素和重金属.光谱学与光谱分析,2008,28(6):1403~1405.
7.方兵兵.蜂蜜为什么会结晶.中国养蜂,2005,56(8) :25.
8.谷筱玉,徐可欣,汪日燕.波长选择算法在近红外光谱法中药有效成分测量中的应用.光谱学与光谱分析,2006,26 (9):1618~1620.
9.桂现才. BP神经网络在MATLAB上的实现与应用.湛江师范学院学报,2004,25(3):80~84.
10.何勇,李晓丽,邵咏妮.基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究.光谱学与光谱分析,2006,26(5):850~853.
11.侯卓成,杨宁,李俊英,徐桂云.傅里叶变换近红外反射用于鸡蛋蛋品质的研究.光谱学与光谱分析,2009,(8):2063~2066.
12.候瑞丽,程玉来,重腾和明.采用近红外光谱技术检测蜂蜜中蔗糖含量的研究.食品工业,2007,28 (2):57~58.
13.候瑞丽.蜂蜜主要成分的近红外光谱检测技术的研究. [硕士学位论文].沈阳:沈阳农业大学, 2005.
14.李鹏飞,王加华,曹楠宁,韩东海. BiPLS结合GA优选可见/近红外光谱MLR变量.光谱学与光谱分析,2009,29(10) :2637~2641.
15.梁逸曾,俞汝勤.分析化学手册(第十分册)化学计量学.北京:化学工业出版社,2000(第二版),332~333.
16.刘燕德.水果糖度和酸度的近红外光谱无损检测研究[博士学位论文].浙江大学,2006.
17.卢太白,陈玲,张中满.蜂蜜品质检测与掺假鉴别.陕西农业科学,2006(2):24~25.
18.陆辉山.水果内部品质可见/近红外光谱实时无损检测关键技术研究[博士学位论文].浙江大学,2006.
19.陆婉珍主编.现代近红外光谱分析技术(第二版).北京:中国石化出版社,2007.
20.陆晓华.化学计量学[M].武汉:华中理工大学出版社,1997.
21.闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化.光谱学与光谱分析,2004,24 (10):1205~1209.
22.闵惜琳,刘国华.用MATLAB神经网络工具箱开发BP网络应用.计算机应用,2001,21(8) :163~164.
23.齐小明,张录达,杜晓林,宋昭娟,张一,徐淑燕. PLS-BP法近红外光谱定量分析研究.光谱学与光谱分析,2003,23 (5):870~872.
24.史琦云,贠建民.蜂蜜品质的花粉检验法研究.中国养蜂,2005,56(2):9~11.
25.田海清.西瓜品质可见/近红外光谱无损检测技术研究[博士学位论文].浙江大学,2006.
26.屠振华,籍保平,孟超英,朱大洲,史波林,庆兆珅.基于遗传算法和间隔偏最小二乘的苹果硬度特征波长分析研究.光谱学与光谱分析,2009,29(10):2760~2764.
27.王冬,叶升锋,闵顺耕,韩晨,黄越.近红外光谱分辨率对液体样品近红外定量模型影响的研究.光谱学与光谱分析,2009,29(7):1813~1817.
28.王多加,周向阳,金同铭,胡祥娜,钟娇娥,吴启堂.近红外光谱检测技术在农业和食品分析上的应用.光谱学与光谱分析,2004,24(4):447~450.
29.王工.用花粉含量鉴定蜂蜜品质的研究.吉林粮食高等专科学校学报,1998,13(3):4~8.
30.王加华,潘璐,李鹏飞,韩东海.苹果糖度近红外光谱分析模型的温度补偿.光谱学与光谱分析,2009,29(6):1517~1520.
31.吴瑾光.近代傅立叶变换红外光谱技术与应用.北京:科技文献出版社,1994.
32.夏柏杨,任芊.近红外光谱分析技术的一些数据处理方法的讨论.光谱实验室,2005,22(3):629~634.
33.徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展.光谱学与光谱分析,2000,20 (3):134~142.
34.许国根,许萍萍,编著.化学化工中的数学方法及Matlab实现.北京:化学工业出版社, 2008.
35.许禄,邵学广.化学计量学方法.北京:科学出版社,2004(第二版),90~175.
36.严衍录,赵龙莲,韩东海编著.近红外光谱分析基础与应用.北京:中国轻工业出版社. 2005: 11~126.
37.叶振生.蜂产品深加工技术[M].北京:中国轻工业出版社,2003,15~24.
38.于海燕.黄酒品质和酒龄的近红外光谱分析方法研究[博士学位论文].浙江大学,2007.
39.张复兴.现代养蜂生产[M].北京:中国农业大学出版社,1998,285~303.
40.张军,陈华才,陈星旦.近红外光谱温度修正定量分析模型的研究.光谱学与光谱分析,2005,25(6):890~893.
41.张萍,闫继红,朱志华,刘庆生,范志影,李为喜.近红外光谱技术在食品品质鉴别中的应用研究.现代科学仪器,2006,(1):60~62.
42.张瑜,谢国秀.温度和时间对蜂蜜中淀粉酶值的影响.养蜂科技,2004,(4):4~5.
43.赵静,李熠,陈兰珍.蜂产品检测实用技术[M].北京:中国农业出版社,2006.
44.中华人民共和国国家标准. GB18796-2005,蜂蜜.
45.中华人民共和国商务部.《出口商品技术指南—蜂蜜》,2005年.
46.周旻,王天志,叶利明,陈聪,黄果,吴严巍.近红外漫反射光谱法测定川产黄柏中小檗碱含量.光谱学与光谱分析,2007,27(8):1527~1530.
47. Alissandrakis E, Daferera D, Tarantilis P A, Polissiou M, Harizanis P C. Ultrasound-assistedextraction of volatile compounds from citrus flowers and citrus honey. Food Chemistry, 2003, 82(4): 575~582.
48. Andrade P, Ferreres F, Amaral M T. Analysis of honey phenolic acids by HPLC, its application to honey botanical characterization. Journal of Liquid Chromatography & Related Technologies, 1997, 20(14): 2281~2288.
49. Anthony M C, Branka R, Tom F. A Preliminary study on the characterization of honey by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 2002, 10 (2): 121~135.
50. AOAC. Official Methods of Analysis. Nr. 44.4.18: C-4 Plant Sugars in Honey, Method 991.41, 16th, ed.,AOAC International, Arlington, Virginia, 1995,USA.
51. Arcos M J, Ortiz M C, Belen V , Sarabia L A. Genetic algorithm based wavelength selection in multicomponent spectrometric determinations by PLS: application on indomethacin and acemethacin mixture. Analytica Chimica Acta, 1997, 339(1-2): 63~77.
52. Baroni M V, Chiabrando G A, Costa C, Wunderlin DA. Assessment of the floral origin of honey by SDS-page immunoblot techniques. Journal of Agriculture and Food Chemistry, 2002, 50(6): 1362~1367.
53. Benedetti S, Mannino S, Sabatini A G, Marcazzan G L. Electronic nose and neural network use for the classification of honey. Apidologie, 2004, 35: 1~6.
54. Berrueta L A, Salces R M A & Héberger K. Supervised pattern recognition in food analysis. Journal of Chromatography A, 2007, 1158(1-2), 196~214.
55. Bianchi F, Careri M, Musci M. Volatile norisoprenoids as markers of botanical origin of Sardinian strawberry-tree (Arbutus unedo L.) honey: Characterisation of aroma compounds by dynamic headspace extraction and gas chromatography-mass spectrometry. Food Chemistry, 2005, 89(4): 527~532.
56. Bogdanov S, Ruoff K, Persano Oddo L. Physico-chemical methods for the characterisation of unifloral honeys: a review. Apidologie, 2004, 35 (special issue): 4~17.
57. Bouseta A C S. Optimized Likens - Nickerson Methodology for quantifying honey flavours. Journal of Agriculture and Food Chemistry, 1995, 43(7): 1890~1896.
58. Bouseta A, Scheirman V, Collin S. Flavor and free amino acid composition of lavender and eucalyptus honey . Journal of Food Science, 1996, 61(4): 683~687.
59. Brookes S T, Barrie A, Davies J E. A rapid 13 C/ 12 C test for determination of corn syrups in honey. Journal of AOAC. 1991,74: 627~629.
60. Burns D A, Ciurczak E W. Handbook of near-infrared analysis 3rd. CRC Press, 2001.
61. Cavia M M, Fernández-Muino M A, Alonso-Torreetal S R, Huidobro J F, Sancho M T. Evolution of acidity of honeys from continental climates: Influence of induced granulation. Food chemistry, 2007, 100(4): 1728~1733.
62. Cen H, Bao Y, He Y, & Sun D W. Visible and Near Infrared Spectroscopy for Rapid Detection of Citric and Tartaric Acids in Orange Juice. Journal of Food Engineering, 2007, 82(2): 253~260.
63. Cercaci L, Rodriguez-Estrada M T, & Lercker G. Solid phase extraction-thin-layerchromatography-gas chromatography method for the detection of hazelnut oil in olive oils by determination of esterified sterols. Journal of Chromatography A, 2003, 985: 211–220.
64. Cho H J, Hong S H. Acacia honey quality measurement by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 1998, 6(5): A329~A331.
65. Codex Alimentarius Committee. Revised Codex Standard for Honey[S]. Codex STAN 12-1981, REV.2(2001), 11: 1~7.
66. Contal L, Leon V, &Downey G. Detection and quantification of apple adulteration in strawberry and raspberry purees using visible and near infrared spectroscopy. Jounral of Near Infrared Spectroscopy, 2002, 10: 289~299.
67. Conti M E. Lazio region (central Italy) honeys: a survey of mineral content and typical quality parameters. Food Control, 2000, 11(6): 459~463.
68. Corbella E, Cozzolino D. The use of visible and near infrared spectroscopy to classify the floral origin of honey samples produced in Uruguay. Journal of Near Infrared Spectroscopy, 2005, 13(2): 63～68.
69. Cotte J F, Casabianca H, Chardon S, Heritier J L, Grenier-Loustalot M F. Application of carbohydrate analysis to verify honey authenticity. Journal of Chromatography A, 2003, 1021(1-2): 145~155.
70. Cozzolino D, Kwiatkowski M J, Parker M, Cynkar W U, Dambergs R G, Gishen M. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 2004, 513 (1): 73~80.
71. Cozzolino D, Smyth H E, Gishen M. Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. Journal of Agriculture and Food Chemistry, 2003, 51(26): 7703~7708.
72. Cozzolino D, Smyth H E, Lattey K A, Cynkar W, Janik L, Francis R L, Gishen M. Relationship between sensory analysis and near infrared spectroscopy in Australian Riesling and Chardonnay wines. Analytica Chimica Acta, 2005, 539: 341~348.
73. D'Arcy B R, Rintoul G B, Rowland C Y E A. Composition of Australian honey extractives. 1. Norisoprenoids, monoterpenes, and other natural volatiles from blue gum (Eucalyptus leucoxylon) and yellow box (Eucalyptus melliodora) honeys. Journal of Agriculture and Food Chemistry, 1997, 45(5): 1834~1843.
74. Davies A M C, Radovic B, Fearn T&Anklam E. A preliminary study on the characterisation of honey by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 2002, 10(2):121~135.
75. Devillers J, Morlot M, Pham D M H, Dore J C. Classification of monofloral honeys based on their quality control data. Food Chemistry, 2004, 86(2): 305~312.
76. Downey G, Fouratier V & Kelly J D. Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy. Journal of Near Infrared Spectroscopy, 2003, 11 (6): 447~456.
77. Downey G, McIntyre P, Davies A N. Detecting and quantifying sunflower oil adulteration in extravirgin olive oils from the eastern mediterranean by visible and near-infrared spectroscopy. Journal of Agriculture and Food Chemistry, 2002, 50(20): 5520~5525.
78. Dvash L, Afik O, Shafir S, Schaffer A, Yeselson Y, Dag A. Determination by near-infrared spectroscopy of perseitol used as a marker for the botanical origin of avocado (Persea americana Mill.) honey. Journal of Agriculture and Food Chemistry, 2002, 50(19): 5283~5287.
79. European Commission. Council Directive 2001/110/EC of 20 December 2001 relating to honey [S]. Official Journal of the European Union, 2002, L10: 47~52.
80. Galtier O, Dupuy N, Le Dr′eau Y, Ollivier D, Pinatel C&Kister J. Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra. Analytica Chimica Acta, 2007, 595(1-2): 136~144.
81. Garcia-Alvarez M , Huidobro J F, Hermida M, Rodriguez-Otero J L. Major components of honey analysis by near-infrared transflectance spectroscopy. Journal of Agriculture and Food Chemistry, 2000, 48 (11): 5154~5158.
82. Garcia-Alvarez M, Ceresuela S, Huidobro J F, Hermida M, Rodriguez-Otero J L. Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy. Journal of Agriculture and Food Chemistry, 2002, 50(3): 419~425.
83. Gishen M, Dambergs R G, Kambouris A. Wine quality grading by near infrared spectroscopy. Proceeding of the tenth International Conference on Near Infrared Spectroscopy, 2002:187~189.
84. Gonzalez-Miret M L, Terrab A, Hernanz D, Fernandez-Recamales M A, Heredia F J. Multivariate correlation between color and mineral composition of honeys and by their botanical origin. Journal of Agriculture and Food Chemistry, 2005, 53(7): 2574~2580.
85. Goodacre R, Radovic B S, Anklam E. Progress toward the rapid nondestructive assessment of the floral origin of European honey using dispersive Raman spectroscopy. Applied spectroscopy, 2002, 56(4): 521~527.
86. Guyot C, Scheirman V, Collin S. Floral origin markers of heather honeys: Calluna vulgaris and Erica arborea. Food Chemistry, 1999, 64(1): 3~11.
87. Ha J, Koo M O H. Determination of the constituents of honey by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 1998, 6(5): 367~369.
88. Holland J H. Adaptation in Natural and Artificial Systems. American: University of Michigan Press, 1975.
89. http://www.shac.gov.cn/zxzx/scfx/jsbl/200904/t20090403_1239395.htm
90. Iglesias M T, Lorenzo C D, Polo M D, Jésus P, Pueyo E. Usefulness of amino acid composition to discriminate between honeydew and floral honeys. Application to honeys from a small geographic area . Journal of Agriculture and Food Chemistry, 2004, 52(1): 84~89.
91. Karoui R, Pillonel L, Schaller E, Bosset J O, Baerdemaeker J. Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study. Food Chemistry, 2007, 101(3): 1121~1129.
92. Kelly J D, Petisco C, Downey G. Potential of near infrared transflectance spectroscopy to detectadulteration of Irish honey by beet invert syrup and high fructose corn syrup. Journal of Near Infrared Spectroscopy, 2006, 14(2): 139~146.
93. Kelly J F D, Downey G., Fouratier V, Initial study of honey adulteration by sugar solutions using mid-infrared (MIR) spectroscopy and chemometrics. Journal of Agriculture and Food Chemistry, 2004, 52(1): 33~39.
94. Kerkvliet J D, Meijer H A J. Adulteration of honey: relation between microscopic analysis and d13C measurements. Apidologie,, 2000, 31: 717~726.
95. Kim S S , Rhyu M R, Kim J M , Lee S H. Authentication of rice using near infrared reflectance spectroscopy.Cereal Chemistry, 2003, 80(3): 346~49.
96. Lachman J, Kolihova D, Miholova D, Kosata J, Titera D, Kult K. Analysis of minority honey components: Possible use for the evaluation of honey quality. Food Chemistry, 2007, 101(3): 973~979.
97. Latorre M J, Pena R,Garcia S E A. Authentication of Galician (NW Spain) honeys by multivariate techniques based on metal content data. Analyst, 2000, 125(2): 307~312.
98. Leardi R, González A L.Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometrics and Intelligent Laboratory Systems, 1998, 41(2): 195~207.
99. Lichtenberg-Kraag B, Hedtke C, Bienefeld K. Infrared spectroscopy in routine quality analysis of honey. Apidologie, 2002, 33(3): 327~337.
100. Louveaux J, Maurizio A, Vorwohl G. Methods of melissopalynology. Bee World, 1978, 59, 139-162.
101. Maeda H, Ozaki M, Tanaka N, Hayashi, Kojima T. Near infrared spectroscopy and chemometrics studies of temperature-dependent spectral variations of water: relationship between spectral changes and hydrogen bonds. Journal of Near Infrared Spectroscopy, 1995, 3 (3): 191~201.
102. Manikis I, Thrasyvoulou A. The relation of physico-chemical characteristics of honey and the crystallization sensitive parameters. Apiacta, 2001, 36(2): 106~112.
103. Martin I G, Macias E M, Sanchez J S, Rivera B G. Detection of honey adulteration with beet sugar using stable isotope methodology. Food Chemistry, 1998, 61(3): 281~286.
104. Martin Y G, Oliveros M C C, Pavon J L P, Pinto C G, Cordero B M. Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: Characterisation of vegetable oils. Analytica Chimica Acta, 2001, 449(1-2): 69~80.
105. Martos I, Ferreres F, Tomás-Barberán F A. Identification of flavonoid markers for the botanical origin of Eucalyptus honey. Journal of Agriculture and Food Chemistry, 2000, 48(5): 1498~1502.
106. Mateo R, Bosch R F. Sugar profiles of Spanish unifloral honeys. Food Chemistry, 1997, 60(1): 33~41.
107. Moar N T. Pollen analysis of New Zealand honey. New Zealand Journal of Agricultural Research, 1985, 28: 39~70.
108. N?rgaard L, Saudland A, Wagner J., Nielsen J P, Munck L. Engelsen S B. Interval partial least squares regression (iPLS): A comparative chemometric study with an example from near-infraredspectroscopy. Applied Spectroscopy, 2000, 54(3):413~419.
109. Ohtani S, Wang T, Nishimura K. Milk Fat Analysis by Fiber-optic Spectroscopy. Asian-australasian journal of animal sciences, 2005, 18 (4): 580~583.
110. Padovan G J, Jong D, Rodrigues L P, Marchini J S. Detection of adulteration of commercial honey samples by the 13C/12C isotopic ratio. Food Chemistry, 2003, 82(4): 633~636.
111. Paradkar M M, Sakhamuri S, Irudayaraj J. Comparison of FTIR, FT-Raman, and NIR spectroscopy in a maple syrup adulteration study. Journal of Food Science, 2002, 67(6): 2009~2015.
112. Paula A,Federico F,Isabel G M,Francisco TB. Determination of phenolic compounds in honeys with different floral origin by capillary zone electrophoresis. Food Chemistry, 1997, 60(1): 79~84.
113. Pérez R A, Sanchez-Brunete C, Calvo R M, Tadeo J L. Analysis of volatiles from Spanish honeys by solid-phase microextraction and gas chromatography-mass spectrometry. Journal of Agriculture and Food Chemistry, 2002, 50(9): 2633~2637.
114. Persano Oddo L , Piazza M G, Sabatini A G, Accorti M. Characterization of unifloral honeys. Apidologie, 1995, 26: 453~465.
115. Persano Oddo L, Piana L, Bogdanov S, Bentabol A, Gotsiou P, Kerkvliet J. Botanical species giving unifloral honey in Europe . Apidologie , 2004, 35(special issue):82~93.
116. Persano Oddo L, Piro R. Main European unifloral honeys: descriptive sheets. Apidologie 2004, 35 (special issue): 38~81.
117. Qiu P Y, Ding H B, Tang Y K, Xu R J. Determination of chemical composition of commercial honey by near-infrared spectroscopy. Journal of Agriculture and Food Chemistry, 1999, 47(7): 2760~2765.
118. Radovic B S, Careri M, Mangia A, Musci M, Gerboles M, Anklam E. Contribution of dynamic headspace GC-MS analysis of aroma compounds to authenticity testing of honey . Food Chemistry, 2001, 72(4): 511~520.
119. Roggo Y, Duponchel L, Huvenne J P. Comparison of supervised pattern recognition methods with McNemar’s statistical test Application to qualitative analysis of sugar beet by near-infrared spectroscopy. Analytica Chimica Acta, 2003, 477(2): 187~200
120. Rossmann A, Koziet J, Martin G J, Dennis M J. Determination of the carbon-13content of sugars and pulp from fruit juices by isotope-ration massspectrometry (internal reference method) A European inter laboratory comparison. Analytica Chimica Acta, 1997, 340(1-3): 21~29.
121. Ruoff K, Iglesias M T, Luginbühl W, Bosset J O, Bogdanov S, AmadòR. Quantitative analysis of physical and chemical measurands in honey by mid-infrared spectrometry. European Food Research and Technology, 2006, 223(1): 22~29.
122. Ruoff K, Karoui R, Eric D , Dufour E, Luginbühl W, Bosset J O, Bogdanov S, Amado R. Authentication of the botanical origin of honey by front-face fluorescence spectroscopy: A preliminary study . Journal of Agriculture and Food Chemistry, 2005, 53(5): 1343~1347.
123. Singh N, Bath P K. Quality evaluation of different types of Indian honey. Food Chemistry, 1997, 58(1-2): 129~133.
124. Sivakesava S, Irudayaraj J. Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy. Journal of Food Science and Technology, 2002, 37(4):351~360.
125. Smith B M, Gemperline P J. Wavelength selection and optimization of pattern recognition methods using the genetic algorithm. Analytica Chimica Acta, 2000, 423(2): 167~177.
126. Stefan B, Kaspar R L P O. Physico-chemical methods for the characterisation of unifloral honeys: a review. Apidologie, 2004, 35 (special issue): 4~17.
127. Swallow K W, Low N H. Analysis and quantitation of the carbohydrates in honey using high-performance liquid chromatography. Journal of Agriculture and Food Chemistry, 1990, 38: 1828~1832.
128. Terrab A, Gonzalez A G, Diez M J E A. Mineral content and electrical conductivity of the honeys produced in Northwest Morocco and their contribution to the characterisation of unifloral honeys. Journal of the Science of Food and Agriculture, 2003, 83(7): 637~664.
129. Tewari J C, Irudayaraj J M K. Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose sensor. Journal of Agriculture and Food Chemistry, 2005, 53(18): 6955~6966.
130. Tomás-Barberán F A, Martos I, Ferreres F, Radovic B S, Anklam E. HPLC flavonoid profiles as markers for the botanical origin of European unifloral honeys. Journal of the Science of Food and Agriculture, 2001, 81(5): 485~496.
131. Von der Ohe W, Persano Oddo L, Piana M L, Morlot M, Martin P. Harmonised methods of melissopalynology. Apidologie, 2004, 35 (special issue): 18~25.
132. Wang L, Lee FSC, Wang X, He Y. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry, 2006, 95(3): 529~536.
133. White J W, Doner L W. Mass Spectrometric Detection of High-fructose Corn Syrup in Honey by use of 13C/12C Ratio: Collaborative Study. Journal of AOAC International, 1978, 61: 746~750.
134. White J W, Winters K, Martin K P, Rossmann A.. Stable Carbon Isotope Ratio Analysis of Honey: Validation of Internal Standard Procedure for Worlwide Application. Journal of AOAC International, 1998, 81: 610~619.
135. White J W, Winters K. Honey protein as internal standard for stable carbon isotope ratio detection of aduheration of honey. Journal of AOAC International, 1992, 72: 907~911.
136. Xing J, Ngadi M, Gunenc A, Prasher S, Gariepy C. Use of visible spectroscopy for quality classification of intact pork meat. Journal of Food Engineering, 2007, 82(2): 135~141.
137. Yao L, Jiang Y, Singanusong R, Datta, N, Raymont K. Phenolic acids and abscisic acid in Australian Eucalyptus honeys and their potential for floral authentication. Food Chemistry, 2004, 86(2): 169~177.
138. Zou X B, Zhao J W, Li Y X. Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of 'Fuji' apple based on BiPLS and FiPLS models. Vibrational Spectroscopy, 2007, 44 (2) : 220~227.
2.岑海燕,鲍一丹,何勇.基于光谱技术的杨梅汁品种快速鉴别方法的研究.光谱学与光谱分析,2007, 27 (3): 503~506.
3.陈建,陈晓,李伟,王加华,韩东海.基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究.光谱学与光谱分析,2008,28(8):1806~1809.
4.褚小立,袁洪福,陆婉珍.近红外光谱分析中光谱预处理及波长选择方法进展与应用.化学进展,2004,16 (4):528~542.
5.丛爽.面向Matlab工具箱的神经网络理论与应用.合肥:中国科学技术大学出版社,2003.
6.陈兰珍,芮玉奎,赵静,叶志华,薛晓锋,王鹏.应用ICP-MS测定不同种类蜂蜜中微量元素和重金属.光谱学与光谱分析,2008,28(6):1403~1405.
7.方兵兵.蜂蜜为什么会结晶.中国养蜂,2005,56(8) :25.
8.谷筱玉,徐可欣,汪日燕.波长选择算法在近红外光谱法中药有效成分测量中的应用.光谱学与光谱分析,2006,26 (9):1618~1620.
9.桂现才. BP神经网络在MATLAB上的实现与应用.湛江师范学院学报,2004,25(3):80~84.
10.何勇,李晓丽,邵咏妮.基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究.光谱学与光谱分析,2006,26(5):850~853.
11.侯卓成,杨宁,李俊英,徐桂云.傅里叶变换近红外反射用于鸡蛋蛋品质的研究.光谱学与光谱分析,2009,(8):2063~2066.
12.候瑞丽,程玉来,重腾和明.采用近红外光谱技术检测蜂蜜中蔗糖含量的研究.食品工业,2007,28 (2):57~58.
13.候瑞丽.蜂蜜主要成分的近红外光谱检测技术的研究. [硕士学位论文].沈阳:沈阳农业大学, 2005.
14.李鹏飞,王加华,曹楠宁,韩东海. BiPLS结合GA优选可见/近红外光谱MLR变量.光谱学与光谱分析,2009,29(10) :2637~2641.
15.梁逸曾,俞汝勤.分析化学手册(第十分册)化学计量学.北京:化学工业出版社,2000(第二版),332~333.
16.刘燕德.水果糖度和酸度的近红外光谱无损检测研究[博士学位论文].浙江大学,2006.
17.卢太白,陈玲,张中满.蜂蜜品质检测与掺假鉴别.陕西农业科学,2006(2):24~25.
18.陆辉山.水果内部品质可见/近红外光谱实时无损检测关键技术研究[博士学位论文].浙江大学,2006.
19.陆婉珍主编.现代近红外光谱分析技术(第二版).北京:中国石化出版社,2007.
20.陆晓华.化学计量学[M].武汉:华中理工大学出版社,1997.
21.闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化.光谱学与光谱分析,2004,24 (10):1205~1209.
22.闵惜琳,刘国华.用MATLAB神经网络工具箱开发BP网络应用.计算机应用,2001,21(8) :163~164.
23.齐小明,张录达,杜晓林,宋昭娟,张一,徐淑燕. PLS-BP法近红外光谱定量分析研究.光谱学与光谱分析,2003,23 (5):870~872.
24.史琦云,贠建民.蜂蜜品质的花粉检验法研究.中国养蜂,2005,56(2):9~11.
25.田海清.西瓜品质可见/近红外光谱无损检测技术研究[博士学位论文].浙江大学,2006.
26.屠振华,籍保平,孟超英,朱大洲,史波林,庆兆珅.基于遗传算法和间隔偏最小二乘的苹果硬度特征波长分析研究.光谱学与光谱分析,2009,29(10):2760~2764.
27.王冬,叶升锋,闵顺耕,韩晨,黄越.近红外光谱分辨率对液体样品近红外定量模型影响的研究.光谱学与光谱分析,2009,29(7):1813~1817.
28.王多加,周向阳,金同铭,胡祥娜,钟娇娥,吴启堂.近红外光谱检测技术在农业和食品分析上的应用.光谱学与光谱分析,2004,24(4):447~450.
29.王工.用花粉含量鉴定蜂蜜品质的研究.吉林粮食高等专科学校学报,1998,13(3):4~8.
30.王加华,潘璐,李鹏飞,韩东海.苹果糖度近红外光谱分析模型的温度补偿.光谱学与光谱分析,2009,29(6):1517~1520.
31.吴瑾光.近代傅立叶变换红外光谱技术与应用.北京:科技文献出版社,1994.
32.夏柏杨,任芊.近红外光谱分析技术的一些数据处理方法的讨论.光谱实验室,2005,22(3):629~634.
33.徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展.光谱学与光谱分析,2000,20 (3):134~142.
34.许国根,许萍萍,编著.化学化工中的数学方法及Matlab实现.北京:化学工业出版社, 2008.
35.许禄,邵学广.化学计量学方法.北京:科学出版社,2004(第二版),90~175.
36.严衍录,赵龙莲,韩东海编著.近红外光谱分析基础与应用.北京:中国轻工业出版社. 2005: 11~126.
37.叶振生.蜂产品深加工技术[M].北京:中国轻工业出版社,2003,15~24.
38.于海燕.黄酒品质和酒龄的近红外光谱分析方法研究[博士学位论文].浙江大学,2007.
39.张复兴.现代养蜂生产[M].北京:中国农业大学出版社,1998,285~303.
40.张军,陈华才,陈星旦.近红外光谱温度修正定量分析模型的研究.光谱学与光谱分析,2005,25(6):890~893.
41.张萍,闫继红,朱志华,刘庆生,范志影,李为喜.近红外光谱技术在食品品质鉴别中的应用研究.现代科学仪器,2006,(1):60~62.
42.张瑜,谢国秀.温度和时间对蜂蜜中淀粉酶值的影响.养蜂科技,2004,(4):4~5.
43.赵静,李熠,陈兰珍.蜂产品检测实用技术[M].北京:中国农业出版社,2006.
44.中华人民共和国国家标准. GB18796-2005,蜂蜜.
45.中华人民共和国商务部.《出口商品技术指南—蜂蜜》,2005年.
46.周旻,王天志,叶利明,陈聪,黄果,吴严巍.近红外漫反射光谱法测定川产黄柏中小檗碱含量.光谱学与光谱分析,2007,27(8):1527~1530.
47. Alissandrakis E, Daferera D, Tarantilis P A, Polissiou M, Harizanis P C. Ultrasound-assistedextraction of volatile compounds from citrus flowers and citrus honey. Food Chemistry, 2003, 82(4): 575~582.
48. Andrade P, Ferreres F, Amaral M T. Analysis of honey phenolic acids by HPLC, its application to honey botanical characterization. Journal of Liquid Chromatography & Related Technologies, 1997, 20(14): 2281~2288.
49. Anthony M C, Branka R, Tom F. A Preliminary study on the characterization of honey by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 2002, 10 (2): 121~135.
50. AOAC. Official Methods of Analysis. Nr. 44.4.18: C-4 Plant Sugars in Honey, Method 991.41, 16th, ed.,AOAC International, Arlington, Virginia, 1995,USA.
51. Arcos M J, Ortiz M C, Belen V , Sarabia L A. Genetic algorithm based wavelength selection in multicomponent spectrometric determinations by PLS: application on indomethacin and acemethacin mixture. Analytica Chimica Acta, 1997, 339(1-2): 63~77.
52. Baroni M V, Chiabrando G A, Costa C, Wunderlin DA. Assessment of the floral origin of honey by SDS-page immunoblot techniques. Journal of Agriculture and Food Chemistry, 2002, 50(6): 1362~1367.
53. Benedetti S, Mannino S, Sabatini A G, Marcazzan G L. Electronic nose and neural network use for the classification of honey. Apidologie, 2004, 35: 1~6.
54. Berrueta L A, Salces R M A & Héberger K. Supervised pattern recognition in food analysis. Journal of Chromatography A, 2007, 1158(1-2), 196~214.
55. Bianchi F, Careri M, Musci M. Volatile norisoprenoids as markers of botanical origin of Sardinian strawberry-tree (Arbutus unedo L.) honey: Characterisation of aroma compounds by dynamic headspace extraction and gas chromatography-mass spectrometry. Food Chemistry, 2005, 89(4): 527~532.
56. Bogdanov S, Ruoff K, Persano Oddo L. Physico-chemical methods for the characterisation of unifloral honeys: a review. Apidologie, 2004, 35 (special issue): 4~17.
57. Bouseta A C S. Optimized Likens - Nickerson Methodology for quantifying honey flavours. Journal of Agriculture and Food Chemistry, 1995, 43(7): 1890~1896.
58. Bouseta A, Scheirman V, Collin S. Flavor and free amino acid composition of lavender and eucalyptus honey . Journal of Food Science, 1996, 61(4): 683~687.
59. Brookes S T, Barrie A, Davies J E. A rapid 13 C/ 12 C test for determination of corn syrups in honey. Journal of AOAC. 1991,74: 627~629.
60. Burns D A, Ciurczak E W. Handbook of near-infrared analysis 3rd. CRC Press, 2001.
61. Cavia M M, Fernández-Muino M A, Alonso-Torreetal S R, Huidobro J F, Sancho M T. Evolution of acidity of honeys from continental climates: Influence of induced granulation. Food chemistry, 2007, 100(4): 1728~1733.
62. Cen H, Bao Y, He Y, & Sun D W. Visible and Near Infrared Spectroscopy for Rapid Detection of Citric and Tartaric Acids in Orange Juice. Journal of Food Engineering, 2007, 82(2): 253~260.
63. Cercaci L, Rodriguez-Estrada M T, & Lercker G. Solid phase extraction-thin-layerchromatography-gas chromatography method for the detection of hazelnut oil in olive oils by determination of esterified sterols. Journal of Chromatography A, 2003, 985: 211–220.
64. Cho H J, Hong S H. Acacia honey quality measurement by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 1998, 6(5): A329~A331.
65. Codex Alimentarius Committee. Revised Codex Standard for Honey[S]. Codex STAN 12-1981, REV.2(2001), 11: 1~7.
66. Contal L, Leon V, &Downey G. Detection and quantification of apple adulteration in strawberry and raspberry purees using visible and near infrared spectroscopy. Jounral of Near Infrared Spectroscopy, 2002, 10: 289~299.
67. Conti M E. Lazio region (central Italy) honeys: a survey of mineral content and typical quality parameters. Food Control, 2000, 11(6): 459~463.
68. Corbella E, Cozzolino D. The use of visible and near infrared spectroscopy to classify the floral origin of honey samples produced in Uruguay. Journal of Near Infrared Spectroscopy, 2005, 13(2): 63～68.
69. Cotte J F, Casabianca H, Chardon S, Heritier J L, Grenier-Loustalot M F. Application of carbohydrate analysis to verify honey authenticity. Journal of Chromatography A, 2003, 1021(1-2): 145~155.
70. Cozzolino D, Kwiatkowski M J, Parker M, Cynkar W U, Dambergs R G, Gishen M. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 2004, 513 (1): 73~80.
71. Cozzolino D, Smyth H E, Gishen M. Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. Journal of Agriculture and Food Chemistry, 2003, 51(26): 7703~7708.
72. Cozzolino D, Smyth H E, Lattey K A, Cynkar W, Janik L, Francis R L, Gishen M. Relationship between sensory analysis and near infrared spectroscopy in Australian Riesling and Chardonnay wines. Analytica Chimica Acta, 2005, 539: 341~348.
73. D'Arcy B R, Rintoul G B, Rowland C Y E A. Composition of Australian honey extractives. 1. Norisoprenoids, monoterpenes, and other natural volatiles from blue gum (Eucalyptus leucoxylon) and yellow box (Eucalyptus melliodora) honeys. Journal of Agriculture and Food Chemistry, 1997, 45(5): 1834~1843.
74. Davies A M C, Radovic B, Fearn T&Anklam E. A preliminary study on the characterisation of honey by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 2002, 10(2):121~135.
75. Devillers J, Morlot M, Pham D M H, Dore J C. Classification of monofloral honeys based on their quality control data. Food Chemistry, 2004, 86(2): 305~312.
76. Downey G, Fouratier V & Kelly J D. Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy. Journal of Near Infrared Spectroscopy, 2003, 11 (6): 447~456.
77. Downey G, McIntyre P, Davies A N. Detecting and quantifying sunflower oil adulteration in extravirgin olive oils from the eastern mediterranean by visible and near-infrared spectroscopy. Journal of Agriculture and Food Chemistry, 2002, 50(20): 5520~5525.
78. Dvash L, Afik O, Shafir S, Schaffer A, Yeselson Y, Dag A. Determination by near-infrared spectroscopy of perseitol used as a marker for the botanical origin of avocado (Persea americana Mill.) honey. Journal of Agriculture and Food Chemistry, 2002, 50(19): 5283~5287.
79. European Commission. Council Directive 2001/110/EC of 20 December 2001 relating to honey [S]. Official Journal of the European Union, 2002, L10: 47~52.
80. Galtier O, Dupuy N, Le Dr′eau Y, Ollivier D, Pinatel C&Kister J. Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra. Analytica Chimica Acta, 2007, 595(1-2): 136~144.
81. Garcia-Alvarez M , Huidobro J F, Hermida M, Rodriguez-Otero J L. Major components of honey analysis by near-infrared transflectance spectroscopy. Journal of Agriculture and Food Chemistry, 2000, 48 (11): 5154~5158.
82. Garcia-Alvarez M, Ceresuela S, Huidobro J F, Hermida M, Rodriguez-Otero J L. Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy. Journal of Agriculture and Food Chemistry, 2002, 50(3): 419~425.
83. Gishen M, Dambergs R G, Kambouris A. Wine quality grading by near infrared spectroscopy. Proceeding of the tenth International Conference on Near Infrared Spectroscopy, 2002:187~189.
84. Gonzalez-Miret M L, Terrab A, Hernanz D, Fernandez-Recamales M A, Heredia F J. Multivariate correlation between color and mineral composition of honeys and by their botanical origin. Journal of Agriculture and Food Chemistry, 2005, 53(7): 2574~2580.
85. Goodacre R, Radovic B S, Anklam E. Progress toward the rapid nondestructive assessment of the floral origin of European honey using dispersive Raman spectroscopy. Applied spectroscopy, 2002, 56(4): 521~527.
86. Guyot C, Scheirman V, Collin S. Floral origin markers of heather honeys: Calluna vulgaris and Erica arborea. Food Chemistry, 1999, 64(1): 3~11.
87. Ha J, Koo M O H. Determination of the constituents of honey by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 1998, 6(5): 367~369.
88. Holland J H. Adaptation in Natural and Artificial Systems. American: University of Michigan Press, 1975.
89. http://www.shac.gov.cn/zxzx/scfx/jsbl/200904/t20090403_1239395.htm
90. Iglesias M T, Lorenzo C D, Polo M D, Jésus P, Pueyo E. Usefulness of amino acid composition to discriminate between honeydew and floral honeys. Application to honeys from a small geographic area . Journal of Agriculture and Food Chemistry, 2004, 52(1): 84~89.
91. Karoui R, Pillonel L, Schaller E, Bosset J O, Baerdemaeker J. Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study. Food Chemistry, 2007, 101(3): 1121~1129.
92. Kelly J D, Petisco C, Downey G. Potential of near infrared transflectance spectroscopy to detectadulteration of Irish honey by beet invert syrup and high fructose corn syrup. Journal of Near Infrared Spectroscopy, 2006, 14(2): 139~146.
93. Kelly J F D, Downey G., Fouratier V, Initial study of honey adulteration by sugar solutions using mid-infrared (MIR) spectroscopy and chemometrics. Journal of Agriculture and Food Chemistry, 2004, 52(1): 33~39.
94. Kerkvliet J D, Meijer H A J. Adulteration of honey: relation between microscopic analysis and d13C measurements. Apidologie,, 2000, 31: 717~726.
95. Kim S S , Rhyu M R, Kim J M , Lee S H. Authentication of rice using near infrared reflectance spectroscopy.Cereal Chemistry, 2003, 80(3): 346~49.
96. Lachman J, Kolihova D, Miholova D, Kosata J, Titera D, Kult K. Analysis of minority honey components: Possible use for the evaluation of honey quality. Food Chemistry, 2007, 101(3): 973~979.
97. Latorre M J, Pena R,Garcia S E A. Authentication of Galician (NW Spain) honeys by multivariate techniques based on metal content data. Analyst, 2000, 125(2): 307~312.
98. Leardi R, González A L.Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometrics and Intelligent Laboratory Systems, 1998, 41(2): 195~207.
99. Lichtenberg-Kraag B, Hedtke C, Bienefeld K. Infrared spectroscopy in routine quality analysis of honey. Apidologie, 2002, 33(3): 327~337.
100. Louveaux J, Maurizio A, Vorwohl G. Methods of melissopalynology. Bee World, 1978, 59, 139-162.
101. Maeda H, Ozaki M, Tanaka N, Hayashi, Kojima T. Near infrared spectroscopy and chemometrics studies of temperature-dependent spectral variations of water: relationship between spectral changes and hydrogen bonds. Journal of Near Infrared Spectroscopy, 1995, 3 (3): 191~201.
102. Manikis I, Thrasyvoulou A. The relation of physico-chemical characteristics of honey and the crystallization sensitive parameters. Apiacta, 2001, 36(2): 106~112.
103. Martin I G, Macias E M, Sanchez J S, Rivera B G. Detection of honey adulteration with beet sugar using stable isotope methodology. Food Chemistry, 1998, 61(3): 281~286.
104. Martin Y G, Oliveros M C C, Pavon J L P, Pinto C G, Cordero B M. Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: Characterisation of vegetable oils. Analytica Chimica Acta, 2001, 449(1-2): 69~80.
105. Martos I, Ferreres F, Tomás-Barberán F A. Identification of flavonoid markers for the botanical origin of Eucalyptus honey. Journal of Agriculture and Food Chemistry, 2000, 48(5): 1498~1502.
106. Mateo R, Bosch R F. Sugar profiles of Spanish unifloral honeys. Food Chemistry, 1997, 60(1): 33~41.
107. Moar N T. Pollen analysis of New Zealand honey. New Zealand Journal of Agricultural Research, 1985, 28: 39~70.
108. N?rgaard L, Saudland A, Wagner J., Nielsen J P, Munck L. Engelsen S B. Interval partial least squares regression (iPLS): A comparative chemometric study with an example from near-infraredspectroscopy. Applied Spectroscopy, 2000, 54(3):413~419.
109. Ohtani S, Wang T, Nishimura K. Milk Fat Analysis by Fiber-optic Spectroscopy. Asian-australasian journal of animal sciences, 2005, 18 (4): 580~583.
110. Padovan G J, Jong D, Rodrigues L P, Marchini J S. Detection of adulteration of commercial honey samples by the 13C/12C isotopic ratio. Food Chemistry, 2003, 82(4): 633~636.
111. Paradkar M M, Sakhamuri S, Irudayaraj J. Comparison of FTIR, FT-Raman, and NIR spectroscopy in a maple syrup adulteration study. Journal of Food Science, 2002, 67(6): 2009~2015.
112. Paula A,Federico F,Isabel G M,Francisco TB. Determination of phenolic compounds in honeys with different floral origin by capillary zone electrophoresis. Food Chemistry, 1997, 60(1): 79~84.
113. Pérez R A, Sanchez-Brunete C, Calvo R M, Tadeo J L. Analysis of volatiles from Spanish honeys by solid-phase microextraction and gas chromatography-mass spectrometry. Journal of Agriculture and Food Chemistry, 2002, 50(9): 2633~2637.
114. Persano Oddo L , Piazza M G, Sabatini A G, Accorti M. Characterization of unifloral honeys. Apidologie, 1995, 26: 453~465.
115. Persano Oddo L, Piana L, Bogdanov S, Bentabol A, Gotsiou P, Kerkvliet J. Botanical species giving unifloral honey in Europe . Apidologie , 2004, 35(special issue):82~93.
116. Persano Oddo L, Piro R. Main European unifloral honeys: descriptive sheets. Apidologie 2004, 35 (special issue): 38~81.
117. Qiu P Y, Ding H B, Tang Y K, Xu R J. Determination of chemical composition of commercial honey by near-infrared spectroscopy. Journal of Agriculture and Food Chemistry, 1999, 47(7): 2760~2765.
118. Radovic B S, Careri M, Mangia A, Musci M, Gerboles M, Anklam E. Contribution of dynamic headspace GC-MS analysis of aroma compounds to authenticity testing of honey . Food Chemistry, 2001, 72(4): 511~520.
119. Roggo Y, Duponchel L, Huvenne J P. Comparison of supervised pattern recognition methods with McNemar’s statistical test Application to qualitative analysis of sugar beet by near-infrared spectroscopy. Analytica Chimica Acta, 2003, 477(2): 187~200
120. Rossmann A, Koziet J, Martin G J, Dennis M J. Determination of the carbon-13content of sugars and pulp from fruit juices by isotope-ration massspectrometry (internal reference method) A European inter laboratory comparison. Analytica Chimica Acta, 1997, 340(1-3): 21~29.
121. Ruoff K, Iglesias M T, Luginbühl W, Bosset J O, Bogdanov S, AmadòR. Quantitative analysis of physical and chemical measurands in honey by mid-infrared spectrometry. European Food Research and Technology, 2006, 223(1): 22~29.
122. Ruoff K, Karoui R, Eric D , Dufour E, Luginbühl W, Bosset J O, Bogdanov S, Amado R. Authentication of the botanical origin of honey by front-face fluorescence spectroscopy: A preliminary study . Journal of Agriculture and Food Chemistry, 2005, 53(5): 1343~1347.
123. Singh N, Bath P K. Quality evaluation of different types of Indian honey. Food Chemistry, 1997, 58(1-2): 129~133.
124. Sivakesava S, Irudayaraj J. Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy. Journal of Food Science and Technology, 2002, 37(4):351~360.
125. Smith B M, Gemperline P J. Wavelength selection and optimization of pattern recognition methods using the genetic algorithm. Analytica Chimica Acta, 2000, 423(2): 167~177.
126. Stefan B, Kaspar R L P O. Physico-chemical methods for the characterisation of unifloral honeys: a review. Apidologie, 2004, 35 (special issue): 4~17.
127. Swallow K W, Low N H. Analysis and quantitation of the carbohydrates in honey using high-performance liquid chromatography. Journal of Agriculture and Food Chemistry, 1990, 38: 1828~1832.
128. Terrab A, Gonzalez A G, Diez M J E A. Mineral content and electrical conductivity of the honeys produced in Northwest Morocco and their contribution to the characterisation of unifloral honeys. Journal of the Science of Food and Agriculture, 2003, 83(7): 637~664.
129. Tewari J C, Irudayaraj J M K. Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose sensor. Journal of Agriculture and Food Chemistry, 2005, 53(18): 6955~6966.
130. Tomás-Barberán F A, Martos I, Ferreres F, Radovic B S, Anklam E. HPLC flavonoid profiles as markers for the botanical origin of European unifloral honeys. Journal of the Science of Food and Agriculture, 2001, 81(5): 485~496.
131. Von der Ohe W, Persano Oddo L, Piana M L, Morlot M, Martin P. Harmonised methods of melissopalynology. Apidologie, 2004, 35 (special issue): 18~25.
132. Wang L, Lee FSC, Wang X, He Y. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry, 2006, 95(3): 529~536.
133. White J W, Doner L W. Mass Spectrometric Detection of High-fructose Corn Syrup in Honey by use of 13C/12C Ratio: Collaborative Study. Journal of AOAC International, 1978, 61: 746~750.
134. White J W, Winters K, Martin K P, Rossmann A.. Stable Carbon Isotope Ratio Analysis of Honey: Validation of Internal Standard Procedure for Worlwide Application. Journal of AOAC International, 1998, 81: 610~619.
135. White J W, Winters K. Honey protein as internal standard for stable carbon isotope ratio detection of aduheration of honey. Journal of AOAC International, 1992, 72: 907~911.
136. Xing J, Ngadi M, Gunenc A, Prasher S, Gariepy C. Use of visible spectroscopy for quality classification of intact pork meat. Journal of Food Engineering, 2007, 82(2): 135~141.
137. Yao L, Jiang Y, Singanusong R, Datta, N, Raymont K. Phenolic acids and abscisic acid in Australian Eucalyptus honeys and their potential for floral authentication. Food Chemistry, 2004, 86(2): 169~177.
138. Zou X B, Zhao J W, Li Y X. Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of 'Fuji' apple based on BiPLS and FiPLS models. Vibrational Spectroscopy, 2007, 44 (2) : 220~227.