蜂蜜质量的近红外光谱分析技术研究
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摘要
蜂蜜是具有重要营养价值和医疗保健作用的天然食品。对蜂蜜质量的检测一般包括理化参数分析、品种鉴别、产地鉴别和真伪检测。高效、快速、低成本检测蜂蜜质量是有效监控蜂蜜质量的前提条件之一。近红外光谱技术具有高效、快速、低成本和绿色环保等常规分析方法无可比拟的优点。开展蜂蜜质量近红外光谱分析技术研究对保证蜂蜜质量、保护消费者权益和保障蜂蜜产业健康发展具有重要意义。本课题的主要研究内容及得到的结论如下：
(1)研究了薄膜袋透反射、样品杯透反射和样本池透射三种不同方式下采集的蜂蜜近红外光谱的重复性,确定样品杯透反射光谱和样本池透射光谱重复性好。进一步比较这两种方式下采集的光谱所建模型的预测能力后,确定样品杯透反射方式为蜂蜜近红外光谱采集的较合理方式。
(2)通过考察扫描次数和分辨率对光谱响应特性和对模型预测能力的影响,确定采集蜂蜜近红外光谱时,扫描次数取32次、分辨率取8cm-1为宜。
(3)用近红外光谱结合偏最小二乘法(PLS)对蜂蜜的12个主要理化参数值进行了定量分析。这12个理化参数包括可溶性固形物含量(SSC)、水分、还原糖、pH值、总酸度、电导率、果糖、葡萄糖、蔗糖、麦芽糖、果糖/葡萄糖和葡萄糖/水。PLS模型对蜂蜜中SSC、水分、还原糖和果糖/葡萄糖的定量分析结果好：验证集预测均方根误差(RMSEP)依次为0.1795、0.1696、1.5270和0.0344,真实值和模型预测值的相关系数(Rp)依次为0.9989、0.9989、0.9191和0.9749。对其它8个理化参数用蒙特卡罗交互检验法(MCCV)剔除奇异样本,再用竞争性自适应重加权采样(CARS)变量选择法结合PLS回归来优化模型,优化后的PLS模型的预测能力都有明显提高。优化后模型对蜂蜜pH值、总酸度、电导率、果糖、葡萄糖、蔗糖、麦芽糖和葡萄糖/水的RMSEP分别为0.1196、0.4674、2.6827、0.4955、0.5704、0.5711、0.2578和0.0394,Rp分别为0.9058、0.9083、0.9679、0.9845、0.9879、0.9386、0.9586和0.9809。
(4)选择4200～5400cm-1的光谱范围,分别采用马氏距离判别分析法(MD-DA)、偏最小二乘判别分析法(PLSDA)和径向基函数神经网络法(RBFNN)对5个品种(苹果、油菜、枣花、枸杞和荆条)蜂蜜进行植物来源的判别分析。MD-DA模型和RBFNN模型对验证集的判别总正确率都达到了94.0%,而PLSDA模型只有78.0%。表明近红外光谱结合MD-DA法或RBFNN法具有快速识别蜂蜜品种的潜力。
(5)光谱用小波变换(WT)进行变量压缩和滤噪。结合滤波前后的光谱信息,分别用RBFNN和PLSDA建立了苹果蜜产地和油菜蜜产地的判别模型,并对验证集进行预测。对苹果蜜,PLSDA、WT-PLSDA、RBFNN和WT-RBFNN模型对验证集的判别总正确率都为96.2%；对油菜蜜,PLSDA、WT-PLSDA、 RBFNN和WT-RBFNN模型对验证集的判别总正确率分别86.4%、90.9%、81.8%和86.4%。结果表明：不同品种蜂蜜的产地判别,同样的建模方法所得预测结果会存在较大差异；线性的WT-PLSDA模型比非线性的WT-RBFNN模型可能更适于峰蜜产地判别；近红外光谱技术具有快速识别蜂蜜产地的潜力。
(6)真蜂蜜样本中分别掺入高果玉米糖浆(HFCS)=甜菜糖浆(BS)、麦芽糖浆(MS)及这三种糖浆的混合物,采集掺假前后蜂蜜样本的近红外光谱。分别用距离匹配法(DM)、MD-DA和PLSDA法对真假蜜进行定性判别分析,结果表明PLSDA模型的判别结果最好：验证集的判别总正确率依次为82.4%、90.2%、90.2%和80.4%。当分别用这三种判别方法对掺假物类型进行判别分析时,只有DM模型和MD-DA模型能较好地识别MS掺假物,验证集的判别正确率都为82.4%。用PLS回归法对不同掺假物的掺假量进行定量分析的结果表明：同一蜂蜜样本掺假,掺假物的定量分析结果好,但这不具现实意义；同一品种或不同品种蜂蜜掺假,只有MS掺假物的定量分析结果较好。
(7)真蜂蜜样本中掺入果糖和葡萄糖的混合溶液,采集真蜂蜜与掺假蜜的近红外光谱。用WT对光谱进行变量压缩和去噪,选择径向基核函数(RBF)作为最小二乘支持向量机(LS-SVM)算法的内核函数,用网格搜索法寻优,得到回归误差的权重(γ)和RBF核的核参数(σ2)分别为222.822和45.170。建立的LSSVM模型对验证集的判别总正确率为95.1%,高于支持向量机算法(SVM)、反向传播人工神经网络法(BPANN)、K最临近距离法(KNN)和线性判别分析法(LDA),表明LSSVM算法比SVM、BPANN、KNN(?)口LDA算法具有更好的泛化能力。近红外光谱技术具有快速识别蜂蜜中掺入混合糖的能力。
Honey is a natural health product with high nutritional value. Quality assessment of honey includes following sections:quantitative analysis of physical and chemical parameters, classification of botanical and geographical origin and detection of adulteration. It is necessary to looking for fast and accurate methods to detect these indexes so as to assess honey quality more effectively. As a new analytical method, near infrared spectroscopy (NIRS) have advantages like highly efficient, fast, low cost and green environmental protection, and so on. Therefore, it is significant to develop NIRS methods to detect honey quality, which will benefit healthy development of honey industry and protect consumers'rights and interests. The dissertation mainly studies:
(1) The different effects of three measurements with thin film transflectance, sample cup transflectance and cuvette transmission on the repeatability of spectra of honey sample were studied. The results showed that both sample cup transflectance and cuvette transmission had better repeatability than thin film transflectance. Further studies results showed that the transflectance sample cup performed better than cuvette transmission in measurement.
(2) The different effects of scanning frequency and resolution on near-infrared spectra of honey samples and the prediction abilities of models were studied. For honey sample, the optimized parameters combination was32times of scanning frequency and8cm-1of resolution.
(3) The quantitative analytic models of12different physical and chemical parameters, soluble solids content (SSC), moisture, reducing sugar (RS), pH-value, total acidity (TA), electrical conductivity (EC), fructose, glucose, sucrose, maltose, fructose/glucose ratio (F/G) and glucose/moisture ratio (G/M) in honey were developed by partial least squares (PLS) regression. Satisfying prediction accuracies were achieved for SSC, moisture, RS and F/G:Root mean square error of prediction (RMSEP) for each of the parameters was respectively0.1795,0.1696,1.5270and0.0344; Coefficient of determination in prediction (Rp) set was0.9989,0.9989,0.9191and0.9749, respectively. After rejection of the outliers by Monte-Carlo cross-validation (MCCV), competitive adaptive reweighted sampling (CARS) combined with PLS regressions were used to choose effective variables so as to optimize PLS models of8other parameters. The results showed that the prediction abilities of PLS models were obviously improved. Using the optimal PLS models, RMSEP of pH-value, TA, EC, fructose, glucose, sucrose, maltose and G/M in honey were0.1196,0.4674,2.6827,0.4955,0.5704,0.5711,0.2578and0.0394, respectively, and Rp were0.9058、0.9083,0.9679,0.9845,0.98790.9386,0.9586and0.9890, respectively.
(4) Three models were developed for detection of five different botanical origin honey samples by Mahalanobis distance-discriminant analysis (MD-DA), partial least squares-discriminant analysis (PLSDA) and radical basis function neural networks (RBFNN) in the NIR region of4200～5400cm-1. The total prediction accuracy (PA) of both MD-DA model and RBFNN model can reach94.0%. However, PA of PLSDA model was only78.0%. Therefore, NIRS combined with MD-DA or RBFNN have a potential for quickly detecting botanical origin of honey.
(5) The spectral data were compressed and de-noised using wavelet transform (WT). RBFNN and PLSDA were applied to develop classification models of the geographical origin of honey samples using either before or after the reconstructed signals, respectively. For apple honey samples, PLSDA, WT-PLSDA, RBFNN and WT-RBFNN produced a same total prediction accuracy of96.2%. For rape honey samples, PLSDA, WT-PLSDA, RBFNN and WT-RBFNN produced total prediction accuracy of86.4%,90.9%,81.8%and86.4%, respectively. The results showed that prediction accuracy varied widely in the different geographical origin honey samples when modeling method were the same. Linear WT-PLSDA model may be more suitable for geographical classification of honey samples than no-linear WT-RBFNN model. NIRS has a potential for quickly detecting geographical origin of honey samples.
(6) Authentic honey samples were adulterated with high fructose corn syrup (HFCS), beet syrup (BS), maltose syrup (MS) and a blend of these syrups. Detection of honey adulteration was developed by distance match (DM), MD-DA and PLSDA. The best performances of classification of authentic and adulterated honeys were obtained by PLSDA:total accuracy for validation sets were82.4%,90.2%,90.2%and80.4%, respectively. The classification of types of adulterants was also developed using the above methods, and the highest accuracy of84.4%was obtained by both DM and MD-DA for MS adulterant. The quantitative analysis of adulterants by PLS regression gave satisfying results if adulterated honey samples were got from the same one authentic honey sample, otherwise it gave dissatisfying results for the adulterated samples from different botanical origins, except the adulteration is solo MS.
(7) Honey samples were adulterated by blend of fructose and glucose. Near infrared spectrum of honeys were collected before and after adulteration. The spectral data were compressed and de-noised by WT. The radial basis function (RBF) was used as kernel function of WT-LSSVM model. By grid search, the selected optimal value of γ(the relative weight of the regression error) and σ2(the kernel parameter of the RBF kernel) was222.822and45.170respectively. The total accuracy of WT-LSSVM model was95.1%for the validation set, which was better than that of support vector machines (SVM), back propagation artificial neural networks (BPANN), K-nearest neighbor (KNN) and linear discriminant analysis (LDA). This study demonstrated that the superiority of LSSVM in building models with better generalization abilities than those obtained from SVM, BPANN, KNN and LDA for the problems studied. NIRS has the potential ability to quickly detect mixed sugar adulterants in honey.
(1)研究了薄膜袋透反射、样品杯透反射和样本池透射三种不同方式下采集的蜂蜜近红外光谱的重复性,确定样品杯透反射光谱和样本池透射光谱重复性好。进一步比较这两种方式下采集的光谱所建模型的预测能力后,确定样品杯透反射方式为蜂蜜近红外光谱采集的较合理方式。
(2)通过考察扫描次数和分辨率对光谱响应特性和对模型预测能力的影响,确定采集蜂蜜近红外光谱时,扫描次数取32次、分辨率取8cm-1为宜。
(3)用近红外光谱结合偏最小二乘法(PLS)对蜂蜜的12个主要理化参数值进行了定量分析。这12个理化参数包括可溶性固形物含量(SSC)、水分、还原糖、pH值、总酸度、电导率、果糖、葡萄糖、蔗糖、麦芽糖、果糖/葡萄糖和葡萄糖/水。PLS模型对蜂蜜中SSC、水分、还原糖和果糖/葡萄糖的定量分析结果好：验证集预测均方根误差(RMSEP)依次为0.1795、0.1696、1.5270和0.0344,真实值和模型预测值的相关系数(Rp)依次为0.9989、0.9989、0.9191和0.9749。对其它8个理化参数用蒙特卡罗交互检验法(MCCV)剔除奇异样本,再用竞争性自适应重加权采样(CARS)变量选择法结合PLS回归来优化模型,优化后的PLS模型的预测能力都有明显提高。优化后模型对蜂蜜pH值、总酸度、电导率、果糖、葡萄糖、蔗糖、麦芽糖和葡萄糖/水的RMSEP分别为0.1196、0.4674、2.6827、0.4955、0.5704、0.5711、0.2578和0.0394,Rp分别为0.9058、0.9083、0.9679、0.9845、0.9879、0.9386、0.9586和0.9809。
(4)选择4200～5400cm-1的光谱范围,分别采用马氏距离判别分析法(MD-DA)、偏最小二乘判别分析法(PLSDA)和径向基函数神经网络法(RBFNN)对5个品种(苹果、油菜、枣花、枸杞和荆条)蜂蜜进行植物来源的判别分析。MD-DA模型和RBFNN模型对验证集的判别总正确率都达到了94.0%,而PLSDA模型只有78.0%。表明近红外光谱结合MD-DA法或RBFNN法具有快速识别蜂蜜品种的潜力。
(5)光谱用小波变换(WT)进行变量压缩和滤噪。结合滤波前后的光谱信息,分别用RBFNN和PLSDA建立了苹果蜜产地和油菜蜜产地的判别模型,并对验证集进行预测。对苹果蜜,PLSDA、WT-PLSDA、RBFNN和WT-RBFNN模型对验证集的判别总正确率都为96.2%；对油菜蜜,PLSDA、WT-PLSDA、 RBFNN和WT-RBFNN模型对验证集的判别总正确率分别86.4%、90.9%、81.8%和86.4%。结果表明：不同品种蜂蜜的产地判别,同样的建模方法所得预测结果会存在较大差异；线性的WT-PLSDA模型比非线性的WT-RBFNN模型可能更适于峰蜜产地判别；近红外光谱技术具有快速识别蜂蜜产地的潜力。
(6)真蜂蜜样本中分别掺入高果玉米糖浆(HFCS)=甜菜糖浆(BS)、麦芽糖浆(MS)及这三种糖浆的混合物,采集掺假前后蜂蜜样本的近红外光谱。分别用距离匹配法(DM)、MD-DA和PLSDA法对真假蜜进行定性判别分析,结果表明PLSDA模型的判别结果最好：验证集的判别总正确率依次为82.4%、90.2%、90.2%和80.4%。当分别用这三种判别方法对掺假物类型进行判别分析时,只有DM模型和MD-DA模型能较好地识别MS掺假物,验证集的判别正确率都为82.4%。用PLS回归法对不同掺假物的掺假量进行定量分析的结果表明：同一蜂蜜样本掺假,掺假物的定量分析结果好,但这不具现实意义；同一品种或不同品种蜂蜜掺假,只有MS掺假物的定量分析结果较好。
(7)真蜂蜜样本中掺入果糖和葡萄糖的混合溶液,采集真蜂蜜与掺假蜜的近红外光谱。用WT对光谱进行变量压缩和去噪,选择径向基核函数(RBF)作为最小二乘支持向量机(LS-SVM)算法的内核函数,用网格搜索法寻优,得到回归误差的权重(γ)和RBF核的核参数(σ2)分别为222.822和45.170。建立的LSSVM模型对验证集的判别总正确率为95.1%,高于支持向量机算法(SVM)、反向传播人工神经网络法(BPANN)、K最临近距离法(KNN)和线性判别分析法(LDA),表明LSSVM算法比SVM、BPANN、KNN(?)口LDA算法具有更好的泛化能力。近红外光谱技术具有快速识别蜂蜜中掺入混合糖的能力。
Honey is a natural health product with high nutritional value. Quality assessment of honey includes following sections:quantitative analysis of physical and chemical parameters, classification of botanical and geographical origin and detection of adulteration. It is necessary to looking for fast and accurate methods to detect these indexes so as to assess honey quality more effectively. As a new analytical method, near infrared spectroscopy (NIRS) have advantages like highly efficient, fast, low cost and green environmental protection, and so on. Therefore, it is significant to develop NIRS methods to detect honey quality, which will benefit healthy development of honey industry and protect consumers'rights and interests. The dissertation mainly studies:
(1) The different effects of three measurements with thin film transflectance, sample cup transflectance and cuvette transmission on the repeatability of spectra of honey sample were studied. The results showed that both sample cup transflectance and cuvette transmission had better repeatability than thin film transflectance. Further studies results showed that the transflectance sample cup performed better than cuvette transmission in measurement.
(2) The different effects of scanning frequency and resolution on near-infrared spectra of honey samples and the prediction abilities of models were studied. For honey sample, the optimized parameters combination was32times of scanning frequency and8cm-1of resolution.
(3) The quantitative analytic models of12different physical and chemical parameters, soluble solids content (SSC), moisture, reducing sugar (RS), pH-value, total acidity (TA), electrical conductivity (EC), fructose, glucose, sucrose, maltose, fructose/glucose ratio (F/G) and glucose/moisture ratio (G/M) in honey were developed by partial least squares (PLS) regression. Satisfying prediction accuracies were achieved for SSC, moisture, RS and F/G:Root mean square error of prediction (RMSEP) for each of the parameters was respectively0.1795,0.1696,1.5270and0.0344; Coefficient of determination in prediction (Rp) set was0.9989,0.9989,0.9191and0.9749, respectively. After rejection of the outliers by Monte-Carlo cross-validation (MCCV), competitive adaptive reweighted sampling (CARS) combined with PLS regressions were used to choose effective variables so as to optimize PLS models of8other parameters. The results showed that the prediction abilities of PLS models were obviously improved. Using the optimal PLS models, RMSEP of pH-value, TA, EC, fructose, glucose, sucrose, maltose and G/M in honey were0.1196,0.4674,2.6827,0.4955,0.5704,0.5711,0.2578and0.0394, respectively, and Rp were0.9058、0.9083,0.9679,0.9845,0.98790.9386,0.9586and0.9890, respectively.
(4) Three models were developed for detection of five different botanical origin honey samples by Mahalanobis distance-discriminant analysis (MD-DA), partial least squares-discriminant analysis (PLSDA) and radical basis function neural networks (RBFNN) in the NIR region of4200～5400cm-1. The total prediction accuracy (PA) of both MD-DA model and RBFNN model can reach94.0%. However, PA of PLSDA model was only78.0%. Therefore, NIRS combined with MD-DA or RBFNN have a potential for quickly detecting botanical origin of honey.
(5) The spectral data were compressed and de-noised using wavelet transform (WT). RBFNN and PLSDA were applied to develop classification models of the geographical origin of honey samples using either before or after the reconstructed signals, respectively. For apple honey samples, PLSDA, WT-PLSDA, RBFNN and WT-RBFNN produced a same total prediction accuracy of96.2%. For rape honey samples, PLSDA, WT-PLSDA, RBFNN and WT-RBFNN produced total prediction accuracy of86.4%,90.9%,81.8%and86.4%, respectively. The results showed that prediction accuracy varied widely in the different geographical origin honey samples when modeling method were the same. Linear WT-PLSDA model may be more suitable for geographical classification of honey samples than no-linear WT-RBFNN model. NIRS has a potential for quickly detecting geographical origin of honey samples.
(6) Authentic honey samples were adulterated with high fructose corn syrup (HFCS), beet syrup (BS), maltose syrup (MS) and a blend of these syrups. Detection of honey adulteration was developed by distance match (DM), MD-DA and PLSDA. The best performances of classification of authentic and adulterated honeys were obtained by PLSDA:total accuracy for validation sets were82.4%,90.2%,90.2%and80.4%, respectively. The classification of types of adulterants was also developed using the above methods, and the highest accuracy of84.4%was obtained by both DM and MD-DA for MS adulterant. The quantitative analysis of adulterants by PLS regression gave satisfying results if adulterated honey samples were got from the same one authentic honey sample, otherwise it gave dissatisfying results for the adulterated samples from different botanical origins, except the adulteration is solo MS.
(7) Honey samples were adulterated by blend of fructose and glucose. Near infrared spectrum of honeys were collected before and after adulteration. The spectral data were compressed and de-noised by WT. The radial basis function (RBF) was used as kernel function of WT-LSSVM model. By grid search, the selected optimal value of γ(the relative weight of the regression error) and σ2(the kernel parameter of the RBF kernel) was222.822and45.170respectively. The total accuracy of WT-LSSVM model was95.1%for the validation set, which was better than that of support vector machines (SVM), back propagation artificial neural networks (BPANN), K-nearest neighbor (KNN) and linear discriminant analysis (LDA). This study demonstrated that the superiority of LSSVM in building models with better generalization abilities than those obtained from SVM, BPANN, KNN and LDA for the problems studied. NIRS has the potential ability to quickly detect mixed sugar adulterants in honey.
引文
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