可见/近红外反射光谱法检测马铃薯抗性 淀粉含量的研究

目的利用可见/近红外反射光谱技术无损检测新鲜马铃薯茎块中抗性淀粉的含量。方法使用光谱仪获取新鲜马铃薯在345~1100 nm波段范围内的漫反射光谱;分别使用Savitzky–Golay(S-G)平滑处理、多元散射校正(MSC)法和一阶导数法(1st-D)对反射光谱进行预处理;对(S-G)反射光谱、MSC处理光谱和1st-D光谱使用逐步回归法判别法选择最优波长组合,建立多元线性回归模型,使用全交叉验证法验证模型。结果结果表明,可见/近红外反射光谱经过一阶导数处理后,确定的8个最优波长(370、569、576、866、868、886、922和963 nm)组合建立模型的校正和验证结果最好:模型的校正结果为相关系数R=0.996,标准差SEC=0.521%;模型交叉验证相关系数Rcv=0.982,验证标准差SECV=0.791%。结论可见/近红外反射光谱技术可以较好地预测新鲜马铃薯茎块的抗性淀粉含量,本研究可为可见近红外光谱技术在马铃薯功能成分的快速检测提供一定的技术基础。     

可见/近红外光谱技术无损检测新鲜鸡蛋pH及蛋白质的研究

本文利用可见/近红外光谱技术检测新鲜鸡蛋p H和蛋白质。分别采集新鲜鸡蛋在400~1000 nm和900~1700 nm波长范围的漫反射光谱,使用多元散射矫正(MSC)、标准正态变量变换(SNV)等光谱预处理技术,选择最佳的预处理方法,使用偏最小二乘法(PLS)建立p H和蛋白质模型并对其进行评价。结果表明,基于900~1700 nm波长范围的光谱获得的p H模型较好,其校正集相关系数为0.948,预测集相关系数为0.855;基于400~1000 nm波长范围的光谱获得的蛋白质模型较好,其校正集相关系数为0.927,预测集相关系数为0.906。研究表明,可见/近红外光谱技术可以较好的预测新鲜鸡蛋的p H和蛋白质,为鸡蛋营养成分的快速无损检测提供新的思路和方法。     

利用可见/近红外光谱判别干枣品种

目的利用可见/近红外反射光谱技术快速判别干枣的品种。方法使用光谱仪获取山西永和枣、山西板枣和新疆和田枣3种干枣在345~1100 nm波段范围内的漫反射光谱;分别使用多元散射校正(MSC)法和一阶导数法(1~(st)-D)和二阶导数法(2~(st)-D)对反射光谱进行预处理;对预处理光谱进行主成分分析,全交差验证法确定最佳主成分数量,提取主成分,结合马氏距离法和线性判别法建立品种判别模型,建立模型过程中使用全交叉验证法确定最佳主成分数,将模型应用于干枣的品种判别。结果可见/近红外反射光谱经过MSC处理后提取主成分建立品种预测模型对枣的品种判别结果最好,利用前4个主成分结合马氏距离法建立的判别模型和利用前5个主成分结合线性判别法建立判别模型,对于3个品种的枣的校正和验证判别准确率都达到了100%。结论可见/近红外反射光谱技术可以较好地判别干枣品种,本研究可为可见/近红外光谱技术在于枣品种和产地的快速鉴别和溯源中的应用提供一定的技术基础。     

基于VIS/NIR高光谱成像技术检测鸡肉嫩度

利用400~1000 nm可见近红外高光谱成像系统对鸡肉嫩度进行快速无损检测研究。采集鸡肉表面的高光谱散射图像,提取样本感兴趣区域反射光谱曲线并用剪切力值表征鸡肉的标准嫩度。以原始光谱和多元散射校正(MSC)预处理光谱数据建立鸡肉嫩度的偏最小二乘回归(PLSR)模型,预处理光谱建立的模型效果更优。基于MSC预处理,采用偏PLS权重系数法结合逐步回归法筛选出了4个特征波长。然后采用PLSR和多元线性回归(MLR)模型分别建立特征波长处光谱反射值和鸡肉嫩度关系的数学模型,优选最佳模型。结果显示:MLR模型预测效果较好,预测相关系数(RP)和均方根误差(RMSEP)分别为0.94和1.97。研究表明:利用可见近红外高光谱成像技术结合多元回归分析法对鸡肉嫩度的快速无损检测是可行的。     

利用可见/近红外反射光谱无损检测小米的粘度

目的建立适用于小米粘度无损检测的可见/近红外反射光谱法。方法使用光谱仪获取小米在367～1020 nm波段范围内的漫反射光谱,采用多元散射校正法(multiple scattering correction,MSC)和一阶导数法(first derivation,1~(st)-D)对原始反射光谱进行处理,并且使用主成分分析确定最佳主成分数,建立小米粘度判别模型,使用全交叉验证法进行模型验证。结果使用原始反射光谱、MSC处理光谱和1~(st)-D处理光谱,分别提取了6、12和12个主成分,建立的峰值粘度模型RCV在0.86以上,对验证集的预测结果 Rp在0.82～0.86之间;而使用1~(st)-D处理光谱提取12个最优主成分,建立的模型可较好地预测小米粘度的破损值,RCV为0.8573,对验证集的预测结果 Rp为0.8309。结论该方法适用于小米粘度的无损检测,为小米加工品质的快速检测提供一定的理论支持。     

利用可见/近红外光谱技术对小米产地进行溯源研究

目的利用可见/近红外光谱技术对产自不同地区的晋谷21号小米进行溯源研究。方法使用近红外光谱仪获取产自洪洞、浮山、沁县3个不同地区的晋谷21号小米400~1004nm波段范围内的漫反射光谱;对光谱分别进行多元散射校正法(multiple scattering correction,MSC)、一阶导数法(first derivative,1St-D)预处理;对预处理光谱进行主成分分析,全交叉验证确定最佳主成分数量,获取主成分;同时选择预处理光谱特征波长。使用马氏距离法、线性判别法建立判别模型,最后用未知样品的验证准确率来表示模型的判别效果。结果原始光谱和MSC处理光谱提取特征波长分别建立的产地判别模型对3个不同产地的小米判别完全准确;1St-D处理光谱基于7个主成分结合马氏距离法和基于9个主成分结合线性判别法建立的2种判别模型对3个不同产地的小米亦实现完全准确判别。结论可见/近红外反射光谱技术用于小米产地的判别具有可行性,本研究可为小米产地的快速判别应用中提供技术基础。     

鸡蛋脂肪含量近红外光谱无损检测技术研究

为建立近红外光谱无损检测鸡蛋脂肪含量的方法,在近红外光谱全波段内采集鸡蛋样品的漫反射光谱图,用酸水解法测定鸡蛋样品中的脂肪含量。对采集的光谱进行最小-最大归一化(Min-max Normalization,MMN)、矢量归一化(Vector Normalization,SNV)、平滑、一阶导数(First Derivative,FD)及多元散射校正(Multiplicative Scatter Correction,MSC)处理,用偏最小二乘法(Partial Least Squares,PLS)对鸡蛋脂肪含量建模验证。结果表明,经多元散射校正(MSC)法预处理,偏最小二乘法(PLS)建模以及杠杆校正(Leverage Correction)检验,鸡蛋的脂肪含量与其近红外光谱信号之间存在线性关系,校正集和验证集相关系数R2分别0.947 5,0.906 3,校正均方差RMSEE为0.173 2,预测均方差RMSEP为0.231 4,模型效果最好,可用于鸡蛋中脂肪含量的无损检测。     

鸡蛋蛋白质含量无损检测技术研究

为了探索傅立叶近红外光谱(FT-NIR)快速无损检测鸡蛋蛋白质的方法,采集83枚新鲜种鸡蛋赤道位和锐端的近红外光谱(12000~4000cm-1),用凯氏定氮法测定对应鸡蛋的粗蛋白含量,运用The Unscrambler软件中的一阶导数(First Derivative,FD)、二阶导数(Second Derivative,SD)、多元散射校正(Multiplication Scatter Correction,MSC)、变量标准化(Standard Normalized Variate,SNV)和光谱转换(Spectroscopic Transformation,ST)方法预处理光谱,结合偏最小二乘法(PLS)建立蛋白质的数学模型并用杠杆校正(Leverage Correction)检验,通过模型的比较来判断FT-NIR快速无损检测鸡蛋蛋白质的可行性。结果表明:鸡蛋蛋白质与其近红外光谱信号间存在线性关系,在未作预处理下用赤道位和锐端原始光谱建立的蛋白质校正模型R2在0.74以上;在不同预处理中,SD处理下建立模型效果最优,赤道位和锐端模型的R2均在0.97以上,RMSEE、RMSEP值都较小且相互接近;SD处理下建立的赤道位和锐端光谱模型差异不显著,R2及RMSEE、RMSEP都较为接近。利用FT-NIR快速无损检测鸡蛋蛋白质的含量是可行的。     

基于近红外光谱技术的马铃薯全粉蛋白质无损检测

为了探索一种简捷、高效、快速、无损的马铃薯全粉品质的检测方法,近红外光谱技术被应用到马铃薯全粉蛋白质的无损检测研究中。以120个经过真空微波冷冻干燥技术处理的不同品种马铃薯为样品,进行光谱采集及相应的化学值测定,采用多元散射校正和SavitzkyGolay卷积平滑对1200~2400 nm波段范围内的原始光谱进行预处理,选取最优的预处理方法;运用主成分回归分析法与偏最小二乘回归系数分别选择特征波长,并根据蛋白质基团的特征光谱区间选取特征波段,建立全波段、特征波段和特征波长下的主成分回归和偏最小二乘蛋白质预测模型。结果为:经过多元散射校正处理后的光谱建模效果最好,且运用偏最小二乘回归系数选择特征波长建立的马铃薯全粉蛋白质校正和验证模型的相关系数和均方根误差分别为0.9693、0.2937和0.9779、0.3304,优于全波段和特征波段建立的模型。研究表明,采用近红外光谱技术对马铃薯全粉蛋白质的无损检测是可行的。     

基于多元散射校正和偏最小二乘(MSC/PLS)的傅里叶变换近红外光谱检测蜂蜜中还原糖

为快速无损测定蜂蜜的还原糖,提出一种用近红外光谱技术进行检测的新方法.选用SN/T0852-2000铁氰化钾滴定法作为光谱还原糖测定的标定方法,用不同光谱预处理方法对模型的影响进行比较.首先用MSC对光谱数据进行校正,在经过Norris导数平滑法对原始光谱进行预处理消除噪声.利用光谱专用分析软件TQ Analyst7.2,建立偏最小二乘(PLS)校正模型.最优光谱模型校正相关系数(R)、校正相关均方差(RMSEC)、预测相关均方差(RMSEP)、交互验证相关系数(Rcv)、交叉验证均方差(REMSECV)分别为0.970 56,1.21,1.54,0.962 67,1.36.结果表明,应用多元散射校正(MSC)处理,并经过Norris二阶导数平滑后的PLS模型优于经典PLS算法所建立的模型. 用MSC对光谱数据进行校正,在经过Norris导数平滑法对原始光谱进行预处理消除噪声.利用光谱专用分析软件TQ Analyst7.2,建立偏最小二乘(PLS)校正模型.最优光谱模型校正相关系数(R)、校正相关均方差(RMSEC)、预测相关均方差(RMSEP)、交互验证相关系数(Rcv)、交叉验证均方差(REMSECV) 别为0.970 56,1.21,1.54,0.962 67,1.36.结果表明,应用多元散射校正(MSC)处理,     

Preliminary study on the use of near‐infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants

Near‐infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole‐wheat plant (Triticum aestivum L). Whole‐wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross‐validation (leave‐one‐out method). Coefficient of determination in calibration (R²_CAL) and the standard error of cross‐validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R²_CAL and SECV for N compared with fresh presentation (R²_CAL > 0.90, SECV < 0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried). Copyright © 2006 Society of Chemical Industry     

Feasibility study of discriminating the manufacturing process and sampling zone in ripened soft cheeses using attenuated total reflectance MIR and fiber optic diffuse reflectance VIS–NIR spectroscopy

The use of visible–near infrared (VIS–NIR) and mid infrared (MIR) spectroscopies for rapid characterisation of 15 traditional and stabilised retail soft cheeses, manufactured with different cheese making procedures was described. A fiber-type, VIS–NIR spectrophotometer (Zeiss Corona 45 VIS–NIR) in a measurement range of 315–1700 nm and a Fourier transform spectrometer (IFS 66V/S, Bruker, Belgium) in a measurement range between 3000 and 900 cm⁻¹ were used to scan spectra in reflectance mode at the external (E) and central (C) zones of the investigated cheeses. The principal component analysis (PCA) applied to the normalised spectral data set (VIS–NIR and MIR) did not provide a good discrimination of cheeses. Therefore, the factorial discriminant analysis (FDA) was applied separately to the first 5 principal components (PCs) of the PCA performed on the VIS–NIR and MIR data sets. Regarding the MIR spectra, the percentage of samples correctly classified into six groups (three for the E and three for the C zones) by the FDA was 64.8% and 33.3% for the calibration and validation samples, respectively. Better classification was obtained from the VIS–NIR spectra since the percentage of samples correctly classified was 85.2% and 63.2% for the calibration and validation samples, respectively. Finally, a concatenation technique was applied on the first 5 PCs of the PCA performed on the VIS–NIR and MIR data sets. This technique allowed a quite satisfactory classification of the investigated cheeses according to their manufacturing process and their sampling zone. In this case, correct classifications (CC) of 90.7% and 80.6% were obtained for the calibration and the validation samples, respectively.     

近红外光谱法测定混合汁中还原糖含量

采用近红外光谱法测定混合汁中还原糖含量 ,获得良好效果 ,所建模型标准误差小而决定系数高。定标决定系数 R2 、交互定标标准误差 (SECV)、交互定标决定系数 (1-VR)分别为 0 .95 1,0 .0 74 ,0 .85 0 ,具有较好的相关性。用 5 2个随机混合汁样品检验该模型 ,近红外法预测结果与传统滴定法测定结果的检验工作标准误差 (SEP(C) )为 0 .0 75 ,检验决定系数 RSQ为 0 .85 0 ,证明测定所建近红外法定标模型具有较好的稳定性。     

近红外光谱法测定清汁中还原糖含量

采用近红外光谱法测定清汁中还原糖含量，获得良好效果，所建模型标准误差小而决定系数高。数据采用修正的最小二乘法（MPLS）进行计算，所得多元回归线性方程的定标决定系数R2、交互定标标准误差（SECV）、交互定标决定系数（1-VR）分别为0．952，0．067，0．813，具有较好的相关性。用36个随机清汁样品检验该模型，近红外法预测结果与传统滴定法测定结果的检验工作标准误差（SEP）为0．070，检验决定系数为0．876，证明测定所建近红外法定标模型具有较好的稳定性。     

Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries

Visible (VIS) and near infrared (NIR) spectroscopy combined with chemometrics was used in an attempt to classify commercial Riesling wines from different countries (Australia, New Zealand, France and Germany). Commercial Riesling wines (n = 50) were scanned in the VIS and NIR regions (400–2500 nm) in a monochromator instrument, in transmission mode. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) based on PCA scores were used to classify Riesling wines according to their country of origin. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. PLS-DA models correctly classified 97.5%, 80% and 70.5% of the Australian, New Zealand and European (France and Germany) Riesling wines, respectively. SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively. These results demonstrated that the VIS and NIR spectra contain information that when used with chemometrics allow discrimination between wines from different countries. To further validate the ability of VIS–NIR to classify white wine samples, a larger sample set will be required.     

软枣猕猴桃总酚的可见-近红外漫反射光谱无损检测

应用可见-近红外漫反射光谱在570～1848nm光谱区域内,建立了软枣猕猴桃总酚定量数学模型。实验将贮藏分三个阶段(采收阶段,贮藏12d,贮藏24d)进行,通过对比分析不同建模方法得到软枣猕猴桃总酚定标模型。结果表明,应用偏最小二乘回归算法、一阶导数处理和反相多元离散校正处理所建软枣猕猴桃总酚定标模型的预测性能较好。定标集样本的交互验证相关系数(RCV)为0.8939,交互验证均方根误差(RMSECV)为11.6734mg/100g;预测集样本的相关系数(RP)为0.8627,预测均方根误差(RMSEP)为16.7690mg/100g。研究表明：可见/近红外漫反射光谱对软枣猕猴桃总酚的快速无损检测具有一定的可行性, 但模型精度有待提高。     

On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy

The chemical composition of industrial scale batches of frozen beef was measured on-line during grinding by near infrared (NIR) reflectance spectroscopy. The MM55E filter based non-contact NIR instrument was mounted at the outlet of a meat grinder, and the fat, moisture and protein contents determined from the average of each filter reading throughout the grinding of the batch. The filters were selected from full spectra measurements to be as insensitive to water crystallization as possible. For on-line calibration and prediction, 55 beef batches of 400–800 kg in the range of 7.66–22.91% fat, 59.36–71.48% moisture, and 17.04–20.76% protein, were ground through 4 or 13 mm hole plates. The regression results, presented as root mean square error of cross validation (RMSECV) were between 0.48 and 1.11% for fat, 0.43 and 0.97% for moisture and 0.41 and 0.47% for protein.