近红外光谱技术预测猪肉的pH、嫩度和蒸煮损失

以冷却猪肉为研究对象,评价近红外光谱（NIR）技术用于肉类物理特性预测的可行性以及不同的光谱处理方法和建模方法对预测准确性的影响。试样取自排酸24h的同一批猪胴体的小里脊肉,采集4000—10000cm-1的光谱。经外部验证的偏最小二乘（PLS）模型在预测pH时表现出良好的相关性（Rc^2=0．88,Rp^2=0．80,SEC=0．08,SEP=0．084）,嫩度与蒸煮损失模型的相关性分别是Rc^2=0．50和0．57,R;=0．34和0．50。在各种光谱预处理方法中,平滑处理结合多元散射校正（MSC）或标准正态变量变换（SNV）的效果最好。     

Quantitation of drug content in a low dosage formulation by transmission near infrared spectroscopy

A transmission near infrared (NIR) spectroscopic method has been developed for the nondestructive determination of drug content in tablets with less than 1% weight of active ingredient per weight of formulation (m/m) drug content. Tablets were manufactured with drug concentrations of ∼0.5%, 0.7%, and 1.0% (m/m) and ranging in drug content from 0.71 to 2.51 mg per tablet. Transmission NIR spectra were obtained for 110 tablets that constituted the training set for the calibration model developed with partial least squares regression. The reference method for the calibration model was a validated UV spectrophotometric method. Several data preprocessing methods were used to reduce the effect of scattering on the NIR spectra and base the calibration model on spectral changes related to the drug concentration changes. The final calibration model included the spectral range from 11 216 to 8662 cm⁻¹ the standard normal variate (SNV), and first derivative spectral pretreatments. This model was used to predict an independent set of 48 tablets with a root mean standard error of prediction (RMSEP) of 0.14 mg, and a bias of only −0.05 mg per tablet. The study showed that transmission NIR spectroscopy is a viable alternative for nondestructive testing of low drug content tablets, available for the analysis of large numbers of tablets during process development and as a tool to detect drug agglomeration and evaluate process improvement efforts. Published: March 24, 2006     

应用近红外光谱法估测小麦叶片糖氮比

糖氮比能够反映作物碳氮代谢的协调程度,及时、准确地监测糖氮比对于作物氮素营养诊断和调控具有重要意义.本研究以不同年份、品种、施氮水平的小麦大田试验为基础,获取鲜叶和粉末状干叶近红外(NIR)光谱及糖氮比信息,分别运用偏最小二乘法(partial least squares, PLS)、BP神经网络(back propagation neural network, BPNN)和小波神经网络(wavelet neural network, WNN)3种方法建立了小麦叶片糖氮比预测模型,并利用随机选择的样品集对所建模型进行测试和检验.结果表明： 小麦鲜叶光谱模型预测性能不佳;而干叶片预测模型表现了较好的准确性,在1655~2378 nm谱区范围内基于3种方法构建的干叶粉末糖氮比估算模型,其预测均方根误差均低于0.3%,决定系数均高于0.9.比较而言,WNN法表现最佳.总体显示,近红外光谱法可以准确预测小麦叶片糖氮比状况,为科学诊断糖氮比提供了理论基础和技术途径.     

近红外光谱分析法测定东北黑土有机碳和全氮含量

以我国东北黑土为研究对象,分析了2004-2005年采集的136个土壤样品在3699～12000 cm^-1范围的近红外光谱,利用偏最小二乘法建立了原始光谱吸光度与土壤有机碳、全氮和碳氮比之间的定量分析模型.结果表明：土壤有机碳和全氮的模型拟合效果良好,决定系数R²分别为0.92和0.91（P<0.001）,相对分析误差RPD分别为3.45和3.36,利用该模型对验证样本土壤有机碳和全氮的预测值与实测值之间的相关系数分别为0.94和0.93（P<0.001）,表明可以用近红外光谱分析法对黑土有机碳和全氮含量进行测定.但是利用近红外光谱分析法对土壤碳氮比的预测并不理想,虽然验证样本集黑土碳氮比模型预测值与实测值呈显著相关（r＝0.74,P<0.001）,但是校正模型的R²为0.61,RPD仅为1.61,建立的模型不能对黑土碳氮比做出合理的估测.     

红外光谱技术在滇龙胆栽培方式选择上的应用

该研究采用傅里叶变换红外光谱结合化学计量学,对条播、撒播、剪根后移栽、扦插和剪枝后移栽的滇龙胆进行了分析,以筛选滇龙胆的最佳栽培方式。结果表明:(1)不同栽培方式的滇龙胆原始谱图在峰形、峰位和峰强上有一定差异;用小波去噪法对光谱进行优化处理并进行偏最小二乘判别分析(Partial least squares discriminant analysis,PLS-DA),能较好地区分不同栽培方式的滇龙胆样品,PLS-DA二维得分图显示同一栽培方式的样品聚在一起,表明相同栽培方式的滇龙胆化学组成和含量差异较小;播种滇龙胆样品(条播和撒播)距离较近,移栽滇龙胆样品(剪根、扦插和剪枝)距离较近,而播种和移栽滇龙胆样品距离较远,表明栽培方式对滇龙胆化学成分的积累有影响。(2)滇龙胆四种主要成分总含量大小依次是剪枝剪根撒播条播扦插,除剪根后移栽,剪枝后移栽滇龙胆中四种主要成分总含量显著高于其他栽培方式下的滇龙胆(P0.05),剪枝后移栽滇龙胆质量最佳。(3)以液相数据为参考值,采用正交信号校正—偏最小二乘回归模型预测不同栽培模式滇龙胆中龙胆苦苷、马钱苷酸、獐牙菜苦苷和当药苷的含量。校正集和验证集的决定系数(R2)均大于0.90,校正均方根误差、交叉验证均方差和预测均方根误差均小于1.65,模型相关性和预测效果好,该方法对红外光谱分析在中药领域的推广应用提供了参考。     

Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics

Two of the primary issues with characterizing the variability of raw materials used in mammalian cell culture, such as wheat hydrolysate, is that the analyses of these materials can be time consuming, and the results of the analyses are not straightforward to interpret. To solve these issues, spectroscopy can be combined with chemometrics to provide a quick, robust and easy to understand methodology for the characterization of raw materials; which will improve cell culture performance by providing an assessment of the impact that a given raw material will have on final product quality. In this study, four spectroscopic technologies: near infrared spectroscopy, middle infrared spectroscopy, Raman spectroscopy, and fluorescence spectroscopy were used in conjunction with principal component analysis to characterize the variability of wheat hydrolysates, and to provide evidence that the classification of good and bad lots of raw material is possible. Then, the same spectroscopic platforms are combined with partial least squares regressions to quantitatively predict two cell culture critical quality attributes (CQA): integrated viable cell density and IgG titer. The results showed that near infrared (NIR) spectroscopy and fluorescence spectroscopy are capable of characterizing the wheat hydrolysate's chemical structure, with NIR performing slightly better; and that they can be used to estimate the raw materials’ impact on the CQAs. These results were justified by demonstrating that of all the components present in the wheat hydrolysates, six amino acids: arginine, glycine, phenylalanine, tyrosine, isoleucine and threonine; and five trace elements: copper, phosphorus, molybdenum, arsenic and aluminum, had a large, statistically significant effect on the CQAs, and that NIR and fluorescence spectroscopy performed the best for characterizing the important amino acids. It was also found that the trace elements of interest were not characterized well by any of the spectral technologies used; however, the trace elements were also shown to have a less significant effect on the CQAs than the amino acids. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers, 33:1127–1138, 2017     

Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms

The goal of this study was to assess the utility of near infrared (NIR) spectroscopy for the determination of content uniformity, tablet crushing strength (tablet hardness), and dissolution rate in sulfamethazine veterinary bolus dosage forms. A formulation containing sulfamethazine, corn starch, and magnesium stearate was employed. The formulations were wet granulated with a 10% (wt/vol) starch paste in a high shear granulator and dried at 60°C in a convection tray dryer. The tablets were compressed on a Stokes B2 rotary tablet press running at 30 rpm. Each sample was scanned in reflectance mode in the wavelengths of the NIR region. Principal component analysis (PCA) of the NIR tablet spectra and the neat raw materials indicated that the scores of the first 2 principal components were highly correlated with the chemical and physical attributes. Based on the PCA model, the significant wavelengths for sulfamethazine are 1514, (1660–1694), 2000, 2050, 2150, 2175, 2225, and 2275 nm; for corn starch are 1974, 2100, and 2325 nm; and for magnesium stearate are 2325 and 2375 nm. In addition, the loadings show large negative peaks around the water band regions (≈1420 and 1940 nm), indicating that the partial least squares (PLS) models could be affected by product water content. A simple linear regression model was able to predict content uniformity with a correlation coefficient of 0.986 at 1656 nm; the use of a PLS regression model, with 3 factors, had anr ² of 0.9496 and a sandard error of calibration of 0.0316. The PLS validation set had anr ² of 0.9662 and a standard error of 0.0354. PLS calibration models, based on tablet absorbance data, could successfully predict tablet crushing strength and dissolution in spite of varying active pharmaceutical ingredient (API) levels. Prediction plots based on these PLS models yielded correlation coefficients of 0.84 and 0.92 on independent validation sets for crushing strength and Q₁₂₀ (percentage dissolved in 120 minutes), respectively. Published: September 20, 2005 The opinions expressed in this paper are of the authors' personal views. They do not necessarily reflect the views or policies of the FDA.     

Application of multivariate curve resolution-alternating least squares (MCR-ALS) for secondary structure resolving of proteins

Fourier transform infrared (FTIR) spectroscopy is the most common spectroscopic technique used for study of protein structure. Initially, band deconvolution techniques were applied to determine the secondary structure of proteins. Recently, several multivariate regression methods have been used to predict the secondary structure of proteins as an alternative to the previous methods. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied on the FTIR spectra of proteins to resolve the fraction and spectral profiles of different structural motifs. Initial estimates of spectral profiles of different protein motifs were built using orthogonal projection approach (OPA). Predicted fractions of α-helix and β-sheet obtained by MCR-ALS technique were compared with those from partial least squares (PLS) modeling which revealed superiority of the former. If we consider the possibility of pure spectra prediction in addition to the prediction of secondary structure from the data set, MCR-ALS can be proposed as a very valuable alternative for qualitative and quantitative study of protein structures.     

近红外光谱技术（NIRS）在人体的应用与展望

近红外光谱技术(NIRS)在人体的应用与研究是近年来在国内外新兴的研究领域,因为其方便无创,成本低等优点,近20年来在不断发展和完善,引起大家的广泛关注。近红外光谱在700-900 mm范围内可以穿透一定深度的组织,组织内含氧血红蛋白、去氧血红蛋白对近红外光的吸收系数存在差异,经过传感器和计算机技术分析,得到组织的血氧参数。其测量参数为微动脉、微静脉和毛细血管中血液的血氧参数之加权平均,反应组织中的血氧参数,其中静脉血占主要地位,不同于普通脉搏式血氧监测仪的指端动脉血的血氧饱和度。基于近红外光谱技术的近红外组织血氧无创监测仪、功能性近红外光谱技术(f NIRS)、近红外光谱荧光技术等在临床医学,运动医学,神经生物学,认知科学,脑力疲劳,人机交互等新兴领域正发挥越来越重要作用。