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     

应用近红外光谱预测水稻叶片氮含量

以水稻(Oryza sativa)新鲜叶片和干叶粉末两种状态的样品为研究对象, 基于近红外光谱(NIRS)技术, 应用偏最小二乘法(PLS)、主成分回归(PCR)和逐步多元回归(SMLR), 建立并评价了水稻叶片氮含量(NC)近红外光谱模型。结果表明, 基于PLS建立的模型表现最好, 鲜叶氮含量近红外光谱校正模型校正决定系数R_C²为0.940, 校正标准误差RMSEC为0.226; 干叶粉末氮含量的近红外光谱校正模型R_C²为0.977, RMSEC为0.136。模型的内部交叉验证分析表明, 预测鲜叶氮含量内部验证决定系数R_CV²为0.866, 内部验证标准误差RMSECV为0.243; 预测干叶粉末氮含量R_CV²为0.900, RMSECV为0.202。模型的外部验证分析表明, 预测水稻鲜叶氮含量的外部验证决定系数R_V²大于0.800, 外部验证标准误差RMSEP小于0.500, 预测干叶粉末氮含量的R_V²为0.944, RMSEP为0.142。说明, 近红外光谱分析技术与化学分析方法一致性较好, 且基于干叶粉末建立的近红外光谱预测模型的准确性和精确度较新鲜叶片高。     

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.     

Evaluation of the bony repair in rat cranial defect using near infrared reflectance spectroscopy and discriminant analysis

We set out to determine whether near infrared reflectance spectroscopy (NIRS) combined with principal component analysis–linear discriminant analysis (LDA) or, variable selection techniques employing successive projection algorithm or genetic algorithm (GA) could evaluate the bone repair in cranial critical‐size (5 mm) defect after stimulation with collagen sponge scaffold and/or infrared low‐level laser therapy directly on the local. Forty‐five Winstar rats were divided into nine groups of five each, namely: group H – healthy, n = 5 (without treatment and without cranial critical‐size defect), (GI positive control – n = 5, 21 days or n = 5, 30 days) without treatment and with cranial critical‐size defect; (GII‐n = 5, 21 days or n = 5, 30 days) cranial critical‐size defect filled with collagen sponge scaffold; (GIII–n = 5, 21 days or n = 5, 30 days) cranial critical‐size defect submitted to low‐level laser therapy; (GIV–n = 5, 21 days or n = 5, 30 days) cranial critical‐size defect submitted to combined collagen sponge scaffold + low‐level laser therapy treatment. In relation to the histological analysis, the collagen sponge scaffold + low‐level laser therapy treatment group (GIV) 30 days showed the best result with the presence of secondary bone, immature bone (osteoid) and newly formed connective tissue (periosteum). GA–LDA model also successfully classified control class of the others classes. Thus, the results provided by the good‐quality classification model revealed the feasibility of NIRS for application to evaluation of the wound healing in rat cranial defect, thanks to the short analysis time of a few seconds and nondestructive advantages of NIRS as an alternative approach for bone repair purposes. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1160–1168, 2017     

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R²CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R²CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R²CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R² of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.     

Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs

Since consumers are showing increased interest in the origin and method of production of their food, it is important to be able to authenticate dietary history of animals by rapid and robust methods used in the ruminant products. Promising breakthroughs have been made in the use of spectroscopic methods on fat to discriminate pasture-fed and concentrate-fed lambs. However, questions remained on their discriminatory ability in more complex feeding conditions, such as concentrate-finishing after pasture-feeding. We compared the ability of visible reflectance spectroscopy (Vis RS, wavelength range: 400 to 700 nm) with that of visible-near-infrared reflectance spectroscopy (Vis-NIR RS, wavelength range: 400 to 2500 nm) to differentiate between carcasses of lambs reared with three feeding regimes, using partial least square discriminant analysis (PLS-DA) as a classification method. The sample set comprised perirenal fat of Romane male lambs fattened at pasture (P, n=69), stall-fattened indoors on commercial concentrate and straw (S, n=55) and finished indoors with concentrate and straw for 28 days after pasture-feeding (PS, n=65). The overall correct classification rate was better for Vis-NIR RS than for Vis RS (99.0% v. 95.1%, P<0.05). Vis-NIR RS allowed a correct classification rate of 98.6%, 100.0% and 98.5% for P, S and PS lambs, respectively, whereas Vis RS allowed a correct classification rate of 98.6%, 94.5% and 92.3% for P, S and PS lambs, respectively. This study suggests the likely implication of molecules absorbing light in the non-visible part of the Vis-NIR spectra (possibly fatty acids), together with carotenoid and haem pigments, in the discrimination of the three feeding regimes.     

Novel approach for the prediction of cell densities and viability in standardized translucent cell culture biochips with near infrared spectroscopy

Near infrared spectroscopy is a rapid and nondestructive method for compositional analysis of biological material. The technology is widely used within bioreactors and possesses potential as a standardized method for quality control in miniaturized microfluidic cell culture systems. Here, we established a method for quantification of cell density and viability of adherent HepaRG cells cultured in a translucent, miniaturized cell culture biochip. The newly developed statistical models for interpretation of near infrared spectroscopy from biochips are the basis for a novel method of fast, continuous, and contact‐free analysis of cell viability and real‐time monitoring of cell growth. The technique thus paves the way for a robust and reliable high‐throughput analysis of biochip‐embedded cell cultures.     

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA×) and Limousin (LIM×) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA× and 106 LIM× were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P < 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA× meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM× meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R(2) (SE(CV), mg/100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM× samples (R(2) = 0.12 to 0.62; SECV = 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA× beef samples. In particular for LIM×, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.     

Functional near infrared spectroscopy using spatially resolved data to account for tissue scattering: A numerical study and arm‐cuff experiment

Functional Near‐Infrared Spectroscopy (fNIRS) aims to recover changes in tissue optical parameters relating to tissue hemodynamics, to infer functional information in biological tissue. A widely‐used application of fNIRS relies on continuous wave (CW) methodology that utilizes multiple distance measurements on human head for study of brain health. The typical method used is spatially resolved spectroscopy (SRS), which is shown to recover tissue oxygenation index (TOI) based on gradient of light intensity measured between two detectors. However, this methodology does not account for tissue scattering which is often assumed. A new parameter recovery algorithm is developed, which directly recovers both the scattering parameter and scaled chromophore concentrations and hence TOI from the measured gradient of light‐attenuation at multiple wavelengths. It is shown through simulations that in comparison to conventional SRS which estimates cerebral TOI values with an error of ±12.3%, the proposed method provides more accurate estimate of TOI exhibiting an error of ±5.7% without any prior assumptions of tissue scatter, and can be easily implemented within CW fNIRS systems. Using an arm‐cuff experiment, the obtained TOI using the proposed method is shown to provide a higher and more realistic value as compared to utilizing any prior assumptions of tissue scatter.     

A novel method for analyzing thick tablets by near infrared spectroscopy

A near-infrared (NIR) spectroscopic method to determine content uniformily of a large, thick tablet using an approach that could facilitate future validations has been developed. A CT ibuprofen 800-mg tablet weighs about 1150 mg and is about 18.6 mm wide and 7.6 mm thick. The FT NIR spectrometer was optimized for transmission spectra of the tablets by moving it to the sample compartment and placing it immediately behind the tablet. In spite of this dedicated setup, the transmission spectra obtained were very poor, indicating that the NIR radiation was not reaching the detector. The spectra of the tablet improved with use of a simple preparation in which a flat-face die applies pressure of 20 000 psi to the tablet, this reduced the thickness of the tablet from 7.6 mm to 3.6 mm. A calibration model was developed for tablets with drug content ranging from 70% to 130% of label. The calibration model was tested using a validation set of tablets with a drug content of 752, 800, and 848 mg. The results obtained were within 1.5% of the known drug content of the validation set, tablets. Even with the sample preparation, the content uniformity results of 10 tablets could be determined using this method in less than 1 hour. The approach described in this article could also be used to validate NIR content uniformity methods for orther formulations. Published: July 12, 2001.     

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。     

Blend uniformity analysis using stream sampling and near infrared spectroscopy

A near infrared spectroscopic method was developed to determine drug content in a 20% (wt/wt) ibuprofen and spray-dried hydous lactose blend. A blending profile was obtained after blending for 0.5, 1, 3, 5, 10, and 20 minutes. Stream sampling was used to collect about 20 blend samples at each of the blending times from a laboratory scale V-blender. The samples collected were used to develop a near infrared calibration model. The calibration model was then used to determine the drug content of unknown samples from 2 validation blends. The validation blends were not included in the calibration model; they were used to evaluate the effectiveness of the calibration model. A total of 45 samples from the 2 validation blends were predicted by the near infrared calibration model and then analyzed by a validated UV spectrophotometric method. The root mean square error of prediction for the first validation blend was 5.69 mg/g and 3.30 mg/g for the samples from the second blend. A paired t test at the 95% confidence level did not indicate any differences between the drug content predicted by the near infrared spectroscopy (NIRS) method and the validated UV method for the 2 blends. The results show that the NIRS method could be developed while the blending profile is generated and used to thoroughly characterize a new formulation during development by analyzing a large number of samples. The new formulation could be transferred to a manufacturing plant with an NIRS method to facilitate blend uniformity analysis.     

应用近红外光谱估测小麦叶片氮含量

研究利用近红外光谱(near-infrared, NIR)和化学计量学方法估测小麦(Triticum aestivum)新鲜叶片和粉末状干叶中全氮含量的可行性, 并建立小麦叶片氮含量估测模型, 以期为小麦氮素营养的精确管理提供理论依据。以3个小麦田间试验观测资料为基础, 分别运用偏最小二乘法(partial least squares, PLS)、反向传播神经网络(back-propagation neural network, BPNN)和小波神经网络(wavelet neural network, WNN), 建立小麦叶片氮含量的鲜叶和粉末状干叶近红外光谱估测模型, 用随机选择的样品集对所建模型进行测试和检验。结果显示, 利用PLS、BPNN和WNN 3种方法构建的近红外光谱模型均能准确地估测小麦叶片氮含量, 其中基于BPNN和WNN的模型优于基于PLS的模型, 且以基于WNN的模型表现最好。对模型进行检验的结果显示, 粉末状干叶模型的预测均方根误差(RMSEP)分别为0.147、0.101和0.094, 鲜叶模型的RMSEP分别为0.216、0.175和0.169, 模型的相关系数均在0.84以上。因此, 利用近红外光谱估算小麦叶片氮素营养精确可行, 对其他作物的氮素营养估测提供了借鉴和参考。     

Short Communication: The potential of portable near infrared spectroscopy for assuring quality and authenticity in the food chain,using Iberian hams as an example

This communication assesses the use of a portable near infrared (NIR) instrument to measure quantitative (fatty acid profile) properties and qualitative (‘Premium’ and ‘Non-premium’) categories of individual Iberian pork carcasses at the slaughterhouse. Acorn-fed Iberian pigs have more unsaturated fats than pigs fed conventional compound feed. Recent advances in miniaturisation have led to a number of handheld NIR devices being developed, allowing processing decisions to be made earlier, significantly reducing time and costs. The most common methods used for assessing quality and authenticity of Iberian hams are analysis of the fatty acid composition of subcutaneous fat using gas chromatography and DNA analysis. In this study, NIR calibrations for fatty acids and classification as premium or non-premium ham, based on carcass fat measured in situ, were developed using a portable NIR spectrometer. The accuracy of the quantitative equations was evaluated through the standard error of cross validation or standard error of prediction of 0.84 for palmitic acid (C16:0), 0.94 for stearic acid (C18:0), 1.47 for oleic acid (C18:1) and 0.58 for linoleic acid (C18:2). Qualitative calibrations provided acceptable results, with up to 98% of samples (n = 234) correctly classified with probabilities ⩾0.9. Results indicated a portable NIR instrument has the potential to be used to measure quality and authenticity of Iberian pork carcasses.     

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.     

Monitoring complex media fermentations with near-infrared spectroscopy: comparison of different variable selection methods

Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.     

Host cell protein quantification by fourier transform mid infrared spectroscopy (FT‐MIR)

Process development in up‐ and downstream processing requires enhanced, non‐time‐consuming, and non‐expensive monitoring techniques to track product purity, for example, the level of endotoxins, viral particles, and host cell proteins (HCPs). Currently, HCP amounts are measured by laborious and expensive HCP‐enzyme‐linked immunosorbent assay (ELISA) assays best suited for measuring HCP amounts in the low concentration regime. The measurement of higher HCP amounts using this method requires dilution steps, adding dilution errors to the measurement. In this work we evaluated the suitability of attenuated total reflection spectroscopy for HCP quantification in process development, using clarified cell culture fluid from monoclonal antibody producing Chinese hamster ovary‐cells after treatment with different polyelectrolytes for semi‐selective clarification. Forty undiluted samples were chosen for multivariate data analysis in the middle infrared range and predicted HCP‐values were in good agreement with results obtained by an ELISA‐assay, suggesting the suitability of this new method for HCP‐quantification. As this method is able to quantify HCP titers ranging from approximately at least 20,000–200,000 ng mL^?1, it is suitable especially for monitoring of process development steps with higher HCP concentrations, omitting dilution errors associated with ELISA assays. Biotechnol. Bioeng. 2013; 110: 252–259. © 2012 Wiley Periodicals, Inc.     

Rapid monitoring of recombinant antibody production by mammalian cell cultures using fourier transform infrared spectroscopy and chemometrics

Fourier transform infrared (FT‐IR) spectroscopy combined with multivariate statistical analyses was investigated as a physicochemical tool for monitoring secreted recombinant antibody production in cultures of Chinese hamster ovary (CHO) and murine myeloma non‐secreting 0 (NS0) cell lines. Medium samples were taken during culture of CHO and NS0 cells lines, which included both antibody‐producing and non‐producing cell lines, and analyzed by FT‐IR spectroscopy. Principal components analysis (PCA) alone, and combined with discriminant function analysis (PC‐DFA), were applied to normalized FT‐IR spectroscopy datasets and showed a linear trend with respect to recombinant protein production. Loadings plots of the most significant spectral components showed a decrease in the C–O stretch from polysaccharides and an increase in the amide I band during culture, respectively, indicating a decrease in sugar concentration and an increase in protein concentration in the medium. Partial least squares regression (PLSR) analysis was used to predict antibody titers, and these regression models were able to predict antibody titers accurately with low error when compared to ELISA data. PLSR was also able to predict glucose and lactate amounts in the medium samples accurately. This work demonstrates that FT‐IR spectroscopy has great potential as a tool for monitoring cell cultures for recombinant protein production and offers a starting point for the application of spectroscopic techniques for the on‐line measurement of antibody production in industrial scale bioreactors. Biotechnol. Bioeng. 2010; 106: 432–442. © 2010 Wiley Periodicals, Inc.