Fourier Transform Mid-infrared Spectra Research Articles

Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra.

During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229-235), Zhang et al. (J I Brewing. 2012;118(4):361-367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (𝜖-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to linear methods, showing a clear out-performance in most cases and being able to meet the model quality requirements defined by the experts at the beer company. Figure Workflow for calibration of non-Linear model ensembles from FT-MIR spectra in beer production .

Supervision of Ethylene Propylene Diene M-Class (EPDM) Rubber Vulcanization and Recovery Processes Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Multivariate Analysis.

Ethylene propylene diene monomer (EPDM) rubber is widely used in a diverse type of applications, such as the automotive, industrial and construction sectors among others. Due to its appealing features, the consumption of vulcanized EPDM rubber is growing significantly. However, environmental issues are forcing the application of devulcanization processes to facilitate recovery, which has led rubber manufacturers to implement strict quality controls. Consequently, it is important to develop methods for supervising the vulcanizing and recovery processes of such products. This paper deals with the supervision process of EPDM compounds by means of Fourier transform mid-infrared (FT-IR) spectroscopy and suitable multivariate statistical methods. An expedited and nondestructive classification approach was applied to a sufficient number of EPDM samples with different applied processes, that is, with and without application of vulcanizing agents, vulcanized samples, and microwave treated samples. First the FT-IR spectra of the samples is acquired and next it is processed by applying suitable feature extraction methods, i.e., principal component analysis and canonical variate analysis to obtain the latent variables to be used for classifying test EPDM samples. Finally, the k nearest neighbor algorithm was used in the classification stage. Experimental results prove the accuracy of the proposed method and the potential of FT-IR spectroscopy in this area, since the classification accuracy can be as high as 100%.

Prediction of naturally-occurring, industrially-induced and total trans fatty acids in butter, dairy spreads and Cheddar cheese using vibrational spectroscopy and multivariate data analysis

This study investigated the use of vibrational spectroscopy [near infrared (NIR), Fourier-transform mid-infrared (FT-MIR), Raman] and multivariate data analysis for (1) quantifying total trans fatty acids (TT), and (2) separately predicting naturally-occurring (NT; i.e., C16:1 t9; C18:1 trans-n, n = 6 … 9, 10, 11; C18:2 trans) and industrially-induced trans fatty acids (IT = TT – NT) in Irish dairy products, i.e., butter (n = 60), Cheddar cheese (n = 44), and dairy spreads (n = 54). Partial least squares regression models for predicting NT, IT and TT in each type of dairy product were developed using FT-MIR, NIR and Raman spectral data. Models based on NIR, FT-MIR and Raman spectra were used for the prediction of NT and TT content in butter; best prediction performance achieved a coefficient of determination in validation (R2V) ∼ 0.91–0.95, root mean square error of prediction (RMSEP) ∼ 0.07–0.30 for NT; R2V ∼ 0.92–0.95, RMSEP ∼ 0.23–0.29 for TT.

Synchrotron based phase contrast X-ray imaging combined with FTIR spectroscopy reveals structural and biomolecular differences in spikelets play a significant role in resistance to Fusarium in wheat.

BackgroundFusarium head blight (FHB), a scab principally caused by Fusarium graminearum Schw., is a serious disease of wheat. The purpose of this study is to evaluate the potential of combining synchrotron based phase contrast X-ray imaging (PCI) with Fourier Transform mid infrared (FTIR) spectroscopy to understand the mechanisms of resistance to FHB by resistant wheat cultivars. Our hypothesis is that structural and biochemical differences between resistant and susceptible cultivars play a significant role in developing resistance to FHB.ResultsSynchrotron based PCI images and FTIR absorption spectra (4000–800 cm−1) of the floret and rachis from Fusarium-damaged and undamaged spikes of the resistant cultivar ‘Sumai3’, tolerant cultivar ‘FL62R1’, and susceptible cultivar ‘Muchmore’ were collected and analyzed. The PCI images show significant differences between infected and non-infected florets and rachises of different wheat cultivars. However, no pronounced difference between non-inoculated resistant and susceptible cultivar in terms of floret structures could be determined due to the complexity of the internal structures. The FTIR spectra showed significant variability between infected and non-infected floret and rachis of the wheat cultivars. The changes in absorption wavenumbers following pathogenic infection were mostly in the spectral range from 1800–800 cm−1. The Principal Component Analysis (PCA) was also used to determine the significant chemical changes inside floret and rachis when exposed to the FHB disease stress to understand the plant response mechanism. In the floret and rachis samples, PCA of FTIR spectra revealed differences in cell wall related polysaccharides. In the florets, absorption peaks for Amide I, cellulose, hemicellulose and pectin were affected by the pathogenic fungus. In the rachis of the wheat cultivars, PCA underlines significant changes in pectin, cellulose, and hemicellulose characteristic absorption spectra. Amide II and lignin absorption peaks, persistent in the rachis of Sumai3, together with increased peak shift at 1245 cm−1 after infection with FHB may be a marker for stress response in which the cell wall compounds related to pathways for lignification are increased.ConclusionsSynchrotron based PCI combined with FTIR spectroscopy show promising results related to FHB in wheat. The combined technique is a powerful new tool for internal visualisation and biomolecular monitoring before and during plant-microbe interactions to understand both the differences between cultivars and their different responses to disease stress.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0357-5) contains supplementary material, which is available to authorized users.

Mechanisms of browning development in aggregates of marine organic matter formed under anoxic conditions: A study by mid-infrared and near-infrared spectroscopy

In this paper we analyze some chemical aspects concerning the browning development associated to the aggregation of marine organic matter (MOM) occurring in anoxic conditions. Organic matter samples obtained by the degradation of different algal samples were daily taken to follow the evolution of the aggregation process and the associated browning process. These samples were examined by Fourier transform mid infrared (FTIR) and Fourier transform near infrared (FTNIR) spectroscopy and the colour changes occurring during the above mentioned aggregation process were measured by means of Colour Indices (CIs). Spectral Cross Correlation Analysis (SCCA) was applied to correlate changes in CI values to the structural changes of MOM observed by FTIR and FTNIR spectra which were also submitted to Two-Dimensional Hetero Correlation Analysis (2HDCORR). SCCA results showed that all biomolecules present in MOM aggregates such as carbohydrates, proteins and lipids are involved in the browning development. In particular, SCCA results of algal mixtures suggest that the observed yellow–brown colour can be linked to the development of non enzymatic (i.e. Maillard) browning reactions. SCCA results for MOM furthermore suggest that aggregates coming from brown algae also showed evidence of browning related to enzymatic reactions. In the end 2HDCORR results indicate that hydrogen bond interactions among different molecules of MOM can play a significant role in the browning development.In this study the combination of spectroscopic techniques such as FTIR and FTNIR with Colour Indices measurements shows a peculiar ability to improve the knowledge of the complex mechanisms related to the aggregation of marine organic matter and its colour development under anoxic conditions such like those present in the marine environments at high depth.

Development and validation of a spectroscopic method to predict wheat protein digestibility1

The CP digestibility is traditionally measured by chemical analyses of CP and marker concentration in digesta and diets. Potentially, CP digestibility can also be predicted by marker concentrations and spectral analyses of digesta and diet. Spectroscopy is a rapid, nondestructive method to ascertain qualitative and quantitative chemical information. Based on Beer's law, a spectroscopic method was developed to predict in vivo CP digestibility. To validate, samples of digesta and diet of wheat grain with predetermined apparent ileal digestibility (AID) of CP were scanned on a Fourier transform midinfrared (FTIR) instrument with a single-reflection attenuated total reflectance attachment. The AID of CP was calculated from peak intensities of spectra and measured marker concentrations in digesta and diet and then compared with in vivo AID of CP. The AID of CP of a wheat-based diet was predicted accurately with a deviation of 0.68 ± 0.86% from in vivo AID of CP ranging from 60.4 to 87.8%. Functional group digestibility based on the peak at 1,643 cm(-1) or the Amide I region was strongly correlated (r ≥ 0.99; P < 0.001) with in vivo AID of CP. In conclusion, instead of predictions based on calibrations, CP digestibility can also be potentially predicted directly from FTIR spectra.

Preliminary evaluation of infrared spectroscopy for the differentiation of Brettanomyces bruxellensis strains isolated from red wines

The objective of this study was to evaluate different infrared spectroscopy methods in combination with chemometrics for the differentiation between Brettanomyces bruxellensis strains. These methods of discrimination were applied to intact yeast cells of B. bruxellensis strains and on wines spoiled by the same strains. Eleven wine isolates of B. bruxellensis were evaluated for volatile phenol production in red wine and their genetic diversity was determined by Restriction Endonuclease Analysis–Pulsed Field Gel Electrophoresis (REA-PFGE). Fourier transform mid-infrared (FTMIR) spectroscopy was used to obtain spectral fingerprints of the spoiled wines. Attenuated total reflectance (ATR) was used to obtain spectral fingerprints from the intact cells of the 11 B. bruxellensis strains. The groupings from the genetic fingerprints obtained with REA–PFGE were used as reference firstly for comparison with the groupings observed with the FTMIR spectral fingerprint of the wines and secondly for the FTIR-ATR spectral fingerprints from the whole cells. Results indicated that ATR-IR spectra obtained by scanning whole cells of B. bruxellensis could be useful for rapid strain typing in comparison or complementary to molecular techniques and FTMIR spectra from wines provide a useful resource for the discrimination between B. bruxellensis contaminated wines.

Application of an electronic tongue based on FT-MIR to emulate the gustative mouthfeel “tannin amount” in red wines

In this work, the ability of an electronic tongue based on Fourier-Transform Mid Infrared (FT-MIR) spectroscopy as a gustative sensor is assessed by emulating the responses of a tasting panel for the gustative mouthfeel "tannin amount". The FT-MIR spectra were modeled against the sensory responses evaluated in 37 red wines by means of partial least squares (PLS) regression models. In order to find the wavenumbers more correlated with the sensorial attribute and thus providing the best predictive models, six different variable selection techniques were tested. The iterative predictor weighting IPW-PLS technique showed the best results with the smallest RMSEC and RMSECV values (0.07 and 0.13, respectively) using 20 selected wavenumbers. The coincident wavenumbers selected by the six variable selection techniques were interpreted based on the absorption bands of tannin and then a calibration model using these wavenumbers was built to validate the interpretation made.

Differentiation of perirenal and omental fat quality of suckling lambs according to the rearing system from Fourier transforms mid-infrared spectra using partial least squares and artificial neural networks analysis

Fourier transform mid-infrared (FT-IR) spectroscopy was evaluated as a tool to discriminate between carcasses of suckling lambs according to the rearing system. Fat samples (39 perirenal and 67 omental) were collected from carcasses of lambs from up to three sheep dairy farms, reared on either ewes milk (EM) or milk replacer (MR). Fatty acid composition of the samples from each fat deposit was first analyzed and, when discriminant-partial least squares regression (PLS) was applied, a perfect discrimination between rearing systems could be established. Additionally, FT-IR spectra of fat samples were obtained and discriminant-PLS and artificial neural network (ANN) based analysis were applied to data sets, the latter using principal component analysis (PCA) or support vector machines (SVM) as processing procedure. Perirenal fat samples were perfectly discriminated from their FT-IR spectra. However, analysis of omental fat showed misclassification rates of 9–13%, with the ANN approach showing a higher discrimination power.

Thermal decomposition of [Mg(NH 3) 6](NO 3) 2, [Ni(NH 3) 6](NO 3) 2 and [Ni(ND 3) 6](NO 3) 2

The thermal decompositions of [Mg(NH 3) 6](NO 3) 2, [Ni(NH 3) 6](NO 3) 2 and [Ni(ND 3) 6](NO 3) 2 were studied by thermal gravimetry analysis (TGA) and simultaneous differential thermal analysis (SDTA) at a constant heating rate. The gaseous products of the decomposition were on-line identified by a quadruple mass spectrometer (QMS).The solid products were identified on the basis of Fourier transform mid infrared spectra (FT-MIR) and X-ray powder diffraction (XRPD) patterns. Deamination of hexaaminemagnesium nitrate(V) to diaminemagnesium nitrate(V) undergoes in two steps and two-third of the all NH 3 molecules are liberated. Deamination of both hexaaminenickel(II) nitrates(V) (non-deuterated and deuterated) to the diamines undergoes in three steps, and at two first steps one half of the NH 3 molecules are liberated. The thermal decomposition of [Ni(NH 3) 2](NO 3) 2 and [Ni(ND 3) 2](NO 3) 2 is also different than that of [Mg(NH 3) 2](NO 3) 2. In both cases the decomposition is connected with the redox processes, but in the case of magnesium compound, contrary to the nickel compounds, in the second stage, besides of liberation of nitrogen, nitrogen oxides and H 2O, undergoes the liberation of NH 3 and the formation of Mg(NO 3) 2, which in turn decomposes next, in the third stage, to the oxygen, nitrogen oxides and MgO. This third stage is not present in the case of both nickel compounds, and the decomposition of diaminenickel(II) nitrates(V) undergoes directly to the final products (NiO 1+ x , nitrogen, nitrogen oxides and H 2O) without the formation of Ni(NO 3) 2, because of the autocatalytic effect of the formed NiO.

Multivariate prediction of eight kerosene properties employing vapour-phase mid-infrared spectrometry ☆

Eight physico-chemical properties of kerosene (aviation jet fuel) are predicted employing vapour-phase generation, Fourier transform mid-infrared (FT-MIR) spectra and partial least squares regression (PLS). Two devices were implemented and studied in order to generate the kerosene vapour from 100 liquid samples from a Spanish refinery. One of them is very simple whilst the other one requires thermostatic and gas flow controls. The FT-MIR spectra are recorded and used to deploy PLS models for each property (distillation curve, flash point, freezing point, percentage of aromatics and viscosity) and each device. In general, the simplest device yields the more satisfactory models. Several criteria are used to evaluate their performance: the average prediction error (corrected to take into account the error in the reference values), the F-test to assess the absence of bias in the predictions, repeatability and reproducibility. In general, all the models provide unbiased predictions, with low average errors and good precision.