Extended Multiplicative Scatter Correction Research Articles

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee.

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. NIR (1100-2400nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ13C, δ18O, δ2H) were moderately to well predicted by NIR (R2: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil.

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R2p) range of 0.910 to 0.971 for different adulterants.

Developing Hyperspectral Indices for Assessing Seasonal Variations in the Ratio of Chlorophyll to Carotenoid in Deciduous Forests

Leaf pigments are sensitive to various stress conditions and senescent stages. Variation in the ratio of chlorophyll to carotenoid content provides valuable insights into the understanding of the physiological and phenological status of plants in deciduous forests. While the use of spectral indices to assess this ratio has been attempted previously, almost all indices were derived indirectly from those developed for chlorophyll and carotenoid contents. Furthermore, there has been little focus on the seasonal dynamics of the ratio, which is a good proxy for leaf senescence, resulting in only a few studies ever being carried out on tracing the ratio over an entire growing season by using spectral indices. In this study, we developed a novel hyperspectral index for tracing seasonal variations of the ratio in deciduous forests, based on a composite dataset of two field measurement datasets from Japan and one publicly available dataset (Angers). Various spectral transformations were employed during this process in order to identify the most robust hyperspectral index. The results show that the wavelength difference (D) type index, using wavelengths of 540 and 1396 nm (calculated from the transformed spectra that were preprocessed by the combination of extended multiplicative scatter correction (EMSC) and first-order derivative), exhibited the highest accuracy for the estimation of the chlorophyll/carotenoid ratio (R2 = 0.57, RPD = 1.52). Further evaluation revealed that the index maintained a good performance at different seasonal stages and can be considered a useful proxy for the ratio in deciduous species. These findings provide a basis for the usage of hyperspectral information in the assessment of vegetation functions. Although promising, extensive evaluations of the proposed index are still required for other functional types of plants.

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy

This study aims to apply near infrared technology as a fast, simultaneous and non-destructive method for quality assessment on intact mango fruit in form of total soluble solids (TSS) and vitamin C. Absorbance spectra of 186 intact mango fruits with four different cultivars were acquired and recorded in wavelength ranging from 1000–2500 nm. Spectra data were enhanced and corrected using three different methods namely moving average smoothing (MAS), extended multiplicative scatter correction (EMSC) and standard normal variate (SNV). In addition, they were divided into two datasets namely calibration (n = 143) and prediction (n = 43) datasets consisting all four mango cultivars. The models used to predict TSS and vitamin C were developed using partial least square regression (PLSR). Prediction performance were quantified using correlation coefficient (r), root mean square error (RMSE), ratio prediction to deviation (RPD) and range to error ratio (RER) indexes. The results showed that the best prediction models for TSS and vitamin C were achieved when the models were constructed using EMSC correction approach with r = 0.86, RMSE = 1.67 Brix, RPD = 2.34 and RER = 9.72 for TSS. Meanwhile, for vitamin C, r = 0.86, RMSE = 6.84 mg·100g⁻¹, RPD = 2.00 and RER = 8.87. From this study, it was concluded that near infrared technology combined with proper spectra enhancement method may be applied as a rapid, simultaneous and contactless method for quality assessment on intact mangoes.

Extended multiplicative signal correction to improve prediction accuracy of protein content in weathered sorghum grain samples

AbstractBackground and objectivesSorghum grains may be subjected to weathering damage in the field as naked grains on the panicle are exposed to adverse environmental conditions during grain development or dry down before harvest. A near infrared (NIR) protein calibration model did not work as expected for weathered grains. Extended multiplicative signal correction was tested as a preprocessing method for spectra to improve the prediction accuracy of the model when weathered grain samples are evaluated.FindingsNIR spectra of weathered sorghum grain samples showed marked distortions with a higher apparent absorbance at lower wavelengths in the 950–1650 nm spectral range. A partial least squares calibration developed with sound grain samples using spectra preprocessed with multiplicative signal correction (MSC) predicted protein content of validation sample set of sound grains with a root mean square error of prediction (RMSEP) = 0.67% and coefficient of determination (r2) = 0.83. However, performance of this calibration model dropped with RMSEP = 13.14% and r2 = 0.04 when used to predict proteins in weathered grain samples. Introduction of weathered grain spectra to the calibration improved predictive performance with RMSEP = 0.92% and r2 = 0.69. Application of extended multiplicative scatter correction (EMSC) to preprocess the spectra of the calibration with weathered grain samples enhanced the prediction performance with RMSEP = 0.61% and r2 = 0.85.ConclusionsInclusion of weathered grain spectra in the calibration and preprocessing spectra with EMSC markedly improved the robustness of the sorghum protein calibration.Significance and noveltySorghum breeders using NIR spectroscopy for evaluation of protein levels in sorghum breeding lines can get the weathered grain samples also analyzed for protein levels by using this method.

An investigation on annular cartilage samples for post-mortem interval estimation using Fourier transform infrared spectroscopy

Many attempts have been made to estimate the post-mortem interval (PMI) using bioanalytical methods based on multiple biological samples. Cartilage tissues could be used as an alternative for this purpose because their rate of degradation is slower than that of other soft tissue or biofluid samples. In this study, we applied Fourier transform infrared (FTIR) spectroscopy to acquire bioinformation from human annular cartilages within 30days post-mortem. Principal component analysis (PCA) showed that sex and causes of death have almost no impact on the overall spectral variations caused by post-mortem changes. With pre-processing approaches, several predicted models were established using a conventional machine learning method, known as the partial least square (PLS) regression. The best model achieved a satisfactory prediction with a low error of 1.49days using the second derivative transform of 3-point smoothing and extended multiplicative scatter correction (EMSC), and the spectral regions from proteins and carbohydrates contributed greatly to the PMI prediction. This study demonstrates the feasibility of cartilage-based FTIR analysis for PMI estimation. Further work will introduce advanced algorithms for more accurate and precise PMI prediction.

Study of the scattering effects on NIR data for the prediction of ash content using EMSC correction factors

AbstractScattering effect is a really common physical phenomenon during near‐infrared analysis. It is an undesired variation in the spectral data due to a deviation of light from a straight trajectory into different paths. The nonlinearities introduced can be handled by using spectral preprocessing techniques. The situation is completely different when the parameter of interest is physical by nature, such as ash content, in this case removing the physical artifacts of scattering would be negative for the final model. In this study, we have decided to investigate the ash content parameter trying to figure out if the information useful for its prediction is related to the scattering effects, the chemical features, or a mixture of them. To this aim, two near‐infrared spectral datasets were taken into consideration: woodchip for energy sector and pellet samples for feed sector. A new regression model (CORR‐PLS) was developed by including principal components analysis scores and extended multiplicative scatter correction (EMSC) factors as physical parameters into the partial least squares (PLS) regression model. The prediction performance of the regular PLS models (PLS on the raw data and MSC pre‐treated data) were compared with that of the CORR‐PLS model both with regard to prediction uncertainty and model complexity in order to evaluate which is the relevant information for prediction of the ash content.

Estimation of phosphorus-based flame retardant in wood by hyperspectral imaging—a new method

It is recognised that flame retardant chemicals degrade and leach out of flame-protected wood claddings when exposed to natural weathering. However, the ability to survey the current state of a flame retardant treatment applied to a wood cladding, an arbitrary length of time after the initial application, is limited today. In this study, hyperspectral imaging in the near infrared to short-wavelength infrared region is used to quantify the amount of flame retardant present on wooden surfaces. Several sets of samples were treated with various concentrations of a flame retardant chemical and scanned with a push broom hyperspectral camera. An inductively coupled plasma (ICP) spectroscopy analysis of the outermost layer of the treated samples was then carried out in order to determine each sample’s phosphorus content, the active ingredient in the flame retardant. Spectra from the hyperspectral images were pre-processed with extended multiplicative scatter correction, and the phosphorus content was modelled using a partial least squares (PLS) regression model. The PLS regression yielded robust predictions of surface phosphorus content with a coefficient of determination, R2, between 0.8 and 0.9 on validation data regardless of whether the flame retardant chemical had been applied to the surface of the wood or pressure-impregnated into it. The result from the study indicates that spectral imaging around the 2400–2531 nm wavelength region is favourable for quantifying the amount of phosphorus-based flame retardant contained in the outermost layer of non-coated wooden claddings. The results also reveal that the uptake of phosphorus-based flame retardant does not occur uniformly throughout the wood surface, but is to a larger extent concentrated in the earlywood regions than in the latewood.

Mie scatter corrections in single cell infrared microspectroscopy.

Strong Mie scattering signatures hamper the chemical interpretation and multivariate analysis of the infrared microscopy spectra of single cells and tissues. During recent years, several numerical Mie scatter correction algorithms for the infrared spectroscopy of single cells have been published. In the paper at hand, we critically reviewed existing algorithms for the correction of Mie scattering and suggest improvements. We developed an iterative algorithm based on Extended Multiplicative Scatter Correction (EMSC), for the retrieval of pure absorbance spectra from highly distorted infrared spectra of single cells. The new algorithm uses the van de Hulst approximation formula for the extinction efficiency employing a complex refractive index. The iterative algorithm involves the establishment of an EMSC meta-model. While existing iterative algorithms for the correction of resonant Mie scattering employ three independent parameters for establishing a meta-model, we could decrease the number of parameters from three to two independent parameters, which reduced the calculation time for the Mie scattering curves for the iterative EMSC meta-model by a factor of 10. Moreover, by employing the Hilbert transform for evaluating the Kramers-Kronig relations based on a FFT algorithm in Matlab, we further improved the speed of the algorithm by a factor of 100. For testing the algorithm we simulate distorted apparent absorbance spectra by utilizing the exact theory for the scattering of infrared light at absorbing spheres, taking into account the high numerical aperture of infrared microscopes employed for the analysis of single cells and tissues. In addition, the algorithm was applied to measured absorbance spectra of single lung cancer cells.

Investigating the effects of simulated transport vibration on tomato tissue damage based on vis/NIR spectroscopy

Visible and near infrared (vis/NIR) spectroscopy combined with chemometrics were investigated to evaluate the effects of simulated transport vibration levels on damage of tomato fruit. A total of 280 tomato samples were randomly divided into 5 groups; each group was subjected to vibration at different acceleration levels. A total of 230 samples (46 from each group) were selected as a calibration set; whereas 50 samples (10 from each group) were selected as a prediction set. Raw spectra, differentiation (the first derivative) spectra, extended multiplicative scatter correction (EMSC) processed spectra and standard normal variant combined with detrending (SNV–DT) processed spectra were used for calibration models. SNV–DT processed spectra had the best performance using for partial least squares (PLS) analysis. The PLS analysis was implemented to calibrate models with different wavelength bands including visible, short-wave near infrared (SWNIR) and long-wave near infrared (LWNIR) regions. The best PLS model was obtained in the vis/NIR (600–1600nm) region. Using a grid search technique and radial basis function (RBF) kernel, four least squares support vector machine (LS–SVM) models with different latent variables (7, 8, 9, and 10 LVs) were compared. The optimal model was obtained with 9 LVs and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by LS–SVM were 0.984, 0.137 and 0.003, respectively. The results showed that vis/NIR spectroscopy could be applied as a reliable and rapid method for predicting the effect of vibration levels on tissue damage of tomato fruit.

Improved Extended Multiplicative Scatter Correction Algorithm Applied in Blood Glucose Noninvasive Measurement with FT-IR Spectroscopy

In order to improve the predictive accuracy of human blood glucose quantitative analysis model with fourier transform infrared (FT-IR) spectroscopy, this paper uses a method named improved extended multiplicative scatter correction (Im-EMSC), which can effectively eliminate the scattering effects caused by human body strong scattering. The principal components of the differential spectra are used instead of the pure spectra of the analytes in this algorithm. Calibrate the unwanted physical characteristic through the shape of the curve of principal components, and extract the original glucose concentration information. Im-EMSC can efficiently remove most of the pathlength difference and baseline shift influences. Firstly, Im-EMSC is used as a preprocessing method, and then partial least squares (PLS) regression method is adopted to establish a quantitative analysis model. In this paper, the result of Im-EMSC is compared with those popular scattering correction algorithms of multiplicative scatter correction (MSC) and extended multiplicative scatter correction (EMSC) preprocessing methods. Experimental results show that the prediction accuracy has been greatly improved with Im-EMSC method, which is helpful for human noninvasive glucose concentration detection technology.

New procedure for improving precision and accuracy of instrumental color measurements of beef

The surface layers of steaks from bovine M. semimembranosus were prepared to have deoxy- (DMb), oxy- (OMb) and metmyoglobin (MMb) states using either chemicals (CHEM) or oxygen partial pressure packaging (OPP). Ninety-six different meat surface areas were measured in reflectance mode (400–1100nm) for each preparation method. Reflectance spectra were converted to absorbance (A) and then transformed by Kubelka–Munk transformation (K/S) and/or extended multiplicative scatter correction (EMSC). Transformed spectra of prepared pure states were used to make calibration models of MMb, DMb and OMb using either selected wavelengths (SW) or partial least square (PLS) regression. Finally, the predicted myoglobin states were normalized to ensure that no state was <0 or >1 and the sum of all states equal to 1. Multivariate calibrations (i.e. PLS) outperformed the univariate calibrations (i.e. SW). The OPP method of preparing pure states was clearly best for OMb while the CHEM method was best for preparing MMb on fresh meat surfaces. Both preparation methods needed improvement concerning DMb. The CHEM(K/S) SW and the OPP EMSC(A) PLS methods predicted MMb, DMb and OMb with root-mean-square errors of cross validation (RMSECV) equal to 0.08, 0.16 and 0.18 (range 0–1) and 0.04, 0.04 and 0.04 (range 0–1), respectively. This new reflectance protocol has potential for routine meat color measurements.

Rapid monitoring of grapevine reserves using ATR–FT-IR and chemometrics

Predictions of grapevine yield and the management of sugar accumulation and secondary metabolite production during berry ripening may be improved by monitoring nitrogen and starch reserves in the perennial parts of the vine. The standard method for determining nitrogen concentration in plant tissue is by combustion analysis, while enzymatic hydrolysis followed by glucose quantification is commonly used for starch. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FT-IR) combined with chemometric modelling offers a rapid means for the determination of a range of analytes in powdered or ground samples. ATR–FT-IR offers significant advantages over combustion or enzymatic analysis of samples due to the simplicity of instrument operation, reproducibility and speed of data collection. In the present investigation, 1880 root and wood samples were collected from Shiraz, Semillon and Riesling vineyards in Australia and Germany. Nitrogen and starch concentrations were determined using standard analytical methods, and ATR–FT-IR spectra collected for each sample using a Bruker Alpha instrument. Samples were randomly assigned to either calibration or test data sets representing two thirds and one third of the samples respectively. Signal preprocessing included extended multiplicative scatter correction for water and carbon dioxide vapour, standard normal variate scaling with second derivative and variable selection prior to regression. Excellent predictive models for percent dry weight (DW) of nitrogen (range: 0.10–2.65% DW, median: 0.45% DW) and starch (range: 0.25–42.82% DW, median: 7.77% DW) using partial least squares (PLS) or support vector machine (SVM) analysis for linear and nonlinear regression respectively, were constructed and cross validated with low root mean square errors of prediction (RMSEP). Calibrations employing SVM-regression provided the optimum predictive models for nitrogen (R2=0.98 and RMSEP=0.07% DW) compared to PLS regression (R2=0.97 and RMSEP=0.08% DW). The best predictive models for starch was obtained using PLS regression (R2=0.95 and RSMEP=1.43% DW) compared to SVR (R2=0.95; RMSEP=1.56% DW). The RMSEP for both nitrogen and starch is below the reported seasonal flux for these analytes in Vitis vinifera. Nitrogen and starch concentrations in grapevine tissues can thus be accurately determined using ATR–FT-IR, providing a rapid method for monitoring vine reserve status under commercial grape production.

Contribution of Light Scattering to near Infrared Absorption in Milk

The presence of milk fat globule emulsion produces scattering phenomena on near infrared (NIR) radiation through the raw milk. Numerically, 80% of milk fat globules have a diameter of less than 3μm interfering with the radiation having a wavelength from 1 μm to 2.5 μm with a radiation dispersion in all directions. Consequently, the NIR transmitted radiation does not behave in accordance with Lambert–Beer's law: the NIR spectra of milk samples with high fat content have high offset values and lower specific absorptions than those of samples with lower fat levels. Usually, this problem is reduced by acting at the level of sample preparation (homogenisation), of optical geometry (transflectance mode coupled with the use of an integrating sphere to collect the widespread radiation) and using chemometric regression models able to optimise non-linear spectral responses. Transmittance measurements have already been exploited for the determination of macro-composition in the agro-food and in the dairy fields. In this work, a set of 150 individual milk samples, collected over three days from a single farm with 49 milking cows, was analysed with Fourier transform-NIR apparatus in order to study the effects of scattering on partial least squares predictors for casein. The spectra of non-homogenised raw whole milk and the respective skimmed samples were collected in transmission mode. Using the true scattering curves obtained by subtraction from the raw milk spectra of skimmed milk spectra the extended multiplicative scatter correction (EMSC) estimate of scattering effects was optimised. EMSC uses polynomial filters in modelling the aspecific absorptions due to the scattering effects. The correct scatter elimination can help in the individuation and interpretation of the true predictors in calibration procedures.

Use of near Infrared Hyperspectral Imaging to Identify Water Matrix Co-Ordinates in Mushrooms (Agaricus Bisporus) Subjected to Mechanical Vibration

In this study, white mushrooms ( Agaricus bisporus) were subjected to physical perturbation by mechanical vibration. Hyperspectral images were obtained after perturbation using a pushbroom line-scanning instrument operating in the wavelength range of 1000–1700 nm (7nm spectral resolution). Changes in sample spectra arising from perturbation were examined by observation of difference spectra and partial least squares regression (PLSR) coefficients. Different spectral pre-treatments [multiplicative scatter correction (MSC), extended multiplicative scatter correction (EMSC) and standard normal variate (SNV)] were employed in order to decrease spectral variability caused by scattering and differences in the optical path length due to physical changes in the mushrooms induced by the perturbation. Candidate water matrix co-ordinates were proposed at 950 nm, 1174 nm, 1398 nm, 1433 nm, 1454 nm, 1496 nm and 1510 nm. Mechanical vibration increased the concentration of weakly hydrogen-bonded water and decreased that of strongly hydrogen-bonded water in the mushrooms without causing changes in the bulk moisture content.

DETECTION AND CLASSIFICATION OF ORGANIC AND ORGANOPHOSPHORUS ANALYTES ON SOIL FROM REFLECTION-ABSORPTION SPECTROSCOPY

Principal Components Analysis (PCA) is a common anomaly detection tool that was used in this work to detect organic and organophosphate analytes on soils using mid-infrared reflection-absorption spectroscopy. Detection is hindered by large variability in sample-to-sample soil reflectivity that is due to the random nature of the soil particle packing. Extended multiplicative scatter correction (EMSC) and Savitzky-Golay derivative preprocessing were examined as methods to reduce this variability and enhance detection capability. Second derivative preprocessing provided results that were at least as good as EMSC for detection and the simplicity of the derivative methodology makes it an attractive preprocessing approach. Typically, PCA is applied to all spectral channels and results from detection events are interrogated to identify a potential cause. In this work, PCA models were developed for specific wavenumber ranges corresponding to functional group frequencies with the objective of providing some classification capability. It was found that detection of CH 2, CH 3 and P = O stretching bands was possible; however, results for a – CH 2 scissors band was less encouraging and detection of O – H stretch, – C – C – skeletal stretch, and PO – C stretch modes was poor. Some limited classification capability may be possible, but it would be difficult to make a unique assignment of the analytes present using the strategies studied.

Detection of Low Volatility Organic Analytes on Soils Using Infrared Reflection Spectroscopy

Previous work on the detection of low-volatility liquid organic (and organophosphorus) analytes on soil indicated a strong signal in Fourier transform infrared spectra in the 2966–2855 cm−1 range attributed to C-H vibrational stretching modes. This range is the focus of detection strategies examined here as an anticipated prelude to subsequent detection and classification approaches utilising additional spectral bands. This laboratory study is being used to test detection algorithms that may be useful for rapid stand-off detection of organic compounds on soil. Six signal processing methods (designed to minimise irrelevant variability in the recorded soil reflection spectra while enhancing signal from analyte compounds possibly present on soil) were studied. These included Savitzky-Golay second derivatives, extended multiplicative scatter correction (EMSC) and a novel alternative to piece-wise EMSC. The pre-processed signal was then used for detection. Previous work utilised principal components analysis (PCA)-based multivariate statistical process control methodologies for detection. Six alternative anomaly detection statistics were examined here that were based on the pre-processed signal, weighted signal and generalised weighted signal. The latter statistic can be considered easier than PCA with many of the same benefits. Two tests corresponding to different monitoring strategies were examined: test data “local” and “non-local” to the calibration data. Although the results are expected to be optimistic, the generalised weighted approach worked extremely well for the “local” detection test, but was less successful for the “non-local” test. Results suggest that good characterisation of analyte-free soil prior to monitoring will lead to the best detection performance. Second derivative pre-processing showed the best results on receiver operator characteristic curves and its ease of application is a distinct advantage. However, it may not be universally successful for all bands to be considered in the future. Both EMSC and the novel “piece-wise” EMSC methodology showed promise, but some improvements may be necessary.

Analysis of Protein Structures and Interactions in Complex Food by Near-Infrared Spectroscopy. 1. Gluten Powder

The potential of near-infrared (NIR) spectroscopy in detailed food analysis was tested in a model system consisting of gluten powder treated with moisture and heat. Second-derivative transformation and extended multiplicative signal correction were applied for improving the band resolution and removing physical and quantitative spectral variations. Subsequent chemometric analyses gave loading spectra, which were interpreted as spectral effects of altered protein structures, induced by the treatments. Moistening of the gluten powder resulted in shifts and intensity changes in the protein bands, which could be explained by a combination of minor secondary structure changes, water binding, and changed microenvironments of the amino acid side chains. Heat denaturation induced increases at 2209 nm and decreases at 2167-2182 nm, indicating an alpha-helix to beta-sheet transformation, in agreement with the expectations.

FTIR investigation of spray-dried milk protein concentrate powders

Fourier transform infrared (FTIR) spectroscopy was used to examine the conformation of proteins in spray-dried milk protein concentrate (MPC) powders and to determine if the spectral changes could be related to nitrogen solubility of these powders. MPC samples (83–92% protein, dry basis) were prepared using a range of processing conditions and stored for 4 weeks at 21 °C. FTIR spectra were collected in the mid infrared (MIR) region between 4000 and 600 cm −1. FTIR data was pre-processed to remove physical effects causing discrimination between samples using firstly second derivatives and normalization and secondly the extended multiplicative scatter correction (EMSC) technique. The FTIR spectral changes were subsequently assessed using second derivative spectroscopy and principal components analysis (PCA) in the amide I and II regions (1700–1400 cm −1) and the fingerprint region (1800–700 cm −1). PCA analysis showed that the different powder preparations could be separated on scores plots but the separation was not related to nitrogen solubility per se. However, changes in nitrogen solubility of individual MPC powders during storage could be correlated to changes in FTIR spectra. PCA analysis of FTIR spectra could generally discriminate between MPC powders that had lost significant nitrogen solubility (9–20%) and those in which nitrogen solubility was preserved on storage. There were changes in intensity and/or position of bands at 1630 cm −1 when the solubility of a stored sample decreased substantially. The results of this work also show that EMSC data pre-processing for these samples gives comparable results when compared with more complicated data pre-processing for the removal of physical effects.