Fourier Transform Near-infrared Research Articles

New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR)

This work aims to explore the potential of near infrared (NIR) spectroscopy to quantify volatile compounds in Vinho Verde wines, commonly determined by gas chromatography. For this purpose, 105 Vinho Verde wine samples were analyzed using Fourier transform near infrared (FT-NIR) transmission spectroscopy in the range of 5435 cm−1 to 6357 cm−1. Boxplot and principal components analysis (PCA) were performed for clusters identification and outliers removal. A partial least square (PLS) regression was then applied to develop the calibration models, by a new iterative approach. The predictive ability of the models was confirmed by an external validation procedure with an independent sample set. The obtained results could be considered as quite good with coefficients of determination (R2) varying from 0.94 to 0.97. The current methodology, using NIR spectroscopy and chemometrics, can be seen as a promising rapid tool to determine volatile compounds in Vinho Verde wines.

Grid search parametric optimization for FT-NIR quantitative analysis of solid soluble content in strawberry samples

Grid search technology is proposed to choose the influencing parameters for interval partial least squares (iPLS) and moving window partial least squares (MWPLS) models in the Fourier transform near infrared (FT-NIR) spectrometric quantitative determination of solid soluble content (SSC) in strawberry samples. The objective of grid search is to find out a robust way to ensure that the selected parameters of iPLS and MWPLS would output the informative wavebands leading to improved modeling results. For the improvement of model accuracy, the grid search technique is designed for locating the informative wavebands via influential algorithmic parameters on FT-NIR regions corresponding to different spectral responses of molecule vibration. The commonly used iPLS and MWPLS algorithms are modified in cooperation with the grid search technique, to identify the informative wavebands for the improvement of the model prediction performances. A further integration of the wavebands from different regions could obtain some more accurate models based on the calibrating-validating-testing (CVT) sample division framework. The optimized parameters have the potential to be applied to the development of online and realtime measurement for fruit industry.

Study of Fourier transform near infrared (FT‐NIR) spectra of ghee (anhydrous milk fat)

Ghee is chemically highly complex in nature. The authentication and characterisation of edible fats and oils by routine chemical methods are highly laborious and time‐consuming. Fourier transform near infrared (FT‐NIR) spectroscopy has emerged as the predominant analytical tool in the study of edible fats/oils. In order to assign absorption bands in the infrared (IR) spectrum, spectra of cow and buffalo ghee samples were acquired in the NIR region (10 000–4000 cm−1). In the FT‐NIR spectrum, a total of nine peaks were obtained for cow and buffalo ghee, with almost equal intensity of absorption. The intensity of absorbance was higher for cow ghee compared to buffalo ghee.

Rapid Diagnosis of Normal and Abnormal Conditions in Solid-State Fermentation of Bioethanol Using Fourier Transform Near-Infrared Spectroscopy

Rapid diagnosis of normal and abnormal fermentation conditions in solid-state fermentation (SSF) of bioethanol is crucial to process and quality controls. The Fourier transform near-infrared (FT-NIR) spectroscopy analysis technique combined with pattern recognition methods was employed to monitor fermentation conditions (i.e., normal and abnormal) in this study. To achieve optimum performance in identification of fermentation conditions, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) classifications were employed to construct the recognition models. The optimization of arithmetical parameters and number of principal components (PCs) were implemented simultaneously using leave-one-out cross-validation (LOOCV) during the recognition model training phase. The results of this study revealed that the SVM model presented better performance compared to the other two models, and the best SVM recognition model was finally established by use of four PCs. The SVM mode...

Chemometric compositional analysis of phenolic compounds in fermenting samples and wines using different infrared spectroscopy techniques

The wine industry requires reliable methods for the quantification of phenolic compounds during the winemaking process. Infrared spectroscopy appears as a suitable technique for process control and monitoring. The ability of Fourier transform near infrared (FT-NIR), attenuated total reflectance mid infrared (ATR-MIR) and Fourier transform infrared (FT-IR) spectroscopies to predict compositional phenolic levels during red wine fermentation and aging was investigated. Prediction models containing a large number of samples collected over two vintages from several industrial fermenting tanks as well as wine samples covering a varying number of vintages were validated. FT-NIR appeared as the most accurate technique to predict the phenolic content. Although slightly less accurate models were observed, ATR-MIR and FT-IR can also be used for the prediction of the majority of phenolic measurements. Additionally, the slope and intercept test indicated a systematic error for the three spectroscopies which seems to be slightly more pronounced for HPLC generated phenolics data than for the spectrophotometric parameters. However, the results also showed that the predictions made with the three instruments are statistically comparable. The robustness of the prediction models was also investigated and discussed.

Fourier Transform Near-Infrared (FTNIR) Spectroscopy and Partial Least-Squares (PLS) Algorithm for Monitoring Compositional Changes in Hydrocarbon Gases under In Situ Pressure

The ability of Fourier transform near-infrared (FTNIR) spectroscopy and chemometric method was investigated to determine the concentration of major hydrocarbon components of natural gases at pressures from 3.44 to 13.78 MPa and temperatures from 278.15 to 313.15 K. Various partial least-squares (PLS) models were developed to determine the concentration of methane, ethane, propane, i-butane, and n-butane simultaneously in gas phase at different pressures and temperatures, using the acquired FTNIR spectra. Several preprocessing techniques were tested prior to the construction of the calibration models. The first Savitzky–Golay derivative with smoothing over five points plus orthogonal signal correction (OSC) was found to be the best method for FTNIR data preprocessing. Good agreement was obtained between the predicted data by PLS models and the measured values with a standard error of prediction (SEP) of 0.184–0.217, 0.165–0.209, 0.136–0.181, 0.098–0.154, and 0.096–0.142 cmol/mol for methane, ethane, propan...

Qualitative Prediction of Yeast Growth Process Based on Near Infrared Spectroscopy

Abstract To improve the yield of industrial fermentation, this study presented a method based on near infrared spectroscopy to predict the growth process of yeast. The spectral data were measured from fermentation sample by Fourier-transform near-infrared (FT-NIR) spectrometer in the process of yeast culture. Each spectrum was acquired over the range of 10000 to 4000 cm −1 . Meanwhile, the optical density (OD) values of fermentation sample were determined with photoelectric turbidity method. A method on the basis of competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelength variables of NIR data, and then extreme learning machine (ELM) algorithm was employed to develop the categorization model about the four growth phases of yeast. The experimental results showed that only 30 characteristic wavelength variables of NIR data were selected by CRAS algorithms, and prediction accuracy of the training set and testing set of the CARS-ELM model was 98.68% and 97.37%, respectively. This study showed that near infrared spectral analysis technique was feasible to predict the growth process of yeast.

Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000–2500nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

A novel method to determine total sugar of Goji berry using FT-NIR spectroscopy with effective wavelength selection

ABSTRACTThis study investigated the feasibility of determining the content of Goji berry total sugar (GBTS) through Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics. A total of 114 Goji berry samples from different regions in China were scanned with a FT-NIR reflectance spectroscopic system. X-loading weights and correlation coefficients were utilised to identify the most influential wavelength after the selection of spectra processing methods. Partial least squares regression was applied to build models, and paired t test was used for external validation. The model with raw spectra after pre-treatment with Norris gap derivatives and effective wavelength of 8700–4000 cm−1, which were determined by the correlation coefficients, exhibited the best predicative ability. In addition, the determination coefficients (R2) of calibration validation set were both greater than 0.9 when principal component number was 6. The model presents good performance, with root mean square errors (RMSE) of 0.406 and 0.695 for the prediction of calibration validation set, relatively. The use of FT-NIR spectroscopy to implement quick determination of GBTS is thus feasible.

Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT‐NIR Spectroscopy

Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near‐infrared (FT‐NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000–4,000 cm−1 (1,000–2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT‐IR/FT‐NIR spectrometer. The DON calibration predicted the DON levels in test samples with R2 = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5–25 ppm of DON was used as the cut‐off to classify samples into low‐ and high‐DON groups, 60.8–82.3% of the low‐DON samples were correctly classified, whereas the classification accuracy of the high‐DON group was 82.3–94.0%. The MC calibration predicted the MC in grain samples with R2 = 0.98 and RMSEP = 0.19%. Therefore, these FT‐NIR calibrations can be used to rapidly prescreen wheat lines to identify low‐DON lines for further evaluation using standard laboratory methods, thereby reducing the time and costs of analyzing samples from FHB screening trials.

Development of calibration models for the evaluation of pomegranate aril quality by Fourier-transform near infrared spectroscopy combined with chemometrics

A Fourier transform NIR (FT-NIR) method was developed combining chemometrics for prediction of organoleptic and phytochemical parameters of pomegranate arils using two different FT-NIR acquisition methods; namely, an integrating sphere (IS) and an emission head (EH) used over a spectral region 800–2500 nm. Several pre-processing methods were investigated. Pre-processing methods that yielded higher coefficient of determination (R2) and residual predictive deviation (RPD), lower root mean square error estimation (RMSEE) and root mean square error of prediction (RMSEP) values were used for model development. Model development using the EH gave the best prediction of total soluble solids (R2 = 87.55, RMSEP = 0.30%), pH (R2 = 85.18, RMSEP = 0.10), titratable acidity (R2 = 85.59, RMSEP = 0.10%), BrimA (R2 = 83.43, RMSEP = 0.43), aril hue (R2 = 88.59, RMSEP = 4.19), total phenolic concentration (R2 = 86.48, RMSEP = 0.11 g l−1), total anthocyanin concentration (R2 = 70.50, RMSEP = 0.13 g l−1) and vitamin C concentration (R2 = 84.86, RMSEP = 0.09 g l−1), while the IS provided the best results for TSS:TA (R2 = 82.20, RMSEP = 1.03), aril firmness (R2 = 68.40, RMSEP = 6.71 N), aril colour components (a* (R2 = 73.54, RMSEP = 1.67) and Chroma (R2 = 78.37, RMSEP = 2.31)). Good prediction was observed for both the models based on EH and IS data acquisition methods. However, better prediction performance was obtained with the model based on EH data acquisition method, resulting in accurate predictions of 8 quality parameters. This study demonstrated that FT-NIR and associated chemometric analysis can holistically evaluate the quality parameters of pomegranate arils.

The Evaluation of the Applicability of Fourier Transform Near-Infrared (FT-NIR) Spectroscopy in the Measurement of Analytical Parameters in Must and Wine

Fourier transform near-infrared (FT-NIR) spectroscopy can be used as a rapid method to measure the percentage of sugar and to discriminate between different must samples in terms of their free amino nitrogen (FAN) values. It can also be used as a rapid method to discriminate between Chardonnay wine samples in terms of their malolactic fermentation (MLF) status. By monitoring the conversion of malic to lactic acid, the samples could be classified on the basis of whether MLF has started, is in progress or has been completed. Furthermore, FT-NIR spectroscopy can be used as a rapid method to discriminate between table wine samples in terms of their ethyl carbamate (EC) content. It is claimed that high concentrations of ethyl carbamate in wine can pose a health threat and has to be monitored by determining the EC content in relation to the regulatory limits set by authorities. For each of the above-mentioned parameters QUANT+™ methods were built and calibrations were derived and it was found that a very strong correlation existed in the sample set for the FT-NIR spectroscopic predictions of the percentage of sugar (r = 0.99, SEP= 0.31 °Brix). However, the correlation for the FAN predictions (r = 0.602, SEP= 272.1 g.L-1), malic acid (r = 0.64, SEP= 1.02 g.L-1), lactic acid (r = 0.61, SEP= 1.35 g.L-1) and EC predictions (r = 0.47, SEP = 3.6 μg.kg·1) were not good. The must samples could be classified in terms of their FAN values when Soft Independent Modelling by Class Analogy (SIMCA) diagnostics and validation were applied as a discriminative method, with recognition rates exceeding 80% in all cases. When SIMCA diagnostics and validation were applied to the Chardonnay and EC wine samples, recognition rates exceeding 88% and 80% respectively were obtained. These results therefore confirm that this method is successful in discriminating between samples.

The Use of Near-Infrared (NIR) Spectroscopy and Principal Component Analysis (PCA) To Discriminate Bark and Wood of the Most Common Species of the Pellet Sector

The pellet energy market is expanding rapidly in Europe and also at the global level, in response to the continuously growing energy demand and because of the high degree of reliability, the easy handling, and the cheap and simple logistics, in comparison to other solid biomasses. The fast growth of this market has highlighted the problem of product quality, which has strong repercussions for technical, environmental, and economic aspects. The biomass quality is defined by several chemical–physical parameters that are directly measurable in the laboratory. In addition, there are quality attributes related to origin and source, difficult to investigate through traditional analyses, such as the type of wood (hardwood/softwood) and the presence of bark. The development of a rapid technique able to provide this information could be an advantageous tool for the energy sector proving indications on biofuel traceability and sustainability. More than 90 samples belonging to three of the most common European species for the energy pellet sector (fir, pine, and beech) have been collected and analyzed by means of Fourier transform near-infrared (FT-NIR) coupled with Principal Component Analysis (PCA). The results show that the main variation in the data is due to both differences between bark and wood, as well as differences between hardwood and softwood. The differences are mainly linked to changes in composition and amounts of cellulose, hemicellulose, lignin, and extractives. Moreover, 30 wood/bark blends (2%–20% (w/w)) were analyzed, indicating the ability of the system to recognize blends from pure material. This study has shown that spectroscopy coupled with multivariate data analysis is a useful tool verifying the compliance of producer declarations and assisting experts in evaluation of the biomass quality.

Computer Assisted Examination of Infrared and Near Infrared Spectra to Assess Structural and Molecular Changes in Biological Samples Exposed to Pollutants: A Case of Study

We present a computer assisted method for the examination of the structural changes present in the probe organism Vicia faba exposed to inorganic arsenic, detected by means of Fourier transform infrared (FTIR) and Fourier transform near infrared (FTNIR) spectroscopy. Like the common ecotoxicological tests, the method is based on the comparison among control and exposed sample spectra of the organisms to detect structural changes caused by pollutants. Using FTIR spectroscopy, we measured and plotted the spectral changes related to the unsaturated to saturated lipid ratio changes (USL), the lipid to protein ratio changes (LPR), fatty and ester fatty acid content changes (FA), protein oxidation (PO) and denaturation, and DNA and RNA changes (DNA-RNA). Using FTNIR spectroscopy, we measured two spectral ranges that belonged to hydrogen bond interactions and aliphatic lipid chains called IntHCONH and Met1overt, respectively. The FTIR results showed that As modified the DNA-RNA ratio and also caused partial protein denaturation in the Vicia faba samples. The FTNIR results supported the FTIR results. The main advantage of the proposed computational method is that it does not require a skilled infrared or near infrared operator, lending support to conventional studies performed by toxicological testing.

On-line monitoring of biomass in 1,3-propanediol fermentation by Fourier-transformed near-infrared spectra analysis

Biomass is an important parameter reflecting the fermentation dynamics. Real-time monitoring of biomass can be used to control and optimize a fermentation process. To overcome the deficiencies of measurement delay and manual errors from offline measurement, we designed an experimental platform for online monitoring the biomass during a 1,3-propanediol fermentation process, based on using the fourier-transformed near-infrared (FT-NIR) spectra analysis. By pre-processing the real-time sampled spectra and analyzing the sensitive spectra bands, a partial least-squares algorithm was proposed to establish a dynamic prediction model for the biomass change during a 1,3-propanediol fermentation process. The fermentation processes with substrate glycerol concentrations of 60 g/L and 40 g/L were used as the external validation experiments. The root mean square error of prediction (RMSEP) obtained by analyzing experimental data was 0.341 6 and 0.274 3, respectively. These results showed that the established model gave good prediction and could be effectively used for on-line monitoring the biomass during a 1,3-propanediol fermentation process.

Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy

Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%–100%). NIR spectra of meat mixtures were measured with dispersive (400–2500 nm) and Fourier transform NIR (FT-NIR) spectrometers (1000–2500 nm). Partial least squares (PLS) regression with full leave-one-out cross-validation was used to build prediction models. The results based on dispersive NIR spectra revealed that the coefficient of determination for cross-validation (Rcv2) ranged from 0.91 for toasted soy grits to 0.99 for Arconet. The results based on FT-NIR spectra exhibited the best prediction for toasted soy grits (Rcv2 = 0.99) and Rcv2 > 0.98 for the other three soy types. For identification of different types of soy products, support vector machine (SVM) classification was used and the total accuracy for dispersive NIR and FT-NIR was 95% and 83.33%, respectively. These results suggest that either dispersive NIR or FT-NIR spectroscopy could be used to predict the content and the discrimination of different soy products added in ground beef products. In application, FT-NIR spectroscopy methods would be recommended if time is a consideration in practice.

Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy.

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6%(w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192(w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.

SPA Combined with Swarm Intelligence Optimization Algorithms for Wavelength Variable Selection to Rapidly Discriminate the Adulteration of Apple Juice

The application of wavelength variable selection before partial least squares (PLS) regression to rapidly discriminate the adulteration of apple juice by Fourier transform near-infrared (FT-NIR) was investigated in this study. Successive projections algorithm (SPA) combined with four swarm intelligence optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), group search optimizer (GSO), and firefly algorithm (FA), was applied to extract effective wavelength variables. The results demonstrated that the variable number of SPA-PSO-PLS models was validly improved with a wavelength variable of four. The accuracy of model was satisfactory with the coefficients of determination of prediction (R 2 p = 0.9986) and good root mean square errors of prediction (RMSEP = 0.0628). The results suggested that SPA combined with swarm intelligence optimization algorithms for wavelength variable selection could rapidly and efficiently discriminate the adulteration of apple juice.

Study on Rapid Measurement of Wood Powder Concentration of Wood-Plastic Composites using FT-NIR and FT-IR Spectroscopy Techniques

Wood-plastic composite (WPC) is a promising and sustainable material, and refers to a combination of wood and plastic along with some binding (adhesive) materials. In comparison to pure wood material, WPCs are in general have advantages of being cost effective, high durability, moisture resistance, and microbial resistance. The properties of WPCs come directly from the concentration of different components in composite; such as wood flour concentration directly affect mechanical and physical properties of WPCs. In this study, wood powder concentration in WPC was determined by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra from WPC in both powdered and tableted form with five different concentrations of wood powder were collected and preprocessed to remove noise caused by several factors. To correlate the collected spectra with wood powder concentration, multivariate calibration method of partial least squares (PLS) was applied. During validation with an independent set of samples, good correlations with reference values were demonstrated for both FT-NIR and FT-IR data sets. In addition, high coefficient of determination (<TEX>${R^2}_p$</TEX>) and lower standard error of prediction (SEP) was yielded for tableted WPC than powdered WPC. The combination of FT-NIR and FT-IR spectral region was also studied. The results presented here showed that the use of both zones improved the determination accuracy for powdered WPC; however, no improvement in prediction result was achieved for tableted WPCs. The results obtained suggest that these spectroscopic techniques are a useful tool for fast and nondestructive determination of wood concentration in WPCs and have potential to replace conventional methods.

Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy

The characteristics of internal quality attributes (firmness, soluble solids content and color values) of the Tokaloglu apricot cultivar (Prunus armeniaca L.) were predicted nondestructively using Fourier Transform-Near Infrared (FT-NIR) spectroscopy. Calibration methods were developed between the physical parameters, which were measured using standard methods, and the spectral measurements (in reflectance mode between 780 and 2500 nm) using Partial Least Squares method (PLS). Good correlations were obtained in calibration and validation procedures for Magness-Taylor (MT) maximum force, with a coefficient of determination (R2) of 0.82 (RMSEE = 4.45) in calibration and 0.80 (RMSECV = 4.68) in validation for multiple-harvest (MH) apricot group. The coefficient of determination (R2) for predicting MT slope was 0.79 (RMSEE = 0.83) in calibration and 0.77 (RMSECV = 0.88) in validation for the MH apricot group while it was 0.56 (RMSEE = 0.69) in calibration and 0.47 (RMSECV = 0.80) in validation for single-harvest (SH) apricot group. Good correlations were obtained for MT area with the coefficient of determination (R2) of 0.75 (RMSEE = 20.1) in calibration and R2 = 0.71 (RMSECV = 21) in validation for MH group. Good prediction values were obtained for soluble solids content for both applications (MH and SH) using FT-NIR spectroscopy: the best coefficient of determination was obtained for MH application with 0.77 (RMSEE = 1.45) in calibration and 0.75 (RMSECV = 1.51) in validation. Correlation values for prediction of chroma and hue were low for MH application, with R2 = 0.55 (RMSECV = 3.38) for chroma and with R2 = 0.16 (RMSECV = 0.49) for hue. The results showed that NIR spectroscopy has a good potential to predict internal quality of apricots non-destructively, however it has a limited ability to predict color features.