Prediction of total and volatile acidity in red wines by Fourier-transform mid-infrared spectroscopy and iterative predictor weighting

Fourier-transform mid-infrared (FT-MIR) spectroscopy, combined with partial least-squares (PLS) regression and IPW as feature selection method, was used to develop reduced-spectrum calibration models based on a few IR bands to provide near-real-time predictions of two key parameters for the characterization of finished red wines, which are essential from a quality assurance standpoint: total and volatile acidity. Separate PLS calibration models, correlating IR data (only considering those regions showing a high signal to noise ratio) with each response studied, were developed. Wavenumber selection was also performed applying IPW-PLS to take into account only significant predictors, in an attempt to improve the quality of the final models constructed. Using both PLS and IPW-PLS regression, prediction of the two responses modelled was performed with very high reliability, with RMSECV and RMSEP values on the order of 1% (comparable in terms of accuracy to the results provided by the respective reference analysis methods). An important advantage derived from the application of the IPW-PLS method had to do with the low number of original variables needed for modelling both total acidity (22 significant wavenumbers) and volatile acidity (only 11 selected predictor variables), in such a way that variable selection contributed to enhance the stability and parsimony properties of the final calibration models. The high quality of the calibration models proposed encourages the feasibility of implementing them as a fast and reliable tool in routine analysis for the determination of critical parameters for wine quality.     

Culture medium optimization for acetic acid production by a persimmon vinegar-derived bacterium

A new acetic acid-producing microorganism, Acetobacter sp. RKY4, was isolated from Korean traditional persimmon vinegar, and we optimized the culture medium for acetic acid production from ethanol using the newly isolated Acetobacter sp. RKY4. The optimized culture medium for acetic acid production using this microorganism was found to be 40 g/L ethanol, 10 g/L glycerol, 10 g/L corn steep liquor, 0.5 g/L MgSO₄·7H₂O, and 1.0 g/L (NH₄H₂PO₄. Acetobacter sp. RKY4 produced 47.1 g/L of acetic acid after 48 h of fermentation in a 250 mL Erlenmeyer flask containing 50 mL of the optimized medium.     

Evolving factor analysis-based method for correcting monitoring delay in different batch runs for use with PLS: On-line monitoring of a transesterification reaction by ATR-FTIR

In this work, the base-catalyzed transesterification of soybean oil with ethanol was monitored on-line using mid-infrared spectroscopy (MIRS) and the yield of fatty acid ethyl esters (biodiesel) was obtained by (1)H NMR spectroscopy. The MIRS monitoring carried out for 12min, was performed using a cylindrical internal reflectance cell of PbSe in the range of 3707-814cm(-1) with eight co-added scans. Two different data treatment strategies were used: in the first, the models were built using the raw data and in the other, evolving factor analysis (EFA) was used to overcome the sensor time delay due to the on-line analysis, producing significantly better results. In addition, models based on partial least squares (PLS) using three batches for calibration and another for validation were compared with models with just one batch for calibration and three for validation. The models were compared between each other using root mean square error of prediction (RMSEP) and root mean square prediction difference (RMSPD), obtaining relative errors under 3%.     

Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel

This paper evaluates analytical methods based on near infrared (NIR) and middle infrared (MIR) spectroscopy and multivariate calibration to monitor the stability of biodiesel. There was a focus on three parameters: oxidative stability index, acid number and water content. Ethylic and methylic biodiesel from different feedstocks were used in experiments of accelerated aging, in order to take into account the wide variety of oilseeds and feedstocks available in Brazil. Partial least squares (PLS) and multiple linear regression (MLR) models were developed. Different pre-processing techniques and spectral variable/regions selection algorithms were evaluated. For MLR models, the successive projection algorithm (SPA) was employed. Interval PLS (iPLS) and selection of variables taking into account the significant regression coefficients were used for PLS models. Results showed that both near and middle infrared regions, and all variable selection methods tested were efficient for predicting these three important quality parameters of B100, the root mean squares error of prediction (RMSEP) values being comparable to the reproducibility of the corresponding standard method for each property investigated.     

Rapid determination of pH in solid-state fermentation of wheat straw by FT-NIR spectroscopy and efficient wavelengths selection

In the work discussed in this paper we investigated the feasibility of determination of the pH of a fermented substrate in solid-state fermentation (SSF) of wheat straw. Fourier-transform near-infrared (FT-NIR) spectroscopy was combined with an appropriate multivariate method of analysis. A genetic algorithm and synergy interval partial least-squares (GA-siPLS) were used to select the efficient spectral subintervals and wavelengths by k-fold cross-validation during development of the model. The performance of the final model was evaluated by use of the root mean square error of cross-validation (RMSECV) and correlation coefficient (R (c)) for the calibration set, and verified by use of the root mean square error of prediction (RMSEP) and correlation coefficient (R (p)) for the validation set. The experimental results showed that the optimum GA-siPLS model was achieved by use of seven PLS factors, when four spectral subintervals were selected by siPLS and then 45 wavelength variables were chosen by use of the GA. The predicted precision of the best model obtained was: RMSECV = 0.0583, R (c) = 0.9878, RMSEP = 0.0779, and R (p) = 0.9779. Finally, the superior performance of the GA-siPLS model was demonstrated by comparison with four other PLS models. The overall results indicated that FT-NIR spectroscopy can be successfully used for measurement of pH in solid-state fermentation, and use of the GA-siPLS algorithm is the best means of calibration of the model.     

Producing Acetic Acid of <Emphasis Type="Italic">Acetobacter pasteurianus</Emphasis> by Fermentation Characteristics and Metabolic Flux Analysis

The acetic acid bacterium Acetobacter pasteurianus plays an important role in acetic acid fermentation, which involves oxidation of ethanol to acetic acid through the ethanol respiratory chain under specific conditions. In order to obtain more suitable bacteria for the acetic acid industry, A. pasteurianus JST-S screened in this laboratory was compared with A. pasteurianus CICC 20001, a current industrial strain in China, to determine optimal fermentation parameters under different environmental stresses. The maximum total acid content of A. pasteurianus JST-S was 57.14?±?1.09 g/L, whereas that of A. pasteurianus CICC 20001 reached 48.24?±?1.15 g/L in a 15-L stir stank. Metabolic flux analysis was also performed to compare the reaction byproducts. Our findings revealed the potential value of the strain in improvement of industrial vinegar fermentation.     

Characterization of a Recombinant Flocculent Saccharomyces cerevisiae Strain That Co-Ferments Glucose and Xylose: II. Influence of pH and Acetic Acid on Ethanol Production

The inhibitory effects of pH and acetic acid on the co-fermentation of glucose and xylose in complex medium by recombinant flocculent Saccharomyces cerevisiae MA-R4 were evaluated. In the absence of acetic acid, the fermentation performance of strain MA-R4 was similar between pH?4.0?C6.0, but was negatively affected at pH?2.5. The addition of acetic acid to batch cultures resulted in negligible inhibition of several fermentation parameters at pH?6.0, whereas the interactive inhibition of pH and acetic acid on the maximum cell and ethanol concentrations, and rates of sugar consumption and ethanol production were observed at pH levels below 5.4. The inhibitory effect of acetic acid was particularly marked for the consumption rate of xylose, as compared with that of glucose. With increasing initial acetic acid concentration, the ethanol yield slightly increased at pH?5.4 and 6.0, but decreased at pH values lower than 4.7. Notably, ethanol production was nearly completely inhibited under low pH (4.0) and high acetic acid (150?C200?mM) conditions. Together, these results indicate that the inhibitory effects of acetic acid and pH on ethanol fermentation by MA-R4 are highly synergistic, although the inhibition can be reduced by increasing the medium pH.     

Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy

Thirty-five representative and suitably selected roasted coffee samples were characterised by near-infrared (NIR) spectroscopy and used to prepare the corresponding espresso samples to be subsequently subjected to sensory evaluation by trained panellists. The main purpose was to investigate the relationships between certain crucial sensory attributes of espresso coffees, including perceived acidity, mouthfeel, bitterness and aftertaste, and near-infrared spectra of original roasted coffee samples, in such a way that non-destructive near-infrared reflectance measurements would be used to predict all these sensory properties with a decisive influence from a quality assurance standpoint. Separate calibration models based on partial least squares regression (PLS), correlating NIR spectral data of roasted coffee samples with each sensory attribute of espresso samples studied, were developed. Wavelength selection was also performed applying iterative predictor weighting-PLS (IPW-PLS) in order to take into account only significant and characteristic spectral features, in an attempt to improve the quality of the final regression models constructed. Using IPW-PLS regression, prediction of the four sensory responses modelled was performed with high accuracy, with root mean square errors of the residuals in cross-validation (RMSECV) ranging from 4.7 to 7.0%. Thus, the results provided by the high-quality calibration models proposed in the present study, comparable in terms of accuracy to the evaluations provided by a trained sensory panel, are promising and prove the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of unknown espresso coffee samples via their respective NIR roasted coffee spectra.     

Growth Inhibition of Thermotolerant Yeast,Kluyveromyces marxianus,in Hydrolysates from Cassava Pulp

In this study, we report the inhibition of Kluyveromyces marxianus TISTR5925 growth and ethanol fermentation in the presence of furan derivatives and weak acids (acetic acid and lactic acid) at high temperatures. Cassava pulp, obtained as the waste from starch processing, was collected from 14 starch factories located in several provinces of Thailand. At a high temperature (42 °C), the cassava pulp hydrolysate from some starch factories strongly inhibited growth and ethanol production of both K. marxianus (strain TISTR5925) and Saccharomyces cerevisiae (strain K3). HPLC detected high levels of lactic acid and acetic acid in the hydrolysates, suggesting that these weak acids impaired the growth of K. marxianus at high temperature. We isolated Trp-requiring mutants that had reduced tolerance to acetic acid compared to the wild-type. This sensitivity to acetic acid was suppressed by supplementation of the medium with tryptophan.     

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.     

Extractive fermentation of acetic acid

In this technoeconomic evaluation of the manufacture of acetic acid by fermentation, the use of the bacterium:Acetobacter suboxydans from the old vinegar process was compared with expected performance of the newerClostridium thermoaceticum bacterium. Both systems were projected to operate as immobilized cells in a continuous, fluidized bed bioreactor, using solvent extraction to recover the product. Acetobacter metabolizes ethanol aerobically to produce acid at 100 g/L in a low pH medium. This ensures that the product is in the form of a concentrated extractable free acid, rather than as an unextractable salt. Unfortunately, yields from glucose by way of the ethanol fermentation are poor, but near the biological limits of the organisms involved.     

Two-parameter determination in vinegar by a flow injection-pervaporation system

A flow injection method (FI) for the sequential determination of ethanol and acetic acid in vinegar is reported. The determination of ethanol is based on the oxidation of the pervaporated ethanol by K2Cr2O7. The acetic acid is determined by an acid-base reaction with Thymol Blue as the indicator. Both reactions are monitored photometrically at 600 nm using a single detector. Optimisation studies and assessment of the sequential Fl method are also reported. The linear determination range is 0-12% v/v for ethanol and 0-10% (grams of acetic acid in 100 ml) for acetic acid. The sample throughput of the sequential manifold is seven per hour. The new method was applied to vinegar samples and the results obtained were in excellent agreement with those from reference methods used in Spain.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Standardization of SPE signals in multicomponent analysis of three benzimidazolic pesticides by spectrofluorimetry

Piecewise direct standardization (PDS) is applied to multivariate standardization of fluorescence signals using partial least squares (PLS) and principal component regression (PCR) as the calibration models. The multivariate standardization was used to transfer spectra obtained after a step of solid phase extraction (SPE) to spectra registered in pure solvent in the determination of carbendazim, fuberidazole and thiabendazole in water samples. The influential parameters, such as tolerance, window size and the number of samples of the standardization subset were optimized by means of the root mean squared error of prediction (RMSEP). Similar RMSEP values were obtained by PLS and PCR using the optimized influential parameters in the standardization. However, better predictions of the compounds were obtained in test set by the PLS model.     

Quantitative determination of acyclovir in plasma by near infrared spectroscopy

The aim of this study was to assess the feasibility of near infrared spectroscopy (NIRS) for analysis of acyclovir in plasma. This methodology was based on the direct measurement of the transmission spectra of liquid samples and a multivariate calibration model (partial least squares, PLS) to determine the acyclovir concentration in plasma sample. The PLS calibration set was built on using the spiked samples by mixing different amounts of acyclovir. Concentration of acyclovir in the plasma samples was calculated employing a 6-factors PLS calibration using the spectral information in the range of 6102-5450 cm^− 1. The root mean square errors of prediction (RMSEP) found was 1.21 for acyclovir. The developed PLS-NIRS procedure allows the determination of 120 samples/h does not require any sample pretreatment and avoids waste generation.     

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).     

Determination of ethanol in fuel ethanol and beverages by Fourier transform (FT)-near infrared and FT-Raman spectrometries

Fourier transform-near infrared (FT-NIR) and FT-Raman spectrometries have been used to design partial least squares (PLS) calibration models for the determination of the ethanol content of ethanol fuel and alcoholic beverages. In the FT-NIR measurements the spectra were obtained using air as reference, and the spectral region for PLS modeling were selected based on the spectral distribution of the relative standard deviation in concentration. In the FT-Raman measurements hexachloro-1,3-butadiene (HCBD) has been used as an external standard. In the PLS/FT-NIR modeling for ethanol fuel analysis 50 ethanol fuel standards (84.9-100% (w/w)) were used (25 in the calibration, 25 in the validation). In the PLS/FT-Raman modeling 25 standards were used (13 in the calibration, 12 in the validation). The PLS/FT-NIR and FT-Raman models for beverage analysis made use of 24 standards (0-100% (v/v)). Twelve of them contained sugars (1-5% (w/w)), one-half was used in the calibration and the other half in the validation. Different spectral pre-processing were used in the PLS modeling, depending on the type of sample investigated. In the ethanol fuel analysis the FT-NIR pre-processing was a 17 points smoothed first derivative and for beverages no spectral pre-processing was used. The FT-Raman spectra were pre-processed by vector normalization in the ethanol fuel analysis and by a second derivative (17 points smoothing) in the beverage analysis. The PLS models were used in the analysis of real ethanol fuel and beverage samples. A t-test has shown that the FT-NIR model has an accuracy equivalent to that of the reference method (ASTM D4052) in the analysis of ethanol fuel, while in the analysis of beverages, the FT-Raman model presents an accuracy equivalent to the reference method. The limits of detection for NIR and Raman calibration models were 0.05 and 0.2% (w/w), respectively. It has also been shown that both techniques, present better results than gas chromatography (GC) in evaluating the ethanol content of beverages.     

H NMR and C-IRMS analyses of acetic acid from vinegar, O-IRMS analysis of water in vinegar: International collaborative study report

An international collaborative study of isotopic methods applied to control the authenticity of vinegar was organized in order to support the recognition of these procedures as official methods. The determination of the ²H/¹H ratio of the methyl site of acetic acid by SNIF-NMR (site-specific natural isotopic fractionation-nuclear magnetic resonance) and the determination of the ¹³C/¹²C ratio, by IRMS (isotope ratio mass spectrometry) provide complementary information to characterize the botanical origin of acetic acid and to detect adulterations of vinegar using synthetic acetic acid. Both methods use the same initial steps to recover pure acetic acid from vinegar. In the case of wine vinegar, the determination of the ¹⁸O/¹⁶O ratio of water by IRMS allows to differentiate wine vinegar from vinegars made from dried grapes. The same set of vinegar samples was used to validate these three determinations.The precision parameters of the method for measuring δ¹³C (carbon isotopic deviation) were found to be similar to the values previously obtained for similar methods applied to wine ethanol or sugars extracted from fruit juices: the average repeatability (r) was 0.45 ‰, and the average reproducibility (R) was 0.91‰. As expected from previous in-house study of the uncertainties, the precision parameters of the method for measuring the ²H/¹H ratio of the methyl site were found to be slightly higher than the values previously obtained for similar methods applied to wine ethanol or fermentation ethanol in fruit juices: the average repeatability was 1.34 ppm, and the average reproducibility was 1.62 ppm. This precision is still significantly smaller than the differences between various acetic acid sources (δ¹³C and δ¹⁸O) and allows a satisfactory discrimination of vinegar types. The precision parameters of the method for measuring δ¹⁸O were found to be similar to the values previously obtained for other methods applied to wine and fruit juices: the average repeatability was 0.15‰, and the average reproducibility was 0.59‰. The above values are proposed as repeatability and reproducibility limits in the current state of the art.On the basis of this satisfactory inter-laboratory precision and on the accuracy demonstrated by a spiking experiment, the authors recommend the adoption of the three isotopic determinations included in this study as official methods for controlling the authenticity of vinegar.     

A study of robustness with multivariate calibration. Application to the polarographic determination of benzaldehyde

A procedure to evaluate the robustness of an analytical method when there are changes in some experimental variables, when using multivariate calibration, is proposed. The procedure consists of analysing the root mean square error of prediction (RMSEP) as a response to a Plackett–Burman experimental design, through which the influence of several experimental factors on the prediction capability of the multivariate partial least squares (PLS) models built is studied. Two different ways of analysing the experimental design response are considered: establishing the residual variance with replicates and using Lenth's method. The proposed methodology has been applied to estimate the robustness of the polarographic determination of benzaldehyde when PLS calibration is used.     

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.