Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganisms.

BACKGROUND: Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups. RESULTS: The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R2) in the 0.94-0.99 and 0.89-0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles. CONCLUSIONS: The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.

Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis.

Detection and Quantification of Alprazolam Added to Long Drinks by Near Infrared Spectroscopy and Chemometrics.

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2-5 mg/L.

Using the right tool for the job: the difference between unsupervised and supervised analyses of multivariate ecological data.

Ecologists often collect data with the aim of determining which of many variables are associated with a particular cause or consequence. Unsupervised analyses (e.g. principal components analysis, PCA) summarize variation in the data, without regard to the response. Supervised analyses (e.g., partial least squares, PLS) evaluate the variables to find the combination that best explain a causal relationship. These approaches are not interchangeable, especially when the variables most responsible for a causal relationship are not the greatest source of overall variation in the data-a situation that ecologists are likely to encounter. To illustrate the differences between unsupervised and supervised techniques, we analyze a published dataset using both PCA and PLS and compare the questions and answers associated with each method. We also use simulated datasets representing situations that further illustrate differences between unsupervised and supervised analyses. For simulated data with many correlated variables that were unrelated to the response, PLS was better than PCA at identifying which variables were associated with the response. There are many applications for both unsupervised and supervised approaches in ecology. However, PCA is currently overused, at least in part because supervised approaches, such as PLS, are less familiar.

Quantification of phenolic acids by partial least squares Fourier-transform infrared (PLS-FTIR) in extracts of medicinal plants.

INTRODUCTION: Phenolic compounds are ubiquitous compounds found in all plants as their secondary metabolites. Phenols are becoming increasingly important particularly because of their beneficial effects on health. OBJECTIVE: To provide a faithful calibration model for the simultaneous determination and quantification of phenolic acids, as salicylic, vanillic, p-hydroxybenzoic acids, eugenol and thymol in different extracts of medicinal plants, a comparative study was made between two methods of infrared measurements based on attenuated total reflectance (ATR) and transmission. METHODS: Characteristic absorbance peak heights of mid-infrared spectra of individual phenolic acids were measured for the compounds. For partial least squares regression (PLS-R) calibration mixtures of phenolic acids, wavenumber ranges, spectra pretreatment and number of latent variables, were assayed to improve the prediction capability of models using different spectral preprocessing techniques after mean centring of infrared data. Plant extracts were prepared by using water/methanol and ethanolic extraction solvents followed by Fourier-transform infrared (FTIR)-spectrometry analysis. The concentrations of phenolic compounds contained in the extracts were obtained by using the best models selected of the PLS calibration. RESULTS: PLS-ATR-mid-infrared (MIR) measurement provided the most accurate results and offers a good methodology for the determination of phenolic acids. The analysis showed that the rate of phenolic acids and monoterpenic phenols in extracts of medicinal plants is in the same range obtained with the Folin-Ciocalteu method, which confirm that the developed method using PLS is therefore, highly specific and selective. CONCLUSION: The simultaneous direct quantification of various phenolic acids in different plant extracts was possible with a fast and simple methodology based on PLS-ATR-FTIR analysis.

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometric analysis for detection and quantification of adulteration in lavender and citronella essential oils.

INTRODUCTION: The growing consumer interest in "naturals" led to an increased application of essential oils (EOs). The market outbreak induced the intensification of EO adulterations, which could affect their quality. OBJECTIVES: Nowadays, little is known about the illegal practice of adulteration of EOs with vegetable oils. Therefore, the application of mid-infrared spectroscopy coupled with chemometrics was proposed for the detection of EO counterfeits. MATERIALS AND METHODS: Two EOs, three seed oils, and their mixtures were selected to build the adulteration model. EO-adulterant mixtures for model calibration and validation were analyzed by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral data were analyzed with principal component analysis (PCA) and partial least-squares (PLS) regression. RESULTS: PCA allowed the discrimination of the EO and adulterant percentages by explaining 97.47% of the total spectral variance with two principal components. A PLS regression model was generated with three factors explaining 97.73% and 99.69% of the total variance in X and Y, respectively. The root mean square error of calibration and the root mean square error of cross-validation were 0.918 and 1.049, respectively. The root mean square error of prediction value obtained from the external validation set was 1.588 and the coefficients of determination R2 CAL and R2 CV were 0.997 and 0.996, respectively. CONCLUSIONS: The results highlighted the robustness of the developed method in quantifying counterfeits in the range from 0 to 50% of adulterants, disregarding the type of EO and adulterant employed. The present work offers a research advance and makes an important impact in phytochemistry, revealing an easily applicable method for EO quality assessment.

Impacts on product quality attributes of monoclonal antibodies produced in CHO cell bioreactor cultures during intentional mycoplasma contamination events.

A mycoplasma contamination event in a biomanufacturing facility can result in costly cleanups and potential drug shortages. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and penetrate the standard 0.2-µm filters used in the clarification of harvested cell culture fluid. Previously, we reported a study regarding the ability of Mycoplasma arginini to persist in a single-use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G 1 (IgG1) antibody. Our previous work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Careful evaluation of certain identified process parameters over time may be used to indicate mycoplasma contamination in CHO cell cultures in a bioreactor before detection from a traditional method. In this report, we studied the changes in the IgG1 product quality produced by CHO cells considered to be induced by the M. arginini contamination events. We observed changes in critical quality attributes correlated with the duration of contamination, including increased acidic charge variants and high mannose species, which were further modeled using principal component analysis to explore the relationships among M. arginini contamination, CHO cell growth and metabolites, and IgG1 product quality attributes. Finally, partial least square models using NIR spectral data were used to establish predictions of high levels (≥104 colony-forming unit [CFU/ml]) of M. arginini contamination, but prediction of levels below 104 CFU/ml were not reliable. Contamination of CHO cells with M. arginini resulted in significant reduction of antibody product quality, highlighting the importance of rapid microbiological testing and mycoplasma testing during particularly long upstream bioprocesses to ensure product safety and quality.

Contrasted effects of temperature during defoliation vs. refoliation periods on the infection of rubber powdery mildew (Oidium heveae) in Xishuangbanna, China.

Rubber powdery mildew caused by the foliar fungi Oidium heveae is one of the main diseases affecting rubber plantations (Hevea brasiliensis) worldwide. It is particularly serious in sub-optimal growing areas, such as Xishuangbanna in SW China. To prevent and control this disease, fungicides causing serious environmental problems are widely used. Strong correlations between the infection level and the temperature variables were reported previously, but they were related to monthly data that did not allow unraveling the patterns during the entire sensitive period. We correlated the infection level of powdery mildew of rubber trees recorded over 2003-2011 with antecedent 365 days daily temperature variables using partial least squares (PLS) regression. Our PLS regression results showed that the infection level of powdery mildew responded differently to the temperature variables of the defoliation and refoliation periods. Further analysis with Kriging interpolation showed that the infection level increased by 20% and 11%, respectively, per 1 °C rise of the daily maximum and mean temperature in the defoliation season, while it decreased by 8% and 10%, respectively, per 1 °C rise of the daily maximum and temperature difference in the refoliation season. This pattern was likely linked to the effects of temperature on leaf phenology. It seems highly possible that the infection level of powdery mildew increases, as increasing trends of maximum temperature and mean temperature during the defoliation continue.

Evaluation of key meteorological determinants of wintering and flowering patterns of five rubber clones in Xishuangbanna, Yunnan, China.

All rubber tree clones (Hevea brasiliensis) exhibit regular annual wintering characterized by senescence and abscission of leaves. After 3-4 weeks, this is followed by the onset of new leaves. It is likely that the timing of leaf onset affects the susceptibility of rubber trees to rubber powdery mildew disease, as this predominantly infests young leaves. However, little information is available on the phenological behavior of different rubber clones, or how meteorological factors affect such behavior. We assessed the wintering and flowering patterns of five rubber clones in Xishuangbanna, southwest China, based on observations made from 1978 to 2011, and evaluated how these patterns responded to different meteorological factors. Partial least squares regression was used to analyze the timing of defoliation, refoliation, and flowering. Our results showed that the two clones RRIM 600 and GT1 defoliated during the last week of December and refoliated in the last week of January, and clones Yunyan 277-5, Yunyan 34-4, and PR 107 defoliated during the first week of January and refoliated in the second week of February. The number of hours of sunshine during both the rainy season and the cold dry period in the dry season were important determinants of phenological changes in the rubber trees. Similarly, higher temperatures tended to delay the onset of defoliation and refoliation, and were a triggering factor for the onset of flowering. These results may help rubber cultivators to schedule appropriate disease control measures, as well as to design hybridization programs aiming at the production of clones which are resistant to foliar disease.

Quantitative Analysis of Organic Liquid Three-Component Systems: Near-Infrared Transmission versus Raman Spectroscopy, Partial Least Squares versus Classical Least Squares Regression Evaluation and Volume versus Weight Percent Concentration Units.

The band shapes and band positions of near-infrared (NIR) and Raman spectra change depending on the concentrations of specific chemical functionalities in a multicomponent system. To elucidate these effects in more detail and clarify their impact on the analytical measurement techniques and evaluation procedures, NIR transmission spectra and Raman spectra of two organic liquid three-component systems with variable compositions were analyzed by two different multivariate calibration procedures, partial least squares (PLS) and classical least-squares (CLS) regression. Furthermore, the effect of applying different concentration units (volume percent (%V) and weight percent (%W) on the performance of the two calibration procedures have been tested. While the mixtures of benzene/cyclohexane/ethylbenzene (system 1) can be regarded as a blended system with comparatively low molecular interactions, hydrogen bonding plays a dominant role in the blends of ethyl acetate/1-heptanol/1,4-dioxane (system 2). Whereas system 1 yielded equally good calibrations by PLS and CLS regression, for system 2 acceptable results were only obtained by PLS regression. Additionally, for both sample systems, Raman spectra generally led to lower calibration performance than NIR spectra. Finally, volume and weight percent concentration units yielded comparable results for both chemometric evaluation procedures.

Fluorescence fingerprint as an instrumental assessment of the sensory quality of tomato juices.

BACKGROUND: Sensory analysis is an important standard for evaluating food products. However, as trained panelists and time are required for the process, the potential of using fluorescence fingerprint as a rapid instrumental method to approximate sensory characteristics was explored in this study. RESULTS: Thirty-five out of 44 descriptive sensory attributes were found to show a significant difference between samples (analysis of variance test). Principal component analysis revealed that principal component 1 could capture 73.84 and 75.28% variance for aroma category and combined flavor and taste category respectively. Fluorescence fingerprints of tomato juices consisted of two visible peaks at excitation/emission wavelengths of 290/350 and 315/425 nm and a long narrow emission peak at 680 nm. The 680 nm peak was only clearly observed in juices obtained from tomatoes cultivated to be eaten raw. The ability to predict overall sensory profiles was investigated by using principal component 1 as a regression target. Fluorescence fingerprint could predict principal component 1 of both aroma and combined flavor and taste with a coefficient of determination above 0.8. CONCLUSION: The results obtained in this study indicate the potential of using fluorescence fingerprint as an instrumental method for assessing sensory characteristics of tomato juices.

Non-destructive age estimation of biological fluid stains: An integrated analytical strategy based on near-infrared hyperspectral imaging and multivariate regression.

From a criminalistic point of view, the accurate dating of biological traces found at the crime scene, together with its compatibility with the estimated crime perpetration timeframe, enables to limit the number of suspects by assessing their alibis and clarifying the sequence of events. The present study delineates, for the first time, the possibility of dating biological fluids such as semen and urine, as well as blood traces, by using a novel non-destructive analytical strategy based on hyperspectral imaging in the near infared region (HSI-NIR), coupled with multivariate regression methods. Investigated aspects of the present study include not only the progressive degradation of the biological trace itself, but also the effects of its interactions with the support on which it is absorbed, in particular the hydrophilic vs. hydrophobic character of fabric tissues. Results are critically discussed, highlighting potential and limitations of the proposed approach for a practical implementation.

New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence.

This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R2CV = 0.939, R2P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R2CV = 0.862, R2P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.

Prediction of quality parameters in straw wine by means of FT-IR spectroscopy combined with multivariate data processing.

This study represents the first attempt to combine mid infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars content and total acidity in straw wine. 302 Italian samples, representing different vintages, production regions and grape varieties, were analysed using FT-MIR spectroscopy and reference methods. New regression functions based on a combination of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of quality parameters: this approach allows overcoming the issue of matrix complexity, reducing spectral interferences and enhancing the information embodied in fingerprinting data. The models proposed are characterised by an excellent reliability, with low error in prediction (alcohol: 0.28%; sugars: 9.9 g/L; acidity: 0.29 g/L) comparable both to reference methods and table wine models. Results demonstrate that vibrational spectroscopy, combined with a proper multivariate data strategy, represents a suitable strategy for the quick and non-destructive assessment of quality parameters of straw wine.

Application of chemometrics for detection and modeling of adulteration of fresh cow milk with reconstituted skim milk powder using voltammetric fingerpriting on a graphite/ SiO2 hybrid electrode.

In the present work, an analytical approach for the voltammetric detection and prediction of adulteration of fresh cow milk with reconstituted skim milk powder is developed. After precipitation of milk proteins upon addition of ethanol and centrifugation, the supernatant liquid of the samples was analyzed by cyclic voltammetry on a novel graphite/SiO2 hybrid working electrode (GSiHE) using LiClO4 as electrolyte. Under these conditions, fresh milk samples gave broadened peaks/plateaus in both forward and backward potential scanning, attributed mainly to oxidases. Such peaks were not evident in the case of reconstituted skim milk powder samples due to inactivation of enzymes and breakdown of certain antioxidants caused by heat and pressure-treatments. The differences between fresh and reconstituted skim milk powder samples in their voltammetric profile were exploited for the detection of fresh milk adulteration by submitting voltammetric data to chemometrics. As datapoints, the differences between forward and backward current values, recorded at the same potentials, were determined and submitted to multivariate analysis. Principal Component Analysis (PCA) provided a clear differentiation between fresh milk and reconstituted skim milk powder samples. Soft independent modeling of class analogy (SIMCA) was employed to model the class of fresh milks, using samples from 12 commercially available fresh milk brands. Prediction of fresh milk adulteration with reconstituted skim milk powders was achieved by means of Partial Least Squares (PLS) regression analysis. Detection limit of the technique was found to be below 6% (v/v) and the linearity of model in terms of observed/predicted values was confirmed up to 100% (v/v). Validation and applicability of both SIMCA and PLS models were confirmed using a suitable test set, consisting of commercial fresh milk and skim milk powder samples as well as synthetic adulterated fresh milk samples.

Method transfer of a near-infrared spectroscopic method for blend uniformity in a poorly flowing and hygroscopic blend.

The challenges in transferring and executing a near-infrared (NIR) spectroscopic method for croscarmellose (disintegrant) in binary blends for a continuous manufacturing (CM) process are presented. This work demonstrates the development of a NIR calibration model and its use to determine the blending parameters needed for binary blends at a development plant and later used to predict CM process blends. The calibration models were developed with laboratory scale powder blends ranging from 4.32%-64.77 (%w/w) of croscarmellose and evaluated using independent test blends. The selected model was then transferred to the continuous manufacturing development site to determine the croscarmellose concentration for spectra collected in real-time. A total of 18 development plant runs were monitored using an in-line NIR spectrometer, however, these spectra showed high baseline variations. The baseline variations were caused by the poor flow of the material within the system. An inconsistent bias which varied from 2.51 to 14.95 (%w/w) was observed in the predictions of croscarmellose. High baseline spectra were eliminated and the bias was significantly reduced by 42-51%. Experiments at lower flow rate speeds did not show significant changes in baseline and bias values showed more consistency. The calibration model was then transferred to two NIR spectrometers installed at-line at the commercial site, where powder samples were collected at the beginning middle and end of each CM plant run. The NIR calibration model predicted disintegrant concentration from the powder samples. Results showed the bias values for the NIR (1) varied from 0.74 to 2.21 (%w/w) and NIR (2) from 0.28 to 3.39 (%w/w). Average concentration values for both equipments were very close to the reference concentration values of 43.18 and 50.98 (%w/w). The study showed the model was able to identify flow issues, identified as baseline shifts, that could be used to alert to problems in the powder bed that may warrant diversion from a production line. These powder flow problems such as air gaps and inconsistent powder bed height affected the NIR spectra collected at the development plant and provided results with high bias. A lower bias was obtained in samples collected at line after blending.

Fast determination of extra-virgin olive oil acidity by voltammetry and Partial Least Squares regression.

In this paper, an approach for the detection of extra-virgin olive oil (EVOO) free-acidity, based on combination of voltammetric profiles (Voltammetry) and Partial Least Squares (PLS) multivariate regression, is described. Voltammetric measurements are performed with a 12.5 µm radius platinum microdisk, directly in the oil samples mixed with 0.5 M of the room temperature ionic liquid (RTIL) tri-hexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide, which acted as a supporting electrolyte, and allows achieving a suitable conductivity in the matrices. Multivariate regression is performed directly on full voltammetric responses recorded over a properly chosen negative potential range and scan rate, where essentially all free fatty acids, characterizing EVOOs, can be reproducibly reduced. PLS regression models are built by employing Italian EVOO samples training sets at different acidity levels (over the range 0.2% w/w - 1.5% w/w; (% w/w) represents mass percentage) and optimized by choosing the optimal complexity, in terms of number of latent variables (LVs). The free-acidity prediction is made through a multivariate model, constructed by using standards of known acidity (determined by the official volumetric titration method) and validated on an external sample set. To show the validity of the proposed method, the PLS/Voltammetry predictions of the free-acidity of a series of commercially available Italian EVOOs, ranging from 0.2 to 0.41 %w/w, are obtained and the values compared with those determined by the official titration approach. Differences found between the two methods are within 5% RSD.

Fast spectroscopic monitoring of inhibitors in the 2G ethanol process.

One of the main challenges of second generation (2G) ethanol production is the high quantities of phenolic compounds and furan derivatives generated in the pretreatment of the lignocellulosic biomass, which inhibit the enzymatic hydrolysis and fermentation steps. Fast monitoring of these inhibitory compounds could provide better control of the pretreatment, hydrolysis, and fermentation processes by enabling the implementation of strategic process control actions. We investigated the feasibility of monitoring these inhibitory compounds by ultraviolet-visible (UV-Vis) spectroscopy associated with partial least squares (PLS) regression. Hydroxymethylfurfural, furfural, vanillin, and ferulic and p-coumaric acids generated during different severities of liquid hot water pretreatment of sugarcane bagasse were quantified with highly accuracy. In cross-validation (leave-one-out), the PLS-UV-Vis method presented root mean square error of prediction (RMSECV) of around only 5.0%. The results demonstrated that the monitoring performance achieved with PLS-UV-Vis could support future studies of optimization and control protocols for application in industrial processes.

Estimation of the critical quality attributes for hydroxypropyl methylcellulose with near-infrared spectroscopy and chemometrics.

With the implementation of quality by design (QbD), critical attributes of raw material (drug substance and excipients) are of significantly importance in pharmaceutical manufacturing process. It is desirable for the quality control of critical material attributes (CMAs) of excipients to ensure the quality of end product. This paper explored the feasibility of an at-line method for the quantitative analysis of hydroxypropoxy group in hydroxypropyl methylcellulose (HPMC) with near infrared spectroscopy (NIRS). Hydroxypropoxy group content can be seen as a CMA of HPMC for quality control. The partial least squares (PLS) model was built with 61 samples including 47 samples as calibration set, 14 samples as validation set by sample set partitioning based on joint x-y distances (SPXY) method. Multiplicative scattering correction (MSC) combined with Savitzkye-Golay (SG) smoothing with first derivative was used as the appropriate pretreatment method. Three variable selection methods including interval partial least-squares (iPLS), competitive adaptive reweighted Sampling (CARS), and the combination of the two methods (iPLS-CARS) were performed for optimizing the model. The results indicated that NIRS could predict rapidly and effectively the content of hydroxypropoxy group in HPMC. NIRS could be a potential method for the quality control of CMAs.