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The construction of a dispersive optical spectrometer using three-dimensional (3D) design software and printing, without applying any optical adjustments, its validation, and application to quantification of ethanol in multiproduct liquids, is the objective of this work. A 3D design software was used to design a near-infrared (NIR) spectrometer in the region from 800 to 1600 nm from the dimensions of commercially available optical components. The project was printed on a polymer filament 3D printer, and the components were fitted to the printed part. Software calculations using the model design parameters were applied to attribute the wavelength values to the abscissa axis in the spectra and estimate errors due to 3D printing limitations. The alignment of the spectrum was proven using the chloroform absorbance spectrum, which presented a maximum mispositioning of 4.1 nm concerning the literature data and effective bandwidths equivalent to commercial instruments. The 3D-printed instrument was applied to quantify ethanol in samples of cachaça, rum, beer, brandy, whiskey, vodka, mouth wash, alcohol gel, and commercial alcohol solutions. Partial least-squares regression models were built for the 3D-printed instrument and two commercial NIR instruments, the MPA II (Bruker) and the NIR DLP NIRscan (Texas Instruments), using a group of 180 standards. The three instruments reached excellent predictive capability with similar root mean square error of cross-validation (2.36-2.68) and prediction (2.31-2.87). The correlation coefficient of cross-validation and prediction for all models were between 0.97 and 0.98. The results show the feasibility of building a 3D-printed dispersive spectrometer ready for application with the simple docking of the optics, presenting acceptable accuracy to the project design concerning the printing limitations.

This study aims to validate and investigate the power of green coconut biomass (mesocarp) for the production of levulinic acid (LA). Several methods have been applied to characterize this biomass physically and chemically, in order to provide an assertive direction in the use of this residue. The LA conversion was performed with different catalysts, hydrochloric acid (HCl) and sulfuric acid (H2SO4). The conversion reactions were performed by varying time (t) and temperature (T) according to the optimum conditions described in the literature for each catalyst. In order to increase the yield of LA, the biomass was pretreated with sulfuric acid (H2SO4), sodium hydroxide (NaOH), and water (H2O in soxhlet) solutions. There were significant increases in the release of sugars, xylose, and arabinose (p

Generic procedures for the determination of extractives and carbohydrates (glucose, xylose, galactose, arabinose, mannose) were improved specifically for sugarcane bagasse. The extraction time was optimized using a specific design of experiments. The optimal condition for sugarcane biomass with up to 20% extractives was 11.5 h of extraction in water, followed by 9 h in ethanol. For the carbohydrate analysis, a separation method using liquid chromatography was improved and optimized to reduce the retention time of furanic compounds. The reduction obtained was approximately 30%. The optimal condition was the isocratic elution of the mobile phase with an aqueous solution containing 15% acetonitrile. The percentages of glycans, xylans, and arabinans in the extractive-free biomass was 33%, 22%, and 6.5%, respectively. The galactans and mannans were not detected. Acetyl groups and uronic acids were not determined.

The aim of this work was to develop and optimize a pH-responsive nanoparticle based on poly(D,L-lactide-co-glycolide) (PLGA) and chitosan (CHIT) for delivery of natural antimicrobial using trans-cinnamaldehyde (TCIN) as a model compound. The optimization was performed using a central composite design and the desirability function approach. The optimized levels of variables considering all significant responses were 4% (w/w) of TCIN and 6.75% (w/w) of CHIT. After, optimized nanoparticles were produced and characterized according to their physicochemical properties and their antimicrobial activity against Salmonella Typhimurium and Staphylococcus aureus. Optimized nanoparticles characterization indicated a satisfactory TCIN encapsulation (33.20 ± 0.85%), spherical shape, pH-responsive controlled release, with faster release in the presence of CHIT at low pH, and enhanced antimicrobial activity against both pathogens. TCIN encapsulation using PLGA coated with CHIT enhanced its antimicrobial activity and generated a delivery system with pH-sensitivity for controlled release with promising properties for food safety applications.

New strategies of ordered predictors selection (OPS) were developed in this work, making this method more versatile and expanding its worldwide use and applicability. OPS is a recognized method to select variables in multivariate regression and is used by analytical chemists and chemometrists. It shows high ability to improve the prediction of models after the selection of a few and important variables. At the core of OPS is sorting variables from informative vectors and systematically investigating the regression models to identify the most relevant set of variables by comparing the cross-validation parameters of the models. Nevertheless, the first version of the OPS method performs variable selection using only one informative vector at a time and is limited to just one variable selection run. Then, three new strategies were proposed. First, an automatic method was developed to perform variable selection using several informative vectors and their combinations. Second, the feedback OPS is presented, in this new strategy the pre-selected variables would return to a new selection. Last, a method to apply OPS in full array subdivisions called OPS intervals was established. Initially, the new strategies were applied in the six datasets used in the original OPS paper to compare the prediction performance with the new OPS algorithms. After that, twelve new datasets were used to test and compare the new OPS approaches with other variable selection methods, genetic algorithm (GA), the interval successive projections algorithm for PLS (iSPA), and recursive weighted partial least squares (rPLS). The new OPS approaches outperformed the first OPS version and the other variable selection methods. Results showed that in addition to greater predictive capacity, the accuracy in the selection of expected variables is highly superior with the new OPS approaches. Overall, the new OPS provided the best set of selected variables to build more predictive and interpretative regression models, proving to be efficient for variable selection in different types of datasets.

The aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer. The sugarcane leaf +1 was directly analyzed with no sample preparation by ultraviolet-visible-near-infrared (UV-VIS-NIR), middle-infrared (MID), and near-infrared (NIR) spectroscopies. Also, laser-induced breakdown spectroscopy (LIBS) was used to analyze pellets of dried and ground leaves and stalks of sugarcane. Classification models were built using PLS-DA. The models built using UV-VIS-NIR, MID or NIR spectra exhibited ideal sensitivity, specificity, and classification errors, i.e., 1 for both sensitivity and specificity and 0 for classification errors. Regarding the models built using LIBS spectra, those using spectra of pellets made from dried and ground leaves also presented ideal sensitivity, specificity, and classification errors; on the other hand, models built using the spectra of pellets made of dried and ground stalks did not present ideal values for these parameters. Thus, the models built, except for the one using LIBS of pellets made of stalks, showed excellent predictive capacity, making them suitable for predicting the resistance or susceptibility of sugarcane genotypes in the early stages of a plant's life.

A central composite experimental design was used to evaluate the influence of pH, extraction time and liquid:solid ratio on the yield and uronic acid content of the pectin from ponkan peel. The response surface methodology showed that the yield is positively influenced by lower pHs, longer extraction times and higher liquid:solid ratio, whereas the uronic acid content decreases with increasing extraction time. The conditions that resulted in the highest yield and highest uronic acid content were defined as pH 1.6, extraction time of 100 min and liquid:solid ratio of 36 mL/g. The pectin obtained under these conditions (PPOP) had an experimental yield of 25.6%, below the predicted theoretical value despite the good fit of the model (R2 = 0.96) and the galacturonic acid content was 84.5%, in close agreement with the predicted theoretical value. PPOP was composed mainly of a homogalacturonan with degree of methyl esterification of 85.7% and a rhamnogalacturonan I region mainly branched by galactans. In addition, PPOP had a very low degree of acetylation (0.1%) and average molar mass of 80,650 g/mol, determined by light scattering. The results showed that ponkan peel may be used as a source of citrus pectin in the regions where this species is cultivated.

The aim of this work was to study dehydration as a way to improve the prediction of sucrose, glucose, and fructose in sugarcane juice using near-infrared (NIR) spectroscopy and partial least squares (PLS) regression models. The temperature, time, and sample volume involved in the dehydration process were optimized using design of experiments. Six different sample supports were assessed, being the thick couche paper the best support. NIR spectra from liquid (LSJ) and dehydrated sugarcane juice (DSJ) were obtained. Sucrose, glucose, and fructose in LSJ were analyzed using high-performance liquid chromatography with an evaporative light scattering detector (HPLC-ELSD). Sucrose, glucose, and fructose ranged from 99.29 to 249.27 mg/mL, 5.96–14.94 mg/mL and 3.99–16.10 mg/mL. PLS models were built using the sugars content and NIR spectra collected from a benchtop and a portable instrument. Ordered predictors selection (OPS) was applied to select the most informative variable. The results indicated better predictions for all sugars using the DSJ for both instruments, being the benchtop statistically better than the portable instrument. On the benchtop instrument, the PLS-OPS models presented root mean square error of prediction (RMSEP) respectively for sucrose, glucose, and fructose 7.98, 0.82, and 1.00 mg/mL using the DSJ against 12.75, 1.00, and 1.35 mg/mL using the LSJ. For the portable instrument, the RMSEP were respectively 15.90, 1.18, and 1.65 mg/mL using DSJ against 23.23, 1.40, and 2.08 mg/mL using LSJ. To sum up, the dehydration approach showed to be a great technique to improve the predictability of PLS-OPS models for sugarcane juice sugars using NIR spectra by removing the water and concentrating the analytes.

The management of soil nutrients is essential for sustainable agricultural production. The time requirements for soil nutrient determinations and the high cost per sample are problems that are attributed to traditional laboratory analyses that limit the adoption of precision agriculture techniques. Such problems arise because the sample density that is required to obtain soil fertility maps is greater than that required by conventional agricultural management. The use of radiometric sensors combined with a diffuse reflectance technique is quicker and less expensive than surveying soil fertility. However, the construction of robust models for the prediction of soil chemical properties based on spectral data requires samples with standardized physical characteristics. The objective of this work was to develop a model to predict the soil phosphorus (P), calcium (Ca), magnesium (Mg), and potassium (K) contents based on a multivariate analysis using spectroscopic data in the visible and near-infrared ranges. Ion-exchange resins were used to extract nutrients from the soil, and then diffuse reflectance spectra were collected. Models were constructed using partial least squares (PLS) regression, and the ordered predictors selection (OPS) algorithm was used for the selection of variables. The coefficients of determination (greater than 90%), ratios of the standard deviation to the root mean square error (higher than 2.20), and relative error percentages (lower than 25%) were obtained using the developed models. The mean values that were predicted by the models were significantly different from those measured in the laboratory only for K ions. For the other analyzed ions, including P, Ca and Mg, no significant differences were observed at the 5% level (p > 0.05). The results indicate that the PLS–OPS models based on the diffuse reflectance of ion-exchange resins are reliable for the fast and accurate prediction of these ions.

The aim of this work was to optimize the acid-catalyzed conversion of glucose into 5-hydroxymethylfurfural (HMF) in an aqueous medium using niobic acid (NbO), niobium phosphate (NbP) and a mixture of both solid acid catalysts. A simplex-centroid mixture design was applied to optimize the mixture ratio between NbO and NbP. A central composite design was applied to process optimization. The studied variables were temperature (T), time (t) and substrate to catalyst weight ratio (R S/C ). The mixture design revealed excellent glucose conversion (55%) and HMF selectivity (56%) when the weight ratio between NbO and NbP was equal to 1:1. The experimental results demonstrate that a mixture of both solid acids provides a better combination of Bronsted and Lewis acidity for effective glucose dehydration into HMF than each individual catalyst.

Combination of β-lactoglobulin (β-Lg) and lactoferrin (Lf), biomacromolecules derived from bovine whey, was used in the formation of supramolecular structures by thermal gelation technique to adjust the pH. Furthermore, the influence of the molar ratio, temperature, pH, and heating time in the formation of supramolecular structures were also studied. The characterization of the protein supramolecular structures was performed using dynamic light scattering, zeta potential measurements, molecular spectrofluorimetry, and circular dichroism spectroscopy. The thermal behavior of the pure proteins was investigated by differential scanning calorimetry. The protein denaturation temperatures were of around 85 °C for the β-Lg and around 52 °C and 85 °C (a small portion) for the Lf. The protein molar ratio of 2:1 Lf/β-Lg was used to form the structures, whose characterization showed that the best conditions of supramolecular structure formation occurred at pH 6.5 and at temperatures of 62.5 °C. In those conditions, more stable systems with reduced hydrophobic surface and average sizes between 30 and 100 nm were generated. The correlation between pH and temperature suggests that the method of preparation of the supramolecular structure affects its size during storage.

Sugarcane is one of the crops of greatest interest in the search for efficient biomass for the production of bioenergy. In addition to the production of sugar and ethanol from sugarcane juice, the industrialization process obtains the biomass, a lignocellulosic material with high energy content, which can be converted into biofuels. Recognized this importance, the objective of this study was to indicate crosses between potential sugarcane genotypes, aiming at the production of sugar and ethanol, and at the use of biomass for the production of bioelectricity or cellulosic ethanol. Ninety sugarcane genotypes of the Active Germplasm Bank of the Federal University of Vicosa were evaluated for 15 chemical and technological variables that characterize the juice and the bagasse. Crosses were defined by the genotypes which presented the best means using the simultaneous selection index, excluding the crosses between the most similar genotypes based on genetic dissimilarity verified by the analyses of diversity. Thus, it was possible to indicate 40 crosses between potential parents with high quality for production of sugar and ethanol and with efficient biomass for production of bioenergy, 19 crosses being for production of bioelectricity, 15 for production of cellulosic ethanol, and 6 for both purposes.

In this work, some physical mixtures of Nb2O5·nH2O and NbOPO4 were prepared to study the role of phosphate groups in the total acidity of samples and in two reactions involving carbohydrate biomass: hydrolysis of polyfructane and dehydration of fructose/glucose to 5-hydroxymethylfurfural (HMF). The acid and catalytic properties of the mixtures were dominated by the phosphate group enrichment. Lewis and Brønsted acid sites were detected by FT-IR experiments with pyridine adsorption/desorption under dry and wet conditions. Lewis acidity decreased with NbP in the composition, while total acidity of the samples, measured by titrations with phenylethylamine in cyclohexane (~3.5 μeq m−2) and water (~2.7 μeq m−2), maintained almost the same values. Inulin conversion took advantage of the presence of surfaces rich in Brønsted sites, and NbOPO4 showed the best hydrolysis activity with glucose/fructose formation. The catalyst with a more phosphated surface showed less deactivation during the dehydration of fructose/glucose into HMF.

A method for early quantification of unripe macaw fruits oil content using near-infrared spectroscopy (NIR) and partial least squares (PLS) is presented. After harvest, the fruit takes about 30 days to reach its maximum oil accumulation. The oil content was quantified thirty days after harvest using Soxhlet extraction. PLS models were built using NIR spectra of shell obtained five days after harvest (Shell5). The Shell5 model was compared with models built using NIR spectra of the shell (Shell30) and mesocarp thirty days after harvest (Pulp30). Ordered predictors selection was used to select the most informative variables. The best models presented root mean square error of prediction and correlation coefficient of prediction of 4.87% and 0.89 for Shell5; 5.83% and 0.85 for Shell30; 4.76% and 0.92 for Pulp30. Thus, the anticipated prediction of oil content could reduce the time and costs of macaw palm quality control and storage.

Study of the production of 5-hydroxymethylfurfural (5-HMF) from glucose, applying niobic acid as catalyst, and the quantification of 5-HMF by selective analytical methods are the goals of this work. The high-performance liquid chromatography (HPLC) was used as reference method. The developed alternative method proposes the use of ultraviolet (UV) spectroscopy technique in combination with multivariate curve resolution alternating least squares (MCR-ALS). In addition, a partial least squares (PLS) regression model was built to compare MCR-ALS and HPLC results. Regression models using MCR-ALS and PLS were built for 5-HMF in the presence of levulinic acid in the range of 2.0–16.0 mg L−1. For HPLC the range used was of 10–800 mg L−1. The models were evaluated by analyzing statistical parameters of quality such as root mean square error (RMSE) and correlation coefficient (R). The calibration parameters obtained for MCR-ALS, PLS and HPLC were, respectively: RMSEC of 0.68, 0.27 and 4.92 mg L−1 and R equal to 0.988, 0.998 and 0.999. Central composite design (CCD) was used to optimize the two variables of reaction, i.e., time and mass of catalyst for glucose conversion into 5-HMF. The samples were analyzed by UV-MCR-ALS, UV-PLS and HPLC. The predicted concentrations of 5-HMF obtained by MCR-ALS and PLS versus HPLC predictions were evaluated. Both correlations were equal to 0.995. According to paired t-test results at a significance level of 0.05 both model predictions were statistically equal to the values predicted by HPLC. The results show that UV-MCR-ALS method can predict the concentration of 5-HMF in reaction mixtures with accuracy and obtain the relative concentration and pure spectra in a mixture without chromatographic separation.

Summary The evaluation of the effects of sugars, metals, acids and other antioxidants on the in vitro antioxidant capacity of purified anthocyanin extract by different techniques was the purpose of this study. Three methods and the ways of expressing their results were evaluated: ABTSTEAC (capacity equivalent to Trolox, by ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid))), DPPHTEAC (capacity equivalent to Trolox, by DPPH (2,2-diphenyl-1-picrylhydrazyl)), DPPHEC50 (50% reduction in the radical, by DPPH), DPPH%Sca (reduction in the scavenging capacity, by DPPH), FRAPTEAC (capacity equivalent to Trolox, by FRAP (reduction power of iron)) and FRAPEC50 (50% reduction in the radical, by FRAP). The way of expressing DPPH and FRAP results as EC50 showed the greater interfering extent, mainly when the medium contained tartaric and ascorbic acids. The most coherent method was ABTSTEAC in which only ascorbic acid interfered. Ascorbic acid was shown to interfere in all methods; thus, it must be removed prior to determining the in vitro antioxidant capacity of anthocyanins in food materials.

Nanostructures from conjugates of tara gum and α-lactalbumin were obtained via the heat-gelation process with pH adjustment. The conjugates were produced by Maillard reaction using the dry-heating method in lyophilized or spray-dried mixtures of TG and α-la and were characterized by browning index (BI) and percentage of free amino groups (% FAG). Nanostructured systems were characterized by dynamic light scattering, ζ-potential, circular dichroism, and intrinsic fluorescence to evaluate the structures. The most appropriate time of conjugation was 2 days. The spray-dried and lyophilized mixtures presented different values of BI and % FAG (p

Author Summary: A new method was developed for the early selection of sugarcane genotypes using near infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and a variable selection method named ordered predictors selection (OPS). The use of the OPS method improved the predictive capacity of PLS-DA models to classify the sugarcane samples correctly according to fiber content (FC) and pol percent (PP).

This study investigated the production of furfural from sugarcane bagasse over niobic acid (Nb2O2·nH2O, called as NbO) or niobium phosphate (NbOPO4·nH2O, called as NbP) in a one-step process performed in water. The variables temperature (T), the mass of catalyst (mcat), reaction time (rt), and vibration time (Vt) for mixing SCB and catalyst were studied through a design of experiments. After 5 h at 150 °C, the furfural yields were 52.1 g kg−1 for NbO and 59.3 g kg−1 for NbP, while the yields of xylose and arabinose were, respectively, 69.7 g kg−1 and 6.4 g kg−1 for NbO and 147.4 g kg−1 and 11.1 g kg−1 when using NbP. It was proved that furfural yield is independent of the SCB genotype. The selective depolymerization and hydrolysis of hemicellulose into monomers and its further dehydration to furfural were observed. The cellulose and lignin remained readily utilizable for further processing. The better catalytic performance of NbP was attributed to the higher ratio of Bronsted to Lewis acid sites on its surface than NbO.

Paprika powder is a widely consumed spice, making it an attractive target for adulteration, which is not easily detected. In this study, a portable near-infrared (NIR) spectrometer was used for fast detection of paprika adulteration. Nine paprika samples from five suppliers were adulterated with potato starch, acacia gum and annatto at different concentrations (0–36% by weight of potato starch and acacia gum, and 0–18% by weight of annatto). The NIR spectrum of each mixture (n = 315) was used as predictors to determine adulteration by partial least squares-discriminant analysis (PLS-DA) and partial least squares regression (PLSR). First, PLS-DA was applied to discriminate between adulterated and non-adulterated samples, as well as the type of adulterant. This method proved to be efficient, with specificity greater than 90 % and error rate lower than 2 %, for all models constructed. PLSR was used to predict the concentration of adulterants in paprika samples. In addition, PLSR models with reduced number of wavelengths (predictors) were built by selecting the variables with larger weights on the regression coefficients. Coefficient of prediction (R2p) and root mean square errors of prediction (RMSEP) obtained were 0.95 and 2.12; 0.97 and 1.68; 0.87 and 1.74, for potato starch, acacia gum and annatto, respectively. In conclusion, results showed that NIR spectroscopy is a useful screening technique for identification and quantification of adulteration in paprika.

This study aimed to estimate the parameters related to the genetic control of the physical characteristics of the fruits, oil content, and oil yield, and to proceed with the selection of macaw palm accessions. Forty-four macaw palm accessions of the active germplasm collection of the Federal University of Vicosa were collected for the evaluation of epicarp dry matter, pulp dry matter, endocarp dry matter, kernel dry matter, oil content, and oil yield. Narrow-sense individual heritability estimates were considered as of intermediate magnitude. The coefficient of repeatability and the accuracy in family selection was of high magnitude. Oil yield per plant presented the highest coefficient of individual genetic variation. Five different accessions contributed to the ten first individuals selected by the individual BLUP. The Mulamba and Mock’s rank index allowed classifying the accessions in a sequence favorable to selection.

A progeny test of half-siblings of Eucalyptus benthamii was analyzed using wood quality traits combined with volumetric information. The test was designed in completely randomized blocks design with a total of 1394 trees planted in the city of Encruzilhada do Sul, Brazil. At six years of age, all the trees were measured and sawing samples were collected from 87 trees, which were processed and read through the NIR. Regression models were calibrated by the partial least squares (PLS) method to correlate the NIR spectra with wet chemical measurements, allowing the assembly of models to estimate values of basic density and pulp yield for all the population. From the values of basic density and pulp yield, obtained by the models, and average annual increment of wood (MAI), the average annual increment of cellulose (MAIpulp) was calculated for each individual in the test. Two types of optimizations were tested: balanced and unbalanced. The balanced optimization consisted of selecting for MAIpulp, on average, 9 individuals in the 40 best families. The unbalanced optimization was performed through the simulation of 72 scenarios. The effect size (Ne), inbreeding rate (F), and accumulated gain were calculated for each scenario. The selection prioritized the best families and the top ranked individuals within each family. Results revealed that the individual heritability in the restricted sense (h²a) for density (0.331) and pulp yield (0.322) were classified as moderate magnitude. For MAIpulp, the h²a was considered high (0.514), which suggested the presence of genetic control and the possibility of obtain gains through selection. The accuracy for all the traits was higher than 77.3%. In addition, the NIR prediction correlation coefficient presented values above 85% in PLS-OPS for basic density and pulp yield, indicating a high predictive potential of technology for selection of E. benthamii genotypes. In the balanced scenario, 369 individuals were selected with a Ne of 119 and a genetic gain of 36%. In the unbalanced scenario, the scenario 53 was selected, which demonstrated the maintenance of 100 individuals and 36 families is necessary to obtain a Ne equal to 54 and a gain of 64.21% relative to the original population. The estimated genetic parameters indicated favorable conditions for selection. The results suggested the necessity to improve the process through the use of NIR technology. For this process, future research may need to adopt models specific to breeding regions and make alterations in the calibration model for each species. The unbalanced optimization was more efficient than the balanced. Using the unbalanced procedure, researchers may be able to accomplish considerable genetic gains with less individuals in the population while maintaining the same rate of genetic variability.

Near-infrared (NIR) spectroscopy and chemometric methods were used to predict the chemical properties of decomposing eucalyptus harvest residues to better understand the decomposition process of these materials. Leaves, twigs, branches, and bark from a decomposition experimental set up in commercial plantations were sampled for one year. The contents of carbon (C), nitrogen (N), extractives (EX), acid-soluble lignin (SL), Klason insoluble lignin (KL) and holocellulose (HC) were determined by the reference method in the collected samples. Principal component analysis (PCA) was employed to distinguish the types of harvest residues throughout the decomposition period. Multi-residue regression models were built from the NIR spectra using partial least squares regression (PLS). Two feature selection methods, i.e., ordered predictors selection (OPS) and genetic algorithm (GA), were applied and compared. The OPS and GA did not differ statistically; however, compared with the GA, OPS was more computationally efficient and selected fewer variables. Using the PLS-OPS models, the root mean square errors of prediction (RMSEP) for C, N, EX, SL, KL and HC were 19.70, 0.08, 0.74, 0.39, 28.13 and 33.99, respectively, and the prediction correlations (Rp) for these properties were 0.94, 0.99, 0.99, 0.99, 0.96 and 0.98, respectively. PLS-discriminant analysis (PLS-DA) was used to classify the samples over the decomposition time and provided a good separation. Some mismatches obtained in the modeled classes were explained by the differences in the decomposition rate and changes in the chemical composition of the different harvest residue components that were evaluated. The results showed the feasibility of NIR spectroscopy and chemometric methods to evaluate the chemistry of decomposing eucalyptus harvest residues, indicating that these methods can be used as rapid and inexpensive alternatives to conventional methods to help understand the decomposition process.

The global demand and production of primary energy is expected to grow at a rate of 1.4 % per year by 2035. In the same period, the production of renewable energy is expected to grow at a rate of 6.6 % per year, rising from 3 % to 9 % of world primary energy. Therefore, the biofuels supply assumes a strategic role for world energy security. In this context, sorghum is a promising alternative in the bioenergetic sector, due to its agronomic characteristics and adaptability to limiting edaphoclimatic conditions. This study aimed at evaluating the productive potential of three sorghum agronomic groups (biomass, saccharin and forage), as well as chemically characterizing the biomass of these materials. For that, a field experiment was carried out using a randomized block design, where agronomic and chemical characteristics (cellulose, hemicellulose, lignin and ash), as well as moisture contents, were evaluated. The biomass sorghum cultivars presented a high yield (about 30 t ha-1 of dry stalk), being, for this reason, more suitable for the generation of solid biofuels, i.e., direct burning. On the other hand, the forage sorghum cultivars presented a lower lignin content in the stalks, in relation to the other cultivars, being indicated for the generation of liquid biofuels. It is also worth mentioning the possibility of producing second-generation bioethanol from saccharin sorghum bagasse. Therefore, sorghum presents different use potentials that may be exploited by the bioenergy sector according to the agronomic group and plant physical part. RESUMO A demanda e produção mundial de energia primária deverá crescer à taxa de 1,4 % ao ano, até 2035. No mesmo período, a produção de energias renováveis deverá crescer à taxa de 6,6 % ao ano, passando de 3 % para 9 % de energia primária mundial. Logo, a oferta de biocombustíveis assume papel estratégico para a segurança energética mundial. Neste contexto, o sorgo é uma alternativa promissora no setor bioenergético, devido às suas características agronômicas e adaptabilidade às condições edafoclimáticas limitantes. Objetivou-se avaliar o potencial produtivo de três grupos agronômicos de sorgo (biomassa, sacarino e forrageiro), bem como caracterizar quimicamente a biomassa desses materiais. Para isso, conduziu-se um experimento utilizando-se delineamento em blocos casualizados, no qual foram realizadas avaliações agronômicas, químicas (celulose, hemicelulose, lignina e cinzas) e teores de umidade. As cultivares de sorgo biomassa apresentaram alta produtividade (cerca de 30 t ha-1 de colmo seco), sendo, por isso, mais indicadas para a geração de biocombustíveis sólidos, isto é, queima direta. Já as cultivares de sorgo forrageiro apresentaram menor teor de lignina nos colmos, em relação às demais cultivares, sendo indicadas para a geração de biocombustíveis líquidos. Destaca-se, ainda, a possibilidade de produção de bioetanol de segunda geração a partir do bagaço de sorgo sacarino. Logo, o sorgo apresenta diferentes potenciais de uso que podem ser explorados pelo setor de bioenergia de acordo com o grupo agronômico e a parte física da planta.

The objective of this study was to select a population of sugarcane clones to develop energy cane cultivars. The genotypic means of 124 clones were estimated for the yield-related traits: mean number of stalks per plant (NS), mean stalk weight (MSW), fiber content (FIB), lignin content (LIG), and sucrose content in cane juice (SPC). From these data, the traits tons of cane per hectare (TCH), tons of fiber per hectare (TFH) and tons of lignin per hectare (TLH) were estimated. The clone effects were statistically significant (p

O desenvolvimento de um algoritmo baseado na decomposição dos valores singulares com visualização gráfica para auxiliar na detecção e no diagnóstico de falhas foi o objetivo deste trabalho. Para testar o algoritmo foram utilizados dados de temperatura reais de um forno industrial de uma unidade de processamento de gás natural. Nove detectores de temperatura foram monitorados no tempo. Além de uma falha real que gerou a parada do equipamento, foram também testadas cinco falhas simuladas nos detectores do forno. Os resultados mostraram que o algoritmo foi eficaz e possui grande potencial para auxiliar no monitoramento de processos industriais. The development of an algorithm based on singular value decomposition with graphic visualization to aid in the fault detection and diagnosis was the aim of this work. In order to test the algorithm, real temperature data from an industrial furnace of a natural gas processing unit were used. Nine temperature detectors were monitored over time. In addition to a real fault that caused the shutdown of the equipment, five simulated faults in the furnace detectors were also tested. Results showed that the algorithm was effective and has great potential to assist in the monitoring of industrial processes.

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000–4000 cm−1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( RP), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.

a b s t r a c t This work aimed to develop edible films based on pectin and acao ´. Films were incorporated with apple skin polyphenols (ASP) and thyme essential oil (TEO) according to the central composite design. Antimicrobial optimal formulation was determined using the response surface methodology and desirability function approach for multi-response optimization. Physical-mechanical properties, including mechanical resistance, thermal stability, wa- ter vapor permeability (WVP), color and film microstructure, were evaluated. Film anti- microbial activity was evaluated against Listeria monocytogenes. ASP improved film mechanical resistance, while TEO had a plasticizing effect. The thermal stability was improved by ASP and film microstructure resulted in smoother surface. Antimicrobials had no effect on WVP. Films presented tendency to redness and yellowness caused by both antimicrobials. Films had antimicrobial activity, with TEO being stronger than ASP against L. monocytogenes. Considering significant film properties, optimal formulation was 6.07% ASP and 3.1% TEO. Therefore, optimal acao ´-pectin films have potential food preser- vation application.

Neste trabalho, foram estudados os perfis cromatográficos dos compostos voláteis de 21 amostras de cafés comerciais brasileiros submetidos a diferentes processos industriais, i.e., descafeinados e com diferentes graus de torra (médio e extraforte). Os voláteis foram coletados por microextração em fase sólida (HS-SPME) e analisados por GC-FID e GC-MS. As matrizes de dados cromatográficos (perfis) obtidas, a partir dessas análises, foram exploradas por análise de componentes principais (PCA) e análise discriminante por quadrados mínimos parciais (PLS-DA). Inicialmente, os dados cromatográficos foram alinhados utilizando-se o algorítimo correlation optimized warping (COW). A PCA demonstrou a diferenciação dos cafés descafeinados em relação às outras classes de café, com ambas as fibras de SPME utilizadas. Essa separação, provavelmente, ocorreu em razão do desaparecimento de algumas espécies precursoras de voláteis durante o processo de descafeinação, tal como sacarose. Os modelos PLS-DA, para ambas as fibras testadas, PDMS/DVB e CX/PDMS, classificaram corretamente 100% das amostras, de acordo com o grau de torra (médio e extraforte); a principal diferença entre as classes foi a diferença de concentração de alguns compostos voláteis, tais como 2-metilfurano, 2-metilbutanal, 2,3-pentanodiona, pirazina, 2-pirrol carboxaldeído, furfural e 2-furan metanol. The fingerprints of the volatile compounds of 21 commercial Brazilian coffee samples submitted to different industrial processing i.e. decaffeinated or different roasting degrees (traditional and dark) were studied. The volatiles were collected by headspace solid phase microextraction (HS-SPME) and analyzed by GC-FID and GC-MS. The chromatographic data matrices (fingerprints) obtained were explored by the principal component analysis (PCA) and partial least squares – discriminative analysis (PLS-DA). Initially the chromatographic profiles were aligned by the algorithm correlation optimized warping (COW). The PCA showed the discrimination of the decaffeinated coffees from the others with both the SPME fibres used. This separation probably occurred due to the loss of some volatile precursors during the decaffeination process, such as sucrose. For both the fibres tested, PDMS/DVB and CX / PDMS SPME, the PLS-DA models correctly classified 100% of the samples according to their roasting degree: (medium and dark), the main differences being the concentrations of some of the volatile compounds such as 2-methyl furan, 2-methylbutanal, 2,3-pentanedione, pyrazine, 2-carboxyaldehyde pyrrole, furfural and 2-furanmethanol.

The present study aimed to conduct a simultaneous optimization of different carrier agents and temperatures for the production of jaboticaba extracts by spray-drying microencapsulation. The 30% maltodextrin (control), 25% arabic gum + 5% maltodextrin and 25% Capsul™ + 5% maltodextrin carriers were used at air-drying inlet temperatures of 140, 160 and 180°C. The following responses were evaluated: anthocyanin retention (AR), moisture content (MC), total solids (TS), hygroscopicity (H), overall color difference and antioxidant activity. Scanning electron microscopy (SEM) analysis was also performed. The AR, MC, H and overall color difference parameters were selected for simultaneous optimization by the desirability approach. The results showed that the highest desirability (0.7–0.8) was achieved when 30% maltodextrin was used at 180°C. According to the SEM analysis, the use of maltodextrin and gum arabic allowed for the formation of more homogeneous particles, which is recommended in spray-drying microencapsulation.

The pH effect of potato, apple, and soil matrices on the chromatographic response of nine pesticides was evaluated. All chromatographic analyses were performed in duplicate on a gas chromatograph with electron capture detection. The matrix effect observed in the chromatographic response of the pesticides was evaluated by comparison. We compared the chromatographic response of each pesticide in pure solvent and in organic extract obtained for the matrices. The organic extracts were obtained by solid–liquid extraction with partition at low temperature. Depending on the matrix pH, a greater or lesser amount of co-extractives can be extracted into the organic phase, which affects the matrix effect. The pH of the samples before the extraction process was modified in order to check their influence on pesticide responses. Statistical analyses involving principal component analysis and marginal means revealed that, in the potato and apple matrices, the co-extractives exerted positive effects on the chromatographic response of the analytes. At lower pH, the extraction of co-extractives from potato and apple was favored, thus increasing the matrix effect for these samples.

The components of seven matrices in the analysis of 11 pesticides by GC-ECD were analysed. The matrix effect was calculated based on the changing of chromatographic response of the analyte in the presence of co-extractives of the matrices in the organic phase obtained by solid–liquid and liquid–liquid extraction with partition at low temperature (ESL-PBT and ELL-PBT), in relation to the response of it in the pure solvent. It was used the Principal Component Analysis (PCA) in evaluating the results obtained for the percentages of the matrix effect. The tomato, grape and pineapple matrices caused greater matrix effect and were grouped. The other matrices such as apple, water and potato caused small matrix effect. For most pesticides the soil matrix caused negative matrix effect. The influence of pH of the samples on the matrix effect was also evaluated showing not to have a direct effect on the phenomenon.

A method for estimation of sugarcane (Saccharum spp.) biomass crystallinity using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as an alternative to the standard method using X-ray diffractometry (XRD) is proposed. Crystallinity was obtained using XRD from sugarcane bagasse. NIR spectra were obtained of the same material. PLS models were built using the NIR and crystallinity values. Cellulose crystallinity ranged from 50 to 81%. Two variable selection algorithms were applied to improve the predictive ability of models, i.e. (a) Ordered Predictors Selection (OPS) and (b) Genetic Algorithm. The best model, obtained with the OPS algorithm, presented values of correlation coefficient of prediction, root mean squared error of prediction and ratio of performance deviation equals to 0.92, 3.01 and 1.71, respectively. A scatter matrix among lignin, α-cellulose, hemicellulose, ash and crystallinity was built that showed that there was no correlation among these properties for the samples studied.

Electrodegradation of atrazine in water was performed using homemade (PA and PB) and purchased (PC) boron-doped diamond anodes. The degradation was monitored off-line by analyzing total organic carbon and high performance liquid chromatography with diode array detector (HPLC-DAD) and at-line by UV spectroscopy. The spectra were recorded every 2 min. The rank deficiency problem was resolved by assembling an augmented column-wise matrix. HPLC was employed to separate the original and byproducts degradation components. Aiming the same goal, multivariate curve resolution - alternating least squares (MCR-ALS) was applied to resolve the UV spectroscopic data. Comparison between HPLC and MCR-ALS separations is presented. By using MCR-ALS the spectra of atrazine and two byproducts were successfully resolved and the resulted concentration profiles properly represented the system studied. The ALS explained variance (R2) for PA, PB and PC was equal to 99.99% for all of them and the lack of fit for PA, PB and PC were 0.39, 0.34 and 0.54 respectively. The correlation (R) between the recovered and pure spectra were calculate for each electrodegradation, validating the MCR-ALS results. The average R was equal to 0.997. The spectral and concentration profiles described with this new approach are in agreement with HPLC-DAD results. The proposed method is an alternative to classical analyses for monitoring of the degradation process, mainly due to the simplicity, fast results and economy.

Thedepositionofwell-adherentdiamondfilmsontotheoutersurfaceofcylindricalsubstrateswasperformed.Severaldiametersizesanddifferentsubstratesmaterialswerestudied.Diamondfilmsadherencewasimprovedbyincreasingthesubstratesurfaceroughnessandbypyrolisingcastoroilorpolyanilinepolymeronsubstratesurfacetoacceleratetheformationofdiamondinitialnuclei.Profilometryand scanning electron microscopy were used respectively to measure roughness of surface and to characterize the morphologies ofthe diamond films. The residual stress in the diamond films was characterized by Raman spectroscopy with depth series analysis.Voltammetric analysis using c-BDD electrodes were performed in acid media, revealing high quality diamond behavior.© 2012 The Electrochemical Society. [DOI: 10.1149/2.001205jss] All rights reserved.Manuscript submitted June 12, 2012; revised manuscript received July 25, 2012. Published August 29, 2012.

Magnetic semiconductors are promising materials for electronic applications, because it is possible to control the quantum state of the electron spin (up or down), providing almost total spin polarization. We present boron-doped nanocrystalline diamond samples with magnetic properties prepared by chemical vapor deposition process and characterized using X-ray photoelectron spectroscopy, energy dispersive X-ray spectroscopy, Raman spectroscopy, scanning electron microscopy and magnetization assays. We believe that ferromagnetic elements were incorporated into the diamond film by diffusion from stainless steel substrates, during film growth.

In order to establish the parameters to obtain aqueous mate extract with the highest content of total phenolic compounds (TPC), the response surface methodology (RSM) was used. The extract obtained at 100 °C, within 3 min and with pH of 6.0 was submitted to nanofiltration to concentrate the phenolic compounds of mate (Ilex paraguariensis A. St. Hill). The total phenolic content values in the permeate and in the concentrate collected in different volumetric reduction factors (VRF) were different from those detected in the feed extract. The concentration of phenolic compounds increased when VRF was increased, reaching the highest retention index (R) of 0.99 when VRF was 4.0. The permeate and the concentrate obtained in VRF 4.0 were verified through HPLC. 4,5-dicaffeoylquinic acid and gallic acid showed retention index of 1.00 and 0.95, respectively, while 3,4-dihydroxybenzoic acid and chlorogenic acid reached 0.99 and 0.98, respectively.

It is well known that no single experimental condition can be found under which the extraction of all the volatile compounds in a gas chromatographic analysis of roasted coffee beans by headspace-solid phase microextraction (HS-SPME) is maximized. This is due to the large number of peaks recorded. In this work, the scores vector of the first principal component obtained from PCA on chromatographic peak areas was used as the response to find the optimal conditions for simultaneous optimization of coffee volatiles extraction via response surface methodology (RSM). This strategy consists in compressing several highly correlated peak areas into a single response variable for a central composite design (CCD). RSM was used to identify an optimal factor combination that reflects a compromise between the partially conflicting behavior of the volatiles groups. This simultaneous optimization approach was compared with the desirability function method. The versatility of the PCA–RSM methodology allows it to be used in other chromatographic applications, resulting in an interpretable procedure to solve new analytical problems.

Lactic beverages have been used as an important vehicle for probiotics, and the utilization of cheese whey and oligofructose would contribute even more to the functional properties of this product. However, because of the short lifespan of probiotics, studies have been carried out aiming at the evaluation of the contribution of the prebiotics in the improvement of the viability of these microorganisms. The technological properties (fermentation time, acidity and syneresis index) and the population of probiotic bacteria in fermented lactic beverages manufactured with different content of cheese whey and oligofructose were evaluated. The results showed that oligofructose, at the concentrations evaluated in this study, did not show any significant influence on the response variables, whereas the content of cheese whey only influenced the syneresis index of lactic beverages.

Summary Pectin was extracted from passion fruit peel using three different acids (citric, hydrochloric or nitric) at different temperatures (40–90 °C), pH (1.2–2.6) and extraction times (10–90 min), with and without skins using a 24 factorial design. Temperature, pH and extraction time had highly significant effects on the pectin yield. A central composite design with face centring was used to optimise the extraction process conditions for citric acid without skins. Pectin yields varied from 10% to 70%. The optimal conditions for maximisation of pectin yield were the use of citric acid at 80 °C and pH 1 with an extraction time of 10 min considering model extrapolation.

A new procedure with high ability to enhance prediction of multivariate calibration models with a small number of interpretable variables is presented. The core of this methodology is to sort the variables from an informative vector, followed by a systematic investigation of PLS regression models with the aim of finding the most relevant set of variables by comparing the cross-validation parameters of the models obtained. In this work, seven main informative vectors i.e. regression vector, correlation vector, residual vector, variable influence on projection (VIP), net analyte signal (NAS), covariance procedures vector (CovProc), signal-to-noise ratios vector (StN) and their combinations were automated and tested with the main purpose of feature selection. Six data sets from different sources were employed to validate this methodology. They originated from: near-Infrared (NIR) spectroscopy, Raman spectroscopy, gas chromatography (GC), fluorescence spectroscopy, quantitative structure-activity relationships (QSAR) and computer simulation. The results indicate that all vectors and their combinations were able to enhance prediction capability with respect to the full data sets. However, regression and NAS informative vectors from partial least squares (PLS) regression,bothbuilt usingmorelatent variablesthanwhen building themodelpresented inmost of testeddata sets, were the best informative vectors for variable selection. In all the applications, the selected variables were quite effective and useful for interpretation. Copyright 2008 John Wiley & Sons, Ltd.

The electrochemical properties of boron-doped diamond (BDD) polycrystalline films grown on tungsten wire substrates using ethanol as a precursor are described. The results obtained show that the use of ethanol improves the electrochemistry properties of "as-grown" BDD, as it minimizes the graphitic phase upon the surface of BDD, during the growth process. The BDD electrodes were characterized by Raman spectroscopy, scanning electronic microscopy, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The boron-doping levels of the films were estimated to be ∼10 20 B/cm 3 . The electro- chemical behavior was evaluated using the Fe CN

Cassava starches are widely used in the production of biodegradable films, but their resistance to hu- midity migration is very low. In this work, commercial cassava starch films were studied and classified according to their physicochemical properties. A nondestructive method for water vapor permeability determination, which com- bines with infrared spectroscopy and multivariate calibration, is also presented. The following commercial cassava starches were studied: pregelatinized (amidomax 3550), carboxymethylated starch (CMA) of low and high viscosi- ties, and esterified starches. To make the films, 2 different starch concentrations were evaluated, consisting of water suspensions with 3% and 5% starch. The filmogenic solutions were dried and characterized for their thickness, gram- mage, water vapor permeability, water activity, tensile strength (deformation force), water solubility, and puncture strength (deformation). The minimum thicknesses were 0.5 to 0.6 mm in pregelatinized starch films. The results were treated by means of the following chemometric methods: principal component analysis (PCA) and partial least squares (PLS) regression. PCA analysis on the physicochemical properties of the films showed that the differences in concentration of the dried material (3% and 5% starch) and also in the type of starch modification were mainly related to the following properties: permeability, solubility, and thickness. IR spectra collected in the region of 4000 to 600 cm −1 were used to build a PLS model with good predictive power for water vapor permeability determination, with mean relative errors of 10.0% for cross-validation and 7.8% for the prediction set.

This work describes, through examples, a simple way to carry out experimental design calculations applying an spreadsheets. The aim of this tutorial is to introduce an alternative to sophisticated commercial programs that normally are too complex in data input and output. An overview of the principal methods is also briefly presented. The spreadsheets are suitable to handle different types of computations such as screening procedures applying factorial design and the optimization procedure based on response surface methodology. Furthermore, the spreadsheets are sufficiently versatile to be adapted to specific experimental designs.