Predicting oil content in ripe Macaw fruits (Acrocomia aculeata) from unripe ones by near infrared spectroscopy and PLS regression.

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.

Comprehensive new approaches for variable selection using ordered predictors selection.

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.

Development and optimization of pH-responsive PLGA-chitosan nanoparticles for triggered release of antimicrobials.

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 ±â€¯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.

Early prediction of sugarcane genotypes susceptible and resistant to Diatraea saccharalis using spectroscopies and classification techniques.

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.

Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods.

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.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression.

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.

Prediction of Lignin Content in Different Parts of Sugarcane Using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS).

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.

Estimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods.

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.

Physical-mechanical and antimicrobial properties of nanocomposite films with pediocin and ZnO nanoparticles.

This work aimed to develop nanocomposite films of methyl cellulose (MC) incorporated with pediocin and zinc oxide nanoparticles (nanoZnO) using the central composite design and response surface methodology. This study evaluated film physical-mechanical properties, including crystallography by X-ray diffraction, mechanical resistance, swelling and color properties, microscopy characterization, thermal stability, as well as antimicrobial activity against Staphylococcus aureus and Listeria monocytogenes. NanoZnO and pediocin affected the crystallinity of MC. Load at break and tensile strength at break did not differ among films. NanoZnO and pediocin significantly affected the elongation at break. Pediocin produced yellowish films, but nano ZnO balanced this effect, resulting in a whitish coloration. Nano ZnO exhibited good intercalation in MC and the addition of pediocin in high concentrations resulted crater-like pits in the film surfaces. Swelling of films diminished significantly compared to control. Higher concentrations of Nano ZnO resulted in enhanced thermal stability. Nanocomposite films presented antimicrobial activity against tested microorganisms.

Correlation of quantitative sensorial descriptors and chromatographic signals of beer using multivariate calibration strategies.

In this study, two important sensorial parameters of beer quality - bitterness and grain taste - were correlated with data obtained after headspace solid phase microextraction - gas chromatography with mass spectrometric detection (HS-SPME-GC-MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME-GC-MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively; while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists.

Optimization of extraction of high-ester pectin from passion fruit peel (Passiflora edulis flavicarpa) with citric acid by using response surface methodology.

A central composite design was employed to optimize the extraction of pectin with citric acid. The independent variables were citric acid concentration (0.086-2.91% w/v) and extraction time (17-102 min). The combined effect of these variables on the degree of esterification was investigated. Results have shown that the generated regression models adequately explained the data variation and significantly represented the actual relationship between the independent variables and the responses. Besides that, the citric acid concentration was the most important factor to affect the degree of esterification, as it exerted a significant influence on the dependent variable. Lower citric acid concentration increased the pectin degree of esterification. The surface response showed the relationships between the independent variables, and thus responses were generated. Through this surface, the satisfactory condition of 0.086% w/v citric acid for 60 min was established for extraction of high-ester yellow passion fruit pectin.

A study of physicochemical and biopharmaceutical properties of amoxicillin tablets using full factorial design and PCA biplot.

The variables that influence the tablets obtained by direct compression method deserve to be studied to minimize formulations costs and to improve the physicochemical and biopharmaceutical properties of the compacts. Here, we explore the adjuvants effects on amoxicillin tablet formulations considering multiple responses, and indicate the most suitable formulation composition. A 2(3) full factorial design was built to different amoxicillin formulations, each one containing three replicate batches, and eight responses (physicochemical and biopharmaceutical parameters) were obtained. The microcrystalline cellulose (MCC) type Avicel PH-102 (low) or PH-200 (high), the absence (low) or presence (high) of spray-dried lactose (LAC), and the absence (low) or presence (high) of disintegrant (DIS) were the levels investigated. The more relevant responses to the distinct formulations from the experimental design were hardness, friability, and the amount of amoxicillin dissolved during the first hour. PCA biplot indicated high values of amount of drug dissolved in 60 min as advantageous responses for the investigated amoxicillin tablet formulations and high values of friability as not desirable. Considering the individual response evaluation, the most suitable amoxicillin tablet formulation should present in its composition the MCC type Avicel PH-102 (low level) and the superdisintegrant agent (DIS high level), croscarmellose sodium, but no LAC (low level).