Color approach to the analysis of white crystal cane sugar for the detection of solid impurities.

BACKGROUND: Sugar is consumed worldwide and so the quality control of sugar cane is necessary. Solid impurities are an inherent part of industrial sugar processing. Dark particles and adulteration with sand must be controlled. Sixty-four samples of white crystal cane sugar analysis in the presence of both kinds of impurities (dark particles and sand) were assessed using an affordable digital image system and a multivariate calibration strategy. RESULTS: The quality parameters for the multivariate calibration models obtained to estimate sugar content were remarkable. Color descriptors from digital images allowed identification of different levels of sugar content for the following three ranges: 0-49.99 wt%, 50.03-78.99 wt%, and 82.99-100 wt%. The multivariate model using red (R), green (G), Blue (B), and luminosity (L) color descriptors showed low standard errors of cross-validation (SECV) and validation (SEV) of 7.63 and 6.01 wt%, respectively. CONCLUSIONS: The method is affordable and reliable, and might aid quick screening in situations where access to a laboratory or instrumentation is restricted. © 2021 Society of Chemical Industry.

Biofortification quality in bananas monitored by energy-dispersive X-ray fluorescence and chemometrics.

Biofortification is a nutritional strategy used to enhance nutrients in a variety of staple foods. As bananas and plantains (Musa spp.) are considered staple food in many developing countries, monitoring zinc (Zn) content in biofortified bananas is crucial to ensure this mineral intake. Bananas were biofortified by injecting Zn sulfate heptahydrate (ZnSO4·7H2O) solutions into banana trees' pseudostem (1%, 2%, and 4%) compared with the control treatment. Zinc content was estimated using energy-dispersive X-ray fluorescence (EDXRF) and multivariate calibration using partial least squares (PLS). The impressive result is the possibility of high throughput analysis of Zn in bananas after biofortification to guarantee the quality when eaten as a central portion of the diet.

Infraspecific Chemical Variability and Biological Activity of Casearia sylvestris from Different Brazilian Biomes.

Casearia sylvestris is an outstanding representative of the Casearia genus. This representability comes from its distinctive chemical profile and pharmacological properties. This species is widespread from North to South America, occurring in all Brazilian biomes. Based on their morphology, 2 varieties are recognized: C. sylvestris var. sylvestris and C. sylvestris var. lingua. Despite the existence of data about their chemical composition, a deeper understanding of the specialized metabolism correlation and variation in respect to environmental factors and its repercussion over their biological activities was still pending. In this study, an UHPLC-DAD-based metabolomics approach was employed for the investigation of the chemical variation of 12 C. sylvestris populations sampled across 4 Brazilian biomes and ecotones. The correlation between infraspecific chemical variability and the cytotoxic and antioxidant activities was achieved by multivariate data analysis. The analyses showed that C. sylvestris var. lingua prevailed at Cerrado areas, and it was correlated with lower cytotoxic activity and high level of glycosylated flavonoids. Among them, narcissin and isorhamnetin-3-O-α-L-rhamnopyranosyl-(1 → 2)-α-L-arabinopyranoside showed good correlation with the antioxidant activity. Conversely, C. sylvestris var. sylvestris prevailed at the Atlantic Forest areas, and it was associated with high cytotoxic activity and high content of clerodane diterpenoids. Different casearins showed good correlation (R2 = 0.3 - 0.70) with the cytotoxic activity. These findings highlighted the great complexity among different C. sylvestris populations, their chemical profile, and the related biological activities. Consequently, it can certainly influence the medicinal properties, as well as the quality and efficacy, of C. sylvestris phytomedicines.

Calibration strategies for the direct determination of Ca, K, and Mg in commercial samples of powdered milk and solid dietary supplements using laser-induced breakdown spectroscopy (LIBS).

This study describes the application of laser-induced breakdown spectroscopy (LIBS) for the direct determination of Ca, K and Mg in powdered milk and solid dietary supplements. The following two calibration strategies were applied: (i) use of the samples to calculate calibration models (milk) and (ii) use of sample mixtures (supplements) to obtain a calibration curve. In both cases, reference values obtained from inductively coupled plasma optical emission spectroscopy (ICP OES) after acid digestion were used. The emission line selection from LIBS spectra was accomplished by analysing the regression coefficients of partial least squares (PLS) regression models, and wavelengths of 534.947, 766.490 and 285.213nm were chosen for Ca, K and Mg, respectively. In the case of the determination of Ca in supplements, it was necessary to perform a dilution (10-fold) of the standards and samples to minimize matrix interference. The average accuracy for powdered milk ranged from 60% to 168% for Ca, 77% to 152% for K and 76% to 131% for Mg. In the case of dietary supplements, standard error of prediction (SEP) varied from 295 (Mg) to 3782mgkg-1 (Ca). The proposed method presented an analytical frequency of around 60 samples per hour and the step of sample manipulation was drastically reduced, with no generation of toxic chemical residues.

Time-Domain Nuclear Magnetic Resonance (TD-NMR) and Chemometrics for Determination of Fat Content in Commercial Products of Milk Powder.

This study shows the use of time-domain (TD)-NMR transverse relaxation (T2) data and chemometrics in the nondestructive determination of fat content for powdered food samples such as commercial dried milk products. Most proposed NMR spectroscopy methods for measuring fat content correlate free induction decay or echo intensities with the sample's mass. The need for the sample's mass limits the analytical frequency of NMR determination, because weighing the samples is an additional step in this procedure. Therefore, the method proposed here is based on a multivariate model of T2 decay, measured with Carr-Purcell-Meiboom-Gill pulse sequence and reference values of fat content. The TD-NMR spectroscopy method shows high correlation (r = 0.95) with the lipid content, determined by the standard extraction method of Bligh and Dyer. For comparison, fat content determination was also performed using a multivariate model with near-IR (NIR) spectroscopy, which is also a nondestructive method. The advantages of the proposed TD-NMR method are that it (1) minimizes toxic residue generation, (2) performs measurements with high analytical frequency (a few seconds per analysis), and (3) does not require sample preparation (such as pelleting, needed for NIR spectroscopy analyses) or weighing the samples.

Prediction of beef color using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate analyses.

Time-domain nuclear magnetic resonance and chemometrics were used to predict color parameters, such as lightness (L*), redness (a*), and yellowness (b*) of beef (Longissimus dorsi muscle) samples. Analyzing the relaxation decays with multivariate models performed with partial least-squares regression, color quality parameters were predicted. The partial least-squares models showed low errors independent of the sample size, indicating the potentiality of the method. Minced procedure and weighing were not necessary to improve the predictive performance of the models. The reduction of transverse relaxation time (T2 ) measured by Carr-Purcell-Meiboom-Gill pulse sequence in darker beef in comparison with lighter ones can be explained by the lower relaxivity Fe2+ present in deoxymyoglobin and oxymyoglobin (red beef) to the higher relaxivity of Fe3+ present in metmyoglobin (brown beef). These results point that time-domain nuclear magnetic resonance spectroscopy can become a useful tool for quality assessment of beef cattle on bulk of the sample and through-packages, because this technique is also widely applied to measure sensorial parameters, such as flavor, juiciness and tenderness, and physicochemical parameters, cooking loss, fat and moisture content, and instrumental tenderness using Warner Bratzler shear force. Copyright © 2016 John Wiley & Sons, Ltd.

Development of a comprehensive method for analyzing clerodane-type diterpenes and phenolic compounds from Casearia sylvestris Swartz (Salicaceae) based on ultra high performance liquid chromatography combined with chemometric tools.

This work describes the development and optimization of an analytical method utilizing liquid chromatography and chemometrics to evaluate and differentiate two varieties of Casearia sylvestris Swartz (Salicaceae) from São Paulo State (Brazil) based on their secondary metabolite profiles. Previously, analytical studies only concerned the analysis of clerodane-type diterpenes. Therefore, considering the importance of including phenolic compounds in such analysis, we used design of experiments to simultaneously extract and detect the largest number of compounds from both chemical classes. This new strategy allowed a comprehensive chromatographic analysis of C. sylvestris, and the results for the two varieties exhibited an interesting distribution according to their original ecosystems, suggesting a strong correlation to the main metabolites found in each species group. Besides their inherent morphological differences, C. sylvestris variety lingua, mainly found in Cerrado areas, predominantly contains phenolic compounds, while C. sylvestris variety sylvestris, mainly found in Atlantic Forest areas, contains mostly clerodane-type diterpenes. Finally, it was also possible to observe differences in the secondary metabolite composition within each group depending on the place where samples were collected.

Fast determination of beef quality parameters with time-domain nuclear magnetic resonance spectroscopy and chemometrics.

The noteworthy of this study is to predict seven quality parameters for beef samples using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate models. Samples from 61 Bonsmara heifers were separated into five groups based on genetic (breeding composition) and feed system (grain and grass feed). Seven sample parameters were analyzed by reference methods; among them, three sensorial parameters, flavor, juiciness and tenderness and four physicochemical parameters, cooking loss, fat and moisture content and instrumental tenderness using Warner Bratzler shear force (WBSF). The raw beef samples of the same animals were analyzed by TD-NMR relaxometry using Carr-Purcell-Meiboom-Gill (CPMG) and Continuous Wave-Free Precession (CWFP) sequences. Regression models computed by partial least squares (PLS) chemometric technique using CPMG and CWFP data and the results of the classical analysis were constructed. The results allowed for the prediction of aforementioned seven properties. The predictive ability of the method was evaluated using the root mean square error (RMSE) for the calibration (RMSEC) and validation (RMSEP) data sets. The reference and predicted values showed no significant differences at a 95% confidence level.

Evaluation of the effects of Candidatus Liberibacter asiaticus on inoculated citrus plants using laser-induced breakdown spectroscopy (LIBS) and chemometrics tools.

This study investigated the organic and inorganic constituents of healthy leaves and Candidatus Liberibacter asiaticus (CLas)-inoculated leaves of citrus plants. The bacteria CLas are one of the causal agents of citrus greening (or Huanglongbing) and its effect on citrus leaves was investigated using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics. The information obtained from the LIBS spectra profiles with chemometrics analysis was promising for the construction of predictive models to identify healthy and infected plants. The major, macro- and microconstituents were relevant for differentiation of the sample conditions. The models were then applied to different inoculation times (from 1 to 8 months). The models were effective in the classification of 82-97% of the diseased samples with a 95% significance level. The novelty of this method was in the fingerprinting of healthy and diseased plants based on their organic and inorganic contents.

Image evaluation with chemometric strategies for quality control of paints.

In this study a complementary analytical methodology for quality of paints evaluation was developed. Four different primers applied to steel substrates and submitted to accelerated laboratory and outdoor exposure tests were taken into account. After this, digitalized images were obtained from these samples using a conventional scanner. The images were converted in gray color scale histograms, the resulting data were organized into a matrix form and analyzed with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). It was possible to identify the best primers performance avoiding subjective interpretation.

Development of a methodology for calcium, iron, potassium, magnesium, manganese, and zinc quantification in teas using X-ray spectroscopy and multivariate calibration.

In this study an analytical methodology for food analyses combining X-ray spectroscopy (XRS) with partial least-squares (PLS) data treatment was developed. Fifteen tea samples were purchased at a local market, and XRS spectra were obtained without sample pretreatment. For comparison of the metal concentrations, the samples were also mineralized, and six elements were determined using flame atomic absorption spectrometry (Ca, Fe, Mg, and Mn), flame atomic emission spectrometry (K), and thermospray flame furnace atomic absorption spectrometry (Zn). The spectral information obtained from XRS and the metal concentrations found using the alternative techniques were employed to generate six PLS models. The Ca and Mn models required four latent variables (LV), Fe, five LV, K, two LV, and Mg and Zn, three LV. The limits of quantification for these models were 614, 134, 761, 140, 85, and 1 mg kg(-1) for Ca, Fe, K, Mg, Mn, and Zn, respectively.