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•Determination/authentication of cassava starch content in wheat flour under study;•Different pre-processed NIR spectra and color histograms as analytical information;•Good prediction ...to quantify cassava starch in certified and commercial wheat flour;•DD-SIMCA had similar results using NIR and CACHAS for the certified wheat flour;•NIR is more useful to offer definitive quantitative and qualitative analysis.
This works proposed a feasibility study on NIR spectroscopy and chemometrics-assisted color histogram-based analytical systems (CACHAS) to determine and authenticate the cassava starch content in wheat flour. Prediction results of partial least squares (PLS) achieved coefficient of correlation (rpred) of 0.977 and root mean square error of prediction (RMSEP) of 1.826 mg kg−1 for the certified additive-free wheat flour, while rpred of 0.995 and RMSEP of 1.004 mg kg−1 were obtained for the commercial wheat flour containing chemical additives. Additionally, Data-Driven Soft Independent Modelling of Class Analogy (dd-SIMCA) presented similar predictive ability using NIR and CACHAS for the certified wheat flour, authenticating all target samples, besides correctly recognizing samples that could represent a fraud. No satisfactory results were obtained for the commercial wheat flour. Therefore, NIR spectroscopy is more useful to offer definitive quantitative and qualitative analysis, while CACHAS can only provide an alternative preliminary analysis.
The main biofuels produced on an industrial large scale are biodiesel and ethanol, which are the most economically viable and widely implemented solutions to replace conventional fossil fuels from a ...greener and more sustainable perspective. In such a scenario, there is an opportunity to produce fully renewable biodiesel using ethanol instead of methanol, which is mainly derived from fossil resources. In this paper, near‐infrared (NIR) spectroscopy was used to discriminate biodiesel/diesel (B7) blends regarding the synthesis route and oil feedstock of biodiesels simultaneously. Data‐Driven Soft Independent Modeling of Class Analogy (DD‐SIMCA) authenticated correctly all ethyl B7 (target) samples into the acceptance area, while rejected all non‐target samples, implying in an efficiency of 100%. Additionally, Partial Least Squares‐Discriminant Analysis coupled with interval selection by the Successive Projections Algorithm (iSPA‐PLS‐DA) discriminated all ethyl B7 samples correctly, considering cottonseed, sunflower, and soybean as oil feedstocks. Moreover, only one ethyl cottonseed B7 sample was incorrectly discriminated when methyl B7 samples from the same three oil feedstocks were included in the model. As advantages, the proposed analytical methodology contributes to the United Nations' Sustainable Development Goal (SDG) #7 (affordable and clean energy) as well as aligns with the principles of Green Analytical Chemistry.
A green analytical method based on near‐infrared spectroscopy was developed to authenticate fully renewable ethyl biodiesel in B7 blends (i.e., samples containing 7% biodiesel and 93% diesel) using Data‐Driven Soft Independent Modeling of Class Analogy (DD‐SIMCA) and to discriminate their feedstock oil (cottonseed, sunflower, and soybean) using the Successive Projections Algorithm‐Partial Least Squares‐Discriminant Analysis (iSPA‐PLS‐DA).
Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by ...the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg−1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww−1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg−1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.
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•Determination of fat content in 70 chicken hamburger samples•NIR spectroscopy associated with PLS, iPLS and iSPA-PLS were used.•Successive Projections Algorithm used for interval selection in PLS regression•Data preprocessed with baseline correction, SNV, MSC and Savitzky-Golay smoothing•iSPA-PLS with Savitzky-Golay smoothing provided significantly better results.
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•Color histograms in the Grayscale, RGB, and HSI were used as analytical fingerprint of the samples.•Coumarin contents of 19.39 to 54.79 µg mL−1 in commercial M. glomerata syrups were ...determined by HPLC.•DD-SIMCA authenticated all samples of commercial sugar-free syrups for diabetics.•PLS achieved a REP of only 2.57% for the quantification of coumarin as chemical marker.•Best results for DD-SIMCA and PLS were achieved by using the Grayscale + RGB + HSV histogram.
This paper aimed at the use of a chemometrics-assisted color histogram-based analytical system (CACHAS) to provide a fast and reliable analytical tool for the qualitative and quantitative quality control of commercial syrups containing Mikania glomerata, popularly known as guaco and widely used in Brazil in the treatment of respiratory problems. For this, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) was initially employed to authenticate sugar-free syrups, and then Partial Least Squares (PLS) was used to quantify coumarin as a chemical marker. The best results were obtained by using the Grayscale + RGB(Red-Green-Blue) + HSV(Hue-Saturation-Value) histogram as analytical information for both the qualitative and quantitative analysis. In the first case, DD-SIMCA (α = 0.01) authenticated all samples of commercial sugar-free syrups for diabetics, achieving sensitivities of 1.00 in both the training and test sets, and specificity of 0.91 in the test set, i.e., with only 3 misclassifications from a total of 60 samples, achieving, therefore, overall efficiency of 0.97. Additionally, for the quantification of coumarin, the predictive ability of PLS achieved coefficient of correlation (R2pred) of 0.9919, root mean square error of prediction (RMSEP) of 0.8969 µg mL−1, ratio of performance to deviation (RPDpred) of 11.38, and relative error of prediction (REP) of only 2.57%. The proposed digital image-based study can be used as a rapid, non-destructive, and promising, green analytical methodology to offer a safe and reliable product to consumers and help regulatory agencies.
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•An improved adaptation of PCA-LDA under study.•Fisher's discriminability as a criterion to choose the most informative scores.•A new algorithm PCA-DP-LDA based on the discriminant ...power was proposed.•PCA-DP-LDA was compared with conventional PLS-DA and PCA-LDA.•PCA-DP-LDA achieved good classifications with parsimony and interpretability.
This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaça adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.
This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type ...(cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.
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•Cottonseed, sunflower, corn and soybean biodiesels under study.•Digital images of biodiesel samples obtained with a webcam.•RGB, HSI and Grayscale histograms were used as analytical information.•Evaluations using SIMCA, PLS-DA and SPA-LDA.•Significantly better results were obtained with SPA-LDA variable selection.
•Brazilian red wines from São Francisco valley were studied.•Classification of geographic origin, winemaker, and grape type were achieved.•An Eco-friendly methodology based on color histograms was ...applied.•PCA-LDA, PLS-DA and SPA-LDA were employed as multivariate classifiers.•Good accuracy, sensitivity, and specificity were obtained.
This work proposes the development of a simple, fast, and inexpensive methodology based on color histograms (obtained from digital images), and supervised pattern recognition techniques to classify red wines produced in the São Francisco Valley (SFV) region to trace geographic origin, winemaker, and grape variety. PCA-LDA coupled with HSI histograms correctly differentiated all of the SFV samples from the other geographic regions in the test set; SPA-LDA selecting just 10 variables in the Grayscale + HSI histogram achieved 100% accuracy in the test set when classifying three different SFV winemakers. Regarding the three grape varieties, SPA-LDA selected 15 variables in the RGB histogram to obtain the best result, misclassifying only 2 samples in the test set. Pairwise grape variety classification was also performed with only 1 misclassification. Besides following the principles of Green Chemistry, the proposed methodology is a suitable analytical tool; for tracing origins, grape type, and even (SFV) winemakers.
The performance of pharmacists in clinical services contributes to improving outcomes in patient drug therapy. In the context of streamlined resources and high health services’ demand, the use of ...patient selection tools can screen those who would benefit more from a pharmaceutical service.
This review aims to map and describe tools developed for patient selection for pharmaceutical services delivered in primary health care and outpatient settings.
The search was conducted in MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and the Latin American and Caribbean Health Sciences. The search strategy included terms relating to patient selection and outpatient pharmaceutical service. We included papers on outpatient settings, and which described the tool developed for the selection of a patient for pharmaceutical service. Two reviewers extracted data of each study concerning the types and items making up the tool. The items composing the tools were grouped into categories.
Twelve studies were included in the literature. Most of the studies were developed in the United States (53.8%), followed by Canada (30.8%). Approximately half of the studies developed tools for selecting patients for a medication review (46.2%), and only 15.4% for drug therapy management. Identification of patients at risk of drug-related problems, the need for pharmaceutical service follow-up, and patients at risk of hospital readmission were the main objective to develop the tools. In total, 92.3% of the developed tools had items related to drug therapy complexity, 76.9% to comorbidities and 61.5% to adherence/subjective aspects. Statistical methods were employed to evaluate the validation parameters, such as the ROC curve and internal consistency.
Few studies that developed tools to select outpatients for pharmaceutical services were found. However, many tools showed unsatisfactory validation parameters. Thus, it is necessary to improve the development of instruments that can identify patients who would benefit from the pharmaceutical service accurately.
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