NIRS and iSPA-PLS for predicting total anthocyanin content in jaboticaba fruit

Mariani, Nathália Cristina Torres; Teixeira, Gustavo Henrique de Almeida; Lima, Kássio M. G.; Morgenstern, Thayara Bittencourt; Nardini, Viviani; Júnior, Luís Carlos Cunha

doi:10.1016/j.foodchem.2014.11.008

Cited by 49 publications

(24 citation statements)

References 16 publications

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“…Determined correlation coeffi cients of the calibration set outperform those coeffi cients of prediction models (R 2 c >R 2 p ), which is substantiated with the values of root mean square error of calibration (RMSEC) and validation (RMSEP), showing lower values in the calibration set, and exactly such trend is expected (10,24). The RPD value (29) was included in the evaluation of the PLS models where a good validation model should have a lower RMSEP and higher R 2 and RPD. Studies of Cervellati et al (17,18) have shown linear relationship between total phenols and antioxidant activity (measured with the DPPH method and BR reaction), and our study confi rms good correlations of the mentioned parameters with the NIR spectra that are also in a positive relation with the RAC and IT.…”

Section: Applied Chemometricsmentioning

confidence: 56%

“…The model performance was evaluated with R 2 and the regression point displacement (RPD). RPD value is the ratio of the standard error of performance and the standard deviation (29). Models with the prediction classifi ed as excellent have RPD>3, while RPD<1.5 indicates that presented calibration model is not suitable for prediction, values between 1.5 and 2 show a possibility to distinguish high and low levels with the presented model, and values in the range of 2-2.5 and 2.5-3 show approximate quantitative prediction that is possible and prediction that is classifi ed as good, respectively.…”

Section: Modellingmentioning

confidence: 99%

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Near-infrared spectroscopic analysis of total phenolic content and antioxidant activity of berry fruits

Kljusurić

Mihalev

Bečić

et al. 2016

Food Technol. Biotechnol.

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SummaryThis study evaluates the feasibility of using near-infrared (NIR) spectroscopy as a rapid and environmentally friendly technique for validation and prediction of the total phenolic content (TPC) and antioxidant activity (AOA) indices (as 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, inhibition time (IT) of the Briggs-Rauscher oscillating reaction, and relative antioxidant capacity (RAC)) of berry fruit extracts. The analysed berry samples originated from Croatia (blackberries, wild blueberries, raspberries, red currants and strawberries) and Bulgaria (wild blueberries, raspberries and strawberries). Principal component analysis and partial least squares (PLS) regression were used from the set of chemometric tools in distinguishing and validating the measured berry fruit extract. ANOVA and PCA showed no signifi cant impact of the origin and freshness of the samples. PLS models were developed to validate the relationship of NIR spectra with TPC and AOA of berry fruits. Representativeness of the models was expressed with the R 2 and the ratio of performance to deviation. Calculated R 2 values were above 0.84 and the ratio of performance to deviation was between 1.8 and 3.1, indicating adequacy of the PLS models.

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Section: Applied Chemometricsmentioning

confidence: 56%

Section: Modellingmentioning

confidence: 99%

Near-infrared spectroscopic analysis of total phenolic content and antioxidant activity of berry fruits

Kljusurić

Mihalev

Bečić

et al. 2016

Food Technol. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…All values were calculated with respect to the i corr values measured with the Tafel method. In order to verify the ability of the PLS models to predict i corr values, root mean square error of prediction (RMSEP) was calculated as follows:

RMSEP = \sqrt{\frac{1}{I_{p} - 1} \sum_{normali = 1}^{Ip} {({normaly}_{normalm} - {normaly}_{normalps})}^{2}}

where y ps is the predicted value for the i th sample from the prediction set, y m is the measured value for the same sample, and I p is the number of observations in the prediction set.…”

Section: Resultsmentioning

confidence: 99%

PLS multivariate analysis applied to corrosion studies on reinforced concrete

Monzón

Ramón

Gandía-Romero

et al. 2018

Journal of Chemometrics

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There are few techniques available to calculate the corrosion rate (icorr) of reinforcing steel in concrete structures. This is due not only to a lack of instrumentation but also because it is necessary to take into account that polarization can irreversibly modify the metal surface and can affect the results or the future state of the metal. This is the reason some researchers prefer to test reinforcing steel with reversible techniques. The main objective of this study is to predict the corrosion rate of reinforced concrete using electrochemical methods combined with statistical tools such as multivariate analysis. Using reinforcements embedded in mortar samples, the corrosion rates were determined at different ages using the Tafel method, and values obtained were compared with other techniques: linear polarization resistance (LPR), potentiostatic pulse testing (PPT), and AC electrochemical impedance spectroscopy (EIS). In addition, these values were compared to those obtained using a mixed technique based on partial least squares (PLS). With this technique, we were able to automatically analyze the current data obtained from LPR, PPT, and EIS and to predict the icorr value. The study allows us to conclude that it is possible to obtain reliable icorr values, very close to those obtained with the Tafel method by using PLS combined with PPT or LPR. Furthermore, it presents several advantages, such as being able to directly treat data without requiring an established Stern‐Geary constant (B) for LPR and not having to use an equivalent circuit (EC) in EIS to calculate icorr because only the impedance spectra are necessary.

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“…In addition, these pretreatments resulted in models with a higher RPD and smaller bias (mean difference between reference values and values estimated by the model). According to Mariani et al ., RPD values of between 1.5 and 2 indicate that the model is able to distinguish between high and low values, for values between 2.0 and 2.5 the model can roughly predict the data and for values between 2.5 and 3, an excellent prediction can be obtained. RPD values varied, respectively, between 1.813 and 2.061 and between 1.794 and 2.966 for the validation set samples.…”

Section: Discussionmentioning

confidence: 99%

Determination of pH and acidity in green coffee using near‐infrared spectroscopy and multivariate regression

et al. 2020

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BACKGROUND Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near‐infrared spectroscopy (FT‐NIR) to determine the pH values and acidity in coffee beans in a practical and non‐destructive way. Partial least squares regression was used during the calibration and the cross‐validation to optimize the number of latent variables. The predictive capacity of the spectral pre‐processing methods was also accessed. RESULTS The results obtained showed that the best methods of pre‐processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of −0.00011 and −0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%. CONCLUSIONS FT‐NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non‐destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry

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NIRS and iSPA-PLS for predicting total anthocyanin content in jaboticaba fruit

Cited by 49 publications

References 16 publications

Near-infrared spectroscopic analysis of total phenolic content and antioxidant activity of berry fruits

Near-infrared spectroscopic analysis of total phenolic content and antioxidant activity of berry fruits

PLS multivariate analysis applied to corrosion studies on reinforced concrete

Determination of pH and acidity in green coffee using near‐infrared spectroscopy and multivariate regression

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