Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN

Liu, Yande; Sun, Xudong; Ouyang, Aiguo

doi:10.1016/j.lwt.2009.10.008

Cited by 191 publications

(111 citation statements)

References 35 publications

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“…Use of chemometrics helps to detect similarities or diff erences of the studied samples and to exclude their fi ngerprint using the analysed TPC and AOA with their corresponding NIR spectra (40). The diff erentiation of the examined samples based on TPC, IT, RAC and DPPH is shown in Fig.…”

Section: Applied Chemometricsmentioning

confidence: 99%

“…2), demonstrating the abundance of the observed parameters (TPC and AOA). Although the PCA was not successful in data separation according to the sample freshness or origin, we assumed that the data set will be a good input matrix for calibration and validation of some analytical parameters (24,(40)(41)(42). The origin was not a parameter provided for calibration or validation, because the ANOVA analysis did not show significant diff erence between samples grown in Croatia and Bulgaria.…”

Section: Applied Chemometricsmentioning

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: 99%

Section: Applied Chemometricsmentioning

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.

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“…Model assessment and predictive capability was evaluated by the following indices: determination coefficient (r 2 ) (Davrieux et al 2010;Chen et al 2011) and root mean square error of prediction (RMSEP) (Castritius et al 2010;Liu et al 2009). …”

Section: Samplesmentioning

confidence: 99%

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Martelo-Vidal¹,

Vázquez²

2014

Czech J. Food Sci.

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Martelo-Vidal M.J., Vázquez M. (2014): Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds. Czech J. Food Sci., 32: 37-47.Spectroscopy of UV-VIS-NIR combined with chemometric analyses was used as a non-destructive technique to build models for the quantitative characterisation of the main compounds of wine. The work in mixtures can give insight into how interferences affect the performance of calibrations in wines. Ethanol, glycerol, glucose, tartaric acid, malic acid, lactic acid, and acetic acid were evaluated as pure compounds and in mixtures. Different pre-treatments for the spectra and modelling strategies such as partial least squares (PLS) regression or Principal Component Regression (PCR) were evaluated. All pure compounds studied showed a good relationship between spectra and concentrations. However, interferences were observed in the mixtures and only good models for ethanol, tartaric acid, and malic acid were obtained. The best model was obtained in the NIR region for ethanol and in the UV region for tartaric acid and malic acid. The results indicate that NIR spectroscopy could be used as an alternative to conventional chemical methods for ethanol determination and UV spectroscopy for the determination of tartaric acid and malic acid.

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“…Near infrared spectroscopy (NIR) provides a rapid, nondestructive, little sample preparation, and good reproducibility method to measure the quality of food and agriculture products (Liu et al 2010a;Liu et al 2010b). Infrared spectroscopy has been widely used to evaluate the internal quality (SSC, pH, acidity and firmness et al) of fruits such as apple (Bobelyn et al 2010;Fan et al 2009), pear (Paz et al 2009;Sun et al 2009), orange (Liu et al 2010a;Liu et al 2010b), grape (Cao et al 2010), peach (Kurz et al 2010;Ziosi et al 2008), and kiwifruit (Clark et al 2004;Moghimi et al 2010) et al.…”

Section: Introductionmentioning

confidence: 99%

“…Infrared spectroscopy has been widely used to evaluate the internal quality (SSC, pH, acidity and firmness et al) of fruits such as apple (Bobelyn et al 2010;Fan et al 2009), pear (Paz et al 2009;Sun et al 2009), orange (Liu et al 2010a;Liu et al 2010b), grape (Cao et al 2010), peach (Kurz et al 2010;Ziosi et al 2008), and kiwifruit (Clark et al 2004;Moghimi et al 2010) et al. The first attempts to establish the relationship between sensory texture and NIRS measurements were made by Mehinagic et al (2003) who reported good correlations between penetrometry and sensory texture but poor correlations between NIR and sensory data were found.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive measurement of fracturability and chewiness of apple by FT-NIRS

Ren

et al. 2013

J Food Sci Technol

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In order to assess quickly and non-destructively the fracturability and chewiness of apple fruit by FT-NIR spectra in the wavelength range of 4000 cm −1 -12000 cm −1 , multivariate models were built using multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Fracturability and chewiness reference data were instrumentally measured using a Texture Profile Analysis (TPA) test. The effects of various pre-processing methods of the spectroscopic data on the performance of the multivariate models were analyzed. Standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Min-Max normalization(MMN) and first derivative (FD) were tested. The performance of the fracturability prediction models was better for the PLSR model (R 2 =0.91, RMSEP=101.90) than for the PCR and MLR models. With regard to chewiness, the performance of the PCR model (R 2 =0.88, RMSEP=13.46) was similar to the one of the PLSR model but better than the one of the MLR model. The results demonstrated that NIR spectra together with stoichiometry could determine precisely fracturability and chewiness of apple, and the predictive ability of the models developed by other methods may be improved in the future.

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Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN

Cited by 191 publications

References 35 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

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Non-destructive measurement of fracturability and chewiness of apple by FT-NIRS

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