Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy

Revilla, I.; Lastras, C.; González-Martín, M.I.; Quintana, Ana María Vivar; Morales-Corts, María Remedios; Gómez-Sánchez, María Ángeles; Sánchez, Rodrigo Pérez

doi:10.1016/j.jfca.2019.01.012

Cited by 32 publications

(17 citation statements)

References 38 publications

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“…The prediction ability of the equation for the NDF fraction over chickpea seed obtained by us in this work is in agreement with previous results reported for this component on grass silage, in which SD SECV −1 of 3.25 and R 2 cv of 0.90 (range from 402 to 669 g kg −1 dry wt) values have been shown [49]. Bruno-Soares et al [29] also reported high R 2 cv (0.95, range from 469 to 772 g kg −1 dry wt) and SD SECV −1 (4.30) values in predicting NDF in green crop cereals through the use of a multi-product calibration involving six species.…”

Section: Nir Calibration Performance For Ndf Fractionsupporting

confidence: 93%

“…In recent decades, near-infrared spectroscopy (NIRS) has been widely used as a rapid and accurate technique for quantitative and qualitative analysis of biological and non-biological materials in the fields of food, agriculture, textiles, petrochemicals, and pharmaceuticals [27]. In the agricultural field, NIRS has shown to be a reliable analytical technique to determine oil, protein, fiber, and moisture concentrations of grains and oilseeds, which are widely used for analyzing the chemical composition of many agro-food products [28][29][30]. The most attractive features of NIRS analysis are its low cost, speed of analysis, and minimal sample preparation to be a non-destructive technique and to be a friendly environmental technique, as no chemicals are used.…”

Section: Introductionmentioning

confidence: 99%

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Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

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Río-Celestino

Luna³

et al. 2021

Agronomy

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The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

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Section: Nir Calibration Performance For Ndf Fractionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

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Río-Celestino

Luna³

et al. 2021

Agronomy

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“…Therefore, it is very important to have fast and reliable tools that allow the classification of the samples according to their origin. Previous studies have shown that a combination of NIR spectroscopy and chemometric tools is very useful for this purpose [9][10][11]16].…”

Section: Discrimination Of the Samples According To Protected Geograp...mentioning

confidence: 99%

“…This technique has been shown to be a powerful tool for discriminating products according to the geographical origin of the samples. Therefore, it has been used for rice wines [8], honey [9], tea [10], and lentils [11] among other products. These results indicate that this technique can be a simple way of distinguishing PGI from non-PGI meat products.…”

Section: Introductionmentioning

confidence: 99%

NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”

Revilla

Quintana

González-Martín

et al. 2020

Sensors

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For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat “cecina de León”, a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.

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“…The significance level at which differences were considered A discriminant analysis of the samples belonging to both periods was carried out. The calibration method applied to this procedure was D-PLS as previously described (Revilla et al, 2019). During the development of the model the group to which the samples belong is known, files are prepared to contain the spectra of all samples belonging to the same category; there is one file for each category.…”

Section: Discussionmentioning

confidence: 99%

Effect of Weather Conditions on the Fatty Acid Composition of Medium-Growth Chicken Reared in Organic Production System

Sarmiento-García

Revilla

Palacios

et al. 2021

Braz. J. Poult. Sci.

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A total of 160 medium-sized one-day-old male chicks reared in organic conditions were studied individually from the first day of their life until slaughter (120 days). Two weather periods were considered, being period C colder than period H. A total of 24 chickens per period were randomly selected, then the breast muscle (m. Pectoralis major) was extracted for analysis. Individual fatty acids were measured by gas chromatography and expressed in grams per 100 g of fat. From the values obtained, total lipid fractions were calculated. Near infrared spectroscopy spectra (NIRS) were recorded on the surface of the breast without manipulating. Breast from chicken reared in H period had significantly lower (p<0.05) saturated fatty acids / polyunsaturated fatty acids (SFA / PUFA) ratio, and increased (p<0.05) content in PUFA and n-6. However, no significant differences were observed on the content of individual fatty acid. NIR system was not able to correctly classify the samples according to the breeding period.

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Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy

Cited by 32 publications

References 38 publications

Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”

Effect of Weather Conditions on the Fatty Acid Composition of Medium-Growth Chicken Reared in Organic Production System

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