Comparison of artificial neural networks and multiple regression tools applied to near infrared spectroscopy for predicting sensory properties of products from quality labels

Hernández-Jiménez, Miriam; Hernández-Ramos, Pedro; Martínez-Martín, Iván; Quintana, Ana María Vivar; González-Martı́n, I.; Revilla, I.

doi:10.1016/j.microc.2020.105459

Cited by 19 publications

(19 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An ANN was constructed for each of the 24 sensory parameters. As previously reported by Hernández-Jiménez et al [16], the best training algorithm for predicting sensory parameters is the Levenberg-Marquardt backpropagation. The hyperbolic tangent sigmoid function was selected for the hidden layer and the pure linear transfer function was used for the output layer.…”

Section: Artificial Neural Network For Predicting Sensory Parametersmentioning

confidence: 68%

“…The panel that carried out the sensory analysis was formed by 10 assessors with a wide previous experience of dry-cured meat analysis as previously described [16,23]. The training for the specific sensory profiling of this product involved 8 sessions lasting 1-1.5 h each.…”

Section: Sensory Analysismentioning

confidence: 99%

“…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%

“…However, there are numerous discriminating methods which could be applied to NIR spectra which have been assayed for discriminating between food products belonging to PDO or PGI quality labels. Examples include principal component analysis for almonds [12], factorial discriminant analysis (FDA) for Swiss cheeses [13], partial least squares discriminant analysis (PLS-DA) for vinegars [14], K-nearest neighbors (KNN) for hazelnuts [15], the residual mean squares method (RMS-X residuals) for dry sausages [16] or artificial neural networks (ANN) for cheeses [17]. However, all this research has also shown that the most suitable distinguishing method is closely linked to the product; a detailed study should be carried out for each specific PDO or PGI label or food matrix.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, previous studies indicate that ANNs are more suitable for predicting sensory parameters than multiple regression tools. In general, higher regression coefficient R squared (RSQ) and lower mean squared error of prediction (MSEP) are observed when ANN are used for predictions [24][25][26] and it is possible to predict a higher number of parameters [16].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Revilla

Quintana

González-Martín

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Artificial Neural Network For Predicting Sensory Parametersmentioning

confidence: 68%

Section: Sensory Analysismentioning

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Revilla

Quintana

González-Martín

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics

et al. 2022

View full text Add to dashboard Cite

Background: In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour.Results: The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R 2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R 2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. Conclusion:The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud.

show abstract

Development of an in-syringe dispersive liquid–liquid micro-extraction process for the measurement of malachite green in aqueous solutions

Gorgich,

Rahmani

2024

Chem. Pap.

View full text Add to dashboard Cite

Comparison of artificial neural networks and multiple regression tools applied to near infrared spectroscopy for predicting sensory properties of products from quality labels

Cited by 19 publications

References 54 publications

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

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

Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics

Development of an in-syringe dispersive liquid–liquid micro-extraction process for the measurement of malachite green in aqueous solutions

Contact Info

Product

Resources

About