Neural networks applied to characterize blends containing refined and extra virgin olive oils

Aroca-Santos, Regina; Cancilla, John C.; Pariente, Enrique S.; Torrecilla, José S.

doi:10.1016/j.talanta.2016.08.033

Cited by 23 publications

(15 citation statements)

References 31 publications

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“…The use of artificial neural networks has been successfully applied to NIR spectrometry in the rapid quantification of wine compounds (Martelo-Vidal and Vázquez, 2015), evaluation of chemical components and properties of the jujube fruit (Guo et al, 2016) and characterization of blends containing refined and extra virgin olive oils (Aroca- Santos et al, 2016).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques

Coronel-Reyes

Ramírez-Morales

Fernández-Blanco

et al. 2018

Computers and Electronics in Agriculture

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Currently, consumers are more concerned about freshness and quality of food. Poultry egg storage time is a freshness and quality indicator in industrial and consumer applications, even though egg marking is not always required outside the European Union. Other authors have already published works using expensive laboratory equipment in order to determine the storage time and freshness of eggs. This paper presents a novel alternative method based on low-cost devices for the rapid and non-destructive prediction of egg storage time at room temperature (23 ± 1 °C). H&N brown flock with 49-week-old hens were used as a source for the sampled eggs. Samples were scanned for a period of 22 days beginning from the time the egg was laid. The spectral acquisition was performed using a low-cost near-infrared reflectance (NIR) spectrometer which has a wavelength range between 740 nm and 1070 nm. The resulting dataset of 660 samples was randomly split according to a 10-fold cross-validation in order to be used in a contrast and optimization process of two machine learning algorithms. During the optimization, several models were tested to develop a robust calibration model. The best model used a Savitzky Golay pre-processing technique with a third derivative order and an artificial neural network with ten neurons in one hidden layer. Regressing the storage time of the eggs, tests achieved a coefficient of determination (R-squared) of 0.8319 ± 0.0377 and a root mean squared error in cross-validation test set (RMSECV) of 1.97 days. Although further work is needed, this technique shows industrial potential and consumer utility to determine an egg's freshness using a low-cost spectrometer connected to a smartphone.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques

Coronel-Reyes

Ramírez-Morales

Fernández-Blanco

et al. 2018

Computers and Electronics in Agriculture

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“…In the literature, there are many papers and reviews relating the concentration of main pigments and other derived quantities (i.e., the ratio between lutein and β-carotene, or the relative ratio between lutein and minor carotenoids), with olive oil authenticity and quality [9,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. Moreover, several works demonstrated the usefulness of pigments’ determination to reveal olive oil adulterations [29,30,31]. The identification and quantification of single pigments is usually performed by means of chromatographic methods, such as high performance liquid chromatographic with ultraviolet-visible detection (HPLC-DAD) [12,14,15,22,23,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

Determination of Pigments in Virgin and Extra-Virgin Olive Oils: A Comparison between Two Near UV-Vis Spectroscopic Techniques

Borello

Domenici

2019

Foods

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The colour of olive oil is due to the presence of natural pigments belonging to the class of carotenoids, chlorophylls, and their derivatives. These substances, other than being responsible for the colour, an important qualitative feature of the oil, have antioxidant and, more generally, nutraceutical properties and their quantification can be related to the product’s quality and authenticity. In this work, we have quantified the total amount of carotenoids and chlorophylls’ derivatives in several virgin and extra-virgin olive oils produced in Italy, by using two different methods that are based on near-ultraviolet-visible absorption spectroscopy. The first method defines two indexes, K670 and K470, related to absorbance values of oil at wavelengths of 670 and 470 nm, respectively. The second method is based on the mathematical deconvolution of the whole absorption spectrum of the oil to obtain the concentrations of four main pigments present in olive oils: β-carotene, lutein, pheophytin A, and pheophytin B. The concentrations of the total carotenoids and total chlorophylls’ derivatives, as obtained by the two spectroscopic methods, are compared and the results are discussed in view of the practical usefulness of spectroscopic techniques for a fast determination of pigments in olive oil.

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“…Here, the inputs for the BP-ANN were the pre-selected peaks, while the bioactivity was set as output. It is known that the type of BP algorithm, the initial weights and bias, and the mu factor have considerable in uence on the performance of networks [15]. The inappropriate parameters will lead to an over-t model and take more training time.…”

Section: Bp-ann Modelmentioning

confidence: 99%

“…Other intrinsic model architecture parameters, including the number of hidden layer, transfer functions, initial bias, initial weights, and mu decrease factor, also greatly affect the performance of BP-ANN model [15]. The hidden layer was assigned with increasing number of neurons, and then the neuron numbers were determined by comparing the resulted MSE.…”

Section: Establishment Of Bp-ann Modelmentioning

confidence: 99%

“…Different machine learning techniques have been used to screen the bioactive compounds and predict the different signatures of food and herbal samples [10][11][12]. Among them, arti cial neural network (ANN) models are very powerful tools to model the complex and non-linear relationships [13][14][15]. The well-trained ANN models have been employed to identify complex patterns in datasets, make real-time predictions and offer adaptive solutions in multiple elds, such as natural products, nanotechnology, bioresource technology [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Integrating Metabolomic Data With Machine Learning Approach for Discovery of Q-markers From Jinqi Jiangtang Preparation Against Type 2 Diabetes

Yang

Xue

Wei

et al. 2020

Preprint

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Background: Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little was known about its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation. Methods: This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishing of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled using an untargeted UPLC-LTQ-Orbitrap metabolomic approach. The obtained chemical features associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection, respectively. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).Results: Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of PLSR calibration model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. Using this machine learning model, 15 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.Conclusions: The proposed artificial intelligence approach is suitable for quick and easy discovery of Q-markers with bioactivity from herbal medicines.

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Neural networks applied to characterize blends containing refined and extra virgin olive oils

Cited by 23 publications

References 31 publications

Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques

Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques

Determination of Pigments in Virgin and Extra-Virgin Olive Oils: A Comparison between Two Near UV-Vis Spectroscopic Techniques

Integrating Metabolomic Data With Machine Learning Approach for Discovery of Q-markers From Jinqi Jiangtang Preparation Against Type 2 Diabetes

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