Sandra Pradana-López's research while affiliated with Complutense University of Madrid and other places
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Publications (15)
In this work, a new method has been developed to detect adulterations in avocado oil by combining optical images and their treatment with deep learning algorithms. For this purpose, samples of avocado oil adulterated with refined olive oil at concentrations from 1% to 15% (v/v) were prepared. Two groups of images of the different samples were obtai...
In this work, an image-based method has been developed to detect the presence of traces of wheat flour in a gluten-free product such as chickpea flour, which may affect the health of the final consumers. For this purpose, a set of ground chickpea samples have been mixed with low amounts of wheat flour ranging from 1 to 50 ppm. Specifically, 12 grou...
An artificial intelligence-based method to rapidly detect adulterated lentil flour in real time is presented. Mathematical models based on convolutional neural networks and transfer learning (viz., ResNet34) have been trained to identify lentil flour samples that contain trace levels of wheat (gluten) or pistachios (nuts), aiding two relevant popul...
This paper combines intelligent algorithms based on a residual neural network (ResNet34) to process thermographic images. This integration is aimed at detecting traces of wheat flour, in concentrations from 1 to 50 ppm, mixed into chickpea flour. Using an image database of over 16 thousand samples to train the ResNet34, and 1712 images to blindly t...
In this research, more than 302,000 images of five different types of extra virgin olive oils (EVOOs) have been collected to train and validate a system based on convolutional neural networks (CNNs) to carry out their classification. Furthermore, comparable deep learning models have also been trained to detect and quantify the adulteration of these...
In this work, a method to detect and quantify melamine in three different powdered milks is presented. The goal has been achieved by training convolutional neural networks (CNN) with a photograph database. The three types of milk are of different brands, each with their own fat content and intended final consumer (age-based distinction). The adulte...
In this work, convolutional neural networks were trained with images of ground coffee captured with a camera. The objective is the quality control and detection of adulterations of Arabica and Robusta coffee with other foods such as chicory and barley. The convolutional algorithms are based on the previously trained ResNet34 convolutional system co...
In order to develop a rice adulteration detection system, a deep learning method was implemented to classify simple photographs of five different types of rice has been established. Firstly, the different types of rice were milled and sieved, enabling the imaging of not only grain, but also rice in flour format. Pure rice types as well as mixtures...
Given the importance of presenting tools capable of combating fraudulent activities such as adulterations, this chapter presents a method for their detection based on capturing representative thermographic images of pure and adulterated extra-virgin olive oil (EVOO) during their cooling process. This thermodynamic evolution, captured by a thermogra...
The quality control of food and, in particular, of extra-virgin olive oil (EVOO), is required to protect the consumer, producers, and economies worldwide. Extra-virgin oil has been the target of fraudulent activities at an international level for many years. This chapter presents a collection of both administrative and chemical tools for quality co...
In this work, a thermographic camera and intelligent algorithms have been used to classify five different types of rice (Oryza sativa L.) in grain or flour format and to detect mixtures of different rice types which act as adulterated samples. For this purpose, more than 63,000 thermographic images of pure rice (Japonica and Indica) and their mixtu...
In this research, more than 27,000 images (samples) of five different types of rice (Oryza sativa L.) have been used to design and validate a deep learning-based system to carry out their classification. A typical photographic camera was used to obtain images from five different varieties of rice, which will be used for their classification after p...
Citations
... Despite its potential, not many reports can be found in the scientific literature on the assessment of the quality parameters of avocado oil using DIC. Perez-Calabuig et al. (2023) used optical images and artificial neural networks to achieve a 95% accuracy classification of avocado oil blended with a range of 1 to 15% refined olive oil [34]. A higher number of reports exist regarding the successful quantification of quality parameters in avocado oil using smartphone images, such as peroxide values [21], chlorophyll and carotenoids [22], and total sterols [20]. ...
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... All aforementioned analytical methods work having the analyte (protein) as the target, however, in the last few years, indirect analysis based on AI methods has been proposed more frequently [18]. They work using large databases, offering quick and cheap solutions to the food industry [19]. ...
... Therewith, sample No. 8 of wholegrain mill wheat flour demonstrated an unusually low gluten level of 16%. J.C. Cancilla et al. (2022) report that according to world standards, soft wheat is regulated by classes: for class 1, the gluten content is 30-32%, class 2 -26-28%, class 3 -22-23%, and class 4 -16-18%; and the FDI index for class 1, 2 of soft wheat ranges from 43-77 units, for classes 3 and 4 -18-102 units. The gluten index of durum wheat by class is 28% for class 1, 25% for class 2, 22% for class 3, and 18% for class 4. The gluten index for durum wheat should be in the range of 18-102 units. ...
... There are various available CNN architectures that are widely used for food and agriculture image classification including VGG, GoogLeNet, AlexNet, LeNEt, ResNet, and Inception. CNN has been successfully used for the adulteration detection in food and quality evaluation of agricultural products [76][77][78][79][80][81]. ...
... Computer-based with image processing techniques are being used to grading seeds automatically, which helps to speed up and improve the accuracy of the process. Rice quality grading is an important aspect of the processes used in the rice-producing businesses to evaluate rice quality and to define rice pricing in the commercial market [3], [4]. ...
... Igualmente se han establecidos investigaciones, basados en aprendizaje automático, tendientes a predecir la temperatura de las paletas a lo largo de la cadena de frio (Loisel, et al., 2022). Así como técnicas de aprendizaje profundo para verificar la calidad del café (Pradana-López, et al., 2021), entre muchas otras investigaciones (Jiménez-Carvelo, et al., 2019;Malvandi, et al., 2022;Pineda, et al., 2022;Zhu, et al., 2021). ...
... Food safety accidents may be caused by cross-contamination in food processing and distribution facilities, and there is much room for future AI applications in food traceability systems [65,86,115,132]. A computer vision system based on CNN or ML models, such as SVM, KNN, J48, and RF, has been seen as a potential technique for automatic food classification, adulterant quantification, and feature extraction [72,84,103,104,145,158,160,161]. ML algorithms have also been used to improve the effectiveness of the drying system for orange slices [82]. ...
... However, the process of manual inspection is quite laborious, inconsistent, subjective, and time-consuming [12]. Near infrared spectroscopy and Fourier-transform infrared spectroscopy are also efficiently employed for adulteration detection in various food materials with minimal sample preparation [13][14][15][16]. In addition, near-infrared hyperspectral imaging has also been used for accurate classification and quantification of adulteration of different foods [17][18][19][20]. ...
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