Toward a general and interpretable umami taste predictor using a multi‑objective machine learning approach

Supplementary Information The online version contains supplementary material available at https://doi.org/10. 1038/s41598-022-25935-3.The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.VIRTUOUS project, funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie-RISE Grant Agreement No. 87218

A survey on computational taste predictors

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.Politecnico di Torino within the CRUI-CARE AgreementEuropean Union's Horizon 2020 research and innovation program 87218

Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil

Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond- CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.project Innosuisse - Swiss Innovation Agency 36761.1 INNO-LSproject "SUSTAINABLE" - European Union's Horizon 2020 H2020-MSCA-RISE-2020 program 101007702project "PARENT" - European Union's Horizon 2020 H2020-MSCA-ITN-2020 program 956394Junta de Andalucia-FEDER-Fondo de Desarrollo Europeo 2018 P18-H0-470

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil

One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality.European Union?s Horizon 2020 Project H2020-MSCA-RISE-2020 101007702Junta de Andalucia-FEDER-Fondo de Desarrollo Europeo P18-H0-470

Blockchain in Agriculture: A PESTELS Analysis JAVIER

Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agriculture represents a sector of significance for overall sustainability challenges that is benefiting from digitalisation and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies paving the way for innovative applications. BC appears to be a promising technology for agriculture introducing new mechanisms for tracing of products and overall agricultural Supply Chain management from the farm to the fork. The authors perform a review of 152 scientific works, providing a concise summary for each and extracting current challenges and open issues for the application of BC in agriculture. By synthesizing their findings, they perform a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, create big research potential for the evolution of the area.SUSTAINABLE Project, funded by the European Union’s Horizon 2020 Research and Innovation Program, through the Marie Skłodowska-Curie-Research and Innovation Staff Exchange (RISE) under Grant 10100770

Combating Salinity Through Natural Plant Extracts Based Biostimulants: A Review

Enhanced crop growth and yield are the recurring concerns in agricultural field, considering the soaring world population and climate change. Abiotic stresses are one of the major limiting factors for constraining crop production, for several economically important horticultural crops, and contribute to almost 70% of yield gap. Salt stress is one of these unsought abiotic stresses that has become a consistent problem in agriculture over the past few years. Salinity further induces ionic, osmotic, and oxidative stress that result in various metabolic perturbations (including the generation of reactive oxygen, carbonyl, and nitrogen species), reduction in water potential (%w), distorted membrane potential, membrane injury, altered rates of photosynthesis, leaf senescence, and reduced nitrogen assimilation, among others); thereby provoking a drastic reduction in crop growth and yield. One of the strategies to mitigate salt stress is the use of natural plant extracts (PEs) instead of chemical fertilizers, thus limiting water, soil, and environmental pollution. PEs mainly consist of seeds, roots, shoots, fruits, flowers, and leaves concentrates employed either individually or in mixtures. Since PEs are usually rich in bioactive compounds (e.g., carotenoids, flavonoids, phenolics, etc.), therefore they are effective in regulating redox metabolism, thereby promoting plant growth and yield. However, various factors like plant growth stage, doses applied, application method, soil, and environmental conditions may greatly influence their impact on plants. PEs have been reported to enhance salt tolerance in plants primarily through modulation of signaling signatures and pathways (e.g., NaC, ANNA4, GIPC, SOS3, and SCaBP8 Ca2C sensors, etc.), and regulation of redox machinery [e.g., superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), non-specific peroxidase (POX), glutathione peroxidase (GPX), peroxiredoxin (Prx), ascorbic acid (AsA), glutathione (GSH), a-tocopherol, etc.]. The current study highlights the role of PEs in terms of their sources, methods of preparation, and mode of action with subsequent physiological changes induced in plants against salinity. However, an explicit mode of action of PEs remains nebulous, which might be explicated utilizing transcriptomics, proteomics, metabolomics, and bioinformatics approaches. Being ecological and economical, PEs might pave the way for ensuring the food security in this challenging era of climate change.European Union's Horizon 2020 Project H2020-MSCA-RISE-2019 872181 European Union's Horizon 2020 Project H2020-MSCA-RISE-2020 101007702FEDER (Fondo Europeo de Desarrollo Regional)- Junta de Andalucia 2018 P18-H0-470

Aprender sobre la clorofila y las antocianinas como posibles indicadores del estado fisiológico de las plantas

Ali Ahmad – Universidad de Granada - 0000-0001-5530-7374María Belén García del Moral Garrido – Universidad de Granada - 0000-0001-9803-9939Vanessa Martos Núñez – Universidad de Granada - 0000-0001-6442-7968Recepción: 14.03.2022 | Aceptado: 18.03.2022Correspondencia a través de ORCID: Vanessa Martos - 0000-0001-6442-79683Área o categoría del conocimiento: Fisiología Vegetal – DocenciaAbstract: Currently, increased crop yield is direly needed considering the ever-increasing world population and climate change. Crops growth and production is mainly dependent upon their physiological state and vigor. Several forms of plant pigments exist naturally. They are categorized into four major groups: namely betalains (betacyanins, betaxanthins), carotenoids (carotenes, xanthophylls), chlorophylls (Chl a and b), and flavonoids (anthocyanins, aurones, chalcones, flavonols, proanthocyanidins). Out of all these, Chlorophylls (Chl) and Anthocyanins (Anth) are the two most important plant pigments that provide valuable insight into the plant physiological state. Primary function of Chl is the conversion of solar energy into chemical energy that is further utilized in the photosynthetic process, whereas Anth are multifunctional molecules that apart from coloration of plant organs also play an important role in stress mitigation. This study might serve as a guide for students interested to learn about plant physiology.Resumen: En la actualidad, el aumento de la producción de los cultivos es una necesidad imperiosa, teniendo en cuenta el incremento de la población mundial y el cambio climático. La producción y el desarrollo de los cultivos dependen principalmente de su estado fisiológico y su vigor. Existen varias formas de pigmentos vegetales en la naturaleza. Normalmente, se clasifican en cuatro grandes grupos: betalaínas (betacianinas, betaxantinas), carotenoides (carotenos, xantofilas), clorofilas (Chl a y b) y flavonoides (antocianinas, auronas, chalconas, flavonoles, proantocianidinas). De todos ellos, las clorofilas (Chl) y las antocianinas (Anth) son los dos pigmentos vegetales más importantes que proporcionan una valiosa información sobre el estado fisiológico de la planta. La función principal de las Chl es la conversión de la energía solar en energía química que se utiliza posteriormente en el proceso fotosintético, mientras que las Anth son moléculas multifuncionales que, además de la coloración de los órganos de la planta, desempeñan un importante papel en la mitigación del estrés. Este estudio podría servir de guía para los estudiantes interesados en aprender sobre fisiología vegetal.VIRTUOUS” funded from the European Union’s Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181“SUSTAINABLE” funded from the European Union’s Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702“Project of Excellence” from FEDER (Fondo Europeo de Desarrollo Regional) - Junta de Andalucía 2018. Ref. P18-H0-470

Inteligencia Artificial (IA) como tecnología complementaria a la Teledetección (RS) agrícola en la enseñanza de la fisiología vegetal

Ali Ahmad – Universidad de Granada - 0000-0001-5530-7374Shab E Noor – Universidad de Granada - 0000-0003-0345-4692Pedro Cartujo Cassinello – Universidad de Granada - 0000-0001-6072-3137Vanessa Martos Núñez – Universidad de Granada - 0000-0001-6442-7968Recepción: 28.10.2022 | Aceptado: 31.10.2022Correspondencia a través de ORCID: Ali Ahmad - 0000-0001-5530-7374Financiación: This work was supported by the projects: “VIRTUOUS” funded from the European Union’s Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, “SUSTAINABLE” funded from the European Union’s Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702, and the “Project of Excellence” from FEDER (Fondo Europeo de Desarrollo Regional)- Junta de Andalucía 2018. Ref. P18-H0-4700.Área o categoría del conocimiento: Fisiología Vegetal – DocenciaAbstract: Agriculture is facing several challenges such as climate change, drought, and loss of fertile land, which could compromise global food safety and security. In this scenario, integration of novel technologies into agriculture could be the possible solution to address these concerns. There are several modern technology tools that can be integrated into agriculture for this purpose. Agricultural remote sensing (RS) technology, being one of the promising tools, has long been used for agriculture, but its potential has not been explored fully. RS involves monitoring and analysis of various crop growth parameters generating huge datasets. But management and interpretation of RS generated data is a complex and costly process. Therefore, artificial intelligence (AI), another promising tool of 5th industrial era, could be used to complement agricultural RS technology to improve data processing and generating visualizing results. Machine learning, a subset of AI, methods have been efficiently employed for disease detection, yield predictions, and biomass estimations. Yet, there remains a huge possibility to develop crop growth and yield simulations, and machine training models from the freely available satellite data. Hence, indicating and instilling this knowledge into young students would result in the novel initiatives in agricultural plant physiology, since most of the parameters analyzed through RS are physiological.Resumen: La agricultura se enfrenta a varios retos, como el cambio climático, la sequía y la pérdida de tierras fértiles, que podrían comprometer la seguridad alimentaria mundial. En este escenario, la integración de tecnologías novedosas en la agricultura podría ser la posible solución para hacer frente a estos problemas. Existen varias herramientas tecnológicas modernas que pueden integrarse en la agricultura con este fin. La tecnología de teledetección agrícola (RS), que es una de las herramientas más prometedoras, se utiliza desde hace tiempo en la agricultura, pero su potencial no se ha explorado plenamente. La teledetección implica el seguimiento y el análisis de diversos parámetros de crecimiento de los cultivos, lo que genera enormes conjuntos de datos. Pero la gestión e interpretación de los datos generados por la RS es un proceso complejo y costoso. Por lo tanto, la inteligencia artificial (IA), otra herramienta prometedora de la quinta era industrial, podría utilizarse para complementar la tecnología de RS agrícola con el fin de mejorar el procesamiento de los datos y generar resultados de visualización. Los métodos de aprendizaje automático, un subconjunto de la IA, se han empleado eficazmente para la detección de enfermedades, la predicción del rendimiento y la estimación de la biomasa. Sin embargo, sigue existiendo una enorme posibilidad de desarrollar simulaciones de crecimiento y rendimiento de los cultivos, así como modelos de entrenamiento de máquinas a partir de los datos satelitales disponibles de forma gratuita. Por lo tanto, indicar e inculcar estos conocimientos a los jóvenes estudiantes daría lugar a iniciativas novedosas en la fisiología de las plantas agrícolas, ya que la mayoría de los parámetros analizados mediante RS son fisiológicos.Universidad de GranadaEuropean Union’s Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181European Union’s Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702“Project of Excellence” from FEDER (Fondo Europeo de Desarrollo Regional)- Junta de Andalucía 2018. Ref. P18-H0-470

Aprendiendo los fundamentos de criptografía con ejemplos prácticos

Shab E Noor – Universidad de Granada - 0000-0003-0345-4692Ali Ahmad – Universidad de Granada - 0000-0001-5530-7374Vanessa Martos Núñez – Universidad de Granada - 0000-0001-6442-7968Miguel J. Hornos Barranco – Universidad de Granada - 0000-0001-5722-9816Recepción: 03.05.2022 | Aceptado: 09.05.2022Correspondencia a través de ORCID: Ali Ahmad - 0000-0001-5530-7374Área o categoría del conocimiento: Estudios de IngenieríaAbstract: Cryptography is a secure technique of data communication and exchange that relies on encryption and decryption protocols. This technique is in use since many centuries. Nonetheless, in today’s world, it supports data protection and privacy while ensuring the authenticity and confidentiality of the data. E-commerce, banking, military, and corporations are the prominent operators of this technology, although it is, directly or indirectly, linked to almost every single person nowadays. Symmetric and asymmetric key cryptography are the two fundamental forms of cryptography. Asymmetric key cryptography is more secure than symmetric but at the cost of greater computational complexity. The use of hash functions and digital signatures also contribute to the security of systems and the privacy of information. This article presents the basic differences between the fundamental types of cryptography as well as practical examples of encrypting information using various cipher systems, which will help to understand them. This is an introductory article that is primarily aimed at undergraduate students in the area of computer science, in order to enrich their understanding of the field.Resumen: La criptografía es una técnica segura de comunicación y de intercambio de datos que se basa en protocolos de cifrado y descifrado. Esta técnica se utiliza desde hace muchos siglos. No obstante, sirve para proteger los datos y la privacidad en el mundo actual, garantizando la autenticidad y la confidencialidad de los datos. El comercio electrónico, la banca, el ejército y las empresas son los principales operadores de esta tecnología, aunque hoy en día está vinculada, directa o indirectamente, a casi todas las personas. La criptografía de clave simétrica y asimétrica son las dos formas fundamentales de criptografía. La criptografía de clave asimétrica es más segura que la simétrica, pero a costa de una mayor complejidad computacional. El uso de funciones hash y firmas digitales también contribuye a la seguridad de los sistemas y a la privacidad de la información. Este artículo presenta las diferencias básicas entre los tipos fundamentales de criptografía, además de ejemplos prácticos de cifrado de información usando varios sistemas, que ayudará a comprenderlos. Se trata de un artículo introductorio que está principalmente dirigido a estudiantes de grado en el área de las ciencias de la computación, con el fin de enriquecer su comprensión del campo

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470