Clasificación de la fermentación del grano de cacao usando información espectral

Cocoa beans are the most important raw material for the chocolate industry and an essential product for the economy of tropical countries such as Colombia. Their price mainly depends on their quality, which is determined by various aspects, such as good agricultural practices, their harvest point, and level of fermentation. The entities that regulate the international marketing of cocoa beans have been encouraging the development of new classification methods that, compared to current techniques, could save time, reduce waste, and increase the number of evaluated beans. In particular, hyperspectral images are a novel tool for food quality control. However, studies that have examined some quality parameters of cocoa using spectroscopy also involve the chemical evaluation of cocoa powder and liquor and the interior of the beans, which implies an invasive analysis, longer times, and waste generation. Therefore, in this paper, we assess the quality of cocoa beans based on their level of fermentation using a noninvasive system to obtain hyperspectral information, as well as fast image processing and spectral classification techniques. We obtained hyperspectral images of 90 cocoa beans in the range between 350 and 950 nm in an optical laboratory. In addition, each cocoa bean was classified according to its fermentation level: slightly fermented (SF), correctly fermented (CF), and highly fermented (HF). We compared this classification with that carried out by experts from the Colombia National Federation of Cocoa Growers and reported in the Colombian technical standard No. 1252. The results show that the level of fermentation of dried cocoa beans can be estimated using noninvasive hyperspectral image acquisition and processing techniques.Los granos de cacao son la materia prima de la industria del chocolate y un producto esencial para la economía de países tropicales como Colombia. El precio del grano depende principalmente de su calidad, determinada por diversos aspectos, tales como, buenas prácticas agrícolas, el punto de cosecha del fruto y la fermentación. Entidades que regulan el comercio internacional de granos de cacao promueven la creación de nuevas metodologías de clasificación que, en comparación con los métodos actuales, disminuyan el tiempo y los residuos y aumenten la cobertura de granos evaluados. Las imágenes hiperespectrales se han venido posicionando como una herramienta novedosa para el control de calidad de alimentos. Sin embargo, trabajos que analizan ciertos parámetros de la calidad del cacao mediante espectroscopía, también involucran etapas de estudio químico del polvo, el licor y el interior de los granos, lo que implica un análisis invasivo, así como un tiempo extenso y producción de residuos. Por lo tanto, este artículo analiza la calidad de granos de cacao a partir del parámetro estado de fermentación, usando un sistema no-invasivo de captura de información hiperespectral y técnicas rápidas de procesamiento de imágenes y clasificación espectral. Imágenes hiperespectrales de 90 granos de cacao en un rango de 350 a 950 nanómetros fueron adquiridos y se asignó una etiqueta a cada grano de cacao según su nivel de fermentación: poco, correcta y altamente fermentado. Esta clasificación se comparó con la realizada por profesionales de la federación nacional de cacaoteros a través de la norma técnica colombiana número 1252. Los resultados obtenidos muestran que es posible estimar el nivel de fermentación de granos secos de cacao usando técnicas no-invasivas de adquisición de y procesamiento de imágenes hiperespectrales

Marker development for the traceability of certified sustainably produced cacao (Theobroma cacao) in the chocolate industry

Theobroma cacao (cocoa) is one of the most studied commodities around the world and the source of one of the world’s most consumed and familiar products, chocolate. The multibillion-pound industry has changed to a higher demand for sustainably certified cacao (Rainforest Alliance, UTZ, and Fair Trade) and closer attention is being paid to how this such cacao can be traced. The present work describes a new concept, “From Shelf to Farm & Cooperative”, a study to identify the geographical origin of the fermented cacao beans used to manufacture premium and bulk chocolate products. The research sought to assess how DNA based approaches for traceability of food products can be utilised within the supply chain of cacao and chocolate. To identify the factors that influence cacao traceability and the importance of assessing it in different supply chain systems, multi-disciplinary stakeholders from policy makers, small-scale farmers in South and Central America, to the biggest cacao and chocolate manufacturers in Europe were interviewed. Two stages in chocolate production were identified as key to be screened for tracking implementation: The farm (Stage 1) to identify cacao trees genotype composition and the cooperative (Stage 2) where fermentation of cacao beans occur. A reliable modified cacao DNA extraction protocol was developed using the DNeasy mericon Food Kit which enable higher DNA yield from a range of chocolate products including, for the first time, ‘cocoa butter’. DNA markers characterising the chloroplast genome of T. cacao were assessed to trace back the chocolate to Stage 1 (farm). Reference genotypes from the International Cocoa Quarantine Centre at the University of Reading were screened with 25 chloroplast single sequence repeat (cpSSR) markers revealing a level of DNA polymorphism sufficient to reliably identify lineages below the species level to characterise farms. Allelic proportions for nine cpSSR were quantified and compared in DNA extracted from 116 chocolate samples revealing distinct clustering in single-origin chocolate produced from beans harvested in Peru, Ecuador, Venezuela, Trinidad and Madagascar. In contrast, no differentiation was observed for bulk chocolate samples (Mars, Nestle) and beans originating from Côte d'Ivoire farms thus reflecting the lack of allelic diversity found in cultivars in West Africa. To identify unique biomarkers for Stage 2 (cooperative), the fermentation microbiome was assessed by performing amplicon Illumina sequencing on 47 single origin chocolate using the universal 16S v3-v4 ribosomal region and three housekeeping genes from Acetobacter pasteurianus. Variation in microbiome diversity was characterised with unique Amplicon Sequence Variants (ASV) identified per continent, country and fermentation location for which signature bacterial profile was found to be conserved across years. Markers identified in Stage 1 and Stage 2 can be used for tracking cocoa beans origin. To make these biomarkers applicable in industrial scenarios, it will be essential to create a machine learning model that could recognize the specific markers from multiple regions

Book of abstracts, 4th World Congress on Agroforestry

International audienc

A Landsat-based analysis of tropical forest dynamics in the Central Ecuadorian Amazon : Patterns and causes of deforestation and reforestation

Tropical deforestation constitutes a major threat to the Amazon rainforest. Monitoring forest dynamics is therefore necessary for sustainable management of forest resources in this region. However, cloudiness results in scarce good quality satellite observations, and is therefore a major challenge for monitoring deforestation and for detecting subtle processes such as reforestation. Furthermore, varying human pressure highlights the importance of understanding the underlying forces behind these processes at multiple scales but also from an interand transdisciplinary perspective. Against this background, this study analyzes and recommends different methodologies for accomplishing these goals, exemplifying their use with Landsat timeseries and socioeconomic data. The study cases were located in the Central Ecuadorian Amazon (CEA), an area characterized by different deforestation and reforestation processes and socioeconomic and landscape settings. Three objectives guided this research. First, processing and timeseries analysis algorithms for forest dynamics monitoring in areas with limited Landsat data were evaluated, using an innovative approach based in genetic algorithms. Second, a methodology based in image compositing, multisensor data fusion and postclassification change detection is proposed to address the limitations observed in forest dynamics monitoring with timeseries analysis algorithms. Third, the evaluation of the underlying driving forces of deforestation and reforestation in the CEA are conducted using a novel modelling technique called geographically weight ridge regression for improving processing and analysis of socioeconomic data. The methodology for forest dynamics monitoring demonstrates that despite abundant data gaps in the Landsat archive for the CEA, historical patterns of deforestation and reforestation can still be reported biennially with overall accuracies above 70%. Furthermore, the improved methodology for analyzing underlying driving forces of forest dynamics identified local drivers and specific socioeconomic settings that improved the explanations for the high deforestation and reforestation rates in the CEA. The results indicate that the proposed methodologies are an alternative for monitoring and analyzing forest dynamics, particularly in areas where data scarcity and landscape complexity require approaches that are more specialized.Landsat-basierte Analyse der Dynamik tropischer Wälder im Zentral-Ecuadorianischen Amazonasgebiet: Muster und Ursachen von Abholzung und Wiederaufforstung Die tropische Entwaldung stellt eine große Bedrohung für den AmazonasRegenwald dar. Daher ist die Überwachung von Walddynamiken eine notwendige Maßnahme, um eine nachhaltige Bewirtschaftung der Waldressourcen in dieser Region zu gewährleisten. Jedoch verschlechtert Bewölkung die Qualität der Satellitenaufnahmen und stellt die hauptsächliche Herausforderung für die Überwachung der Entwaldung sowie die Detektierung einhergehender Prozesse, wie der Wiederaufforstung, dar. Darüber hinaus zeigt der unterschiedliche menschliche Nutzungsdruck, wie wichtig es ist, die zugrundeliegenden Kräfte hinter diesen Prozessen auf mehreren Ebenen, aber auch interund transdisziplinär, zu verstehen. Variierender anthropogener Einfluss unterstreicht die Notwendigkeit, unterschwellige Prozesse (oder "Driver") auf multiplen Skalen aus interund transdisziplinärer Sicht zu verstehen. Darauf basierend analysiert und empfiehlt die vorliegende Studie unterschiedliche Methoden, welche unter Verwendung von LandsatZeitreihen und sozioökonomischen Daten zur Erreichung dieser Ziele beitragen. Die Untersuchungsgebiete befinden sich im ZentralEcuadorianischen Amazonasgebiet (CEA). Einem Gebiet, das einerseits durch differenzierte Entwaldungsund Aufforstungsprozesse, andererseits durch seine sozioökonomischen und landschaftlichen Gegebenheiten geprägt ist. Das Forschungsprojekt hat drei Zielvorgaben. Erstens werden auf genetischen Algorithmen basierten Verfahren zur Verarbeitung der Zeitreihenanalyse für die Überwachung der Walddynamik in Gebieten, für die nur begrenzte LandsatDaten vorhanden waren, bewertet. Zweitens soll eine Methode in Anlehnung an Satellitenbildkompositen, Datenfusion von mehreren Satellitenbildern und Veränderungsdetektion gefunden werden, die Einschränkungen der Walddynamik durch Entwaldung mithilfe von ZeitreihenAlgorithmen thematisiert. Drittens werden die Ursachen der Entwaldung/Abholzung im CEA anhand der geographischen gewichteten RidgeRegression, die zur einen verbesserten Analyse der sozioökonomischen Information beiträgt, bewertet. Die Methodik für das WalddynamikMonitoring zeigt, dass trotz umfangreicher Datenlücken im LandsatArchiv für das CEA alle zwei Jahre die historischen Entwaldungsund Wiederaufforstungsmuster mit einer Genauigkeit von über 70% gemeldet werden können. Eine verbesserte Analysemethode trägt außerdem dazu bei, die für die Walddynamik verantwortlichen treibenden Kräfte zu identifizieren, sowie lokale Treiber und spezifische sozioökonomische Rahmenbedingungen auszumachen, die eine bessere Erklärung für die hohen Entwaldungsund Wiederaufforstungsraten im CEA aufzeigen. Die erzielten Ergebnisse machen deutlich, dass die vorgeschlagenen Methoden eine Alternative zum Monitoring und zur Analyse der Walddynamik darstellen; Insbesondere in Gebieten, in denen Datenknappheit und Landschaftskomplexität spezialisierte Ansätze erforderlich machen

Simulated prototype for the manufacturing process of chocolate bars based on industry 4.0 concepts

La industria 4.0 o cuarta revolución industrial, es un concepto que integra la industria automatizada, intercambio de información, y las tecnologías de manufactura modernas traducidas en computación, análisis de datos, conectividad, sistemas ciber físicos, Internet de las cosas (IoT), automatización, y sistemas en la nube. Basado en lo anterior, el presente proyecto de investigación se centró en el desarrollo de un prototipo simulado para el proceso de fabricación de barras de chocolate basado en conceptos de industria 4.0. Fue realizada la minería de datos a partir de la construcción de una base de datos mediante un instrumento de consulta tomando como referencia una muestra de 451 personas para estructurar un producto de alta participación y crecimiento de mercado, utilizando algoritmos de Inteligencia Artificial en la estimación de costos de materias primas en donde se hizo la validación del aprendizaje de los algoritmos y se estableció el modelo con mejores métricas de evaluación para el análisis de los resultados siendo el modelo Random Forest en la medida en que alcanzó una precisión del 96,64%, resultado que se pudo validar evaluando las métricas a través de la matriz de confusión. Finalmente, se implementó software de negocio inteligente para generar las órdenes de pedido de acuerdo con la información recopilada y visualizar el análisis de datos realizado. El aporte más importante del trabajo es mostrar que se puede realizar un despliegue de Negocio Inteligente a partir de la información recopilada de una muestra poblacional en función de sus preferencias en la fabricación de barras de chocolate mediante la minería de datos para la estructuración y normalización de una base de datos que contribuya en la identificación de información de valor para lograr una implementación de modelos de Machine Learning con métricas de evaluación superiores al 95%.RESUMEN ABSTRACT 1. INTRODUCCIÓN 1.1 Planteamiento del Problema 1.2 Justificación 1.3 Objetivo General 1.4 Objetivos Específicos 1.5 Hipótesis 2. MARCO REFERENCIAL Y TEÓRICO 2.1 Marco Referencial 2.2 Marco Teórico 2.2.1 Minería de datos 2.2.2 Machine Learning 2.2.2.1 Redes Neuronales Artificiales 2.2.2.2 Árboles de decisión 2.2.2.3 Random Forest 2.2.2.4 K Vecinos Más Cercanos 2.2.2.5 Regresión Logística 2.2.2.6 Máquinas de Vectores de Soporte 2.2.3 Negocio Inteligente 3. INGENIERÍA DEL PROYECTO 3.1 Metodología 3.2 Desarrollo 3.2.1 Definición del dataset 3.2.2 Preprocesamiento de datos 3.2.3 Procesamiento analítico de datos 3.2.4 Aplicación técnicas de optimización para cálculo de dimensiones 3.2.5 Aplicación de algoritmos de Machine Learning 3.2.6 Construcción del ERD 3.2.7 Implementación de Negocio Inteligente 4. ANÁLISIS DE RESULTADOS 4.1 Minería de datos 4.2 Métricas de evaluación de los algoritmos de Machine Learning 4.3 Dashboard del Negocio Inteligente 5. CONCLUSIONES 6. REFERENCIASIndustry 4.0, or fourth industrial revolution, is a concept that integrates automated industry, information exchange, and modern manufacturing technologies translated into computing, data analysis, connectivity, cyber-physical systems, Internet of Things (IoT), automation, and cloud systems. Based on the above, the present research project focused on the development of a simulated prototype for the chocolate bar manufacturing process based on Industry 4.0 concepts. Data mining was performed from the construction of a database by means of a query tool taking as reference a sample of 451 people to structure a product of high market participation and growth, using Artificial Intelligence algorithms in the estimation of raw material costs where the validation of the learning of the algorithms was done and the model with the best evaluation metrics for the analysis of the results was established being the Random Forest model insofar as it reached an accuracy of 96, 64%, a result that could be validated by evaluating the metrics through the confusion matrix. Finally, Business Intelligent software was implemented to generate orders according to the information collected and to visualize the data analysis performed. The most important contribution of the work is to show that an Intelligent Business deployment can be performed from the information collected from a population sample based on their preferences in the manufacture of chocolate bars through data mining for the structuring and normalization of a database that contributes to the identification of valuable information to achieve an implementation of Machine Learning models with evaluation metrics higher than 95%.Maestrí

Agroforestry-Based Ecosystem Services

As a dynamic interface between agriculture and forestry, agroforestry has only recently been formally recognized as a relevant part of land use with ‘trees outside forest’ in important parts of the world—but not everywhere yet. The Sustainable Development Goals have called attention to the need for the multifunctionality of landscapes that simultaneously contribute to multiple goals. In the UN decade of landscape restoration, as well as in response to the climate change urgency and biodiversity extinction crisis, an increase in global tree cover is widely seen as desirable, but its management by farmers or forest managers remains contested. Agroforestry research relates tree–soil–crop–livestock interactions at the plot level with landscape-level analysis of social-ecological systems and efforts to transcend the historical dichotomy between forest and agriculture as separate policy domains. An ‘ecosystem services’ perspective quantifies land productivity, flows of water, net greenhouse gas emissions, and biodiversity conservation, and combines an ‘actor’ perspective (farmer, landscape manager) with that of ‘downstream’ stakeholders (in the same watershed, ecologically conscious consumers elsewhere, global citizens) and higher-level regulators designing land-use policies and spatial zoning

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products