<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0377-9424</journal-id>
<journal-title><![CDATA[Agronomía Costarricense]]></journal-title>
<abbrev-journal-title><![CDATA[Agron. Costarricense]]></abbrev-journal-title>
<issn>0377-9424</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Costa Rica. Colegio de Ingenieros y Agrónomos. Ministerio de Agricultura y Ganadería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0377-94242024000200031</article-id>
<article-id pub-id-type="doi">10.15517/rac.v48i2.62465</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis de calidad de cacao utilizando espectroscopía de infrarrojo cercano portable: Desafíos para diferenciación geográfica]]></article-title>
<article-title xml:lang="en"><![CDATA[Quality analysis of cocoa using portable near-infrared spectroscopy (nir): Challenges towards geographical differentiation]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quesada-Arguedas]]></surname>
<given-names><![CDATA[Paola]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-Aguirre]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vargas-Martínez]]></surname>
<given-names><![CDATA[Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mencía-Guevara]]></surname>
<given-names><![CDATA[Alejandra]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Laboratorio de Calidad e Innovación Agroalimentaria (LABCIAGRO Universidad Nacional ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Escuela de Ciencias Agrarias ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Laboratorio de Calidad e Innovación Agroalimentaria (LABCIAGRO Universidad Nacional ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>48</volume>
<numero>2</numero>
<fpage>31</fpage>
<lpage>43</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0377-94242024000200031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0377-94242024000200031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0377-94242024000200031&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Introducción. La combinación de curvas de calibración generadas por espectroscopía de infrarrojo cercano (NIR) con aprendizaje profundo ofrece una oportunidad para desarrollar métodos de discriminación de la calidad y origen del cacao, apoyando estrategias de valorización territorial y trazabilidad de cacaos diferenciados. Objetivo. Validar la aplicabilidad de un método de discriminación de cacaos según su origen geográfico, mediante técnicas de espectroscopía NIR portátil y aprendizaje profundo. Materiales y métodos. Se recolectaron 193 muestras de granos de cacao fermentados y secos de diversas regiones de Costa Rica, utilizando 72 muestras para calibración y 121 para predicción. Las muestras se analizaron para determinar su composición proximal, acidez titulable y compuestos fenólicos. Se empleó un espectrofotómetro NIR para recopilar datos espectrales (400-1700 nm). El preprocesamiento de los datos espectrales permitió desarrollar modelos de regresión para predecir características químicas. Para el modelo de clasificación geográfica, se eliminó el ruido de los espectros y se realizó un análisis de conglomerados, usando la distancia Gower y el método de agrupamiento Ward sobre los componentes obtenidos mediante Análisis de Componentes Principales (ACP). Resultados. La grasa fue el principal componente presente en el conjunto de muestras (&gt;39,67%). El análisis espectral demostró que el NIR puede diferenciar el cacao según el grado de fermentación y el contenido de compuestos fenólicos. El modelo de regresión de mínimos cuadrados parciales extendida (XLS) mostró la mejor capacidad predictiva para las propiedades químicas. El agrupamiento por origen geográfico identificó cuatro grupos influenciados principalmente por propiedades químicas relacionadas con prácticas poscosecha. Conclusión. El modelo de regresión lineal empleado demostró ser superior en la predicción de características químicas proximales. Se observó que la limitada diversidad genética y las prácticas poscosecha estandarizadas podrían reducir la variabilidad de calidad asociada al origen geográfico, limitando la utilidad del NIR en la identificación del origen y la trazabilidad. Se sugiere explorar espectros más amplios y equipos adicionales para análisis multivariados avanzados.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Introduction.The combination of calibration curves generated by near-infrared (NIR) spectroscopy with deep learning offers an opportunity to develop methods for discriminating the quality and origin of cocoa, supporting strategies for territorial valorization and traceability of differentiated cocoas. Objective. To validate the applicability of a method for discriminating cocoa according to its geographical origin using portable NIR spectroscopy and deep learning techniques. Materials and methods. A total of 193 samples of fermented and dried cocoa beans from different regions of Costa Rica were collected, using 72 samples for calibration and 121 for prediction. The samples were analyzed to determine their proximal composition, titratable acidity, and phenolic compounds. A NIR spectrophotometer was used to collect spectral data (400-1700 nm). Spectral data preprocessing allowed for the development of regression models to predict chemical characteristics. For the geographical classification model, noise was removed from the spectra, and cluster analysis was performed using the Gower distance and Ward's clustering method on components obtained through Principal Component Analysis (PCA). Results. Fat was the main component present in the sample set (&gt;39.67%). Spectral analysis demonstrated that NIR can differentiate cocoa based on the degree of fermentation and phenolic compound content. The extended partial least squares (XLS) regression model showed the best predictive capacity for chemical properties. Clustering by geographical origin identified four groups mainly influenced by chemical properties related to post-harvest practices. Conclusion. The linear regression model used proved superior in predicting proximal chemical characteristics. It was observed that limited genetic diversity and standardized post-harvest practices could reduce the quality variability associated with geographical origin, limiting the utility of NIR in origin identification and traceability. It is suggested to explore broader spectra and additional equipment for advanced multivariate analyses.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Theobroma cacao L.]]></kwd>
<kwd lng="es"><![CDATA[NIR]]></kwd>
<kwd lng="es"><![CDATA[análisis multivariado]]></kwd>
<kwd lng="es"><![CDATA[origen geográfico]]></kwd>
<kwd lng="es"><![CDATA[poscosecha.]]></kwd>
<kwd lng="en"><![CDATA[Theobroma cacao L.]]></kwd>
<kwd lng="en"><![CDATA[NIR]]></kwd>
<kwd lng="en"><![CDATA[multivariate analysis]]></kwd>
<kwd lng="en"><![CDATA[geographic origin]]></kwd>
<kwd lng="en"><![CDATA[post-harvest.]]></kwd>
</kwd-group>
</article-meta>
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