Monitoring spinach shelf-life with hyperspectral image through

Different procedures for monitoring the evolution of leafy vegetables, under plastic covers during cold storage, have been studied. Fifteen spinach leaves were put inside Petri dishes covered with three different plastic films and stored at 4 °C for 21 days. Hyperspectral images were taken during this storage. A radiometric correction is proposed in order to avoid the variation in transmittance of the plastic films during time in the hyperspectral images. Afterwards, three spectral pre-processing procedures (no pre-process, Savitsky–Golay and Standard Normal Variate, combined with Principal Component Analysis) were applied to obtain different models. The corresponding artificial images of scores were studied by means of Analysis of Variance to compare their ability to sense the aging of the leaves. All models were able to monitor the aging through storage. Radiometric correction seemed to work properly and could allow the supervision of shelf-life in leafy vegetables through commercial transparent films.

Application of Improved Direct Calibration for Hyperspectral image processing: Detecting peanut traces in wheat flour.

NIR Hyperspectral imaging (1000-2500 nm) combined with IDC allowed the detection of peanut traces down to adulteration percentages 0.01% Contrary to PLSR, IDC does not require a calibration set, but uses both expert and experimental information and suitable for quantification of an interest compound in complex matrices. The obtained results shows the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA

Monitoring leafy vegetables through packaging films with

Fresh-cut or minimally processed fruit and vegetables have been physically modified from its original form (by peeling, trimming, washing and cutting) to obtain a 100% edible product that is subsequently packaged (usually under modified atmosphere packaging –MAP) and kept in refrigerated storage. In fresh-cut products, physiological activity and microbiological spoilage, determine their deterioration and shelf-life. The major preservation techniques applied to delay spoilage are chilling storage and MAP, combined with chemical treatments antimicrobial solutions antibrowning, acidulants, antioxidants, etc.). The industry looks for safer alternatives. Consequently, the sector is asking for innovative, fast, cheap and objective techniques to evaluate the overall quality and safety of fresh-cut products in order to obtain decision tools for implementing new packaging materials and procedures. In recent years, hyperspectral imaging technique has been regarded as a tool for analyses conducted for quality evaluation of food products in research, control and industries. The hyperspectral imaging system allows integrating spectroscopic and imaging techniques to enable direct identification of different components or quality characteristics and their spatial distribution in the tested sample. The objective of this work is to develop hyperspectral image processing methods for the supervision through plastic films of changes related to quality deterioration in packed readyto-use leafy vegetables during shelf life. The evolutions of ready-to-use spinach and watercress samples covered with three different common transparent plastic films were studied. Samples were stored at 4 ºC during the monitoring period (until 21 days). More than 60 hyperspectral images (from 400 to 1000 nm) per species were analyzed using ad hoc routines and commercial toolboxes of MatLab®. Besides common spectral treatments for removing additive and multiplicative effects, additional correction, previously to any other correction, was performed in the images of leaves in order to avoid the modification in their spectra due to the presence of the plastic transparent film. Findings from this study suggest that the developed images analysis system is able to deal with the effects caused in the images by the presence of plastic films in the supervision of shelf-life in leafy vegetables, in which different stages of quality has been identified.

Generación de una librería RVC - CAL para la etapa de estimación de abundancias en el proceso de análisis de imágenes hiperespectrales

Las imágenes hiperespectrales permiten extraer información con una gran resolución espectral, que se suele extender desde el espectro ultravioleta hasta el infrarrojo. Aunque esta tecnología fue aplicada inicialmente a la observación de la superficie terrestre, esta característica ha hecho que, en los últimos años, la aplicación de estas imágenes se haya expandido a otros campos, como la medicina y, en concreto, la detección del cáncer. Sin embargo, este nuevo ámbito de aplicación ha generado nuevas necesidades, como la del procesado de las imágenes en tiempo real. Debido, precisamente, a la gran resolución espectral, estas imágenes requieren una elevada capacidad computacional para ser procesadas, lo que imposibilita la consecución de este objetivo con las técnicas tradicionales de procesado. En este sentido, una de las principales líneas de investigación persigue el objetivo del tiempo real mediante la paralelización del procesamiento, dividiendo esta carga computacional en varios núcleos que trabajen simultáneamente. A este respecto, en el presente documento se describe el desarrollo de una librería de procesado hiperespectral para el lenguaje RVC - CAL, que está específicamente pensado para el desarrollo de aplicaciones multimedia y proporciona las herramientas necesarias para paralelizar las aplicaciones. En concreto, en este Proyecto Fin de Grado se han desarrollado las funciones necesarias para implementar dos de las cuatro fases de la cadena de análisis de una imagen hiperespectral - en concreto, las fases de estimación del número de endmembers y de la estimación de la distribución de los mismos en la imagen -; conviene destacar que este trabajo se complementa con el realizado por Daniel Madroñal en su Proyecto Fin de Grado, donde desarrolla las funciones necesarias para completar las otras dos fases de la cadena. El presente documento sigue la estructura clásica de un trabajo de investigación, exponiendo, en primer lugar, las motivaciones que han cimentado este Proyecto Fin de Grado y los objetivos que se esperan alcanzar con él. A continuación, se realiza un amplio análisis del estado del arte de las tecnologías necesarias para su desarrollo, explicando, por un lado, las imágenes hiperespectrales y, por otro, todos los recursos hardware y software necesarios para la implementación de la librería. De esta forma, se proporcionarán todos los conceptos técnicos necesarios para el correcto seguimiento de este documento. Tras ello, se detallará la metodología seguida para la generación de la mencionada librería, así como el proceso de implementación de una cadena completa de procesado de imágenes hiperespectrales que permita la evaluación tanto de la bondad de la librería como del tiempo necesario para analizar una imagen hiperespectral completa. Una vez expuesta la metodología utilizada, se analizarán en detalle los resultados obtenidos en las pruebas realizadas; en primer lugar, se explicarán los resultados individuales extraídos del análisis de las dos etapas implementadas y, posteriormente, se discutirán los arrojados por el análisis de la ejecución de la cadena completa, tanto en uno como en varios núcleos. Por último, como resultado de este estudio se extraen una serie de conclusiones, que engloban aspectos como bondad de resultados, tiempos de ejecución y consumo de recursos; asimismo, se proponen una serie de líneas futuras de actuación con las que se podría continuar y complementar la investigación desarrollada en este documento. ABSTRACT. Hyperspectral imaging collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for example, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. For that reason, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization: in order to reduce the computational load, this solution executes image analysis in several processors simultaneously; in that way, this computational load is divided among the different cores, and real-time specifications can be accomplished. This document describes the construction of a new hyperspectral processing library for RVC - CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This Diploma Project develops the required library functions to implement two of the four stages of the hyperspectral imaging processing chain - endmember and abundance estimations -. The two other stages - dimensionality reduction and endmember extraction - are studied in the Diploma Project of Daniel Madroñal, which complements the research work described in this document. The document follows the classical structure of a research work. Firstly, it introduces the motivations that have inspired this Diploma Project and the main objectives to achieve. After that, it thoroughly studies the state of the art of the technologies related to the development of the library. The state of the art contains all the concepts needed to understand the contents of this research work, like the definition and applications of hyperspectral imaging and the typical processing chain. Thirdly, it explains the methodology of the library implementation, as well as the construction of a complete processing chain in RVC - CAL applying the mentioned library. This chain will test both the correct behavior of the library and the time requirements for the complete analysis of one hyperspectral image, either executing the chain in one processor or in several ones. Afterwards, the collected results will be carefully analyzed: first of all, individual results -from endmember and abundance estimations stages - will be discussed and, after that, complete results will be studied; this results will be obtained from the complete processing chain, so they will analyze the effects of multithreading and system parallelization on the mentioned processing chain. Finally, as a result of this discussion, some conclusions will be gathered regarding some relevant aspects, such as algorithm behavior, execution times and processing performance. Likewise, this document will conclude with the proposal of some future research lines that could continue the research work described in this document.

Implementación de una Support Vector Machine en RVC – CAL para imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar el clasificador conocido como Support Vector Machine – SVM. Cabe mencionar que este trabajo complementa el realizado en [1] y [2] donde se desarrollaron las funciones necesarias para implementar una cadena de procesado que utiliza el método unmixing para procesar la imagen hiperespectral. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Trabajo de Investigación y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como sus métodos de procesado y, en concreto, se detallará el método que utiliza el clasificador SVM. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para convertir una versión en Matlab del clasificador SVM optimizado para analizar imágenes hiperespectrales; un punto importante en este apartado es que se desarrolla la versión secuencial del algoritmo y se asientan las bases para una futura paralelización del clasificador. Tras explicar el método utilizado, se exponen los resultados obtenidos primero comparando ambas versiones y, posteriormente, analizando por etapas la versión adaptada al lenguaje RVC – CAL. Por último, se aportan una serie de conclusiones obtenidas tras analizar las dos versiones del clasificador SVM en cuanto a bondad de resultados y tiempos de procesado y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement the Support Vector Machine – SVM - classifier. This research complements the research conducted in [1] and [2] where the necessary functions to implement the unmixing method to analyze hyperspectral images were developed. The document is divided in several chapters. The first of them introduces the motivation of the Master Thesis and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images, their processing methods and, concretely, the SVM classifier. Once we have exposed the theoretical bases, we will explain the followed methodology to translate a Matlab version of the SVM classifier optimized to process an hyperspectral image to RVC – CAL language; one of the most important issues in this chapter is that a sequential implementation is developed and the bases of a future parallelization of the SVM classifier are set. At this point, we will expose the results obtained in the comparative between versions and then, the results of the different steps that compose the SVM in its RVC – CAL version. Finally, we will extract some conclusions related with algorithm behavior and time processing. In the same way, we propose some future research lines according to the results obtained in this document.

Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.