24 research outputs found
Non-destructive determination of pre-symptomatic biochemical markers for Peteca spot and evaluation of edible coatings for reducing the incidence of the disorder on ‘Eureka’ lemons
Masters degree. University of KwaZulu-Natal, Pietermaritzburg.International markets that import citrus fruit from South Africa have imposed regulations that involve cold sterilization at low temperatures, which cause physiological disorders such as peteca spot in lemon. The aim of this study was to, non-destructively determine pre-symptomatic biochemical markers for Peteca spot and the evaluation of edible coatings for reducing the incidence of the disorder on ‘Eureka’ lemons. The first chapter is general background which introduces the key words and clearly outlines the aim and objectives of the study. The second chapter is review of literature, which motivated the three research chapters due to the gaps found. Presymptomatic biochemical markers that are related to peteca spot were evaluated in the third chapter. The Principal Component Analysis (PCA) was able to separate fruit harvested from the inside and outside canopy positions based on their susceptibility to the disorder. Fruit harvested in the inside canopy were more susceptible to peteca spot and these were correlated with physic-chemical properties, which were typically low in the inside canopy. The efficacy of carboxymethyl cellulose (CMC) and chitosan (CH) incorporated with moringa leaf extracts (M) edible coatings on reducing the incidence of peteca spot was also evaluated in the fourth chapter. Fruit harvested from inside and outside canopy positions were assigned to five coating treatments: control, M+CMC, CMC, CH and M+CH. The most effective coating treatment in reducing the susceptibility of ‘Eureka’ lemon to peteca spot was M+CMC followed by CMC and CH. The fifth chapter focused on, non-destructively predicting peteca spot using visible to near infrared spectroscopy (vis/NIRS). Presymptomatic biochemical markers that have been related to peteca spot were successfully predicted. Lastly, general discussions and conclusions were made in chapter six as well as recommendations
Application of Visible to Near-Infrared Spectroscopy for Non-Destructive Assessment of Quality Parameters of Fruit
The accuracy and robustness of prediction models are very important to the successful commercial application of visible to near-infrared spectroscopy (Vis-NIRS) on fruit. The difference in physiological characteristics of fruit is very wide, which necessitates variance in the type of spectrometers applied to collect spectral data, pre-processing of the collected data and chemometric techniques used to develop robust models. Relevant practices of data collection, processing and the development of models are a challenge because of the required knowledge of fruit physiology in addition to the Vis-NIRS expertise of a researcher. This chapter deals with the application of Vis-NIRS on fruit by discussing commonly used spectrometers, data chemometric treatment and common models developed for assessing quality of specific types of fruit. The chapter intends to create an overview of commonly used techniques for guiding general users of these techniques. Current status, gaps and future perspectives of the application of Vis-NIRS on fruit are also discussed for challenging researchers who are experts in this research field
Evaluation of quality and potential bioactive of fruit drinks : application of spectroscopy on infrared and chemometric
Orientador: Juliana Azevedo Lima PalloneDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: O Brasil é o terceiro maior produtor de frutas no mundo. Dentre as frutas produzidas no território nacional, o caju (2 milhões toneladas/ano), goiaba (345 mil toneladas/ano) e uva (1,4 milhões de toneladas/ano), merecem destaque pela grande produção e variedade de produtos gerados a partir deles (sucos, néctares, polpas, doces, refrigerantes, etc). O setor de bebidas, sucos e néctares, tem sido responsável pela movimentação de US 1.9 billion from the sale of 476 million liters/ year, this share of the market has been driven by consumers looking for healthier products. Nectars of cashew apple and guava, present high content of vitamin C found in fruits (average 230 mg/100g and 600 mg/100g, respectively) and grape juice has a significant amount of phenolic compounds, especially anthocyanins. Ascorbic acid, phenolic compounds, including anthocyanins are labile compounds and subject to oxidative degradation, especially in aqueous media, such as drinks. To ensure the quality of nectars Brazilian legislation defined acidity parameters (TA) and total sugars (TS), soluble solids (SS), pH and ascorbic acid (AA) that are commonly performed by traditional analyzes involving the use of toxic substances, danger to the analyst and the environment, and the need for specific equipment. Similar scenario is found to analyze concentration of total phenolics content (TPC) and anthocyanins content (TAC) of grape juice, with the aggravation that these analyzes, in general, are time consuming and therefore impair the stability of the bioactive compounds during the process. As an alternative to these problems, this work proposes to use the spectroscopic analysis NIR and/or MIR along with chemometrics as an alternative to analyze nectars cashew and guava (ACT, AT, SS, pH and AA) and grape juice (TF and TA), to replace the traditional analysis. The spectra obtained by transflectance were preprocessed to reduce multiplicative effects (MSC/SNV), to improve signal/noise ratio (smoothing using Savitzky-Golay), baseline correction (derived by Savitzky-Golay) and mean centered. Among the PLS calibration models constructed for the analyzes in nectars stood out the AA model for cashew¿s nectar (RMSEP= 4.8 mg/100g and RMSEC= 4.6 mg/100g) and AT for guava¿s nectar (RMSEP = 0.315% and RMSEC = 0.297%), the other models presented good values of R² (> 0.7) in addition to low values of RMSEP and RMSEC. Calibration models using NIR and MIR constructed to determine TA and TF in grape juice showed similar performance. MIR and NIR models for TA forecast RMSEP had low values (4,22mg / 100mL and 4,44mg / 100 mL, respectively) and for predicting TF, MIR presented a slightly smaller RMSEP than presented by NIR (2.12 EqAGmg/ml and 3.71 EqAGmg/mL, respectively). The RMSEP values found for all models built in this work show that spectroscopic analysis, NIR and MIR, can act as a substitute for traditional analyzes for quality control nectars and bioactive compounds in grape juice, with the advantages of being chemically green, fast and efficient, and do not require sample preparation, avoiding errors due to instability of the compounds evaluatedMestradoCiência de AlimentosMestra em Ciência de Alimentos145658/2014-72015/15848-0CNPQFAPES
Automatic early detection of decay in citrus fruit using optical technologies and machine learning techniques
Los cítricos representan el cultivo frutal de mayor valor en términos de comercio internacional, siendo España el primer exportador mundial de cítricos para consumo en fresco. Sin embargo, la presencia de podredumbres causadas por hongos del género Penicillium se encuentra entre los principales problemas que afectan la postcosecha y comercialización de cítricos. Un número reducido de frutas infectadas puede contaminar una partida completa de cítricos durante el almacenamiento de la fruta por largos períodos de tiempo o en el transporte al extranjero, lo que conlleva grandes pérdidas económicas y el desprestigio de los productores de cítricos. Por lo tanto, la detección temprana de infecciones por hongos de forma efectiva y la eliminación de la fruta infectada son asuntos de especial interés en los almacenes de confección de fruta para impedir la propagación de las infecciones fúngicas, asegurando de esta forma una excelente calidad de la fruta y la ausencia total de fruta infectada. En este sentido, la presente tesis doctoral se centra en abordar un reto tan importante para la industria citrícola como es la automatización del proceso de detección de podredumbres incipientes, con el fin de proporcionar alternativas a la inspección manual con peligrosa luz ultravioleta que permitan realizar esta detección de forma más eficiente y, en consecuencia, reducir potencialmente el uso de fungicidas. En concreto, esta tesis doctoral avanza en el campo de la detección automática de podredumbres en cítricos mediante sistemas ópticos y técnicas de aprendizaje automático. Específicamente, se investigan tres técnicas ópticas diferentes que operan en las regiones del visible e infrarrojo cercano del espectro electromagnético, incluyendo la técnica de imagen basada en backscattering, visión hiperespectral y espectroscopía. Los sistemas ópticos usados en esta tesis no están limitados a la parte visible del espectro, por lo que sus capacidades superan a las del ojo humano y a las de los sistemas de visión convencionales basados en cámaras de color, lo cual resulta de especial interés para detectar daños en cítricos que son difícilmente visibles a simple vista, como las podredumbres en estadios tempranos de infección. Además, se exploran numerosas técnicas de aprendizaje automático de reducción de la dimensionalidad de los datos y clasificación, con la finalidad de usar las medidas ópticas de los cítricos para discriminar la fruta afectada por podredumbre de la fruta sana. Las tres técnicas ópticas, junto con métodos de aprendizaje automático adecuados, proporcionan buenos resultados en la clasificación de la piel de los frutos cítricos en sana o podrida, consiguiendo un porcentaje de muestras bien clasificadas superior al 90% para ambas clases, a pesar de la gran similitud entre ellas. En vista de los resultados obtenidos, esta tesis doctoral sienta las bases para la futura implementación de las técnicas ópticas estudiadas en un sistema comercial de clasificación automática de fruta destinado a la detección de podredumbres en cítricos.Citrus fruit is the highest value fruit crop in terms of international trade, with Spain being the first worldwide exporter of citrus fruit for fresh consumption. However, the presence of decay caused by Penicillium spp. fungi is among the main problems affecting postharvest and marketing processes of citrus fruit. A small number of decayed fruit can infect a whole consignment, during long-term storage or fruit shipping to export markets, thus involving enormous economic losses and the blackening of the reputation of citrus producers. Therefore, effective early detection of fungal infections and removal of infected fruit are issues of major concern in commercial packinghouses in order to prevent the spread of the infections, thus ensuring an excellent fruit quality and absolute absence of infected fruit. In this respect, this doctoral thesis focuses on addressing such an important challenge for the citrus industry as the automation of the detection of early symptoms of decay, in order to provide alternatives to human inspection under dangerous ultraviolet illumination, thus accomplishing this detection task more efficiently and, consequently, leading to a possible reduction of the use of fungicides. Specifically, this doctoral thesis advances in the field of the automatic detection of decay in citrus fruit using optical systems and machine learning methods. In particular, three different optical techniques operating in the visible and near-infrared spectral regions are investigated, including hyperspectral imaging, light backscattering imaging and spectroscopy. The optical systems used in this thesis are not limited to the visible part of the electromagnetic spectrum, thus presenting capabilities beyond those of the naked human eye and traditional computer vision systems based on colour cameras, this fact being of special interest for detecting hardly-visible damage in citrus fruit, such as decay at early stages. Furthermore, a vast number of machine learning techniques aimed at data dimensionality reduction and classification are explored for dealing with the optical measurements of citrus fruit in order to discriminate fruit with symptoms of decay from sound fruit. The three optical techniques, coupled with suitable machine learning methods, investigated in this doctoral thesis provide good results in the classification of skin of citrus fruit into sound or decaying, with a percentage of well-classified samples above 90% for both classes despite their similarity. In the light of the results, this doctoral thesis lays the foundation for the future establishment of the explored optical technologies on a commercial fruit sorter aimed at decay detection in citrus fruit
Effects Of White Light Emitting Diodes And Halogen Lamp On Spectroscopic Measurement Of Sala Mango Intrinsic Qualities
Spektroskopi merupakan salah satu teknik yang paling berkesan dalam penilaian kualiti buah-buahan terutamanya mangga. Spektrum pantulan yang diperolehi daripada sistem spektrometer sangat dipengaruhi oleh cahaya yang digunakan dalam system ini
Spectroscopy is among one of the most promising techniques for assessment of fruit quality particularly mango. Reflectance spectrum obtained from spectrometer system is greatly affected by lighting used in the syste
Functional Coatings for Food Packaging Applications
The food packaging industry is experiencing one of the most relevant revolutions associated with the transition from fossil-based polymers to new materials of renewable origin. However, high production costs, low performance, and ethical issues still hinder the market penetration of bioplastics. Recently, coating technology was proposed as an additional strategy for achieving a more rational use of the materials used within the food packaging sector. According to the packaging optimization concept, the use of multifunctional thin layers would enable the replacement of multi-layer and heavy structures, thus reducing the upstream amount of packaging materials while maintaining (or even improving) the functional properties of the final package to pursue the goal of overall shelf life extension. Concurrently, the increasing requirements among consumers for convenience, smaller package sizes, and for minimally processed, fresh, and healthy foods have necessitated the design of highly sophisticated and engineered coatings. To this end, new chemical pathways, new raw materials (e.g., biopolymers), and non-conventional deposition technologies have been used. Nanotechnology, in particular, paved the way for the development of new architectures and never-before-seen patterns that eventually yielded nanostructured and nanocomposite coatings with outstanding performance. This book covers the most recent advances in the coating technology applied to the food packaging sector, with special emphasis on active coatings and barrier coatings intended for the shelf life extension of perishable foods
Innovations in non-destructive techniques for fruit quality control applied to manipulation and inspection lines
Tesis por compendioLa industria alimentaria, concretamente el sector poscosecha, necesita innovar en sus procesos productivos, optimizando los mismos para rentabilizar sus actividades, garantizando productos de calidad capaces de satisfacer las necesidades de los consumidores.
La presente tesis doctoral se centra en evaluar el potencial de la espectroscopia VIS-NIR para la caracterización e inspección de la calidad de la fruta tanto fuera de línea como a tiempo real en procesos automatizados.
En un primer lugar, la viabilidad de la técnica se estudió a nivel de laboratorio en estado estático (off-line), con el fin de conocer y optimizar las condiciones de medición. Posteriormente, se evaluó la calidad interna y externa de diferentes tipos de frutas como son caqui, nectarina y mango. En una segunda etapa, se llevó a cabo una automatización de los procesos de inspección mediante el desarrollo de nuevos prototipos in-line. Para este propósito, y con el objetivo de completar y corroborar los resultados obtenidos de manera estática, se estudió la integración de dos sondas VIS-NIR en una garra robótica capaz de manipular mangos. Finalmente, se estudió la integración de una sonda VIS-NIR a una cinta transportadora.
Los resultados obtenidos a nivel estático han demostrado que la espectroscopia VIS-NIR es un método no destructivo muy prometedor para predecir la astringencia en caqui. Así mismo, ha demostrado ser una adecuada herramienta para clasificar al 100% entre variedades de nectarinas como "Big Top" y "Diamond Ray" con una apariencia externa e interna muy similar, pero con diferentes propiedades organolépticas. De manera similar, fue posible clasificar al 100% variedades como "Big Top" y "Magique" de apariencia externa y composición similar pero distinto color de pulpa., y además se desarrolló un índice de calidad interna (IQI) para evaluar la calidad de las nectarinas. Por lo que respecta a los trabajos off-line realizados con mangos de la variedad "Osteen", fue posible predecir su calidad interna mediante los índices de madurez (RPI) y de calidad (IQI) con un gran rendimiento. A su vez, los ensayos experimentales efectuados con estos mismos mangos bajo la manipulación no destructiva de una garra robótica, demostraron que los mejores modelos eran capaces de predecir tanto la firmeza mecánica, el contenido en sólidos solubles, la luminosidad de la pulpa, así como el índice RPI de las muestras en base a la información obtenida por los acelerómetros instalados en los dedos de la garra robótica.
En cuanto a los ensayos realizados de manera in-line, el primer prototipo desarrollado se basó en la integración de dos sondas VIS-NIR en una garra robótica dispuesta con dos acelerómetros. El sistema desarrollado permitió alcanzar una buena estimación de la calidad del mango a través del índice RPI fusionando la información tanto de los espectros VIS-NIR como del impacto no destructivo de los acelerómetros. De este modo quedó demostrado que era posible obtener una predicción similar trabajando de forma in-line como trabajando de manera off-line para la predicción del mismo índice de calidad en mangos. El segundo prototipo in-line desarrollado se basa en la integración de una sonda VIS-NIR en una cinta transportadora para la identificación de distintas variedades y orígenes de manzanas. El prototipo desarrollado permitió registrar resultados de clasificación tan buenos como los efectuados de manera off-line con, por ejemplo, nectarina.
De este modo, se puede concluir que la espectroscopia VIS-NIR permite monitorear la calidad y clasificar fruta poscosecha tanto en modo off-line como in-line. Los nuevos prototipos desarrollados aportan claras ventajas respecto a los procesos tradicionales realizados a mano, como son la reducción del tiempo de inspección, la disminución de la cantidad de residuos generados y la posibilidad de inspeccionar toda la producción, obteniendo así un análisis más estandarizThe food industry, concretely the post-harvest sector, needs to innovate in their production processes, optimizing them to make their activities profitable, guaranteeing quality products capable of satisfying the needs of consumers.
The present doctoral thesis focuses on evaluating the potential of visible and near infrared spectroscopy (VIS-NIR) for the characterization and inspection of fruit quality both off-line and in real time in automated processes.
Firstly, the viability of the technique was studied at the laboratory level in a static mode (off-line), in order to know and optimise the measurement conditions. Subsequently, the internal and external quality of different types of fruits such as persimmon, nectarine and mango were evaluated. Secondly, an automation of the inspection processes was carried out through the development of new in-line prototypes. For this purpose, and with the aim of completing and corroborating the results obtained in a static mode, the integration of two VIS-NIR probes in a robotic gripper capable of manipulating mangoes was studied. Finally, the integration of a VIS-NIR probe to a conveyor belt was studied as an in-line monitoring tool on the inspection process of different apple varieties.
The results obtained in static mode have shown that VIS-NIR spectroscopy is a very promising non-destructive method to predict the astringency in persimmon. Likewise, it has demonstrated to be an adequate tool to classify 100% between nectarine varieties such as 'Big Top' and 'Diamond Ray' with very similar external and internal appearance, but with different organoleptic properties. Similarly, it was possible to classify 100% varieties such as 'Big Top' and 'Magique' with external appearance and similar composition but different pulp colour. An internal quality index (IQI) was developed to evaluate the quality of nectarines, which can be predicted through VIS-NIR spectroscopy. Regarding the off-line work carried out with mangoes of 'Osteen' variety, it was possible to predict its internal quality through the indexes of maturity (RPI) and quality (IQI) with a high performance. Moreover, the experimental tests carried out with these same mangoes under the non-destructive manipulation of a robotic gripper, showed that the best models were able to predict both the mechanical firmness, the soluble solids content, the brightness of the pulp, as well as the RPI index of the samples based on the information obtained by the accelerometers installed on the fingers of the robotic gripper.
Regarding the tests carried out in an in-line mode, the first developed prototype was based on the integration of two VIS-NIR probes in a robotic gripper fitted with two accelerometers. The developed system allowed reaching a good estimation of mango quality through the RPI index. In this way, it was demonstrated that it was possible to obtain a similar prediction working in-line as off-line mode for the prediction of the same quality index in mangoes. The second developed in-line prototype is based on the integration of a VIS-NIR probe in a conveyor belt for the identification of different varieties and origins of apples, achieving a success rate of 98% with the system. The developed prototype allowed to register classification results as good as those carried out off-line with, for example, nectarine.
In this way, it can be concluded that VIS-NIR spectroscopy allows monitoring the quality and classifying post-harvest fruit in both off-line and in-line mode, being a tool that allows improving and guaranteeing the correct quality and food safety. The new developed prototypes provide clear advantages over the traditional processes performed by hand, such as the reduction of inspection time, the reduction of the amount of waste generated by destructive quality analysis and the possibility of inspecting full production, obtaining a more standardised analysis of the quality of the products.La indústria alimentària, concretament el sector postcollita, necessita innovar en els seus processos productius, optimitzant els mateixos per a rendibilitzar les seues activitats, garantint productes de qualitat capaços de satisfer les necessitats dels consumidors.
La present tesi doctoral es centra en avaluar el potencial de l'espectroscòpia visible i infraroig pròxim (VIS-NIR) per a la caracterització i la inspecció de la qualitat de la fruita tant fora de línia com a temps real en processos automatitzats.
En un primer lloc, la viabilitat de la tècnica es va estudiar a nivell de laboratori en estat estàtic (off-line), a fi de conéixer i optimitzar les condicions de mesurament. Posteriorment, es va avaluar la qualitat interna i externa de diferents tipus de fruites com són caqui, nectarina i mango. En una segona etapa, es va dur a terme una automatització dels processos d'inspecció per mitjà del desenvolupament de nous prototips in-line. Per aquest propòsit, i amb l'objectiu de completar i corroborar els resultats obtinguts de manera estàtica, es va estudiar la integració de dos sondes VIS-NIR en una garra robòtica capaç de manipular. Finalment, es va estudiar la integració d'una sonda VIS-NIR a una cinta transportadora.
Els resultats obtinguts a nivell estàtic han demostrat que l'espectroscòpia VIS-NIR és un mètode no destructiu molt prometedor per a predir l'astringència en caqui. Així mateix, ha demostrat ser una adequada ferramenta per a classificar al 100% entre varietats de nectarines com "Big Top" i "Diamond Ray" amb una aparença externa i interna molt semblant, però amb diferents propietats organolèptiques. De manera semblant, va ser possible classificar al 100% varietats com "Big Top" i "Magique" d'aparença externa i composició semblant però distint color de polpa. Es va desenvolupar un índex de qualitat interna (IQI) per avaluar la qualitat de les nectarines. Pel que fa als treballs off-line realitzats amb mangos de la varietat "Osteen" va ser possible predir la seua qualitat interna mitjançant els índexs de maduresa (RPI) i de qualitat (IQI) amb un gran rendiment. Al mateix temps, els assajos experimentals efectuats amb estos mateixos mangos baix la manipulació no destructiva d'una garra robòtica, van demostrar que els millors models eren capaços de predir tant la fermesa mecánica, el contingut en sòlids solubles, la lluminositat de la polpa, així com l'índex RPI de les mostres basant-se en l'informació obtinguda pels acceleròmetres instal¿lats en els dits de la garra robòtica.
En quant als assajos realitzats de manera in-line, el primer prototip desenvolupat es va basar en la integració de dos sondes VIS-NIR en una garra robòtica disposada amb dos acceleròmetres. El sistema desenvolupat va permetre aconseguir una bona estimació de la qualitat del mango a través de l'índex RPI fusionant l'informació tant dels espectres VIS-NIR com de l'impacte no destructiu dels acceleròmetres. D'esta manera va quedar demostrat que era possible obtindre una predicció semblant treballant de forma in-line com off-line per a la predicció del mateix índex de qualitat en mangos. El segon prototip in-line desenvolupat es va basar en la integració d'una sonda VIS-NIR en una cinta transportadora per a l'identificació de distintes varietats i orígens de pomes. El prototip desenvolupat va permetre registrar resultats de classificació tan bons com els efectuats de manera off-line.
D'aquesta manera, es pot concloure que l'espectroscòpia VIS-NIR permet monitorar la qualitat i classificar fruita postcollita tant en mode off-line com in-line. Els nous prototips desenvolupats aporten clars avantatges respecte als processos tradicionals realitzats a mà, com són la reducció del temps d'inspecció, la disminució de la quantitat de residus generats pels anàlisis destructives de qualitat i la possibilitat d'inspeccionar tota la producció, obtenint així un anàlisi més estandarditzCortés López, V. (2018). Innovations in non-destructive techniques for fruit quality control applied to manipulation and inspection lines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/110969TESISCompendi
Efficient least angle regression for identification of linear-in-the-parameters models
Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm
Multivariate analysis for quality control of agrifood materials using near infrared spectroscopy
Seguridad y calidad alimentaria son uno de los conceptos más
demandados actualmente en la industria agroalimentaria. La mayoría de análisis
de control de los productos alimentarios se lleva a cabo mediante métodos
tradicionales (vía húmeda). Los principales problemas relacionados con este tipo
de análisis son el consumo de tiempo para la obtención de los resultados de una
sola muestra, el coste del análisis, así como la limitación en cuanto a su
implantación en la línea de producción o en el campo, entre otros.
Paralelamente al desarrollo e innovación tecnológica, numerosos métodos
han sido implementados para la determinación, evaluación y control de la calidad
de los productos agroalimentarios en las últimas décadas. Estos métodos están
basados en la detección de varias propiedades tanto físicas como químicas
correlacionadas con ciertos factores cualitativos de los productos. Uno de los
métodos más difundido y aún en desarrollo debido a su gran aplicabilidad, es la
espectroscopía de infrarrojo cercano (tecnología NIRS, Near Infrared
Spectroscopy). Han pasado más de 20 años desde su primera introducción como
potente herramienta hecha por Karl Norris en el análisis de la composición de los
cereales.
El planteamiento de esta tesis nace de la necesidad, cada vez mayor, del
control de los parámetros de calidad de los productos agroalimentarios de manera
rápida y precisa. La categorización del trigo en función de su calidad o el valor
añadido que adquiere la soja según el porcentaje de proteína o grasa presente en
una determinada variedad ha llevado al estudio de la aplicación de la
espectroscopía de infrarrojo cercano en dichos productos.
El objetivo general de la investigación ha consistido en la aplicación de la
tecnología NIRS para la determinación de parámetros de calidad en muestras de...Food safety and quality are currently the most popular concepts in the
food industry. Usually, most control analyses of food products are carried out by
conventional methods (wet chemistry). However, some of the main negative
issues of these methods are: they are time consuming in order to obtain the results
of a single sample, the raising price and the limitation on its implementation in
the production line or in the field, among others.
At the same time to the technological innovation and development, during
the last decades many methods have been implemented for the identification,
assessment and quality control of food products. These methods are based on the
detection of various physical and chemical properties correlated with certain
product quality factors. One of the most widespread due to its wide applicability
is the near-infrared spectroscopy (NIRS technology, Near Infrared Spectroscopy).
It has been over 20 years since its first introduction as a powerful tool made by
Karl Norris in the analysis of the composition of the grains.
The approach of this thesis arises from the increasing need of fast and
accurate analyses of quality parameters control on food products. The
categorization of wheat in terms of quality and the added value acquired by the
percentage of soy protein or fat in a particular variety has led to the study of the
application of near infrared spectroscopy in these products.
The general objective of the research has been the application of NIRS
technology for the determination of quality parameters in wheat and soybean
samples. As a result, this study has led to the development of four chapters:
- "Development of robust soybean NIR Calibration Models with temperature
compensation and high variability in the data basis." This chapter was focused on
the development of robust calibrations by adding in the group of samples
instrumental and environmental variability..
Food Recognition and Ingredient Detection Using Electrical Impedance Spectroscopy With Deep Learning Techniques to Facilitate Human-food Interactions
Food is a vital component of our everyday lives closely related to our health, well-being, and human behavior. The recent advancements of Spatial Computing technologies, particularly in Human-Food interactive (HFI) technologies have enabled novel eating and drinking experiences, including digital dietary assessments, augmented flavors, and virtual and augmented dining experiences. When designing novel HFI technologies, it is essential to recognize different food and beverages and their internal attributes (i.e., food sensing), such as volume and ingredients. As a result, contemporary research employs image analysis techniques to identify food items, notably in digital dietary assessments. These techniques, often combined with AI algorithms, analyze digital food images to extract various information about food items and quantities. However, these visual food analyzing methods are ineffective when: 1) identifying food’s internal attributes, 2) discriminating visually similar food and beverages, and 3) seamlessly integrating with people’s natural interactions while consuming food (e.g., automatically detecting the food when using a spoon to eat). This thesis presents a novel approach to digitally recognize beverages and their attributes, an essential step towards facilitating novel human-food interactions. The proposed technology has an electrical impedance measurement unit and a recognition method based on deep learning techniques. The electrical impedance measurement unit consists of the following components: 1) a 3D printed module with electrodes that can be attached to a paper cup, 2) an impedance analyzer to perform Electrical Impedance Spectroscopy (EIS) across two electrodes to acquire measurements such as a beverage’s real part of impedances, imaginary part of impedances, phase angles, and 3) a control module to configure the impedance analyzer and send measurements to a computer that has the deep learning framework to conduct the analysis. Two types of multi-task learning models (hard parameter sharing multi-task network and multi-task network cascade) and their variations (with principal component analysis and different combinations of features) were employed to develop a proof-of-concept prototype to recognize eight different beverage types with various volume levels and sugar concentrations: two types of black tea (LiptonTM and TwiningsTM English-Breakfast), two types of coffee (StarbucksTM dark roasted and medium roasted), and four types of soda (regular and diet coca-cola, and regular and diet Pepsi). Measurements were acquired from these beverages while changing volume levels and sugar concentrations to construct training and test datasets. Both types of networks were trained using the training dataset while validated with the test dataset. Results show that the multi-task network cascades outperformed the hard parameter sharing multi-task networks in discriminating against a limited number of drinks (accuracy = 96.32%), volumes (root mean square error = 13.74ml), and sugar content (root mean square error = 7.99gdm3). Future work will extend this approach to include additional beverage types and their attributes to improve the robustness and performance of the system and develop a methodology to recognize solid foods with their attributes. The findings of this thesis will contribute to enable a new avenue for human-food interactive technology developments, such as automatic food journaling, virtual flavors, and wearable devices for non-invasive quality assessment