Detection and quantification of paprika powder adulteration by near infrared (NIR) spectroscopy

Orientador: Douglas Fernandes BarbinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: A páprica é uma das especiarias mais consumidas no mundo, e devido aos seus atributos sensoriais desejáveis, ela apresenta um alto valor de mercado. Embora especiarias como o pó de páprica sejam usadas e consumidas apenas em pequenas quantidades, elas estão presentes em muitos alimentos processados. Em razão disso, ela se torna susceptível a adulteração por motivação econômica. Por esse motivo, muitos esforços têm sido feitos no desenvolvimento de técnicas analíticas para detecção dessas práticas fraudulentas. No entanto, muitas dessas técnicas tradicionais são destrutivas, utilizam reagentes químicos e seu uso é dispendioso e demorado. Por outro lado, técnicas de espectroscopia vibracional, aliadas a quimiometria, surgem como uma alternativa promissora na detecção de adulteração na indústria de ervas e especiarias. O uso dessas técnicas traz como vantagens a rapidez e a natureza não-destrutiva das análises. Dessa forma, a espectroscopia de infravermelho próximo (NIR) tem sido utilizada com êxito, na verificação da autenticidade e no controle de qualidade desses produtos. Diante disso, o presente trabalho teve como objetivo investigar as potencialidades da espectroscopia NIR, em conjunto com a análise multivariada, na detecção e quantificação de substâncias estranhas (fécula de batata, goma arábica e urucum), em páprica em pó. Na determinação dos níveis de adulteração, foi utilizada a regressão por mínimos quadrados parciais (PLSR). Melhores resultados da calibração PLSR foram obtidos com um número reduzido de variáveis, aplicando o método de seleção de variáveis a partir do gráfico dos coeficientes de regressão. Como resultado, para os modelos PLSR reduzidos construídos a partir dos dados espectrais de NIR, os coeficientes de determinação de predição (R2p) foram 0,960, 0,968 e 0,874 para fécula de batata, goma arábica e urucum, respectivamente e os erros quadráticos médios de predição (RMSEP) foram 1,86, 1,68 e 1,74, respectivamente. Finalmente, a análise discriminante de mínimos quadrados parciais (PLS-DA) foi o método utilizado para estabelecer um modelo de classificação para discriminar amostras de páprica adulteradas e não adulteradas e também identificar o tipo de adulteração. Assim, este método de classificação mostrou-se bastante eficiente, com especificidade maior que 90% e taxa de erro menor que 2%, para todos os modelos construídos. Os resultados obtidos neste estudo mostraram que a espectroscopia NIR, combinada com a quimiometria podem ser uteis para a rápida detecção e/ou quantificação da adulteração em páprica em póAbstract: Paprika is one of the most consumed spices in the world, and because of its desirable sensory attributes, it has a high market value. Although spices such as paprika powder are used and consumed only in small amounts, they are present in many processed foods. Because of this, it becomes susceptible to adulteration by economic motivation. For this reason, much effort has been expended in developing analytical techniques to detect such fraudulent practices. However, many of these traditional techniques are destructive, use chemical reagents and their use is expensive and time consuming. On the other hand, techniques of vibrational spectroscopy, combined with chemometrics, appear as a promising alternative in the detection of adulteration in the herb and spice industry. The use of these techniques brings as advantages the speed and the non-destructive nature of the analyses. Thus, near infrared spectroscopy (NIR) has been successfully used to verify the authenticity and quality control of these products. The objective of this study was to investigate the potential of NIR spectroscopy, in conjunction with the multivariate analysis, in the detection and quantification of foreign substances (potato starch, acacia gum and annatto) in powdered paprika. In the determination of adulteration levels, partial least squares regression (PLSR) was used. The best results of the PLSR calibration were obtained with a reduced number of variables, applying the method of selection of variables from the graph of the regression coefficients. As a result, for the reduced PLSR models built with NIR spectral data, the prediction determination coefficients (R2p) were 0.960, 0.968 and 0.874 for potato starch, acacia gum and annatto, respectively, and the mean squared errors of prediction (RMSEP) were 1.86, 1.68 and 1.74, respectively. Finally, the discriminant analysis of partial least squares (PLS-DA) was the method used to establish a classification model to discriminate adulterated and unadulterated paprika samples and also to identify the type of adulteration. Hence, this method of classification proved to be efficient, with specificity greater than 90% and error rate lower than 2%, for all models constructed. The results obtained in this study showed that NIR spectroscopy, combined with chemometrics may be useful for the rapid detection and / or quantification of paprika powder adulterationMestradoEngenharia de AlimentosMestre em Engenharia de AlimentosCAPE

Geochemical and spectroscopic fingerprinting for authentication and geographical traceability of high-quality lemon fruits.

Geochemical (mineral element and Sr isotope ratio) and spectroscopical fingerprinting (Near Infrared Spectroscopy) were proposed to authenticate and track the two high-quality lemon fruits from the Campania region (Limone di Sorrento PGI and Limone Costa d'Amalfi PGI) to protect them from frauds. Considering the geochemical indicators, we built different chemometric discriminant models based on mineral profile and 87Sr/86Sr isotope ratio. These two techniques were applied to discriminate fruits from different territorial scales, small territorial scales (region scale), and large territorial scales. The results of different discriminant models applied on mineral profiles of lemon juices, both on a small and large territorially scale, showed good discrimination according to provenance, especially for non-essential elements as Rb, Ba, Sr, Ti, and Co. These same elements have shown a good correlation with cultivation soils and stability between the two production years. It is worth noting that although, the performance of the whole elemental profile gave a better result than the profile of the non-essential elements, the reliability of the two models, calculated as the ratio between the percentage of correctly validated and classification samples, was similar. In addition, the Sr isotope ratio had shown a clear differentiation among the fruits from the Campania region and extra-regional samples, and by analysis of 86Sr/87Sr of soils, it was clear that the strontium isotope ratio of lemon juices was closely related to that of the bioavailable fractions of the soil. Furthermore, combining both isotopic and mineral profiles in lemon juices by a low-level data fusion approach, the results showed a better clustering according to geographical origins than the two-determination taken separately, although on an explorative level. In addition, the spectroscopical data (NIR) on intact lemon fruits showed the strong influence of environmental growing conditions on the samples. For this, the application of Linear Discriminant Analysis (LDA) models suggested building the discrimination models according to origins (PGI and not PGI productions) based on one production year. In the same way, the application of MLR models, that showed a strong relationship between quality properties of lemon fruits and NIR spectra, suggested the applicability of this technique to build predictive models for the quality properties. In addition, on a part of the total samples collected only in 2019 (intact lemons and juices), have been successfully applied two different chemometrics models i.e., LDA and Partial Least Square Discriminant Analysis (PLS-DA). The results showed better provenance discrimination using the lemon juices than the intact lemons. Comparing the results obtained, of the two approaches used, the results of geochemical fingerprinting have shown more stability for discriminate lemon fruits derived from two different production years, especially for not essential elements. However, considering the various vantages of the application of NIR spectroscopy (non-destructive, rapid, and cheap) and the results obtained, this technique can be used for rapid screening of samples in order to verify the quality and origins of lemon fruits during the year. The study of the pedoclimatic features was fundamental to understand the nature of discriminating variables, in both approaches. Additional research should be conducted to include a greater number of lemon farms (or sampling points) in the PGI area and to enlarge the existing database including lemon samples from other regions and validate the models built. These discriminant models based on geochemical and spectroscopical profiles of lemon fruits could substantially contribute to implementing a blockchain system for Campanian lemon traceability, providing real-time information not only to the final consumers but also to manufacturers, distributors, and retailers

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

A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Imagens hiperespectrais para o controle da qualidade de alimentos : híbridos de graos de cacau e vida de prateleira de sementes de chia

Orientador: Douglas Fernandes BarbinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: A imagem hiperespectral (HSI) permite a aquisição simultânea de informações espectrais e espaciais. Neste trabalho, HSI foi utilizado para o controle de qualidade de produtos agrícolas, que inclui a autenticação de híbridos de cacau e a estimativa do prazo de validade de sementes de chia. Para o trabalho com sementes de chia, as amostras foram armazenadas a 25, 35 e 45 ° C por 180 dias, para análises aceleradas do prazo de validade. Periodicamente, as amostras de chia eram removidas do armazenamento para obter imagens hiperespectrais (900 - 2500 nm), análise de acidez e perfil de ácidos graxos. O objetivo foi usar imagens hiperespectrais e análises multivariadas para desenvolver uma metodologia para estimar a vida de prateleira de sementes de chia, denominada Multivariate Accelerated Shelf Life Testing (MASLT). A Análise de Componentes Principais (PCA) foi usada para estudar a variabilidade durante o armazenamento e, em seguida, as pontuações do PC foram usadas para modelar a cinética e estimar os parâmetros da Equação de Arrhenius e, finalmente, para estimar a vida de prateleira. Além disso, pela primeira vez, uma nova estratégia foi proposta para validar essa metodologia, que chamamos de "Re-sampling", onde as amostras do conjunto de validação foram projetadas no conjunto de calibração com um número razoável de iterações. Os escores PC1 e gráficos cinéticos foram construídos ajustando os escores PC1 relacionados ao tempo versus o tempo por um modelo cinético fundido (R2> 0,85). Os espectros de sementes de chia onde a acidez aumentou em 75% a partir do valor inicial foram usados para calcular o valor de corte (-0,9853). As estimativas de vida de prateleira foram 1300, 798 e 90 dias para sementes de chia armazenadas a 25, 35 e 45 ° C, respectivamente. Pela primeira vez, uma metodologia confiável é proposta para validar que todas as amostras foram previstas corretamente usando as pontuações PC1. No segundo estudo, cinco híbridos de cacau foram cultivados e processados nas mesmas condições na CEPLAC (Medicilândia, Pará, Brasil). Os grãos de cacau foram então transportados para o Wallonie Research Center (Bélgica), onde foram obtidas imagens hiperespectrais na faixa de 1100 - 2500 nm. A análise parcial discriminante dos mínimos quadrados (PLS-DA) e a máquina de vetores de suporte (SVM) foram implementadas para classificar os híbridos de cacau, (1) duas classes de híbridos e (2) cinco classes de híbridos. Além disso, um novo conjunto de imagens foi usado para validação externa pixel a pixel. Os resultados mostraram que PLS-DA e SVM tiveramresultados comparáveis para modelos de duas classes (híbridos), mas o SVM (erro de previsão de 3,8 a 23,1%) foi superior ao PLS-DA (erro de previsão de 4,4 a 34,4%) quando todas as cinco classes de híbridos foram incluídas em um modelo. Os resultados de previsão pixel a pixel em um conjunto de imagens externas mostraram uma taxa de classificação correta de 50 a 100%. Os resultados para os modelos de duas classes e cinco foram comparáveis às técnicas de reação em cadeia da polimerase. Os resultados mostram o potencial do HSI para o controle de qualidade de produtos agrícolas, tanto para autenticação quanto para estimativa do prazo de validadeAbstract: Hyperspectral imaging (HSI) enables simultaneous acquisition of spectral and spatial information. In this work, HSI was used for quality control of agricultural products, which includes the authentication of cocoa bean hybrids and the estimation of shelf-life of chia seeds. Regarding the chia seeds study, samples were stored at 25, 35 and 45 ° C for 180 days, for accelerated shelf life analyzes. From time to time, chia samples were removed from storage to acquire hyperspectral images (900 - 2500 nm), acidity analysis, and fatty acid profile. The objective was to use hyperspectral images and multivariate analysis to develop a methodology for estimating the shelf-life of chia seeds, called Multivariate Accelerated Shelf Life Testing (MASLT). Principal Component Analysis (PCA) was used to study the variability during storage, and then, the PC scores were used to model the kinetics and estimate the parameters of the Arrhenius Equation, and finally to estimate the shelf life. Furthermore, for the first time a new strategy was proposed to validate this methodology, which we called "Re-sampling", where the samples from the validation set were projected onto the calibration set with a reasonable number of iterations. PC1 scores and kinetic charts were built fitting the time-related PC1 scores versus time by a fused kinetic model (R2 > 0.85). The spectra of chia seeds where acidity increased at 75% from initial value were used to calculate the cut-off value (-0.9853). The shelf life estimations were 1300, 798 and 90 days for chia seeds stored at 25, 35 and 45 °C, respectively. For the first time, a reliable methodology is proposed to validate that all samples were correctly predicted using PC1 scores. In the second study, cocoa beans hybrids (five) were grown and processed under the same conditions in CEPLAC (Medicilândia, Para, Brazil). The cocoa beans were then transported to the Wallonie Research Center (Belgium), where hyperspectral images in the 1100 - 2500 nm range were acquired. Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) was implemented to classify cocoa bean hybrids, (1) two classes of hybrids and (2) five classes of hybrids. Additionally, a new set of images was used for external pixel-to-pixel validation. The results showed that PLS-DA and SVM demonstrate comparable results for two-class (hybrids) models, but SVM (3.8–23.1% prediction error) was superior to PLS-DA (4.4–34.4% prediction error) when all five classes (hybrids) were included in a model. Pixel-to-pixel prediction results on a set of external images showed a correct classification rate of 50 - 100%. The results for both the two-class models and the five-class model were comparable with polymerase chain reaction techniques. The results show the potential of HSI for quality control of agricultural products, both for authentication and estimation of shelf lifeMestradoEngenharia de AlimentosMestre em Engenharia de Alimentos2018/02500-4; 2019/04833-3; 2015/24351-288882.329557/2019-01FAPESPCAPE

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