Evaluation of vibrational and image analytical techniques associated with chemometrics for the quality control of high value foods

Orientador: Juliana Azevedo Lima PalloneTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: Métodos tradicionais (MT) empregados no controle de qualidade (CQ) de alimentos utilizam reagentes químicos, têm elevado custo e tempo de execução. Nesse contexto, a espectroscopia no infravermelho próximo e médio (NIR e MIR) e técnicas de imagem hiperespectral (NIR-HSI) e smartphone (SBI), são alternativas verdes aos MT. Sendo assim, objetivou-se avaliar o potencial de técnicas analíticas verdes associadas a quimiometria para CQ, aspectos nutricionais e bioativos de repolho roxo, polpa de açaí e arroz integral. Inicialmente, 60 amostras de repolho roxo tiveram os parâmetros relacionados aos compostos bioativos (antocianinas e polifenois totais, ORAC, DPPH e TEAC) determinados por MT, os espectros NIR e MIR coletados e os dados foram empregados para a obtenção de modelos de calibração multivariada. Os modelos de calibração construídos, baseados em NIR e MIR, apresentaram performance satisfatória na predição dos paramêtros avaliados. Para polpas de açaí, espectros de NIR e MIR também foram obtidos para verificar a possibilidade de utilização das técnicas para detecção de amostras adulteradas, com farinha de tapioca, mandioca, trigo e emulsificante. Determinou-se cartas de controle multivariada e modelos PLS-DA e KNN. Como resultado, ambas técnicas foram capazes de detectar amostras adulteradas com 100% de precisão através dos modelos PLS-DA. Além disso, 96 amostras de polpa de açaí liofilizadas foram utilizadas para obtenção de modelos PLS, baseados em NIR e SBI, para a determinação dos parâmetros bioativos obtidos através de MT. Os modelos PLS obtidos por NIR e SBI se mostraram satisfatórios para todos os parâmetros avaliados, com exceção do modelo ORAC-SBI. Em adição, verificou-se a possibilidade de utilização de minerais essenciais como marcadores de adulterações. Para essa fase do trabalho, polpa de açaí liofilizada autêntica e adulterada, com beterraba, suco de uva, maltodextrina, farinhas de tapioca e mandioca, tiveram os teores de Ca, Mn, Fe e K determinados por FAAS e baseado nesses valores foram construídos modelos de classificação por PLS-DA, OCPLS e SIMCA, onde obteve-se 90% de precisão na detecção de polpas adulteradas através do modelo OCPLS, demonstrando que a composição mineral pode ser útil para a detecção de fraudes em açaí. Na etapa seguinte, os teores de Ca, Mn, Fe e K, determinados via FAAS, de polpas de açaí liofilizadas e NIR foram utilizados para a construção de modelos PLS para predição indireta do teor desses minerais. Exceto para o Ca, os modelos obtidos por PLS apresentaram desempenho insatisfatório. A seleção de variáveis (iPLS) foi realizada, as porções do espectro selecionadas resultaram em modelos PLS satisfatórios para Mn, Fe e K. Amostras de arroz integral orgânico e convencional foram analisadas por instrumentos NIR-bancada, NIR-portátil e NIR-HSI, para a construção de modelos discriminativos entre os grãos. Todos modelos PLS-DA mostraram-se capazes de discriminar as amostras (85% de precisão), sendo o NIR-bancada a técnica de melhor desempenho. Sendo assim, foi possível concluir que as técnicas analíticas verdes, conhecidas como rápidas e não destrutivas, associadas a quimiometria, foram consideradas adequadas como alternativas para CQ, nutricional, bioativo e detecção de adulteração em alimentos diferentes alimentos de origem vegetal, com alto valor agregadoAbstract: Traditional methods (TM) applied in food quality control (QC) use chemical reagents, present high cost and time consuming. In this context, the near and mid infrared spectroscopy (NIR e MIR), hyperspectral imaging system (NIR-HSI) and smartphones (SBI) are green alternatives for TM. Therefore, it is intended to evaluate the potential of green analytical techniques associated to chemometric for QC, nutritional and bioactive aspects of red cabbage, açai pulp and brown rice. Initially, 60 red cabbage samples had parameters related to bioactive compounds (anthocyanins and phenolics content, ORAC, DPPH and TEAC) determined by TM, the NIR and MIR spectra collected and chemical data were implemented to obtain models of multivariate calibration. The calibration models built, based on NIR and MIR, provided satisfactory performance in the prediction of the evaluated parameters. Regard açaí pulp, NIR and MIR spectra were also obtained to verify the possibility of utilization of techniques that aimed to detect adulterated samples, with tapioca flour, cassava and wheat and emulsifier. It was stablished multivariate control charts and PLS-DA and KNN models. The outcome showed that both techniques were capable to detect adulterated samples with 100% precision through PLS-DA models. Furthermore, 96 freeze-dried açaí pulp samples were employed to obtain PSL models, based on NIR and SBI, in order to determined bioactive parameters acquired through TM. The PLS models collected by NIR and SBI had a satisfactory result to all the evaluated parameters, with an exception of ORAC-SBI model. In addition, it was found the possibility of the utilization of essential minerals as adulterations markers. In this stage, authentic and adulterated freeze-dried açaí pulp, with beet pulp, grape juice, maltodextrin, tapioca flour and cassava presented contents of Ca, Mn, Fe and K determined by FAAS and based on these amounts were built models of classification by PLS-DA, OCPLS and SIMCA, attaining 90% of precision on the detection of adulterated pulps via OCPLS model, revealing that the mineral composition can be useful to expose fraud in açai. In the next stage, the contents of Ca, Mn, Fe and K, determined by FAAS, of freeze-dried açai pulp samples and NIR were used to build PLS models for the indirect prediction of the content of these minerals. Except for Ca, the models acquired by PLS presented unsatisfactory performance. The selection of variables (iPLS) was executed, the portions of spectra selected resulted in satisfactory PLS models for Mn, Fe and K. Samples of organic and conventional brown rice were analyzed by NIR-benchtop, NIR-portable and NIR-HIS instruments, in order to build discriminative models among grains. All the PLS-DA models were able to discriminate the samples (85% of precision) and NIR-benchtop achieved the best performance. Accordingly, it was possible to conclude that green analytical techniques, known as fast and non-destructive, associated with chemometric, were considered suitable as alternatives for QC, nutritional, bioactive and detection of adulterations in different food of plant origin, with high value-addedDoutoradoCiência de AlimentosDoutora em Ciência de Alimentos142414/2016-62018/09759-3CNPQFAPES

Rare Earth Elements analysis to identify anthropogenic signatures at Valle del Serpis (Spain) Neolithic settlements

Due to their particular geochemical properties and stability Rare Earth Elements (REE) can act as a ‘fingerprint’ for soils, and as a consequence have been employed in a variety of different archaeological scenarios in order to identify past human activities.In this study, for the first time, we apply REE signatures in different Spanish Neolithic settlements, all located in the Valle del Serpis region. More than 100 Neolithic settlements have been identified in this area, and most of these open sites are characterised by dark brown strata that are in contrast with the light brown soils of the valley. These dark brown deposits are usually covered by paleosols and have been interpreted as markers of anthropogenic activities. However, in order to demonstrate whether these strata are anthropogenic or natural features requires a better understand-ing of soil development processes. A total of fifty samples were taken across six different sites, and from each site the sam-pling was carried out at different depths through 3m deep sections. Four sites are clearly associated with archaeological findings (sites BF, LP, PB and AC); another one is from a natural section near the Neolithic site of Mas d’Is (MD) and has been radiocarbon dated to the beginning of the Holocene (7751-7611 cal BC); and the last corresponds to a place of uncertain attribution (BK). Major, minor and trace elements including REE were determined using XRF and ICP- MS, with Principal Components Analysis (PCA) used to statistically analyze these data. Results were then compared with the strata soil properties analysed by XRD and particle size analysis, and cross-referenced with archaeological data to aid interpretation. The results demonstrate that REE analyses provide significant details regarding anthropogenic activities and strata development history, and in this instance confirm and elaborate on the archaeological interpretation that these dark brown deposits are evidence of a region-wide agricultural system in the Neolithic Valle del Serpis

The source of the building stones from the Sagunto Castle archaeological area and its surroundings

A multidisciplinary study was carried out on the building stones of the masonries belonging to the Castle of Sagunto (Valencia, Spain), an important historical and archeological complex, characterized by several construction phases from the Roman Period to the Modern Ages. For the first time, the stones of the Sagunto Castle have been analysed to determine their chemical, mineralogical and petrographic features, the main physical and mechanical properties, and to understand their decay, use and recycling dynamics in the different building during the entire occupational period. Geochemical and mineralogical analyses employing X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS) and X-ray diffraction (XRD) were carried out together with optical and electronic microscope analysis to observe the stone macro- and micro-structures. The collected data were processed by Principal Component Analysis (PCA) to highlight differences among the studied structures. The results show that the stones employed to build Sagunto`s structures during the different historical periods are related to a specific quarried area located nearby Sagunto Castle hill and differences between the studied samples are mostly related to the conservation state of the buildings. Therefore, geochemical analyses confirm the origin of the raw materials, while petrographic and physical analyses have been useful to evaluate the conservation state of the studied Sagunto Castle structures

TECHNART 2017. Non-destructive and microanalytical techniques in art and cultural heritage. Book of abstracts

440 p.TECHNART2017 is the international biannual congress on the application of Analytical Techniques in Art and Cultural Heritage. The aim of this European conference is to provide a scientific forum to present and promote the use of analytical spectroscopic techniques in cultural heritage on a worldwide scale to stimulate contacts and exchange experiences, making a bridge between science and art. This conference builds on the momentum of the previous TECHNART editions of Lisbon, Athens, Berlin, Amsterdam and Catania, offering an outstanding and unique opportunity for exchanging knowledge on leading edge developments. Cultural heritage studies are interpreted in a broad sense, including pigments, stones, metal, glass, ceramics, chemometrics on artwork studies, resins, fibers, forensic applications in art, history, archaeology and conservation science. The meeting is focused in different aspects: - X-ray analysis (XRF, PIXE, XRD, SEM-EDX). - Confocal X-ray microscopy (3D Micro-XRF, 3D Micro-PIXE). - Synchrotron, ion beam and neutron based techniques/instrumentation. - FT-IR and Raman spectroscopy. - UV-Vis and NIR absorption/reflectance and fluorescence. - Laser-based analytical techniques (LIBS, etc.). - Magnetic resonance techniques. - Chromatography (GC, HPLC) and mass spectrometry. - Optical imaging and coherence techniques. - Mobile spectrometry and remote sensing

Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest

Tesis por compendio[ES] El objetivo de esta tesis doctoral es evaluar la técnica de imagen hiperespectral en el rango visible e infrarrojo cercano, en combinación con técnicas quimiométricas para la evaluación de la calidad de la fruta en poscosecha de manera eficaz y sostenible. Con este fin, se presentan diferentes estudios en los que se evalúa la calidad de algunas frutas que por su valor económico, estratégico o social, son de especial importancia en la Comunidad Valenciana como son el caqui 'Rojo Brillante', la granada 'Mollar de Elche', el níspero 'Algerie' o diferentes cultivares de nectarina. En primer lugar se llevó a cabo la monitorización de la calidad poscosecha de nectarinas 'Big Top' y 'Magique' usando imagen hiperespectral en reflectancia y transmitancia. Al mismo tiempo se evaluó la transmitancia para la detección de huesos abiertos. Se llevó a cabo también un estudio para distinguir los cultivares 'Big Top' y "Diamond Ray", los cuales poseen un aspecto muy similar pero sabor diferente. En cuanto al caqui 'Rojo Brillante', la imagen hiperespectral fue estudiada por una parte para monitorear su madurez, y por otra parte para evaluar la astringencia de esta fruta, que debe ser completamente eliminada antes de su comercialización. Las propiedades físico-químicas de la granada 'Mollar de Elche' fueron evaluadas usando imagen de color e hiperespectral durante su madurez usando la información de la fruta intacta y de los arilos. Finalmente, esta técnica se usó para caracterizar e identificar los defectos internos y externos del níspero 'Algerie'. En la predicción de los índices de calidad IQI y RPI usando imagen en reflectancia y transmitancia se obtuvieron valores de R2 alrededor de 0,90 y en la discriminación por firmeza, una precisión entorno al 95 % usando longitudes de onda seleccionadas. En cuanto a la detección de huesos abiertos, el uso de la imagen hiperespectral en transmitancia obtuvo un 93,5 % de clasificación correcta de frutas con hueso normal y 100 % con hueso abierto usando modelos PLS-DA y 7 longitudes de onda. Los resultados obtenidos en la clasificación de los cultivares 'Big Top' y 'Diamond Ray' mostraron una fiabilidad superior al 96,0 % mediante el uso de modelos PLS-DA y 14 longitudes de onda seleccionadas, superando a la imagen de color (56,9 %) y a un panel entrenado (54,5 %). Con respecto al caqui, los resultados obtenidos indicaron que es posible distinguir entre tres estados de madurez con una precisión del 96,0 % usando modelos QDA y se predijo su firmeza obteniendo un valor de R2 de 0,80 usando PLS-R. En cuanto a la astringencia, se llevaron a cabo dos estudios similares en los que en el primero se discriminó la fruta de acuerdo al tiempo de tratamiento con altas concentraciones de CO2 con una precisión entorno al 95,0 % usando QDA. En el segundo se discriminó la fruta de acuerdo a un valor de contenido en taninos (0,04 %) y se determinó qué área de la fruta era mejor para realizar esta discriminación. Así se obtuvo una precisión del 86,9 % usando la zona media y 23 longitudes de onda. Los resultados obtenidos para la granada indicaron que la imagen de color e hiperespectral poseen una precisión similar en la predicción de las propiedades fisicoquímicas usando PLS-R y la información de la fruta intacta. Sin embargo, cuando se usó la información de los arilos, la imagen hiperespectral fue más precisa. En cuanto a la discriminación del estado de madurez usando PLS-DA, la imagen hiperespectral ofreció mayor precisión, 95,0 %, usando la información de la fruta intacta y del 100 % usando la de los arilos. Finalmente, los resultados obtenidos para el níspero indicaron que la imagen hiperespectral junto con el método de clasificación XGBOOST pudo discriminar entre muestras con y sin defectos con una precisión del 97,5 % y entre muestras sin defectos o con defectos internos o externos con una precisión del 96,7 %. Además fue posible distinguir entre los dife[CA] L'objectiu de la present tesi doctoral se centra en avaluar la capacitat de la imatge hiperespectral en el rang visible i infraroig pròxim, en combinació amb mètodes quimiomètrics, per a l'avaluació de la qualitat de la fruita en post collita de manera eficaç i sostenible. A aquest efecte, es presenten diferents estudis en els quals s'avalua la qualitat d'algunes fruites que pel seu valor econòmic, estratègic o social, són d'especial importància a la Comunitat Valenciana com són el caqui 'Rojo Brillante', la magrana 'Mollar de Elche', el nispro 'Algerie' o diferents cultivares de nectarina. En primer lloc es va dur a terme la monitorització de la qualitat post collita de nectarines 'Big Top' i 'Magique' per mitjà d'imatge hiperespectral en reflectància i trasnmitancia. Així mateix es va avaluar la transmitància per a la detecció d'ossos oberts. Es va dur a terme també un estudi per distingir els cultivares 'Big Top' i 'Diamond Ray', els quals posseeixen un aspecte molt semblant però sabor diferent. Pel que fa al caqui 'Rojo Brillante', la imatge hiperespectral va ser estudiada d'una banda per a monitoritzar la seua maduresa, i per un altre costat per avaluar l'astringència, que ha de ser completament eliminada abans de la seua comercialització. Les propietats fisicoquímiques de la magrana 'Mollar de Elche' van ser avaluades per la imatge de color i hiperespectral durant la seua maduresa usant la informació de la fruita intacta i els arils. Finalment, aquesta tècnica es va fer servir per caracteritzar i identificar els defectes interns i externs del nispro 'Algerie'. En la predicció dels índexs de qualitat IQI i RPI usant imatge en reflectància com en trasnmitancia es van obtindre valors de R2 al voltant de 0,90 i en la discriminació per fermesa una precisió entorn del 95,0 % utilitzant longituds d'ona seleccionades. Pel que fa a la detecció d'ossos oberts, l'ús de la imatge hiperespectral en transmitància va obtindre un 93,5 % classificació correcta de fruites amb os normal i 100 % amb os obert usant models PLS-DA i 7 longituds d'ona. Els resultats obtinguts en la classificació dels cultivares 'Big Top' i 'Diamond Ray' van mostrar una fiabilitat superior al 96,0 % per mitjà de l'ús de models PLS-DA i 14 longituds d'ona, superant a la imatge de color (56,9 %) i a un panell sensorial entrenat (54,5 %). Quant al caqui, els resultats obtinguts van indicar que és possible distingir entre tres estats de maduresa amb una precisió del 96,0 % usant models QDA i es va predir la seua fermesa obtenint un valor de R2 de 0,80 usant PLS-R. Pel que fa a l'astringència, es van dur a terme dos estudis similars en què el primer es va discriminar la fruita d'acord al temps de tractament amb altes concentracions de CO2 amb una precisió al voltant del 95,0 % usant QDA. En el segon, es va discriminar la fruita d'acord a un valor de contingut en tanins (0,04 %) i es va determinar quina part de la fruita era millor per a realitzar aquesta discriminació. Així es va obtindre una precisió del 86,9 % usant la zona mitjana i 23 longituds d'ona. Els resultats obtinguts per la magrana van indicar que la imatge de color i hiperespectral posseïxen una precisió semblant a la predicció de les propietats fisicoquímiques usant PLS-R i la informació de la fruita intacta. No obstant això, quan es va usar la informació dels arils, la imatge hiperespectral va ser més precisa. Quant a la discriminació de l'estat de maduresa usant PLS-DA, la imatge hiperespectral va oferir major precisió (95,0 %) usant la informació de la fruita intacta i del 100 % usant la dels arils. Finalment, els resultats obtinguts pel nispro indiquen que la imatge hiperespectral juntament amb el mètode de classificació XGBOOST va poder discriminar entre mostres amb i sense defectes amb una precisió del 97,5 % i entre mostres sense defectes o amb defectes interns o externs amb una precisió del 96,7 %. A més, va ser possible distingir entre[EN] The objective of this doctoral thesis is to evaluate the potential of the hyperspectral imaging in the visible and near infrared range in combination with chemometrics for the assessment of the postharvest quality of fruit in a non-destructive, efficient and sustainable manner. To this end, different studies are presented in which the quality of some fruits is evaluated. Due to their economic, strategic or social value, the selected fruits are of special importance in the Valencian Community, such as Persimmon 'Rojo Brillante', the pomegranate 'Mollar de Elche', the loquat 'Algerie' or different nectarine cultivars. First, the quality monitoring of 'Big Top' and 'Magique' nectarines was carried out using reflectance and transmittance images. At the same time, transmittance was evaluated for the detection of split pit. In addition, a classification was performed to distinguish the 'Big Top' and 'Diamond Ray' cultivars, which look very similar but have different flavour. Whereas that for the 'Rojo Brillante' persimmon, the hyperspectral imaging was studied on the one hand to monitor its maturity, and on the other hand to evaluate the astringency of this fruit, which must be completely eliminated before its commercialization. The physicochemical properties of the 'Mollar de Elche' pomegranate were evaluated by means of hyperspectral and colour imaging during its maturity using the information from the intact fruit and arils. Finally, this technique was used to characterise and identify the internal and external defects of the 'Algerie' loquat. In the prediction of the IQI and RPI quality indexes using reflectance and transmittance images, R2 values around 0.90 were obtained and in the discrimination according to firmness, accuracy around 95.0 % using selected wavelengths was obtained. Regarding the split pit detection, the use of the hyperspectral image in transmittance mode obtained a 93.5 % of fruits with normal bone correctly classified and 100% with split pit using PLS-DA models and 7 wavelengths. The results obtained in the classification of 'Big Top' and 'Diamond Ray' fruits show accuracy higher than 96.0 % by using PLS-DA models and 14 selected wavelengths, higher than the obtained with colour image (56.9 %) and a trained panel (54.5 %). According to persimmon, the results obtained indicated that it is possible to distinguish between three states of maturity with an accuracy of 96.0 % using QDA models and its firmness was predicted obtaining a R2 value of 0.80 using PLS-R. Regarding astringency, two similar studies were carried out. In the first study, the fruit was classified according to the time of treatment with high concentrations of CO2 with a precision of around 95.0 % using QDA. In the second, the fruit was discriminated according to a threshold value of soluble tannins (0.04 %) and was determined what fruit area was better to perform this discrimination. Thus, an accuracy of 86.9 % was obtained using the middle area and 23 wavelengths. The results obtained for the pomegranate indicated that the use of colour and hyperspectral images have a similar precision in the prediction of physicochemical properties using PLS-R and the intact fruit information. However, when the information from the arils was used, the hyperspectral image was more accurate. Regarding the discrimination by the state of maturity using PLS-DA, the hyperspectral image offered greater precision, of 95.0 % using the information from the intact fruit and 100 % using that from the arils. Finally, the results obtained for the 'Algerie' loquat indicated that the hyperspectral image with the XGBOOST classification method could discriminate between sound samples and samples with defects with accuracy of 97.5 % and between sound samples or samples with internal or external defects with an accuracy of 96.7 %. It was also possible to distinguish between the different defects with an accuracy of 95.9 %.Munera Picazo, SM. (2019). Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/125954TESISCompendi

Investigation of Volatile Organic Compounds (VOCs) released as a result of spoilage in whole broccoli, carrots, onions and potatoes with HS-SPME and GC-MS

Vegetable spoilage renders a product undesirable due to changes in sensory characteristics. The aim of this study was to investigate the change in the fingerprint of VOC composition that occur as a result of spoilage in broccoli, carrots, onions and potatoes. SPME and GC-MS techniques were used to identify and determine the relative abundance of VOC associated with both fresh and spoilt vegetables. Although a number of similar compounds were detected in varying quantities in the headspace of fresh and spoilt samples, certain compounds which were detected in the headspace of spoilt vegetables were however absent in fresh samples. Analysis of the headspace of fresh vegetables indicated the presence of a variety of alkanes, alkenes and terpenes. Among VOCs identified in the spoilt samples were dimethyl disulphide and dimethyl sulphide in broccoli; Ethyl propanoate and Butyl acetate in carrots; 1-Propanethioland 2-Hexyl-5-methyl-3(2H)-furanone in onions; and 2, 3-Butanediol in potatoes. The overall results of this study indicate the presence of VOCs that can serve as potential biomarkers for early detection of quality deterioration and in turn enhance operational and quality control decisions in the vegetable industry