Characterization of vasskveite (water halibut) syndrome for automated detection

In recent years, cases of vasskveite (water halibut) syndrome in halibut have been increasing. At the moment, there exists no way to screen for the syndrome immediately after capture, which is problematic for both exporters and purchasers. In this article, we compared good quality halibut and halibut exhibiting the syndrome using a variety of techniques. Hyperspectral imaging was used to quantify the relative amounts of fat and water in the tissue. Diffusion tensor imaging was used to characterize tissue structure. Histology was performed to provide direct visual characterization of the tissue. Results indicate the muscle fibers in afflicted fish exhibit disordered growth and the tissue is lacking in fat. These results are in line with the current theory that the syndrome stems from a nutritional deficiency in the halibut diet. Hyperspectral imaging appears to be a promising technology to rapidly identify afflicted halibut immediately after capture

Nir Spectral Techniques and Chemometrics Applied to Food Processing

Tesis por compendio[ES] Las técnicas rápidas, no destructivas y libres de químicos tienen una demanda creciente en muchos campos de la industria. Las técnicas de espectroscopia de infrarrojo cercano (NIRS) y imágenes hiperespectrales NIR (NIR-HSI) han mostrado un gran potencial para determinar los parámetros de calidad de los alimentos, autenticar productos alimenticios, detectar el fraude, entre otras. En la NIRS, las medidas se toman en puntos específicos, detectando solo una pequeña porción; en la NIR-HSI, la información espectral y espacial se combinan, lo que la convierte en una opción adecuada para muchos productos alimenticios, ya que son matrices muy heterogéneas. Por lo tanto, este estudio tuvo como objetivo revisar la aplicación de NIRS (dispersivos), NIR de Transformada de Fourier (FT) y HSI en la evaluación de los parámetros de calidad de harina de trigo y productos a base de trigo, así como para la autenticación y determinación de la composición de estos productos. Además, este trabajo tuvo como objetivo identificar y clasificar diferentes tipos de muestras de fibra agregadas a la semolina y pasta producidas por estas formulaciones, y monitorear el proceso de cocción de esta pasta enriquecida en fibra mediante técnicas espectrales. Además, se objetivó aplicar HSI a otro producto en polvo, por lo que se cuantificó el contenido de pectina en las cáscaras de naranja. Primero, se adquirieron espectros NIR para comparar la precisión en la clasificación de muestras enriquecidas con fibra, para cuantificar la cantidad de estas fibras y verificar su distribución en muestras de semolina. Para la clasificación se utilizaron el Análisis de Componentes Principales (PCA) y el Soft Independent Modelling of Class Analogy (SIMCA). Los modelos de regresión de mínimos cuadrados parciales (PLSR) aplicados a espectros NIR-HSI mostraron R²P entre 0,85 y 0,98 y RMSEP entre 0,5 y 1, y los modelos se utilizaron para construir los mapas químicos para verificar la distribución de fibra en las superficies de las muestras. Además, se probó el NIR-HSI junto con Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) para investigar la capacidad de evaluación, resolución y cuantificación de la distribución de fibra en la pasta. Los resultados mostraron R²P entre 0.28 y 0.89,% de falta de ajuste (LOF) <6%, varianza explicada sobre 99% y similitud entre espectros puros y recuperados sobre 96% y 98%. Además, se probó VIS/NIR-HSI en el modo de transmisión como una alternativa objetiva para la clasificación de muestras de pasta según el tiempo de cocción. El análisis discriminante lineal (LDA) mostró valores de sensibilidad y especificidad entre 0,14-1,00 y 0,51-1,00, respectivamente, y una tasa de ausencia de error (NER) superior a 0,62. El análisis discriminante de mínimos cuadrados parciales (PLSDA) mostró valores de sensibilidad y especificidad entre 0,67-1,00 y 0,10-1,00, respectivamente, y NER superiores a 0,80. Los resultados de este trabajo mostraron que la técnica NIR-HSI se puede utilizar para la identificación y cuantificación de la fibra agregada a la semolina. Además, NIR-HSI y MCR-ALS pueden identificar la fibra en la pasta. La HSI en el modo de transmisión demostró ser una técnica adecuada como alternativa objetiva para la clasificación de muestras de pasta según el tiempo de cocción como una forma de automatizar la determinación de los atributos de la pasta. La determinación del contenido de pectina en cáscaras de naranja se investigó usando NIR-HSI. LDA mostró mejores resultados de discriminación considerando tres grupos: bajo (0-5%), intermedio (10-40%) y alto (50-100%) contenido. Los modelos PLSR basados en espectros completos mostraron mayor precisión (R2> 0,93, RMSEP entre 6,50 y 9,16% de pectina) que los basados en pocas longitudes de onda seleccionadas (R2 entre 0,92 y 0,94, RMSEP entre 8,03 y 9,73% de pectina). Los resultados demuestran el potencial de NIR-HSI para cuantificar el contenido de pectina en las cáscaras de naranja, proporcionando una técnica valiosa para los productores de naranja y las industrias de procesamiento.[CA] Les tècniques ràpides, no destructives i lliures de químics tenen una demanda creixent en molts camps de la indústria. Les tècniques d'espectroscopia d'infraroig proper (NIRS) i d'imatges hiperespectrals NIR (NIR-HSI) han demostrat tindre un gran potencial per a determinar paràmetres de qualitat d'aliments, autenticar productes alimentaris, detectar frau entre altres aplicacions. Mentre que en la NIRS proper les mesures es prenen en punts específics de la mostra i es detecta una porció menuda, en la HSI es combina informació espectral i espacial de tal manera que és una opció adient per a molts tipus de productes alimentaris, ja que són matrius molt heterogènies. Per tant, este estudi va tindre com objectiu revisar tota l'aplicació de NIRS (dispersius), NIR de Transformada de Fourier (FT) i HSI en l'avaluació dels paràmetres de qualitat de la farina de blat i els productes a base de blat, així com per a l'autenticació i determinació de la composició d'estos productes. A més a més, este estudi va tindre com objectiu identificar i classificar diferents tipus de mostres de fibra afegides a la semolina i pasta produïdes per formulació de fibra i semolina, i monitorar mitjançant tècniques espectrals el procés de cocció d'aquesta pasta enriquida amb fibra. A més, este treball va tindre com objectiu aplicar HSI a un altre producte en pols, de tal manera que es va quantificar el contingut de pectina en les corfes de taronja. Primer, es van adquirir espectres NIR per comparar la precisió en la classificació de mostres enriquides amb fibra, per quantificar estes fibres i verificar la seua distribució en mostres de sèmola. Per a la classificació es van emprar l'Anàlisi de Components Principals (PCA) i el SIMCA (Soft Independent Modelling of Class Analogy). Els models de regressió de mínims quadrats parcials (PLSR) aplicats a espectres NIR-HSI mostraren R²P entre 0,85 i 0,98 i RMSEP entre 0,5 i 1% de contingut de fibra, i els models s'utilitzaren per construir els mapes químics per verificar la distribució de fibra en les superficies de les mostres. Així mateix, es va provar NIR-HSI amb Multivariate Curve Resolution-Alternating Least Square (MCR-ALS) per a investigar la capacitat d'avaluació, resolució i quantificació de la distribució de fibra en la pasta enriquida. Els resultats mostraren un R²P entre 0,28 i 0,89%, lack of fit (LOF) 0,93, RMSEP entre 6,50 i 9,16% de pectina) que els basats en longituds d’ona seleccionades (R2 entre 0,92 i 0,94, RMSEP entre 8,03 i 9,73% de pectina). Els resultats demostren el potencial de NIR-HSI per a quantificar el contingut de pectina en corfa de taronja i proporcionen una tècnica valuosa per als productors de taronja i les indústries de processament.[EN] Fast, non-destructive and chemical-free techniques are in increasing demand in many fields of the industry. Near-infrared spectroscopy (NIRS) and NIR hyperspectral imaging (NIR-HSI) techniques have shown great potential in determining food quality parameters, authenticating food products, detecting food fraud, among many other applications. While in near infrared spectroscopy, the measurements are taken at specific points on the sample, detecting only a small portion; in hyperspectral imaging, spectral and spatial information are combined, making it a suitable choice for many food products, since they are very heterogeneous matrices. Therefore, this study aimed to review all the application of (dispersive) NIRS, Fourier Transform (FT) NIR, and HSI in assessing wheat flour and wheat-based products quality parameters, as well for the authentication and determination of composition of these products. Moreover, this work aimed to identify and classify different types of fibre samples added to the semolina and pasta produced by semolina-fibre formulations, and to monitor the cooking process of this fibre-enriched pasta by spectral techniques. In addition, this work had the aim of applying HSI to other powdered product, so the pectin content in orange peels was quantified. First, NIR spectra were acquired to compare the accuracy in the classification of fibre-enriched samples, to quantify the amount of these fibres and verify their distribution on semolina samples. Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were used for classification. Partial Least Squares Regression (PLSR) models applied to NIR-HSI spectra showed R2P between 0.85 and 0.98, and RMSEP between 0.5 and 1% of fibre content, and the models were used to construct the chemical maps to check the fibre distribution on the samples surface. Moreover, NIR-HSI together with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), was tested to investigate the ability for the evaluation, resolution and quantification of fibre distribution in enriched pasta. Results showed coefficient of determination of validation (R²V) between 0.28 and 0.89, % of lack of fit (LOF) 0.93, RMSEP between 6.50 and 9.16% of pectin) than those based on few selected wavelengths (R² between 0.92 and 0.94, RMSEP between 8.03 and 9.73%). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries.This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior- Brasil (CAPES) [Finance Code 001]; São Paulo Research Foundation (FAPESP) [grant numbers 2015/24351-2, 2017/17628-3, 2019/06842- 0]; and by projects AEI PID2019-107347RR-C31 and PID2019-107347RR-C32, and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014-2020. The authors would like to thank Nutrassim Food Ingredients company for the donation of the fibre samples, the support provided by Enrique Aguilar María, Carlos Alberto Velasquez Hernández, Diego Hernández Catalán, Carlos Ruiz Catalá and Andrés Estuardo Prieto López during system installation, experimental analysis and data acquisition.Teixeira Badaró, A. (2021). Nir Spectral Techniques and Chemometrics Applied to Food Processing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178758Compendi

Towards a quantum cascade laser-based implant for the continuous monitoring of glucose

The continuous surveillance of the body’s glucose concentration is pivotal for the prevention of short- and long-term health complications for people with diabetes. In this thesis, mid-infrared absorption spectroscopy is investigated as an alternative to conventional enzyme-based glucose monitors. To this end, a quantum cascade laser-based sensor system is designed and implemented with the goal to serve as an optical port for continuous, real-time spectroscopy in vivo. This transflection sensor shows high sensitivity in vitro with a glucose error of prediction as low as 4mg/dL in pure glucose solutions, 10mg=dL in the presence of proteins and 21mg=dL in the presence of other carbohydrates. The impact of the temperature on the optical signal is investigated both theoretically and experimentally. Even under temperature variations up to 15C a glucose prediction error as low as 18:5mg/dL can be achieved. Adding a porous membrane hinders the diffusion of large molecules into the sensor while enabling glucose diffusion on the targeted time scale (<5 minutes). Finally, a stable glucose permeation and concentration prediction over more than 1 month demonstrates the potential of a quantum cascade laser-based transflection technology for application in a long-term implant for continuous glucose sensing

Nir spectroscopy for non-destructive quality evaluation of fish

Fish freshness is regarded as one major parameter for seafood quality. However, it is lost inevitably in practice after catching and fish death, owing to the natural autolysis process which, in turn, trigger the growth of microorganisms and, consequently, the progressive loss of food characteristics and quality. This phenomenon is perceptible by changes in the sensory characteristics such as appearance, odour, taste and texture of fresh fish as well as in chemical, biochemical and microbiological changes. Fish market prices is highly depended on accurately predict its freshness and shelf-life. Predicted storage time in ice is defined as the number of days that the fish has been stored in ice and it is possible to use these results to estimate the remaining shelf life. Quality Index Method (QIM) is currently the most wholesome and straightforward method of describing freshness. However, it is time consuming and subjective and it is not always suitable for largescale applications. NIR spectroscopy has been proven to be a rapidly and non-destructive method for evaluating fish components (moisture, protein, fat, …) as well as it has shown good predictions errors associated with fish storage time prediction. The purpose of this research is to test the possibility of using NIR spectroscopy for non-destructively predicting freshness levels of plaice fish in Flanders, Belgium. In the preliminary study, spectroscopic measurements were performed on tested plaice samples (n=10) subjected to different storage times assisted with Partial Least Squares Discriminant Analysis (PLSDA) indicated that NIR spectroscopy had great potential for nondestructive plaice freshness discrimination. In the next step, the main study employing NIR diffuse reflectance measurements for plaice samples (n=90) graded using commercial QIM scoring method at ILVO (Flanders, Belgium) together with Partial Least Squares (PLS) regression culminated on two different calibration models for predicting freshness expressed as storage days in ice (converted from the graded QIM scores): one for dark skin measurements with prediction performances of 1.82, 2.22 and 0.804 for RMSECV, RMSEP and R2p, respectively, using the selected wavelength range of 1400 to 1580 nm; and one for white skin measurements with those parameters of 2.356, 2.59 and 0.677 for RMSECV, RMSEP and R2p, respectively, using the full wavelength range studied of 940 to 1700 nm.A frescura do peixe é considerada um dos parâmetros mais importantes na caracterização da qualidade dos produtos aquáticos. No entanto, esta é inevitavelmente perdida devido ao processo de autólise que, por sua vez, desencadeia o crescimento de microrganismos e, consequentemente, a perda progressiva de qualidade. Este fenómeno é visível através das alterações das características sensoriais tais como a aparência, odor, paladar e textura assim como alterações químicas, bioquímicas e microbiológicas. O preço de mercado do peixe depende da previsão exata da frescura e do tempo de vida útil. Prever o tempo de armazenamento no gelo define-se como o número de dias que o peixe está armazenado no gelo e é possível utilizar este valor para estimar o tempo de vida útil remanescente. O método do índex de qualidade (QIM) é atualmente o método mais completo e direto para descrever a frescura do peixe. No entanto, é um método lento e subjetivo e que não é exequível para aplicações em grande escala. A espectroscopia na zona do infravermelho próximo (NIR) provou ser um método rápido e não destrutivo para avaliação dos constituintes do peixe (água, proteína, gordura, …) assim como exibiu bons erros de previsão do tempo de armazenamento. O objetivo desta investigação é testar a possibilidade de utilizar esta técnica para prever de forma não destrutiva níveis de frescura em amostras de solha em Flandres, Bélgica. No estudo preliminar, análise espectral assistida pela técnica PLSDA foi utilizada em amostras de solha (n=10) sujeitas a diferentes tempos de armazenamento e os resultados indicaram que a espectroscopia na zona do infravermelho próximo tem um grande potencial para a discriminação não destrutiva da frescura da solha. Na etapa seguinte, o estudo principal, realizado no modo de reflectância difusa, em amostras de solha (n=90) classificadas pelo sistema de pontuação QIM na ILVO (Flandres, Bélgica) em conjunto com a técnica PLS, culminou em dois modelos aperfeiçoados para previsão da frescura expressada em dias de armazenamento em gelo (obtidos pela conversão dos pontos QIM): um para as medições na parte da pele escura, com parâmetros de 1.82, 2.22 e 0.804 para RMSECV, RMSEP e R2p no intervalo de comprimento de onda entre os 1400 e os 1580 nm; e um segundo para as medições na parte da pele branca, com parâmetros de 2.36, 2.59 e 0.677 para RMSECV, RMSEP e R2p utilizando a extensão completa de comprimentos de onda estudados de 940 a 1700 nm

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

Spatial variability in sea-ice algal biomass: an under-ice remote sensing perspective

Sea-ice algae are a paramount feature of polar marine ecosystems and ice algal standing stocks are characterized by a high spatio-temporal variability. Traditional sampling techniques, e.g., ice coring, are labor intensive, spatially limited and invasive, thereby limiting our understanding of ice algal biomass variability patterns. This has consequences for quantifying ice-associated algal biomass distribution, primary production, and detecting responses to changing environmental conditions. Close-range under-ice optical remote sensing techniques have emerged as a capable alternative providing non-invasive estimates of ice algal biomass and its spatial variability. In this review we first summarize observational studies, using both classical and new methods that aim to capture biomass variability at multiple spatial scales and identify the environmental drivers. We introduce the complex multi-disciplinary nature of under-ice spectral radiation profiling techniques and discuss relevant concepts of sea-ice radiative transfer and bio-optics. In addition, we tabulate and discuss advances and limitations of different statistical approaches used to correlate biomass and under-ice light spectral composition. We also explore theoretical and technical aspects of using Unmanned Underwater Vehicles (UUV), and Hyperspectral Imaging (HI) technology in an under-ice remote sensing context. The review concludes with an outlook and way forward to combine platforms and optical sensors to quantify ice algal spatial variability and establish relationships with its environmental drivers