341 research outputs found
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
Applications of Infrared and Raman Spectroscopies to Probiotic Investigation
In this review, we overview the most important contributions of vibrational spectroscopy based techniques in the study of probiotics and lactic acid bacteria. First, we briefly introduce the fundamentals of these techniques, together with the main multivariate analytical tools used for spectral interpretation. Then, four main groups of applications are reported: (a) bacterial taxonomy (Subsection 4.1); (b) bacterial preservation (Subsection 4.2); (c) monitoring processes involving lactic acid bacteria and probiotics (Subsection 4.3); (d) imaging-based applications (Subsection 4.4). A final conclusion, underlying the potentialities of these techniques, is presented
Sensors and biosensors for pathogen and pest detection in agricultural systems : recent trends and oportunities
Pathogen and pest-linked diseases across agriculture and ecosystems are a major issue towards enhancing current thresholds in terms of farming yields and food security. Recent developments in nanotechnology allowed the designing of new generation sensors and biosensors in order to detect and mitigate these biological hazards. However, there are still important challenges concerning its respective applications in agricultural systems, typically related to point-of-care testing, cost reduction and real-time analysis. Thus, an important question arises: what are the current state-of-the-art trends and relationships among sensors and biosensors for pathogen and pest detection in agricultural systems? Targeted to meet this gap, a comparative study is performed by a literature review of the past decade and further data mining analysis. With the majority of the results coming from recent studies, leading trends towards new technologies were reviewed and identified, along with its respective agricultural application and target pathogens, such as bacteria, viruses, fungi, as well as pests like insects and parasites. Results have indicated lateral flow assay, lab-on-a-chip technologies and infrared thermography (both fixed and aerial) as the most promising categories related to sensors and biosensors driven to the detection of several different pathogenic varieties. The main existing interrelations between the results are especially associated to cereals, fruits and nuts, meat and dairy along with vegetables and legumes, mostly caused by bacterial and fungal infections. Additional results also presented and discussed, providing a fertile groundwork for decision-making and further developments in modern smart farming and IoT-based agriculture
Applications of Infrared and Raman Spectroscopies to Probiotic Investigation
In this review, we overview the most important contributions of vibrational spectroscopy based techniques in the study of probiotics and lactic acid bacteria. First, we briefly introduce the fundamentals of these techniques, together with the main multivariate analytical tools used for spectral interpretation. Then, four main groups of applications are reported: (a) bacterial taxonomy (Subsection 4.1); (b) bacterial preservation (Subsection 4.2); (c) monitoring processes involving lactic acid bacteria and probiotics (Subsection 4.3); (d) imaging-based applications (Subsection 4.4). A final conclusion, underlying the potentialities of these techniques, is presented.Facultad de Ciencias Exacta
Radio frequency dielectric heating and hyperspectral imaging of common foodborne pathogens
Doctor of PhilosophyDepartment of Food ScienceRandall K. PhebusIntervention techniques to control foodborne pathogens, and rapid identification of pathogens in food are of vital importance to ensure food safety. Therefore, the first objective of this research was to study the efficacy of radio frequency dielectric heating (RFDH) against C. sakazakii and Salmonella spp. in nonfat dry milk (NDM) at 75, 80, 85, or 90°C. Using thermal-death-time (TDT) disks, D-values of C. sakazakii in high heat (HH)- and low heat (LH)-NDM were 24.86 and 23.0 min at 75°C, 13.75 and 7.52 min at 80°C, 8.0 and 6.03 min at 85°C, and 5.57 and 5.37 min at 90°C, respectively. D-values of Salmonella spp. in HH- and LH-NDM were 23.02 and 24.94 min at 75°C, 10.45 and 12.54 min at 80°C, 8.63 and 8.68 min at 85°C, and 5.82 and 4.55 min at 90°C, respectively. The predicted (TDT) and observed (RFDH) destruction of C. sakazakii and Salmonella spp. were in agreement, indicating that the organisms' behavior was similar regardless of the heating system (conventional vs. RFDH). However, RFDH can be used as a faster and more uniform heating method for NDM to achieve the target temperatures. The second objective of this research was to study if hyperspectral imaging can be used for the rapid identification and differentiation of various foodborne pathogens. Four strains of C. sakazakii, 5 strains of Salmonella spp., 8 strains of E. coli, and 1 strain each of L. monocytogenes and S. aureus were used in the study. Principal component analysis and kNN (k-nearest neighbor) were used to develop classification models, which were then validated using a cross-validation technique. Classification accuracy of various strains within genera including C. sakazakii, Salmonella spp. and E. coli, respectively was 100%; except within C. sakazakii, strain BAA-894, and within E. coli, strains O26, O45 and O121 had 66.67% accuracy. When all strains were studied together (irrespective of their genera) for the classification, only C. sakazakii P1, E. coli O104, O111 and O145, S. Montevideo, and L. monocytogenes had 100% classification accuracy; whereas, E. coli O45 and S. Tennessee were not classified (classification accuracy of 0%)
SPECTROSCOPY, IMAGE ANALYSIS AND HYPERSPECTRAL IMAGING FOR FOOD SAFETY AND QUALITY: A CHEMOMETRIC APPROACH
Questo progetto di dottorato studia le differenti applicazioni delle tecniche ottiche non distruttive per la valutazione della qualit\ue0 e della shelf-life di prodotti vegetali cos\uec come l\u2019identificazione precoce di sviluppi microbici su superfici industriali. La spettroscopia, l\u2019analisi dell\u2019immagine e l\u2019analisi dell\u2019immagine iperspettrale possono giocare un ruolo importante nella valutazione sia della qualit\ue0 che della sicurezza degli alimenti grazie alla rapidit\ue0 e sensibilit\ue0 della tecnica, specialmente quando si utilizzano strumenti semplificati portatili. Un approccio statistico multivariato (chemiometria) \ue8 richiesto al fine di estrarre informazioni dal segnale acquisito, riducendo la dimensionalit\ue0 dei dati e mantenendo le informazioni spettrali pi\uf9 utili.
Lo scopo del primo studio presentato \u2013 Testing of a Vis-NIR system for the monitoring of long-term apple storage \u2013 \ue8 la valutazione dell\u2019applicabilit\ue0 della spettroscopia nel visibile e vicino infrarosso (Vis-NIR) per il monitoraggio e la gestione delle mele durante lo stoccaggio a basse temperature. Per sette mesi \ue8 stata seguita l\u2019evoluzione in termini di grado zuccherino e consistenza delle mele suddivise in classi di maturazione. I risultati hanno indicato che la spettroscopia \ue8 una tecnica non-distruttiva che consente una stima accurata dei parametri chimico-fisici per la classificazione delle mele in lotti omogenei.
Il lavoro descritto nel secondo paragrafo - Wavelength selection with a view to a simplified handheld optical system to estimate grape ripeness \u2013 \ue8 finalizzato all\u2019identificazione delle tre lunghezze d\u2019onda pi\uf9 importanti per il riconoscimento, direttamente in campo, dell\u2019uva pronta per essere raccolta al fine della messa a punto di un sistema semplificato e a basso costo. I coefficienti di regressione standardizzati del modello PLS (Partial Least Square) sono stati utilizzati per selezionare le variabili pi\uf9 importanti, che racchiudono l\u2019informazione pi\uf9 utile lungo l\u2019intero spettro. La stessa procedura \ue8 stata condotta per determinare la freschezza delle foglie di Valerianella durante la shelf-life - Selection of optimal wavelengths for decay detection in fresh-cut Valerianella Locusta laterr (terzo paragrafo).
Lo scopo del lavoro presentato nel quarto paragrafo del primo capitolo - Comparison between FT-NIR and Micro-NIR in the evaluation of Acerola fruit quality, using PLS and SVM regression algorithms \u2013 \ue8 stimare l\u2019acidit\ue0 titolabile e il contenuto di acido ascorbico all\u2019interno del frutto acerola, utilizzando uno strumento compatto e a basso costo denominato Micro-NIR, che lavora nell\u2019intervallo spettrale 950-1650 nm. I dati spettrali sono stati modellati mediante l\u2019applicazione di due algoritmi PLS e SVM (Support Vector Machine). La capacit\ue0 predittiva dello strumento semplificato \ue8 risultata interessante per applicazioni di monitoraggio in campo, soprattutto modellizzando i dati in modo non lineare.
Nel secondo capitolo, \ue8 presentata l\u2019applicazione di immagini RGB per la valutazione delle superfici - Image texture analysis, a non-conventional technique for early detection of biofilm. La texture dell\u2019immagine \ue8 definita come una differenza nella distribuzione spaziale, nella frequenza e nell\u2019intensit\ue0 dei livelli di grigio in ogni pixel dell\u2019immagine. Questo metodo \ue8 stato determinante per l\u2019identificazione precoce dello sviluppo microbico su superfici normalmente impiegate nell\u2019industria alimentare.
L\u2019approccio chemiometrico \ue8 stato cruciale in ogni fase del progetto di dottorato ed \ue8 definito come un approccio statistico multivariato che si applica ai dati chimici per estrarre informazione utile, ridurre il rumore di fondo e l\u2019informazione ridondante. Il lavoro presentato all\u2019inizio del terzo capitolo - Hyperspectral image analysis: a tutorial - propone una procedura standard per l\u2019elaborazione di dati tridimensionali, presentando un esempio relativo alla predizione del raffermamento del pane in cassetta.
Il secondo paragrafo del terzo capitolo, presenta una applicazione dell\u2019immagine iperspettrale su acerola, focalizzata sul contenuto di vitamina C - HSI for quality evaluation of vitamin C content in Acerola fruit. In questo lavoro, \ue8 stata acquisita l\u2019immagine di dieci acerola, raccolte in funzione del livello di maturazione, definito in base al colore della buccia (cinque acerola verdi e cinque rosse). Lo spettro della polvere di vitamina C pura \ue8 stato utilizzato come riferimento per l\u2019applicazione di due algoritmi di correlazione (spectral angle mapping e correlation coefficient), consentendo la costruzione di mappe qualitative di distribuzione dell\u2019acido ascorbico all\u2019interno del frutto.
Lo scopo dell\u2019ultimo lavoro presentato \ue8 la valutazione della qualit\ue0 post raccolta dell\u2019acerola - Selection of NIR wavelengths from hyperspectral imaging data for the quality evaluation of Acerola fruit. Le immagini iperspettrali di venti acerola sono state acquisite per cinque giorni consecutivi. La valutazione delle modificazioni spettrali durante il tempo ha consentito la selezione delle tre lunghezze d\u2019onda caratterizzanti il processo di maturazione/degradazione del frutto. L\u2019immagine in falsi colori, derivante dalla composizioni delle immagini alle tre lunghezze d\u2019onda di interesse, consente l\u2019identificazione precoce del processo degradativo in maniera rapida e non distruttiva.
Le tre tecniche non distruttive impiegate in questo progetto di dottorato hanno dimostrato efficienza e applicabilit\ue0 per la valutazione della qualit\ue0 e della sicurezza degli alimenti, rispondendo alla necessit\ue0 dell\u2019industria alimentare di tecniche accurate, veloci e obiettive per assicurare produzioni ottimali lungo l\u2019intero processo produttivo.This PhD project regards different applications of non-destructive optical techniques to evaluate quality and shelf life of agro-food product as well as the early detection of biofilm on food plants. Spectroscopy, image analysis and hyperspectral imaging could play an important role in the assessment of both quality and safety of foods due to their rapidity and sensitivity especially when using simplified portable devices. Due to the huge amount of collected data, chemometric, a multivariate statistical approach, is required, in order to extract information from the acquired signals, reducing dimensionality of the data while retaining the most useful spectral information.
The thesis is organized in four chapters, one for each technique and a final chapter including the overall conclusion. Each chapter is divided in case studies according to the matrix analysed and the data acquisition and elaboration carried out.
The first chapter is about spectroscopy. The aim of the first study - Testing of a Vis-NIR system for the monitoring of long-term apple storage - is to evaluate the applicability of visible and near-infrared (Vis-NIR) spectroscopy to monitor and manage apples during long-term storage in a cold room. The evolution of the apple classes, originally created, was analysed during 7 months of storage by monitoring TSS and firmness. Vis-NIR allows an accurate estimation of chemical-physical parameters of apples allowing a non-destructive classification of apples in homogeneous lots and a better storage management.
The work reported in the second paragraph - Wavelength selection with a view to a simplified handheld optical system to estimate grape ripeness - is aimed to identify the three most significant wavelengths able to discriminate grapes ready to be harvested directly in the field. Wavelengths selection was carried out with a view to construct a simplified handheld and low-cost optical device. Standardized regression coefficients of the PLS model were used to select the relevant variables, representing the most useful information of the full spectral region. The same approach was followed to discriminate freshness levels during shelf-life of fresh-cut Valerianella leaves - Selection of optimal wavelengths for decay detection in fresh-cut Valerianella Locusta Laterr. (third paragraph).
The aim of the work presented in the fourth paragraph of the first chapter - Comparison between FT-NIR and Micro-NIR in the evaluation of Acerola fruit quality, using PLS and SVM regression algorithms - is to estimate titratable acidity and ascorbic acid content in acerola fruit, using a MicroNIR, an ultra-compact and low-cost device working between 950 \u2013 1650 nm. The spectral data were modelled using two different regression algorithms, PLS (partial least square) and SVM (support vector machine). The prediction ability of Micro-NIR appears to be suitable for on field monitoring using non-linear regression modelling (i.e. SVM).
In the second chapter, image analysis was performed. The traditional RGB imaging for the evaluation of image texture, a specific surface characteristic, is presented. The texture of an image is given by differences in the spatial distribution, in the frequency and in the intensity of the values of the grey levels of each pixel of the image. This technique was applied for the early detection of biofilm in its early stages of development, when it is still difficult to observe it by the naked eye, was evaluated (Image texture analysis, a non-conventional technique for early detection of biofilm).
In the third paragraph, image and spectroscopy were combined in hyperspectral imaging applications. Data analysis by chemometric was crucial in any stage of my PhD project. Chemometric is a multivariate statistical approach that is applied on chemical data to extract the useful information avoiding noise and redundant data. At the beginning of the third chapter - Hyperspectral image analysis: a tutorial - proposes an original approach, developed as a flow sheet for three-dimensional data elaboration. The method was applied, as an example, to the prediction of bread staling during storage.
The first application about hyperspectral on acerola is focused on the vitamin C content - HIS for quality evaluation of vitamin C content in Acerola fruit. Ten different acerola fruits picked up according to two different stages of maturity, based on the colour of the peel (5 green and 5 red acerola), were analysed. The spectra of pure vitamin C powder was used as references for computing models with two different correlation techniques: spectral angle mapping and correlation coefficient allowing the construction of a qualitative distribution map of ascorbic acid inside the fruit.
The aim of the last one work presented is to evaluate acerola post-harvest quality - Selection of NIR wavelengths from hyperspectral imaging data for the quality evaluation of Acerola fruit. Hyperspectral images of 20 acerolas were acquired for five consecutive days and an investigation of time trends was carried out to highlight the most important three wavelengths that characterized the ripeness/degradation process of the Acerola fruit. The false-colour RGB images, derived from the composition of the three interesting wavelengths selected, data enable early detection of the senescence process in a rapid and non-destructive manner.
In conclusion, the three non-destructive optical techniques applied in this PhD project have proved to be one of the most efficient and advanced tools for safety and quality evaluation in food industry answering the need for accurate, fast and objective food inspection methods to ensure safe production throughout the entire production process
Raman Mapping: Emerging Applications
Raman mapping is a noninvasive, label‐free technique with high chemical specificity and high potential to become a leading method in biological and biomedical applications. As opposed to Raman spectroscopy, which provides discrete chemical information at distinct positions within the sample, Raman mapping provides chemical information coupled with spatial information. The laser spot scans the investigated sample area with a preset step size and acquires Raman spectra pixel by pixel. The Raman spectra are then discriminated from each other by chemometric analysis, and the end result is a false color map, an image of the sample that contains highly precise structural and chemical information. Raman imaging has been successfully used for label‐free investigations at cellular and subcellular level. Cell compartments, cell responses to drugs and different stages of the cell cycle from the stem cell to the completely differentiated cell were successfully distinguished. This technique is also able to differentiate between healthy and cancer cells, indicating great potential for replacing conventional cancer detection tools with Raman detection in the future
Phytoplankton dynamics and bio-optical variables associated with Harmful Algal Blooms in aquaculture zones
The surveillance of Harmful Algal Blooms (HABs) in aquaculture zones is a crucial component in monitoring and mitigation of adverse effects caused by accumulation of high biomass of algal cells and/or associated toxins. Integrated findings of this thesis strongly stress the significance of synoptic bio-optical and conventional measures for efficient surveillance of HABs and their environmental triggers over required spatio-temporal scales, here shown for a case study in the Ebro Delta, NW Mediterranean. In particular, the installation of an environmental observatory in the Ebro Delta aquaculture area, and the capability of a radiometric sensor system as key component are highly motivated by study results. Yet it was clearly shown that for the interpretation of bio-optical data, detailed knowledge on bloom characteristics is crucial. By such effective coverage of bloom dynamics, combined with insights on environmental scenarios that promote the proliferation of certain taxa, public and private responses can be optimised. In a future scenario, this knowledge can be transferred to predictive models of HABs. In this sense, these future steps may advance towards preventive measures rather than mitigation actions to deal with this environmental hazard
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