Investigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction

Near infrared spectroscopy is a susceptible technique which can be affected by various factors including the surface of samples. According to the Lambertian reflection, the uneven and matte surface of fruits will provide Lambertian light or diffuse reflectance where the light enters the sample tissues and that uniformly reflects out in all orientations. Bunch of researches were carried out using near infrared diffuse reflection mode in non-destructive soluble solids content (SSC) prediction whereas fewer of them studying about the geometrical effects of uneven surface of samples. Thus, this study aims to investigate the parameters that affect the near infrared diffuse reflection signals in non-destructive SSC prediction using intact pineapples. The relationship among the reflectance intensity, measurement positions, and the SSC value was studied. Next, three independent artificial neural networks were separately trained to investigate the geometrical effects on three different measurement positions. Results show that the concave surface of top and bottom parts of pineapples would affect the reflectance of light and consequently deteriorate the predictive model performance. The predictive model of middle part of pineapples achieved the best performance, i.e. root mean square error of prediction (RMSEP) and correlation coefficient of prediction (Rp) of 1.2104 °Brix and 0.7301 respectively

Non-destructive prediction of ‘marsh’ grapefruit (citrus x paradisi MacFad) postharvest quality and physiological rind disorders using visible to near infrared spectroscopy.

Master of Science in Horticulture. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file

Postharvest technologies for predicting and reducing susceptibility of ‘Marsh’ grapefruit (Citrus paradisi MacFad.) to rind pitting disorder.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Citrus fruit is globally one of the most important fruit due to their nutritional value and sensorial attributes, however, they were susceptible to various postharvest disorders, especially during shipping period. Therefore, the aim of this study was to determine the effect of edible coating, carboxymethyl cellulose (CMC) infused with moringa leaf extracts (MLE) on reducing postharvest physiological rind pitting disorder in ‘Marsh’ grapefruit (Citrus paradisi MacFad.). The study also reviewed the literatures on the ability of edible coatings to improve fruit quality and extend shelf life of citrus fruit. Edible coatings recently received attention due to their ability to enhance fruit quality without compromising human health. The first experimental chapter was conducted to evaluate the ability of CMC and MLE as edible coatings to control the disorder in ‘Marsh’ grapefruit. A total of 300 fruit (150 from outside canopy and 150 from inside canopy) were harvested from a commercial orchard at Dole Bolton Citrus Estate in Nkwalini at Showe, KwaZulu Natal, South Africa. Fruit were subjected to different treatments, control (untreated), CMC (0.5%) + MLE (10%), CMC (1%) + MLE (10%), CMC 0.5% and CMC 1%. Treatments were organised in a factorial design. Fruit were stored at 3 ± 0.5 °C and 90-95 % relative humidity (RH), for nine weeks and thereafter taken to room temperature (22 ± 2 °C) for two weeks to simulate shelf life. The physicochemical attributes (total soluble solids. titratable acidity, maturity index, fruit mass loss, fruit colour, rind dry matter) of the fruit were analysed during this period. Rind pitting as well as sensory quality was evaluated at the end of storage period. This study identified that CMC 0.5% + MLE 10% and CMC 1% + MLE 10% reduced postharvest rind pitting disorder incidence compared to CMC 0.5%, CMC 1% and the control treatment. High mass loss contributes largely to rind pitting development, however, edible coatings managed to provide semi-permeable barrier to the fruit. Uncoated fruit had high mass loss which may be due to high water loss from the rind, most probably rind cell collapsed thereby leading to visible pitting in fruit rind. Coated fruit with low rind pitting incidence had low rind dry matter (RDM) compared to uncoated fruit with high rind pitting incidence. This study reported that total soluble solids (TSS) increased with storage time, however, low rate of increase was noticed in coated fruit compared to uncoated fruit. Fruit with higher TSS at the end of storage had high rind pitting incidence compared to fruit with low TSS. Rind colour was expressed as citrus colour index (CCI). Citrus colour index was noticed to increase with storage, however, the rate of increase in coated fruit was lower than that of uncoated fruit. At the end of storage, CCI was therefore higher in uncoated fruit than coated fruit, while higher CCI was correlated with high rind pitting incidence. These physicochemical quality parameters can be used to predict rind pitting occurrence in ‘Marsh’ grapefruit. The second experimental chapter investigated rind phytochemical quality attributes that can be used as pre-symptomatic markers of rind pitting disorder in ‘Marsh’ grapefruit. Treatments used for this chapter were similar to the abovementioned. Treatments were organised in a factorial design. Visible to near infrared spectroscopy (Vis/NIRS) as a non-destructive technique was used to develop models that can assist in rind pitting disorder prediction. Partial least square (PLS) regression models were developed to predict rind phytochemical quality attributes such as ascorbic acid, phenolics, flavonoids, antioxidant capacity and activity, pigments (chlorophyll a and b, β carotene and total carotenoids) and sugars (sucrose, glucose and fructose), and these models were developed to predict rind pitting disorder of ‘Marsh’ grapefruit. Noticeably, CMC when combined with MLE were able to reduce the incidence of rind pitting disorder when compared to their counterparts. This could be due to the fact that moringa is believed to have high content of flavonoids, phenolics and antioxidants, which may be released to fruit and act as free radical scavengers from the cell matrix and protect fruit from external stress. These studies further investigated the effect of canopy position on susceptibility of rind pitting development. It was found that outside canopy (OC) fruit were more susceptible to rind pitting disorder compared to fruit from inside canopy (IC). This may be due to that OC fruit are exposed to different climate during fruit growth and development which could lead to rind quality stress and damage rind cells. Since OC fruit were more prone to disorder development than IC fruit, it would make financial sense to export fruit to the low demanding market with less penalties if fruit develop pitting prior to destination. Alternatively, fruit with higher chances of developing disorders (OC fruit) must be sent to local markets or fruit may be processed to other sellable products such as juices and dried fruits

Multivariate analysis and artificial neural network approaches of near infrared spectroscopic data for non-destructive quality attributes prediction of Mango (Mangifera indica L.)

There is a need for fast and reliable quality and authenticity control tools of pharmaceutical ingredients. Among others, hormone containing drugs and foods are subject to scrutiny. In this study, terahertz (THz) spectroscopy and THz imaging are applied for the first time to analyze melatonin and its pharmaceutical product Circadin. Melatonin is a hormone found naturally in the human body, which is responsible for the regulation of sleep-wake cycles. In the THz frequency region between 1.5 THz and 4.5 THz, characteristic melatonin spectral features at 3.21 THz, and a weaker one at 4.20 THz, are observed allowing for a quantitative analysis within the final products. Spectroscopic THz imaging of different concentrations of Circadin and melatonin as an active pharmaceutical ingredient in prepared pellets is also performed, which permits spatial recognition of these different substances. These results indicate that THz spectroscopy and imaging can be an indispensable tool, complementing Raman and Fourier transform infrared spectroscopies, in order to provide quality control of dietary supplements and other pharmaceutical products

Non-destructive evaluation of external and internal table grape quality

Thesis (PhDAgric)--Stellenbosch University, 2021.ENGLISH ABSTRACT: Determining the correct harvest maturity parameters of table grapes is an essential step before harvesting. The chemical analysis of table grapes to determine harvest and quality parameters such as total soluble solids (TSS), titratable acidity (TA) and pH, is very time-consuming, expensive, and destructive. Developing faster and more cost-effective methods to obtain the information can benefit the table grape industry by reducing losses suffered at the postharvest stage. There are multitudes of factors that can influence table grape postharvest quality leading to huge losses. These losses are exacerbated even further by the long list of postharvest external and internal defects that can occur, including browning in all its various manifestations. The application of cutting-edge technologies such as Fourier Transform Near-Infrared (FT-NIR) spectroscopy that can accurately assess the external and internal quality of fruit is, therefore, essential. This particularly concerns the identification of defects or assessment of the risks of defects that are likely to develop during post storage. The aim of this application would thus be to evaluate these new technologies to monitor table grape quality non-destructively, before, during, and/or after harvest. This study, therefore, focussed on the development and optimisation of faster, cost- effective, and fit-for-purpose methods to monitor harvest maturity and quality of table grapes in the vineyard before harvesting and during packaging and cold storage. Harvest of three different cultivars, namely, Thompson Seedless, Regal Seedless and Prime, happened over two seasons (2016 and 2017) from six different commercial vineyards. Five of these vineyards were in the Western Cape (two in the Hex River Valley, three in Wellington) and one in the Northern Cape (Kakamas), South Africa. Harvest occurred twice at each vineyard, at optimum ripeness and two weeks later (after the optimum harvest date). The incidence and intensity of browning on each berry on a bunch were evaluated for different defects and browning phenotypes. Quantitative harvest maturity and indicative quality parameters such as TSS, TA and pH, as well as the sensory-related parameters – sugar:acid ratio (TSS:TA ratio) and BrimA, were investigated by scanning whole table grape bunches contactless with Bruker’s MATRIX-F spectrometer in the laboratory. Partial Least Squares (PLS) regression was used to build prediction models for each parameter. Two different infrared spectrometers, namely the Bruker Multipurpose Analyser Fourier Transform Near-Infrared (MPA FT-NIR) and MicroNIR Pro 1700 were also used to determine TSS on whole table grape berries. The MicroNIR Pro 1700 was utilised in the vineyard and the laboratory and the MPA only in the laboratory. The same spectral dataset used to build the quantitative models was used to build classification models for two browning phenotypes, namely chocolate browning and friction browning. Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANN) were used for the classification tasks. Key results showed that the incidence and intensity of different defects and browning phenotypes such as sulphur dioxide (SO2) damage were prevalent on all three white seedless table grape cultivars. The incidences of fungal infection, sunburn and abrasion damage were high on Regal Seedless and Thompson Seedless in 2016. Contact browning, mottled browning and friction browning and bruising damage had higher incidences in 2017 than in 2016. Overall, the intensity of defects was very high in 2016 except on Regal Seedless from Hex River Valley. Prime from Kakamas and Wellington had the highest intensity of defects in 2017, which appeared on the grapes after 7 weeks of cold storage. Prediction models were successfully developed for TSS, TA, TSS:TA, pH, and BrimA minus acids on intact table grape bunches using FT-NIR spectroscopy in a contactless measurement mode, and applying spectral pre-processing techniques for regression analysis with PLS. The combination of Savitzky-Golay first derivative coupled with multiplicative scatter correction on the original spectra delivered the best models. Statistical indicators used to evaluate the models were the number of latent variables (LV) used to build the model, the prediction correlation coefficient (R2p) and root mean square error of prediction (RMSE). For the respective parameters TSS, TA, TSS:TA ratio, pH, and BrimA, the number of LV used when the models were build according to a random split of the calibration and validation set were 6, 4, 5, 5 and 10, the R2p = 0.81, 0.43, 0.66, 0.27, and 0.71, and the RMSEP = 1.30 °Brix, 1.09 g/L, 7.08, 0.14, and 1.80. When 2016 was used as the calibration set and 2017 as the validation set in model building the number of LV used were 9, 5, 5, 4 and the R2p = 0.44, 0.06, 0.17, 0.05, and 0.05 and the RMSEP = 3.22 °Brix, 2.41 g/L, 14.53, 0.21, and 8.03 for for the respective parameters. Determining TSS of whole table grape berries in the vineyard before and after harvesting using handheld and benchtop spectrometers on intact table grape berries showed that spectra taken in the laboratory with the MicroNIR were more homogenous than those taken in the vineyard with the same spectrometer, over the two years investigated. The results obtained with the MPA were not as good as those obtained with the MicroNIR in the laboratory were. The model constructed with the combined data of 2016 and 2017 taken in the laboratory with the MicroNIR had the best statistics in terms of R2p (0.74) and RPDp (1.97). The model constructed with the 2017 data obtained in the laboratory with the MicroNIR had the lowest prediction error (RMSEP = 1.13°Brix). Good models were obtained using PLS-DA and ANN to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact table grape bunches with the MATRIX-F spectrometer. The classification error rate (CER), specificity and sensitivity were used to evaluate the models constructed using PLS-DA and the kappa score was used for ANN. The CER for chocolate browning (25%) was better than that of friction browning (46%) after Weeks 3 and 4 in cold storage for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 of friction browning were not as good as for chocolate browning. With ANN, the testing kappa score to classify table grape bunches as clear or having chocolate browning or friction browning showed that chocolate browning could be classified with the strong agreement after Weeks 3 and 4 and Weeks 5 and 6 and that friction browning could be classified with moderate agreement after three and four weeks in cold storage. Classification of chocolate browning and friction browning phenotypes was done using PLS-DA and ANN and the result showed that both types of browning can be classified with moderate agreement. The implications of the results of this study for the table grape industry are that the industry can move beyond just assessing methods and techniques in the laboratory towards implementation in the vineyard and the packhouse. Much quicker decisions regarding grape quality and destination of export can now be made using a combination of the MicroNIR handheld and MATRIX-F instruments for onsite quality measurement and the models to predict internal (e.g. TSS) and external (browning) quality attributes.AFRIKAANSE OPSOMMING: Die bepaling van die korrekte oesrypheidsparameters van tafeldruiwe is 'n noodsaaklike stap voor oes. Chemiese ontleding van tafeldruiwe om oes- en kwaliteitsparameters te bepaal, soos totale oplosbare vaste stowwe (TOVS), titreerbare suur (TS) en pH, is baie tydrowend, duur en vernietigend. Die ontwikkeling van vinniger en kostedoeltreffender maniere om die inligting te bekom, kan die tafeldruifbedryf bevoordeel deur verliese wat in die na-oesstadium gely word, te verminder. Dit sluit die menigte faktore in wat die gehalte van tafeldruiwe ná oes kan beïnvloed en tot verliese lui. Hierdie verliese word nog verder vererger deur die lang lys van verskillende na-oes-verwante gebreke wat kan voorkom, insluitend verbruining in al sy verskillende manifestasies. Die toepassing van toonaangewende tegnologieë soos Fourier-transform-naby- infrarooi (FT-NIR) spektroskopie wat die eksterne en interne kwaliteit van vrugte akkuraat kan beoordeel, is dus noodsaaklik. Dit is veral die identifisering van gebreke, of die beoordeling van die risiko's van gebreke, wat waarskynlik tydens die opberging kan ontstaan. Die doel van hierdie toepassing was dus om hierdie nuwe tegnologieë te evalueer om die kwaliteit van tafeldruiwe nie-vernietigend te monitor, voor, tydens en/of ná oes. Hierdie studie het dus gefokus op die ontwikkeling en optimalisering van vinniger, koste- effektiewe en geskikte doeleindes om oesrypheid en kwaliteit van tafeldruiwe in die wingerd te monitor voor oes en tydens verpakking en koelopberging. Druiwe-oes van drie verskillende kultivars (Thompson Seedless, Regal Seedless en Prime) het gedurende twee jare (2016 en 2017) uit ses verskillende kommersiële wingerde plaasgevind. Vyf van hierdie wingerde was in die Wes-Kaap (twee in die Hexriviervallei, drie in Wellington) en een in die Noord-Kaap (Kakamas), Suid-Afrika. Die oes het twee keer by elke wingerd plaasgevind, dit wil sê op die beste rypheid en twee weke later ná die optimale oesdatum. Die voorkoms en intensiteit van verbruining op elke korrel op 'n tros is op verskillende defekte en verbruiningsfenotipes geëvalueer. Kwantitatiewe oesrypheid en kwaliteitsindikatiewe parameters, naamlik TOVS, TS en pH, sowel as sensoriese verwante parameters suiker:suur-verhouding (TOVS:TS- verhouding) en BrimA is ondersoek deur heel tafeldruiftrosse sonder kontak met die Bruker se MATRIX-F-spektrometer in die laboratorium te skandeer. Gedeeltelike minste kwadrate (GMK) regressie is gebruik om modelle vir die parameters te bou. Twee verskillende infrarooi- spektrometers naamlik (a) die Bruker Multipurpose Analyzer Fourier Transform Near-Infrared (MPA FT-NIR) en (b) MicroNIR Pro 1700 is ook gebruik om TOVS op heel tafeldruifkorrels te bepaal. Die MicroNIR Pro 1700 is in die wingerd en in die laboratorium gebruik en die MPA slegs in die laboratorium. Met behulp van dieselfde spektrale datastel as die een wat gebruik word om die kwantitatiewe modelle op te stel, is klassifikasiemodelle vir twee verskillende verbruiningsfenotipes (sjokoladeverbruining en wrywingverbruining) gebou. Hierdie keer is gedeeltelike minste-kwadrate-diskriminant-analise (GMK-DA) en kunsmatige neurale netwerke (KNN) gebruik. Die belangrike resultate het getoon dat die voorkoms en intensiteit van verskillende defekte en verbruiningsfenotipes soos swaeldioksied (SO2)-skade op al drie wit pitlose tafeldruifkultivars voorgekom het. Die voorkoms van swaminfeksie, sonbrand en skaafskuur was hoog op Regal Seedless en Thompson Seedless in 2016. Kontak-, gevlekte- en wrywing verbruining sowel as kneusplekke het in 2017 'n hoër voorkoms as in 2016 gehad. Oor die algemeen was die intensiteit van defekte baie hoog in 2016 behalwe op Regal Seedless vanaf die Hexriviervallei. Prime van Kakamas en Wellington het in 2017 die hoogste intensiteit van gebreke gehad wat ná 7 weke se koelopberging op die druiwe verskyn het. Die suksesvolle ontwikkeling van modelle vir TOVS, TS, TOVS:TS verhouding, pH en BrimA op heel tafeldruiftrosse met behulp van FT-NIR-spektroskopie is bewys as inderdaad moontlik – veral as GMK met verskillende spektrale voorverwerkingstegnieke gepaard gaan. Statistiese aanwysers wat gebruik is om die modelle te evalueer, was die aantal latente veranderlikes (LV) wat gebruik is om die model te bou, die voorspellingskorrelasiekoëffisiënt (R2p) en wortelgemiddelde vierkante voorspellingsfout (WGVVF). Die kombinasie van die eerste afgeleide Savitzky-Golay tesame met die vermenigvuldigende verstrooiingskorreksie op die oorspronklike spektra het die beste modelle gelewer. Statistiese aanwysers wat gebruik is om die modelle te evalueer, was die aantal latente veranderlikes (LV) wat gebruik is om die model te bou, die voorspellingskorrelasiekoëffisiënt (R2p) en wortelgemiddelde vierkante voorspellingsfout (RMSE). Vir die onderskeie parameters TSS, TA, TSS: TA-verhouding, pH en BrimA, was die aantal LV wat gebruik is toe die modelle volgens 'n ewekansige verdeling van die kalibrasie- en valideringstel gebou is, 6, 4, 5, 5 en 10, die R2p = 0,81, 0,43, 0,66, 0,27 en 0,71, en die RMSEP = 1,30 ° Brix, 1,09 g / l, 7,08, 0,14 en 1,80. Toe 2016 as die kalibrasiestel gebruik is en 2017 as die validasieset in modelbou, was die aantal gebruikte LV 9, 5, 5, 4 en die R2p = 0,44, 0,06, 0,17, 0,05 en 0,05 en die RMSEP = 3,22 ° Brix, 2,41 g / l, 14,53, 0,21 en 8,03 vir die onderskeie parameters. Die bepaling van TOVS van heel tafeldruifkorrels in die wingerd voor en ná oes oor twee jaar met behulp van hand- en tafelbladspektrometers het getoon dat spektra wat in die laboratorium met die MicroNIR geneem is meer homogeen was as dié wat in die wingerd met dieselfde spektrometer geneem is. Die resultate wat met die MPA behaal is, was nie so goed soos met die MicroNIR in die laboratorium nie. Die model wat saamgestel is met die gekombineerde data van 2016 en 2017 wat in die laboratorium met die MicroNIR geneem is, het die beste statistieke gehad in terme van die R2p (0.74) en die RPDp (1.97). Die model wat opgestel is met die 2017 data wat in die laboratorium met die MicroNIR verkry is, het die laagste voorspellingsfout (RMSEP = 1.13°Brix) gehad. Goeie modelle is verkry met behulp van GMK-DA en KNN om trosse as skoon te klassifiseer, of as sjokoladeverbruining en wrywingsverbruining gebaseer op die spektra van die heel tafeldruiftrosse wat met die MATRIX-F-spektrometer geneem is. Die klassifikasiesyfer (KS), spesifisiteit en sensitiwiteit is gebruik om die modelle wat met behulp van GMK-DA saamgestel is, te evalueer en die kappa-telling is vir KNN gebruik. Die KS vir sjokoladeverbruining (25%) was beter as dié van wrywingsverbruining (46%) vir week 3 en week 4 vir beide klas 0 (afwesigheid van verbruining) en klas 1 (teenwoordigheid van verbruining). Beide die spesifisiteit en sensitiwiteit van klas 0 en klas 1 vir wrywingverbruining was nie so goed soos vir sjokoladeverbruining nie. Met KNN het die toetskappa-telling om tafeldruiftrosse as skoon of sjokoladeverbruining of wrywingsverbruining te klassifiseer, getoon dat sjokoladeverbruining tydens Week 3 en Week 4 en Week 5 en Week 6 met 'n matige ooreenstemming geklassifiseer kan word en dat wrywingsverbruining met matige ooreenstemming tydens Week 3 en Week 4 geklassifiseer kan word. Die implikasies van hierdie resultate vir die tafeldruifbedryf is van so 'n aard dat die bedryf nou verder kan gaan as om net metodes en tegnieke in die laboratorium te beoordeel, maar kan beweeg na implementering in die wingerd en die pakhuis. Die neem van baie vinniger besluite rakende die kwaliteit van die druiwe, dit wil sê in watter klas druiwe geplaas kan word en na watter uitvoermark druiwe gestuur kan word, is nou moontlik. Veel vinniger besluite rakende druiwekwaliteit en bestemming van uitvoer kan nou geneem word met behulp van 'n kombinasie van die MicroNIR-hand- en MATRIX-F-instrumente vir kwaliteitsmeting in situ en die modelle om interne (bv. TOVS) en eksterne (verbruining) kwaliteitseienskappe te voorspel.Doctora

Automatic early detection of decay in citrus fruit using optical technologies and machine learning techniques

Los cítricos representan el cultivo frutal de mayor valor en términos de comercio internacional, siendo España el primer exportador mundial de cítricos para consumo en fresco. Sin embargo, la presencia de podredumbres causadas por hongos del género Penicillium se encuentra entre los principales problemas que afectan la postcosecha y comercialización de cítricos. Un número reducido de frutas infectadas puede contaminar una partida completa de cítricos durante el almacenamiento de la fruta por largos períodos de tiempo o en el transporte al extranjero, lo que conlleva grandes pérdidas económicas y el desprestigio de los productores de cítricos. Por lo tanto, la detección temprana de infecciones por hongos de forma efectiva y la eliminación de la fruta infectada son asuntos de especial interés en los almacenes de confección de fruta para impedir la propagación de las infecciones fúngicas, asegurando de esta forma una excelente calidad de la fruta y la ausencia total de fruta infectada. En este sentido, la presente tesis doctoral se centra en abordar un reto tan importante para la industria citrícola como es la automatización del proceso de detección de podredumbres incipientes, con el fin de proporcionar alternativas a la inspección manual con peligrosa luz ultravioleta que permitan realizar esta detección de forma más eficiente y, en consecuencia, reducir potencialmente el uso de fungicidas. En concreto, esta tesis doctoral avanza en el campo de la detección automática de podredumbres en cítricos mediante sistemas ópticos y técnicas de aprendizaje automático. Específicamente, se investigan tres técnicas ópticas diferentes que operan en las regiones del visible e infrarrojo cercano del espectro electromagnético, incluyendo la técnica de imagen basada en backscattering, visión hiperespectral y espectroscopía. Los sistemas ópticos usados en esta tesis no están limitados a la parte visible del espectro, por lo que sus capacidades superan a las del ojo humano y a las de los sistemas de visión convencionales basados en cámaras de color, lo cual resulta de especial interés para detectar daños en cítricos que son difícilmente visibles a simple vista, como las podredumbres en estadios tempranos de infección. Además, se exploran numerosas técnicas de aprendizaje automático de reducción de la dimensionalidad de los datos y clasificación, con la finalidad de usar las medidas ópticas de los cítricos para discriminar la fruta afectada por podredumbre de la fruta sana. Las tres técnicas ópticas, junto con métodos de aprendizaje automático adecuados, proporcionan buenos resultados en la clasificación de la piel de los frutos cítricos en sana o podrida, consiguiendo un porcentaje de muestras bien clasificadas superior al 90% para ambas clases, a pesar de la gran similitud entre ellas. En vista de los resultados obtenidos, esta tesis doctoral sienta las bases para la futura implementación de las técnicas ópticas estudiadas en un sistema comercial de clasificación automática de fruta destinado a la detección de podredumbres en cítricos.Citrus fruit is the highest value fruit crop in terms of international trade, with Spain being the first worldwide exporter of citrus fruit for fresh consumption. However, the presence of decay caused by Penicillium spp. fungi is among the main problems affecting postharvest and marketing processes of citrus fruit. A small number of decayed fruit can infect a whole consignment, during long-term storage or fruit shipping to export markets, thus involving enormous economic losses and the blackening of the reputation of citrus producers. Therefore, effective early detection of fungal infections and removal of infected fruit are issues of major concern in commercial packinghouses in order to prevent the spread of the infections, thus ensuring an excellent fruit quality and absolute absence of infected fruit. In this respect, this doctoral thesis focuses on addressing such an important challenge for the citrus industry as the automation of the detection of early symptoms of decay, in order to provide alternatives to human inspection under dangerous ultraviolet illumination, thus accomplishing this detection task more efficiently and, consequently, leading to a possible reduction of the use of fungicides. Specifically, this doctoral thesis advances in the field of the automatic detection of decay in citrus fruit using optical systems and machine learning methods. In particular, three different optical techniques operating in the visible and near-infrared spectral regions are investigated, including hyperspectral imaging, light backscattering imaging and spectroscopy. The optical systems used in this thesis are not limited to the visible part of the electromagnetic spectrum, thus presenting capabilities beyond those of the naked human eye and traditional computer vision systems based on colour cameras, this fact being of special interest for detecting hardly-visible damage in citrus fruit, such as decay at early stages. Furthermore, a vast number of machine learning techniques aimed at data dimensionality reduction and classification are explored for dealing with the optical measurements of citrus fruit in order to discriminate fruit with symptoms of decay from sound fruit. The three optical techniques, coupled with suitable machine learning methods, investigated in this doctoral thesis provide good results in the classification of skin of citrus fruit into sound or decaying, with a percentage of well-classified samples above 90% for both classes despite their similarity. In the light of the results, this doctoral thesis lays the foundation for the future establishment of the explored optical technologies on a commercial fruit sorter aimed at decay detection in citrus fruit

Functional Coatings for Food Packaging Applications

The food packaging industry is experiencing one of the most relevant revolutions associated with the transition from fossil-based polymers to new materials of renewable origin. However, high production costs, low performance, and ethical issues still hinder the market penetration of bioplastics. Recently, coating technology was proposed as an additional strategy for achieving a more rational use of the materials used within the food packaging sector. According to the packaging optimization concept, the use of multifunctional thin layers would enable the replacement of multi-layer and heavy structures, thus reducing the upstream amount of packaging materials while maintaining (or even improving) the functional properties of the final package to pursue the goal of overall shelf life extension. Concurrently, the increasing requirements among consumers for convenience, smaller package sizes, and for minimally processed, fresh, and healthy foods have necessitated the design of highly sophisticated and engineered coatings. To this end, new chemical pathways, new raw materials (e.g., biopolymers), and non-conventional deposition technologies have been used. Nanotechnology, in particular, paved the way for the development of new architectures and never-before-seen patterns that eventually yielded nanostructured and nanocomposite coatings with outstanding performance. This book covers the most recent advances in the coating technology applied to the food packaging sector, with special emphasis on active coatings and barrier coatings intended for the shelf life extension of perishable foods

Fast Discrimination of Nanfeng Mandarin Varieties Based on Near Infrared Spectroscopy Technique

International audienceThe potential of visible and near infrared (Vis/NIR) spectroscopy was investigated for discriminating the varieties of Nanfeng mandarin fruit nondestructively. The spectra were collected by a spectrophotometer in the wavelength range of 600–1040 nm. Relationship between the spectra and Nafeng mandarin varieties was established using principal component analysis (PCA), supervised independent modeling of class analogy (SIMCA) and backward propagation neural network (BPNN). By comparison the best result was obtained by BPNN with recognition rate of 97.5%. The results suggested Vis/NIR spectroscopy combination with BPNN was a new approach to discriminate of the varieties of Nanfeng mandarin fruit nondestructively