1,682 research outputs found

    Deep learning in food category recognition

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    Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

    Strategic pricing possibilities of grocery retailers : an empirical study

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    The right pricing of products is one of the most important issues concerning the development of companies’ financial performance. Prices should be low enough to attract customers and at the same time high enough to cover all the emerged costs and expected profits. This research illustrates how self-organizing maps (SOM) can be used for pricing purposes. We show how changes in a company’s pricing policies would affect the company’s pricing position. The study illustrates clearly that companies have different possibilities to change their pricing positions. The SOM method is new and can be applied in many different ways through different pricing simulations.La tarifación correcta de los productos es una de los problemas más importantes, en relación con el desarrollo del desempeño financiero de las compañías. Los precios deberían ser lo suficientemente bajos para atraer a los clientes, y al mismo tiempo, lo suficientemente altos para cubrir todos los costes emergentes y los beneficios previstos. Esta investigación ilustra cómo los mapas auto-organizadores (SOM en inglés) pueden ser usados para fines de tarifación. Mostramos cómo los cambios en las políticas de tarifación en una empresa, pueden afectar a la posición de tarifación de la misma. El estudio muestra claramente que las empresas tienen diferentes posibilidades para cambiar dichas posiciones. El método SOM es nuevo y puede ser aplicado de muchas maneras mediante varias simulaciones de tarifación

    The development of a conceptual rural logistics system model to improve products distribution in Indonesia

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    Purpose: The role of speculators in distributing products across rural areas is increasing the poverty rate in Indonesia. Therefore, this study aims to develop a conceptual framework of the rural logistics system model to influence the welfare and sustainability of farmers. Design/methodology/approach: Conceptual framework was used to evaluate logistics and supply chain networks. The method consists of developing stages based on four components, namely network structure, management, resources, and business processes. Furthermore, it also proposed the management function of the rural logistics system models. Findings: The model of a rural logistics system obtained in this study consists of 1) a trade related to the network of business, 2) a freight, related to the flow of goods, and 3) management functions related to crucial activities in rural logistics management. Research limitations/implications: This model is conceptual, therefore, future studies must accommodate optimizing models to predict the performance of rural logistics systems when they are applied in Indonesia. Practical implications: This study promotes the actors in intermediaries of the rural logistics system to synergize the distribution of goods effectively and efficiently. It also reduces the role of speculators in product distribution in form of availability and price in rural areas. Social implications: This model is a strategy to achieve the Rural Sustainable development Goals (Rural-SDGs) agenda and complements the Blueprint of The National Logistics System. Originality/value: There are fewer studies in rural logistics compared to other fields such as agricultural logistics, food logistics, disaster logistics, etc. Therefore, this study organizes the actors in the rural logistics network and plans management functions for the efficient distribution of products across Indonesia. It also raises the awareness of logistics management to improve the welfare of rural communitiesPeer Reviewe

    Comparative Analytics on Chilli Plant Disease using Machine Learning Techniques

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    This thesis concerns the detection of diseases in chilli plants using machine learning techniques. Three algorithms, viz., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), and their variants have been employed. Chilli-producing countries, India, Mexico, China, Indonesia, Spain, the United States, and Turkey. India has the world’s largest chilli production of about 49% (according to 2020). Andhra Pradesh (Guntur) is the largest market in India, where their varieties are more popular for pungency and color. This study classifies five kinds of diseases that affect the chilli, namely, leaf spot, whitefly, yellowish, healthy, and leaf curl. A comparison among deep learning techniques CNN, RNN, MLP, and their variants to detect the chilli plant disease. 400 images are taken from the Kaggle dataset, classified into five classes, and used for further analytics. Each image is analyzed with CNN (with three variants), RNN (with three variants), and MLP (with two variants). Comparative analytics shows that the higher number of epochs implies a higher execution time and vice versa for lower values. The research implies that MLP-1 (36.08 in seconds) technique is the fastest, requiring 15 epochs. More hidden layers imply higher execution time. This research implies that the MLP-1 technique yields the lowest number of hidden layers. Thereby giving the highest execution time (349.1 in seconds) for RNN-3. Lastly, RNN and MLP have the highest accuracy of 80% (for all variants). The inferences are that these approaches could be used for disease management in terms of the use of proper pesticides in the right quantity using proper spraying techniques. Based on these conclusions, an agricultural scientist can propose a set of right regulations and guidelines

    Graph pangenome captures missing heritability and empowers tomato breeding

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    Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits(1,2). The solution to this problem is to identify all causal genetic variants and to measure their individual contributions(3,4). Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used forgenome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding

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

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    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

    Non-destructive evaluation of external and internal table grape quality

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    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

    A Systematic Review of Real-Time Monitoring Technologies and Its Potential Application to Reduce Food Loss and Waste: Key Elements of Food Supply Chains and IoT Technologies

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    Continuous monitoring of food loss and waste (FLW) is crucial for improving food security and mitigating climate change. By measuring quality parameters such as temperature and humidity, real-time sensors are technologies that can continuously monitor the quality of food and thereby help reduce FLW. While there is enough literature on sensors, there is still a lack of understanding on how, where and to what extent these sensors have been applied to monitor FLW. In this paper, a systematic review of 59 published studies focused on sensor technologies to reduce food waste in food supply chains was performed with a view to synthesising the experience and lessons learnt. This review examines two aspects of the field, namely, the type of IoT technologies applied and the characteristics of the supply chains in which it has been deployed. Supply chain characteristics according to the type of product, supply chain stage, and region were examined, while sensor technology explores the monitored parameters, communication protocols, data storage, and application layers. This article shows that, while due to their high perishability and short shelf lives, monitoring fruit and vegetables using a combination of temperature and humidity sensors is the most recurring goal of the research, there are many other applications and technologies being explored in the research space for the reduction of food waste. In addition, it was demonstrated that there is huge potential in the field, and that IoT technologies should be continually explored and applied to improve food production, management, transportation, and storage to support the cause of reducing FLW
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