Non-destructive Quality Monitoring of Fresh Fruits and Vegetables

Quality determines the shelf life as well as selling price of fresh fruit or vegetable and therefore, quality monitoring and testing of fresh commodities have paramount importance in their postharvest handling and supply chain management. Most of the methods used to assess fruits and vegetables quality are destructive in nature. Now-a-days, various mechanical, optical, electromagnetic, and dynamic non-destructive methods are gaining importance due to ease in operations, faster turn over and reliability. Some of the non-destructive techniques (NDT) are currently being used in laboratories, research institutions and food packaging and processing industries, whereas, some methods are still at developmental stage. Various NDT with respect to their principle and applications such as impact test, electronic nose, time-resolved reflectance spectrometry (TSR), near infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), X-Ray, ultra sonic, acoustic impulse response method, electrical conductivity methods etc., are discussed in this review

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques

The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment

Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)

La tesi di dottorato è incentrata sull'analisi di tecnologie non distruttive per il controllo della qualità dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi riguarda l'applicazione del sistema di visione artificiale per valutare la qualità delle foglie di rucola fresh-cut. La tesi è strutturata in tre parti (introduzione, applicazioni sperimentali e conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio della qualità dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i) la variabilità dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli più semplici rispetto al machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di dottorato è stato svolto dall'Università di Foggia, dall'Istituto di Scienze delle Produzioni Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). L’attività di ricerca è stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualità della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione non distruttiva della qualità di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the control quality of agri-food products, along the whole supply chain. In particular, the thesis concerns the application of computer vision system to evaluate the quality of fresh rocket leaves. The thesis is structured in three parts (introduction, experimental applications and conclusions) and in 5 chapters, the first and second focused on non-destructive technologies and in particular on computer vision systems for monitoring the quality of agri-food products, respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the estimation of quality aspects, considering different aspects: (i) the variability due to the different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii) development and exploitation of the advantages of new models simpler than the machine learning used in the previous experiments. The research work of this doctoral thesis was carried out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of National Research Council (CNR). It was conducted within the Project SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR- PRIN 2017, and aimed at sustaining quality of production and of the environment using low input agricultural practices and non-destructive quality evaluation

Application of Hyperspectral Imaging and Acoustic Emission Techniques for Apple Quality Prediction

There is a growing demand for developing effective non-destructive quality assessment methods with quick response, high accuracy, and low cost for fresh fruits. In this study, hyperspectral reflectance imaging (400 to 1000 nm) and acoustic emission (AE) tests were applied to ‘GoldRush‘ apples (total number, n = 180) to predict fruit firmness, total soluble solids (TSS), and surface color parameters (L*, a*, b*) during an eight-week storage period. Partial least squares (PLS) regression, least squares support vector machine (LS-SVM), and multivariate linear regression (MLR) methods were used to establish models to predict the quality attributes of the apples. The results showed that hyperspectral imaging (HSI) could accurately predict all the attributes except TSS, while the AE method was capable of predicting fruit firmness, b* color index, and TSS. Overall, HSI regression using PLS had better comprehensive ability for predicting firmness, TSS, and color parameters (L*, a*, b*) than AE, with correlation coefficients of prediction (rp) of 0.92, 0.41, 0.83, 0.87, and 0.94 and root mean square errors of prediction (RMSEP) of 4.32 (N), 1.78 (°Brix), 3.41, 2.28, and 4.29, respectively, while AE regression using LS-SVM gave rp values of 0.88, 0.74, 0.34, 0.37, and 0.81 and RMSEP values of 4.26 (N), 0.64 (°Brix), 4.69, 1.8, and 5.17 for firmness, TSS, and color parameters (L*, a*, b*), respectively. The results show the potential of these two non-destructive methods for predicting some of the quality attributes of apples

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

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

Innovations in non-destructive techniques for fruit quality control applied to manipulation and inspection lines

Tesis por compendioLa industria alimentaria, concretamente el sector poscosecha, necesita innovar en sus procesos productivos, optimizando los mismos para rentabilizar sus actividades, garantizando productos de calidad capaces de satisfacer las necesidades de los consumidores. La presente tesis doctoral se centra en evaluar el potencial de la espectroscopia VIS-NIR para la caracterización e inspección de la calidad de la fruta tanto fuera de línea como a tiempo real en procesos automatizados. En un primer lugar, la viabilidad de la técnica se estudió a nivel de laboratorio en estado estático (off-line), con el fin de conocer y optimizar las condiciones de medición. Posteriormente, se evaluó la calidad interna y externa de diferentes tipos de frutas como son caqui, nectarina y mango. En una segunda etapa, se llevó a cabo una automatización de los procesos de inspección mediante el desarrollo de nuevos prototipos in-line. Para este propósito, y con el objetivo de completar y corroborar los resultados obtenidos de manera estática, se estudió la integración de dos sondas VIS-NIR en una garra robótica capaz de manipular mangos. Finalmente, se estudió la integración de una sonda VIS-NIR a una cinta transportadora. Los resultados obtenidos a nivel estático han demostrado que la espectroscopia VIS-NIR es un método no destructivo muy prometedor para predecir la astringencia en caqui. Así mismo, ha demostrado ser una adecuada herramienta para clasificar al 100% entre variedades de nectarinas como "Big Top" y "Diamond Ray" con una apariencia externa e interna muy similar, pero con diferentes propiedades organolépticas. De manera similar, fue posible clasificar al 100% variedades como "Big Top" y "Magique" de apariencia externa y composición similar pero distinto color de pulpa., y además se desarrolló un índice de calidad interna (IQI) para evaluar la calidad de las nectarinas. Por lo que respecta a los trabajos off-line realizados con mangos de la variedad "Osteen", fue posible predecir su calidad interna mediante los índices de madurez (RPI) y de calidad (IQI) con un gran rendimiento. A su vez, los ensayos experimentales efectuados con estos mismos mangos bajo la manipulación no destructiva de una garra robótica, demostraron que los mejores modelos eran capaces de predecir tanto la firmeza mecánica, el contenido en sólidos solubles, la luminosidad de la pulpa, así como el índice RPI de las muestras en base a la información obtenida por los acelerómetros instalados en los dedos de la garra robótica. En cuanto a los ensayos realizados de manera in-line, el primer prototipo desarrollado se basó en la integración de dos sondas VIS-NIR en una garra robótica dispuesta con dos acelerómetros. El sistema desarrollado permitió alcanzar una buena estimación de la calidad del mango a través del índice RPI fusionando la información tanto de los espectros VIS-NIR como del impacto no destructivo de los acelerómetros. De este modo quedó demostrado que era posible obtener una predicción similar trabajando de forma in-line como trabajando de manera off-line para la predicción del mismo índice de calidad en mangos. El segundo prototipo in-line desarrollado se basa en la integración de una sonda VIS-NIR en una cinta transportadora para la identificación de distintas variedades y orígenes de manzanas. El prototipo desarrollado permitió registrar resultados de clasificación tan buenos como los efectuados de manera off-line con, por ejemplo, nectarina. De este modo, se puede concluir que la espectroscopia VIS-NIR permite monitorear la calidad y clasificar fruta poscosecha tanto en modo off-line como in-line. Los nuevos prototipos desarrollados aportan claras ventajas respecto a los procesos tradicionales realizados a mano, como son la reducción del tiempo de inspección, la disminución de la cantidad de residuos generados y la posibilidad de inspeccionar toda la producción, obteniendo así un análisis más estandarizThe food industry, concretely the post-harvest sector, needs to innovate in their production processes, optimizing them to make their activities profitable, guaranteeing quality products capable of satisfying the needs of consumers. The present doctoral thesis focuses on evaluating the potential of visible and near infrared spectroscopy (VIS-NIR) for the characterization and inspection of fruit quality both off-line and in real time in automated processes. Firstly, the viability of the technique was studied at the laboratory level in a static mode (off-line), in order to know and optimise the measurement conditions. Subsequently, the internal and external quality of different types of fruits such as persimmon, nectarine and mango were evaluated. Secondly, an automation of the inspection processes was carried out through the development of new in-line prototypes. For this purpose, and with the aim of completing and corroborating the results obtained in a static mode, the integration of two VIS-NIR probes in a robotic gripper capable of manipulating mangoes was studied. Finally, the integration of a VIS-NIR probe to a conveyor belt was studied as an in-line monitoring tool on the inspection process of different apple varieties. The results obtained in static mode have shown that VIS-NIR spectroscopy is a very promising non-destructive method to predict the astringency in persimmon. Likewise, it has demonstrated to be an adequate tool to classify 100% between nectarine varieties such as 'Big Top' and 'Diamond Ray' with very similar external and internal appearance, but with different organoleptic properties. Similarly, it was possible to classify 100% varieties such as 'Big Top' and 'Magique' with external appearance and similar composition but different pulp colour. An internal quality index (IQI) was developed to evaluate the quality of nectarines, which can be predicted through VIS-NIR spectroscopy. Regarding the off-line work carried out with mangoes of 'Osteen' variety, it was possible to predict its internal quality through the indexes of maturity (RPI) and quality (IQI) with a high performance. Moreover, the experimental tests carried out with these same mangoes under the non-destructive manipulation of a robotic gripper, showed that the best models were able to predict both the mechanical firmness, the soluble solids content, the brightness of the pulp, as well as the RPI index of the samples based on the information obtained by the accelerometers installed on the fingers of the robotic gripper. Regarding the tests carried out in an in-line mode, the first developed prototype was based on the integration of two VIS-NIR probes in a robotic gripper fitted with two accelerometers. The developed system allowed reaching a good estimation of mango quality through the RPI index. In this way, it was demonstrated that it was possible to obtain a similar prediction working in-line as off-line mode for the prediction of the same quality index in mangoes. The second developed in-line prototype is based on the integration of a VIS-NIR probe in a conveyor belt for the identification of different varieties and origins of apples, achieving a success rate of 98% with the system. The developed prototype allowed to register classification results as good as those carried out off-line with, for example, nectarine. In this way, it can be concluded that VIS-NIR spectroscopy allows monitoring the quality and classifying post-harvest fruit in both off-line and in-line mode, being a tool that allows improving and guaranteeing the correct quality and food safety. The new developed prototypes provide clear advantages over the traditional processes performed by hand, such as the reduction of inspection time, the reduction of the amount of waste generated by destructive quality analysis and the possibility of inspecting full production, obtaining a more standardised analysis of the quality of the products.La indústria alimentària, concretament el sector postcollita, necessita innovar en els seus processos productius, optimitzant els mateixos per a rendibilitzar les seues activitats, garantint productes de qualitat capaços de satisfer les necessitats dels consumidors. La present tesi doctoral es centra en avaluar el potencial de l'espectroscòpia visible i infraroig pròxim (VIS-NIR) per a la caracterització i la inspecció de la qualitat de la fruita tant fora de línia com a temps real en processos automatitzats. En un primer lloc, la viabilitat de la tècnica es va estudiar a nivell de laboratori en estat estàtic (off-line), a fi de conéixer i optimitzar les condicions de mesurament. Posteriorment, es va avaluar la qualitat interna i externa de diferents tipus de fruites com són caqui, nectarina i mango. En una segona etapa, es va dur a terme una automatització dels processos d'inspecció per mitjà del desenvolupament de nous prototips in-line. Per aquest propòsit, i amb l'objectiu de completar i corroborar els resultats obtinguts de manera estàtica, es va estudiar la integració de dos sondes VIS-NIR en una garra robòtica capaç de manipular. Finalment, es va estudiar la integració d'una sonda VIS-NIR a una cinta transportadora. Els resultats obtinguts a nivell estàtic han demostrat que l'espectroscòpia VIS-NIR és un mètode no destructiu molt prometedor per a predir l'astringència en caqui. Així mateix, ha demostrat ser una adequada ferramenta per a classificar al 100% entre varietats de nectarines com "Big Top" i "Diamond Ray" amb una aparença externa i interna molt semblant, però amb diferents propietats organolèptiques. De manera semblant, va ser possible classificar al 100% varietats com "Big Top" i "Magique" d'aparença externa i composició semblant però distint color de polpa. Es va desenvolupar un índex de qualitat interna (IQI) per avaluar la qualitat de les nectarines. Pel que fa als treballs off-line realitzats amb mangos de la varietat "Osteen" va ser possible predir la seua qualitat interna mitjançant els índexs de maduresa (RPI) i de qualitat (IQI) amb un gran rendiment. Al mateix temps, els assajos experimentals efectuats amb estos mateixos mangos baix la manipulació no destructiva d'una garra robòtica, van demostrar que els millors models eren capaços de predir tant la fermesa mecánica, el contingut en sòlids solubles, la lluminositat de la polpa, així com l'índex RPI de les mostres basant-se en l'informació obtinguda pels acceleròmetres instal¿lats en els dits de la garra robòtica. En quant als assajos realitzats de manera in-line, el primer prototip desenvolupat es va basar en la integració de dos sondes VIS-NIR en una garra robòtica disposada amb dos acceleròmetres. El sistema desenvolupat va permetre aconseguir una bona estimació de la qualitat del mango a través de l'índex RPI fusionant l'informació tant dels espectres VIS-NIR com de l'impacte no destructiu dels acceleròmetres. D'esta manera va quedar demostrat que era possible obtindre una predicció semblant treballant de forma in-line com off-line per a la predicció del mateix índex de qualitat en mangos. El segon prototip in-line desenvolupat es va basar en la integració d'una sonda VIS-NIR en una cinta transportadora per a l'identificació de distintes varietats i orígens de pomes. El prototip desenvolupat va permetre registrar resultats de classificació tan bons com els efectuats de manera off-line. D'aquesta manera, es pot concloure que l'espectroscòpia VIS-NIR permet monitorar la qualitat i classificar fruita postcollita tant en mode off-line com in-line. Els nous prototips desenvolupats aporten clars avantatges respecte als processos tradicionals realitzats a mà, com són la reducció del temps d'inspecció, la disminució de la quantitat de residus generats pels anàlisis destructives de qualitat i la possibilitat d'inspeccionar tota la producció, obtenint així un anàlisi més estandarditzCortés López, V. (2018). Innovations in non-destructive techniques for fruit quality control applied to manipulation and inspection lines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/110969TESISCompendi

PENGEMBANGAN ALAT UJI KEMATANGAN JERUK PAMELO DENGAN METODE IMPEDANSI

Citrus Pamela / Citrus Maxima adalah jeruk dengan ukuran besar dan kulit tebal. Pamelo setengah matang atau matang berwarna hijau atau hijau kekuningan sehingga agak sulit untuk dibedakan. Dalam penelitian ini, dikembangkan perangkat untuk menentukan kematangan pamelo menggunakan metode impedansi. Sifat listrik pamelo dibandingkan untuk menentukan kematangan buah. Pengukuran impedansi dan fase dilakukan dengan menyuntikkan arus bolak-balik menggunakan probe dua-elektroda yang terhubung ke buah. Frekuensi bolak-balik dipilih antara 1 kHz hingga 100 kHz. Kami juga mengukur keasaman dan kadar gula pamelo dengan menggunakan pH meter dan Refractometer Brix. Hasil penelitian menunjukkan AD5933 dapat digunakan untuk mengukur rangkaian ekuivalen model cole dan juga mengukur impedansi jeruk pamelo. Pengukuran kadar gula (obrix) pada sampel jeruk menunjukkan nilai antara 10.5 % hingga 14.00 % dan pH dari 4.00 hingga 5.85.Kata kunci : kematangan buah, citrus pamelo, sifat kelistrikan buah, bio-impedans

Drying characteristics of Saskatoon berries under microwave and combined microwave-convection heating

The study on dehydration of frozen saskatoon berries and the need for dried fruits has been strategically identified in the prairies. Our motivation was to find a suitable method for dehydration in order to extend saskatoon berry shelf life for preservation. Microwave, convection and microwave-convection combination drying processes were identified to finish-dry saskatoon berries after osmotic dehydration using sucrose and high fructose corn syrup (HFCS) sugar solutions. Osmotic dehydration removes moisture in small quantities and also introduces solutes into the fruit that acts as a preservative and also reduces the total drying time. Due to the very short harvesting season of saskatoon berries, an accelerated process like microwave combination drying can bring down the moisture to safe storage level, immediately after harvest. Untreated and osmotically dehydrated berries were subjected to convection (control), microwave and microwave-convection combination drying conditions at different product drying temperatures (60, 70 and 80C) until final moisture content was 25% dry basis. A laboratory-scale microwave combination dryer was developed, built with temperature and moisture loss data acquisition systems using LabView 6i software. Thin-layer cross flow dryer was used for convection-only drying and for comparison. Drying kinetics of the drying processes were studied and curve fitting with five empirical equations including Page equation, was carried to determine drying constant, R2 and standard error values. The microwave-combination drying method proved to be the best for drying saskatoon berries. Dehydrated product quality analysis by means of color changes, rehydration ratio measurements and observed structural changes with scanning electron microscope technique were the factors in drying method selection for saskatoon berries. This research was instrumental in the modification and development of a novel drying system for high-moisture agricultural materials. Microwave-convection combination drying at 70oC, yields good results with higher drying rates and better end-product quality