Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties

Grafting by vegetables is a practice with many benefits, but also with some unknown influences on the chemical composition of the fruits. Our goal was to assess the effects of grafting and storage on the extracted juice of four orange-fleshed Cantaloupe type (Celestial, Donatello, Centro, Jannet) melons and two green-fleshed Galia types (Aikido, London), using sensory profile analysis and analytical instruments: An electronic tongue (E-tongue) and near-infrared spectroscopy (NIRS). Both instruments are known for rapid qualitative and quantitative food analysis. Linear discriminant analysis (LDA) was used to classify melons according to their varieties and storage conditions. Partial least square regression (PLSR) was used to predict sensory and standard analytical parameters. Celestial variety had the highest intensity for sensory attributes in Cantaloupe variety. Both green and orange-fleshed melons were discriminated and predicted in LDA with high accuracies (100%) using the E-tongue and NIRS. Galia and Cantaloupe inter-varietal classification with the E-tongue was 89.9% and 82.33%, respectively. NIRS inter-varietal classification was 100% with Celestial variety being the most discriminated as with the sensory results. Both instruments, classified different storage conditions of melons (grafted and self-rooted) with high accuracies. PLSR showed high accuracy for some standard analytical parameters, where significant differences were found comparing different varieties in ANOVA

Towards Sweetness Classification of Orange Cultivars Using Short‑Wave NIR Spectroscopy

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices

Visible and near-infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality

[EN] Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. 'Big Top' and cv. 'Magique', were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (R (2) (P) of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by the INIA and FEDER funds through projects RTA2012-00062-C04-01 and 03, and RTA2015-00078-00-00. Victoria Lopez Cortes thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202). The authors are also grateful to Fruits de Ponent (Lerida) for providing the fruit.Cortes-Lopez, V.; Blasco Ivars, J.; Aleixos Borrás, MN.; Cubero García, S.; Talens Oliag, P. (2017). Visible and near-infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality. 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Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor.

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields

Deteksi Benih Varietas Padi Menggunakan Gelombang Near Infrared Dan Model Jaringan Saraf Tiruan

Seed is an important input component in rice production. Of the many varieties that had been release, the distinction among varieties is not always clear. Among a large number of varieties error may happen in seed processing, storage and distribution, because of the similarity of their physical shape and size, and the seed appearances are difficult to be distinguished. An alternative to distinguish rice seed varieties is using near infrared (NIR) as sensors and using artificial neural network (ANN) as data processor. This research was aimed to study the accuracy of NIR spectroscopy and ANN for detecting rice seed varieties. NIR reflectances (1000-2500 nm) of seeds of 12 varieties were given pretreatment data such as first derivative, second derivative, normalization and standard normal variates. The pretreatment data were used as input in ANN models. Each variety consisted of 12 samples, each sample was 40 grams. ANN model used backpropagation multilayer perceptron with three layers as input, hiden, and output. Network weights were estimated using gradient descent algorithm. The wave form of NIR spectra was similar among varieties, but had different absorptions in intensities, so they could be used for determining the rice seed varieties. The best model was an ANN with standard normal variate pretreatment as input data. The accuracy of varieties prediction was 100% for training, 99.1% for testing and 98.1% for validation. Results showed that the NIR spectra and ANN model can be used as detection methods in rice varieties

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

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Mechanical harvest of grapes is one of the operations that mostly influence the quality of the future wine. The shaking frequency of the harvesting machine is usually adjusted on the basis of the grape berry characteristics in order to limit grape juice production that is a potential cause of uncontrolled fermentations. These evaluations usually require time, personnel and laboratory analyses. The introduction of a vis NIR system to rapidly and reliably evaluate the berry properties in field before mechanical harvest could be a good alternative. The aim of this study was to evaluate the feasibility of applying vis NIR spectroscopy as a non-destructive technique on grapes cv. Syrah and Chardonnay to predict pedicel detachment force, pH and total soluble solids before mechanical harvest. The spectral acquisitions were performed using a portable vis NIR device (600-1000 nm). An Ordinary Least Square evaluation was applied to assess vis NIR prediction ability on grapes. The system gave excellent performance in predicting pH for both varieties (R 2 = 0.99), also confirmed by the indicators SECV/M and Bias/M respectively equal to 0.024 and 0.014 for cv. Syrah and 0.002 and -0.009 for Chardonnay. The vis NIR device showed satisfactory prediction ability even regarding total soluble solids (R 2 = 0.997 for Syrah and 0.9935 for Chardonnay) with SECV/M = 0.090, Bias/M = 0.071 for cv. Syrah and SECV/M = 0.00, Bias/M = -0.002 for Chardonnay. However, the results showed the low vis NIR ability to predict detachment force for Chardonnay grapes (R 2 = 0.85, SECV/M = 1.008; Bias/M = -0.834), and an acceptable one for Syrah grapes (R 2 = 0.87; SECV/M = 0.362; Bias/M = -0.109). Since detachment force has an enormous importance in grapes mechanical harvest, the possibility of applying vis NIR spectroscopy in field before harvest is very encouraging for cv. Syrah (red grapes) and needs to be improved for cv. Chardonnay (white grapes)