Examination of the quality of spinach leaves using hyperspectral imaging

Diezma, Belén; Lleó, L.; Roger, Jean‐Michel; Herrero-Langreo, Ana; Lunadei, Loredana; Ruiz-Altisent, Margarita

doi:10.1016/j.postharvbio.2013.04.017

Cited by 61 publications

(35 citation statements)

References 30 publications

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“…The increase in the LSI values noted in the images responds to a Clear differences between the LSI values from the pixels of the leaves corresponding to the control treatment (Ct) and the pixels of the leaves corresponding to the highest salinity treatment (S3) were observed ( Figure 7). The increase in the LSI values noted in the images responds to a lateral displacement of the red edge to the right [28,41], which concurs with the findings obtained from the first derivative spectra analysis. Table 2 presents eleven narrow band indexes that are related to pigment, water content and salinity effects, all of them extracted from a literature review on this topic.…”

Section: A New Index On the Red Edge Region: Level Salinity Index Lsisupporting

confidence: 89%

Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

et al. 2016

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Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky-Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.

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Section: A New Index On the Red Edge Region: Level Salinity Index Lsisupporting

confidence: 89%

Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

et al. 2016

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“…The increase in the LSI values noted in the images responds to a lateral displacement of the red edge to the right (Lara et al, 2013;Diezma et al 2013), which concurs with the findings obtained from the first derivative spectra analysis. Table III.2 presents eleven narrow band indexes that are related to pigment, water content and salinity effects, all of them extracted from the bibliography.…”

Section: Eq Iii5 Lsi = [(R675 + R745) / 2] -R710supporting

confidence: 89%

“…Working in reflectance mode, in the VIS/NIR range (400 -1000 nm), Díaz et al (2004) developed a hyperspectral imaging system to classify olives in function of their quality (presence of defects), with an accuracy of over 90% using artificial neural networks. Diezma et al (2013), working also in reflectance mode (400 -1000 nm) was able to classify spinach leaves according to their quality grade (deterioration stage). Razmjooy et al (2012) determined quality in potatoes and ElMasry et al (2007) in strawberries.…”

Section: Physical Properties Inspectionmentioning

confidence: 99%

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Hyperspectral image applied to determine quality parameters in leafy vegetables

Lara¹,

Ángel²

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MASTER IN AGRO-ENGINEERING CO-SUPERVISOR: BELÉN DIEZMA IGLESIAS CO-SUPERVISOR: LOURDES LLEÓ GARCÍA MADRID, 2016He nacido hoy de madrugada, viví AGRADECIMIENTOS -ACKNOWLEDGEMENTSDesde que comencé este reto que ha sido la tesis doctoral, muchas cosas han cambiado. No ha sido un camino fácil. Muchas horas de esfuerzo y dedicación. Cambios que han puesto patas arriba mis metas y aspiraciones. Tener que reinventarse, volver a empezar, adaptarse a los cambios… Por suerte, no estoy solo en el camino. Muchas personas me lo han demostrado en este tiempo y me gustaría agradecérselo en estas líneas.Quisiera dar las gracias de forma muy especial a mis tutoras, Belén Diezma Iglesias y Lourdes Lleó García, por toda la ayuda prestada a lo largo de estos años. Por su supervisión, apoyo y empuje. Para mí han sido más que tutoras, amigas, y su empuje y ánimo han sido determinantes para que hoy pueda escribir este párrafo. Gracias de corazón y ojalá podamos seguir colaborando en el futuro.Special thanks also to Jean Michel Roger for his unconditional support and technical advice in this research, and also for his assistance during my stay in CEMAGREF, now IRSTEA, Montpellier (France). It is always a great pleasure working with him.A todas las personas que componen el grupo de investigación LPF-TAGRALIA. Me he sentido muy a gusto trabajando con vosotros y en los momentos que hemos compartido. Ojalá hubieran podido ser muchos más. En especial, quisiera agradecer su ayuda y apoyo a Margarita Ruiz-Altisent y a Pilar Barreiro Elorza, siempre dispuestas a compartir su conocimiento. Podéis contar conmigo cuando lo necesitéis.Al Ministerio de Ciencia e Innovación (MICINN) que ha financiado los trabajos de investigación llevados a cabo en esta tesis doctoral con los proyectos Multihort (AGL2008-05666-C02-01) y QualityLeaf (AGL2013-48529-R), junto con el proyecto Durasfrut II (AL11-P(I+D)-06) de la Universidad Politécnica de Madrid.A María Isabel Gil y Yolanda Garrido, del Centro de Edafología y Biología Aplicada del Segura, perteneciente al Consejo Superior de Investigaciones Científicas (CEBAS-CSIC), por su colaboración y ayuda en la generación de muestras y en la interpretación de resultados de una parte fundamental de este trabajo.A mis antiguos compañeros de la Escuela de Ingeniería Técnica Agrícola que se preocuparon por mí y me apoyaron en momentos muy difíciles. En especial, me gustaría dar las gracias a las profesoras Eva Cristina Correa Hernando y Virginia Díaz Barcos, que se volcaron en ayudarme en aquellos momentos y, gracias a ellas, pude encontrar pronto un nuevo reto profesional. Os estaré siempre agradecido.A mi familia. A mis padres y hermanos. A los amigos que han sido conscientes de este duro camino y se han preocupado por mí, dándome ánimos. A Devin Townsend por haber sido la banda sonora de esta Tesis Doctoral.A mi mujer, Laura, porque sus pasos han sido mi inspiración para decidirme a cumplir este reto. Todo llega y, no me cabe duda, nuestro trabajo también tendrá su recompensa.A Alicia, porque aún no lo sabe, pero...

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“…14 Some recent work demonstrating the feasibility of multispectral systems are the supervision of quality deterioration in ready-to-use leafy spinach during storage, 15 the comparison of multispectral indexes related to maturity in peaches, 16 non-destructive detection of transgenic rice seeds, 17 non-destructive determination of aerobic plate count (APC) in cooked pork sausages, 18 mitosis detection in cancer cells with multispectral imaging 19 and detection of frass contamination on mature Campari tomatoes. 20 In these studies, multispectral imaging systems proved to be an effective alternative to the traditional analytical procedures, offering cost savings and being easier to apply in an industrial context.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour

Mishra

Herrero-Langreo²,

Barreiro

et al. 2015

J. Spectral Imaging

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In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and compared with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

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Examination of the quality of spinach leaves using hyperspectral imaging

Cited by 61 publications

References 30 publications

Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

Hyperspectral image applied to determine quality parameters in leafy vegetables

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour

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