Monitoring spinach shelf-life with hyperspectral image through packaging films

Lara, Miguel Ángel; Lleó, L.; Diezma, Belén; Roger, Jean‐Michel; Ruiz-Altisent, Margarita

doi:10.1016/j.jfoodeng.2013.06.005

Cited by 44 publications

(35 citation statements)

References 24 publications

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“…For the calibration set, containing the 28 mean spectra, two different preprocessing were applied in order to remove the additive and the multiplicative effect such as it has been previously reported on spinach leaves by [28]. Savitzky-Golay smoothing and differentiation algorithm (SG) Figure 2.…”

Section: Data Preprocessing and Processing Computation Of Modelsmentioning

confidence: 99%

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: Data Preprocessing and Processing Computation Of Modelsmentioning

confidence: 99%

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%

“…This displacement to the left is typically related to the loss of activity and the degradation of chlorophyll (Ustin et al, 2009). Lara et al (2013) confirmed this fact in spinach leaves during shelf life and attributed it to aging processes and the loss of cell structure and chlorophyll. However, the effect of aging in harvested leaves over time is not similar to the effect of saline stress in freshly harvested leaves.…”

Section: First Derivative Of the Spectramentioning

confidence: 53%

“…An experimental design has been carried out, similar to the one conducted in spinach leaves, described by Lara et al (2013). For that, 15 leaves from a commercial bag of ready-to-eat watercress leaves have been selected and put individually into Petri dishes, with a reference material (the same grey plastic than in the case of spinach), and covered with a plastic film (polypropylene 35 microns width) for simulating similar storage conditions than in the original plastic bag (figure II.1).…”

Section: Methodsmentioning

confidence: 99%

“…This LSI index is an approximation to the second derivative at 710 nm and it is a value related to the red edge displacements (Lara et al 2013):…”

Section: Index On the Red Edge Regionmentioning

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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