Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content

Mendoza, Fernando; Lu, Renfu; Ariana, Diwan P.; Cen, Haiyan; Bailey, Benjamin B.

doi:10.1016/j.postharvbio.2011.05.009

Cited by 76 publications

(77 citation statements)

References 27 publications

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“…Mendoza et al [65,66] explored various image processing methods to extract features from hyperspectral scattering images, which were used, coupled with the features from 1-D scattering profiles, for prediction of apple firmness and SSC. The authors extracted image texture features based on grey-level co-occurrence matrix, first-order statistics and Fourier analysis, as well as multi-resolution image features obtained by wavelet transform.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Mean and SD spectra were extracted from hyperspectral scattering images, and neural network modelling combined with PCA were used to develop calibration models, which produced the best results with r 2 = 0.76 and 0.55 and SEP = 6.2 and 6.1 N, for 'Golden Delicious' and 'Delicious', respectively. Peng and Lu [54] used the MLD function for characterizing hyperspectral scattering profiles and achieved the firmness prediction of apples with r = 0.89 and SEP = 6.1 N. Mendoza et al [65] investigated the textural features of hyperspectral scattering images for prediction of apple firmness. Integrating textural features and wavelet coefficients of mean spectra, coupled with PLS modelling, was more effective than when they were used alone, achieving the predictions with r = 0.84-0.95 and SEP = 5.9-8.7 N.…”

Section: Applementioning

confidence: 99%

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Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Applementioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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“…Hyperspectral imager provides the applications to monitor the quality of fruits. Peng and Lu [75] developed a reflective system to identify apple firmness and solids contents with the use of steady object stage. With the help of optical fiber and focusing lenses, 2-dimensional hyperspectral images were obtained.…”

Section: (Ijacsa) International Journal Of Advanced Computermentioning

confidence: 99%

The Role of Hyperspectral Imaging: A Literature Review

Mateen¹,

Wen²,

Nasrullah³

et al. 2018

ijacsa

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Abstract-Optical analysis techniques are used recently to detect and identify the objects from a large scale of images. Hyperspectral imaging technique is also one of them. Vision of human eye is based on three basic color (red, green and blue) bands, but spectral imaging divides the vision into many more bands. Hyperspectral remote sensors achieve imagery data in the form of hundreds of adjoining spectral bands. In this paper, our purpose is to illustrate the fundamental concept, hyperspectral remote sensing, remotely sensed information, methods for hyperspectral imaging and applications based on hyperspectral imaging. Moreover, in the forensic context, the novel methods involving deep neural networks are elaborated in this paper. The proposed idea can be useful for further research in the field of hyperspectral imaging using deep learning.

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“…It produces spectral data, generally in the range of VIS-NIR, from each spatial position of the product studied. This technique generates a huge amount of information that can be related to many characteristics of the product, such as firmness (Mendoza et al, 2011a, Cen et al, 2012, soluble solids content (SSC) (Mendoza et al, 2011b;Leiva-Valenzuela et al, 2012), maturity (Lleó et al, 2011;HerreroLangreo et al, 2011), external defects (Cho et al, 2013;, faecal contamination (Lee et al, 2014;Kang et al, 2011), microbial or insect infestation (Gómez-Sanchis et al, 2013;Lu and Ariana, 2013), and so on. Hyperspectral imaging was widely employed for quality determination in fruits and vegetables, but there is not so much research focused on using this technology for quality inspection in RTU leafy vegetables.…”

Section: Problem Statementmentioning

confidence: 99%

“…A lot of efforts have been made using hyperspectral imaging (mainly in the range between 500 -1000 nm) for the determination of firmness of fruits and vegetables, including apple (Huang et al, 2012b;Mendoza et al, 2011aMendoza et al, , 2011b) (figure 14), banana (Rajkumar et al, 2012), strawberries Tallada et al, 2006), blueberries Leiva-Valenzuela et al, 2012), pear (Zhao et al, 2010b) and peach (Cen et al, 2012). Rajkumar et al (2012), working in the range of 400 -1000 nm, developed a multiple linear regression (MLR) model for predicting firmness in banana fruits, obtaining a correlation coefficient of R = 0´91.…”

Section: Physical Properties Inspectionmentioning

confidence: 99%

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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Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content

Cited by 76 publications

References 27 publications

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

The Role of Hyperspectral Imaging: A Literature Review

Hyperspectral image applied to determine quality parameters in leafy vegetables

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