Nondestructive Evaluation of Food Quality

Jha, Shyam Narayan

doi:10.1007/978-3-642-15796-7

Cited by 43 publications

(5 citation statements)

References 205 publications

(286 reference statements)

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“…This technique is based on an instrument that allows the detection and discrimination of complex odors by using a sensor array (Bhattacharyya et al, 2005). It is composed of three elements: (I) sample handling system, (II) detection system or sensor array, and (III) a data processing system such as classification and prediction models (Bhattacharyya & Bandhopadhyay, 2010). The detection system is a nonspecific sensor, which is usually metal oxides treated with chemical materials.…”

Section: Aroma and Flavor Sensorsmentioning

confidence: 99%

“…Another technique used for the detection of adulterants in powdered spices is the electronic tongue (e-tongue) technology that measures and compares the chemical compounds responsible for taste (Bhattacharyya & Bandhopadhyay, 2010). These taste recognition systems, similar to human tongues, are composed of three elements: (I) the receptor or group of sensors, which will depend on the samples analyzed; (II) the circuit, in charge of quantifying, digitalizing, and recording the results, and (III) the software with the algorithm to process the information (Peris & Escuder-Gilabert, 2016).…”

Section: Aroma and Flavor Sensorsmentioning

confidence: 99%

“…These taste recognition systems, similar to human tongues, are composed of three elements: (I) the receptor or group of sensors, which will depend on the samples analyzed; (II) the circuit, in charge of quantifying, digitalizing, and recording the results, and (III) the software with the algorithm to process the information (Peris & Escuder-Gilabert, 2016). Each receptor has a spectrum of reactions, and their combination generates a specific signal, which is interpreted by the statistical software and recognized in the taste pattern database (Bhattacharyya & Bandhopadhyay, 2010).…”

Section: Aroma and Flavor Sensorsmentioning

confidence: 99%

See 2 more Smart Citations

Relationships between amylopectin internal molecular structure and physicochemical properties of starch

Zhu

2018

Trends in Food Science & Technology

167

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Section: Aroma and Flavor Sensorsmentioning

confidence: 99%

Section: Aroma and Flavor Sensorsmentioning

confidence: 99%

Section: Aroma and Flavor Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Relationships between amylopectin internal molecular structure and physicochemical properties of starch

Zhu

2018

Trends in Food Science & Technology

167

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“…Added only H 2 O 2 into the reaction chamber and reacting at 90 °C did not increase the GP yield because the excessive O 2 coupled with the high temperature rapidly oxidized GP further into AMPA, thus reducing GP yield. A room temperature of 25 °C and a high ultrasound intensity increased the ultrasonic vibrations and enhanced the catalyzing process [49]. At high temperatures, the microwave also rapidly altered the orientation of the polar PMIDA molecules for rapid conversion to GP.…”

Section: Catalytic Performance Of Ganoderma Acsmentioning

confidence: 99%

Chemical modifications of activated carbons prepared from different Ganoderma residues, their adsorption, and catalytic application

Chen,

Liu,

Liu

et al. 2024

Matéria (Rio J.)

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This study aimed to prepare Ganoderma-based activated carbons for chemical adsorption and catalyzing N-(phosphonomethyl)iminodiacetic acid to glyphosate. The activated carbons were prepared by applying different activation conditions. These carbon samples were applied to catalyze N-(phosphonomethyl)iminodiacetic acid to glyphosate. The results showed that the black Ganoderma char activated at 800 °C for 90 min using 40% phosphoric acid had the highest methylene blue adsorption capacity and DPPH scavenging activities, but the methylene blue adsorption capacity of the black Ganoderma sample activated at 800 °C for 90 min using 40% acid-base solution was lower than the red AC sample. These carbon samples had high thermal stability. Chemical modifications of these chars with the carbon modifiers at an optimized activation condition increased the Langmuir-specific surface areas of these carbon samples up to 2055.09 m 2 /g. The carbon sample of black Ganoderma activated using the 40% acid-base solution at 800 °C for 90 min and coupled with the addition of hydrogen peroxide during ultrasonication and microwave-assisted reactions had the highest glyphosate catalyzing rate. The glyphosate yield obtained from the catalyzing at atmospheric pressure was 58.78 ± 0.28%. These Ganoderma-based activated carbons can replace other carbon materials for catalysis applications and as adsorbents in food and pharmaceutical industries.

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“…Todas las interacciones aportan información sobre la composición y estructura de la materia [38]. Dependiendo de la técnica empleada se estudian una o varias de estas interacciones.…”

Section: Radiación Electromagnéticaunclassified

Desarrollo de modelos para la identificación de variedades y fitopatologías mediante técnicas fotónicas

Borraz-Martínez¹

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In the cultivation of fruit trees, the most common is to work with cuttings. In this way, the grafted fruit tree variety offers specific fruit qualities and the rootstock offers certain agronomic and adaptation characteristics. The grafts that come from varieties of the same species usually present a great genetic proximity, which makes them very difficult to identify by means of agro-morphological characters. For this reason, it is necessary to implement more complex methods to be able to distinguish them. Nowadays, the most widely used methods for varietal identification are based on DNA analysis, which allow obtaining molecular profiles. However, these biomolecular techniques require sophisticated instruments and facilities, time consuming, and are expensive. These drawbacks make the implementation of molecular techniques unfeasible as methods for the routine analysis of large volumes of samples. In this context, the nursery plant sector demands a rapid, no-destructive, accurate, and economic methods. The main objective of this thesis is to develop a method that allows identifying and differentiating varieties at the nursery plant level, together with the ability to assess their physiological status. The thesis is articulated in 4 articles published in indexed journals together with essays presented in the present work. The main results are to a) Protocols have been developed for the use of near infrared spectroscopy techniques, hyperspectral camera and artificial vision coupled to deep learning in fruit cuttings and cherry rootstocks; b) Near-infrared spectroscopy proved to be a technique capable of differentiating, with a very high degree of specificity, six varieties of almond (Prunus dulcis), but it presented limitations for its implementation in a production line; c) The use of the hyperspectral camera has proven adequate for the differentiation of three rootstock varieties and the analysis of the physiological state, including the premature detection of Pseudomonas syringae; d) The use of artificial vision coupled with deep learning has proven to be effective in the differentiation of almond varieties; e) The joint analysis of the results indicates that the best solution to be implemented in a production line would go through the implementation of the spectral camera for the control of the physiological state of the plants and the artificial vision coupled to deep learning for the varietal identification. En el cultivo de árboles frutales lo más habitual es trabajar con plantas injertadas. De esta forma, la variedad de árbol frutal injertada ofrece unas cualidades de fruto específicas y el portainjerto ofrece unas características agronómicas y de adaptación determinadas. Los injertos que provienen de variedades de una misma especie suelen presentar una gran proximidad genética, lo que genera que sean muy difíciles de identificar mediante caracteres agromorfológicos. Por esta razón, es necesario la implementación de métodos más complejos para poder distinguirlos. En la actualidad, los métodos más utilizados para la identificación varietal se basan en el análisis del ADN, los cuales permiten obtener perfiles moleculares. Sin embargo, estas técnicas biomoleculares requieren de instrumentos e instalaciones sofisticadas, no son rápidas de ejecutar y presentan un coste elevado. Estos inconvenientes hacen inviable la implementación de las técnicas moleculares como métodos de análisis rutinario de grandes volúmenes de muestras. En este contexto, el sector viverístico demanda nuevos métodos que seas rápidos, no destructivos, eficaces y con un coste por análisis bajo. El objetivo principal de esta tesis es desarrollar un método que permita identificar y diferenciar variedades a nivel de planta de vivero junto con la capacidad de evaluar el estado fisiológico de las mismas. La tesis se articula en 4 artículos publicados en revistas indexadas junto con otros ensayos expuestos en el presente trabajo. Los principales resultados son: a) Se han desarrollado protocolos para el uso de técnicas de espectroscopia de infrarrojo cercano, cámara hiperespectral y visión artificial acoplado a deep learning en esquejes de frutales y portainjertos de cerezo; b) La espectroscopia de infrarrojo cercano demostró ser una técnica capaz de diferenciar, con un grado muy elevado de especificidad, seis variedades de almendro (Prunus dulcis), pero presentó limitaciones para su implementación en una línea de producción; c) El uso de la cámara hiperespectrales ha demostrado adecuado para la diferenciación de tres variedades de portainjerto y el análisis del estado fisiológico, incluyendo la detección prematura de Pseudomonas syringae; d) El uso de la visión artificial acoplada a deep learning ha demostrado ser eficaz en la diferenciación de variedades de almendro; e) El análisis conjunto de los resultados indican que la mejor solución para ser implementada en una línea de producción pasaría por la implementación de la cámara espectral para el control del estado fisiológico de las plantas y la visión artificial acoplada a deep learning para la identificación varietal.

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Nondestructive Evaluation of Food Quality

Cited by 43 publications

References 205 publications

Relationships between amylopectin internal molecular structure and physicochemical properties of starch

Relationships between amylopectin internal molecular structure and physicochemical properties of starch

Chemical modifications of activated carbons prepared from different Ganoderma residues, their adsorption, and catalytic application

Desarrollo de modelos para la identificación de variedades y fitopatologías mediante técnicas fotónicas

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