Citas bibligráficas
Cruz, L., (2020). Hyperspectral imaging for food quality control: cocoa beans hybrids and chia seeds shelf-life [Universidade Estadual de Campinas]. https://renati.sunedu.gob.pe/handle/sunedu/3290671https://hdl.handle.net/20.500.12733/1641036
Cruz, L., Hyperspectral imaging for food quality control: cocoa beans hybrids and chia seeds shelf-life []. BR: Universidade Estadual de Campinas; 2020. https://renati.sunedu.gob.pe/handle/sunedu/3290671https://hdl.handle.net/20.500.12733/1641036
@mastersthesis{renati/3933,
title = "Hyperspectral imaging for food quality control: cocoa beans hybrids and chia seeds shelf-life",
author = "Cruz Tirado, Luis Jam Pier",
publisher = "Universidade Estadual de Campinas",
year = "2020"
}
Hyperspectral imaging (HSI) enables simultaneous acquisition of spectral and spatial information. In this work, HSI was used for quality control of agricultural products, which includes the authentication of cocoa bean hybrids and the estimation of shelf-life of chia seeds. Regarding the chia seeds study, samples were stored at 25, 35 and 45 ° C for 180 days, for accelerated shelf life analyzes. From time to time, chia samples were removed from storage to acquire hyperspectral images (900 - 2500 nm), acidity analysis, and fatty acid profile. The objective was to use hyperspectral images and multivariate analysis to develop a methodology for estimating the shelf-life of chia seeds, called Multivariate Accelerated Shelf Life Testing (MASLT). Principal Component Analysis (PCA) was used to study the variability during storage, and then, the PC scores were used to model the kinetics and estimate the parameters of the Arrhenius Equation, and finally to estimate the shelf life. Furthermore, for the first time a new strategy was proposed to validate this methodology, which we called “Resampling“, where the samples from the validation set were projected onto the calibration set with a reasonable number of iterations. PC1 scores and kinetic charts were built fitting the time-related PC1 scores versus time by a fused kinetic model (R² > 0.85). The spectra of chia seeds where acidity increased at 75% from initial value were used to calculate the cut-off value (-0.9853). The shelf life estimations were 1300, 798 and 90 days for chia seeds stored at 25, 35 and 45 °C, respectively. For the first time, a reliable methodology is proposed to validate that all samples were correctly predicted using PC1 scores. In the second study, cocoa beans hybrids (five) were grown and processed under the same conditions in CEPLAC (Medicilândia, Para, Brazil). The cocoa beans were then transported to the Wallonie Research Center (Belgium), where hyperspectral images in the 1100 - 2500 nm range were acquired. Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) was implemented to classify cocoa bean hybrids, (1) two classes of hybrids and (2) five classes of hybrids. Additionally, a new set of images was used for external pixel-to-pixel validation. The results showed that PLS-DA and SVM demonstrate comparable results for two-class (hybrids) models, but SVM (3.8–23.1% prediction error) was superior to PLS-DA (4.4– 34.4% prediction error) when all five classes (hybrids) were included in a model. Pixelto- pixel prediction results on a set of external images showed a correct classification rate of 50 - 100%. The results for both the two-class models and the five-class model were comparable with polymerase chain reaction techniques. The results show the potential of HSI for quality control of agricultural products, both for authentication and estimation of shelf life.
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
CruzTiradoLJP.pdf Acceso restringido | Disertación (abierta en repositorio de origen) | 3.21 MB | Adobe PDF | Visualizar/Abrir Solicita una copia |
Autorizacion.pdf Acceso restringido | Autorización del registro | 509.05 kB | Adobe PDF | Visualizar/Abrir Solicita una copia |
IMPORTANTE
La información contenida en este registro es de entera responsabilidad de la universidad, institución o escuela de educación superior que administra el repositorio académico digital donde se encuentra el trabajo de investigación y/o proyecto, los cuales son conducentes a optar títulos profesionales y grados académicos. SUNEDU no se hace responsable por los contenidos accesibles a través del Registro Nacional de Trabajos de Investigación – RENATI.