Determinación de adulteración de ají paprika con ladrillo y piel de tomate usando imágenes hiperespectrales y NIR portátil
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Date
2025
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Universidad Nacional de Trujillo
Abstract
La páprica en polvo es uno de los condimentos más utilizados en todo el mundo, debido a sus características sensoriales y color. Debido a su alto precio en el mercado, es susceptible de adulteración por adición de materiales de menor valor económico, como cáscara de tomate y ladrillo en polvo. El objetivo de este trabajo fue implementar un espectrómetro NIR (900 – 1700 nm) portátil y un sistema de imágenes hiperespectrales NIR (900 – 1700 nm) para clasificar muestras de páprica pura y páprica adulterada, así como cuantificar la concentración del adulterante. Las muestras de páprica en polvo se mezclaron con cáscara de tomate y ladrillo en polvo en concentraciones de 0 – 30% p/p. Los espectros fueron adquiridos en modo absorbancia, se crearon matrices de datos y se analizaron los datos usando Análisis de Componentes Principales (PCA), Analogía de clase de modelado independiente suave (SIMCA) y Regresión parcial de mínimos cuadrados (PLSR). El análisis PCA mostró una mejor separación entre las muestras de páprica pura y páprica adulterada usando la información obtenida de las imágenes hiperespectrales. Además, los modelos SIMCA basado en las imágenes hiperespectrales obtuvieron Sensibilidad = 100% para todas las clases, indicando que son altamente eficientes para la autenticación de páprica pura. Los modelos PLSR basados en el espectrómetro NIR portátil e imágenes hiperespectrales obtuvieron una excelente capacidad predictiva (RPD > 6.0) para predecir el contenido de ladrillo en polvo en páprica en polvo. Sin embargo, las imágenes hiperespectrales (RPD = 5.5) fueron superiores para predecir el contenido de cáscara de tomate en páprica en polvo que el espectrómetro NIR portátil (RPD = 2.2). Finalmente, podemos concluir que ambas técnicas pueden implementarse como métodos de screening para detectar fraude en páprica en polvo.
ABSTRACT Papric powder is one of the most used seasonings worldwide, due to its sensory characteristics and color. Due to its high price in the market, it is susceptible to adulteration by adding materials of lower economic value, such as tomato peel and brick powder. The objective of this work was to implement a portable NIR spectrometer (900 – 1700 nm) and a NIR hyperspectral imaging system (900 – 1700 nm) to classify samples of pure paprica and adulterated paprica, as well as quantify the concentration of the adulterant. Paprica powder samples were mixed with tomato peel and brick powder at concentrations of 0 – 30% w/w. Spectra were acquired in absorbance mode, data matrices were created, and data were analyzed using Principal Component Analysis (PCA), Soft Independent Modeling Class Analogy (SIMCA), and Partial Least Squares Regression (PLSR). PCA analysis showed better separation between pure paprica and adulterated paprica samples using information obtained from hyperspectral images. Furthermore, the SIMCA models based on the hyperspectral images obtained Sensitivity = 100% for all classes, indicating that they are highly efficient for the authentication of pure papric. The PLSR models based on the portable NIR spectrometer and hyperspectral imaging obtained excellent predictive ability (RPD > 6.0) for predicting the content of brick powder in papric powder. However, hyperspectral imaging (RPD = 5.5) was superior in predicting tomato peel content in paprica powder than the portable NIR spectrometer (RPD = 2.2). Finally, we can conclude that both techniques can be implemented as screening methods to detect fraud in paprica powder.
ABSTRACT Papric powder is one of the most used seasonings worldwide, due to its sensory characteristics and color. Due to its high price in the market, it is susceptible to adulteration by adding materials of lower economic value, such as tomato peel and brick powder. The objective of this work was to implement a portable NIR spectrometer (900 – 1700 nm) and a NIR hyperspectral imaging system (900 – 1700 nm) to classify samples of pure paprica and adulterated paprica, as well as quantify the concentration of the adulterant. Paprica powder samples were mixed with tomato peel and brick powder at concentrations of 0 – 30% w/w. Spectra were acquired in absorbance mode, data matrices were created, and data were analyzed using Principal Component Analysis (PCA), Soft Independent Modeling Class Analogy (SIMCA), and Partial Least Squares Regression (PLSR). PCA analysis showed better separation between pure paprica and adulterated paprica samples using information obtained from hyperspectral images. Furthermore, the SIMCA models based on the hyperspectral images obtained Sensitivity = 100% for all classes, indicating that they are highly efficient for the authentication of pure papric. The PLSR models based on the portable NIR spectrometer and hyperspectral imaging obtained excellent predictive ability (RPD > 6.0) for predicting the content of brick powder in papric powder. However, hyperspectral imaging (RPD = 5.5) was superior in predicting tomato peel content in paprica powder than the portable NIR spectrometer (RPD = 2.2). Finally, we can conclude that both techniques can be implemented as screening methods to detect fraud in paprica powder.
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Keywords
Quimiometria, Espectroscopia, Condimento, Autenticación