Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical-chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900-1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.
Este estudio tiene como objetivo evaluar el futuro de los casos confirmados de Covid-19 en el Perú, utilizando el modelo de regresión no lineal de Gompertz. Los datos utilizados están basados en los informes oficiales del Ministerio de Salud del Perú (MINSA). El valor acumulado de casos fatales fue sometido a un análisis interactivo por el método de mínimos cuadrados no lineales para proyectar el modelo. A partir de la derivada de primer orden del modelo predictivo fue obtenido la curva de casos fatales diarios. Usando la tasa de letalidad como proporción entre infectados y casos fatales, además del desfase promedio de días entre estos, fue posible estimar la curva epidémica. Por el momento, el modelo predictivo sugiere que el Perú estaría en un descenso lento de la curva epidémica, distanciandose del pico de contagios por día. Asimismo, se observó una tendencia de alcanzar unos 550 mil infectados y unos 19 mil fallecidos hasta fines del año 2020. Las predicciones de los modelos matemáticos pueden variar con la actualización periódica de datos, predicciones actualizadas serán publicadas en www.yupay-dynamic.com
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