Worldwide, the concern on food safety, for example, on agriculture products, has become a topic with huge relevance. Nowadays, hyperspectral imaging systems for rapid detection of dangerous agents have emerged in response to these needs. In this research project, we proposed a new algorithm for Salmonella typhimurium detection on tomato surfaces in visible range (400-1000 nm). Gaussian model was used as a way to take out a model that could be calculated its definite integral; the final result of this algorithm is the area under curve (AUC), which gives a quantitative approach of spectral signatures. Three doses (5, 10, and 15 μL) and a control response (0 μL) were spread out on 20 tomatoes' surface. Subsequently, it was observed that some decrease responses with higher dose; also, numerically this pattern was seen with the help of AUC value. As a last step, a single factor analysis of variance showed no significance due to doses. Despite this outcome, the algorithm provides to be a good methodology for pathogen detection.
Corn grain drying modelling is used to estimate moisture loss along time, and with this information improve the drying process, maximizing energy resources. The numerical method of Runge-Kutta is an alternative for the solution of ordinary differential equations, since obtaining the coefficients of the method is possible to simulate a very real approximation of the actual behaviour of drying process. In this research, the constants of the fourth order Runge-Kutta numerical method were calculated, a determination coefficient with R^2=1 between analytical solution and a numerical method was found. The mean error between the two solutions was 3.13x10 -5 .
Mexico is the world's leading producer of Opuntia ficus-indica. This kind of prickly pear is the most widespread and most commercially important cactus in Mexico. Morphological and colorimetric descriptors are among the most important agronomic traits because these parameters affect the yield, in such a way, the objective of the present research was to present a fast and reliable methodology to obtain the functional relationship in shape and color parameters of O. ficus indica cladodes, using a smartphone, a color meter, and open-access software. The acquisition and processing of images discovered interesting relationships between the Opuntia cladode's morphological characteristics, as well as colorimetric parameters of the cladodes. The non-linear data behaviors were fitted using deterministic models and CurveExpert software. Results of the study revealed that the best morphological descriptors were Circularity vs. Perimeter (r= 0.9815) and Aspect ratio vs. Roundness (r= 0.9999). In addition, mean values of the L*, C, and H color parameters were displayed in a window of a computer program online. It was found that the a-C relationship of the color parameters had the highest correlation coefficient (0.999). Therefore, it can be concluded that the morphological descriptors Circularity vs. Perimeter, Aspect Rate vs. Roundness, and a*-C color parameter can predict quickly and precisely the quality of O. ficus-indica.
A corn dryer prototype was manufactured for Mexican small-scale farmers in order to avoid them paying fines for corn with a high-moisture content when selling their corn on to stores. The dryer comprised two large boxes perforated by round holes and containing stainless steel trays subjected to a hot air temperature of 45°C within the batch. The accumulated grain in both boxes was 200 mm and the airflow rate were 0.56 m3 s-1. The corn ears layer was of 80 mm of depth in each of the boxes. The airflow rate was 0.34 m3 s-1. Within eight hours, we sampled corn grain in nine points of each box and found that the mean corn grain moisture content was reduced from 30.36% to 10.47% for box 1 whereas for box 2 it was reduced until 14.72%. The fuel consumption for drying was 0.55 kg h-1 of kerosene. In Box1, the exponential regression model for corn grain moisture content had an R² of 0.9143 whereas Box 2 exponential regression model had an R² was of 0.6642. In Box 1, the exponential regression model for corn ear moisture content had an R² of 0.9616 whereas Box 2 had an R² was of 0.9400. Both models for corn cob moisture content had an R² of 0.9639. Two-layer corn dryers can be used to harness gas or fuel energy to speed up drying for storage.
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