Analysis of dry matter and bromatological compounds of fruit and vegetable products from western Honduras
DOI:
https://doi.org/10.5377/nrc.v37i01.18323Keywords:
Horticulture, Dry Matter, Specific Heat, Thermal Conductivity, Bromatological AnalysisAbstract
This work focuses on the evaluation of the performance of 23 fruit and vegetable products from the Western Region of Honduras, dry matter and moisture content in the raw materials were determined in a sample of 100 (g) of fresh fruit, in the same way it was determined by bromatological analysis, in a dry matter sample of 100 (g). The percentage of participation of protein, carbohydrates, etheric or lipid extract, fiber and ash of each product studied was established, it was evaluated by preparing two replications of each sample and/or treatment, at 105 degrees Celsius and time between six (6 hr) and (12 hr), designing the relative predictive potential of an exponential model, is denoted, R² equal to or greater if its significance level is 0.05, the corresponding confidence level greater than 0.95 confidence level, obtaining the following result.
The scientific onion name (Allium cepa) yellow variety or Vidalia, R² = 0.9848, prediction potential of an exponential model is denoted R² the closer to 1 is better is the exponential equation model, shelled potato (Solanum tuberosum), prediction potential R² = 0.9865, tender green mango (Mangifera indica), R² = 0.9734, strawberry (Fragaria × ananassa), R² = 0.9785, potato with skin (Solanum tuberosum), R² = 0.9616, Tomato (Solanum lycopersicum), Cherokee variety, R² = 0.964, Carrot with skin (Daucus carota), R² = 0.9621, Chile (Capsicum annuum), Jalapeño variety, R² = 0.9674, Pineapple with skin (Ananas comosus), sugar variety, R² = 0.9548, Melon with skin (Cucumis melo), cantaloupe variety, R² = 0.9548; Confidence interval at 95%, protocols should be improved, to achieve efficiency in the prediction of imported green apple (Granny Smith), the relative predictive power of an exponential model is denoted by R² = 0.9409, chayote (Sechium edule), R² = 0.9167, green bell pepper (Capsicum annuum), statistical prediction of R² = 0.9058, Avocado (Persea Americana) Hass variety, R² = 0.9135, Watermelon (Citrullus lanatus), R² = 0.9406.
In the following samples of the trial much more research is needed, since the relative predictive potential of an exponential model is below 90%, it is best to do more repetitions in new research to optimize the prediction model of the exponential curve, Rambutan pulp (Nephelium lappaceum), prediction potential of R² = 0.6879, pineapple pulp (Ananas comosus), sugar variety, R² = 0.8332, rambutan with shell (Nephelium lappaceum), R² = 0.8002, this probably due to the fact that the sample weight times were not efficient, oven temperature not homogeneous, samples closer to the resistance of the oven experience greater moisture loss, so the appropriate protocols must be established to achieve an experiment with a higher level of confidence, for samples below 95%.
With the estimation of thermal variables such as specific heat and thermal conductivity we find that there is a direct relationship with the water content of the raw material, in the same way there is no relationship with the percentage of dry matter of these raw materials, which are a contribution to be used as primary data in thermodynamic analyses when these agricultural raw materials are transformed.
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