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www.cien= ciadigital.org               

 
                                                                        =                                           ISSN: 2602-8085=

Vol. 3, N°3.4, p. 19 - 28, septiembre, 2019

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 Estudio comparativo entre el método calorimétrico y el de estimación matemática par= a la determinación energía bruta en subproductos agrícolas <= /span>

 

Comparative study between the calorimetric method = and the mathematical estimation method for the determination of gross energy in agricultural by products

 

[1], Marcelo Ramos Flores.[2],  Manfredy Chugńay Cargua.[3] & María Eugenia Ra= mos Flores.[4]

 

Recibido: 22-06-2019 / Revisado: 28-0= 7-209 /Aceptado: 09-08-2019/ Publicado: 10-09-2019

 

Abstract                          DOI:  https://doi.org/10.33262/cienciadigital.v= 3i3.4.831

Two methods used to determine the gross energy val= ue of food: calorimetric and mathematical estimation were compared, in order to determine if there is statistical similarity between their results, and thus consider them both equivalent. To this end, duplicate samples of eight agricultural by-products destined for animal feed were prepared, one of whi= ch was subject to proximal analysis for later application of the mathematical estimation method, and the other was analyzed directly in bomb-calorimetric= . Both sets of results were characterized by descriptive statistics, showing both normal distribution with non-homogeneous variances; so, they were compared using the t test with the Welch variation. The test determined that the mea= ns of the results of both methods show a significant difference; in addition, = the existence of positive bias was observed, where the calorimetric method deli= vers numerically higher results than those of the mathematical estimation method= .

Keywords: Gross energy, comparison of methods, proximal analysis, calorimetric bomb, statistical bias

 

Resumen=

Se compararon dos métodos utilizados para determinar el valor de energía bruta= de alimentos: calorimétrico, y de estimación matemática, para determinar si ex= iste similaridad estadística entre sus resultados, y así considerarlos equiparab= les. Para ello se prepararon muestras duplicadas de ocho subproductos agrícolas destinados a alimentación animal, una de las cuales se sometió a análisis proximal para posterior aplicación del método de estimación matemática, y la otra se analizó directamente en una bomba calorimétrica. Ambos conjuntos de resultados se caracterizaron mediante estadísticos descriptivos, mostrando ambos distribución normal con varianzas no-homogéneas; por lo que se compar= aron mediante test t con la variación de Welch. El test determinó que las medias= de los resultados de ambos métodos presentan diferencia significativa, observándose además la existencia de sesgo positivo, donde el método calori= métrico entrega resultados numéricamente mayores a los del método de la estimación matemática.

Palabras clave: energía bruta, comparación de métodos, análisis proximal, bomba calorimétrica, sesgo estadístico.

 

Introducción

El Ecuador en sus tres regiones es un = país altamente agrícola que posee recursos vegetales disponibles para su utiliza= ción en la alimentación de animales; residuos que no son utilizados por desconocimiento del valor nutricional y energético. En los últimos ańos se = ha puesto atención al uso de residuales agrícolas para la alimentación de gana= do porque, además de maximizar la eficiencia del uso de los recursos, disminuye costos de producción, mismos que representan entre un 60 a 70% del costo to= tal de producción (Torres Navarrete, y otros, 2017), reduce la competición por alimento entre el hombre y el ganado. (García, Henry, & Schulmeister, 201= 5) y lleva a una reducción del impacto ambiental de la actividad agroganadera (Núńez Torres, 2017).

La información sobre el valor energéti= co de alimentos es esencial para ejecutar estudios nutricionales sobre dietas destinadas a tratar la malnutrición calórica o la obesidad en animales (Hern= ández & Sastre, 1999). Los alimentos pueden valorarse inicialmente por sus propiedades organolépti= cas, para evaluar si están en condiciones de ser utilizados, así también, por su composición química, para conocer si responden con las especificaciones establecidas y su aporte de energía. Las principales fuentes de energía en = los alimentos son las proteínas y el extracto etéreo, la energía bruta es la má= xima energía potencial presente en un alimento (Hernández & Sastre, 1999).

La “energía bruta”, EB, es la energía química total almacenada en la materia orgánica que constituye los alimentos. Se expresa como calor de combustión por unidad de peso del alimento (Caravaca Rodríguez, Castel Genís, & Otros, 2005). De acuerdo a = la primera ley de termodinámica indica que todas las diferentes formas energía pueden transformarse en calor, de allí que, cuando una sustancia se combustiona, el calor que se desprende es equivalente a la energía bruta almacenado en dicho alimento (Cańas Cruchaga, Aguilar González, García Gómez, Quiroz, & otros, 1992).

La valoración del contenido de energía bruta presente en los alimentos puede ser determinada de forma directa utilizando una bomba calorimétrica, donde el alimento se combustiona (Santos, 2010); o por el méto= do de estimación matemática, partiendo de resultados de un análisis químico proxi= mal previo (Mora Brautigan, = 2007). Entre estas d= os opciones, el método calorimétrico toma menos tiempo, es más económico y no demanda de análisis previos, pero también es considerado como un método “grueso” o “rápido” (Mora Brautigan, 2007)

No obstante, antes de considerar que el método calorimétrico pueda reemplazar a la estimación matemática, usada tradicionalmente y que da resultados ampliamente aceptados; se hace necesar= io comparar estadísticamente sus resultados.

Importancia

Se carece de investigaciones que anali= cen a nivel estadístico si los dos métodos presentan similaridad o diferencia e= ntre ellos, además, a la fecha de realización del estudio, en Ecuador no se disp= onen de estudios en donde se haya determinado la cantidad de energía bruta de subproductos no convencionales con interés para la alimentación animal.

Estudios similares realizados para comparar el método de estimación matemática y el calorimétrico para la determinación del contenido energético de alimentos, como el de  = (Batres Gracias, = 2004) determinó que = los valores de energía bruta obtenidos por estimación matemática, partiendo del análisis químico proximal fueron menores a los valores cuantificados por el calorimétrico, en el caso de productos para alimentación de cerdos. Por otra parte, (Posada, Rosero, Rodríguez, & Costa, 2012), en el caso de alimentos para rumiantes, determinaron por medio de la prueba de Tukey que los dos métodos diferían entre sí, con valores calorimétricos mayores que los procedentes d= e la estimación matemática.

La investigación realizada consistió en análisis de similaridad estadística entre las medias de valores de energía bruta de ocho subproductos agrícolas no-convencionales, de los cuales se tomaron muestras por duplicado, una de las cuales se analizaron por calorimetría y la otra por estimación matemática. Los resultados permitiero= n responder a la interrogante si, ambos métodos puedan considerarse equiparables desde = un punto de vista de los resultados obtenidos al medir energía bruta.

Metodología

La población de estudio estuvo constit= uida por ocho subproductos agrícolas no-convencionales de la sierra ecuatoriana:= trabajando un total de 44 muestras, compuestas por: tronco de brócoli: 12, zanahoria: = 13, chaqui sara: 2, hoja de chocho: 6, hoja de maíz: 3, hoja de plátano: 4, polvillo de quinua: 2, hoja de camote: 2. Las muestras fueron recolectadas y selecciona= das según su biodisponibilidad, y sometidas a tratamiento previo de secado y molienda, separando una porción para aplicación del método calorimétrico y = otra para estimación matemática.

 

La investigación se realizó en la Escu= ela Superior Politécnica de Chimborazo, en la ciudad de Riobamba, Ecuador. El tratamiento de muestras y las mediciones se efectuaron en el laboratorio de Bromatología de la Facultad de Ciencias Pecuarias y el procesamiento y anál= isis de los datos en la Escuela de Ingeniería Química de la Facultad de Ciencias= .

Análisis químico proximal

El análisis químico proximal, es probablemente el método más utilizado para expresar la calidad nutritiva de= un alimento, midiendo seis parámetros: humedad, cenizas, extracto etéreo (EE), fibra bruta (FB), proteína bruta (PB), y extracto libre de nitrógeno (ELN)<= w:Sdt Citation=3D"t" ID=3D"-1509202363"> (Barrera, Tapia, & Monteros, 2004)<= !--[if supportFields]>. La medición se realizó sobre muestras secas de los mencionados subproductos.

Energía bruta por estimación matemátic= a

Para determinar el valor de energía bruta por= el método de estimación matemática se partió de los resultados del análisis químico proximal. Para ello se operaron los valores de extracto etéreo (EE), fibra bruta (FB), proteína bruta (PB) y extracto libre de nitrógeno (ELN, carbohidratos) del alimento a través de la siguiente ecuación (Batr= es Gracias, 2004):

<= !--[if gte msEquation 12]>EB=3D9EE+4FB+4PB+4(ELN)<= /m:e> ×10

Don= de:

EE = =3D Extracto etéreo (%)

FB = =3D Fibra bruta (%)

PB = =3D Proteína bruta (%)

EB = =3D Energía bruta (cal/g)

ELN= =3D Extracto libre de nitrógeno (%)

 

 

Estimación por método calorimétrico

Para el método calorimétrico se usó la bomba calorimétrica IKA C 2000, que consiste en una cámara cerrada inyectada con oxígeno, en cuyo interior se combustiona la muestra de alimento mediante ignición con un conductor eléctrico. La cámara está sumergida en agua a temperatura ambiente y, al combustionar la muestra, provoca un aumento de temperatura del agua circundante. El valor de energía bruta es determinado directamente por el equipo (IKA, s.a.).

Análisis Estadístico

Los resultados de energía bruta de cada subproducto aplicando cada método, fuer= on analizados, para determinar sus estadígrafos descriptivos (medias, desviaci= ones estándar, varianzas), adicionalmente se aplicó el Test Anderson-Darling (Mohd Razali & Bee Wah, 2011) para determina= r si los valores obtenidos responden a una distribución Normal.

&nbs= p;

Se definió como hipótesis nula que las medias = de los resultados obtenidos por ambos métodos son similares, con un nivel de confianza del 95%.

<= span style=3D'font-size:12.0pt;line-height:150%;font-family:"Times New Roman",se= rif'>Ho: μ cal =3D μ mat

<= span style=3D'font-size:12.0pt;line-height:150%;font-family:"Times New Roman",se= rif'>Ha: μ cal ≠ μ mat

Las características de cada conjunto de resultados: datos continuos o discretos, tamańo de muestras, tipo de distribución de resultados, homogeneidad de varianzas, y tipo de análisis (comparación de medias) se utilizaron para definir el tipo de test estadíst= ico a aplicar (MinitabŽ, 2017).

Resultados

Los resultados de análisis químico proximal se indican en la Tabla 1:

Tabla 1. Resultados de determinación de energía bruta, por estimación matemática y por calorimetrí= a.

 

En la Tabla 2, se indican los valores = de energía determinados por ambos métodos, sus desviaciones estándar y sus varianzas.<= o:p>

Tabla 2. Resultados de= energía, por estimación matemática y calorimetría

Sub Produc= to

Met. Calorimétrico

cal /g

Met. Matem= ática

cal/g=

Tronco de brócoli

3522,7

3445,6

Hoja de zanahoria

3629,1

3426,2

Chaqui sara

4288,5

4197,6

Hoja de chocho

4064,9

3646,2

Hoja de maíz

3868,2

3643,6

Polvillo de quinua

3779,2

3773,8

Hoja de camote

3825,0

3439,5

Hoja de plátano

4016,4

3836,3

Los valores obtenidos presentaron las siguientes características para cada subproducto, y en ambos métodos:<= /o:p>

ˇ&nb= sp;        Datos continuos

ˇ&nb= sp;        Muestra de tamańo moderado

ˇ&nb= sp;        Distribución de frecuencias de tipo normal

ˇ&nb= sp;        Varianzas heterogéneas

Este conjunto de características llevó= a elegir al Test t a dos colas, con variación de Welch (Welch, 1947), como método inferencial.

Los resultados de energía bruta se ingresaron y analizaron utilizando la aplicación informática Minitab 17 dem= o, aplicando el Test de Welch a dos colas y con un nivel de significancia del = 5% (α=3D0,05), de modo que la probabilidad P debería superar al valor α para consider= ar que se cumple la hipótesis nula. Se obtuvieron los siguientes resultados:

 

Tabla 3. Resultados test t de Welch, p= ara hipótesis de similaridad de medias entre método calorimétrico y de estimaci= ón matemática

SUBPRODUCTO

P

α

RELACIÓN

RESULTADO

OBSERVACIÓN

Tronco de brócoli

0,025

0,25

P < α<= /o:p>

Ho1 se rechaza

μ cal ≠ μ mat

Hoja de zanahoria

0,000

0,25

P < α<= /o:p>

Ho1se rechaza

μ cal ≠ μ mat

Chaqui Sara

0,615

0,25

P ˃ α

Ho1se ac= epta

μ mat similar μ cal

Hoja de chocho=

0,000

0,25

P < α<= /o:p>

Ho1 se rechaza

μ cal ≠ μ mat

Hoja de maíz

0,000

0,25

P < α<= /o:p>

Ho1se re= chaza

μ cal ≠ μ mat

Hoja de camote=

0,000

0,25

P < α<= /o:p>

Ho1se rechaza

μ cal ≠ μ mat

Polvillo de quinua<= o:p>

0,858

0,25

P ˃ α

Ho1 se acepta

μ mat similar μ cal

Hoja de plátano

0,015

0,25

P < α<= /o:p>

Ho1 se rechaza

μ cal ≠ μ mat

 

Durante las mediciones se observó que = los valores de energía bruta obtenidos por estimación matemática son menores re= specto a los obtenidos por método calorimétrico. Para verificar esta observación se hizo un análisis a la totalidad de datos, comprobándose que la media de ene= rgía bruta por estimación matemática fue significativamente menor que la media de energía bruta por el método calorimétrico.

Figura 1. Prueba de la media de la totalidad de mediciones.

<= /span>

Fuente: Autores, Ecuador, 2017

Discusión

En el presente estudio los valores de energía bruta obtenidos oscilan entre los 3175 y 4226 cal/g por el método de estimación matemática, y por el calorimétrico valores entre 3324 y 4340 cal= / g. Teniendo en consideración lo indicado por (Fillat, García-González, Gómez, & Reiné, 2008), quienes estab= lecen que, en promedio los vegetales poseen un valor de 4400 cal /g de energía br= uta, y que el contenido energético de las plantas varía con el tiempo, aumentand= o en los tejidos maduros, puede afirmarse que los valores encontrados están dent= ro de lo estimado en la bibliografía. Por otro lado, la mediana de los valores= de energía de estos subproductos, procedentes de la sierra ecuatoriana, 3644.9 cal, es menor a la mediana de la energía medida con subproductos propios de= la costa ecuatoriana, 4619 cal (Torres Navarrete, y otros, 2017).

Respecto a la comparación de los valor= es de las medias, únicamente en los casos de los subproductos chaqui sara y polvillo de quinua se verificó igualdad estadística; mientras que en los ot= ros seis sub productos el test muestra que los valores medidos por calorimetría= no son estadísticamente iguales a los de la estimación matemática. En todos los casos, además se observó y confirmó la presencia de un sesgo positivo, donde los valores obtenidos por calorímetro son entre 4% y 10% mayores que los de= estimación matemática.

Si bien no corresponde a este estudio = el determinar la naturaleza y causas de este sesgo, algunas posibilidades incluyen: efecto del alto contenido de carbohidratos en alimentos de tipo harina, siendo ést= os donde se verificaron medias estadísticamente iguales; contenido de extracto etéreo puesto que los componentes grasos se caracterizan por generar altas cantidades de energía al ser combustionados (MdDonald, Edwards, & Morgan, 2006)= . Otra posibili= dad es que la estimación calorimétrica, al medir calor generado por combustión, to= me en cuenta energía producida por componentes no considerados en el cálculo q= ue se aplica en la estimación matemática, tal sea el caso de la fibra, misma q= ue, según (Cardona, y otros, 2002) hace que las materias primas y subproductos sean menos energéticos y menos digeribles. Un aspecto interesante, es que los dos subproductos que present= aron similaridad, también fueron aquellos con menor contenido proteico, y al mis= mo tiempo mayor cantidad de extracto libre de nitrógeno.

Conclusión

Los valores de energía bruta de subpro= ductos agrícolas de la sierra ecuatoriana, obtenidos por estimación matemática presentan un sesgo respecto a los valores obtenidos por el método calorimétrico; siendo los últimos entre 4% y 10% mayores; con excepción de chaqui sara y polvillo de quinua, en los que puede considerarse que los resultados obtenidos por ambos métodos son estadísticamente similares.=

Adicional a la diferencia de medias, la existencia de un sesgo indica la necesidad de estudios posteriores para cor= relacionar el efecto de diferentes componentes de los subproductos en los valores de energía bruta.

Referencias

 

ˇ      Barrera, V., Tapia,= C., & Monteros, Á. (2004). Raíces y tubérculos andinos: Alternativas p= ara la conservación y uso sostenible en el Ecuador. Quito, Ecuador: INIAP, CIP.

ˇ     &nbs= p;   Batres Gracias, R. A. (2004). <= i>Comparación entre el método de estimación matemática y el método de calorimetría para determinar el contenido energético del subproducto de galleta y de panade= ría para la alimentación de cerdos en desarrollo. Guatemala: Universidad = San Carlos de Guatemala.

ˇ     &nbs= p;   Cańas Cruchaga, R., Aguilar González, C., García Gómez, F., Quiroz, R. A., & otros. (1992). Si= mulación de sistemas pecuarios. San José: RISPAL.

ˇ     &nbs= p;   Caravaca Rodríguez, F. P., Cast= el Genís, J. M., & Otros. (2005). Bases de la producción animal. Sevilla: Universidad de Sevilla.

ˇ     &nbs= p;   Cardona, M., Sorza, J., Posada,= S., Carmona, J., Ayala, S., & Álvarez, O. (2002). Establecimiento de una = base de datos para la elaboración de tablas de contenido nutricional de alimen= tos para animales. Revista Colombiana de Ciencia Pecuarias, 15(2), 240-246.

ˇ     &nbs= p;   Fillat, F., García-González, R., Gómez, D., & Reiné, R. (2008). Pastos del Pirineo. Madrid.

ˇ     &nbs= p;   García, M., Henry, T., & Schulmeister, T. (2015). Nutrición an= imal en sistemas tropicales: Uso de residuos agrícolas en la producción animal= . Maskana, 1er Congreso Internacional de Producción Animal Especializada.

ˇ     &nbs= p;   Hernández, M., & Sastre, A. (1999). Tratado de nutrición. Madrid: Días de Santos S.A.

ˇ     &nbs= p;   IKA. (s.f.). IKA Calorimeter System C2000 basic. IKA.

ˇ     &nbs= p;   MdDonald, P., Edwards, R., & Morgan, C. (2006). Nutrición animal. Zaragoza, Espańa: Editorial Acribia S.A.

ˇ     &nbs= p;   Mohd Razali, N., & Bee Wah, Y. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling Tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.<= o:p>

ˇ     &nbs= p;   Mora Brautigan, I. (2007). N= utrición animal (1ra ed.). San José, Costa Rica: EUNED.

ˇ     &nbs= p;   Núńez Torres, O. (2017). Utilización de alimentos no tradicionales y subproductos agrícolas en la nutrición animal. Journal of the Selva Andina Animal Science, 4(1), 1-2.

ˇ     &nbs= p;   Posada, S., Rosero, R., Rodrígu= ez, N., & Costa, A. (2012). Comparación de métodos para la determinación = del valor energético de alimentos para rumiantes. Revista MVZ Córdoba, 17<= /i>(3), 3184-3192.

ˇ     &nbs= p;   Santos, R. (2010). O valor energético dos alimentos. Exemplo de uma determinaçăo experimental, usando calorimetria de combustăo. Quimica nova, 220-224.

ˇ     &nbs= p;   Torres Navarrete, E., Sánchez Laíńo, A., Díaz Ocampo, R., Solórzano Robinson, M., Barrera Álvarez, A., & Jácome López, G. (2017). Composición química de productos y subproductos agrícolas utilizados en alimentación animal por pequeńos productores en la zona de Quevedo, Ecuador. Revista Amazónica Ciencia y Tecnología.

ˇ     &nbs= p;   Welch, D. (1947). The generalization of "Studnet's" problem when sever= al different population varlances are involved. Biometrika, 34(1-2), 28-35.

 

 

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PARA CITAR EL ARTÍCULO INDEXADO.

 

Chugńay Cargua, A., Ramos Flores, M., Chugńay Cargua, M., & Ramos Flores, M. (2019). Estudio comparativo entre el método calorimétrico y el de estimación matemática par= a la determinación energía bruta en subproductos agrícolas. Ciencia Digital, 3(3.4.), 19-28. = https://doi.org= /10.33262/cienciadigital.v3i3.4.831

 

 

3Deditorial1.png

 

 

 

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[1] Escuela Sup= erior Politécnica de Chimborazo, angelica.chugnay@espoch.edu.ec

[2] Escuela Superior Politécnica de Chimborazo, jmramos@espoch.edu.ec

[3] Escuela Supe= rior Politécnica de Chimborazo, machugnay@espoch.edu.ec

[4]  = Universidad Estatal Amazónica, mramos@uea.edu.ec

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Vol. 3, N°3.4, p. 19 - 28, septiembre, 2019

 

Desarrollo & Crecimiento (Volumen Especial)=                =                                 Página 319=

 

 

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