Publicado
Cómo citar
Análisis estadistico multivariado como herramienta Estratégica para el Control de procesos de calidad en la industria agroalimentaria
El uso de técnicas de estadística multivariada para el control de procesos y calidad en la industria agroalimentaria ha venido en crecimiento significativo desde la mitad de los años setenta, como una consecuencia de la revolución informática que facilitó el análisis de matrices de datos de gran tamaño. A diferencia de los métodos univariados de exploración de datos, la estadística multivariada utiliza como gran pilar el análisis de información descrita por tres o más variables que pueden ser estudiadas simultáneamente y comprendidas de una manera rápida, eficiente y sencilla. Debido al extraordinario avance en las máquinas informáticas, hoy es posible aplicar estas metodologías para resolver problemas extremadamente complejos. Este artículo presenta las técnicas de estadística multivariada más reconocidas, así como la compilación de algunos trabajos que sirven como demostración de su aplicabilidad en el campo de los alimentos.
Kvalheim o. (1987). Latent-structure decompositions (projections) of multivariate data. Chemometrics and Intelligent Laboratory Systems Vol. 2, pp.283-290. Leardi R. (2001). Genetic algorithms in che- mometrics and chemistry: a review. Journal of Chemometrics Vol. 15, No.7, pp.559-569.
Stahle L. y Wold S. (1987). Partial least squares analysis with cross-validation for the two- class problem: a monte carlo study. Journal of chemometrics Vol. 1, pp.185-196.
Wold S., Esbesen K. y Geladi P. (1987). Principal Component Analysis. Chemometrics and Intelligent- Laboratory Systems Vol. 2, pp.37-52.
Mclachlan G. (1992). Discriminant Analysis and Statistical Pattern Recognition. NY, USA, Wiley.
Zupan J. (1994). Introduction to artificial neural network (ann) methods: what they are and how to use them. Acta Chimica Slovenica Vol. 41, pp.327-352.
Poulsen J. y French A. (1996). Discriminant Function Analysis (DA). San Francisco State University, USA Vol.Razmi-Rad E., Ghanbarzadeh B., Mousavi S. M., Emam-Djomeh Z. y Khazaei J. (2007).
Zupan J., Novic M. y Ruisanchez I. (1997). Kohonen and counterpropagation artificial neural net- works in analytical chemistry. Chemometrics and Intelligent Laboratory Systems Vol. 38, pp.1-23.
Zupan J. y Gasteiger J. (1999). Neural networks in chemistry and drug design. Weinheim, Germany, VCH.
McLachlan G. (2004). Discriminant Analysis and Statistical Pattern Recognition. New Jersey, USA., John Wiley & Sons, Inc.Moros J., Iñón F. A., Garrigues S. y de la Guardia M. (2005). Determination of the energetic value of fruit and milk-based beverages through partial-least-squares attenuated total reflectance-Fourier transform infrared spectrometry. Analytica Chimica Acta Vol. 538, No.1–2, pp.181-193.
Bereton R. (2007). Applied Chemometrics for Scientists. USA, John Wiley & Sons.
Garcia D. (2009). Fault detection using Principal Component Analysis (PCA) in a wastewater treatment plant (WWTP). Valladolid, Spain, Department of Systems Engineering and Automatic Control. University of Valladolid.
Massart D. L., Vandeginste B., Buydens L., De Jong S., Lewi P. y J. S. (1997). Handbook of Chemometrics and Qualimetrics: Part A. Amsterdam, The Netherlands, Elsevier.
Ballabio D. (2006). Chemometric characterisation of physical-chemical fingerprints of food products. Dipartimento di Scienze e Tecnologie Alimentari e Microbiologiche. Milano, Università degli Studi di Mila- no. Dottorato di Ricerca in Biotecnologie degli Alimenti.
WOK (2012). Web of knowledge. Recuperado de http://apps.webofknowledge.com/WOS_General-search_input.do?product=WOS&search_mode=GeneralSearch&SID=3Clo@fn8K4O325jaJ3o&preferen cesSaved=&highlighted_tab=WOS.
SIC (2004). Denominaciones de Origen en la Comunidad Andina. Bogotá, Colombia., Superintendencia de Industria y Comercio.
James M. (1985). Classification Algorithms. London, UK, Collins.
Hand D. (1997). Construction and Assessment of Classification Rules. Chichester, UK, Wiley.
Duda R., Hart P. y Stork D. (2000). Pattern Classification and Scene Analysis. Wiley, 2nd Ed. Vol.
Frank I. y Friedman J. (1989). Classification: oldtimers and newcomers. Journal of Chemometrics Vol. 3, pp.463-475.
Webb A. (2002). Statistical Pattern Recognition. Arnold, 2nd Ed. Vol.
Distante C., Ancona N. y Siciliano P. (2003). Support Vector Machines for Olfatory Signals Recognition. Sensors and Actuators B Vol. 88, pp.30-39.
Brudzewski K., Osowski S. y Markiewicz T. (2004). Classification of milk by means of an electronic nose and SVM neural network. Sensors and Actuators B: Chemical Vol. 98, No.2-3, pp.291-298.
Leardi R. y Lupianez-Gonzalez (1998). Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometrics and Intelligent Laboratory Systems Vol. 41, No.2, pp.195-207.
Alcázar Á., Jurado J. M., Palacios-Morillo A., de Pablos F. y Martín M. J. (2012). Recognition of the geographical origin of beer based on support vector machines applied to chemical descriptors. Food Control Vol. 23, No.1, pp.258-262.
Baardseth P., N1s T. y Vogt G. (1995). Roll-in shortenings effects on Danish pastries sensory properties studied by principal component analysis. LWT - Food Science and Technology Vol. 28, No.1, pp.72-77.
Ballabio D. y Todeschini R. (2009). Chapter 4 - Multivariate Classification for Qualitative Analysis. Infrared Spectroscopy for Food Quality Analysis and Control. San Diego, Academic Press: 83-10.
Borin A., Ferrão M. F., Mello C., Maretto D. A. y Poppi R. J. (2006). Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta Vol. 579, No.1, pp.25-32.
Buratti S., Ballabio D., Benedetti S. y Cosio M. S. (2007). Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models. Food Chemistry Vol. 100, No.1, pp.211-218.
Cerretani L., Maggio R. M., Barnaba C., Toschi T. G. y Chiavaro E. (2011). Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil. Food Chemistry Vol. 127, No.4, pp.1899-1904.
Cevoli C., Cerretani L., Gori A., Caboni M. F., Gallina Toschi T. y Fabbri A. (2011). Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC–MS analysis of volatile compounds. Food Chemistry Vol. 129, No.3, pp.1315-1319.
Cimpoiu C., Cristea V.-M., Hosu A., Sandru M. y Seserman L. (2011). Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chemistry Vol. 127, No.3, pp.1323-1328.
Cozzolino D., Murray I., Chree A. y Scaife J. R. (2005). Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near- infrared spectroscopy. LWT - Food Science and Technology Vol. 38, No.8, pp.821 - 828.
De Belie N., De Smedt V. y De Baerdemaeker J. (2000). Principal component analysis of chewing sounds to detect differences in apple crispness. Postharvest Biology and Technology Vol. 18, No.2, pp.109-119.
E. M. y Romero C. D. (2011). Differentiation of potato cultivars experimentally cultivated based on their chemical composition and by applying linear discriminant analysis. Food Chemistry In press.
Geeraerd A. H., Herremans C. H., Cenens C. y Van Impe J. F. (1998). Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food pro- ducts. International Journal of Food Microbiology Vol. 44, No.1–2, pp.49-68.
Gonçalves E. C., Minim L. A., Coimbra J. S. R. y Minim V. P. R. (2005). Modeling sterilization process of canned foods using artificial neural networks. Chemical Engineering and Processing: Process Intensi- fication Vol. 44, No.12, pp.1269- 1276.
González-Sáiz J.-M., Pizarro C. y Garrido-Vidal D. (2008). Modelling gas–liquid and liquid-gas trans- fers in vinegar production by genetic algorithms. Journal of Food Engineering Vol. 87, No.1, pp.136-147.
González Martín Y., Luis Pérez Pavón J., Moreno Cordero B. & García Pinto C. (1999). Classification of vegetable oils by linear discriminant analysis of Electronic Nose data. Analytica Chimica Acta Vol. 384, No.1, pp.83-94.
Guerreiro J. S., Barros M., Fernandes P., Pires P. y Bardsley R. (2012). Principal componentanalysis of proteolytic profiles as markers of authenticity of PDO cheeses. Food Chemistry Vol., No.0.
Díez L. M. (2011). Principal component analysis (PCA) and multiple linear regression (MLR) statistical tools to evaluate the effect of E-beam irradiation on ready-to-eat food. Journal of Food Composition and Analysis Vol. 24, No.3, pp.456-464.
Horimoto Y. y Nakai S. (1998). Classification of pasteurized milk using principal component similarity analysis of off-flavours. Food Research International Vol. 31, No.4, pp.279-287.
Izadifar M. y Jahromi M. Z. (2007). Application of genetic algorithm for optimization of vegetable oil hydrogenation process. Journal of Food Engineering Vol. 78, No.1, pp.1 - 8.
Jaya Shankar T. y Bandyopadhyay S. (2004). Optimization of Extrusion Process Variables Using a Genetic Algorithm. Food and Bioproducts Processing Vol. 82, No.2, pp.143-150.
Kallithraka S., Arvanitoyannis I. S., Kefalas P., El-Zajouli A., Soufleros E. y Psarra E. 2001). Instrumental and sensory analysis of Greek wines; implementation of principal component analysis (PCA) for classification according to geographical origin. Food Chemistry Vol. 73, No.4, pp.501 -514.
Keeratipibul S., Phewpan A. y Lursinsap C. (2011). Prediction of coliforms and Escherichia coli on tomato fruits and lettuce leaves after sanitizing by using Artificial Neural Networks. LWT - Food Science and Technology Vol. 44, No.1, pp.130-138.
Lerma-García M. J., Simó-Alfonso E. F., Méndez A., Lliberia J. L. y Herrero-Martínez J. M. (2011). Classification of extra virgin olive oils according to their genetic variety using linear discriminant analysis of sterol profiles established by ultra-performance liquid chromatography with mass spectrometry detec- tion. Food Research International Vol. 44, No.1, pp.103 -108.
Lou W. y Nakai S. (2001). Application of artificial neural networks for predicting the thermal inacti- vation of bacteria: a combined effect of temperature, pH and water activity. Food Research International Vol. 34, No.7, pp.573-579.
Llave Y. A., Hagiwara T. y Sakiyama T. (2012). Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods.
Journal of Food Engineering Vol. 109, No.3, pp.553-560.
Maggio R. M., Kaufman T. S., Carlo M. D., Cerretani L., Bendini A., Cichelli A. y Compagnone D. (2009). Monitoring of fatty acid composition in virgin olive oil by Fourier transformed infrared spectrosco- py coupled with partial least squares. Food Chemistry Vol. 114, No.4, pp.1549-1554.
Mahesar S. A., Kandhro A. A., Cerretani L., Bendini A., Sherazi S. T. H. y Bhanger M. I. (2010). Determination of total trans fat content in Pakistani cereal-based foods by SB-HATR FT-IR spectroscopy coupled with partial least square regression. Food Chemistry Vol. 123, No.4, pp.1289-1293.
Nashat S. y Abdullah M. Z. (2010). Multi-class colour inspection of baked foods featuring support vector machine and Wilk’s analysis. Journal of Food Engineering Vol. 101, No.4, pp.370-380.
Ni Y. y Liu C. (1999). Artificial neural networks and multivariate calibration for spectrophotometric differential kinetic determinations of food antioxidants. Analytica Chimica Acta Vol. 396, No.2–3, pp.221-230.
Papadopoulou O. S., Tassou C. C., Schiavo L., Nychas G.-J. E. y Panagou E. Z. (2011). Rapid Assessment of Meat Quality by Means of an Electronic Nose and Support Vector Machines. Procedia Food Science Vol. 1, No.0, pp.2003-2006.
Pardo M. y Sberveglieri G. (2005). Classification of electronic nose data with support vector machines. Sensors and Actuators B Vol. 107, pp.730-737.
Patras A., Brunton N. P., Downey G., Rawson A., Warriner K. y Gernigon G. (2011). Application of principal component and hierarchical cluster analysis to classify fruits and vegetables commonly con- sumed in Ireland based on in vitro antioxidant activity. Journal of Food Composition and Analysis Vol. 24, No.2, pp.250-256.
Penza M. y Cassano G. (2004). Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks. Food Chemistry Vol. 86, No.2, pp.283-296.
Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. Journal of Food Engineering Vol. 81, No.4, pp.728-734.
Rodriguez-Campos J., Escalona-Buendía H. B., Orozco-Avila I., Lugo-Cervantes E. y Jaramillo-Flores M. E. (2011). Dynamics of volatile and non-volatile compounds in cocoa (Theobroma cacao L.) during fermentation and drying processes using principal components analysis. Food Research International Vol. 44, No.1, pp.250-258.
Rodríguez-Delgado M.-Á., González-Hernández G., Conde-González J.-E. a. y Pérez- Trujillo J.-P. (2002). Principal component analysis of the polyphenol content in young red wines. Food Chemistry Vol. 78, No.4, pp.523-532.
Shin E.-C., Craft B. D., Pegg R. B., Phillips R. D. y Eitenmiller R. R. (2010). Chemometric approach to fatty acid profiles in Runner-type peanut cultivars by principal component analysis (PCA). Food Che- mistry Vol. 119, No.3, pp.1262-1270.
Torrecilla J. S., Otero L. & Sanz P. D. (2005). Artificial neural networks: a promising tool to design and optimize high-pressure food processes. Journal of Food Engineering Vol. 69, No.3, pp.299-306.
Vandeginste B., Massart D. L., Buydens L., De Jong S., Lewi P. y J. S. (1998). Handbook of Chemometrics and Qualimetrics. Part B. Amsterdam, The Netherlands, Elsevier.
Yuzgec U., Becerikli Y. y Turker M. (2006). Nonlinear predictive control of a drying process using genetic algorithms. ISA Transactions Vol. 45, No.4, pp.589-602.
Zuluaga C., Diaz C., Henao N. & Quicazan M. (2010). Diferenciación por origen de mieles colombianas de acuerdo a su contenido mineral y perfil aromático.
Encuentro Nacional de Investigación y Desarrollo. Universidad Nacional de Colombia.
Cuando PUBLICACIONES E INVESTIGACIÓN recibe la postulación de un original por parte de su autor, ya sea a través de correo electrónico o postal, considera que puede publicarse en formatos físicos y/o electrónicos y facilitar su inclusión en bases de datos, hemerotecas y demás sistemas y procesos de indexación. PUBLICACIONES E INVESTIGACIÓN autoriza la reproducción y citación del material de la revista, siempre y cuando se indique de manera explícita el nombre de la revista, los autores, el título del artículo, volumen, número y páginas. Las ideas y conceptos expresados en los artículos son responsabilidad de los autores y en ningún caso reflejan las políticas institucionales de la UNAD