Artificial neural networks for prediction of antioxidant capacity of cruciferous sprouts

Abstract The aim of this work was to show that artificial neural networks (ANNs) are the convenient tool for modeling the biological properties of food. For this reason, as a good example, known contents of bioactive compounds of cruciferous sprouts were taken for the prediction of their trolox equivalent antioxidant capacity–one of the most important biological properties of food. The input data reflected the contents of the following compounds in cruciferous seeds in the course of germination: total phenolic compounds (TPC), inositol hexaphosphate (InsP 6 ), glucosinolates (GLS), soluble proteins (SP), ascorbic acid (AH 2 ), and total tocopherols (T tot ). Additionally, the trolox equivalent antioxidant capacity of germinated cruciferous seeds (TEAC exp ) was determined. The used ANN which was trained on the learning set, generalized the obtained prediction ability in respect to the data contained in validating and testing sets. The predicted TAEC values calculated by ANN were satisfactory correlated with experimental TAEC values. Therefore, the ANN seems to find application in the quality analysis of functional properties of food of plant origin for the prediction of the trolox equivalent antioxidant capacity.

[1]  A. Sandberg,et al.  HPLC Method for Determination of inositol Tri-, Tetra-, Penta-, and Hexaphosphates in Foods and Intestinal Contents , 1986 .

[2]  M. Serafini,et al.  Total antioxidant capacity as a tool to assess redox status: critical view and experimental data. , 2000, Free radical biology & medicine.

[3]  Bruce G. Batchelor.  Automated inspection of bread and loaves , 1993, Other Conferences.

[4]  N. Temple.  Antioxidants and disease: More questions than answers , 2000 .

[5]  C. Rice-Evans,et al.  Spectrophotometric determination of antioxidant activity. , 1996, Redox report : communications in free radical research.

[6]  M. Nestle.  Broccoli sprouts in cancer prevention. , 2009, Nutrition reviews.

[7]  長嶋 雲兵,et al.  分子の構造活性相関解析のためのニューラルネットワークシミュレータ : Neco (NEural network simulator for structure-activity COrrelation of molecules) の開発 , 1994 .

[8]  G. Jones,et al.  High Molecular Weight Plant Polyphenolics (Tannins) as Biological Antioxidants. , 1998, Journal of agricultural and food chemistry.

[9]  G Schneider,et al.  Artificial neural networks for computer-based molecular design. , 1998, Progress in biophysics and molecular biology.

[10]  Johann Gasteiger,et al.  Neural networks in chemistry and drug design , 1999 .

[11]  M. M. Bradford.  A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. , 1976, Analytical biochemistry.

[12]  M R Brickley,et al.  Neural networks: a new technique for development of decision support systems in dentistry. , 1998, Journal of dentistry.

[13]  A. Tepsuwan,et al.  Vegetables, Fruits and Cancer Prevention , 2003 .

[14]  G. Block,et al.  Fruit, vegetables, and cancer prevention: a review of the epidemiological evidence. , 1992, Nutrition and cancer.

[15]  R. Kaliszan,et al.  Prediction of gradient retention from the linear solvent strength (LSS) model, quantitative structure‐retention relationships (QSRR), and artificial neural networks (ANN) , 2003 .

[16]  R. Prior,et al.  Total Antioxidant Capacity of Fruits , 1996 .

[17]  Jules Thibault,et al.  pH prediction and final fermentation time determination in lactic acid batch fermentations , 1993 .

[18]  H. Zieliński,et al.  Antioxidant activity and total phenolics in selected cereal grains and their different morphological fractions. , 2000, Journal of agricultural and food chemistry.

[19]  J. Potter,et al.  Vegetables, fruit, and cancer prevention: a review. , 1996, Journal of the American Dietetic Association.

[20]  H. Zieliński.  Peroxyl radical-trapping capacity of germinated legume seeds. , 2002, Die Nahrung.

[21]  Chang W. Lee,et al.  Assessment of HIV/AIDS-related health performance using an artificial neural network , 2001, Inf. Manag..

[22]  Johann Gasteiger,et al.  Neural Networks for Chemists: An Introduction , 1993 .

[23]  A. Qureshi,et al.  Genotype and environment effects on tocols of Barley and oats , 1993 .

[24]  José Manuel Cruz,et al.  Natural antioxidants from residual sources , 2001 .

[25]  F. Shahidi,et al.  Methods of Analysis and Quantification of Phenolic Compounds , 2003 .

[26]  C. Rice-Evans,et al.  Antioxidants – the case for fruit and vegetables in the diet , 1995 .

[27]  S. So,et al.  Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors. , 1992, Journal of medicinal chemistry.

[28]  A Bucinski,et al.  Pharmacological classification of drugs based on neural network processing of molecular modeling data. , 2000, Combinatorial chemistry & high throughput screening.

[29]  Umpei Nagashima,et al.  Development of NEural network simulator for structure-activity COrrelation of molecules: Neco(2). The structure-activity relationship between 13C-NMR chemical shifts and the carcinogenicity of polycyclic aromatic hydrocarbons(PAH). , 1994 .

[30]  H. Kozłowska,et al.  Content of glucosinolates in cruciferous vegetables grown at the same site for two years under different climatic conditions. , 2000, Journal of agricultural and food chemistry.

[31]  Pekka Linko,et al.  Neural network based food extrusion cooker control , 1995 .

[32]  D M Rodvold,et al.  Artificial neural networks in clinical urology. , 1999, Urology.

[33]  C. Rice-Evans,et al.  Polyphenolic flavanols as scavengers of aqueous phase radicals and as chain-breaking antioxidants. , 1995, Archives of biochemistry and biophysics.

[34]  J. Zupan,et al.  Neural networks: A new method for solving chemical problems or just a passing phase? , 1991 .