Multiple regression proteins

Multiple regression analysis is a useful statistical tool for the prediction of the effect of pH, suspension percentage, and composition of soluble and insoluble fractions of oilseed vegetable protein products on foam properties. Similar studies were completed with emulsion properties of cottonseed and peanut seed protein products (23, 24, 29, 30, 31). As observed with the emulsion statistical studies, these regression equations are not optimal, and predicted values outside the range of the experimental data should be used only with caution. Extension of these studies to include nonlinear (curvilinear) multiple regression equations have proven useful in studies on the functionality of peanut seed products (33). 

Use of multiple regression techniques in the study of functional properties of food proteins is not new I76) Most food scientists have some familiarity with basic statistical concepts and some access to competent statistical advice. At least one good basic text on statistical modelling for biological scientists exists (7 ). A number of more advanced texts covering use of regression in modelling are available (, ). ... 

Schmidt, R. H., Illingworth, B. L., Deng, J. C. and Cornell, J. A. 1979. Multiple regression and response surface analysis of the effects of calcium chloride and crysteine on heat-induced whey protein gelation. J. Agr. Food Chem. 27, 529-532. 

Based on the earlier work of Meyer and Overton, who showed that the narcotic effect of anesthetics was related to their oil/water partition coefficients, Hansch and his co-workers have demonstrated unequivocally the importance of hydrophobic parameters such as log P (where P is, usually, the octanol/water partition coefficient) in QSAR analysis.28 The so-called classical QSAR approach, pioneered by Hansch, involves stepwise multiple regression analysis (MRA) in the generation of activity correlations with structural descriptors, such as physicochemical parameters (log P, molar refractivity, etc.) or substituent constants such as ir, a, and Es (where these represent hydrophobic, electronic, and steric effects, respectively). The Hansch approach has been very successful in accurately predicting effects in many biological systems, some of which have been subsequently rationalized by inspection of the three-dimensional structures of receptor proteins.28 The use of log P (and its associated substituent parameter, tr) is very important in toxicity,29-32 as well as in other forms of bioactivity, because of the role of hydrophobicity in molecular transport across cell membranes and other biological barriers. 

Bohm (221, 222) analyzed 45 protein-ligand complexes (affinity range = -9 to -76 kJ/mol) and found the following equation by multiple regression analysis ... 

Multiple regression analysis performed for succinylated rapeseed protein isolates indicated that emulsification activity was related to protein solubility, hydrophobicity, zeta potential, and flow behavior of aqueous dispersions of the proteins. Emulsion stability was affected by protein solubility, zeta potential, apparent viscosity of protein dispersions, and difference in density between aqueous and oil phases [76],... 

Multiple regression analysis based on Eq. 2 revealed that the cytotoxic activity mainly depended on the log P and a low Elumo value. It has been suggested that receptor protein tryptophan residues containing an aromatic ring moiety should be the best electron donor for charge transfer interactions with phenols because of their high Ehomo value [67]-... 

In total, = 30 calibration samples are available. The NIR spectrum of one sample is given in Figure 6.8. We will use the inverse calibration method (cf. Eq. (6.81)), that is, according to the general equation of multiple regression (Eq. (6.41)), the protein content is arranged in the y vector and the matrix X contains the NIR spectra. In order to keep the calibration model small, only the five most... 

In a paper that addresses both these topics, Gordon et al.11 explain how they followed a com mixture fermented by Fusarium moniliforme spores. They followed the concentrations of starch, lipids, and protein throughout the reaction. The amounts of Fusarium and even com were also measured. A multiple linear regression (MLR) method was satisfactory, with standard errors of prediction (SEP) for the constituents being 0.37% for starch, 4.57% for lipid, 4.62% for protein, 2.38% for Fusarium, and 0.16% for com. It may be inferred from the data that PLS or PCA (principal components analysis) may have given more accurate results. 

Fig. 1.1. Examples for standard curves resulting from multiple determinations of different amounts of BSA. Line with circies protocol according to Lowry et al. Soiid iine. nonlinear regression dotted iine linear regressions wavelength 720 nm. Line with squares BCA protein determination. Soiid iine nonlinear regression dotted iine linear regression wavelength 562 nm). Findings means an example for graphical evaluation...

Multiple r2 values of 0.9346 and 0.9280 were obtained for these equations of capacity and stability, respectively. The relative importance of each respective partial regression coefficient was determined by comparison of B values (32). These evaluations indicate that the most important variables in the two models for foam capacity and stability are soluble protein, soluble and insoluble carbohydrate and ash, and insoluble fiber. 

---