A novel method for color determination of edible oils in L*a*b* format

A simple method that uses visible spectrophotometer data and an artificial neural network (ANN) was developed to determine edible oil color based on the L*a*b* format. The 100 oil samples consisted of nine pure oils, a sesame oil blend and three heated oils. Binary, ternary and quaternary mixtures of these 13 oils in different ratios were prepared, and absorbance values of the samples were measured in the visible region (380–700 nm). The absorbance values at wavelengths of 416, 456, 483, 537, 611 and 672 nm were used to train, validate and test the network. Strong correlations between the instrumental L*a*b*ΔE and the estimated L*a*b*ΔE were found for the test samples, with correlation coefficients (R²) of 0.989, 0.984, 0.996 and 0.992 for L*, a*, b*, and ΔE, respectively. The effects of number and combination of the wavelengths used for training of the ANN on the estimation capability of the network for the test samples were also investigated. Although a good agreement, average R² of 0.991– 0 993 for L*a*b*, was obtained for combinations composed of three to six wavelengths with 483 and 537 nm in common, the best R² value was obtained when all six wavelengths were used to train the ANN. The developed method is objective, cost effective and simple, and allows the color measurement with a basic visible spectrophotometer and disposable cuvettes.     

Estimation of Dielectric Properties of Cakes Based on Porosity, Moisture Content, and Formulations Using Statistical Methods and Artificial Neural Networks

Dielectric constant (DC) and dielectric loss factor (DLF) are the two principal parameters that determine the coupling and distribution of electromagnetic energy during radiofrequency and microwave processing. In this study, chemometric methods [classical least square (CLS), principle component regression (PCR), partial least square (PLS), and artificial neural networks (ANN)] were investigated for estimation of DC and DLF values of cakes by using porosity, moisture content and main formulation components, fat content, emulsifier type (Purawave™, Lecigran™), and fat replacer type (maltodextrin, Simplesse). Chemometric methods were calibrated firstly using training data set, and then they were tested using test data set to determine estimation capability of the method. Although statistical methods (CLS, PCR and PLS) were not successful for estimation of DC and DLF values, ANN estimated the dielectric properties accurately (R ², 0.940 for DC and 0.953 for DLF). The variation of DC and DLF of the cakes when the porosity value, moisture content, and formulation components were changed were also visualized using the data predicted by trained network.     

Determination of kinetic parameters of pectolytic enzymes at low pectin concentrations by a simple method

A simple and sensitive method for determination of the pectolytic enzyme activity was improved for soluble and immobilized forms of the enzyme. During enzymatic hydrolyzation of pectin, samples were collected from substrate solution at certain time intervals. Pectin in the samples was precipitated by alcohol and then pectin concentration was determined by measuring the refractive index of a prepared aqueous solution of these precipitates. The validity of this technique was evaluated using the kinetic behavior of soluble and immobilized enzymes. The kinetics of free and Duolite A568-immobilized pectinase was investigated. Michaelis-Menten constants and maximal reaction rates were found as K _m=20.71 g l ^-1 and V _max=81.30 g l ^-1s ^-1 for free enzyme and K _m=1.02 g l ^-1 and V _max=0.035 g s ^-1 g ^-1 particles for immobilized pectinase at 20 °C.     

Rapid detection of fat adulteration in bakery products using Raman and near-infrared spectroscopies

Adulteration is frequently encountered in the food industry and can be identified using currently available techniques. Infrared and Raman spectroscopic procedures are the most attractive techniques regarding fats and oils. The objective of this study was to determine the adulteration of the fat source (margarine or butter) in bakery products using Raman and near-infrared (NIR) spectroscopies. Margarine and butter samples were purchased at local markets in Turkey and examined using Raman and NIR devices. A mixture (50 % margarine : 50 % butter) of fat samples was examined as well. The NIR and Raman spectral output data of all the fat samples were processed using principal component analysis (PCA). Good classification was obtained for margarine, butter and the 1:1 adulterated mixture. The chosen bakery product (cake) was produced using the same fat samples according to the method of the American Association of Cereal Chemists. Then, the fat fraction was extracted from the cakes with n-hexane. Extracted fat samples from the cakes were examined as before. PCA was applied to Raman and NIR spectral data to achieve the separation of fat sources in the cakes. PCA was also validated in each of the two stages. Significant decomposition was observed in the Raman study in contrast to the NIR study. A chemometric comparison was also applied to processed (baked) fat samples in cakes and purchased samples by PCA to assess the effects of heat treatment on sample spectra. Raman spectroscopy with multivariate analyses such as PCA can be used to detect the adulteration of the fat source in bakery products in a faster and more suitable way than the other methods.     

Optimizing a submerged Monascus cultivation for production of red pigment with bug damaged wheat using artificial neural networks

The combined effect of temperature, agitation speed, and light on red pigment production by Monascus purpureus (M. purpureus) Went DSM 1604 using bug damaged wheat was studied using an artificial neural network (ANN). Information retrieved from the ANN was used to determine the optimal operating conditions for pigment production by M. purpureus using bug damaged wheat meal. The developed ANN had R ² values for training, validation, and testing data sets of 0.993, 0.961, and 0.944, respectively. According to the model, the highest pigment production of 1.874 absorbance units at 510 nm (A_{510 nm}) would be achieved at 29°C and 150 rpm under light conditions. The mean value of the experimental results obtained under these optimum conditions was 1.787±0.072 A_{510 nm}, corresponding to a pigment yield of 35.740 A_{510 nm}/g. The study showed that bug damaged wheat can be used as a substrate for red pigment production by M. purpureus.     

Scanner-based color measurement in L * a * b * format with artificial neural networks (ANN)

A computerized inspection system (CIS) that uses a flat-bed scanner, a computer, and an algorithm and graphical user interface coded and designed in Matlab^® 7.0 was developed to determine food color based on CIE L ^* a ^* b ^*, a color format. The USA Federal Color Standard printouts (SP) comprised of 456 different colors were used to train and test the artificial neural network (ANN) integrated CIS. Strong correlations were found between the results estimated from ANN-integrated CIS and those obtained from spectrophotometer (R ², 0.991, 0.989, and 0.995 for L ^*, a ^*, and b ^*, respectively) for test images data set. Various food samples were also evaluated to test the performance of the CIS. A good agreement, R ², 0.958, 0.938, and 0.962 for L ^*, a ^*, and b ^*, respectively, was found between color measurement with CIS and a spectrophotometer. CIS with a mean error of 0.60% and 2.34% for test and various food samples, respectively, has an ability to imitate the results obtained from a spectrophotometer. CIS allows users to store the captured picture for further use and estimate the overall color or the color of selected region of the samples either heterogeneous in color or amorphous in shape.     

Investigating the effects of ingredient levels on physical quality properties of cooked hamburger patties using response surface methodology and image processing technology

A three-factor central composite design was adopted to determine the interactive effects of fat (15–30%), water (10–20%) and textured soy protein (3–9%) content on the shrinkage, fat loss and moisture loss of hamburger patties after cooking. Image processing was used to estimate the shrinkage of hamburger patties. Textured soy protein (TSP) content was found to be the most important factor for minimizing fat and moisture loss. Both fat and water content were found to be significantly effective (P < 0.05) in the model for shrinkage and moisture loss in linear form. The changes in shrinkage due to fat, water and TSP content were also in linear form. The model for fat loss was in linear and quadratic form, whereas the model for moisture loss was in full quadratic form. The models for shrinkage, fat loss and moisture loss had the R-square values of 0.954, 0.969 and 0.964, respectively.