Pretreatment of different food rest materials for bioconversion into fungal lipid-rich biomass.

Food rest materials have the potential to be used as media components in various types of fermentations. Oleaginous filamentous fungi can utilize those components and generate a high-value lipid-rich biomass, which could be further used for animal and human use. One of the main limitations in this process is the pretreatment of food rest materials, needed to provide homogenization, sterilization and solubilization. In this study, two pretreatment processes-steam explosion and enzymatic hydrolysis-were evaluated for potato and animal protein-rich food rest materials. The pretreated food rest materials were used for the production of fungal lipid-rich biomass in submerged fermentation by the oleaginous fungus Mucor circinelloides. Cultivation media based on malt extract broth and glucose were used as controls of growth and lipid production, respectively. It was observed that media based on food rest materials can support growth and lipid production in M. circinelloides to a similar extent as the control media. More specifically, the use of potato hydrolysate combined with chicken auto-hydrolysate resulted in a higher fungal total biomass weight than using malt extract broth. When the same C/N ratio was used for glucose and rest materials-based media, similar lipid content was obtained or even higher using the latter media.

The relationship between fatty acid profiles in milk identified by Fourier transform infrared spectroscopy and onset of luteal activity in Norwegian dairy cattle.

To investigate the feasibility of milk fatty acids as predictors of onset of luteal activity (OLA), 87 lactations taken from 73 healthy Norwegian Red cattle were surveyed over 2 winter housing seasons. The feasibility of using frozen milk samples for dry-film Fourier transform infrared (FTIR) determination of milk samples was also tested. Morning milk samples were collected thrice weekly (Monday, Wednesday, Friday) for the first 10 wk in milk (WIM). These samples had bronopol (2-bromo-2-nitropropane-1,3-diol) added to them before being frozen at -20°C, thawed, and analyzed by ELISA to determine progesterone concentration and the concentrations of the milk fatty acids C4:0, C14:0, C16:0, C18:0, and cis-9 C18:1 as a proportion of total milk fatty acid content using dry-film FTIR, and averaged by WIM. Onset of luteal activity was defined as the first day that milk progesterone concentrations were >3 ng/mL for 2 successive measurements; the study population was categorized as early (n=47) or late (n=40) OLA, using the median value of 21 DIM as the cutoff. Further milk samples were collected 6 times weekly, from morning and afternoon milkings, these were pooled by WIM, and one proportional sample was analyzed fresh for fat, protein, and lactose content by the dairy company Tine SA, using traditional FTIR spectrography in the wet phase of milk. Daily energy-balance calculations were performed in 42 lactations and averaged by WIM. Animals experiencing late OLA had a more negative energy balance in WIM 1, 3, 4, and 5, with the greatest differences been seen in WIM 3 and 4. A higher proportion of the fatty acids were medium chained, C14:0 and C16:0, in the early than in the late OLA group from WIM 1. In WIM 4, the proportion of total fatty acid content that was C16:0 predicted late OLA, with 74% sensitivity and 80% specificity. The long-chain proportion of the fatty acids C18:0 and cis-9 C18:1 were lower in the early than in the late OLA group. Differences were greatest in WIM 4 and 5. Differences in concentrations of cis-9 C18:1 were seen between the groups from WIM 1. No relationship was seen between OLA and milk concentrations of either protein or fat, or between OLA and the milk fat:protein ratio. The differences in milk fatty acid proportions between the 2 groups are most likely related to differences in energy balance. The study shows that frozen milk samples can be tested for fatty acids by FTIR spectroscopy and that FTIR spectroscopy of milk can be used to provide real-time information about cow reproductive function.

Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks.

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fatty acids of 16.0, 20.5, 22.6 and fat, respectively.

Caprine CSN1S1 haplotype effect on gene expression and milk composition measured by Fourier transform infrared spectroscopy.

The Norwegian dairy goat population has a high frequency of a CSN1S1 (alphaS1-casein) haplotype with negative effects on protein and fat content. It is characterized by a single point deletion in exon 12 of CSN1S1, leading to a truncated protein and hence a low content of alphaS1-casein in the milk. This haplotype together with another haplotype with a deletion in exon 9 are called "weak" haplotypes. "Strong" haplotypes, on the other hand, have positive effects on important milk production traits. We show that expression of CSN1S1 in the mammary gland of lactating goats is significantly lower in animals with 2 weak haplotypes. Moreover, the effects of defective alleles were not detected in animals having 1 strong and 1 weak haplotype. Expression levels of other genes in the casein cluster were not affected by the CSN1S1 haplotypes investigated. Milk samples from goats with 2 weak haplotypes could be distinguished from the other milk samples using Fourier transform infrared (FTIR) spectroscopy and partial least squares discriminant analysis (PLS-DA). The PLS-DA components were related to spectra of pure caseins and whey proteins, hence FTIR has a potential for identifying milk samples with low alphaS1-casein content and different protein composition. The results indicate that FTIR-based measurements can be incorporated into breeding plans, or for selection of milk samples with high casein content, which in turn may improve cheese-making properties of the milk.

Raman and near-infrared spectroscopy for quantification of fat composition in a complex food model system.

Raman and near-infrared (NIR) spectroscopy have been evaluated for determining fatty acid composition and contents of main constituents in a complex food model system. A model system consisting of 70 different mixtures of protein, water, and oil blends was developed in order to create a rough chemical imitation of typical fish and meat samples, showing variation both in fatty acid composition and in contents of main constituents. The model samples as well as the pure oil mixtures were measured using Raman and NIR techniques. Partial least squares regression was utilized for prediction, and fatty acid features were expressed in terms of the iodine value and as contents of saturated, monounsaturated, and polyunsaturated fatty acids. Raman spectroscopy provided the best results for predicting iodine values of the model samples, giving validated estimation errors accounting for 2.8% of the total iodine value range. Both techniques provided good results for predicting the content of saturated, monounsaturated, and polyunsaturated fatty acids in the model samples, yielding validated estimation errors in the range of 2.4-6.1% of the total range of fatty acid content. Prediction results for determining fatty acid features of the pure oil mixtures were similar for the two techniques. NIR was clearly the best technique for modeling content of main constituents in the model samples.