Structural identification and combination mechanism of iron (II)-chelating Atlantic salmon (Salmo salar L.) skin active peptides.

In order to utilize salmon skin for high value, and investigate the structural identification and combination mechanism of iron (II)-chelating peptides systemically, Atlantic salmon (Salmo salar L.) skin, a by-product of Atlantic salmon processing, was treated by two-step enzymatic hydrolysis to obtain salmon skin active peptides (SSAP). Then they reacted with iron (II) to obtain iron (II)-chelating salmon skin active peptides (SSAP-Fe) with a high iron (II) chelating ability of 98.84%. The results of Fourier transform infrared spectroscopy (FTIR), circular dichroism (CD) spectroscopy, 8-anilino-1-naphthalenesulfonic acid ammonium salt hydrate (ANS) combined fluorescence measurement, isothermal titration calorimetry (ITC) and full wavelength ultraviolet (UV) scanning showed that the structural characteristics of SSAP changed before and after chelating iron (II). Reverse phase high performance liquid chromatography (RP-HPLC) and mass spectrometry were used to identify and quantify the peptides in SSAP-Fe. Four peptide sequences (STEGGG, GIIKYGDDFMH, PGQPGIGYDGPAGPPGPPGPPGAP and QNQRESWTTCRSQSSLPDG) were identified. The content of PGQPGIGYDGPAGPPGPPGPPGAP was the highest, at 25.17 µg/mg. The pharmacokinetic and pharmacodynamic properties of these four peptides were also investigated, and the results indicated that they have satisfactory predicted ADMET properties. Molecular docking technology was used to analyze the binding sites between iron (II) and SSAP, and it was found that PGQPGIGYDGPAGPPGPPGPPGAP had the lowest predicted binding energy with iron (II) and the most stable predicted binding energy with iron (II). This results showed that the stability of SSAP-Fe were closely related to the number of covalent bonds and the types of amino acids. This study revealed the structure and combination mechanism of SSAP-Fe, and indicated that SSAP-Fe prepared by chelation may be used as a Fe supplement that can be applied in functional foods or ingredients.

Preparation, characterization, identification, and antioxidant properties of fermented acaí (Euterpe oleracea).

Fermentation technology was used to prepare the acaí (Euterpe oleracea) fermentation liquid. The optimal fermentation parameters included a strain ratio of Lactobacillus paracasei: Leuconostoc mesenteroides: Lactobacillus plantarum = 0.5:1:1.5, a fermentation time of 6 days, and a nitrogen source supplemental level of 2.5%. In optimal conditions, the ORAC value of the fermentation liquid reached the highest value of 273.28 ± 6.55 µmol/L Trolox, which was 55.85% higher than the raw liquid. In addition, the FRAP value of the acaí, as well as its scavenging ability of DPPH, hydroxyl, and ABTS free radicals, increased after fermentation. Furthermore, after fermentation treatment, the microstructure, basic physicochemical composition, amino acid composition, γ-aminobutyric acid, a variety of volatile components, and so on have changed. Therefore, fermentation treatment can significantly improve the nutritional value and flavor of the acaí. This provides a theoretical basis for the comprehensive utilization of acaí.

[Rapid evaluation of primary nutrients during plant-field chicken manure composting using near-infrared reflectance spectroscopy].

The present study explored the efficiency of quantitative analysis for the contents of total nitrogen (TN), total phosphorus (TP) and total potassium (TK) during chicken manure composting with chrysanthemum residue in a plant field using a Thermo Nicolet Antaris near-infrared reflectance spectral apparatus equipped with InGaAs detectors (Thermo Nicolet Corporation, Chicago, USA). The samples used in this study were collected from different positions during composting and were scanned in polyethylene bags at 2 cm(-1) interval from 10 000 to 4 000 cm(-1) with 32 co-added scans. Regression models were developed using spectral data and reference data by partial least square (PLS). In order to enhance chemical information and reduce data systemic noise, different data preprocessing methods such as smoothing, 1st and 2nd derivative, standard normal variety (SNV) and multiplicative scatter correction (MSC) were tested. The optimum preprocessing method was selected with the lowest root mean square error of cross-validation (RMSECV). Outliers were removed on the basis of being labeled as compositional outliers by the criteria that the predicted-actual difference for the sample was three standard deviations from the mean difference. According to the concentration gradient of each parameter, all samples were divided into a calibration set (3/4 samples) and a validation set (1/4 samples). Leave-one-out cross validation was performed to avoid over-fitting on the calibration sets. Based on the values of determination coefficient (R2) and relative prediction deviation (RPD) in validation sets, the prediction results were evaluated as excellent for TP and TK, and approximate for TN. Hereinto, R2 and RPD values were greater than 0.82 and 2.0 for TN, and greater than 0.90 and 3.0 for TP and TK, respectively.

[Statistical comparison of independent validation results for near infrared spectroscopy models predicting calorific value of straw].

Two hundred and twenty-two straw samples, consisting of 170 rice straw samples and 50 wheat straw samples, were collected from 24 provinces of China. Near infrared spectroscopy (NIRS)was applied to build quantitative models for calorific value of straw combining the use of principal component regression (PCR), partial least square regression (PLS)and modified partial least square regression (MPLS). Different scatter correction methods and derivative treatments were adopted to help improve the accuracy of NIRS models. A total of 54 NIRS models were obtained and independent validations were conducted using the same validation set of samples. A statistical comparison of independent validation results was then introduced to evaluate whether the models perform significantly. Bias and bias corrected standard error of prediction (SEP(C)), which are the mean and the standard deviation of the prediction residuals respectively, were compared by the proposed statistical procedures. It was concluded that near infrared spectroscopy was able to predict the calorific value of straw samples rapidly and accurately, with resulting SEP(C)s between 134 and 178 J x g(-1); statistical comparison of biases and SEP(C)s was a reasonable and efficient way to compare spectral pre-processing methods, and select NIRS models predicting calorific value of straw.

[Proximate analysis of straw by near infrared spectroscopy (NIRS)].

Proximate analysis is one of the routine analysis procedures in utilization of straw for biomass energy use. The present paper studied the applicability of rapid proximate analysis of straw by near infrared spectroscopy (NIRS) technology, in which the authors constructed the first NIRS models to predict volatile matter and fixed carbon contents of straw. NIRS models were developed using Foss 6500 spectrometer with spectra in the range of 1,108-2,492 nm to predict the contents of moisture, ash, volatile matter and fixed carbon in the directly cut straw samples; to predict ash, volatile matter and fixed carbon in the dried milled straw samples. For the models based on directly cut straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.92% and 0.76% for moisture, 0.94% and 0.84% for ash, 0.88% and 0.82% for volatile matter, and 0.75% and 0.65% for fixed carbon, respectively. For the models based on dried milled straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.98% and 0.54% for ash, 0.95% and 0.57% for volatile matter, and 0.78% and 0.61% for fixed carbon, respectively. It was concluded that NIRS models can predict accurately as an alternative analysis method, therefore rapid and simultaneous analysis of multicomponents can be achieved by NIRS technology, decreasing the cost of proximate analysis for straw.

[Methods of analyzing soybean meal adulteration in fish meal based on visible and near infrared reflectance spectroscopy].

The present study investigated the feasibility of visible and near infrared reflectance spectroscopy (NIRS) method for the detection of fish meal adulteration with vegetable meal. Here the authors collected fish meal and soybean meal (representative vegetable meal) which were common used in our country. Fish meal was adulterated with different proportion of soybean meal and then the doping test samples were prepared. Qualitative discriminant analysis and quantitative analysis were studied with representative fish meal adulterated with soybean meal. Two hundred and six calibration samples and 103 validation samples were used in the qualitative discriminant analysis. The effects of different spectrum pre-treatment methods and spectrum regions were considered when the qualitative discriminant analysis model was established. Based on the smallest standard error of cross validation (SECV) and the correct rate, the spectrum region of visible and NIR was chosen as the best region. The eventually established pre-treatment methods were the standard multi-scatter correction (Std MSC) combined with the second derivative (2, 4, 4, 1). Then the independent external validation set was used to test the model, and there was no false positive samples and false negative samples. The correct discriminant rate was 96.12%. In quantitative analysis, 130 fish meal samples adulterated with soybean meal were used as calibration set. The calibration model was established by partial least squares (PLS). Furthermore, the effect of different spectrum pre-treatment methods and the spectrum region were considered. The results showed that the best pre-treatment method was the standard normalized variate (SNV) combined with the second derivative (2, 4, 4, 1). The coefficient of determination (R2) and the standard errors of calibration (SEC) were 0.989 0 and 1.539 0 respectively between the predictive value and the actual value. Sixty five fish meal samples adulterated with soybean meal were used as independent validation set. The coefficient of determination (R2) and the standard errors of prediction (SEP) were 0.988 8 and 1.786 0 respectively, and the ratio of standard deviation of reference data in prediction sample set to the standard errors of prediction (RPD) was 8.61. The results showed that the NIRS could be used as a method to detect the existence and the content of soybean meal in fish meal.

[NIRS method for determination of meat and bone meal content in ruminant concentrates].

Feed contaminated with MBM is commonly accepted as the main transmission carrier of bovine spongiform encephalopathy (BSE). To prevent BSE many countries have banned MBM as a feed ingredient. In the People's Republic of China, the ban was first applied to ruminant feed. In order to investigate the feasibility of near infrared diffuse reflectance spectroscopy method for rapidly quantitative determination of meat and bone meal content in ruminant concentrates, 225 representatively commercial ruminant concentrates samples and 75 meat and bone meal (including cattle, sheep, pig and poultry meat and bone meal) samples were collected in the People's Republic of China. Two hundred twenty five ruminant concentrates samples of adulterated meat and bone meal (0.5%-35%) were prepared including 135 calibration samples and 90 independent validation samples. For the calibration set samples, 3 samples were prepared at each concentration. For validation set samples, 2 samples were prepared at each concentration. Any one commercial ruminant concentrates was used once only. The spectra were scanned by raster near infrared diffuse reflectance spectroscopy instrument, and the effect of spectrum pretreatment methods (mathematic pretreatments and scatter correction) and spectrum region (visible and NIR) on the calibration results was considered. The calibration equation was established by modified partial least squares method. The result showed that the calibration gave r2 of 0.979, a standard error of calibration (SEC) of 1.522% and a standard error of cross validation (SECV) of 1.582%. The 90 independent validation samples were used to validate the quantitative equation. The r2, a standard error of prediction (SEP) and ratio of performance to standard deviation (RPD) were 0.972, 1.764% and 5.99 respectively. The results of this study indicated that near infrared diffuse reflectance spectroscopy method could provide rapidly quantitative prediction for meat and bone meal percent in ruminant concentrates. This method was significant in practice for enriching the rapidly quantitative methods of determining animal feed materials.

[Discriminant analysis of near infrared diffuse reflectance spectra to detect adulteration of non-ruminant meat and bone meal].

In order to study the feasibility of using near infrared (NIR) diffuse reflectance spectroscopy to discriminate adultera tion of non-ruminant meat and bone meal (MBM) with ruminant MBM, a total of 39 MBM samples made up of 15 from pig, 15 from poultry, 5 from cattle and 4 from sheep produced in different areas in China were chosen. The MBM samples were ground with 0. 5 mm sieve. 252 specimens were prepared by non-ruminant MBM deliberately adulterated with different proportion of ruminant MBM. The specimens were scanned by FOSS NIRSystem 6500. A calibration set of 180 specimens and an independent validation set of 72 specimens were randomly selected by the WINISI software. Discriminant analysis model was developed by partial least squares (PLS) on the calibration set and validated with independent validation set. The best discriminant model was obtained using standard normal variate and detrend (SNVD) and second derivative for spectrum pretreatment; this model had a coefficient of determination (R2(CV)) of 0.83 and a standard error of cross-validation (SECV) of 0. 147 1. For the independent validation set, the correct classification rate is 90%. There were a false negative specimen (0.5%) and two uncertain specimens (1%, 1.5%) in validation set. Results showed that it is feasible to use NIR diffuse reflectance spectroscopy to discriminate adulteration of non-ruminant MBM with ruminant MBM, but for specimens adulterated with ruminant MBM at less than 2%, the accuracy of calibration model needs to be improved. NIR was a rapid and non-destructive approach to discriminating adulteration of non-ruminant MBM with ruminant MIBM.

Prediction of human skin permeability using artificial neural network (ANN) modeling.

AIM: To develop an artificial neural network (ANN) model for predicting skin permeability (log K(p)) of new chemical entities. METHODS: A large dataset of 215 experimental data points was compiled from the literature. The dataset was subdivided into 5 subsets and 4 of them were used to train and validate an ANN model. The same 4 datasets were also used to build a multiple linear regression (MLR) model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log K(p) and Abraham descriptors were investigated. RESULTS: The regression results of the MLR model were n=215, determination coefficient (R(2))=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R(2)=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log K(p) and Abraham descriptors is non-linear. CONCLUSION: The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.

[Rapidly determination of compositions of animal manure compost using near infrared reflectance spectroscopy].

Composting is a process of aerobic thermophilic microbial degradation or an exothermic biological oxidation of various wastes by many populations of the indigenous microorganisms, which lead to a stabilized, mature, deodorized and hygienic product, free of pathogens and plant seeds, rich in humic substances, less volume, easy to store and marketable as organic amendment or fertilizer. Compared to the conventional wet chemical method, near-infrared reflectance spectroscopy (NIRS), a rapid, nondestructive, cost-effective technique, has been extensively used for qualitative and quantitative analysis in the field of agriculture. This study was to explore the capability of NIRS to analyze the compositions of Chinese animal manure compost. A representative population of 120 animal manure compost samples from 22 provinces in China was selected as research object, and this study explored the feasibility of analyzing animal manure compost compositions, which included moisture (Moist), volatile solid (VS), total organic carbon (TOC), total nitrogen (TN), C : N, pH and Electronic conductivity (EC) using NIRS. Original samples were scanned with a SPECTRUM ONE NTS (Perkin Elmer, New Jersey, USA) from 10 000 to 4 000 cm(-1). NIRS calibrations of a series of chemical parameters were developed by means of partial least-squares (PLS) regression. Results showed that the determination coefficient of calibration (r2) and the standard error of estimate (SEE) were Moist (0.981 6, 21.98), VS (0.936 5, 37.29), TOC (0.961 0, 16.46), TN (0.987 4, 1.61), C : N (0.741 0, 2.29), pH (0.788 0, 0.48) and EC (0.870 4, 1.74), respectively. The determination coefficient of validation (r2(V)) and the standard error of prediction (SEP) were Moist (0.983 2, 20.99), VS (0.938 1, 35.07), TOC (0.912 8, 26.34), TN (0.973 5, 3.96), C : N (0.830 8, 2.01), pH (0. 615 8, 0.60) and EC (0.895 3, 1.87), respectively. The value of RPD (SD/SEP) for Moist, VS, TOC, TN and EC were all greater than 3.0, 2.39 for C : N and 1.63 for pH. Together, results showed the feasibility and efficiency of NIRS to determinate compositions of animal manure compost.

[Feasibility of using near-infrared reflectance spectroscopy for the analysis of compositions of straw silage].

Near-infrared reflectance spectroscopy (NIRS) calibrations of chemical composition in 158 straw silage samples were developed by means of partial least-squares (PLS) regression. Results showed that the correlation coefficients of calibration (R2) were 0.95, 0.90, 0.86, 0.91, 0.86, 0.95 and 0.90 for crude protein, neutral detergent fibre, acid detergent fibre, hemicellulose, dry matter, crude ash and acid detergent lignin respectively; the R2 of pH, lactic acid, acetic acid, propionic acid, butyric acid and ammonia were 0.98, 0.83, 0.85, 0.36, 0.90 and 0.92 respectively. The RPD (SD/SECV) of these parameters were all greater than 2.5 except acetic acid, propionic acid and butyric acid, and the correlation coefficients of validation (Rv(2)) of the parameters were all greater than 0.80 except lactic acid, acetic acid, propionic acid and butyric acid. These results are of great practical importance in rapid evaluation of silage quality.