Effect of sub- and supercritical water treatments on the physicochemical properties of crab shell chitin and its enzymatic degradation.

This study examined the effects of sub- and supercritical water pretreatments on the physicochemical properties of crab shell α-chitin and its enzymatic degradation to obtain N,N'-diacetylchitobiose (GlcNAc)2. Following sub- and supercritical water pretreatments, the protein in the crab shell was removed and the residue of crab shell contained α-chitin and CaCO3. Prolonged pretreatment led to α-chitin decomposition. The reaction of pure α-chitin in sub- and supercritical water pretreatments was investigated separately; we observed lower mean molecular weight and weaker hydrogen bonds compared with untreated α-chitin. (GlcNAc)2 yields from enzymatic degradation of subcritical (350 °C, 7 min) and supercritical water (400 °C, 2.5 min) pretreated crab shell were 8% and 6%, compared with 0% without any pretreatment. This study shows that sub- and supercritical water pretreatments of crab shell provide to an alternative method to the use of acid and base for decalcification and deproteinization of crab shell required for (GlcNAc)2 production.

Effects of supercritical water and mechanochemical grinding treatments on physicochemical properties of chitin.

This study examined the effects of a combined pretreatment with supercritical water and mechanochemical grinding with a ball mill on the physicochemical properties of chitin and its enzymatic degradation. Following pretreatment with a combination of supercritical water and grinding, chitin had a lower mean molecular weight, a lower crystallinity index, a lower crystallite size, greater d-spacing, weaker hydrogen bonds, and the amide group was more exposed compared with untreated chitin. These properties increased the hydrophilicity of the chitin and enhanced its enzymatic degradation. The N,N'-diacetylchitobiose (GlcNAc)(2) yield after enzymatic degradation of chitin following pretreatment with supercritical water (400 °C, 1 min) and grinding (800 rpm, 10 min) was 93%, compared with 5% without any treatment, 37% with supercritical water pretreatment alone (400 °C, 1 min), and 60% with grinding alone (800 rpm, 30 min).

Discriminant Analysis of Undaria pinnatifida Production Areas Using Trace Elemental Analysis.

Increasingly, attention is being paid to declaring the origin of agricultural and marine products after the advent of the bovine spongiform encephalopathy (BSE; commonly known as mad-cow disease). The display of the production centers on U. pinnatifida has been required in Japan since 2006. As an example of testing in another marine product, near-infrared spectra (NIR) and trace elemental analysis of U. pinnatifida are proven effective methods for discriminating production centers by us and Food and Agricultural Materials Inspection Center (FAMIC). In the present study, we found that X-ray fluorescence analysis of Br was also effective for the discrimination of production centers. The results of our study suggest that a combination of NIR and X-ray fluorescence analysis is a convenient and efficient method for determination due simple sampling procedures and increased effectiveness.

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.

Systematization method for distinguishing plastic groups by using NIR spectroscopy.

A systematic classification method for polymers is not yet available in case of using near infrared spectra (NIR). That is why we have been searching for a systematic method. Because raw NIR spectra usually have few obvious peaks, NIR spectra have been pretreated by 2nd derivation for taking well modulated spectra. After the pretreatment, we applied classification and regression trees (CART) to the discrimination between the spectra and the species of polymers. As a result, we obtained a relatively simple classification tree. Judging from the obtained splitting conditions and the classified polymers, we concluded that obtained knowledge on the chemical function groups estimated by the important wavelength regions is not always applicable to this classification tree. However, we clarified the splitting rules for polymer species from the NIR spectral point of view.