Detection of meat-borne trimethylamine based on nanoporous colorimetric sensor arrays.

Trimethylamine (TMA) is a key measurement indicator for meat spoilage. In order to develop simple, cheap, and sensitive sensors for TMA detection, a nanoporous colorimetric sensor array (NCSA) was developed. A sol-gel method has been used to obtain TiO2 nanoporous film as substrate material to improve the sensitivity and stability of the CSA. The sensor enabled the visual detection of TMA gas from the permissible exposure limits (PEL) 10 ppm to 60 ppb concentrations with significant response. Principal component analysis (PCA) was used to characterize the functional relationship between the color difference data and TMA concentrations. Furthermore, the NCSA was used to predict the presence of TMA in Yao-meat. A partial least square (PLS) prediction model was obtained with the correlation coefficients of 0.896 and 0.837 in calibration and prediction sets, respectively. This research suggested that the NCSA offers a useful technology for quality evaluation of TMA in meat.

A new room temperature gas sensor based on pigment-sensitized TiO2 thin film for amines determination.

A new room temperature gas sensor was fabricated with pigment-sensitized TiO2 thin film as the sensing layer. Four natural pigments were extracted from spinach (Spinacia oleracea), red radish (Raphanus sativus L), winter jasmine (Jasminum nudiflorum), and black rice (Oryza sativa L. indica) by ethanol. Natural pigment-sensitized TiO2 sensor was prepared by immersing porous TiO2 films in an ethanol solution containing a natural pigment for 24h. The hybrid organic-inorganic formed films here were firstly exposed to atmospheres containing methylamine vapours with concentrations over the range 2-10 ppm at room temperature. The films sensitized by the pigments from black-rice showed an excellent gas-sensitivity to methylamine among the four natural pigments sensitized films due to the anthocyanins. The relative change resistance, S, of the films increased almost linearly with increasing concentrations of methylamine (r=0.931). At last, the black rice pigment sensitized TiO2 thin film was used to determine the biogenic amines generated by pork during storage. The developed films had good sensitivity to analogous gases such as putrscine, and cadaverine that will increase during storage.

Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine.

More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ=0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (R(p)) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar.