Study On Detection Of Yao-meat Freshness Based On Pigment Gas Sensing Technology

Salted pork in jelly (Yao-meat) is a kind of jellied low-temperature pork product, which combines traditional craft with modern meat processing technology. Yao-meat is vulnerable to microorganisms due to its nutrient-rich components and the diversity of the starting materials. Furthermore, to keep its unique flavor, Yao-meat could not be treated with high temperature sterilizing, which makes it more liable to be spoiled. Therefore, builting a fast and nondestructive method to evaluat Yao-meat freshness becomes a subject of great importance. To overcome the disadvantages of the present method for Yao-meat freshness evaluation, pigment gas sensor technology was combined with chemometric method to discriminate Yao-meat freshness grade qualitatively and predict the freshness evaluation indexs quantitatively. It was conducted to obtain a new method for fast and nondestructive evaluation of meat freshness. The main research works have been summed as following:(1) Analysis of the freshness indictors during Yao-meat storage. The Yao-meat samples were stored at 4℃. Tests were conducted every 5 days, up to 35 days. For each test date, four indicators, i.e., the total viable count (TVC), total volatile basic nitrogen (TVB-N), Biogenic amine indicators (BAI) and trimethylamine (TMA) content, were measured for 12 samples. With reference to related national standards, the changing rules of Yao-meat freshness were analyzed according to the measured indicators, which provided the foundation for further investigation.(2) Evaluation of Yao-meat freshness by conventional pigment sensor array based on TiO2 nanoporous film. To overcome the uneven distribution of the pigments on the common used base material caused by the irregular surface, nano colorimetric sensor arrays were prepared with TiO2 nanoprorous fim as base, porphyrins and pH indicator as sensitive materials. Compared with C2 reverse silica gel plate, the advantages of the TiO2 nanoporous films are due to amenable surface properties and microporous structures. The chromogenic agents on the TiO2 nanoporous film are more uniformity and orderly. The response time of the nanoporous pigment based sensor was decreased from 720 to 540 s. The detection limit for TMA was decreased from 0.06 mL/m3. Linear discriminate analysis (LDA), support vector machine (SVM) and back propagation neural network (BPNN) pattern recognition combined with principal component analysis (PCA) were used to build discriminate models. The BPNN model exhibited best results for discrimination of Yao-meat freshness and the discriminating rates for train set and prediction set were 93.75%, and 90.63%. The classical partial least squares (PLS), genetic algorithm-partial least squares (GA-PLS) and ant colony optimization-partial least squares (ACO-PLS) were adopted to build the related regression models to predict the evaluation indexes of Yao-meat freshness. Results showed that the ACO-PLS model preformed best:the correlation coefficient (Rp) for predicting TVC, TVB-N, BAI and TMA were 0.903,0.874,0.906 and 0.871 in prediction set.(3) Evaluation of Yao-meat freshness by nanoporous natural pigment sensor array. Since the water-insoluble porphyrins have to be dissolved in toxic organic solvent to fabricate the pigment sensor arrays, such sensors are unsuitable for food detection. To overcome this disadvantage, a new natural pigment sensor array based on TiO2 nanoporous film substrate was developed for Yao-meat freshness detection. The response time of the natural pigment based sensor decreased from 540 to 480s. LDA, SVM and BPNN combined with PCA were used to build discriminate models. The comparison results showed that the BPNN model could effectively discriminate the Yao-meat in different freshness, and the discriminating rates for train set and prediction set were 93.75% and 87.50%. The classical PLS, GA-PLS and ACO-PLS method were adopted to build the related regression model between the color reaction features of nanoporous natural pigment sensor array and evaluation indexes of Yao-meat freshness. Result showed that ACO-PLS could effectively improve the models’performance:for predicting TVC, TVB-N, BAI and TMA, the Rp were 0.871,0.829,0.887and 0.909 in prediction set.(4) Evaluation of Yao-meat freshness by pigment-sensitized TiO2 metal oxide gas sensors. Usually, the metal oxide gas sensors only work at high temperature, which means that they are unsuitable for detection of meat freshness. In this study, two methods were used to prepare pigment-sensitized TiO2 nano gas sensor to obtain operable gas sensor at room temperature. The preparation method, surface structure, crystal form and gas sensing performance of the 2 prepared TiO2 films (TiO2 nanotube and TiO2 nanoparticles) were analyzed and compared. Results showed that the TiO2 nanotube sensor has a better performance than the TiO2 nanoparticles sensor. The pigment-sensitized TiO2 nanotube sensor was used to detect the Yao-meat freshness. LDA, SVM and BPNN pattern recognition combined with PCA were used to build discrimination models. The comparison results showed that the BPNN could effectively discriminate the Yao-meat in different freshness, and the discriminating rates for calibration set and prediction set were 98.44% and 93.75%. The classical PLS, GA-PLS and ACO-PLS were adopted to build the related regression model between the color reaction features of natural pigment sensor array and evaluation indexes of Yao-meat freshness. Result showed that ACO-PLS could effectively improve the models’performance on predicting TVC, TVB-N,BAI and TMA, the Rp are 0.820、0.842、0.812 and 0.866 in prediction set.(5) Detection of Yao-meat freshness based on the integration of the optical and electrical signal of pigment gas sensor. The feature variables were extracted from optical and electrical signals and integrated to be new features. The discriminating rates of calibration set and prediction set were both 100%. Compared with the optimal model of single sensor (pigment-sensitized TiO2 metal oxide gas sensors), the recognition accuracy was increased 2%. The models’performance used to predict TVC, TVB-N, BAI, and TMA, the Rp are 0.914,0.922,0.903 and 0.931 in prediction set. Compared with the optimal model of single sensor (nanoporous natural pigment sensor array), the prediction accuracy was increased 10%. The optical and electrical signal integration can improve detection accuracy of Yao-meat freshness.This research offers a new idea to detect meat products freshness, and there is also of great significance in monitoring meat products quality during production, storage, transportation and consumption.