The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi‐sensor fusion technology based on a novel cost‐effective electronic nose (E‐nose) and a voltammetric electronic tongue (E‐tongue) was proposed. The E‐nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E‐Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E‐nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost‐effective E‐nose created and voltammetric E‐tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.

A low-cost electronic nose (E-nose) based on colorimetric sensors fused with Fourier transform-near-infrared (FT-NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total volatile basic nitrogen, protein, fat, total sugar and ash contents were measured to investigate the differences of basic properties between raw beef and duck; GC-MS was employed to analyze the difference of the volatile organic compounds emitted from these two types of meat. For variable selection and spectra denoising, the simple T-test (p < 0.05) separately intergraded with first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction were performed and the results compared. Extreme learning machine models were built to identify the adulterated beef and predict the adulteration levels. Results showed that for recognizing the independent samples of raw beef, beef–duck mixtures, and raw duck, FT-NIR offered a 100% identification rate, which was superior to the E-nose (83.33%) created herein. In terms of predicting adulteration levels, the root means square error (RMSE) and the correlation coefficient (r) for independent meat samples using FT-NIR were 0.511% and 0.913, respectively. At the same time, for E-nose, these two indicators were 1.28% and 0.841, respectively. When the E-nose and FT-NIR data were fused, the RMSE decreased to 0.166%, and the r improved to 0.972. All the results indicated that fusion of the low-cost E-nose and FT-NIR could be employed for rapid and convenient testing of beef adulterated with duck.

The purpose of this study was to investigate the potential of taste sensors coupled with chemometrics for rapid determination of beef adulteration. A total of 228 minced meat samples were prepared and analyzed via raw ground beef mixed separately with chicken, duck, and pork in the range of 0 ~ 50% by weight at 10% intervals. Total sugars, protein, fat, and ash contents were also measured to validate the differences between raw meats. For sensing the water‐soluble chemicals in the meats, an electronic tongue based on multifrequency large‐amplitude pulses and six metal electrodes (platinum, gold, palladium, tungsten, titanium, and silver) was employed. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were used to model the identification of raw and the adulterated meats. While an adulterant was detected, the level of adulteration was predicted using partial least squares (PLS) and ELM and the results compared. The results showed that superior recognition models derived from ELM were obtained, as the recognition rates for the independent samples in different meat groups were all over 90%; ELM models were more precisely than PLS models for prediction of the adulteration levels of beef mixed with chicken, duck, and pork, with root mean squares error for the independent samples of 0.33, 0.18, and 0.38% and coefficients of variance of 0.914, 0.956, and 0.928, respectively. The results suggested that taste sensors combined with ELM could be useful in the rapid detection of beef adulterated with other meats.

To improve the thermostability of tryptophan synthase, the molecular modification of tryptophan synthase was carried out by rational molecular engineering. First, B-FITTER software was used to analyze the temperature factor (B-factor) of each amino acid residue in the crystal structure of tryptophan synthase. A key amino acid residue, G395, which adversely affected the thermal stability of the enzyme, was identified, and then, a mutant library was constructed by site-specific saturation mutation. A mutant (G395S) enzyme with significantly improved thermal stability was screened from the saturated mutant library. Error-prone PCR was used to conduct a directed evolution of the mutant enzyme (G395S). Compared with the parent, the mutant enzyme (G395S /A191T) had a K_m of 0.21 mM and a catalytic efficiency k_cat/K_m of 5.38 mM⁻¹∙s⁻¹, which was 4.8 times higher than that of the wild-type strain. The conditions for L-tryptophan synthesis by the mutated enzyme were a L-serine concentration of 50 mmol/L, a reaction temperature of 40 °C, pH of 8, a reaction time of 12 h, and an L-tryptophan yield of 81%. The thermal stability of the enzyme can be improved by using an appropriate rational design strategy to modify the correct site. The catalytic activity of tryptophan synthase was increased by directed evolution.