Electrical impedance spectroscopy (EIS), as an effective analytical technique for electrochemical system, has shown a wide application for food quality and safety assessment recently. Individual differences of livestock cause high variation in quality of raw meat and fish and their commercialized products. Therefore, in order to obtain the definite quality information and ensure the quality of each product, a fast and on-line detection technology is demanded to be developed to monitor product processing. EIS has advantages of being fast, nondestructive, inexpensive, and easily implemented and shows potential to develop on-line detecting instrument to replace traditional methods to realize time, cost, skilled persons saving and further quality grading. This review outlines the fundamental theories and two common measurement methods of EIS applied to biological tissue, summarizes its application specifically for quality assessment of meat and fish, and discusses challenges and future trends of EIS technology applied for meat and fish quality assessment.

Short wave infrared hyperspectral imaging (SWIR) (1000-2500 nm) was used to detect aflatoxin B 1 (AFB 1) in single maize kernels. One hundred and twenty kernels of four varieties artificially inoculated with a toxigenic strain of Aspergillus flavus in the field were examined. Normalisation and principal component analysis (PCA) were applied on average spectra of each kernel to reduce dimensionality and noise. Combining with support vector machine (SVM) classification methods, the first five principal components (PCs) were used to qualitatively classify the AFB 1 contamination levels (<20 ppb, 20-100 ppb, ≥100 ppb) in single kernels without effect of maize variety. Classification accuracies were 83.75% and 82.50% for calibration and validation set, respectively. It was also noted that a general correlation exists between categorical AFB 1 content and the first three PCs. Coefficients of determination (R 2) of the support vector machine regression model were 0.77 and 0.70 for calibration and validation set separately. A possible distribution map of AFB 1 was also made by applying the regression model on every pixel of the hyperspectral image. Moreover, using loading plots of the mutual first three PCs, five wavelengths (1317, 1459, 1865, 1934 and 2274 nm) were selected as characteristic wavelengths. Results indicated that hyperspectral imaging could be used to classify AFB 1 level qualitatively in individual maize kernels, however

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