This article proposes and describes a data fusion detection method based on computer vision and spectroscopic techniques for fish freshness classification.

For rapid evaluation of fish freshness, a colorimetric sensor array has been developed for the sensitive detection to measure simultaneously TVB-N and K value of fish during its storage period.

This work measures the total polyphenols content in cocoa beans by using a novel approach of integrating near infrared spectroscopy and electronic tongue, 110 samples of cocoa beans were analysed.

A colorimetric sensor array is a rapid and high sensitivity sensor for the detection and identification of volatile organic compounds. Theoretical investigations are performed to study the binding ability of the colorimetric sensor array with volatile organic compounds. Cobalt-porphyrin is selected to investigate the binding ability of the colorimetric sensor array with small volatile organic compounds. The binding energy of cobalt-porphyrin with small volatile organic compounds, such as O 2 , N 2 , H 2 S, trimethylamine, propanol, propane, ethyl acetate, butanone and so on, is investigated using density functional theory (DFT) methods at three different spin multiplicities: low-spin (singlet), intermediate-spin (triplet) and high-spin (quintet) states. The relative and absolute binding energies of all the complexes are obtained at the optimized geometries. The triplet state is found to have the lowest energy for the CoP-O 2 complex, whereas the singlet state has the lowest energy for the other complexes. The binding energies for the complexes considered are in order starting from the lowest energy state: H 2 S < propane < O 2 < N 2 < ethyl acetate < butanone < propanol < trimethylamine. This theoretical result can be used to optimize the sensor to increase the detection ability of the colorimetric sensor array.

\Aroma is a significant index to reflect the quality of vinegar. This paper intends to investigate the volatile organic compounds (VOCs) of vinegar's substrate during solid-state fermentation. Gas chromatography and mass spectrometry (GC-MS), as well as a colorimetric sensor array, was used comparatively to characterize the VOCs in the different stages of the acetic acid fermentation. It was found from GC-MS that the chemical components of ethanol, 3-methyl-1-butanol, acetic acid, and ethyl acetate were remarkably changed during the solid-state fermentation. Furthermore, the colorimetric sensor array technique was also used to characterize the VOCs of the solid-state fermentation. The color changes of the colorimetric sensor array before and after exposure to the vinegar's substrate samples were obtained by a Charge Coupled Device (CCD) camera. The digital data (i.e., RGB components of the image) representing the color change profiles for the vinegar samples were analyzed. A principle components analysis (PCA) was employed to present the trends in the fermentation process through analyzing the signals obtained from the colorimetric sensor array. A linear discriminant analysis (LDA) model based on the PCA scores was used to distinguish vinegar's substrate samples per day during the whole fermentation process. The result show that around 60 percent of samples were correctly identified corresponding to their fermenting day; and 92.3 percent of samples were correctly identified within an error range of three days. Therefore, the colorimetric sensor array technique was considered to be an excellent method for VOCs measurement, based on its advantages of accuracy, no need for a pretreatment, fast, and low cost.