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Key aroma compounds in Chinese popular fried food of youtiao were characterized by solvent assisted flavor evaporation combined with gas chromatography‐mass spectrometry (GC‐MS) and aroma extract dilution analysis of gas chromatography‐olfactometry (AEDA/GC‐O), quantitation, and aroma recombination and omission. Four samples of youtiao fried with sunflower oil, soybean oil, rapeseed oil, and palm oil, respectively, differing in fatty acid composition, were selected to be investigated. A total of thirty‐five odorants were identified, among which twenty‐two odorants were quantitated by aid of authentic chemicals and with correction of recovery coefficients. Differences in odorants composition and aroma profile were observed among the four samples of youtiao fried with different oils. However, it was demonstrated in common thirteen compounds; that is, 3‐hydroxy‐2‐butanone, 3‐methylbutanal, furfural, 3‐(methylthio)propanal, 2‐furfurylthiol, phenylacetaldehyde, 2,5‐dimethyl‐4‐hydroxy‐3(2H)‐furanone, 2‐ethyl‐3,5‐dimethylpyrazine, 3‐ethyl‐2,5‐dimethylpyrazine, (E,E)‐2,4‐decadienal, 1‐octen‐3‐ol, (E,Z)‐2,6‐nonadienal, and (E)‐2‐nonenal contributed significantly to youtiao aroma. The work can provide some guidance to maintain the preferred youtiao flavor when modifying its preparation technology for safety concerns.

Grilled mutton shashlik is highly preferred by Chinese consumers. In this study, the key odorants in grilled mutton shashlik prepared in the traditional Chinese way under open carbon fire with or without suet (mutton fat) brushing during grilling were identified. Solvent‐assisted flavour evaporation (SAFE), combined with gas chromatography‐mass spectrometry (GC‐MS) and gas chromatography‐olfactometry (GC‐O), quantitation, recovery factor correction, and aroma recombination and omission were performed. In total, 57 odorants were identified, which predominantly included aliphatic aldehydes, sulphur‐containing compounds and pyrazines. In comparison, the suet‐brushed mutton shashlik had stronger fatty and sheep‐like odours due to higher levels of odorants generated via lipid degradation. However, the key odorants in both mutton shashlik with or without suet brushing during grilling were identified to be 3‐(methylthio)propanal, 2‐methyl‐3‐furanthiol, 2‐acetylthiazole, 2‐furfurylthiol, bis(2‐methyl‐3‐furyl)disulphide, 2‐acetylpyrazine, 2,5‐dimethylpyrazine, 2‐ethyl‐3,5‐dimethylpyrazine, 2,5‐dimethyl‐4‐hydroxy‐3(2H)furanone, 3‐hydroxy‐2‐butanone, 1‐octen‐3‐ol, (E)‐2‐octenal, (E,Z)‐2,6‐nonadienal, octanal, (E)‐2‐nonenal, nonanal, 4‐methyloctanoic acid and 4‐methylphenol. Notably, (E,E)‐2,4‐decadienal, which usually contributes significantly to cooked meat aroma, was excluded as a key odorant here. The results can help understand effect of fat on meat flavour and provide guidance for preparation of processed meat flavourings with the preferred flavour of grilled mutton shashlik.

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As a nondestructive testing technology, γ-photon imaging shows immense potential in the industrial field. However, the limitations of γ-photon imaging theory and detection technology result in various problems, such as low image resolution and edge blur. The technology is particularly difficult to apply to industrial detection that requires high imaging speed and high resolution. Therefore, this study proposes a reconstruction algorithm for regions of interest (ROI) in γ-photon images. The proposed algorithm is suitable for fast industrial detection and is based on the reconstruction algorithm for sinusoidal graph data, that is, the ordered subset expectation maximization (OSEM) image reconstruction algorithm. It is an improvement of the traditional point-and-line system matrix (SM) model. In the application of the proposed algorithm, the probability weight of a pixel is determined by the solid angle of the crystal bar at both ends of the line of response (LOR) to the pixel it passes through. In this work, the known contour parameters of industrial parts are used to describe the area of nuclide distribution as the ROI. Only the pixels through which the LOR passed in the ROI are counted, and the probability weights of these pixels are calculated to construct the SM. Gaussian filters are added in each iteration to suppress the clutter of scattered noise inside the image. The effectiveness of the algorithm was verified in two model experiments. A closed cavity detection experiment on industrial hydraulic parts was also conducted to compare the image reconstruction effects before and after the improvement. Results showed that the proposed algorithm can effectively improve image resolution and image edge contours. In the tee pipe model experiment and cavity detection experiment on hydraulic parts, the image reconstruction speed increased by more than 6 and 10 times, respectively. Hence, the proposed algorithm provided a feasible solution for quickly obtaining images with clear edges and high resolution under a large aperture detector ring.INDEX TERMS γ-photon imaging, OSEM, ROI, system matrix, industrial nondestructive testing