Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging

2019 ◽  
Vol 52 (12) ◽  
pp. 1845-1859 ◽  
Author(s):  
Xue Wang ◽  
Jiajia Shan ◽  
Shuqing Han ◽  
Junbo Zhao ◽  
Yituo Zhang
2014 ◽  
Vol 32 (No. 4) ◽  
pp. 320-325 ◽  
Author(s):  
F. Giarratana ◽  
D. Muscolino ◽  
Ch. Beninati ◽  
G. Ziino ◽  
A. Giuffrida ◽  
...  

We evaluated the effects of Gymnorhynchus gigas on the freshness and hygienic quality of Lepidopus caudatus. Total Volatile Basic Nitrogen (TVB-N), Trimethylamine Nitrogen (TMA-N), as well as Specific Spoilage Organisms (SSOs) are the most important freshness indicators in fish. Our study was carried-out on 65 specimens of L. caudatus kept in ice and stored at 2°C for different period of time. The microbiological charge of SSOs recovered on a portion of parasitised muscles (MP) was compared with those recovered on portions of parasite-free muscles (M). The contents of TVB-N and TMA-N on MP, M, and G. gigas larva/ae were measured using the Conway microdiffusion method. High prevalence (72.31%) of G. gigas in the specimens of L. caudatus from the Mediterranean sea was observed. No statistically significant differences (P < 0.05) between M and MP were found during storage. However, massive infestation of G. gigas on the muscle of the silver scabbardfish could negatively influence TVB-N values, without compromising the sensorial characteristic of fish.


2007 ◽  
Vol 15 (5) ◽  
pp. 333-340 ◽  
Author(s):  
Weena Srisawas ◽  
Vinod K. Jindal ◽  
Warunee Thanapase

Fourteen varieties of Thai indica rice, cooked with five water-to-rice ratios ranging from 1.3 to 2.5 on a weight basis, were characterised by sensory and instrumental texture profile analysis. The potential of near infrared (NIR) reflectance spectroscopy was investigated as an alternative tool for evaluating eating quality attributes of cooked rice by developing predictive models for sensory hardness, stickiness and glossiness. Partial least squares regression models were developed which predicted sensory hardness and stickiness slightly better than the glossiness with r2v values ranging from 0.88 to 0.91 and standard errors of prediction ( SEP) lower than 0.4 unit score on nine-point sensory intensity scales. Results indicated that NIR spectroscopy-based models could be used for estimating the sensory hardness, stickiness and glossiness scores of cooked rice with higher accuracy (lower SEP) compared to the instrumental texture profile analysis based-models.


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