Measurement of Instrumental Texture Profile Analysis (TPA) of Foods

2021 ◽  
pp. 427-465
Author(s):  
Mohammad Shafiur Rahman ◽  
Zahir Humaid Al-Attabi ◽  
Nasser Al-Habsi ◽  
Mohammed Al-Khusaibi
Keyword(s):  
Profile Analysis ◽  
Texture Profile Analysis ◽  
Texture Profile ◽  
Instrumental Texture

Texture profile analysis of low fat milk nuggets prepared with 2% fat corrected milk and skim milk coagulum and extended with optimum levels of barnyard millet flour and finger millet flour

2017 ◽  
Vol 36 (04) ◽  
Author(s):  
Jyanendr Kumar Shahi ◽  
Ragini Kumari ◽  
Rakesh Kumar ◽  
Geeta Chauhan ◽  
Sanjeev Kumar Roy
Keyword(s):  
Finger Millet ◽  
Profile Analysis ◽  
Skim Milk ◽  
Food Product ◽  
Texture Profile Analysis ◽  
Consumer Acceptability ◽  
Low Fat ◽  
Barnyard Millet ◽  
Texture Profile ◽  
Instrumental Texture

Texture profile analysis of any food product shows correlation with sensory and overall consumer acceptability of the developed milk products. Instrumental texture profile analysis was conducted on low fat milk nuggets prepared with 2% fat milk coagulum and skim milk coagulum and extended with optimum levels of barnyard millet flour and finger millet flour. The texture profile analysis results showed higher values for hardness, springiness, cohesiveness, gumminess and chewiness for the milk nuggets prepared with 2% fat milk coagulum as compared to the nuggets prepared with skim milk coagulum.

Instrumental Texture Profile Analysis (TPA) of Shelled Sunflower Seed Caramel Snack Using Response Surface Methodology

2007 ◽  
Vol 13 (6) ◽  
pp. 455-460 ◽  
Author(s):  
R.K. Gupta ◽  
Alka Sharma ◽  
R. Sharma
Keyword(s):  
Response Surface Methodology ◽  
Product Quality ◽  
Response Surface ◽  
Sunflower Seed ◽  
Profile Analysis ◽  
Texture Profile Analysis ◽  
Texture Profile ◽  
Texture Analyzer ◽  
Instrumental Texture

Models capable of predicting product quality of shelled sunflower seed caramel snack have been developed using response surface methodology. The textural profile analysis was conducted on the snacks using a texture analyzer. The quality attributes measured were hardness, cohesiveness, springiness, chewiness, and resilience as a function of sugar and sunflower kernels content. The sugar and shelled seed proportions affect the textural characteristics of the product significantly (p<0.05). The values of hardness, cohesiveness, springiness, chewiness, and resilience varied from 2.048 to 42.030 N, 1.002 to 5.003, 1.138 to 1.69, 2.773 to 228.146N, and 0.301 to 0.779, respectively. The highest values of hardness and chewiness were attained for the product with 70:30 sugar and shelled sunflower seed proportion respectively. Similarly the highest values of cohesiveness, springiness and resilience were observed in 50 : 30, 50 : 40, and 50 : 50 proportions respectively. The lowest values of hardness and chewiness were observed in 50 : 50 (sugar: shelled sunflower seed) proportion respectively. Similarly the lowest values of cohesiveness were observed in 70: 50 whereas the lowest values of springiness and resilience were observed in 70 : 30 proportions respectively. Hardness, cohesiveness, and chewiness trended to increase whereas springiness and resilience decreased with increase in sugar proportion.

Relationship between Sensory Textural Attributes and near Infrared Spectra of Cooked Rice

2007 ◽  
Vol 15 (5) ◽  
pp. 333-340 ◽  
Author(s):  
Weena Srisawas ◽  
Vinod K. Jindal ◽  
Warunee Thanapase
Keyword(s):  
Near Infrared ◽  
Profile Analysis ◽  
Indica Rice ◽  
Nir Spectroscopy ◽  
Texture Profile Analysis ◽  
Eating Quality ◽  
Least Squares Regression ◽  
Texture Profile ◽  
Cooked Rice ◽  
Instrumental Texture

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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