Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish

Cozzolino, Daniel; Murray, Ian; Scaife, J. R.

doi:10.1046/j.1365-2095.2002.00176.x

Cited by 41 publications

(21 citation statements)

References 18 publications

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“…An increase in the transaminase activity in the NG corn-fed group could be attributed to lower availability of energy from the NG diet, which in turn demands a greater proportion of the amino acids to be metabolized for energy purposes. Dietary modification of carbohydrate like gelatinization of starch improved its utilization in fish (Mohapatra et al 2002;Yengkokpam et al 2006) as indicated by increased blood glucose or liver glycogen levels (data not shown). Alanine and aspartate are both glycogenic amino acids, and therefore it is likely that their rates of deamination and transamination depressed with high liver glycogen (Fynn-Aikins et al 1993).…”

Section: Discussionmentioning

confidence: 80%

Modulation of key enzymes of glycolysis, gluconeogenesis, amino acid catabolism, and TCA cycle of the tropical freshwater fish Labeo rohita fed gelatinized and non-gelatinized starch diet

Kumar

Sahu

Pal

et al. 2010

Fish Physiol Biochem

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A 60-day experiment was conducted to study the effect of dietary gelatinized (G) and non-gelatinized (NG) starch on the key metabolic enzymes of glycolysis (hexokinase, glucokinase, pyruvate kinase, and lactate dehydrogenase), gluconeogenesis (glucose-6 phosphatase and fructose-1,6 bisphosphatase), protein metabolism (aspartate amino transferase and alanine amino transferase), and TCA cycle (malate dehydrogenase) in Labeo rohita juveniles. In the analysis, 234 juveniles (2.53 +/- 0.04 g) were randomly distributed into six treatment groups each with three replicates. Six semi-purified diets containing NG and G cornstarch, each at six levels of inclusion (0, 20, 40, 60, 80, and 100) were prepared viz., T1 (100% NG, 0% G starch), T2 (80% NG, 20% G starch), T3 (60% NG, 40% G starch), T4 (40% NG, 60% G starch), T5 (20% NG, 80% G starch), and T6 (0% NG, 100% G starch). Dietary G:NG starch ratio had a significant (P < 0.05) effect on the glycolytic enzymes, the highest activities were observed in the T6 group and lowest in the T1 group. On the contrary, the gluconeogenic enzymes, the glucose-6-phosphatase and fructose-1,6 bisphosphatase activities in the organs, liver and kidney were recorded highest in the T1 group and lowest in the T6 group. The liver aspartate amino transferase activity showed an increasing trend with the decrease in the dietary G level. However, the muscle aspartate amino transferase activity was not significantly (P > 0.05) influenced by the type of dietary starch. The alanine amino transferase activity in both liver and muscle showed an increasing trend with the decrease in the dietary G level. The liver and muscle malate dehydrogenase activities were lowest in the T6 group and highest in the T1 group. Results suggest that NG (100%) starch diet significantly induced more the enzyme activities of amino acid metabolism, gluconeogenesis, and TCA cycle, whereas partial or total replacement of raw starch by gelatinized starch increased the glycolytic enzyme activity.

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Section: Discussionmentioning

confidence: 80%

Modulation of key enzymes of glycolysis, gluconeogenesis, amino acid catabolism, and TCA cycle of the tropical freshwater fish Labeo rohita fed gelatinized and non-gelatinized starch diet

Kumar

Sahu

Pal

et al. 2010

Fish Physiol Biochem

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“…NIR can therefore predict the lipid content in a nondestructive way (Rasco, Miller, & King, 1991;Lee, Cavinato, Mayes, & Rasco, 1992;Isaksson, Tøgersen, Iversen, & Hildrum, 1995;Downey, 1996). Prediction models have been established for a wide range of fish species (Lee et al, 1992;Wold & Isaksson, 1997;Nortvedt, Torrissen, & Tuene, 1998;Shimamoto, Hasegawa, Fujii, & Kawano, 2000;Cozzolino, Murray, & Scaife, 2002), but not yet for whole herring or herring fillets.…”

Section: Introductionmentioning

confidence: 99%

Lipid content in herring (Clupea harengus L.)—influence of biological factors and comparison of different methods of analyses: solvent extraction, Fatmeter, NIR and NMR

Nielsen¹,

Hyldig²,

Nielsen³

et al. 2005

LWT - Food Science and Technology

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“…Moreover, compared to traditional laboratory analyses, NIRS is a non-destructive method that preserves the sample's wholeness, enables the measurement of a large number of parameters using a single sample, and reduces the cost of routine analysis (Blanco and Villarroya, 2002). In aquaculture, NIRS is currently used to measure the chemical composition of oysters (Brown, 2011;Guévélou and Allen, 2016), to check flesh and body quality in fishes (reviewed by Cozzolino et al, 2002;Folkestad et al, 2008), to characterize ovarian follicular atresia (Servid et al, 2011), to trace fish geographical origins (Liu et al, 2015) and to assess protein and energy digestibility in rainbow trout (Glencross et al, 2015). Beyond these applications, NIRS could also be used in studies on integrated farming systems where it is essential to determine the quality of waste produced by one species that becomes the main food source for other species with different trophic levels.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The potential of near infrared spectroscopy (NIRS) to measure the chemical composition of aquaculture solid waste

et al. 2017

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In aquaculture, it is extremely important to determine the composition of fish farm waste to evaluate its potential impacts and to improve its reuse. Near-infra red spectroscopy (NIRS), an alternative to standard chemical analytical techniques, is a quick non-invasive method to assess physical and chemical composition, reducing the cost of routine analysis. We developed NIRS calibration models for organic matter (OM), total organic carbon (TOC), total organic nitrogen (TON), the carbon/nitrogen ratio (C/N), total phosphorus (TP) and the lipid content of marine fish particulate waste. To obtain a wide range of compositions of fish waste, decomposition time, feed loss, and inter-specific variations were taken into account. The NIRS calibration models were built using three sub-datasets: in Scenario 1) the calibration was species-specific, including only seabass waste (SeabassWaste), in Scenario 2), the calibration included data from two other species (MultiSpeciesWaste) and in Scenario 3), the general calibration included all data as well as simulation of extreme feed loss (up to 50%) (Faeces&Feed). All calibrations performed using either dried or wet samples gave equations with high coefficients of determination (R2) and reasonably low standard error of cross validation (SECV) values. (Résumé d'auteur

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Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish

Cited by 41 publications

References 18 publications

Modulation of key enzymes of glycolysis, gluconeogenesis, amino acid catabolism, and TCA cycle of the tropical freshwater fish Labeo rohita fed gelatinized and non-gelatinized starch diet

Modulation of key enzymes of glycolysis, gluconeogenesis, amino acid catabolism, and TCA cycle of the tropical freshwater fish Labeo rohita fed gelatinized and non-gelatinized starch diet

Lipid content in herring (Clupea harengus L.)—influence of biological factors and comparison of different methods of analyses: solvent extraction, Fatmeter, NIR and NMR

The potential of near infrared spectroscopy (NIRS) to measure the chemical composition of aquaculture solid waste

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