2‐Step Optimization of the Extraction and Subsequent Physical Properties of Channel Catfish (Ictalurus punctatus) Skin Gelatin

Abstract

To optimize the extraction of gelatin from channel catfish (Ictalurus punctatus) skin, a 2-step response surface methodology involving a central composite design was adopted for the extraction process. After screening experiments, concentration of NaOH, alkaline pretreatment time, concentration of acetic acid, and extraction temperature were selected as the independent variables. In the 1st step of the optimization the dependent variables were protein yield (YP), gel strength (GS), and viscosity (V). Seven sets of optimized conditions were selected from the 1st step for the 2nd-step screen. Texture profile analysis and the 3 dependent variables from the 1st step were used as responses in the 2nd-step optimization. After the 2nd-step optimization, the most suitable conditions were 0.20 M NaOH pretreatment for 84 min, followed by a 0.115 M acetic acid extraction at 55 degrees C. The optimal values obtained from these conditions were YP = 19.2%, GS = 252 g, and V = 3.23 cP. The gelatin obtained also showed relatively good hardness, cohesiveness, springiness, and chewiness. The yield of protein and viscosity can be predicted by a quadratic and a linear model, respectively.

Fish gelatin: a renewable material for developing active biodegradable films

“…Accordingly, a mild acid pre-treatment is normally used for type-A gelatin extraction from fish skins (Norland, 1990). In the past 10 years considerable effort has been expended on studying gelatin extraction from different fish species, e.g., cod 25 (Gudmundsson & Hafsteinsson, 1997), tilapia (Jamilah & Harvinder, 2002), megrim (Montero & Gómez-Guillén, 2000), sole and squid (Gómez-Guillén et al 2002;Giménez, Gómez-Estaca, Alemán, Gómez-Guillén & Montero, 2008), pollock (Zhou & Regenstein, 2004), Nile perch (Muyonga, Cole & Duodu, 2004b), yellowfin tuna (Cho, Gu & Kim, 2005;Rahman, Al-Saidi & Guizani, 2008), Atlantic salmon (Arnesen & Gildberg, 2007), skipjack tuna (Aewsiri, Benjakul, Visessanguan & Tanaka, 2008); shark (Cho et al, 2004), skate (Cho, Jahncke, Chin & Eun, 2006), grass carp (Kasankala, Xue, Weilong, Hong & He, 2007), bigeye snapper and brownstripe red 5 snapper (Jongjareonrak, Benjakul, Visessanguan & Tanaka, 2006b) and channel catfish (Yang, Wang, Jiang, Oh, Herring & Zhou, 2007;Zhang et al, 2007). Table 1 summarizes the gel strength and thermal stability (gelling and melting temperatures) of different gelatins extracted from the skins of several fish species and squid, indicating shortly some details of both pre-treatment and water extracting conditions.…”

Optimization and physical properties of gelatin extracted from pangasius catfish (Pangasius sutchi) bone

“…The seafood processing usually leads to enormous amounts of waste such as skin, bone and scale with high collagen content thereby promising alternative materials for gelatin extraction (Zhou et al 2006;Yang et al 2007). Bones are mineralized material with collagen fibril as the basic building block.…”

Collagen Extraction from Malaysian Cultured Catfish (Hybrid Clarias Sp.): Kinetics and Optimization of Extraction Conditions Using Response Surface Methodology

“…However, health-conscious consumers are opposing the utilization of collagen extracted from these land animals due to health concerns. Besides the risks of transferring bovine spongiform encephalopathy (BSE), foot-and-mouth disease, and avian flu, porcine-derived collagen is not permitted to be used by Muslims and Jews for religious reasons, while collagens extracted from bovine sources are prohibited for Sikhs and Hindus [4]. Therefore, raw materials from fishery products have attracted attention as alternative sources of consumerfriendly collagen [5].…”

“…Under such circumstances where multiple variables may affect the extraction yield, response surface methodology (RSM) is an effective technique in optimizing the process [8]. RSM is a mathematical modeling technique that relates independent and dependent variables and subsequently generates regression equations that represent the interrelations between the input parameters and output properties [4]. In short, the principles and applications of RSM involve three steps: (1) experimental design where the independent variables and the corresponding experimental levels are set using statistical experimental designs such as the central composite design (CCD) or Box-Behnken design (BBD); (2) response surface modeling through regression analysis; and (3) process optimization using the response surface model [8].…”