Ensuring aquafeeds meet the expected nutritional and physical specifications for a species is paramount in research and for the industry. This study aimed to examine the feasibility of predicting the proximate composition and starch gelatinisation (or cook) of aquaculture feeds (aquafeeds) regardless of their intended target species by near infrared (NIR) spectroscopy. Aquafeed samples used for nutrition experiments on various aquatic species with different nutritional requirements, as well as aquafeeds manufactured under varying extrusion conditions and steaming time to generate variable starch cook were used in this study. The various size pellets were ground before scanning by NIR spectroscopy, then models were developed to estimate dry matter, ash, total lipid, crude protein, and gross energy as well as starch cook. Proximate prediction models were successfully produced for diets with R2 values between 0.88 and 0.97 (standard error of cross-validation (SECV) 0.43 to 1.46, residual predictive deviation (RPD) 4.6 to 15.6), while starch cook models were produced with R2 values between 0.91 and 0.97 (SECV 3.60 to 5.76, RPD 1.2 to 1.9). The developed NIR models allow rapid monitoring of the nutritional composition, as well as starch cook, one of the major physical properties of aquafeeds. Models that provide rapid quality control assessment of diet characteristics is highly desirable in aquaculture research and the aquafeed industry.
GlencrossBDBoothMAllanGL.A feed is only as good as its ingredients – a review of ingredient evaluation strategies for aquaculture feeds. Aquac Nutr. 2007;
13: 17–34.
2.
MasagounderKRamosSReimannI, et al. Optimizing nutritional quality of aquafeeds. In: Nates, Sergio F. (eds)Aquafeed formulation. Amsterdam: Elsevier, 2016, pp.239–264.
3.
AlanderJTBochkoVMartinkauppiB, et al.
A review of optical nondestructive visual and near-infrared methods for food quality and safety. Int J Spectrosc. 2013;
2013: 1–36.
4.
CozzolinoDRestainoELa MannaA, et al.
Usefulness of near infrared reflectance (NIR) spectroscopy and chemometrics to discriminate between fishmeal, meat meal and soya meal samples. Cienc Inv Agr. 2009;
36: 209–214.
5.
IsakssonTTøgersenGIversenA, et al.
Non-destructive determination of fat, moisture and protein in salmon fillets by use of near-infrared diffuse spectroscopy. J Sci Food Agric. 1995;
69: 95–100.
6.
OsborneBGFearnTHindlePH.Practical NIR spectroscopy with applications in food and beverage analysis.
Harlow:
Longman Scientific and Technical, 1993, p.227.
7.
de BoeverJLCottynBGVanackerJM, et al.
The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Sci Technol. 1995;
51: 243–253.
8.
SimonCJRodemannTCarterCG.Near-infrared spectroscopy as a novel non-invasive tool to assess spiny lobster nutritional condition. Plos One. 2016;
11: e0159671.
9.
ManeerotJTerdwongworakulATanaphaseW, et al.
Application of near infrared spectroscopy to predict crude protein in shrimp feed. Agric Nat Resour. 2006;
40: 172–180.
10.
ValdesELeesonS.Near infrared reflectance analysis as a method to measure metabolizable energy in complete poultry feeds. Poultry Sci. 1992;
71: 1179–1187.
11.
XiccatoGTrocinoACarazzoloA, et al.
Nutritive evaluation and ingredient prediction of compound feeds for rabbits by near-infrared reflectance spectroscopy (NIRS). Animal Feed Sci Technol. 1999;
77: 201–212.
12.
ZhuFChengSWuD, et al.
Rapid discrimination of fish feeds brands based on visible and short-wave near-infrared spectroscopy. Food Bioprocess Technol. 2011;
4: 597–602.
13.
GlencrossBBourneNIrvinS, et al.
Using near-infrared reflectance spectroscopy to predict the digestible protein and digestible energy values of diets when fed to barramundi, lates calcarifer. Aquac Nutr. 2017;
23: 397–405.
14.
Van BarneveldRNuttallJFlinnP, et al.
Near infrared reflectance measurement of the digestible energy content of cereals for growing pigs. J Near Infrared Spectrosc. 1999;
7: 1–7.
FapojuwoOOMagaJAJansenGR.Effect of extrusion cooking on in vitro protein digestibility of sorghum. J Food Sci. 1987;
52: 218–219.
17.
Pérez-NavarreteCBetancur-AnconaDCasottoM, et al.
Effect of extrusion on protein and starch bioavailability in corn and Lima bean flour blends. Arch Latinoam Nutr. 2007;
57: 278–286.
18.
CamireMCamireAKrumharK.Chemical and nutritional changes in foods during extrusion. Crit Rev Food Sci Nutr. 1990;
29: 35–57.
19.
MuramatsuKMassuquettoADahlkeF, et al.
Factors that affect pellet quality: a review. J Agric Sci Technol. 2015;
5: 717–722.
20.
RomanoNKumarV.Starch gelatinization on the physical characteristics of aquafeeds and subsequent implications to the productivity in farmed aquatic animals. Rev Aquac. 2019;
11: 1271–1284.
21.
HemreGIMommsenTPKrogdahlÅ.Carbohydrates in fish nutrition: effects on growth, glucose metabolism and hepatic enzymes. Aquac Nutr. 2002;
8: 175–194.
22.
KrogdahlÅHemreGIMommsenTP.Carbohydrates in fish nutrition: digestion and absorption in postlarval stages. Aquac Nutr. 2005;
11: 103–122.
23.
OsborneBGDouglasS.Measurement of the degree of starch damage in flour by near infrared reflectance analysis. J Sci Food Agric. 1981;
32: 328–332.
24.
BlažekJJirsaOHruškováM.Prediction of wheat milling characteristics by near-infrared reflectance spectroscopy. Czech J Food Sci. 2011;
23: 145–151.
25.
NzabonimpaR. Determination of the degree of gelatinisation of starch by near infrared spectroscopy, near infrared spectroscopy. In: Proceedings of the 9th international conference, Chichester: NIR Publications 1999, pp.75–80.
26.
TamburiniEMicheleBLuciaS, et al. Real time quality control of dried semolina pasta and egg pasta processing using at-line FT-NIR Spectroscopy. In: Proceedings of the Atti del 7th Simposio Italiano di Spettroscopia NIR, SISNIR: Lodi, Italy, 12–14 October 2016; pp. 11–16, ISBN 9788894115314.
27.
ZardettoS. Measurement of the degree of starch gelatinisation in fresh pasta by near infrared reflectance analysis. Tecnica Molitoria, 2004; 55: 662–668.
28.
ShenkJSWesterhausMO. Calibration the ISI way. In: Near infrared spectroscopy: the future waves 1996, pp.198–202.
29.
WilliamsP.Near infrared technology: getting the best out of light. South Africa: African Sun Media, 2019.
30.
BurnsDACiurczakEW.Handbook of near-infrared analysis. Boca Raton: CRC Press, 2007.
31.
ForteDYoungG.Food and feed extrusion technology: an applied approach to extrusion theory. Australia: Food Industry Engineering, 2016.
32.
BeMillerJN.6 – Starches: molecular and granular structures and properties. In: BeMillerJN (ed.) Carbohydrate chemistry for food scientists. 3rd ed.USA:
AACC International Press, 2019, pp.159–189.
33.
SinghNSinghJKaurL, et al.
Morphological, thermal and rheological properties of starches from different botanical sources. Food Chem. 2003;
81: 219–231.
34.
FredrikssonHSilverioJAnderssonR, et al.
The influence of amylose and amylopectin characteristics on gelatinization and retrogradation properties of different starches. Carbohydr Polym. 1998;
35: 119–134.
35.
LiuW-CHalleyPJGilbertRG.Mechanism of degradation of starch, a highly branched polymer, during extrusion. Macromolecules. 2010;
43: 2855–2864.
36.
CozzolinoDDegnerSEglintonJ.A review on the role of vibrational spectroscopy as an analytical method to measure starch biochemical and biophysical properties in cereals and starchy foods. Foods. 2014;
3: 605–621.
