ORIGINAL PAPER
Improved by potassium chloride (KCl) dilution predictive
ability of attenuated total reflectance Fourier transform
infrared (ATR-FTIR) spectroscopy to determine nutrient contents
of sunflower meal
1
Igdir University, Faculty of Agriculture, Department of Animal Science, 76000, Iğdır, Turkey
2
IgdirUniversity, Rectorate, 76000, Iğdır, Turkey
3
Kahramanmaras Sutcu Imam University, Faculty of Forestry, Department of Forest Industrial Engineering, 46100, Kahramanmaras, Turkey
Publication date: 2019-09-23
Corresponding author
R. Tosun
Igdir University, Faculty of Agriculture, Department of Animal Science, 76000, Iğdır, Turkey
Igdir University, Faculty of Agriculture, Department of Animal Science, 76000, Iğdır, Turkey
J. Anim. Feed Sci. 2019;28(3):282-290
KEYWORDS
TOPICS
animal breedingdairy productsfeed additivesfeed materialsfeed processingmathematic modelsnutritionpoultry
ABSTRACT
Predictive ability of attenuated total reflectance (ATR) Fourier
transform (FT) infrared (IR) spectroscopy to determine nutrient contents of
sunflower meal (SFM) high in fibre and ash was investigated by testing the
effect of potassium chloride (KCl) spiking. The partial least square regression
(PLSR) models were generated using original IR spectrum, and its first and
second derivate data followed by the normalisation, smoothing and multiplicative
scatter correction in order to predict nutrient contents of spiked and non-spiked
SFM samples. The results showed that the best model for the prediction was the
one derived from the second derivate IR data with high degree of precision and
accuracy (R2 = 0.99, r2 = 0.95, residual mean square error of cross-validation
(RMSECV) = 0.66 for dry matter and 1.30 for crude ash). Furthermore, the
precision and accuracy of all three models were improved by the KCl levels spiked
at 27.5 and 57.0%, while no effects of further spiking with KCl was observed.
In conclusion, the model generated from the second derivate IR data was highly
recommended to predict the SFM nutrient contents, and the KCl dilution up to 57%
improved the prediction accuracy and precision irrespective to the models used.
REFERENCES (31)
1.
AOAC, 1990. Official Methods of Analysis of the Association of Official Analytical Chemists. 15th Edition. Arlington, VA (USA).
2.
Bjørsvik H.-R., Martens H., 2007. Data analysis: calibration of NIR instruments by PLS regression. In: D.A. Burns, E.W. Ciurczak (Editors). Handbook of Near-Infrared Analysis. 3rd Edition. CRC Press, Taylor & Francis Group, Boca Raton, FL (USA), pp.189–206.
3.
Hell J., Prückler M., Danner L., Henniges U., Apprich S., Rosenau T., Kneifel W., Böhmdorfer S., 2016. A comparison between nearinfrared (NIR) and mid-infrared (ATR-FTIR) spectroscopy for the multivariate determination of compositional properties in wheat bran samples. Food Control 60, 365–369, https://doi.org/10.1016/j.food....
4.
Hódsági M., Gergely S., Gelencsér T., Salgó A., 2012. Investigations of native and resistant starches and their mixtures using nearinfrared spectroscopy. Food Bioprocess Technol. 5, 401–407, https://doi.org/10.1007/s11947....
5.
Isaksson T., Næs T., 1988. The Effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl. Spectrosc. 42, pp. 1273-1284, https://doi.org/10.1366/000370....
6.
Jensen A., Dam-Johansen K., Wójtowicz M.A., Serio M.A., 1998. TG-FTIR study of the influence of potassium chloride on wheat straw pyrolysis. Energy Fuels 12, 929–938, https://doi.org/10.1021/ef9800....
7.
Li J., Danao M.-G.C., Chen S.-F., Li S., Singh V., Brown P.J., 2015. Prediction of starch content and ethanol yields of sorghum grain using near infrared spectroscopy. J. Near Infrared Spectrosc. 23, 85–92, https://doi.org/10.1255/jnirs.....
8.
Lindberg J.J., Kaila T., 1980. NIR and spin probe studies on amylose and amylopectin. Acta Chem. Scand. B 34, 757–761, https://doi.org/10.3891/acta.c....
9.
Mahesar S.A., Sherazi S.T.H., Kandhro A.A., Bhanger M.I., Khaskheli A.R., Talpur M.Y., 2011. Evaluation of important fatty acid ratios in poultry feed lipids by ATR FTIR spectroscopy. Vib. Spectrosc. 57, 177–181, https://doi.org/10.1016/j.vibs....
10.
Morrey J.R., 1968. On determining spectral peak positions from composite spectra with a digital computer. Anal. Chem. 40, 905–914, https://doi.org/10.1021/ac6026....
11.
Perten, 2019. Perten Instruments Application Note. Analysis of Sunflower Meal Using the DA 7250 Analyzer. Perten, Hägersten (Sweden), https://www.perten.com/Global/....
12.
Pizzo B., Pecoraro E., Alves A., Macchioni N., Rodriguez J.C., 2015. Quantitative evaluation by attenuated total reflectance infrared (ATR-FTIR) spectroscopy of the chemical composition of decayed wood preserved in waterlogged conditions. Talanta 131, 14–20, https://doi.org/10.1016/j.tala....
13.
Ravindran V., Blair R., 1992. Feed resources for poultry production in Asia and the Pacific. II. Plant protein sources. Worlds Poult. Sci. J. 48, 205–231, https://doi.org/10.1079/WPS199....
14.
Ribeiro L.F., Peralta-Zamora P.G., Maia B.H.L.N.S., Ramos L.P., Pereira-Netto A.B., 2013. Prediction of linolenic and linoleic fatty acids content in flaxseeds and flaxseeds flours through the use of infrared reflectance spectroscopy and multivariate calibration. Food Res. Int. 51, 848–854, https://doi.org/10.1016/j.food....
15.
Samadi, Theodoridou K., Yu P., 2013. Detect the sensitivity and response of protein molecular structure of whole canola seed (yellow and brown) to different heat processing methods and relation to protein utilization and availability using ATR-FT/IR molecular spectroscopy with chemometrics. Spectrochim. Acta A Mol. Biomol. Spectrosc. 105, 304–313, https://doi.org/10.1016/j.saa.....
16.
Senkoylu N., Dale N., 1999. Sunflower meal in poultry diets: a review. Worlds Poult. Sci. J. 55, 153–174, https://doi.org/10.1079/WPS199....
17.
Shiroma C., Rodriguez-Saona L., 2009. Application of NIR and MIR spectroscopy in quality control of potato chips. J. Food Comp. Anal. 22, 596–605, https://doi.org/10.1016/j.jfca....
18.
Sivam A.S., Sun-Waterhouse D., Perera C.O., Waterhouse G.I.N., 2013. Application of FT-IR and Raman spectroscopy for the study of biopolymers in breads fortified with fibre and polyphenols. Food. Res. Int. 50, 574–585, https://doi.org/10.1016/j.food....
19.
Steiner J.,Termonia Y., Deltour J., 1972. Smoothing and differentiation of data by simplified least square procedure. Anal. Chem. 44, 1906–1909, https://doi.org/10.1021/ac6031....
20.
Ścibisz I., Reich M., Bureau S., Gouble B., Causse M., Bertrand D., Renard C.M.G.C., 2011. Mid-infrared spectroscopy as a tool for rapid determination of internal quality parameters in tomato. Food Chem. 125, 1390–1397, https://doi.org/10.1016/j.food....
21.
Theodoridou K., Yu P., 2013. Application potential of ATR-FT/IR molecular spectroscopy in animal nutrition: revelation of protein molecular structures of canola meal and presscake, as affected by heat-processing methods, in relationship with their protein digestive behavior and utilization for dairy cattle. J. Agric. Food Chem. 61, 5449–5458, https://doi.org/10.1021/jf4003....
22.
Torres Mariani N.C., de Almeida Teixeira G.H., Gomes de Lima K.M., Morgenstern T.B., Nardini V., Cunha Jr L.C., 2015. NIRS and iSPA-PLS for predicting total anthocyanin content in jaboticaba fruit. Food Chem. 174, 643–648, https://doi.org/10.1016/j.food....
23.
Whitaker S., Pigford R.L., 1960. An approach to numerical differentiation of experimental data. Ind. Eng. Chem. 52, 185–187, https://doi.org/10.1021/ie5060....
24.
Williams P.C., Sobering D.C., 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Sceptosc. 1, 25–32, https://doi.org/10.1255/jnirs.....
25.
Wilson R.H., Belton P.S., 1988. A Fourier-transform infrared study of wheat starch gels. Carbohydr. Res.180, 339–344, https://doi.org/10.1016/0008-6....
26.
Windig W., Shaver J., Bro R., 2008. Loopy MSC: a simple way to improve multiplicative scatter correction. Appl. Spectrosc. 62, 1153–1159, https://doi.org/10.1366/000370....
27.
Wolkers W.F., Bochicchio A., Selvaggi G., Hoekstra F.A., 1998. Fourier transform infrared microspectroscopy detects changes in protein secondary structure associated with desiccation tolerance in developing maize embryos. Plant Physiol. 116, 1169–1177, https://doi.org/10.1104/pp.116....
28.
Xu N., Liu J., Yu P., 2018. Using vibrational molecular spectroscopy to reveal association of steam-flaking induced carbohydrates molecular structural changes with grain fractionation, biodigestion and biodegradation. Spectrochim. Acta A Mol. Biomol. Spectrosc. 194, 181–188, https://doi.org/10.1016/j.saa.....
29.
Yang H., Irudayaraj J., Paradkar M.M., 2005. Discriminant analysis of edible oils and fats by FTIR, FT-NIR and FT-Raman spectroscopy. Food Chem. 93, 25–32, https://doi.org/10.1016/j.food....
30.
Yang J., Yen H.E., 2002. Early salt stress effects on the changes in chemical composition in leaves of iceplant and Arabidopsis. A Fourier transform infrared spectroscopy study. Plant Physiol. 130, 1032–1042, https://doi.org/10.1104/pp.004....
31.
Zhang H., Ma J., Miao Y., Tuchiya T., Chen J.Y., 2015. Analysis of carbonyl value of frying oil by Fourier transform infrared spectroscopy. J. Oleo Sci. 64, 375–380, https://doi.org/10.5650/jos.es....
CITATIONS (1):
1.
Infrared Spectroscopic Determination of Acetamiprid Pesticide in Apple
Sulhattin Yasar, Nizam Nizamlioglu, Arslan Sen, Mehmet Gucus
Communications in Soil Science and Plant Analysis
Sulhattin Yasar, Nizam Nizamlioglu, Arslan Sen, Mehmet Gucus
Communications in Soil Science and Plant Analysis
Share
RELATED ARTICLE
| ISSN: | 1230-1388 |
Assigning DOI numbers, introducing articles from supplements and recent publications to POL-index database, maintaining anti-plagiarism detection, electronic system to proceed manuscripts and webpage of the Journal with interactive pdf files, and language correction of manuscripts published in Journal of Animal and Feed Sciences are financed in the years 2018–2019 by the Ministry of Science and Higher Education from the funds for science popularization activities, Agreement No. 631/P-DUN/2018.
© 2006-2024 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
