Application of NIR transmission spectroscopy with effective wavelength selection in non-destructive determination of essential amino acid content of foxtail millet
Main Article Content
Keywords
near infrared transmission spectroscopy, successive projections algorithm, interval partial least squares, jack-knife algorithm
Abstract
The near infrared (NIR) spectroscopy models developed for rapid and accurate determination of nine essential amino acids and protein in foxtail millet were investigated. The effects of the status of the samples (intact or ground), amino acid/protein correlation and variable selection methods on the predictive ability of the models were analysed. According to results, although the average spectral patterns of the intact and ground samples were similar, the absolute values of peaks and valleys for the intact samples were slightly higher. The modelling results of intact samples were similar to those obtained with ground samples. In the results of the partial least squares models with full spectra, 81-94% of the amino acid variance could be explained, except for lysine, tryptophan and methionine with the coefficients of determination of cross-validation less than 0.70. The correlation between various amino acids and protein might affect the predictive ability of NIR spectroscopy for essential amino acids. After variable selection, the models for isoleucine, leucine, phenylalanine, threonine, tryptophan and valine were remarkably improved by the successive projections algorithm method. The results indicated that short-wave NIR spectroscopy coupled with variable selection is a promising approach for determination of essential amino acids in foxtail millet.
References
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Anderssen, E., Dyrstad, K., Westad, F. and Martens, H., 2006. Reducing over-optimism in variable selection by cross-model validation. Chemometrics and Intelligent Laboratory Systems 84: 69-74.
Araújo, M.C.U., Saldanha, T.C.B., Galvão, R.K.H., Yoneyama, T., Chame, H.C. and Visani, V., 2001. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems 57: 65-73.
Association of Analytical Communities International (AOAC), 2016. Official methods of analysis, 20th edition. AOAC International, St Paul, MN, USA.
Burns, D.A. and Ciurczak, E.W., 2007. Handbook of near-infrared analysis, 35. CRC press, Boca Raton, FL, USA.
Cao, N., 2013. Calibration optimization and efficiency in near infrared spectroscopy. Graduate Theses and Dissertations Thesis, Iowa State University, Ames, IA, USA, 184 pp.
Chen, G.L., Zhang, B., Wu, J.G. and Shi, C.H., 2011. Nondestructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy. Animal Feed Science and Technology 165: 111-119.
Chen, J., Ren, X., Zhang, Q., Diao, X. and Shen, Q., 2013. Determination of protein, total carbohydrates and crude fat contents of foxtail millet using effective wavelengths in NIR spectroscopy. Journal of Cereal Science 58: 241-247.
De Oliveira, G.A., Bureau, S., Renard, C., Pereira-Netto, A.B. and De Castilhos, F., 2014. Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chemistry 143: 223-230.
Faber, N.K.M., 1999. Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemometrics and Intelligent Laboratory Systems 49: 79-89.
Fontaine, J., Horr, J. and Schirmer, B., 2001. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. Journal of Agricultural and Food Chemistry 49: 57-66.
Fontaine, J., Schirmer, B. and Horr, J., 2002. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn, triticale, wheat bran/middlings, rice bran, and sorghum. Journal of Agricultural and Food Chemistry 50: 3902-3911.
Galvão, R.K.H., Araujo, M.C.U., José, G.E., Pontes, M.J.C., Silva, E.C. and Saldanha, T.C.B., 2005. A method for calibration and validation subset partitioning. Talanta 67: 736-740.
Gu, S. and Liu, S., 1989. Study on protein content and amino acid composition of Chinese foxtail millet. Acta Agriculturae Boreali-Sinica 4: 8-15.
Guy, F., Prache, S., Thomas, A., Bauchart, D. and Andueza, D., 2011. Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). Food Chemistry 127: 1280-1286.
Huang, L., Wu, D., Jin, H., Zhang, J., He, Y. and Lou, C., 2011. Internal quality determination of fruit with bumpy surface using visible and near infrared spectroscopy and chemometrics: a case study with mulberry fruit. Biosystems Engineering 109: 377-384.
Huang, Z., Sha, S., Rong, Z., Chen, J., He, Q., Khan, D.M. and Zhu, S., 2013. Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed. Industrial Crops and Products 43: 654-660.
International Association for Cereal Science and Technology (ICC), 1986. ICC Standard Methods 202. Procedure for near infrared (NIR) reflectance analysis of ground wheat and milled wheat products. Available at: https://www.icc.or.at/standard_methods
International Association for Cereal Science and Technology (ICC), 1995. ICC Standard Methods 159. Determination of protein by near infrared reflectance (NIR) Spectroscopy. Available at: https://www.icc.or.at/standard_methods
Isaksson, T. and Næs, T., 1988. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy 42: 1273-1284.
Jolliffe, I.T., 2002. Graphical representation of data using principal components, principal component analysis. Springer, New York, NY, USA, pp. 78-110.
Kasaoka, S., Oh-hashi, A., Morita, T. and Kiriyama, S., 1999. Nutritional characterization of millet protein concentrates produced by a heat-stable ?-amylase digestion. Nutrition Research 19: 899-910.
Kovalenko, I.V., Rippke, G.R. and Hurburgh, C.R., 2006. Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 54: 3485-3491.
Li, Y. and Wu, S., 1996. Traditional maintenance and multiplication of foxtail millet (Setariaitalica (L.) P. Beauv.) landraces in China. Euphytica 87: 33-38.
Liu, F., Jin, Z.L., Naeem, M.S., Tian, T., Zhang, F., He, Y., Fang, H., Ye, Q.F. and Zhou, W.J., 2010. Applying near-infrared spectroscopy and chemometrics to determine total amino acids in herbicide-stressed oilseed rape leaves. Food and Bioprocess Technology 4: 1314-1321.
Lu, W.Z., Yuan, H.F., Xu, G.T. and Qiang, D.M., 2000. The technology of modern near infrared spectral analysis. China Petrochemical Press, Beijing, China P.R.
Matsumoto, K., Koba, T., Hamada, K., Sakurai, M., Higuchi, T. and Miyata, H., 2009. Branched-chain amino acid supplementation attenuates muscle soreness, muscle damage and inflammation during an intensive training program. Journal of Sports Medicine and Physical Fitness 49: 424-431.
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J.P., Munck, L. and Engelsen, S., 2000. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 54: 413-419.
Pazdernik, D.L., Killam, A.S. and Orf, J.H., 1997. Analysis of amino and fatty acid composition in soybean seed, using near infrared reflectance spectroscopy. Agronomy Journal 89: 679-685.
Pojic, M., Mastilovic, J. and Majcen, N., 2012. Robustness of the near infrared spectroscopy method determined using univariate and multivariate approach. Food Chemistry 134: 1699-1705.
Rinnan, Å., Berg, F.v.d. and Engelsen, S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28: 1201-122Rinnan, Å., Berg, F.v.d. and Engelsen, S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28: 1201-1222.
Wang, L., Wang, Q., Liu, H., Liu, L. and Du, Y., 2012. Determining the contents of protein and amino acids in peanuts using near-infrared reflectance spectroscopy. Journal of the Science of Food and Agriculture 93: 118-124.
Wei, X., Xu, N., Wu, D. and He, Y., 2014. Determination of branched-amino acid content in fermented Cordyceps sinensis mycelium by using FT-NIR spectroscopy technique. Food and Bioprocess Technology 7: 184-190.
Williams, P.C. and Sobering, D.C., 1996. How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In: Davies, A.M.C., Williams, P.C. (eds.) Infrared Spectroscopy: The future waves. NIR Publications, Chichester, UK, pp. 185-188.
Wu, J.G.G., Shi, C.H. and Zhang, X.M., 2002. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy. Field Crops Research 75: 1-7.
Yang, X.S., Wang, L.L., Zhou, X.R., Shuang, S.M., Zhu, Z.H., Li, N., Li, Y., Liu, F., Liu, S.C., Lu, P., Ren, G.X. and Dong, C., 2013. Determination of protein, fat, starch, and amino acids in foxtail millet Setaria italica (L.) Beauv. by Fourier transform near-infrared reflectance spectroscopy. Food Science and Biotechnology 22: 1495-1500.
Yoshiji, H., Noguchi, R., Ikenaka, Y., Kaji, K., Aihara, Y., Douhara, A., Yamao, J., Toyohara, M., Mitoro, A. and Sawai, M., 2012. Combination of branched-chain amino acid and angiotensin-converting enzyme inhibitor improves liver fibrosis progression in patients with cirrhosis. Molecular Medicine Reports 5: 539.
Zhang, B., Rong, Z.Q., Shi, Y., Wu, J.G. and Shi, C.H., 2011. Prediction of the amino acid composition in brown rice using different sample status by near-infrared reflectance spectroscopy. Food Chemistry 127: 275-281.
Zhang, M.H., Luypaert, J., Fernández Pierna, J.A., Xu, Q.S. and Massart, D.L., 2004. Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration. Talanta 62: 25-35.
Zhou, L.J., Zhang, L.Y., Zhang, E.X., Li, J.T., Yang, W.J. and Wang, Z.Y., 2012. Rapid determination of swine available energy and amino acids in corn distillers dried grains with solubles by near-infrared reflectance spectroscopy. Animal Feed Science and Technology 175: 198-202.
Zou, X.B., Zhao, J.W., Povey, M.J.W., Holmes, M. and Mao, H., 2010. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta 667: 14-32.
American Association of Cereal Chemists International (AACCI), 2011b. AACCI Method 08-21.01. Prediction of ash content in wheat flour – near-infrared method. Approved Methods of the American Association of Cereal Chemists, 11th edition. AACC, St Paul, MN, USA. Available at:http://methods.aaccnet.org/summaries/08-21-01.aspx
Anderssen, E., Dyrstad, K., Westad, F. and Martens, H., 2006. Reducing over-optimism in variable selection by cross-model validation. Chemometrics and Intelligent Laboratory Systems 84: 69-74.
Araújo, M.C.U., Saldanha, T.C.B., Galvão, R.K.H., Yoneyama, T., Chame, H.C. and Visani, V., 2001. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems 57: 65-73.
Association of Analytical Communities International (AOAC), 2016. Official methods of analysis, 20th edition. AOAC International, St Paul, MN, USA.
Burns, D.A. and Ciurczak, E.W., 2007. Handbook of near-infrared analysis, 35. CRC press, Boca Raton, FL, USA.
Cao, N., 2013. Calibration optimization and efficiency in near infrared spectroscopy. Graduate Theses and Dissertations Thesis, Iowa State University, Ames, IA, USA, 184 pp.
Chen, G.L., Zhang, B., Wu, J.G. and Shi, C.H., 2011. Nondestructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy. Animal Feed Science and Technology 165: 111-119.
Chen, J., Ren, X., Zhang, Q., Diao, X. and Shen, Q., 2013. Determination of protein, total carbohydrates and crude fat contents of foxtail millet using effective wavelengths in NIR spectroscopy. Journal of Cereal Science 58: 241-247.
De Oliveira, G.A., Bureau, S., Renard, C., Pereira-Netto, A.B. and De Castilhos, F., 2014. Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chemistry 143: 223-230.
Faber, N.K.M., 1999. Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemometrics and Intelligent Laboratory Systems 49: 79-89.
Fontaine, J., Horr, J. and Schirmer, B., 2001. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. Journal of Agricultural and Food Chemistry 49: 57-66.
Fontaine, J., Schirmer, B. and Horr, J., 2002. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn, triticale, wheat bran/middlings, rice bran, and sorghum. Journal of Agricultural and Food Chemistry 50: 3902-3911.
Galvão, R.K.H., Araujo, M.C.U., José, G.E., Pontes, M.J.C., Silva, E.C. and Saldanha, T.C.B., 2005. A method for calibration and validation subset partitioning. Talanta 67: 736-740.
Gu, S. and Liu, S., 1989. Study on protein content and amino acid composition of Chinese foxtail millet. Acta Agriculturae Boreali-Sinica 4: 8-15.
Guy, F., Prache, S., Thomas, A., Bauchart, D. and Andueza, D., 2011. Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). Food Chemistry 127: 1280-1286.
Huang, L., Wu, D., Jin, H., Zhang, J., He, Y. and Lou, C., 2011. Internal quality determination of fruit with bumpy surface using visible and near infrared spectroscopy and chemometrics: a case study with mulberry fruit. Biosystems Engineering 109: 377-384.
Huang, Z., Sha, S., Rong, Z., Chen, J., He, Q., Khan, D.M. and Zhu, S., 2013. Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed. Industrial Crops and Products 43: 654-660.
International Association for Cereal Science and Technology (ICC), 1986. ICC Standard Methods 202. Procedure for near infrared (NIR) reflectance analysis of ground wheat and milled wheat products. Available at: https://www.icc.or.at/standard_methods
International Association for Cereal Science and Technology (ICC), 1995. ICC Standard Methods 159. Determination of protein by near infrared reflectance (NIR) Spectroscopy. Available at: https://www.icc.or.at/standard_methods
Isaksson, T. and Næs, T., 1988. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy 42: 1273-1284.
Jolliffe, I.T., 2002. Graphical representation of data using principal components, principal component analysis. Springer, New York, NY, USA, pp. 78-110.
Kasaoka, S., Oh-hashi, A., Morita, T. and Kiriyama, S., 1999. Nutritional characterization of millet protein concentrates produced by a heat-stable ?-amylase digestion. Nutrition Research 19: 899-910.
Kovalenko, I.V., Rippke, G.R. and Hurburgh, C.R., 2006. Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 54: 3485-3491.
Li, Y. and Wu, S., 1996. Traditional maintenance and multiplication of foxtail millet (Setariaitalica (L.) P. Beauv.) landraces in China. Euphytica 87: 33-38.
Liu, F., Jin, Z.L., Naeem, M.S., Tian, T., Zhang, F., He, Y., Fang, H., Ye, Q.F. and Zhou, W.J., 2010. Applying near-infrared spectroscopy and chemometrics to determine total amino acids in herbicide-stressed oilseed rape leaves. Food and Bioprocess Technology 4: 1314-1321.
Lu, W.Z., Yuan, H.F., Xu, G.T. and Qiang, D.M., 2000. The technology of modern near infrared spectral analysis. China Petrochemical Press, Beijing, China P.R.
Matsumoto, K., Koba, T., Hamada, K., Sakurai, M., Higuchi, T. and Miyata, H., 2009. Branched-chain amino acid supplementation attenuates muscle soreness, muscle damage and inflammation during an intensive training program. Journal of Sports Medicine and Physical Fitness 49: 424-431.
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J.P., Munck, L. and Engelsen, S., 2000. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 54: 413-419.
Pazdernik, D.L., Killam, A.S. and Orf, J.H., 1997. Analysis of amino and fatty acid composition in soybean seed, using near infrared reflectance spectroscopy. Agronomy Journal 89: 679-685.
Pojic, M., Mastilovic, J. and Majcen, N., 2012. Robustness of the near infrared spectroscopy method determined using univariate and multivariate approach. Food Chemistry 134: 1699-1705.
Rinnan, Å., Berg, F.v.d. and Engelsen, S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28: 1201-122Rinnan, Å., Berg, F.v.d. and Engelsen, S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28: 1201-1222.
Wang, L., Wang, Q., Liu, H., Liu, L. and Du, Y., 2012. Determining the contents of protein and amino acids in peanuts using near-infrared reflectance spectroscopy. Journal of the Science of Food and Agriculture 93: 118-124.
Wei, X., Xu, N., Wu, D. and He, Y., 2014. Determination of branched-amino acid content in fermented Cordyceps sinensis mycelium by using FT-NIR spectroscopy technique. Food and Bioprocess Technology 7: 184-190.
Williams, P.C. and Sobering, D.C., 1996. How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In: Davies, A.M.C., Williams, P.C. (eds.) Infrared Spectroscopy: The future waves. NIR Publications, Chichester, UK, pp. 185-188.
Wu, J.G.G., Shi, C.H. and Zhang, X.M., 2002. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy. Field Crops Research 75: 1-7.
Yang, X.S., Wang, L.L., Zhou, X.R., Shuang, S.M., Zhu, Z.H., Li, N., Li, Y., Liu, F., Liu, S.C., Lu, P., Ren, G.X. and Dong, C., 2013. Determination of protein, fat, starch, and amino acids in foxtail millet Setaria italica (L.) Beauv. by Fourier transform near-infrared reflectance spectroscopy. Food Science and Biotechnology 22: 1495-1500.
Yoshiji, H., Noguchi, R., Ikenaka, Y., Kaji, K., Aihara, Y., Douhara, A., Yamao, J., Toyohara, M., Mitoro, A. and Sawai, M., 2012. Combination of branched-chain amino acid and angiotensin-converting enzyme inhibitor improves liver fibrosis progression in patients with cirrhosis. Molecular Medicine Reports 5: 539.
Zhang, B., Rong, Z.Q., Shi, Y., Wu, J.G. and Shi, C.H., 2011. Prediction of the amino acid composition in brown rice using different sample status by near-infrared reflectance spectroscopy. Food Chemistry 127: 275-281.
Zhang, M.H., Luypaert, J., Fernández Pierna, J.A., Xu, Q.S. and Massart, D.L., 2004. Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration. Talanta 62: 25-35.
Zhou, L.J., Zhang, L.Y., Zhang, E.X., Li, J.T., Yang, W.J. and Wang, Z.Y., 2012. Rapid determination of swine available energy and amino acids in corn distillers dried grains with solubles by near-infrared reflectance spectroscopy. Animal Feed Science and Technology 175: 198-202.
Zou, X.B., Zhao, J.W., Povey, M.J.W., Holmes, M. and Mao, H., 2010. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta 667: 14-32.
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Jan 11, 2017
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