Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising

Qiang Zhang, Nian Liu, Shuangshuang Wang, Leiqing Pan

Abstract


The objective of this study was to analyze the content of γ-aminobutyric acid (GABA) in germinated brown rice (GBR) by using near-infrared spectroscopy (NIRS) and the pretreatment method of wavelet de-noising (WD). The prediction accuracy of the NIRS model established by the Daubechies5 wavelet basis function at 3 level denoising treatment is the highest, the correlation coefficient for calibration (rc) is 0.931, the root mean square error of calibration (RMSEC) is 0.4038 mg/100 g, the Bias of calibration is 0.006, the correlation coefficient for prediction (rp) is 0.916, the root mean square error of prediction (RMSEP) is 0.4329 mg/100 g, the Bias of prediction is 0.010, and the ratio of performance to deviation (RPD) is 4.911. Results showed that the predicted and actual values had high correlation. Therefore, these results indicate that the NIRS model treated by WD is feasible to detect GABA content in GBR rapidly and nondestructively.
Keywords: near-infrared spectroscopy, wavelet transformation, germinated brown rice, γ-aminobutyric acid, quantitative analysis
DOI: 10.25165/j.ijabe.20211403.6178

Citation: Zhang Q, Liu N, Wang S S, Pan L Q. Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising. Int J Agric & Biol Eng, 2021; 14(3): 200–206.

Keywords


near-infrared spectroscopy, wavelet transformation, germinated brown rice, γ-aminobutyric acid, quantitative analysis

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References


Cáceres P J, Peñas E, Martinez-Villaluenga C, Amigo L, Frias J. Enhancement of biologically active compounds in germinated brown rice and the effect of sun-drying. J Cereal Sci, 2017; 73: 1–9.

Zhang Q, Liu N, Wang S S, Pan L Q. Effects of germination and aeration treatment following segmented moisture conditioning on the γ-aminobutyric acid accumulation in germinated brown rice. Int J Agr Biol Eng, 2020; 13(5): 234–240.

Zhang Q, Liu N, Wang S S, Liu Y, Lan H P. Effects of cyclic cellulase conditioning and germination treatment on the γ-aminobutyric acid content and the cooking and taste qualities of germinated brown rice. Food Chem, 2019; 289: 232–239.

Li Y, Liu K L, Chen F S, Cheng Y Q. Comparative proteomics analysis reveals the effect of germination and selenium enrichment on the quality of brown rice during storage. Food Chem, 2018; 269: 220–227.

Patil S B, Khan M K. Germinated brown rice as a value added rice product: A review. J Food Sci Technol, 2011; 48(6): 661–667.

Cho D H, Lim S T. Changes in phenolic acid composition and associated enzyme activity in shoot and kernel fractions of brown rice during germination. Food Chem, 2018; 256: 163–170.

Yodpitak S, Mahatheeranont S, Boonyawan D, Sookwong P, Roytrakul S, Norkaew O. Cold plasma treatment to improve germination and enhance the bioactive phytochemical content of germinated brown rice. Food Chem, 2019; 289: 328–339.

Imam M U, Azmi N H, Bhanger M I, Ismail N, Ismail M. Antidiabetic properties of germinated brown rice: A systematic review. Evid-Based Compl Alt, 2012; 2012(3): 1–12.

Bown A W, Shelp B J. The metabolism and functions of γ-aminobutyric acid. Plant Physiol, 1997; 115(1): 1–5.

Thompson J F, Morris C J. Determination of amino acids from plants by paper chromatography. Anal Chem, 1959; 31(6): 1031–1037.

Zhang G J, Brown A W. The rapid determination of γ-aminobutyric acid. Phytochemistry, 1997; 44(6): 1007–1009.

Varanyanond W, Tungtrakul P, Surojanametakul V, Watanasiritham L, Luxiang W. Effects of water soaking on gamma-aminobutyric acid (GABA) in germ of different Thai rice varieties. Kasetsart J-Nat Sci, 2005; 39: 411–415.

Zhang Q, Xiang J, Zhang L Z, Zhu X F, Evers J, Werf W V D, et al. Optimizing soaking and germination conditions to improve gamma-aminobutyric acid content in japonica and indica germinated brown rice. J Funct Foods, 2014; 10: 283–291.

Komatsuzaki N, Tsukahara K, Toyoshima H, Suzuki T, Shimizu N, Kimura T. Effect of soaking and gaseous treatment on GABA content in germinated brown rice. J Food Eng, 2007; 78(2): 556–560.

Wu C L, Huang Y H, Lai X F, Lai R H, Zhao W X, Zhang M, et al. Study on quality components and sleep-promoting effect of GABA Maoyecha tea. J Funct Foods, 2014; 7: 180–190.

Zhang Q, Jia F G, Zuo Y J, Fu Q, Wang J T. Optimization of cellulase conditioning parameters of germinated brown rice on quality characteristics. J Food Sci Technol, 2015; 52(1): 465–471.

Zhang Q, Jia F G, Liu C H, Sun J K, Zheng X Z. Rapid detection of aflatoxin B1 in paddy rice as analytical quality assessment by near infrared spectroscopy. Int J Agr Biol Eng, 2014; 7(4): 127–133.

Moscetti R, Haff R P, Monarca D, Cecchini M, Massantini R. Near-infrared spectroscopy for detection of hailstorm damage on olive fruit. Postharvest Biol Technol, 2016; 20: 204–212.

Siriphollakul P, Nakano K, Kanlayanarat S, Ohashi S, Sakai R, Rittiron R, et al. Eating quality evaluation of Khao Dawk Mali 105 rice using near-infrared spectroscopy. LWT-Food Sci Technol, 2017; 79: 70–77.

Zhang J, Li M L, Pan T, Yao L J, Chen J M. Purity analysis of multi-grain rice seeds with non-destructive visible and near-infrared spectroscopy. Comput Electron Agr, 2019; 164. doi: 10.1016/ j.compag.2019.104882.

Xie L H, Tang S Q, Chen N, Luo J, Jiao G A, Shao G N, et al. Optimisation of near-infrared reflectance model in measuring protein and amylose content of rice flour. Food Chem, 2014; 142: 92–100.

Heman A, Hsieh C L. Measurement of moisture content for rough rice by visible and near-infrared (NIR) spectroscopy. Eng Agr Environ Food, 2016; 9(3): 80–290.

Guo Y, Ni Y N, Kokot S. Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. Spectrochim Acta Part A, 2016; 153: 79–86.

Maniwara P, Nakano K, Boonyakiat D, Ohashi S, Hiroi M, Tohyama T. The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality. J Food Eng, 2014; 143: 33–43.

Ning Y, Zhang H M, Zhang Q, Zhang X R. Rapid identification and quantitative pit mud by near infrared Spectroscopy with chemometrics. Vib Spectrosc, 2020; 110. doi: 10.1016/j.vibspec.2020.103116.

Ying Y B, Liu Y D, Fu X P. Sugar content prediction of apple using near-infrared spectroscopy treated by wavelet transform. Spectrosc Spect Anal, 2006; 26(1): 63–66. (in Chinese)

Chalus P, Walter S, Ulmschneider M. Combined wavelet transform–artificial neural network use in tablet active content determination by near-infrared spectroscopy. Anal Chim Acta, 2007; 591(2): 219–224.

Liang L W, Wang B, Guo Y, Ni H, Ren Y L. A support vector machine-based analysis method with wavelet denoised near-infrared spectroscopy. Vib Spectrosc, 2009; 49(2): 274–277.

Singh C B, Choudhary R, Jayas D S, Paliwal J. Wavelet analysis of signals in agriculture and food quality inspection. Food Bioprocess Technol, 2010; 3: 2–12.

Zhang Q, Liu N, Tu M, Wang S S. Effect of conditioning treatment parameters of cellulases solution on milling characteristics of brown rice. Can Biosyst Eng, 2018; 60: 31–39.

Le B T. Application of deep learning and near infrared spectroscopy in cereal analysis. Vib Spectrosc, 2020; 106. doi: 10.1016/ j.vibspec.2019.103009.

Plumier B M, Danao M G, Singh V, Rausch K. Analysis and prediction of unreacted starch content in corn using FT-NIR spectroscopy. Transactions of the ASABE, 2013; 56(5): 1877–1884.

Hao Y, Chen B, Zhu R. Analysis of several methods for wavelet denoising used in near infrared spectrum pretreatment. Spectrosc Spect Anal, 2006; 26(10): 1838–1841. (in Chinese)

Luo S, Li Z Y, Zhang M, Chen G Q. Detection and analysis of alcohol near-infrared spectrum in vitro and vivo based on wavelet transform. Spectrosc Spect Anal, 2012; 32(6): 1541–1546. ( in Chinese)

Kicey C J, Lennard C J. Unique reconstruction of band-limited signals by a Mallat-Zhong wavelet transform algorithm. J Fourier Anal Appl, 1997; 3(1): 63–82.

Albanell E, Martínez M, De Marchi M, Manuelian C L. Prediction of bioactive compounds in barley by near-infrared reflectance spectroscopy (NIRS). J Food Compos Ana, 2021; 97(3). doi: 10.1016/ j.jfca.2020.103763.

Onmankhong J, Sirisomboon P. Texture evaluation of cooked parboiled rice using nondestructive milled whole grain near infrared spectroscopy. J Cereal Sci, 2020. doi: 10.1016/j.jcs.2020.103151.

Jiang H, Liu T, Chen Q S. Dynamicmonitoring of fatty acid value in rice storage based on a portable near-infrared spectroscopy system. Spectrochim Acta Part A, 2020; 240. doi: 10.1016/j.saa.2020.118620.




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