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Establishment of a near-infrared reflectance spectroscopy model for predicting β-glucan content in naked barley grain

Li Ying,Shi Xiao-Xu,Liu Hai-Cui,Shi Lyu,Xue Ya-Guang,Wei Ya-Feng*   

  1. Jiangsu Yanjiang Area Institute of Agricultural Sciences / Key Laboratory of Recycling Agriculture of Nantong City, Nantong 226001, Jiangsu, China
  • Received:2025-08-06 Revised:2025-11-18 Accepted:2025-11-18 Published:2025-11-20
  • Contact: 魏亚凤, E-mail: w-yafeng@163.com E-mail:1185077523@qq.com
  • Supported by:
    This study was supported by the Jiangsu Agricultural Science and Technology Innovation Fund (CX (24) 3089).

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Abstract:

β-glucan, known for its roles in regulating blood sugar, lowering blood lipids, and exhibiting anti-tumor and antioxidant properties, is one of the most important quality indicators of naked barley. Establishing a near-infrared (NIR) prediction model for β-glucan content enables rapid and non-destructive evaluation of this trait in early-generation naked barley materials, which can significantly enhance breeding efficiency. In this study, NIR spectra were collected from 215 naked barley grain samples, and β-glucan content was determined using enzymatic methods. Based on spectral scanning and Monte Carlo cross-validation, 10 outlier samples were excluded. The remaining samples were divided into calibration and validation sets using the SPXY method. Fourteen preprocessing techniques, including normalization, first derivative, and second derivative, were applied to the spectral data. Partial least squares regression (PLSR), support vector machine (SVM), and principal component regression (PCR) were employed to construct prediction models for β-glucan content. Among these, the SVM model combined with the second derivative preprocessing showed the best predictive performance. To further optimize this model, six feature variable selection methods—successive projections algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), variable iterative space shrinkage algorithm (VISSA), bootstrapping soft shrinkage (BOSS), and iteratively retained informative variables (IRIV)—were evaluated. The CARS-SD-SVM model achieved the highest accuracy, with R2C, RMSEC, R2P, RMSEP, and RPD values of 0.859, 0.272, 0.877, 0.237, and 2.790, respectively. This model not only demonstrated excellent predictive performance but also high reliability, making it suitable for the rapid analysis and prediction of β-glucan content in naked barley grains. It holds great potential for application in naked barley breeding and quality assessment.

Key words: naked barley, β-glucan content, near infrared spectroscopy (NIRS), prediction model, feature variable selection

[1] 白羿雄, 郑雪晴, 姚有华, 等. 青稞种质资源表型性状的遗传多样性分析及综合评价. 中国农业科学, 2019, 52: 4201–4214.

Bai Y X, Zheng X Q, Yao Y H, et al. Genetic diversity analysis and comprehensive evaluation of phenotypic traits in hulless barley germplasm resources. Sci Agric Sin, 2019, 52: 4201–4214 (in Chinese with English abstract).

[2] 侯殿志, 沈群. 我国29种青稞的营养及功能组分分析. 中国食品学报, 2020, 20: 289–298.

Hou D Z, Shen Q. Analysis of nutrition and functional components of 29 kinds of highland barley in China. J Chin Inst Food Sci Technol, 2020, 20: 289–298 (in Chinese with English abstract).

[3] Meints B, Hayes P M. Breeding naked barley for food, feed, and malt. Plant Breed Rev, 2019, 43: 95–119.

[4] Iaccarino N, Khakimov B, Mikkelsen M S, et al. Structurally different mixed linkage β-glucan supplements differentially increase secondary bile acid excretion in hypercholesterolaemic rat faeces. Food Funct, 2020, 11: 514–523.

[5] Zhang J J, Zheng M J, Zhou L Y, et al. Oat β-glucan alleviates muscle atrophy via promoting myotube formation and suppressing protein degradation. J Sci Food Agric, 2023, 103: 6252–6262.

[6] Wu Y S, Shao Y, Shao X Y, et al. Qingke β-glucan and Lactobacillus mitigate neuroinflammation and enhance cognitive function in an Alzheimer’s disease mouse model. Int J Biol Macromol, 2025, 319: 145427.

[7] Mcclear B V, Glennie-Holmes M. Enzymic quantification of (1 to 3) (1 to 4)- β-d -glucan in barley and malt. J Inst Brew, 1985, 91: 285–295.

[8] 胡美玲, 郅晨阳, 薛晓梦, 等. 单粒花生蔗糖含量近红外预测模型的建立. 作物学报, 2023, 49: 2498–2504.

Hu M L, Zhi C Y, Xue X M, et al. Establishment of near-infrared reflectance spectroscopy model for predicting sucrose content of single seed in peanut. Acta Agron Sin, 2023, 49: 2498–2504 (in Chinese with English abstract).

[9] 王若楠, 张颖星, 于筱菡, 等. 基于近红外快速检测技术的谷子淀粉多样性分析及模型构建. 作物学报, 2025, 51: 1757–1768.

Wang R N, Zhang Y X, Yu X H, et al. Near-infrared spectroscopic evaluation of starch diversity and model construction in foxtail millet. Acta Agron Sin, 2025, 51: 1757–1768 (in Chinese with English abstract).

[10] Xu Y H, Dong Y, Liu J M, et al. Combination of near infrared spectroscopy with characteristic interval selection for rapid detection of rice protein content. J Food Compos Anal, 2025,137: 106995.

[11] Schmidt J, Gergely S, Schönlechner R, et al. Comparison of different types of NIR instruments in ability to measure β-glucan content in naked barley. Cereal Chem, 2009, 86: 398–404.

[12] 杨焰婷, 江谧, 詹瑞玲, 等. 青稞特征营养成分含量快速检测模型的建立及优化. 食品工业科技, 2024, 45(18): 228–238.

Yang Y T, Jiang M, Zhan R L, et al. Development and optimization of a rapid detection model for characteristic nutrient content in highland barley. Sci Technol Food Ind, 2024, 45(18): 228–238 (in Chinese with English abstract).

[13] Li L L, Li L, Gou G Y, et al. Feasibility analysis of nondestructive detection of multiple parameters of highland barley by near-infrared spectroscopy. J Sci Food Agric, 2025, 105: 5194–5206.

[14] 李鑫瑜, 侯名语, 崔顺立, 等. 红色和黑色花生籽仁黄酮含量近红外模型的构建. 中国农业科学, 2025, 58: 1284–1295.

Li X Y, Hou M Y, Cui S L, et al. Construction of near infrared spectrometry model for flavonoids content of peanut with red and black testa. Sci Agric Sin, 2025, 58: 1284–1295 (in Chinese with English abstract).

[15] Liu J M, Luo X, Zhang D J, et al. Rapid determination of rice protein content using near-infrared spectroscopy coupled with feature wavelength selection. Infrared Phys Technol, 2023, 135: 104969.

[16] Galvão R K H, Araujo M C U, José G E, et al. A method for calibration and validation subset partitioning. Talanta, 2005, 67: 736–740.

[17] Zhang W, Feng W W, Cai Z Q, et al. A deep one-dimensional convolutional neural network for microplastics classification using Raman spectroscopy. Vib Spectrosc, 2023, 124: 103487.

[18] Deng G M, Liu H G, Li J Q, et al. Rapid prediction of nucleosides content and origin traceability of Boletus bainiugan using Fourier transform near-infrared spectroscopy combined with chemometrics. Spectrochim Acta Part A Mol Biomol Spectrosc, 2025, 327: 125421.

[19] Khatun M S, Al Masum A, Islam M H, et al. Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model. J Food Compos Anal, 2024, 136: 106745.

[20] Jiang H, Wang J N, Chen Q S. Comparison of wavelength selected methods for improving of prediction performance of PLS model to determine aflatoxin B1 (AFB1) in wheat samples during storage. Microchem J, 2021, 170: 106642.

[21] 李杨, 李翠玲, 王秀, 等. 高光谱成像的黄瓜病虫害识别和特征波长提取方法. 光谱学与光谱分析, 2024, 44: 301–309.

Li Y, Li C L, Wang X, et al. Identification of cucumber disease and insect pest based on hyperspectral imaging. Spectrosc Spectr Anal, 2024, 44: 301–309 (in Chinese with English abstract).

[22] Deng B C, Yun Y H, Cao D S, et al. A bootstrapping soft shrinkage approach for variable selection in chemical modeling. Anal Chim Acta, 2016, 908: 63–74.

[23] 邵园园, 王永贤, 玄冠涛, 等. 基于高光谱成像的肥城桃品质可视化分析与成熟度检测. 农业机械学报, 2020, 51(8): 344–350.

Shao Y Y, Wang Y X, Xuan G T, et al. Visual detection of ssc and firmness and maturity prediction for Feicheng peach by using hyperspectral imaging. Trans CSAM, 2020, 51(8): 344–350 (in Chinese with English abstract).

[24] Li P, Jin Q T, Liu H M, et al. Determination of soluble solids content in loquat using near-infrared spectroscopy coupled with broad learning system and hybrid wavelength selection strategy. LWT, 2024, 206: 116570.

[25] Zhang H L, Zhan B S, Pan F, et al. Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models. Postharvest Biol Technol, 2020, 163: 111148.

[26] Lin C, Chen X, Jian L, et al. Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley. Food Chem, 2014, 162: 10–15.

[27] Han Z G, Cai S G, Zhang X L, et al. Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy. Food Chem, 2017, 227: 342–348.

[28] Albanell E, Martínez M, De Marchi M, et al. Prediction of bioactive compounds in barley by near-infrared reflectance spectroscopy (NIRS). J Food Compos Anal, 2021, 97: 103763.

[29] 李栓明, 郭银巧, 王克如, 等. 小麦籽粒蛋白质光谱特征变量筛选方法研究. 中国农业科学, 2015, 48: 2317–2326.

Li S M, Guo Y Q, Wang K R, et al. Research on variable selection of wheat kernel protein content with near-infrared spectroscopy. Sci Agric Sin, 2015, 48: 2317–2326 (in Chinese with English abstract).

[30] Yu Y, Chai Y H, Li Z T, et al. Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics. Food Chem, 2025, 462: 141033.

[31] Xiong C H, She Y X, Jiao X, et al. Rapid nondestructive hardness detection of black highland Barley Kernels via hyperspectral imaging. J Food Compos Anal, 2024, 127: 105966.

[32] 陈淼, 侯名语, 崔顺立, 等. 不同种皮颜色花生糖含量近红外模型的构建. 光谱学与光谱分析, 2022, 42: 2896–2902.

Chen M, Hou M Y, Cui S L, et al. Construction of near-infrared model of peanut sugar content in different seed coat colors. Spectrosc Spectr Anal, 2022, 42: 2896–2902 (in Chinese with English abstract).

[33] Yang Z Y, Cheng Z, Su P Y, et al. A model for the detection of β-glucan content in oat grain based on near infrared spectroscopy. J Food Compos Anal, 2024, 129: 106105.

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