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基于NIR定量模型快速预测发酵麸皮中还原糖和可溶性蛋白成分的含量
王春媛1,2,齐景伟1,2*,安晓萍1,2,刘娜1,2,王园1,2,王步钰2,3
0
(1.内蒙古农业大学 动物科学学院,呼和浩特 010018;2.内蒙古自治区草食家畜饲料技术研究中心/国家乳业技术创新中心奶牛繁育与养殖技术研究中心,呼和浩特 010018;3.内蒙古农业大学 计算机与信息工程学院,呼和浩特 010018)
摘要:
为建立一种能快速预测发酵麦麸还原性糖和可溶性蛋白含量的定量分析模型,本研究以发酵麦麸为样本,采用3,5-二硝基水杨酸比色法(3,5-dinitrosalicylic acid, DNS)和BCA蛋白浓度测定法分别测定样品的还原性糖和可溶性蛋白含量;利用近红外光谱技术(Near infrared spectrum instrument, NIR)结合偏最小二乘法(Partial least squares regression, PLS),比较预处理方法、最佳波长及主成分因子的决定系数R2,建立发酵麦麸还原性糖和可溶性蛋白含量的NIR快速检测定量模型。结果表明:1)发酵麦麸还原性糖定量模型的预处理方法使用一阶导数 (First derivative, FD)+二阶导数 (Second derivative, SD)+标准正态变换 (Standard normal variate, SNV),光谱范围为908~1 670 nm ,主因子数为7时,模型效果最优,其决定系数Rc2为0.904 8,校正均方根误差(Square error corrected, SEC)为1.576 1,相对分析误差(Relative percentage difference, RPD)为3.240 8;外部验证集决定系数Rp2为0.954 9且还原糖活性成分的验证集样本的测定值与NIR光谱预测值的P值为0.959 5>0.05。2)发酵麦麸可溶性蛋白定量模型的预处理方法使用一阶导数 (First derivative, FD)+二阶导数 (Second derivative, SD)+标准正态变换 (Standard normal variate, SNV),光谱范围为908~1 670 nm ,主因子数为10时,模型效果最优,其决定系数Rc2为0.938 2,校正均方根误差(Square error corrected, SEC)为2.003,相对分析误差(Relative percentage difference, RPD)为4.021 9;外部验证集决定系数Rp2为0.994 4,且可溶性蛋白活性成分的验证集样本测定值与NIR光谱预测值的P值为0.901 9>0.05。综上,建立的NIR光谱定量模型稳定性和准确性较好,且预测准确度良好,可用于快速预测发酵麦麸样品的还原性糖和可溶性蛋白含量。
关键词:  近红外光谱法  化学计量学法  定量分析模型  活性成分  发酵麸皮
DOI:10.11841/j.issn.1007-4333.2024.08.16
投稿时间:2023-10-17
基金项目:内蒙古自治区科技重大专项(2021ZD0023-3、2021ZD0024-4);国家乳业技术创新中心创新能力建设重点项目(2022-科研攻关-2);内蒙古自治区科技计划项目(2022YFSJ0029,2022YFHH0072)
Rapid prediction of reducing sugar and soluble protein content in fermented bran based on NIR quantitative analysis model
WANG Chunyuan1,2, QI Jingwei1,2*, AN Xiaoping1,2, LIU Na1,2, WANG Yuan1,2, WANG Buyu2,3
(1.College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China;2.Inner Mongolia Autonomous Region Forage Technology Research Center for Herbivoring Livestock/Dairy Cow Breeding and Breeding Technology Research Center of National Dairy Technology Innovation Center, Hohhot 010018, China;3.College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)
Abstract:
This study aims to establish a quantitative analysis model that can quickly predict the content of reducing sugars and soluble proteins in fermented wheat bran(FWB). FWB was taken as the study material, and the reducing sugar and soluble protein contents of the samples were determined by the colorimetric method of 3,5-dinitrosalicylic acid (3,5-dinitrosalicylic acid, DNS) and the BCA protein concentration assay (Bicinchoninic Acid Assay, BCA), respectively. Using Near infrared spectrum instrument (NIR) combined with Partial least squares regression (PLS), the pre-processing method, the optimal wavelength and the coefficient of determination R2 of the principal component factors were compared, and a quantitative model for the rapid detection of NIR in the content of reducing sugars and soluble proteins in FWB were established. The results showed that: 1) The pretreatment method of quantitative model of reduced sugar in fermented wheat bran used First derivative (FD)+ Second derivative (SD)+ Standard normal variate (SNV). When the spectral range was 908-1 670 nm and the number of principal factors was 7, the model had the best performance. The determination coefficient Rc2 was 0.904 8, and the square error corrected (SEC) was 1.576 1. The Relative percentage difference (RPD) was 3.240 8. The determination coefficient Rp2 of the external validation set was 0.954 9, and the P value between the measured values of the validation set samples of reducing sugar active ingredients and the predicted values of NIR spectra was 0.959 5>0.05. 2) The pretreatment method for quantitative model of fermented wheat bran soluble protein was First derivative (FD)+ Second derivative (SD)+ Standard normal variate (SNV). When the spectral range was 908-1 670 nm and the number of principal factors was 10, the model had the best performance. The determination coefficient Rc2 was 0.938 2, and the corrected square error corrected (SEC) was 2.003. The Relative percentage difference (RPD) was 4.021 9. The coefficient of determination (Rp2) of the external validation set was 0.994 4, and the P value between the measured values of the validation set samples and the predicted values of the NIR spectra of the soluble active components was 0.901 9>0.05. To sum up, established NIR spectral quantitative model in the study has good stability and accuracy, and can be used to rapidly predict the contents of reducing sugar and soluble protein in FWB samples.
Key words:  near infrared spectroscopy  stoichiometric method  quantitative analysis model  active ingredient  fermented bran