[背景]牛奶中钠(sodium，Na)、钾(potassium，K)和镁(magnesium，Mg)含量的准确检测有助于奶牛的健康养殖，同时也是稳定乳制品质量的前提。但目前检测牛奶中矿物质含量的常规方法昂贵且耗时，因此需要一种低成本且快速检测牛奶Na、K和Mg含量的方法。[目的]利用中红外光谱(mid-infrared spectroscopy，MIRS)预测中国荷斯坦牛牛奶中Na、K和Mg含量的潜力，为测定牛奶中Na、K和Mg含量提供快速检测技术，为牛群饲养管理和奶牛遗传育种提供大量表型数据支撑。此外，比较不同特征波段选择算法改进预测牛奶中Na、K和Mg含量的MIRS定量预测模型的能力。[方法]以来自华北地区的255份健康中国荷斯坦牛牛奶样本为研究对象。首先，使用MilkoScanTMFT+收集牛奶样本的MIRS数据，并使用电子耦合等离子体发射光谱法测定牛奶样本中Na、K和Mg含量的真实值。随后，以MIRS数据为预测变量，Na、K和Mg含量的真实值为因变量，利用4种光谱预处理算法(一阶导数、二阶导数、SG平滑和标准正态变换)、4种特征选择算法(无信息变量消除(uninformative variable elimination，UVE)、竞争性自适应重加权算法(Competitive adaptive reweighted sampling，CARS)、遗传算法(Genetic Algorithm，GA)及最小角回归算法(Least Angle Regression，LAR))和9种建模算法(偏最小二乘回归、支持向量机、随机森林和弹性网络等)，分别建立预测牛奶中Na、K和Mg含量的MIRS定量预测模型，并选出最优模型组合(特征选择算法+光谱预处理算法+建模算法)。[结果]CARS特征波段选择算法对Na、K和Mg含量预测模型的改进效果优于 UVE、GA 和 LAR 算法。基于 CARS 特征选择算法、一阶导数预处理和弹性网络建模算法开发的 Na含量预测模型效果最好，该模型预测集决定系数(coefficient of determination of prediction set，R 2 p)=0。72，预测集均方根误差(root mean squared error of prediction，RMSEp)=63。28 mg·kg-1，预测集平均绝对误差(mean absolute error of prediction set，MAEp)=49。03 mg·kg-1，性能偏差比(ratio of performance to deviation，RPD)=1。90;基于CARS特征选择算法、原始光谱和支持向量机建模算法开发的K含量预测模型效果最好，该模型R 2 p=0。57，RMSEp=141。49 mg·kg-1，MAEp=116。24 mg·kg-1，RPD=1。57;基于CARS特征选择算法、原始光谱和偏最小二乘回归建模算法开发的Mg含量预测模型效果最好，该模型R 2 p=0。51，RMSEp=12。08 mg·kg-1，MAEp=9。84 mg·kg-1，RPD=1。30。[结论]利用MIRS预测中国荷斯坦牛牛奶中Na和K含量的方法可行，可以较准确地预测Na含量，近似地定量预测K含量(用于区分低浓度K和高浓度K样品)。在建模之前利用CARS算法提取特征波段提高了MIRS预测模型的准确性，并大大减少了运算时间，可提高MIRS模型预测表型数据的效率。
Establishment of Prediction Models for Sodium,Potassium and Magnesium Content in Milk of Chinese Holstein Cows Based on Mid-Infrared Spectroscopy
[Background]Accurate detection of sodium(Na),potassium(K)and magnesium(Mg)content in milk contributes to healthy dairy farming and is a prerequisite for stabilizing the quality of dairy products.However,the current conventional methods for detecting mineral content in milk are expensive and time-consuming,so there is a need for a low-cost and rapid method to measure the Na,K and Mg content in milk.[Objective]The purpose of this study was to investigate the potential of using milk-infrared spectroscopy(MIRS)to predict the Na,K and Mg content in milk from Chinese Holstein cows,to provide a rapid detection technique for the determination of Na,K and Mg content in milk,and to provide a large amount of phenotypic data for the herd management and genetic breeding of dairy cows.In addition,the ability of different feature selection algorithms to improve the MIRS quantitative prediction model for predicting Na,K and Mg content in milk were compared.[Method]A total of 255 milk samples from healthy Holstein cows from North China were used for this study.Firstly,MIRS data of milk samples were collected using MilkoScanTMFT+,and the true values of Na,K and Mg content in milk samples were determined using inductively coupled plasma atomic emission spectrometry.Subsequently,using the MIRS data as the predictor variables and the true values of Na,K and Mg content as the dependent variables,four spectral preprocessing algorithms(first-order derivative,second-order derivative,SG smoothing,and standard normal transform),four feature selection algorithms[uninformative variable elimination(UVE),competitive adaptive reweighted sampling(CARS),genetic algorithm,and least angle regression(LAR)]and nine modelling algorithms(partial least squares regression,support vector machines,Random Forest and Elasticity Network,etc.)were used to establish MIRS quantitative prediction models for predicting Na,K and Mg content in milk,respectively,and the optimal model combination(Feature Selection Algorithm+Spectral Preprocessing Algorithm+Modelling Algorithm)was selected.[Result]Overall,the CARS algorithm improved the Na,K and Mg content prediction models better than the UVE,GA and LAR algorithm.The Na content prediction model developed based on CARS feature selection algorithm,first-order derivative preprocessing and elastic network modelling algorithm was the most effective,and the model had a coefficient of determination of prediction set(RP2)=0.72,root mean squared error of prediction set(RMSEp)=63.28 mg·kg-1,mean absolute error of prediction set(MAEp)=49.03 mg·kg-1,and performance deviation ratio(ratio)=1.90.The best K content prediction model was developed based on the CARS feature selection algorithm,raw spectra and support vector machine modelling algorithm,which had RP2=0.57,RMSEp=141.49 mg·kg-1,MAEp=116.24 mg·kg-1,RPD=1.57.Mg content prediction model developed based on CARS feature selection algorithm,raw spectra and partial least squares regression modelling algorithm was the most effective,the model RP2=0.51,RMSEp=12.08 mg·kg-1,MAEp=9.84 mg·kg-1,and RPD=1.30.[Conclusion]It was feasible to use MIRS to predict Na and K content in milk from Chinese Holstein cows,which could predict Na content with a high degree of accuracy and approximate quantitative prediction of K content(for distinguishing between low and high K concentration samples).The use of the CARS algorithm to extract the characteristic bands before modelling improved the accuracy of the MIRS prediction model,and greatly reduced the computing time to improve the efficiency of the MIRS model in predicting phenotypic data.
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