Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (14): 2862-2873.doi: 10.3864/j.issn.0578-1752.2024.14.013

• FOOD SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Establishment of Prediction Models for Sodium, Potassium and Magnesium Content in Milk of Chinese Holstein Cows Based on Mid-Infrared Spectroscopy

HAO LeiXiao1(), CHU Chu1(), WEN PeiPei1, PENG SongYue1, YANG Zhuo1, ZOU HuiYing1, FAN YiKai1, WANG HaiTong1, LIU WenJu1, WANG DongWei1, LIU WeiHua2, YANG JunHua2, ZHAO Juan2, LI WeiQi3, WEN Wan3, ZHOU JiaMin3, ZHANG ShuJun1()   

  1. 1 College of Animal Science and Technology, School of Animal Medicine, Huazhong Agricultural University, Wuhan 430070
    2 Ningxia Veterinary Drug and Feed Supervision Institute, Yinchuan 750000
    3 Ningxia Hui Autonomous Region Animal Husbandry Workstation, Yinchuan 750000
  • Received:2024-01-04 Accepted:2024-04-01 Online:2024-07-24 Published:2024-07-24
  • Contact: ZHANG ShuJun

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Abstract:

【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.

Key words: milk, mid-infrared spectroscopy, minerals, sodium, potassium, magnesium, machine learning

Table 1

Coefficients of determination and root mean square errors in prediction data sets of MIRS models developed by different references for predicting Na, K and Mg content in milk"

数据量
Number of data		 特征选择方法
Feature selection method		 光谱预处理
Spectral preprocessing		 建模算法
Modelling algorithm		 钠Nab 钾Kb 镁Mgb 参考文献
Reference
1281 212个波点a
212 wavepoints		 一阶导数
First-order derivative		 PLSR 0.49 (62.24) 0.52 (106.69) 0.69 (7.78) [1]
923 UVE 一阶导数
First-order derivative		 PLSR 0.40 (70) 0.69 (120) 0.65 (12.5) [5]
92 - - PLSR 0.65 (64) 0.36 (136) 0.65 (11) [22]
153 去除水后的区域
Areas after water removal		 - PLSR 0.25 (51.6) 0.34 (78.5) 0.26 (4.1) [25]
91 BiPLS - BiPLS 0.75 (40.2) 0.55 (71.4) 0.68 (6.4) [26]
526 去除水后的区域
Areas after water removal		 无预处理
No pre-processing		 PLSR 0.63 (70) 0.33 (110) 0.46 (10) [27]

Table 2

Descriptive statistics of sodium, potassium and magnesium content (mg∙kg-1) in milk derived from the ICP-AES analysis"

矿物质
Mineral		 数据量
Number of samples		 平均值
Mean (mg∙kg-1)		 标准差
SD		 变异系数
CV (%)		 最小值
Min (mg∙kg-1)		 最大值
Max (mg∙kg-1)
钠 Na 246 488.17 120.11 24.60 319.59 1090.150
钾 K 255 1470.76 222.50 15.13 926.75 2085.168
镁 Mg 255 113.95 15.66 13.74 66.61 155.960

Fig. 1

Mean spectra of milk samples SWIR: Short-wavelength infrared or near-infrared; MWIR: Mid-wavelength infrared (3.0-8.0 µm); LWIR: Long-wavelength infrared (8.0-15.0 µm). The gray area represents the mean ± standard deviation range of absorbance"

Table 3

Effects of different feature selection methods on the performance of Na, K and Mg prediction model"

性状
Trait		 特征提取
Feature
selection		 特征数量
Feature
number		 最优预处理
Best
pretreatment		 指标 Metrics
校准集 Calibration set 预测集 Prediction set
RMSEc MAEc Rc2 RMSEp MAEp RP2 RPD
钠
Na		 全波段 Full band 1060 SNV 92.21 67.69 0.42 98.30 76.92 0.28 1.22
GA 423 2D 84.31 59.86 0.52 100.66 77.43 0.26 1.19
CARS 82 1D 64.58 50.03 0.72 66.18 51.23 0.69 1.81
LAR 100 SG 101.87 70.37 0.32 90.31 68.74 0.28 1.33
UVE 290 None 88.75 63.20 0.49 78.61 61.90 0.42 1.53
钾
K		 全波段 Full band 1060 SNV 247.90 156.33 0.29 186.39 150.65 0.29 1.19
GA 437 1D 225.38 137.11 0.43 178.13 130.68 0.32 1.25
CARS 81 None 181.23 126.76 0.62 162.80 128.80 0.48 1.37
LAR 100 2D 231.57 142.46 0.36 213.00 169.86 0.28 1.04
UVE 105 2D 233.16 133.09 0.38 168.15 134.39 0.37 1.32
镁
Mg		 全波段 Full band 1060 2D 14.11 9.30 0.43 13.59 10.75 0.32 1.15
GA 396 None 13.71 9.94 0.47 13.13 10.66 0.31 1.19
CARS 81 None 12.34 8.66 0.55 12.08 9.84 0.51 1.30
LAR 100 SNV 14.28 9.48 0.42 13.45 10.59 0.30 1.16
UVE 466 SG 13.52 9.17 0.47 12.96 11.36 0.43 1.21

Table 4

Effect of different modelling algorithms on the model performance of Na, K and Mg prediction models"

性状
Trait		 建模算法
Modeling algorithm		 最优预处理
Best pretreatment		 指标Metrics
校准集 Calibration set 预测集 Prediction set
RMSEc MAE Rc2 RMSEp MAE RP2 RPD
钠
Na		 PLSR 2D 64.58 50.03 0.72 66.18 51.23 0.69 1.81
RR 1D 71.43 55.61 0.66 67.90 52.79 0.65 1.77
LASSO 1D 62.93 48.76 0.73 63.54 49.36 0.71 1.89
EN 1D 62.97 48.78 0.73 63.28 49.03 0.72 1.90
SVM SG 59.85 35.54 0.80 96.33 71.01 0.28 1.25
SSR 1D 71.17 55.28 0.66 67.54 52.60 0.65 1.78
PPR 2D 46.07 35.26 0.86 76.30 58.81 0.69 1.57
RF None 41.03 27.97 0.95 104.51 73.13 0.14 1.15
GBM None 58.04 34.07 0.81 101.02 74.19 0.22 1.19
钾
K		 PLSR None 181.23 126.76 0.62 162.80 128.80 0.48 1.37
RR None 185.17 124.12 0.61 154.38 125.82 0.50 1.44
LASSO None 181.48 125.26 0.62 161.40 130.77 0.48 1.38
EN None 177.75 122.73 0.63 160.71 126.61 0.49 1.38
SVM None 144.79 50.26 0.79 141.49 116.24 0.57 1.57
SSR None 187.47 125.51 0.60 157.15 128.85 0.49 1.42
PPR SG 105.24 81.34 0.87 152.53 129.32 0.54 1.46
RF None 130.01 70.37 0.92 156.31 121.46 0.54 1.42
GBM None 186.74 92.54 0.64 168.64 134.17 0.40 1.32
镁
Mg		 PLSR None 12.34 8.66 0.55 12.08 9.84 0.51 1.30
RR None 12.56 8.63 0.54 12.34 9.99 0.49 1.27
LASSO SNV 12.97 8.89 0.51 12.30 9.62 0.48 1.27
EN SNV 13.01 8.89 0.51 12.27 9.51 0.49 1.28
SVM 1D 9.12 3.12 0.79 13.12 10.99 0.42 1.19
SSR 1D 13.97 9.39 0.44 13.32 11.22 0.39 1.18
PPR 2D 8.16 6.11 0.80 15.01 11.25 0.26 1.04
RF 1D 8.32 5.18 0.94 15.06 12.43 0.37 1.04
GBM 1D 11.51 6.97 0.70 14.73 12.01 0.28 1.06

Fig. 2

Scatter plots between predicted and true values on the calibration set (red triangles) and the prediction set (blue circles) for the best model for predicting Na, K and Mg content in milk Blue dashed line: y=x"

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