【摘要】 脂肪作为牛奶中的重要营养成分，是评价牛奶质量的一项重要指标。高光谱图像技术能够提供几十到数千波长的数据，能够反映牛奶中不同组成成分细微的光谱差异；另一方面，相邻波段之间往往具有很强的相关性，不仅增加了计算量，而且容易造成维数灾难等问题，因此对高光谱数据进行波段选择非常重要。工作中提出了PLS-ACO特征波段选择方法，并与遗传算法结合，组合成了PLS-ACO-GA的特征波段选择新方法。提出的两种方法以蚁群算法为基础，PLS回归模型回归系数的绝对值作为评价波长重要性的主要依据，以此作为蚁群算法的启发式信息，利用蚁群算法进行智能搜索，结合遗传算法，产生更多优秀的特征波段组合，避免PLS-ACO算法得到的只是局部最优解，得到的最优波段组合能够更好的反映牛奶中脂肪成分的信息；通过计算波长贡献率，筛选出最优波段组合，并与遗传算法，CARS算法和基本蚁群算法光谱特征选择方法比较，最后比较不同特征选择方法下的PLS回归模型预测效果。PLS-ACO, PLS-ACO-GA, CARS, GA和ACO分别筛选了牛奶样品光谱中的18, 16, 40, 43和42个特征波段。其中PLS-ACO-GA筛选波段后的PLS预测模型效果最好，预测集R■和RMSEP分别为0.997 6和0.062 2,PLS-ACO次之，预测集R■和RMSEP分别为0.997 0和0.077 8。PLS-ACO和PLS-ACO-GA不仅减少了特征波段数量，而且提高了模型的精度。对PLS-ACO-GA进行特征波段选择后的数据，建立MLR, RFR和PLS回归预测模型。MLR预测模型的R■和RMSEP分别为0.997 6和0.062 3。RFR回归模型R■和RMSEP分别为0.999 9和0.003 0, PLS回归模型的R■和RMSEP分别为0.997 6和0.062 2。RFR模型在三种回归预测模型中表现最好。研究结果表明PLS-ACO和PLS-ACO-GA这两种方法可以实现光谱数据特征波段选择，高光谱技术可以实现牛奶中脂肪含量的检测，为牛奶脂肪含量检测提供了一种新的、快速无损的方法。更多还原

【Abstract】 As an important nutritional component in milk, fat is an important index to evaluate milk quality. Hyperspectral image technology can provide tens to thousands of bands of data and can reflect the subtle spectral differences of different components in milk. On the other hand, there is often a strong correlation between adjacent bands, which increases the amount of calculation and easily causes problems such as dimension disaster. Therefore, it is very important to select bands for hyperspectral data. This paper proposes a PLS-ACO feature band selection method combined with a genetic algorithm to form a new feature band selection method of PLS-ACO-GA. The two methods proposed in this paper are based on ant colony optimization. The absolute value of the regression coefficient of the PLS regression model is the main basis for evaluating the importance of wavelength, which is used as the heuristic information of ant colony optimization. Ant colony optimization is used for intelligent search, combined with genetic algorithm to produce more excellent characteristic band combinations. To avoid that pls-aco algorithm only obtains the optimal local solution. The optimal band combination can better reflect the information of fat composition in milk. By calculating the wavelength contribution rate, the optimal band combination is selected and compared with the spectral feature selection methods of genetic algorithm, cars algorithm and basic ant colony optimization. Finally, the prediction effects of the PLS regression model under different feature selection methods are compared. PLS-ACO, PLS-ACO-GA, CARS, GA and ACO screened 18, 16, 40, 43 and 42 characteristic bands in the spectrum of milk samples, respectively. The PLS prediction model after the PLS-GA-ACO screening band has the best effect. The prediction sets R■ and RMSEP are 0.997 6 and 0.062 2 respectively, followed by PLS-ACO, and the prediction sets R■ and RMSEP are 0.997 0 and 0.077 8 respectively. PLS-ACO and PLS-ACO-GA reduce the number of characteristic bands and improve the accuracy of the model. MLR, RFR and PLS regression prediction models are established based on PLS-ACO-GA data after characteristic band selection. The R■ and RMSEP of the MLR prediction model are 0.997 6 and 0.062 3 respectively. R■ and RMSEP of the RFR regression model were 0.999 9 and 0.003 0 respectively, and R■ and RMSEP of the PLS regression model were 0.997 6 and 0.062 2 respectively. RFR model performs best among the three regression prediction models. The results show that hyperspectral technology can detect the fat content in milk, which provides a new, rapid and non-destructive method for the detection of fat content in milk.更多还原