摘要
目的:解决猕猴桃糖度无损检测方法存在的准确性差和效率低等问题。方法:提出一种将高光谱检测技术、最小二乘支持向量机和改进的鲸鱼算法相结合的猕猴桃糖度无损检测方法。通过高光谱检测系统采集猕猴桃的高光谱信息,对其进行预处理和特征波长筛选后,输入改进鲸鱼算法优化的最小二乘支持向量机模型,实现猕猴桃糖度的快速无损检测,并验证其性能。结果:所提方法可以实现猕猴桃糖度的快速无损检测,测试集决定系数为0.9652,测试集均方根误差为0.8805,平均检测时间为1.06 s。结论:将机器学习算法与高光谱检测技术相结合,可以实现猕猴桃糖度的快速无损检测。
Objective:Addressing the issues of poor accuracy and low efficiency in non-destructive testing methods for kiwifruit sugar content.Methods:Proposing a non-destructive testing method for kiwifruit sugar content that combined hyperspectral detection technology,least squares support vector machine,and improved whale algorithm.By collecting hyperspectral information of kiwifruit through a hyperspectral detection system,after preprocessing and feature wavelength screening,and then input into an improved whale algorithm optimized least squares support vector machine model to achieve rapid and non-destructive detection of kiwifruit sugar content,and verify its performance.Results:The proposed method could achieve rapid and non-destructive detection of kiwifruit sugar content,with a determination coefficient of 0.9652 for the test set,a root mean square error of 0.8805 for the test set,and an average detection time of 1.06 seconds.Conclusion:Combining machine learning algorithms with hyperspectral detection technology can achieve rapid and non-destructive detection of kiwifruit sugar content.
作者
章恺
朱丽芳
李入林
王子异
ZHANG Kai;ZHU Lifang;LI Rulin;WANG Ziyi(Nanyang Vocational College,Nanyang,Henan 473000,China;Nanyang Eucommia Ulmoides Gum Extraction Engineering Technology Research Center,Nanyang,Henan 473000,China;Nanyang Institute of Technology,Nanyang,Henan 473000,China;Henan Agricultural University,Zhengzhou,Henan 450046,China)
出处
《食品与机械》
CSCD
北大核心
2024年第5期107-112,226,共7页
Food and Machinery
基金
河南省科技攻关项目(编号:22104370125)。
关键词
猕猴桃
高光谱检测
糖度
机器学习算法
鲸鱼优化算法
最小二乘支持向量机
kiwi fruit
hyperspectral detection
sugar content
machine learning algorithms
whale optimization algorithm
least squares support vector machine
作者简介
章恺(1984-),男,南阳职业学院讲师,硕士。E-mail:zniq189@yeah.net。
