摘要
为优选食醋总酸对应的特征波长并提高模型的预测精度,采用模拟退火算法优化窗口宽度和特征波数点,并结合偏最小二乘法建立食醋总酸光谱模型。在全国范围内收集90个不同品牌的食醋样本,用近红外光谱仪采集近红外光谱数据(波数4000~10000cm-1)和常规理化分析方法检测总酸含量。采用模拟退火算法共优选出17个总酸特征波数点,结合样本总酸含量建立偏最小二乘光谱模型,模型对应的预测集相关系数为0.9210,优于全光谱偏最小二乘法和区间偏最小二乘法对应的预测效果。结果表明,近红外光谱技术结合模拟退火算法能够快速预测食醋中总酸含量。
To extract the characteristic information of total acid and improve model accuracy,simulated annealing algorithm was used to optimize window width and wavenumber in a total acid model obtained by the partial least squares analysis.Ninety different brands of vinegar samples collected from the whole country were used to acquire near infrared spectral data,and total acid content was measure by the general analytical method.Pre-processing methods were used to smooth the vinegar NIR spectra.A total of 17 wavenumbers were selected and the predictive model for total acid model revealed a correlation coefficient of prediction set of 0.9210 and its prediction ability was superior to that of the whole spectral partial least square model and interval partial least square model.These results showed near infrared spectroscopy in combination with simulated annealing algorithm allows to quickly predict total acid content of vinegar.
出处
《食品科学》
EI
CAS
CSCD
北大核心
2011年第10期120-123,共4页
Food Science
基金
全国优秀博士基金项目(200968)
国家博士后基金项目(20070411024)
关键词
食醋
总酸
近红外光谱技术
模拟退火算法
波数优选
vinegar
total acid
near infrared spectroscopy
simulated annealing algorithm
wavenumber selection