摘要
为准确识别酱香型高温大曲类型,对30个出仓曲检测了中红外光谱,在主成分分析中,3类大曲(黑曲、黄曲、白曲)呈现出各自聚类的趋势;进一步建立了偏最小二乘偏最小二乘判别(PLS-DA)模式识别方法,模型R2Y为0.956,Q^(2)为0.906,可有效判别不同质量大曲类型,为生产投料配比提供数据依据。为快速定量高温大曲中类黑素,对发酵过程高温大曲建立了基于近红外光谱技术的类黑素定量模型,以60个样品建立模型,光谱经多元散射校正(MSC)结合一阶导数处理,在10 000~4 000 cm^(-1)范围,主成分数为8时,偏最小二乘(PLS)模型效果最优,校正集决定系数R_(Val)^(2)为0.987 7,校正均方根误差(RMSEC)为0.169 6,验证集决定系数R_(Val)^(2)为0.900 7,交叉验证均方根误差(RMSECV)为0.491 1;以15个样品做外部预测以验证模型可靠性,预测均方根误差(RMSEP)为0.460 6,标准偏差与预测标准偏差比值(RPD)为2.63,且与参考方法之间无显著性差异(P=0.772),可较好地预测未知大曲中类黑素含量。该方法操作简便,检测分析时间仅为10~15 min,效率比传统方法提高至少8倍。
To accurately identify the three different types of high-temperature sauce-flavor Daqu,30 samples were collected from storage and subjected to mid-infrared spectra.The three Daqu categories(black,yellow,and white)clustered separately in the principal component analysis.Furthermore,a pattern recognition model was established based on mid-infrared spectroscopy combed with partial least squares discriminant analysis(PLS-DA).With an R^(2)Y of 0.956 and a Q^(2) of 0.906,the model effectively distinguished the different qualities and types of Daqu,offering a data-driven basis for feeding the materials during production.A near-infrared spectroscopy based quantitative model involving 60 samples during different fermentation processes was established to rapidly quantitate melanoidins in Daqu.The obtained spectrum was processed by multiplicative scatter correction(MSC)and first-order derivatives.The PLS model achieved optimal results in the range of 10000~4000^(-1) cm and when the principal component was 8.The coefficient of determination for the calibration set(R_(Val)^(2)) was 0.9877,root mean square error of calibration(RMSEC)was 0.1696,coefficient of determination for the validation set(R_(Val)^(2))was 0.9007,and cross-validation root mean square error(RMSECV)was 0.4911.An external prediction with 15 samples was conducted to validate the reliability of the model,yeilding a root mean square error of prediction(RMSEP)of 0.4606.The ratio of the standard deviation to the prediction standard deviation(RPD)was 2.63.Furthermore,there is no significant differences between the near-infrared method and the reference method(P=0.772).Therefore,this model can effectively predict melanoidin content in unknown Daqu samples.This method could be applied to the rapid quality evaluation of Daqu due to its convenience,with a detection time of only 10~15 min and an efficiency that is at least eight times higher than the traditional method.
作者
王凡
山其木格
卢君
唐平
冯海燕
王丽
毕荣宇
李长文
WANG Fan;SHANQI Muge;LU Jun;TANG Ping;FENG Haiyan;WANG Li;BI Rongyu;LI Changwen(Guizhou Guotai Liquor Group Co.Ltd.,Renhuai 564501,China;Guizhou Guotai Liquor Group Institute,Tianjin 300410,China)
出处
《现代食品科技》
CAS
北大核心
2024年第9期325-332,共8页
Modern Food Science and Technology
基金
贵州省工信厅发展专项资金科技创新项目(202209)
贵州省科技成果应用及产业化计划项目(黔科合成果[2020]2Y045)
合肥市科技计划项目(遵市科合R&D[2020]31号
遵市科合支撑GY(2021)40号)。
关键词
高温大曲
白酒
红外光谱
近红外光谱
类黑素
偏最小二乘
high-temperature Daqu
Baijiu
infrared spectroscopy
near infrared spectroscopy
melanoidins
partial least squares
