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基于NIR和GC-MS融合技术的浓香型白酒原酒等级鉴别

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以蒸馏过程中不同等级的浓香型白酒原酒为研究对象,分别获取原酒的近红外光谱(near infrared spectroscopy,NIR)数据和气相色谱-质谱(gas chromatography-mass spectrometry,GC-MS)数据.采用5点2次卷积平滑对NIR数据进行预处理后,利用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)筛选光谱特征波数;结合Spearman等级相关系数、最大信息系数和随机森林变量重要性筛选GC-MS中影响原酒等级划分的关键风味成分(key flavor components,KC).然后利用极端梯度提升树分别建立基于NIR和GC-MS以及融合数据的原酒等级鉴别模型.结果表明,基于CARS选择的光谱特征变量建立的模型预测准确率为89.66%,基于特征选择后的KC建立的模型预测准确率为94.83%,基于CARS+KC融合数据建立的模型分类准确率达到了98.28%.研究表明,将GC-MS数据和NIR数据的有效特征信息进行数据融合,可以改善单一检测技术对不同等级原酒特征信息表征不全面的缺点,在单一数据源的基础上提高原酒等级鉴别的准确率和稳定性,实验结果为原酒的等级鉴别以及白酒其他的质量控制提供了新的思路和理论基础.
Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry
Raw Nongxiangxin Baijiu of different grades were collected during the distillation process,and their near infrared spectroscopy(NIR)data and gas chromatography-mass spectrometry(GC-MS)data were acquired.After preprocessing the NIR data through 5-point 2-fold convolutional smoothing,spectral feature wavelengths were selected using the competitive adaptive reweighted sampling(CARS)algorithm;combining Spearman's rank correlation coefficient,maximum information coefficient(MIC)and random forest(RF)variable importance,the key flavor components(KC)identified by GC-MS affecting the grading of raw Baijiu were determined.Then,extreme gradient boosting tree(XGBoost)was applied to establish three grade identification models for raw Baijui based on NIR,GC-MS and their fused data.The results showed that the prediction accuracy of the model based on the spectral feature variables selected by CARS was 89.66%,the prediction accuracy of the model based on KC after feature selection was 94.83%,and the classification accuracy of the model based on the fused data of CARS+KC reached as high as 98.28%.This study shows that the fusion of effective feature information from GC-MS and NIR data can enable more accurate and stable grade identification of raw Nongxiangxin Baijiu than either analytical technique alone,which provides a new idea and theoretical basis for the grade identification and quality control of raw Baijiu.

raw Nongxiangxin Baijiunear infrared spectroscopygas chromatography-mass spectrometrydata fusionextreme gradient boosting tree

张维、张贵宇、庹先国、付妮、李晓平、庞婷婷、刘科材

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四川轻化工大学自动化与信息工程学院,四川 宜宾 644000

四川轻化工大学 人工智能四川省重点实验室,四川 宜宾 644000

四川轻化工大学工程实践中心,四川 宜宾 644000

浓香型白酒原酒 近红外光谱 气相色谱-质谱联用 数据融合 极端梯度提升树

泸州老窖研究生创新基金项目酿酒生物技术及应用四川省重点实验室开放课题五粮液产学研合作项目中国轻工业酿酒生物技术及智能制造重点实验室项目四川轻化工大学《横向科研项目结余经费出资科技成果转化专项》项目四川轻化工大学2023年度"652"科研创新团队资助项目

LJCX-2022-8NJ2022-06CXY2022ZR0072023-01HXJY01SUSE652B005

2024

食品科学
北京食品科学研究院

食品科学

CSTPCD北大核心
影响因子:1.327
ISSN:1002-6630
年,卷(期):2024.45(21)