食品与发酵工业 >

2022 , Vol. 48 >Issue 4: 255 - 260

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.027968

分析与检测

白酒糟醅中酸度值的高光谱检测方法

  • 鞠杰 ,
  • 田建平 ,
  • 胡新军 ,
  • 黄丹 ,
  • 黄浩平 ,
  • 彭兴辉 ,
  • 罗惠波
展开
  • 1(四川轻化工大学 机械工程学院,四川 宜宾,644000)
    2(四川轻化工大学 生物工程学院,四川 宜宾,644000)
    3(酿酒生物技术及应用四川省重点实验室,四川 宜宾,644000)
硕士研究生(田建平教授为通信作者,E-mail:409507648@qq.com)

收稿日期: 2021-05-18

  修回日期: 2021-09-23

  网络出版日期: 2022-03-16

基金资助

四川省科技厅重点研发项目(2019YJ0475);中国轻工业浓香型白酒固体发酵重点实验室项目(2019JJ016)

收起

Hyperspectral detection method for the acidity detection of Baijiu fermented grains

  • JU Jie ,
  • TIAN Jianping ,
  • HU Xinjun ,
  • HUANG Dan ,
  • HUANG Haoping ,
  • PENG Xinghui ,
  • LUO Huibo
Expand
  • 1(College of Mechanical Engineering, Sichuan University of Science & Engineering, Yibin 644000, China)
    2(College of Biotechnology Engineering, Sichuan University of Science & Engineering, Yibin 644000, China)
    3(Liquor Making Biotechnology and Application Key Laboratory of Sichuan Province, Yibin 644000, China)

Received date: 2021-05-18

  Revised date: 2021-09-23

  Online published: 2022-03-16

Fold

摘要

酸度值是评价白酒糟醅质量的重要指标之一,为进一步提高糟醅酸度值的检测精度,提出了一种应用高光谱成像技术检测糟醅酸度值的方法。采用高光谱成像系统,在900～1 700 nm内采集糟醅样本的光谱信息,并提取全部样本的平均光谱数据。采用3种预处理方法对原始光谱进行预处理,得到多元散射校正(multiplicative scatter correction,MSC)为最佳预处理方法。在保证检测精度的基础上,为提高检测效率,采用竞争自适应加权抽样(competitive adaptive reweighted sampling,CARS)算法选择特征波段作为优化方法,建立偏最小二乘回归(partial least squares regression,PLSR)和最小二乘支持向量机(least squares-support vector machine,LS-SVM)预测模型。结果表明,CARS算法选取38个特征波长所建立的LS-SVM预测模型显示出良好的预测效果,其中预测集相关系数R2p为0.961 8,预测集均方根误差为0.058 0 g/kg。研究结果表明,高光谱成像技术相对于其他检测技术在糟醅酸度检测中有更好的检测精度,同时针对白酒工艺过程的其他成分含量检测探索出有效的检测途径。

关键词： 糟醅; 高光谱成像; 酸度值; 快速检测; 建模算法

本文引用格式

鞠杰 , 田建平 , 胡新军 , 黄丹 , 黄浩平 , 彭兴辉 , 罗惠波 . 白酒糟醅中酸度值的高光谱检测方法[J]. 食品与发酵工业, 2022 , 48(4) : 255 -260 . DOI: 10.13995/j.cnki.11-1802/ts.027968

Abstract

Acidity value is one of the important indexes to evaluate the quality of fermented grains. In order to further improve the detection accuracy of the acidity value of fermented grains, a hyperspectral imaging method has been proposed to detect the acidity value of fermented grains. A hyperspectral imaging system was used to collect the spectral information of fermented grains samples in the range of 900-1 700 nm and the average spectral data of all samples was extracted. Multiple scattering correction (MSC) was identified as the best pretreatment method of three tested pretreatment methods. On the basis of ensuring the detection accuracy and improving the detection efficiency, the competitive adaptive reweighted sampling (CARS) algorithm adopted to select feature bands was developed as the optimization method, and the PLSR and LS-SVM prediction models were established. The results showed that the LS-SVM prediction model, based on 38 characteristic wavelengths selected by CARS algorithm presented good prediction effect. The correlation coefficient of the prediction set R2p was 0.961 8, and the root mean square error of the prediction set was 0.058 0 g/kg. The results indicated that hyperspectral imaging had better accuracy than other techniques in detecting the acidity of fermented grains. At the same time, an effective way to detect the content of other ingredients in liquor process was explored.

Key words： fermented grains; hyperspectral imaging; acidity value; rapid detection; modeling algorithm

参考文献

[1] 魏志阳, 李秋志, 邢爽, 等.HPLC法同时测定清香类酒醅中主要酸和酯类物质[J].中国酿造, 2018, 37(8):167-171.
WEI Z Y, LI Q Z, XING S, et al.Simultaneous determination of main acids and esters in fermented grains of light-flavor Baijiu by HPLC[J].China Brewing, 2018, 37(8):167-171.
[2] 刘晓, 刘广瑞, 隋璐, 等.应用HS-SPME结合GC-MS分析半固态发酵浓香型酒醅中挥发性成分[J].酿酒科技, 2020(4):102-106;110.
LIU X, LIU G R, SUI L, et al.HS-SPME-GC-MS analysis of volatile components in nongxiang fermented grains by semi-solid fermentation[J].Liquor-Making Science & Technology, 2020(4):102-106;110.
[3] 余松柏, 赵小波, 田敏, 等.近红外光谱技术在快速检测白酒酒醅中的应用[J].酿酒科技, 2021(2):59-64.
YU S B, ZHAO X B, TIAN M, et al.Application of near infrared spectrometry in rapid detection of fermented grains of Baijiu[J].Liquor-Making Science & Technology, 2021(2):59-64.
[4] 石吉勇, 胡雪桃, 朱瑶迪, 等.高光谱图像技术定量检测香醋醋醅水分分布均匀性[J].中国食品学报, 2018, 18(2):250-255.
SHI J Y, HU X T, ZHU Y D, et al.Quantitative detection of homogeneity of moisture content distribution in vinegar culture by hyperspectral imaging technique[J].Journal of Chinese Institute of Food Science and Technology, 2018, 18(2):250-255.
[5] 于宏威, 王强, 石爱民, 等.高光谱成像技术结合化学计量学可视化花生中蛋白质含量分布[J].光谱学与光谱分析, 2017, 37(3):853-858.
YU H W, WANG Q, SHI A M, et al.Visualization of protein in peanut using hyperspectral image with chemometrics[J].Spectroscopy and Spectral Analysis, 2017, 37(3):853-858.
[6] 陈彩虹, 张淑娟, 孙海霞, 等.高光谱成像技术在核桃壳仁检测中的应用[J].山西农业大学学报(自然科学版), 2018, 38(11):27-32.
CHEN C H, ZHANG S J, SUN H X, et al.Application of hyperspectral imaging technology in identification of walnut shell and kernels[J].Journal of Shanxi Agricultural University(Natural Science Edition), 2018, 38(11):27-32.
[7] 吴龙国, 王松磊, 何建国.基于高光谱技术的土壤水分无损检测[J].光谱学与光谱分析, 2018, 38(8):2 563-2 570.
WU L G, WANG S L, HE J G.Study on soil moisture mechanism and establishment of model based on hyperspectral imaging technique[J].Spectroscopy and Spectral Analysis, 2018, 38(8):2 563-2 570.
[8] 韩仲志, 刘杰.高光谱亚像元分解预测花生中的黄曲霉毒素B1[J].中国食品学报, 2020, 20(3):244-250.
HAN Z Z, LIU J.Detecting aflatoxin B1 in peanuts by hyperspectral subpixel decomposition[J].Journal of Chinese Institute of Food Science and Technology, 2020, 20(3):244-250.
[9] 陈李品, 于繁千惠, 陶然, 等.基于高光谱成像技术预测牡蛎干制加工过程中的水分含量[J].中国食品学报, 2020, 20(7): 261-268.
CHEN L P, YU F Q H, TAO R, et al.Prediction of moisture content in oyster drying process based on hyperspectral imaging[J].Journal of Chinese Institute of Food Science and Technology, 2020, 20(7):261-268.
[10] XIA C J, REN M, WANG B, et al.Acquisition and analysis of hyperspectral data for surface contamination level of insulating materials[J].Measurement, 2021, 173:108560.
[11] ZHANG L, SUN H, RAO Z H, et al.Non-destructive identification of slightly sprouted wheat kernels using hyperspectral data on both sides of wheat kernels[J].Biosystems Engineering, 2020, 200:188-199.
[12] OUYANG Q, WANG L, PARK B, et al.Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology[J].Food Chemistry, 2021, 350(6):129141.
[13] 许建东, 张淑娟, 郑小南, 等.高光谱技术结合变量选择方法的甘薯冻害检测研究[J].食品与发酵工业, 2021, 47(8):197-203.
XU J D, ZHANG S J, ZHENG X N, et al.Study on the detection of sweet potato freezing damage based on hyperspectral technology and variable selection method[J].Food and Fermentation Industries, 2021, 47(8):197-203.
[14] HU J, XU Z, LI M P, et al.Discriminant analysis and quantitative study of antibiotics in infant milk powder based on hyperspectral detection[J].Vibrational Spectroscopy, 2021, 114:103244.
[15] DE ÖZDOĞAN G, LIN X H, SUN D W.Rapid and noninvasive sensory analyses of food products by hyperspectral imaging:Recent application developments[J].Trends in Food Science & Technology, 2021, 111:151-165.
[16] ACHATA E M, ESQUERRE C, GOWEN A A, et al.Feasibility of near infrared and Raman hyperspectral imaging combined with multivariate analysis to assess binary mixtures of food powders[J].Powder Technology, 2018, 336:555-566.
[17] 孙红, 刘宁, 吴莉, 等.高光谱成像的马铃薯叶片含水率分布可视化[J].光谱学与光谱分析, 2019, 39(3):910-916.
SUN H, LIU N, WU L, et al.Visualization of water content distribution in potato leaves based on hyperspectral image[J].Spectroscopy and Spectral Analysis, 2019, 39(3):910-916.
[18] MARTÍNEZ GILA D M, CANO MARCHAL P, GÁMEZ GARCÍA J, et al.On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples[J].Computers and Electronics in Agriculture, 2015, 116(C):1-7.
[19] 张大凤, 李可, 刘森, 等.中国浓香型白酒窖池糟醅中微生物群落演替分析[J].食品科学, 2012, 33(15):183-187.
ZHANG D F, LI K, LIU S, et al.Microbial community succession of Chinese Luzhou-flavor liquor lees[J].Food Science, 2012, 33(15):183-187.
[20] 胡晓龙, 王康丽, 余苗, 等.浓香型酒醅微生物菌群演替规律及其空间异质性[J].食品与发酵工业, 2020, 46(10):66-73.
HU X L, WANG K L, YU M, et al.Microbial community succession pattern and spatial heterogeneity in fermented grains of strong-flavor Baijiu[J].Food and Fermentation Industries, 2020, 46(10):66-73.
[21] 曹建全, 刘雪, 李霞, 等.近红外光谱快速分析景芝白酒酒醅指标的研究[J].酿酒科技, 2015(4):109-111.
CAO J Q, LIU X, LI X, et al.Rapid detection of fermented grains of Jingzhi Baijiu(liquor) by near-infrared spectroscopy[J].Liquor-Making Science & Technology, 2015(4):109-111.
[22] 刘翠英, 张津瑞, 曾涛, 等.傅里叶变换红外光谱的土壤团聚体有机碳和全氮含量估测[J].光谱学与光谱分析, 2020, 40(12):3 818-3 824.
LIU C Y, ZHANG J R, ZENG T, et al.Determination of soil organic carbon and total nitrogen contents in aggregate fractions from Fourier transform infrared spectroscopy[J].Spectroscopy and Spectral Analysis, 2020, 40(12):3 818-3 824.
[23] 郭俊先, 马永杰, 郭志明, 等.流形学习方法及近红外透射光谱的新疆冰糖心红富士水心鉴别[J].光谱学与光谱分析, 2020, 40(8):2 415-2 420.
GUO J X, MA Y J, GUO Z M, et al.Watercore identification of Xinjiang Fuji apple based on manifold learning algorithm and near infrared transmission spectroscopy[J].Spectroscopy and Spectral Analysis, 2020, 40(8):2 415-2 420.
[24] 熊雅婷, 李宗朋, 王健, 等.近红外光谱波段优化在白酒酒醅成分分析中的应用[J].光谱学与光谱分析, 2016, 36(1):84-90.
XIONG Y T, LI Z P, WANG J, et al.The near infrared spectral bands optimal selection in the application of liquor fermented grains composition analysis[J].Spectroscopy and Spectral Analysis, 2016, 36(1):84-90.
Options
文章导航

/

〈 〉

技术支持：北京玛格泰克科技发展有限公司