Distribution of total acid in pit mud based on VIS/NIR hyperspectral technology
ZHU Min1, SUN Ting2, BAI Zhizhen2, LUO Huibo1, TIAN Jianping2, HUANG Dan1*
1 (College of Biotechnology Engineering, Sichuan University of Science and Engineering, Yibin 644000, China) 2 (College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China)
Abstract: The total acid distribution in pit mud was rapidly assessed using visible (VIS)/near-infrared (NIR) hyperspectral technology. The hyperspectral data of pit mud in the near-infrared and visible wavebands were analyzed by stoichiometry combined with computer technology. Two prediction models, the partial least squares regression (PLSR) and a least squares-support vector machine (LS-SVM) were constructed based on these measurements. The model performances indicated that the optimal model was the Standard Normal Variable Correction-Successive Projection Algorithm-SVM (SNV-SPA-SVM) in the visible region. The coefficient of determination R2cal of the calibration set was 0.998 5, root mean square error of calibration (RMSEC) was 0.004 9 g/kg, and the coefficient of the determination R2pre of the prediction set was 0.999 1. Furthermore, the root mean square error of prediction (RMSEP) was 0.003 8 g/kg and a visual distribution map of the total acid in pit mud was obtained. The results showed that it was feasible to employ hyperspectral technology for rapid and non-destructive detection of the total acid in pit mud, enabling baijiu enterprises to identify problems quickly, adjust processes in a timely manner, prevent pit mud acidification and aging, as well as provide strong technical support for the transformation and upgrading of traditional technology in the Chinese baijiu industry and intelligent online real-time monitoring of pit mud quality.
朱敏,孙婷,白直真,等. 基于可见光/近红外高光谱技术的窖泥总酸的分布[J]. 食品与发酵工业, 2020, 46(8): 111-117.
ZHU Min,SUN Ting,BAI Zhizhen,et al. Distribution of total acid in pit mud based on VIS/NIR hyperspectral technology[J]. Food and Fermentation Industries, 2020, 46(8): 111-117.
WANG Xueshan, DU Hai, XU Yan. Source tracking of prokaryotic communities in fermented grain of Chinese strong-flavor liquor[J]. International Journal of Food Microbiology, 2017, 244: 27-35.
章发盛,张学英. 预防酿酒窖泥老化的研究[J]. 酿酒, 2010, 37(6):45-46.
HEGE E K,O'CONNELL D,JOHNDON W, et al. Hyperspectral imaging for astronomy and space surveillance in imaging Spectrometry IX[J]. International Society for Optics and Photonics,2004,5 159:380-391.
EDELMAN G J,GASTON E,LEEOWEN G V T. Hyperspectral imaging for non-contact analysis of forensic traces[J]. Forensic Science International, 2012, 223(1-3): 28-39.
MALKOFF D B, OLIVER W R. Hyperspectral imaging applied to forensic medicine[J]. Proceedings of Spie-the International Society for Optical Engineering, 2000, 3 920: 108-116.
KUULA J, PÖLÖNENI H,PUUPPONEN H H, et al. Using VIS/NIR and IR spectral cameras for detecting and separating crime scene details[J]. Proceedings of Spie-the International Society for Optical Engineering, 2012, 8 359: 13.
SCHULER R L,KISH P E,PLESE C A.Preliminary observations on the ability of hyperspectral imaging to provide detection and visualization of bloodstain patterns on black fabrics[J]. Journal of Forensic Sciences, 2015, 57(6): 1 562-1 569.
FISCHER C,KAKOULLI I. Multispectral and hyperspectral imaging technologies in conservation: Current research and potential applications[J]. Studies in Conservation, 2006, 51(Sup 1): 3-16.
LIANG Haida. Advances in multispectral and hyperspectral imaging for archaeology and art conservation[J]. Applied Physics A, 2012, 106(2): 309-323.
AFROMOWITZ M A, CALLIS J B,HEIMBACH D M, et al. Multispectral imaging of burn wounds: A new clinical instrument for evaluating burn depth[J].IEEE transactions on bio-medical engineering, 1988, 35(10): 842-850.
CARRASCO O,GOMEZ R B,CHAINANI A, et al. Hyperspectral imaging applied to medical diagnoses and food safety[J]. Proceedings of Spie-the International Society for Optical Engineering, 2003, 5 097: 215-221.
ADAM E,MUTANGA O,RUGEGE D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review[J]. Wetlands Ecology & Management, 2010, 18(3): 281-296.
GOVENDER M,CHETTY K,BULCOCK H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies[J]. Water SA, 2009, 33(2):141-145.
REN J,ZABALZA J,MARSHALL S, et al. Effective feature extraction and data reduction in remote sensing using hyperspectral imaging[J]. IEEE Signal Processing Magazine, 2014, 31(4): 149-154.
ZHU Yaodi,ZOU Xiaobo,SHEN Tingting, et al. Determination of total acid content and moisture content during solid-state fermentation processes using hyperspectral imaging[J]. Journal of Food Engineering, 2016, 174: 75-84.
MUNERA S,BESADA C,ALEIXOS N, et al. Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging[J]. LWT - Food Science and Technology, 2017, 77: 241-248.
LING Yan,XIONG Chuanwu,HAO Qu, et al. Non-destructive determination and visualisation of insoluble and soluble dietary fibre contents in fresh-cut celeries during storage periods using hyperspectral imaging technique[J]. Food Chemistry, 2017, 228: 249-256.
FERREIRA D S,GALÃO O F,PALLONE J A L, et al. Comparison and application of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for determination of quality parameters in soybean samples[J]. Food Control, 2014, 35(1): 227-232.
HE Hongju, WU Di, SUN Dawen. Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis-NIR) hyperspectral imaging[J]. Food Chemistry, 2014, 156:394-401.