{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T02:03:30Z","timestamp":1784081010691,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Academic Degree and Postgraduate Education Reform Project of Jiangsu Province","award":["KYCX25_1402"],"award-info":[{"award-number":["KYCX25_1402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional methods for wine bottle packaging leakage detection often suffer from low efficiency, high false-positive rates, or an inability to detect micro-leakages. This paper proposes a near-infrared laser leakage detection system based on tunable diode laser absorption spectroscopy at 1392 nm, combined with a LightGBM machine learning model. The system detects gaseous ethanol vapor escaping from leaking bottles, addressing the spectral interference caused by ambient water vapor. A total of 1410 samples were collected, and each raw 2000-point spectral contour was compressed into a 200-dimensional feature vector through baseline correction, Z-score normalization, and uniform down-sampling. A two-stage hyperparameter optimization strategy yielded the optimal LightGBM configuration with a 5-fold cross-validation. For the binary classification task, the model achieved an AUC of 0.9949 and an inference speed of 0.0058 ms per sample on a CPU, outperforming Random Forest, PLS, and four deep learning models. For the regression task, the model achieved an R2 of 0.5854 \u00b1 0.0919. An anti-interference experiment on 422 samples under varying flow rates, temperatures, and commercial wine types confirmed the model\u2019s robustness, achieving an overall accuracy of 0.94 and an alcohol recall of 0.99. To further validate the system under realistic conditions, a simulated micro-leakage test was conducted using a negative-pressure extraction method: 320 samples were collected from artificially damaged commercial wine bottles placed in a custom-built acrylic vacuum chamber that replicates the production line enclosure. The model achieved an accuracy of 0.95 with zero false negatives. The complete detection cycle takes no more than 5 s per bottle, enabling non-destructive, rapid, and online packaging integrity assessment. The results demonstrate that the proposed system provides a low-cost and reliable solution for wine bottle leakage detection suitable for industrial deployment.<\/jats:p>","DOI":"10.3390\/s26144474","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:05:58Z","timestamp":1784077558000},"page":"4474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Near-Infrared Laser Leakage Detection System for Wine Bottles"],"prefix":"10.3390","volume":"26","author":[{"given":"Xinyu","family":"Chen","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shugui","family":"Ding","sequence":"additional","affiliation":[{"name":"Lehui International Institute of Intelligent Packaging, Nanjing Forestry University, Nanjing 211102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5087-6877","authenticated-orcid":false,"given":"Xiaojun","family":"Jin","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0533-4837","authenticated-orcid":false,"given":"Ying","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1179\/sic.1994.39.3.169","article-title":"Leakage prediction for buildings, cases, bags and bottles","volume":"39","author":"Michalski","year":"1994","journal-title":"Stud. Conserv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6004","DOI":"10.1109\/TIM.2020.2969300","article-title":"A mechanism and method of leak detection for pressure vessel: Whether, when, and how","volume":"69","author":"Gao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.ymssp.2018.05.023","article-title":"Detection of air leakage into vacuum packages using acoustic measurements and estimation of defect size","volume":"114","author":"Koruk","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bouzid, A.-H. (2021). A study on liquid leak rates in packing seals. Appl. Sci., 11.","DOI":"10.3390\/app11041936"},{"key":"ref_5","unstructured":"(2009). Standard Test Method for Nondestructive Detection of Leaks in Packages by Vacuum Decay Method (Standard No. ASTM F2338-09)."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"04014064","DOI":"10.1061\/(ASCE)CP.1943-5487.0000371","article-title":"Damage detection strategy using strain-mode residual trends for long-span bridges","volume":"29","author":"Xu","year":"2015","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1061\/(ASCE)BE.1943-5592.0000195","article-title":"Energy damage detection strategy based on strain responses for long-span bridge structures","volume":"16","author":"Xu","year":"2011","journal-title":"J. Bridge Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1515\/REVCE.2007.23.2.65","article-title":"Tunable diode laser absorption spectroscopy (TDLAS) in the process industries\u2014A review","volume":"23","author":"Lackner","year":"2007","journal-title":"Rev. Chem. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Workman, J., and Weyer, L. (2007). Practical Guide to Interpretive Near-Infrared Spectroscopy, CRC Press.","DOI":"10.1201\/9781420018318"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117183","DOI":"10.1016\/j.saa.2019.117183","article-title":"Near-infrared spectroscopic study of molecular interaction in ethanol-water mixtures","volume":"222","author":"Dong","year":"2019","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jqsrt.2017.06.038","article-title":"The HITRAN2016 molecular spectroscopic database","volume":"203","author":"Gordon","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115401","DOI":"10.1088\/0957-0233\/27\/11\/115401","article-title":"Simultaneous imaging of temperature and concentration of ethanol\u2013water mixtures in microchannel using near-infrared dual-wavelength absorption technique","volume":"27","author":"Kakuta","year":"2016","journal-title":"Meas. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21433","DOI":"10.1364\/OE.26.021433","article-title":"Stand-off detection of ethanol vapor based on a tunable ICL","volume":"26","author":"Gao","year":"2018","journal-title":"Opt. Express"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Du, Z., Zhang, S., Li, J., Gao, N., and Tong, K. (2019). Mid-Infrared Tunable Laser-Based Broadband Fingerprint Absorption Spectroscopy for Trace Gas Sensing: A Review. Appl. Sci., 9.","DOI":"10.3390\/app9020338"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/S0022-4073(03)00119-5","article-title":"Absorption by water vapour in the 1 to 2\u03bcm region","volume":"83","author":"Smith","year":"2004","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Echeveria, L., Hao, Y., Rushford, M.C., Chavez, G., Tardif, S., Chang, A., Aubry, S., Murialdo, M., Carter, J.C., and Foley, B. (2025). Fiber-Coupled Multipass NIR Sensor for In Situ, Real-Time Water Vapor Outgassing Monitoring. Sensors, 25.","DOI":"10.3390\/s25123824"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125167","DOI":"10.1016\/j.renene.2025.125167","article-title":"Hybrid machine learning approach in forecasting renewable energy production: Extra trees, catBoost and LightGBM based stacking model","volume":"260","author":"Colak","year":"2026","journal-title":"Renew. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.20517\/ir.2024.01","article-title":"Recognition of the behaviors of dairy cows by an improved YOLO","volume":"4","author":"Bai","year":"2024","journal-title":"Intell. Robot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"143","DOI":"10.20517\/ir.2025.09","article-title":"An overview of industrial image segmentation using deep learning models","volume":"5","author":"Wang","year":"2025","journal-title":"Intell. Robot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.20517\/ir.2024.02","article-title":"Pig-ear detection from the thermal infrared image based on improved YOLOv8n","volume":"4","author":"Han","year":"2024","journal-title":"Intell. Robot."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1177\/058310249803000201","article-title":"A summary review of vibration-based damage identification methods","volume":"30","author":"Doebling","year":"1998","journal-title":"Shock Vib. Dig."},{"key":"ref_23","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. (2017). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Curran Associates Inc."},{"key":"ref_24","first-page":"3133","article-title":"Do we need hundreds of classifiers to solve real world classification problems?","volume":"15","author":"Cernadas","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, Y.-Q., Gu, M., and Xu, Z.-D. (2023). Research on the improvement of semi-global matching algorithm for binocular vision based on lunar surface environment. Sensors, 23.","DOI":"10.3390\/s23156901"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_27","first-page":"1215","article-title":"Damage detection for space truss structures based on strain mode under ambient excitation","volume":"138","author":"Xu","year":"2012","journal-title":"J. Eng. Mech."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109799","DOI":"10.1016\/j.asoc.2022.109799","article-title":"A multi-neural network fusion algorithm for fire warning in tunnels","volume":"131","author":"Sun","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1007\/s11053-023-10249-6","article-title":"A novel scheme for mapping of MVT-Type Pb\u2013Zn Prospectivity: LightGBM, a highly efficient gradient boosting decision tree machine learning algorithm","volume":"32","author":"Hajihosseinlou","year":"2023","journal-title":"Nat. Resour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_32","first-page":"1525","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_34","unstructured":"Platt, U., and Stutz, J. (2008). Differential absorption spectroscopy. Differential Optical Absorption Spectroscopy: Principles and Applications, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2007.914731","article-title":"An introduction to compressive sampling","volume":"25","author":"Wakin","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_36","unstructured":"Atkins, P.W., De Paula, J., and Keeler, J. (2023). Atkins\u2019 Physical Chemistry, Oxford University Press."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"043102","DOI":"10.1063\/1.4895394","article-title":"A fundamental equation of state for ethanol","volume":"43","author":"Schroeder","year":"2014","journal-title":"J. Phys. Chem. Ref. Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2408212","DOI":"10.1080\/14686996.2024.2408212","article-title":"Sensing signal augmentation by flow rate modulation of carrier gas for accurate differentiation of complex odours","volume":"25","author":"Feng","year":"2024","journal-title":"Sci. Technol. Adv. Mater."},{"key":"ref_39","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Alves, R.L.S., De Melo, J.D., Neto, A.D., and Albuquerque, A.C.M. (2002). New parallel algorithms for back-propagation learning. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN\u201902 (Cat. No. 02CH37290), IEEE.","DOI":"10.1109\/IJCNN.2002.1007571"},{"key":"ref_41","unstructured":"Jernelv, I.L., Hjelme, D.R., Matsuura, Y., and Aksnes, A. (2020). Convolutional neural networks for classification and regression analysis of one-dimensional spectral data. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6622927","DOI":"10.1155\/2020\/6622927","article-title":"A CNN-LSTM-based model to forecast stock prices","volume":"2020","author":"Lu","year":"2020","journal-title":"Complexity"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/14\/4474\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:07:02Z","timestamp":1784077622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/14\/4474"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"references-count":42,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26144474"],"URL":"https:\/\/doi.org\/10.3390\/s26144474","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]}}}