未知物成分分析是通过综合的分离和分析手段对复杂的未知化学品的成分进行定性和定量分析，为科研、产品生产、产品开发、改进生产工艺提供科学依据，为企业引进、消化吸收再创新提供强大技术支撑。
未知物成分分析覆盖电子、纺织、日化、塑料、橡胶等各个领域，具体包括：
Ø 助剂产品：纺织、皮革助剂（柔软剂、匀染剂、整理剂等）；电镀（锌、铜、铬、镍、贵重金属）助剂（前处理添加剂、光亮剂、辅助光亮剂等）；塑料和橡胶制品助剂（增塑剂、抗氧剂、阻燃剂、光和热稳定剂、发泡剂、填充剂、抗静电剂等）；涂料助剂（乳化剂、润湿分散剂、消泡剂、阻燃剂等）；线路板制造化学品助剂；电子助焊剂；陶瓷助剂；铝合金表面处理助剂；其它精细化工助剂
Ø 油墨产品：墨水，感光油墨等
Ø 化妆品：洗发、护发用品、护肤用品、美容用品、口腔卫生制品等
Ø 香精、香料
Ø 表面活性剂、民用和工业用清洗剂
Ø 有机溶剂: 油漆稀释剂，天那水，脱漆剂，电子、纺织、印刷行业用溶剂
Ø 水处理剂：缓蚀剂、混凝剂和絮凝剂、阻垢剂等
Ø 石油化学品：润滑油，切削液等
Ø 气雾剂、光亮剂、杀虫剂、脱模剂、致冷剂、空气清新剂等
Ø 高分子材料
Ø 其它化工产品
工业诊断分析是指通过样品或生产过程中微量污染物的鉴定，来查找工业生产过程中的质量事故原因的方法。
工业诊断分析需要综合运用各类常量、微量和痕量检测技术，主要成分与杂质成分鉴定并举，有机分析与无机分析并重，成分分析与生产工艺流程分析结合，尤其是对检测结果的分析和综合判断能力要求很高，才能对产品质量事故原因进行分析诊断。
工业诊断分析业务已涉及精细化工、医疗制品及临床、造纸、电镀、精密仪器制造、汽车生产等工业领域。
行业资讯：
Abstract： Single-cell mass spectrometry analysis enables metabolic profiling of individual cells， helps to reveal the heterogeneity among cells，which is of great significance in oncology research ． Bladder cancer is the most common malignant tumor in the urinary system at present．Accurate iden⁃ tification on the types of bladder cancer cells has an important value in life science and clinical appli⁃ cation in the selection of treatment plan，prognosis judgment and drug resistance evaluation of pa⁃ tients． In this paper， single-cell mass spectrometry combined with machine learning was used to identify bladder cancer cells．The metabolic profiles for different bladder cancer cell subtypes were investigated by single-cell mass spectrometry analysis system， and classification algorithms were studied. based on the collected single cell metabolic data，t-distributed stochastic neighbor embed⁃ ding（t-SNE） clustering algorithm was used for dimensionality reduction analysis on the data，and the difference between the single cell metabolic profile was visualized in the two-dimensional space．In order to accurately identify different types of bladder cancer cells，linear discriminant analysis，ran⁃ dom forest，support vector machine and logistic regression were respectively used to establish ma⁃ chine learning classification models，and grid search method and 5-fold cross-validation were used to optimize the model parameters．Then，five repeats of 10-fold cross-validation were performed on all data sets，and the averaged statistical result was taken as the final result．Accuracy，sensitivity， specificity，receiver operating characteristic（ROC） analysis and other indicators were used to com⁃ doi：10. 19969/j. fxcsxb. 22122804 收稿日期：2022－12－28；修回日期：2023－03－20 基金项目：国家重点研发计划资助项目（2022YFF0705002）；国家自然科学基金资助项目（81902604）；浙江省重点研发计划项目 （2020C03026，2020C02023）；宁波市 3315创新团队项目（2017A-17-C）；宁波市重点研发计划项目（2022Z130）；广州市 番禺区创新创业**团队资助项目（2017-R01-5）；宁波大学王宽诚幸福基金项目 ∗ 通讯作者：金百冶，博士，主任医师，研究方向：泌尿系肿瘤的临床与基础研究，E-mail：jinbaiye1964@zju. edu. cn 陈 腊，博士，助理研究员，研究方向：科学分析仪器研究与开发，E-mail：chenla@nbu. edu. cn 闻路红，博士，教授，研究方向：科学分析仪器研究与开发，E-mail：wenluhong@nbu. edu. cn 分析测试学报 第 42 卷 prehensively evaluate the performance of the model．The results showed that the metabolites of a sin⁃ gle bladder cancer cell，such as ADP，ATP，glutamic acid，pyroglutamic acid，glutathione，etc， were successfully detected by the single-cell mass spectrometry system．There were significant differ⁃ ences among different types of bladder cancer cells，as well as large differences among single cells of the same type，indicating the high heterogeneity of single cell in the tumor．In addition，the four machine learning models all had good typing ability for bladder cancer cells，with a comprehensive accuracy not less than 94. 9%， a sensitivity not less than 88. 6% and a specificity not less than 93. 3%．Compared with other methods，the random forest algorithm has the highest classification ac⁃ curacy，sensitivity and specificity，which are all up to ****，and the area under the ROC curve （AUC） of the model is up to 1，indicating that this method has obvious advantages in classification performance. The method presented in this paper realized the detection of metabolites and differentia⁃ tion of cell subtypes at single cell level of bladder cancer，paving the way for more single cell metabo⁃ lomics research in future. Key words：single-cell mass spectrometry；bladder cancer；metabolite detection；cell typing