Portable near-infrared spectroscopy with variable selection-linear discriminant analysis technology for accurate and nondestructive detection of sulfur-fumigated Citri Reticulatae Pericarpium
文献类型： 外文期刊
作者： Qiu, Xiwen 1 ; Dong, Yiqing 1 ; Jiang, Liwen 1 ; Fan, Wei 1 ; Du, Guorong 3 ; Li, Pao 1 ;
作者机构： 1.Hunan Agr Univ, Coll Food Sci & Technol, Changsha 410128, Peoples R China
2.Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha 410125, Peoples R China
3.Shanghai Tobacco Grp Co Ltd, Technol Ctr, Beijing Work Stn, Beijing 101121, Peoples R China
4.Shaoguan Univ, Guangdong Prov Key Lab Utilizat & Conservat Food &, Shaoguan 512005, Peoples R China
关键词： Citri reticulatae pericarpium; Sulfur fumigation; Nondestructive detection; Near-infrared spectroscopy; Variable selection
期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.0； 五年影响因子：6.0 ）
ISSN： 0023-6438
年卷期： 2024 年 205 卷
页码：
收录情况： SCI
摘要： Citri Reticulatae Pericarpium (CRP) is a kind of important traditional Chinese medicine and food. Sulfurfumigation may induce chemical transformation of CRPs with no difference in appearance, leading to harmful effects on human health. In this paper, an accurate and nondestructive detection method of sulfur-fumigated CRPs was established based on portable near-infrared spectroscopy (NIRS) and chemometrics. 389 normal CRPs and 350 sulfur-fumigated CRPs were collected, while the spectra of outer skin and inner capsule were obtained directly without destroying the samples. A new variable selection-linear discriminant analysis (LDA) and other existing four LDA based on pattern recognition methods were used to establish the identification models of sulfur-fumigated CRPs combined spectral pretreatment methods. The models were evaluated by using the validation set and the external validation set collected one month later. The results show that the identification accuracies of outer skin are better than those of inner capsule. The second-order derivative (2nd)competitive adaptive reweighted sampling (CARS)-LDA model has the best identification ability with 98.92% accuracy for validation set and 99.46% accuracy for external validation set. Besides, the randomization test (RT)LDA method developed in this paper combines the advantages of high robustness and accuracy.
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