Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

• Published in: Journal of chemometrics Vol. 36; no. 9
• Main Authors: Li, Lian, Zhang, Song, Zuo, Zhi‐Tian, Wang, Yuan‐Zhong
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.09.2022
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Artificial neural networks; Chemometrics; Cluster analysis; Data integration; Discriminant analysis; feature variable extraction; Fourier transforms; Fungi; geographical traceability; Information technology; Infrared spectroscopy; Machine learning; multiple‐information strategy; Near infrared radiation; Spectrum analysis; Strategy; Support vector machines; Wolfiporia cocos
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.3436

Owing to the widespread concern relating to herb safety and quality, there is a momentum to discriminate the geographical traceability of fungus with multiple‐information technologies. In this study, we attempted to evaluate the fusion strategy of multiple‐information for the geographical traceability of this fungus based on Fourier transform near infrared spectroscopy (FT‐NIR) and Fourier‐transform mid infrared spectroscopy (FT‐MIR) with chemometrics. From all results, (1) comparative visualization of t‐distributed stochastic neighbor embedding (t‐SNE) emerged that the superior effect of FT‐NIR spectroscopy was better than FT‐MIR, which were separated according to the samples of different regions; (2) in view of optimized spectrum, the geographical traceability of extreme learning machine (ELM) model was better than the partial least squares discriminant analysis (PLS‐DA) and support vector machine (SVM) models that the accuracy of training set for all models was 100%; (3) the accuracy of train and test for ELM model were 100% with single spectrum, low‐level, and mid‐level fusion of FT‐NIR and FT‐MIR based on principal components (PCs) and competitive adaptive weighting algorithm (CARS), which was suitable for geographical traceability and evaluation of this fungus. (4) The three models based on successive projection algorithm (SPA) were not dissatisfied for the geographical traceability of this fungus. (5) The result of hierarchical cluster analysis (HCA) was consistent with the above results, which could distinguish samples according to different regions. All results showed that models could be developed as superior model on the foundation of single spectrum based on FT‐NIR, low‐level or mid‐level (except SPA) fusion of ELM, which could be employed in geographical traceability of Wolfiporia cocos, effectively. These partial least squares discriminant analysis (PLS‐DA), support vector machine (SVM), and extreme learning machine (ELM) models were successfully identified for the geographical traceability of Wolfiporia cocos based on data fusion strategy of Fourier transform near infrared spectroscopy (FT‐NIR) and Fourier‐transform mid infrared spectroscopy (FT‐MIR).