Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy

Abstract

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk’s lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

First Steps in Developing a Fast, Cheap, and Reliable Method to Distinguish Wild Mushroom and Truffle Species

“…In accordance with previous studies [43,44,65], the LDA classification model was able to accurately identify the 14 wild MT species by their smellprint (Figure 3), and both the LDA and PLS-DA classification models could accurately distinguish between the edible and non-edible species (Figure 4). These classification models have been largely used for statistical treatment of the volatile compounds of food matrices, and have been showing high accuracy [66][67][68]. For example, the combined use of the e-nose and PLS-DA contributed to the successful identification of filamentous fungal [67] and plant species [66], monitoring product quality during and after production processing [68][69][70][71], product quality [72], and establishing geographic origin [73].…”

“…These classification models have been largely used for statistical treatment of the volatile compounds of food matrices, and have been showing high accuracy [66][67][68]. For example, the combined use of the e-nose and PLS-DA contributed to the successful identification of filamentous fungal [67] and plant species [66], monitoring product quality during and after production processing [68][69][70][71], product quality [72], and establishing geographic origin [73]. Moreover, comprehensive datasets encompassing a wide range of wild MT species should be established to enhance the accuracy and robustness of the e-nose's identification capabilities.…”

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

“…Artificial intelligence sensory technologies can be used to quantify multiple quality signals, including sensory information obtained from bionic sensory systems, and to perform pattern recognition for sample classification. This approach provides fast, accurate, comprehensive sample data, and has been widely used in detection and analysis of drugs and foods in the past ( Lu et al, 2022 ; Gui et al, 2023 ). Data fusion technology consists in merging complementary information to obtain more data points; this technology was originally used in the military, but has gradually been applied in various types of quality evaluation of traditional Chinese medicine such as origin identification ( Ru et al, 2019 ; Wang et al, 2021 ), species identification ( Lan et al, 2020 ; Sun et al, 2020 ), quality control of production process ( Zhang et al, 2022 ), and evaluation of preparation quality ( Wang et al, 2017 ; Yan and Sun, 2018 ).…”

Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose

“…Modern identification methods include GC ( Xu et al, 2018 ), GC-MS( Wang et al, 2021a ; Gu et al, 2022 ), and DNA barcoding ( Huang et al, 2017 ; Lu et al, 2021 ) which have accurate and reliable results; however, the sample pretreatment is cumbersome, time-consuming and has high technical operational requirements. Therefore, a rapid and accurate quality identification method for A. fructus is urgently needed ( Gui et al, 2023 ).…”

“…Bionic sensory technologies such as e-tongue and e-nose ( Weng et al, 2022 ; Gui et al, 2023 ), can be used to objectify the trait characteristics of medicine or food. The integration of bionic sensory and modern analysis instrumental yields the advantages of both “fast” sensory response and “quantitative” instrumental analysis, affording fast analysis, high sensitivity, strong reliability, good repeatability, and strong integrity ( Xie et al, 2016 ).…”