Rapid assessment of quality of deer antler slices by using an electronic nose coupled with chemometric analysis

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares–Regression (PLS-R) and support vector machine–regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error—RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Determination of the geographical origin of Tetrastigma hemsleyanum Diels & Gilg using an electronic nose technique with multiple algorithms

Tetrastigma hemsleyanum Diels & Gilg, an herbal medicinal plant, is planted widely in bamboo forests in southern China to promote economic benefits. Volatile compounds (VOCs) of T. hemsleyanum from different geographical regions are difficult to identify in field forests. In this study, VOCs from leaf samples of different geographical origins were analyzed using an electronic nose with 10 different sensors. Principal component analysis (PCA), partial least-squares regression (PLS), hierarchical cluster analysis (HCA), and radial basis function (RBF) neural networks were used to determine differences among different local samples. The results demonstrated that PCA achieved an accurate discrimination percentage of 91.31% for different samples and HCA separated the samples into different groups. The RBF neural network was successfully applied to predict samples with no specified localities. T. hemsleyanum samples from geographically close regions tended to group together, whereas those from distant geographical regions showed obvious differences. These results indicate that an electronic nose is an effective tool for detecting VOCs and discriminating the geographical origins of T. hemsleyanum. This study provides insights for further studies on the fast detection of VOCs from plants and effect of forests and plant herbal medicines on improving air quality.

Discrimination of Two Cultivars of Alpinia Officinarum Hance Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Chemometrics

Background: Alpinia officinarum Hance is both an herbal medicine and a condiment, and generally has different cultivars such as Zhutou galangal and Fengwo galangal. The appearance of these A. officinarum cultivars is similar, but their chemical composition and quality are different. It is therefore important to discriminate between different A. officinarum plants to ensure the consistency of the efficacy of the medicine. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two cultivars for fast and robust discrimination. Methods: Odor and volatile components of all A. officinarum samples were detected by the E-nose and gas chromatography-mass spectrometry (GC-MS), respectively. The E-nose sensors and GC-MS data were analyzed respectively by principal component analysis (PCA), the correlation between E-nose sensors and GC-MS data were analyzed by partial least squares (PLS). Results: It was found that Zhutou galangal and Fengwo galangal can be discriminated by combining the E-nose with PCA, and the E-nose sensors S2, S6, S7, S9 were important sensors for distinguishing different cultivars of A. officinarum. A total of 56 volatile components of A. officinarum were identified by the GC-MS analysis, and the composition and content of the volatile components from the two different A. officinarum cultivars were different, in particular the relative contents of 1,8-cineole and α-farnesene. The classification result by PCA analysis based on GC-MS data was consistent with the E-nose results. The PLS analysis demonstrated that the volatile terpene, alcohol and ester components primarily interacted with the sensors S2 and S7, indicating that particular E-nose sensors were highly correlated with some aroma constituents. Conclusions: Combined with advanced chemometrics, the E-nose detection technology can discriminate two cultivars of A. officinarum, with GC-MS providing support to determine the material basis of the E-nose sensors’ response.

Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms?

Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose) response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose.