Journal of Chemometrics最新文献

Seed purity assurance is an important aspect of maintaining the quality standards of wheat seeds. It relies significantly on quality parameters, like varietal classification and geographical origin identification. Hyperspectral imaging (HSI) has emerged as an advanced nondestructive technique to determine various quality parameters. In recent years, several studies have utilized HSI for varietal classification, although a limited number of varieties were considered. Additionally, no attention has been paid to determining the geographical origin of wheat seeds. To address these gaps, two separate experiments were performed for varietal classification and geographical origin identification. The seeds from 96 varieties grown across 5 different agricultural regions in India were collected. Hyperspectral images of wheat seeds were acquired in the wavelength ranging 900–1700 nm. The spectral reflectance values were obtained from the region of interest (ROI) corresponding to each seed. Subsequently, the deep learning models (convolutional neural networks [CNNs]) were established and compared with two conventional algorithms, including support vector machines (SVMs) and K-nearest neighbors (KNNs). The experimental results indicated that the proposed CNN models outperformed the SVM and KNN models, achieving an overall accuracy of 94.88% and 99.02% for varietal classification and geographical origin identification, respectively. These results demonstrate that HSI combined with deep learning has the potential to accurately classify a large number of wheat varieties. Moreover, HSI can be used to precisely identify the geographical origins of wheat seeds. This study provides an accurate and nondestructive method that can assist in breeding, quality evaluation, and the development of high-quality wheat seeds.

Novelty and similarity are complex concepts that have numerous applications in various fields, including biology and medicine. Novelty detection is a technique used to determine whether a dataset is different from another dataset considered as a standard. Similarity detection is a technique used to determine whether two datasets belong to the same population. Novelty and similarity are closely related concepts; however, they are not complementary. Novelty is a much more popular one, and there are many publications about it. Similarity is, in fact, a new concept that has not yet been explored in depth. Classical statistics offers a large number of tools suitable for detection of similarity, mostly in the univariate case. At the same time, this topic has been insufficiently studied in the field of machine learning. This paper suggests several principles which are important for this research and also present a method for the detection of both novelty and similarity. The method uses a one-class classifier, known as Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). Three examples illustrate our approach. The first one uses simulated data and demonstrates the performance of DD-SIMCA for the detection of novelty. The second example uses a real-world data and studies similarity of two groups of patients who participate in the evaluation of the effectiveness of the treatment of primary angle-closure glaucoma. The third example comes from medical diagnostics. This is a real-world publicly available data used for comparison of various classification algorithms.

Gastrodia elata Blume from different regions varies in growth conditions, soil types, and climate, which directly affects the content and quality of its medicinal components. Accurately identifying the origin can effectively ensure the medicinal value of G. elata Bl., prevent the circulation of counterfeit products, and thus protect the interests and health of consumers. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is a rapid and effective method for verifying the authenticity of traditional Chinese medicines. However, the presence of scattering effects in the spectra poses challenges in establishing reliable discrimination models. Therefore, employing appropriate scattering correction techniques is crucial for improving the quality of spectral data and the accuracy of discrimination models. This study uses two ensemble preprocessing approaches; the first type is series fusion of scatter correction technologies (SCSF), and another method is sequential preprocessing through orthogonalization (SPORT). Four discriminant models were established using a single scattering correction technique and two ensemble preprocessing approaches. The results show that the data-driven version of the soft independent modeling of class analogy (DD-SIMCA) model built based on multiplicative scatter correction (MSC) preprocessing has a sensitivity of 0.98 and a specificity of 0.91, able to effectively distinguish whether a sample of G. elata Bl. originates from Zhaotong. In addition, three discriminant models including support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), and three gradient boosting machine (GBM) algorithms built using the ensemble preprocessing approach have good classification and generalization capabilities. Among them, the SCSF-PLS-DA model has the best performance with 99.68% and 98.08% accuracy for the training and test sets, respectively, and F1 of 0.97; the SPORT-SVM model achieved the second-best classification ability. The results show that the ensemble preprocessing approach used can improve the success rate of G. elata Bl. geographical origin classification.

Firefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA-ELM is compared with full-spectrum PLS, ELM, genetic algorithm-ELM (GA-ELM), and particle swarm optimization-ELM (PSO-ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near-infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA-ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples.

This study discusses potential toxic elements detection in conventional nail polish, including Cr and Pb. The noteworthy results highlight well-established potential risks of elevated Cr and Pb concentrations. These elements are not allowed in the European Union. Implementing the minimum spanning tree (MST) approach and the isolation forest algorithm effectively clustered samples. Forty-five samples were analyzed, and four clusters were identified. Two presented six samples with high concentrations of Fe (Cluster 1 with four samples) and Cr and Pb (Cluster 2 with two samples). The other 39 samples presented low concentrations of the determined elements (Co, Cr, Cu, Fe, Ni, and Pb). Cadmium, Zn, and Mn were not detected in any of the analyzed samples. Furthermore, integrating energy-dispersive x-ray fluorescence (ED-XRF) and laser-induced breakdown spectroscopy (LIBS) enabled fast direct analysis of nail polish samples, streamlining a swift and reliable data acquisition process. This research underscores the importance of ongoing vigilance and monitoring of potential health hazards associated with nail polish formulations, especially in regions with regulatory restrictions on certain elements.