Classification of cereal flour species using Raman spectroscopy in combination with spectra quality control and multivariate statistical analysis

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Deep metric learning framework combined with Gramian angular difference field image generation for Raman spectra classification based on a handheld Raman spectrometer

Rapid identification of unknown material samples using portable or handheld Raman spectroscopy detection equipment is becoming a common analytical tool. However, the design and implementation of a set of Raman spectroscopy-based devices for substance identification must include spectral sampling of standard reference substance samples, resolution matching between different devices, and the training process of the corresponding classification models. The process of selecting a suitable classification model is frequently time-consuming, and when the number of classes of substances to be recognised increases dramatically, recognition accuracy decreases dramatically. In this paper, we propose a fast classification method for Raman spectra based on deep metric learning networks combined with the Gramian angular difference field (GADF) image generation approach. First, we uniformly convert Raman spectra acquired at different resolutions into GADF images of the same resolution, addressing spectral dimension disparities induced by resolution differences in different Raman spectroscopy detection devices. Second, a network capable of implementing nonlinear distance measurements between GADF images of different classes of substances is designed based on a deep metric learning approach. The Raman spectra of 450 different mineral classes obtained from the RRUFF database were converted into GADF images and used to train this deep metric learning network. Finally, the trained network can be installed on an embedded computing platform and used in conjunction with portable or handheld Raman spectroscopic detection sensors to perform material identification tasks at various scales. A series of experiments demonstrate that our trained deep metric learning network outperforms existing mainstream machine learning models on classification tasks of different sizes. For the two tasks of Raman spectral classification of natural minerals of 260 classes and Raman spectral classification of pathogenic bacteria of 8 classes with significant noise, our suggested model achieved 98.05% and 90.13% classification accuracy, respectively. Finally, we also deployed the model in a handheld Raman spectrometer and conducted identification experiments on 350 samples of chemical substances attributed to 32 classes, achieving a classification accuracy of 99.14%. These results demonstrate that our method can greatly improve the efficiency of developing Raman spectroscopy-based substance detection devices and can be widely used in tasks of unknown substance identification.

Influence of silicon nano-particles on Avena sativa L. to alleviate the biotic stress of Rhizoctonia solani

Avena sativa L. a cereal crop that is badly affected by several abiotic and biotic stresses. In the current study, silicon nanoparticles are used to mitigate the harmful effects of root rot disease caused by Rhizoctonia solani Kuhn on the growth of A. sativa. In vitro (Petri plates) and in vivo (pots experiment) were performed to measure the various physiological and biochemical parameters i.e. osmotic potential, chlorophyll, proline content, growth parameters, sugar, fresh and dry weight, and disease index. Results revealed that physiological and biochemical parameters were reduced under fungal stress with silicon nanoparticles treatment as compared to the control group. Si nanoparticles helped to alleviate the negative effects caused by fungus i.e. germination percentage upto 80%, germination rate 4 n/d, radical and plumule length was 4.02 and 5.46, dry weight 0.08 g, and relative water content was (50.3%) increased. Fungus + Si treatment showed the maximum protein content, i.e. 1.2 µg/g as compared to Fungus (0.3 µg/g) treated group. The DI was maximum (78.82%) when the fungus directly attacked the target plant and DI reduced (44.2%) when the fungus was treated with Si nanoparticles. Thus, silicon nanoparticles were potentially effective against the stress of R. solani and also used to analyze the plant resistance against fungal diseases. These particles can use as silicon fertilizers, but further studies on their efficacy under field conditions and improvement in their synthesis are still needed.

Rapid identification of ore minerals using multi-scale dilated convolutional attention network associated with portable Raman spectroscopy

Electron portable Raman spectroscopy tools for ore mineral identification are widely used in raw ore analysis and mineral process engineering. This paper demonstrates an extremely fast and accurate method for identifying unknown ore mineral samples by portable Raman spectroscopy from the RRUFF database. Resampling and background subtraction procedures are used to eliminate the influence of the Raman spectrometer and fluorescence scattering. For the complex mineral spectral classification task, a multi-scale dilated convolutional attention network is designed. In addition, to investigate the identification performance of our method, several machine learning and two basic deep learning models, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), cosine similarity, extreme gradient boosting machine (XGBoost), Alexnet and ResNet 18, are also developed on the mineral spectra database and applied for mineral identification. Comparative studies show that our CNN network outperforms other models with state-of-the-art results, achieving a top-1 accuracy of 89.51% and a top-3 accuracy of 96.54%. The function of each module and the explanations of the feature extraction in our CNN network were analyzed by ablation experiments and the Grad-CAM algorithm. The identification of ore mineral samples also proves the outstanding performance of our method. In conclusion, the proposed novel approach that exploits the advantages of portable Raman spectroscopy and a deep learning method is promising for rapidly identifying ore mineral samples.