Determination of benzo(a)pyrene in peanut oil based on Raman spectroscopy and machine learning methods

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

Causal inference of whole-grain foods' risk based on a generative adversarial network and Bayesian network

Whole-grain foods (WGFs) constitute a large part of humans' daily diet, making risk identification of WGFs important for health and safety. However, existing research on WGFs has paid more attention to revealing the effects of a single hazardous substance or various hazardous substances on food safety, neglecting the mutual influence between individual hazardous substances and between hazardous substances and basic information. Therefore, this paper proposes a causal inference of WGFs' risk based on a generative adversarial network (GAN) and Bayesian network (BN) to explore the mutual influence between hazardous substances and basic information. The experiment results show that the proposed GAN outperformed several traditional data-imputation methods, producing at least a 13.65% reduction of the root mean square error (RMSE). The classification accuracy of the BN model reached 91%. In conclusion, we distinguish the provinces, periods, food categories, and hazardous substances cause the absolute risk of WGFs and the high risk of mycotoxins and compounds (MaCs) and cadmium. PRACTICAL APPLICATION: This research can be applied to impute missing values for whole-grain foods (WGFs) sampling data, and explore the causality among hazardous substances themselves, that between hazardous substances and basic information in WGFs. Additionally, it can be applied to infer root cause of existing or potential WGFs risk (e.g., provinces, periods, food categories, and hazardous substances).

Chemistry of Street Art: Neural Network for the Spectral Analysis of Berlin Wall Colors

This research starts with the analysis of some fragments of the Berlin Wall street art for the characterization of the painting materials. The spectroscopic results provide a general description of the paint executive technique but more importantly open the way to a new advantage of Raman application to the analytic analysis of acrylic colors. The study highlights the correlation between peak intensity and compound percentage and explores the powerful application of deep learning for the quantification of a pigment mixture in the acrylic commercial products from Raman spectra acquired with hand-held equipment (BRAVO by Bruker). The study reveals the ability of the convolutional neural network (CNN) algorithm to analyze the spectra and predict the ratio between the coloring compounds. The reference materials for calibration and training were obtained by the dilution of commercial acrylic colors commonly practiced by street artists, using Schmincke brand paints. For the first time, Raman investigation provides valuable insights into calibrations for determining dye dilution in mixtures of commercial products, offering a new opportunity for analytical quantification with Raman hand-held spectrometers and contributing to a comprehensive understanding of artists' techniques and materials in street art.

Dual-template magnetic molecularly imprinted polymers for selective extraction and sensitive detection of aflatoxin B1 and benzo(α)pyrene in environmental water and edible oil

The coexistence of multiple contaminates in the environment and food is of growing concern due to their extremely hazard as a well-known class I carcinogen, like aflatoxin B1 (AFB1) and benzo(α)pyrene (BaP). AFB1 and BaP are susceptible to coexistence in environmental water and edible oil, posing a significant potential risk to environmental monitoring and food safety. The remaining challenges in detecting multiple contaminates include unsatisfied sensitivity, insufficient targets selectivity, and interferences in complex matrices. Here, we developed dual-template magnetic molecularly imprinted polymers (DMMIPs) for selective extraction of dual targets in complex matrices from the environment and food. The DMMIPs were fabricated by surface imprinting with vinyl-functionalized Fe3O4 as carrier, 5,7-dimethoxycoumarin and pyrene as dummy templates, and methacrylamide as functional monomer. The DMMIPs showed excellent adsorption ability (12.73-15.80 mg/g), imprinting factors (2.01-2.58), and reusability of three adsorption-desorption cycles for AFB1 and BaP. The adsorption mechanism including hydrogen bond, electrostatic interaction and van der Waals force was confirmed by physical characterization and DFT calculation. Applying DMMIPs in magnetic solid phase extraction (MSPE) followed by high-performance liquid chromatography (HPLC) analysis enabled detection limits of 0.134 μg/L for AFB1 and 0.107 μg/L for BaP. Recovery rates for water and edible oil samples were recorded as 86.2%-110.3% with RSDs of 4.1%-11.9%. This approach demonstrates potential for simultaneous identification and extraction of multiple contaminants in environmental and food.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC-MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells.

Raman spectroscopy-based prediction of ofloxacin concentration in solution using a novel loss function and an improved GA-CNN model

BACKGROUND

A Raman spectroscopy method can quickly and accurately measure the concentration of ofloxacin in solution. This method has the advantages of accuracy and rapidity over traditional detection methods. However, the manual analysis methods for the collected Raman spectral data often ignore the nonlinear characteristics of the data and cannot accurately predict the concentration of the target sample.

METHODS

To address this drawback, this paper proposes a novel kernel-Huber loss function that combines the Huber loss function with the Gaussian kernel function. This function is used with an improved genetic algorithm-convolutional neural network (GA-CNN) to model and predict the Raman spectral data of different concentrations of ofloxacin in solution. In addition, the paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRU) models to conduct multiple experiments and use root mean square error (RMSE) and residual predictive deviation (RPD) as evaluation metrics.

RESULTS

The proposed method achieved an [Formula: see text] of 0.9989 on the test set data and improved by 3% over the traditional CNN. Multiple experiments were also conducted using RNN, LSTM, BiLSTM, and GRU models and evaluated their performance using RMSE, RPD, and other metrics. The results showed that the proposed method consistently outperformed these models.

CONCLUSIONS

This paper demonstrates the effectiveness of the proposed method for predicting the concentration of ofloxacin in solution based on Raman spectral data, in addition to discussing the advantages and limitations of the proposed method, and the study proposes a solution to the problem of deep learning methods for Raman spectral concentration prediction.

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.