Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Data integrity of food and machine learning: Strategies, advances and prospective

Data integrity is an emerging concept aimed at recording real food properties in the form of data throughout the food lifecycle. However, due to the one-sided nature of current food control data, the comprehensive implementation of data integrity has not been fully achieved. Cause food data integrity realization is required to establish the connection of data-algorithm-application. Machine learning (ML) provides a possibility for the practical carrier of food data integrity. Despite ML is one of top-trend in food quality and safety, ML applications are floating on the surface. The current review does not reveal the relationships behind different algorithms and data patterns. Similarly, due to the rapid development of ML, the current advanced concepts and data explanation tools have not been systematically reviewed. This paper expounds the feasibility of machine learning to achieve data integrity and looks forward to the future vision brought about by artificial intelligence to data integrity.

Accurate Coal Classification Using PAIPSO-ELM with Near-Infrared Reflectance Spectroscopy

China has vast proven coal reserves, encompassing a wide variety of types. However, traditional coal classification methods have limitations, often leading to inaccurate classification and inefficient utilization of coal resources. To address this issue, this paper introduces the Extreme Learning Machine (ELM) as a novel coal classification method, based on the near-infrared reflectance spectroscopy (NIRS) of coal. Initially, we collected NIRS data from coal samples using the SVC-HR-1024 spectrometer. Given the high dimensionality and strong linear correlations in NIRS data, we conducted preprocessing to enhance the usefulness of the data. In experiments, the ELM model demonstrated good classification performance. However, due to the random generation of input layer weights and hidden layer biases in the ELM model, its performance can be unstable, preventing the model from fully realizing its potential. To overcome this shortcoming, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the ELM model. Simulation results showed that the PSO-ELM model achieved a 9.68% improvement in classification accuracy compared to the original ELM model. Furthermore, we optimized the PSO algorithm by introducing exponentially decaying inertia factors and position-variant particles to further reduce the risk of the algorithm falling into local optima. The improved Position-Adaptive Inertia PSO-ELM (PAIPSO-ELM) model achieved an additional 2% increase in classification accuracy over the PSO-ELM model, without a significant increase in training time. In summary, this paper proposes a coal spectral classification method based on the PAIPSO-ELM model, effectively overcoming the limitations of traditional classification methods while meeting industrial demands for classification accuracy and speed.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS)

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

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.

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

Quantitative detection of zearalenone in wheat grains based on near-infrared spectroscopy

Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (R_P) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 μg·kg^-1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

Application of multi-wavelength dual-position absorption spectrum to improve the accuracy of leukocyte spectral quantitative analysis based on "M + N" theory

Leukocytes are the most important immune cells in human body, which are very important to maintain the immune function of human body. They can phagocytose foreign bodies and produce antibodies to resist the invasion of pathogens. Nowadays, the abuse of antibiotics is widespread, and the detection and analysis of leukocytes is very important for clinical diagnosis. It is of great medical significance to use chemical quantitative analysis method based on spectrum to realize the rapid and trace detection of leukocytes in clinic. It is the development direction of clinical detection in the future and provides a new way to improve the abuse of antibiotics. However, due to the influence of nonlinearity introduced by the measurement, the relationship between absorbance and concentration deviates from Lambert-Beer law, which leads to low measurement accuracy and restricts its development in clinical application. In order to improve the accuracy of spectral analysis, this paper with the guidance of "M + N" theory measured the transmission spectra of 392 whole blood samples under two different optical path lengths, and subtracts them to obtain the multi-band differential absorption spectra for modeling and prediction of leukocyte concentration. The following experiments were designed: using the transmission spectra measured at one position and the absorption spectra obtained by subtracting the transmission spectra measured at position 1 and position 2 as the input of modeling. Partial least squares (PLS) method was proposed in this paper for modeling and predicting the concentration of leukocyte. The experimental results show that the modeling results of dual-position absorption spectrum have been significantly improved and promoted compared with the modeling results of transmission spectrum in one position, and the calibration set correlation coefficient (R_C) values has increased by 57.92% to 0.864904, where the prediction set correlation coefficient (R_P) increased by 106.81% to 0.8502. The root mean square error of the calibration set (RMSEC) decreased by 40.01% from 3.1149 to 1.8686. The results suggest that modelling and analysing leukocytes with a multi-band dual-position absorption spectrum may reduce the influence of nonlinearity to a certain amount, significantly increase the model's prediction precision and accuracy, and obtain satisfactory results. This paper provides the possibility for rapid clinical micro-detection of leukocytes, as well as the ideas and directions for improving the accuracy of spectral quantitative analysis of components in complex solutions.

Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration

An autoencoder architecture was adopted for near-infrared (NIR) spectral analysis by extracting the common features in the spectra. Three autoencoder-based networks with different purposes were constructed. First, a spectral encoder was established by training the network with a set of spectra as the input. The features of the spectra can be encoded by the nodes in the bottleneck layer, which in turn can be used to build a sparse and robust model. Second, taking the spectra of one instrument as the input and that of another instrument as the reference output, the common features in both spectra can be obtained in the bottleneck layer. Therefore, in the prediction step, the spectral features of the second can be predicted by taking the reverse of the decoder as the encoder. Furthermore, transfer learning was used to build the model for the spectra of more instruments by fine-tuning the trained network. NIR datasets of plant, wheat, and pharmaceutical tablets measured on multiple instruments were used to test the method. The multi-linear regression (MLR) model with the encoded features was found to have a similar or slightly better performance in prediction compared with the partial least-squares (PLS) model.

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.