Preliminary study on classification of rice and detection of paraffin in the adulterated samples by Raman spectroscopy combined with multivariate analysis

A method to assess industrial paraffin contamination levels in rice and its transferability analysis based on transfer component analysis

Accurate assessment of industrial paraffin contamination levels (IPCLs) in rice is critical for food safety. However, time-consuming and labor-intensive experiments to produce labels for targeted adulterated rice have hindered the development of IPCL estimation methods. In this paper, a transfer learning method (TCA-LSSVR) has been developed. The algorithm integrates transfer component analysis (TCA) with domain adaptive capabilities to produce accurate estimates. Rice from 7 different regions and 3 industrial paraffins were used to generate 4,680 samples from 9 datasets for benchmarking. The test results showed that the established algorithm achieved good estimation performance in various modelling strategies, and only 20 % of off-site samples were needed to supplement the source dataset, the average determination coefficient R2 reached 0.7045, the average RMSE reached 0.140 %, and the average RPD reached 2.023. This work highlights the prospect of rapidly developing a new generation of adulteration detection algorithms using only previous trial data.

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.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin

With the events of fake and inferior rice and food products occurring frequently, how to establish a rapid and high accuracy monitoring method for rice food identification becomes an urgent problem. In this work, we investigate using combined laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) with machine learning algorithms to identify the place of origin of rice production. Six geographical origin rice samples grown in different parts of China are selected and pretreated, and measured by the atomic emission spectra of LIBS and the reflection spectra of HSI, respectively. The principal component analysis (PCA) is utilized to realize data dimensionality and extract the data feat of LIBS, HSI and fusion data, and based on this, three models employing the partial least squares discriminant analysis (PLS-DA), the support vector machine (SVM) and the extreme learning machine (ELM) are used to identify the rice geographical origin. The results show that the accuracy of LIBS and HSI analysis with the SVM machine learning algorithm can reach 93.06% and 88.07%, respectively, and the accuracy of combined LIBS and HSI data fusion recognition can reach 99.85%. Besides, the classification accuracy of the three models measured after pretreatment is basically all above 95%, and up to 99.85%. This study proves the effectiveness of using the combined LIBS and HSI with the machine learning algorithm in rice geographical origin identification, which can achieve rapid and accurate rice quality and identity detection.

Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging

The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice.

Fraud vulnerability quantitative assessment of Wuchang rice industrial chain in China based on AHP-EWM and ANN methods

Vulnerability assessment has been used in the food fraud mitigation based on the subjective judgement of industry participants and simple calculation. To have a more objective result, an improved vulnerability quantitative assessment method was proposed. The overall fraud vulnerability was described by the vulnerability of fraud factors and the health and economic impact of fraud incidents. The fraud factors were related to opportunity, motivation and control measure. Analytic hierarchy process combined with entropy weighting method (AHP-EWM) and artificial neural networking (ANN) to improve judgment accuracy. In the application in Wuchang rice industrial chain, 51 fraud factors were used in the assessment and 10 experts, 36 farmers, 15 suppliers and 15 supervisors were interviewed. Results showed that Wuchang rice industrial chain was highly vulnerable to fraud. The opportunity for fraud was high, the motivation to commit it was moderate, and controls to prevent it needed reinforcing. Fraud vulnerability differed between farmers and suppliers. To reduce the fraud vulnerability, improved regulations and policies and stiffer penalties were strongly recommended.

Detection of fraud in high-quality rice by near-infrared spectroscopy

A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near-infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS-DA), and a support vector machine (SVM) to detect whether high-quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high-quality rice contains other varieties of rice. PRACTICAL APPLICATION: The work of this article is based on the current background of increasingly serious rice fraud, using near-infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Fourier transform infrared and Raman spectroscopic study of the effect of the thermal treatment and extraction methods on the characteristics of ayocote bean starches

Starches isolated from four ayocote bean varieties were modified by thermal treatment to determinate the effect of the treatment on the structural changes of ayocote bean starch. Scanning electron microscopy indicates that the starch granules have oval and round shapes, with heterogeneous sizes and fractures when the extraction method is used. The presence of new bands at 2850 and 1560 cm-1 in the FT-IR spectra showed that the thermal treatment of ayocote beans induced an interaction between the protein or lipid and the amylose or amylopectin, while the sharpest band at 3400 cm-1 indicated a dehydration process in the starch granule in addition to the presence of the band at 1260 cm-1, indicating the product of the retrogradation process. The thermal treatment reduced the crystallinity as well as short-range order. Raman spectroscopy revealed that acute changes occurred in the polysaccharide bonds after thermal treatment. This study showed that the thermal treatment affected the structural properties of ayocote bean starches, the interactions of the lipids and proteins with starch molecules and the retrogradation process of starch.

Sandwich Immunoassays of Multicomponent Subtrace Pathogenic DNA Based on Magnetic Fluorescent Encoded Nanoparticles

A novel magnetic fluorescent encoded nanoimmunoassay system for multicomponent detection and separation of the subtrace pathogenic DNA (hepatitis B virus surface gene, HBV; hepatitis A virus poly the protein gene, HAV) was established based on new type of magnetic fluorescent encoded nanoparticles and sandwich immunoassay principle. This method combines multifunctional nanoparticles, immunoassay technique, fluorescence labeling, and magnetic separation of multicomponent technology. It has many advantages such as high sensitivity, low detection limit, easy operation, and great potential for development. The results of this work show that, based on nanoimmunoassay system, it could quantitatively detect the multicomponent trace pathogenic HAV and HBV DNA, as well as detection limit up to 0.1 pM and 0.12 pM. Furthermore, with the improvement of the performances of magnetic fluorescent encoded nanoparticles, the sensitivity will be further improved. In this experiment, a new nanoimmunoassay system based on magnetic fluorescent encoded nanoparticles was established, which will provide a new way for the immunoassay and separation of multicomponent biomolecules.

A facile strategy applied to simultaneous qualitative-detection on multiple components of mixture samples: a joint study of infrared spectroscopy and multi-label algorithms on PBX explosives

We combined infrared spectroscopy with multi-label algorithms to propose a facile yet efficient strategy to realize simultaneous qualitative-detection on multiple components of mixture explosives without pre-separation.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.