Discrimination of corn variety using Terahertz spectroscopy combined with chemometrics methods

Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted sampling - whale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

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.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

Study on qualitative identification of aflatoxin solution based on terahertz metamaterial enhancement

Aflatoxin is the main carcinogen that contaminates agricultural products and foods such as peanuts and corn. There are many kinds of aflatoxins, mainly including aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2 (AFG2). Different types of aflatoxins have different toxicity and different levels of contamination to agricultural products as well as food. Therefore, the rapid, non-destructive and highly sensitive qualitative identification of aflatoxin species is of great significance to maintain people's life and health. The conventional terahertz detection method can only qualitatively identify the samples at the milligram level, but it is not suitable for the qualitative analysis of trace samples. In this paper, a terahertz metamaterial sensor with "X" composite double-peak structure was designed based on electromagnetic theory to investigate the feasibility of THz-TDS technology based on a metamaterial sensor for the qualitative identification of trace aflatoxin B2, G1 and G2 solutions. Firstly, the terahertz transmission spectra of eight different concentrations of aflatoxin B2, G1 and G2 were collected respectively, and then the differences of terahertz transmission spectra of different aflatoxin species were investigated. Finally, the terahertz transmission spectra of aflatoxin B2, G1 and G2 solutions were modeled and analyzed using chemometric methods. It was found that there were significant differences in the transmission peak curves of different kinds of aflatoxin. Through the comparative analysis of different models, it was concluded that the prediction accuracy of the CARS-RBF-SVM model was the highest, and the accuracy of the calibration set reached 100%. 119 out of 120 predicted samples were correctly predicted, and the prediction accuracy was 99.17%. This study verified the feasibility of qualitative identification of trace aflatoxin B2, G1 and G2 solutions by a metamaterial sensor based on the "X" composite double-peak structure combined with THz-TDS technology, and provided a theoretical basis and a new detection method for the qualitative identification of trace aflatoxins. This will facilitate the rapid, non-destructive and highly sensitive qualitative detection of different kinds of aflatoxins in food and agricultural products. At the same time, this study has important implications for promoting the qualitative detection of other trace substances.

Research on highly sensitive quantitative detection of aflatoxin B2 solution based on THz metamaterial enhancement

Food such as cereal crops, oil crops and dairy products are very easy to produce highly toxic and carcinogenic aflatoxins during inappropriate storage. Therefore, it is of great significance to achieve rapid, non-destructive and highly sensitive detection of aflatoxin. A terahertz metamaterial sensor with "X" compound double-peak structure is designed based on electromagnetic theory to realize highly sensitive detection of aflatoxin B2 solution. It is found that the amplitude of the transmission peak of the terahertz transmission spectrum of aflatoxin B2 (AFB2) solution around 1.2 THz and 2.0 THz gradually decreased with the increase of the concentration of aflatoxin B2 solution, and the frequency of the transmission peak gradually shifted to high frequency with the increase of the concentration of aflatoxin B2 solution, hence a full concentration model was established. And a strategy of first classifying concentration intervals and then building a grouped quantitative model was proposed. The Limit of Detection (LOD) of the interval sub-model of low and medium concentration of aflatoxin B2 solution has been greatly improved with the LOD of the optimal grouping model was 7.28 × 10^-11 mg/ml, 4.19 × 10^-9 mg/ml and 1.22 × 10^-7 mg/ml, respectively. This research verifies the feasibility of terahertz metamaterial sensor based on "X" composite double-peak structure combined with THz-TDS technology for highly sensitive detection of aflatoxin B2 solution. And it provides a new rapid, non-destructive and highly sensitive detection of aflatoxin in food.

Determination of Corn Protein Content Using Near-Infrared Spectroscopy Combined with A-CARS-PLS

In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV = 0.0336, R² _c = 0.9951 in the calibration set; RMSEP = 0.0688, R² _p = 0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.

To Estimate Performance of Artificial Neural Network Model Based on Terahertz Spectrum: Gelatin Identification as an Example

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.

Terahertz signal analysis and substance identification via Zernike moments

Terahertz (THz) spectra contain chemical information, along with noise and variable backgrounds. Measurement environmental changes and spectral signal differences caused by changes in the sample state can degrade the accuracy of the calibration models. This problem obviously hinders practical applications of THz spectroscopy. To tackle this problem, a three-dimensional spectrum was first self-constructed and converted into an intensity image. Zernike moments with inherently invariant properties were then used to describe the THz intensity image and extract the invariant features for further analysis. Considering the reconstruction error and computational cost, the highest order of Zernike moments and the most effective moments were selected and applied to multi-classifiers including support vector machines, naive Bayes, and regularized linear discriminant analysis. Experiments used a THz dataset collected from four chemical substances (melamine, tartaric acid, lactose, and glucose) at five thicknesses (1.0 mm, 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm). The results confirmed the effectiveness of the proposed approach. The obtained results show that compare to traditional absorption spectrum features, Zernike moment features are less sensitive to spectral variations caused by changes in sample status. They have better feature representation ability with lower feature vector dimensions. This suggests that they can be integrated into the design of systems for THz spectral classification to increase the robustness and generalization capability of the classifier.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

Determination of wheat moisture using terahertz spectroscopy combined with the tabu search algorithm

The detection of the wheat moisture content plays a key role in grain storage and classification. Harvested wheat grains were taken as samples in the current research. A total of 240 reaped wheat samples with different moisture contents were tested by applying terahertz (THz) spectroscopy. The frequency domain spectra and absorption coefficient spectra of wheat were obtained in the band of 0.1-1.2 THz, and the spectra were pretreated by mean centering, Savitzky-Golay (S-G), Multiplicative Scatter Correction (MSC) and Stand Normal Variate (SNV), respectively. Then a special algorithm of Tabu Search (TS) was used to find out the effective variables and remove the useless variables from the terahertz spectrum of the sample. Finally, the partial least squares (PLS) of chemometrics were used for quantitative model building and prediction. The correlation coefficient of calibration (R_c) is 0.9522. The root mean square error of calibration (RMSEC) is 0.4730. The correlation coefficient of prediction (R_p) is 0.9531. The root mean square error of prediction (RMSEP) is 0.5396. The results demonstrated that an accurate quantitative analysis of moisture in wheat samples could be achieved by terahertz time-domain spectroscopy combined with the TS algorithm. In addition, the results show that the model S-G + MSC + TS + PLS can effectively predict wheat moisture, and provide a rapid quantitative detection and analysis method for the detection of wheat moisture.