Serum Raman spectroscopy combined with Deep Neural Network for analysis and rapid screening of hyperthyroidism and hypothyroidism.

Hyperthyroidism and hypothyroidism may cause a series of clinical complications have a high incidence, and early diagnosis is beneficial to treatment. Based on Raman spectroscopy and deep learning algorithms, we propose a rapid screening method to distinguish serum samples of hyperthyroidism patients, hypothyroidism patients and control subjects. We collected 99 serum samples, including 38 cases from hyperthyroidism patients, 32 cases from hypothyroidism patients and 29 cases from control subjects. By comparing and analyzing the Raman spectra of the three, we found differences in the peak intensity of the spectra, indicating that Raman spectra can be used for the subsequent identification of diseases. After collecting the spectral data, Vancouver Raman algorithm (VRA) was used to remove the fluorescence background of the data, and kernel principal component analysis (KPCA) was used to extract the spectral data features with a cumulative explained variance ratio of 0.9999. Then, five neural network models, the adjusted AlexNet, LSTM-CNN, IndRNNCNN, the adjusted GoogLeNet and the adjusted ResNet, were constructed for classifications. The total accuracy was 91%, 84%, 82%, 75% and 71% respectively. The results of our study show that it is feasible to use Raman spectroscopy combined with deep learning to distinguish hyperthyroidism, hypothyroidism and control subjects. After comparing the models, we found that as the neural network deepens and the complexity of the model increases, the classification effect of Raman spectroscopy gradually deteriorates, and we put forward three conjectures for this.

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction.

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm-1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks.

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.