Evaluation of medicinal plants using laser-induced breakdown spectroscopy (LIBS) combined with chemometric techniques.

Medicinal plants play a vital role in herbal medical field and allopathic medicine field industry. Chemical and spectroscopic studies of Taraxacum officinale, Hyoscyamus niger, Ajuga bracteosa, Elaeagnus angustifolia, Camellia sinensis, and Berberis lyceum are conducted in this paper by using a 532-nm Nd:YAG laser in an open air environment. These medicinal plant's leaves, roots, seed, and flowers are used to treat a range of diseases by the locals. It is crucial to be able to distinguish between beneficial and detrimental metal elements in these plants. We demonstrated how various elements are categorized and how roots, leaves, seeds and flowers of same plants differ from each other on the basis of elemental analysis. Furthermore, for classification purpose, different classification models, partial least square discriminant analysis (PLS-DA), k-nearest neighbors (kNN), and principal component analysis (PCA) are used. We found silicon (Si), aluminum (Al), iron (Fe), copper (Cu), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), manganese (Mn), phosphorous (P), and vanadium (V) in all of the medicinal plant samples with a molecular form of carbon and nitrogen band. We detected Ca, Mg, Si, and P as primary components in all of the plant samples, as well as V, Fe, Mn, Al, and Ti as essential medicinal metals, and additional trace elements like Si, Sr, and Al. The result's findings show that the PLS-DA classification model with single normal variate (SNV) preprocessing method is the most effective classification model for different types of plant samples. The average correct classification rate obtained for PLS-DA with SNV is 95%. Moreover, laser-induced breakdown spectroscopy (LIBS) was successfully employed to perform rapid, sensitive, and quantitative trace element analysis on medicinal herbs and plant samples.

Evaluation of human melanoma and normal formalin paraffin-fixed samples using Raman and LIBS fused data.

In this research, we developed a novel method of quantitative analysis to increase the detection potential for screening and classification of skin cancer (melanoma). We fused two distinct optical approaches, an atomic spectroscopic detection technique laser-induced breakdown spectroscopy (LIBS) and a vibrational molecular spectroscopic technique known as Raman spectroscopy. Melanoma is a kind of skin cancer, also known as malignant melanoma, that developed in melanocytes cells, which produced melanin. Classification of melanoma cancerous tissues is a fundamental problem in biomedicine. For early melanoma cancer diagnosis and treatment, precise and accurate categorizing is critically essential. Laser-based spectroscopic approaches can be used as an operating instrument for simultaneous tissue ablation and ablated tissue elemental and molecular analysis. For this purpose, melanoma and normal paraffin-embedded tissues are used as a sample for LIBS and Raman measurement. We studied the data provided by laser-based spectroscopic methods using different machine learning classification techniques of extreme learning machine (ELM), partial least square discriminant analysis (PLS-DA), and K nearest neighbors (kNN). For visualization of melanoma and normal data, principal component analysis (PCA) is also used. Three different ways are used to process the data, LIBS measurement, Raman measurement, and combine data measurement (merged/fused data), and then compared the results. ELM classification model achieved the highest accuracy (100%) for combined data as well as for Raman and LIBS data, respectively. According to the experimental results, we can assume that Raman spectroscopy and LIBS combine can significantly improve the identification and classification accuracy of melanoma and normal specimens.

Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning.

To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue.

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C2 molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

In-vitro study on the identification of gastrointestinal stromal tumor tissues using laser-induced breakdown spectroscopy with chemometric methods.

Early-stage detection of tumors helps to improve patient survival rate. In this work, we demonstrate a novel discrimination method to diagnose the gastrointestinal stromal tumor (GIST) and its healthy formalin fixed paraffin embedded (FFPE) tissues by combining chemometric algorithms with laser-induced breakdown spectroscopy (LIBS). Chemometric methods which include partial least square discrimination analysis (PLS-DA), k-nearest neighbor (k-NN) and support vector machine (SVM) were used to build the discrimination models. The comparison of PLS-DA, k-NN and SVM classifiers shows an increase in accuracy from 94.44% to 100%. The comparison of LIBS signal between the healthy and infected tissues shows an enhancement of calcium lines which is a signature of the presence of GIST in the FFPE tissues. Our results may provide a complementary method for the rapid detection of tumors for the successful treatment of patients.

Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.

The identification and preservation of parathyroid glands (PGs) is a major issue in thyroidectomy. The PG is particularly difficult to distinguish from the surrounding tissues. Accidental damage or removal of the PG may result in temporary or permanent postoperative hypoparathyroidism and hypocalcemia. In this study, a novel method for identification of the PG was proposed based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS spectra were collected from the smear samples of PG and non-parathyroid gland (NPG) tissues (thyroid and neck lymph node) of rabbits. The emission lines (related to K, Na, Ca, N, O, CN, C2, etc.) observed in LIBS spectra were ranked and selected based on the important weight calculated by random forest (RF). Three machine learning algorithms were used as classifiers to distinguish PGs from NPGs. The artificial neural network classifier provided the best classification performance. The results demonstrated that LIBS can be adopted to discriminate between smear samples of PG and NPG, and it has a potential in intra-operative identification of PGs.

Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis.

Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.

Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network.

Quick and accurate diagnosis helps shorten intraoperative waiting time and make a correct plan for the brain tumor resection. The common cryostat section method costs more than 10 minutes and the diagnostic accuracy depends on the sliced and frozen process and the experience of the pathologist. We propose the use of molecular fragment spectra (MFS) in laser-induced breakdown spectroscopy (LIBS) to identify different brain tumors. Formation mechanisms of MFS detected from brain tumors could be generalized into 3 categories, for instance, combination, reorganization and break. Four kinds of brain tumors (glioma, meningioma, hemangiopericytoma, and craniopharyngioma) from different patients were used as investigated samples. The spiking neural network (SNN) classifier was proposed to combine with the MFS (MFS-SNN) for the identification of brain tumors. SNN performed better than conventional machine learning methods for the analysis of similar and limited MFS information. With the ratio data type, the identification accuracy achieved 88.62% in 2 seconds.