Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer.

Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts.

Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts-two factors that have been suggested to cause classification deterioration-affect classifiers' performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14-21% decrease) with no difference between factors (p > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes' challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.

Unraveling the Chicken Meat Volatilome with Nanostructured Sensors: Impact of Live and Dehydrated Insect Larvae Feeding.

Incorporating insect meals into poultry diets has emerged as a sustainable alternative to conventional feed sources, offering nutritional, welfare benefits, and environmental advantages. This study aims to monitor and compare volatile compounds emitted from raw poultry carcasses and subsequently from cooked chicken pieces from animals fed with different diets, including the utilization of insect-based feed ingredients. Alongside the use of traditional analytical techniques, like solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), to explore the changes in VOC emissions, we investigate the potential of S3+ technology. This small device, which uses an array of six metal oxide semiconductor gas sensors (MOXs), can differentiate poultry products based on their volatile profiles. By testing MOX sensors in this context, we can develop a portable, cheap, rapid, non-invasive, and non-destructive method for assessing food quality and safety. Indeed, understanding changes in volatile compounds is crucial to assessing control measures in poultry production along the entire supply chain, from the field to the fork. Linear discriminant analysis (LDA) was applied using MOX sensor readings as predictor variables and different gas classes as target variables, successfully discriminating the various samples based on their total volatile profiles. By optimizing feed composition and monitoring volatile compounds, poultry producers can enhance both the sustainability and safety of poultry production systems, contributing to a more efficient and environmentally friendly poultry industry.

Improved pore structure characterization and classification of strong diagenesis sandstones by data-mining analytics in Tazhong area, Tarim Basin.

The Silurian system in Tazhong area is characterized by extensive, low-abundance lithological reservoirs with strong diagenesis, resulting in significant heterogeneity. The complex pore structure in this area significantly impacts fluid control, making accurate characterization and classification of pore structures crucial for understanding reservoir properties and their influence on oil and gas distribution. Based on 314 Mercury Injection Capillary Pressure (MICP) samples in combination with core slices and thin casting slices observation, a pipeline of characterization and classification scheme by data-mining analytics of strong diagenesis sandstone pore structure types in the study zone is established, and the characteristics of different pore structures are clarified. According to the pore structure parameter abstracted by MICP data compression and variable analysis based on hierarchical clustering and principal component analysis (PCA) analysis, the variables are reasonably evaluated and screened, and the screened variables can be divided into three groups: mean pore throat radius-maximum pore throat radius-median pore throat radius-pore throat diameter mean variable group, microscopic mean coefficient variable group, and median pressure displacement pressure-relative sorting coefficient variable group. The combination of classification schemes analysed by decision tree model and linear discriminant analysis (LDA) model was determined. In the two-dimensional projection diagram of LDA model, a relatively obvious distribution of low displacement pressure, middle displacement pressure and high displacement pressure was obtained, and three distribution lines were nearly parallel. Based on the relevant information, 6 combined classification schemes suitable for final pore structure modelling were determined verified by microscopic observation. The correct characterization and classification of pore structure can be applied to the prediction of pore type, which can be used to improve the prediction of oil and gas distribution and oil and gas recovery in the future.

Urine Metabolites Changes in Acute Myocardial Infarction Rats via Metabolomic Analysis.

OBJECTIVES: To screen biomarkers for forensic identification of acute myocardial infarction (AMI) by non-targeted metabolomic studies on changes of urine metabolites in rats with AMI. METHODS: The rat models of the sham surgery group, AMI group and hyperlipidemia + acute myocardial infarction (HAMI) group were established. Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the changes of urine metabolic spectrometry in AMI rats. Principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis were used to screen differential metabolites. The MetaboAnalyst database was used to analyze the metabolic pathway enrichment and access the predictive ability of differential metabolites. RESULTS: A total of 40 and 61 differential metabolites associated with AMI and HAMI were screened, respectively. Among them, 22 metabolites were common in both rat models. These small metabolites were mainly concentrated in the niacin and nicotinamide metabolic pathways. Within the 95% confidence interval, the area under the curve (AUC) values of receiver operator characteristic curve for N8-acetylspermidine, 3-methylhistamine, and thymine were greater than 0.95. CONCLUSIONS: N8-acetylspermidine, 3-methylhistamine, and thymine can be used as potential biomarkers for AMI diagnosis, and abnormal metabolism in niacin and nicotinamide may be the main causes of AMI. This study can provide reference for the mechanism and causes of AMI identification.

Improving Hand Gesture Recognition Robustness to Dynamic Posture Variations by Multimodal Deep Feature Fusion.

Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based on multimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight non-disabled subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control.

Quality evaluation of Qiangli Tianma Duzhong (QLTMDZ) employing UHPLC-MS: A multivariate statistical analysis across multiple dosage forms and manufacturers.

Traditional Chinese medicine (TCM) and its preparations have become increasingly popular in recent years. Nonetheless, due to the high complexity of the compounds in Traditional Chinese Patent Medicine (TCPM), the quality differences between different dosage forms and products from various manufacturers pose numerous challenges and difficulties in quality evaluation. The Qiangli Tianma Duzhong (QLTMDZ) prescription, comprising twelve TCM, is widely used in China. Despite its prevalence, current research on QLTMDZ is limited and lacks in-depth and systematic analysis of the chemical composition of the prescription. In this study, a comprehensive strategy was proposed for characterizing the chemical profile of QLTMDZ based on UHPLC-Q-TOF-MS. A total of 122 compounds were identified in QLTMDZ under both positive and negative ion modes. Subsequently, multivariate statistical methods such as principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were conducted in the MS-DIAL software to further elucidate quality differences among 55 batches of QLTMDZ samples from seven manufacturers. Lastly, multiple reaction monitoring (MRM) mode was utilized in conjunction with UHPLC-QQQ-MS, for the precise quantification of the identified 24 compounds within the QLTMDZ preparation and providing supplementary information in quality evaluation. The established analytical method in this study is sensitive and efficient, enabling qualitative and quantitative analysis of the chemical constituents within QLTMDZ. The application of multivariate statistical analyses effectively discriminates samples based on different dosage forms and manufacturers, thereby providing new research directions and scientific support for further studies on the quality control of the prescription.

In-house validation of a visible and near infrared spectroscopy non-targeted method to support panel test of virgin olive oils.

In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods.

Exploring accurate mass measurements in pixel-based chemometrics: Advancing coffee classification with GC-HRMS-A proof of concept study.

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.

Comparative analysis of the differences in volatile organic components of three lavender essential oils in Ili region using sensory evaluation, GC-IMS and GC-MS techniques.

Lavender (Lavandula angustifolia Mill.) is a widely utilized aromatic plant, with the economic value of its essential oil (EO) largely dependent on its aroma. This study investigated the differences in volatile organic compounds (VOCs) within the EOs of three species of lavender (H70-1, French blue, Taikong blue) in Ili region from 2019 to 2023 with the combination of sensory evaluation, gas chromatography-ion mobility spectrometry (GC-IMS), and gas chromatography-mass spectrometry (GC-MS). The EO from Taikong blue lavender exhibited greater stability in VOC composition compared to the other two varieties. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) effectively distinguished the aromas of the three EOs aroma. Combining odor activity value (OAV) and variable importance in projection (VIP) values identified five VOCs crucial for discriminating among the three lavender EO types. This study provides theoretical support for the cultivation and commercialization of lavender as an industrial crop, as well as for quality control of EO production in the Ili region.

Discrimination of bottled mineral water from tap water using a Dip-Type colorimetric paper-based sensor array and chemometrics.

Mineral water is a natural water that originated from an underground water table, a well, or a natural spring which is considered microbiologically intact. The revenue from the bottled mineral water industry will be USD 342.40 billion in 2023, and it is expected to grow at a compound annual growth rate (CAGR) of 5.24 %. Consequently, the discrimination of original bottled mineral water from tap water is an important issue that requires designing sensors for simple and portable identification of these two types of water. In this work, we have developed a Dip-Type colorimetric paper-based sensor array with three organic dyes (Bromothymol Blue, Bromophenol Blue, and Methyl Red) followed by chemometrics' pattern recognition methods (PCA and LDA) for discrimination of original bottled mineral waters from tap waters based on differences in ion variety and ion quantity. Forty brands of mineral water and twenty-six Tap water samples from different regions of Shiraz and other Iranian cities were analyzed by this sensor array. Moreover, these experiments were performed in two consecutive years to check the versatility of the sensor with seasonal changes in waters. This sensor array was able to discriminate these two water types from each other with an accuracy of > 95 % based on the analysis of 85 water samples.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Elemental profile of wheat in the las vegas market: Geographic origin discrimination and probabilistic health risk assessment.

This study investigates concentrations of toxic and potentially toxic elements (PTEs) in organic and conventional wheat flour and grains marketed in Las Vegas. Geographic origins of the samples were evaluated using Linear Discriminant Analysis (LDA). Monte Carlo Simulation technique was also employed to evaluate non-carcinogenic risk in four life stages. Concentrations of Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, Sr, and Zn were determined using inductively coupled plasma mass spectrometry (ICP-MS) following hot block-assisted digestion. Obtained results showed non-significant differences in contents of toxic and PTEs between conventional and organic wheat grains/flour. Using LDA, metal (loid)s were found to be indicative of geographical origin. The LDA produced a total correct classification rate of 95.8% and 100% for US and West Pacific Region samples, respectively. The results of the present study indicate that the estimated non-carcinogenic risk associated with toxic element intakes across the four life stages were far lower than the threshold value (Target Hazard Quotient (THQ) > 1). However, the probability of exceeding the threshold value for Mn is approximately 32% in children aged between 5 and 8 years. The findings of this study can aid in understanding dietary Mn exposure in children in Las Vegas.

Estimation of biological variance in coherent Raman microscopy data of two cell lines using chemometrics.

Broadband Coherent Anti-Stokes Raman Scattering (BCARS) is a valuable spectroscopic imaging tool for visualizing cellular structures and lipid distributions in biomedical applications. However, the inevitable biological changes in the samples (cells/tissues/lipids) introduce spectral variations in BCARS data and make analysis challenging. In this work, we conducted a systematic study to estimate the biological variance in BCARS data of two commonly used cell lines (HEK293 and HepG2) in biomedical research. The BCARS data were acquired from two different experimental setups (Leibniz Institute of Photonics Technology (IPHT) in Jena and Politecnico di Milano (POLIMI) in Milano) to evaluate the reproducibility of results. Also, spontaneous Raman data were independently acquired at POLIMI to validate those results. First, Kramers-Kronig (KK) algorithm was utilized to retrieve Raman-like signals from the BCARS data, and a pre-processing pipeline was subsequently used to standardize the data. Principal component analysis - Linear discriminant analysis (PCA-LDA) was performed using two cross-validation (CV) methods: batch-out CV and 10-fold CV. Additionally, the analysis was repeated, considering different spectral regions of the data as input to the PCA-LDA. Finally, the classification accuracies of the two BCARS datasets were compared with the results of spontaneous Raman data. The results demonstrated that the CH band region (2770-3070 cm-1) and spectral data in the 1500-1800 cm-1 region have significantly contributed to the classification. A maximum of 100% balanced accuracies were obtained for the 10-fold CV for both BCARS setups. However, in the case of batch-out CV, it is 92.4% for the IPHT dataset and 98.8% for the POLIMI dataset. This study offers a comprehensive overview for estimating biological variance in biomedical applications. The insights gained from this analysis hold promise for improving the reliability of BCARS measurements in biomedical applications, paving the way for more accurate and meaningful spectroscopic analyses in the study of biological systems.

Different stages of flavor variations among canned Antarctic krill (Euphausia superba): Based on GC-IMS and PLS-DA.

The aim of the present study was to explore changes in the profile of volatile compounds (VCs) in canned Antarctic krill (Euphausia superba) at different processing stages using partial least squares discriminant analysis (PLS-DA) and gas chromatography-mass spectrometry (GC-IMS). A total of 43 VCs were detected using GC-IMS in all krill meat samples, which included mainly alcohols, aldehydes, ketones, esters, and furans. Considering the different processing stages, the highest variation in VCs and the highest VC content were observed in krill meat which underwent both blanching and salt addition. PLS-DA further revealed flavor differences in canned Antarctic krill meat at different processing stages, with octanal, 2-hexanol, 2-octane, 2,3,5-trimethyl pyrazine, and cis-3-hexanol as the main contributors to observed differences in VC profiles. These findings contribute to the production of high-quality canned krill meat, enhancing its flavor quality and providing a feasible theoretical basis for future krill meat pretreatment and industry development.

An Electrochemical Strip to Evaluate and to Discriminate Drug Encapsulation in Lipid Nanovectors.

Lipid nanovectors (LNVs) represent potent and versatile tools in the field of drug delivery for a wide range of medical applications including cancer therapy and vaccines. With this Technical Note, we introduce a novel "portable", easy-to-use, and low-cost strategy for double use: (1) it allows one to both quantify the amount of cargo in LNV formulation and (2) classify the nature of formulation with the aim of chemometrics. In particular, an electrochemical strip, based on a screen-printed electrode, was exploited to detect methylene blue (MB) as the model cargo encapsulated in various liposomes (used as model LNV). The experimental setup, including release of the MB content and its electrochemical quantification were optimized through a multivariate design of experiment (DoE), obtaining a satisfactory 88-95% accuracy in comparison to standard methods. In addition, the use of principal component analysis-linear discriminant analysis (PCA-LDA) highlighted the satisfactory differentiation of liposomes. The combination of portable electroanalysis and multivariate analysis is a potent tool for enhancing quality control in the field of pharmaceutical technologies, and also in the field of diagnostics, this approach might be useful for application toward naturally occurring lipid nanoparticles, i.e., exosomes.

Non-invasive anticipation of infusion taste in fine-manipulated green teas through hyperspectral appearance analysis guided by ECG content.

The high catechin content in summer-to-autumn tea leaves often results in strong, unpleasant tastes, leading to significant resource waste and economic losses due to lignification of unpicked leaves. This study aims to improve the taste quality of summer-to-autumn green teas by combining fine manipulation techniques with hyperspectral observation. Fine manipulation notably enhanced infusion taste quality, particularly in astringency and its aftertaste (aftertasteA). Using Partial Least Squares Discriminant Analysis (PLSDA) on hyperspectral data, 100% prediction accuracy was achieved for dry tea appearance in the near-infrared spectrum. Astringency and aftertasteA correlated with hyperspectral data, allowing precise estimation with over 90% accuracy in both visible and near-infrared spectrums. Epicatechin gallate (ECG) emerged as a key taste compound, enabling non-invasive taste prediction. Practical applications in processing and quality control are demonstrated by the derived equations (Astringency = -0.88 × ECG + 45.401, AftertasteA = -0.353 × ECG + 18.609), highlighting ECG's role in shaping green tea taste profiles.

Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds.

Rapid identification of soybean seed varieties is crucial for agricultural production and seed quality. Identifying varieties of soybean seed using conventional chemical methods is time-consuming, destructive, and inappropriate for seed quality evaluation. This study utilized hyperspectral imaging technology (HSI) to identify four varieties of soybean seeds. The hyperspectral images of soybean seeds were collected in the spectral range of 400-1000 nm. A multi-level data fusion strategy based on spectral and image information was proposed to improve the accuracy of model. Subsequently, the multi-level data fusion strategy based on partial least squares discriminant analysis (PLS-DA) was used to establish the classification models of soybean seeds. Compared with the models using individual analytical sources, the results demonstrated that the models with multi-level data fusion strategy obtained better prediction performance. The high-level data fusion (HLDF) based on Bayesian consensus provided the optimal results with an accuracy (Acc) and F1-score of 93.13 % and 93.70 % in the prediction phase, respectively. Therefore, the multi-level data fusion strategy can be used as an identification method for soybean seed varieties and an effective approach to enhance the discriminatory capability of models.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction.

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.