Applied Spectroscopy最新文献

{"title":"Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images Using Machine Learning.","authors":"Harrison Edmonds, Sudipta S Mukherjee, Brooke Holcombe, Kevin Yeh, Rohit Bhargava, Ayanjeet Ghosh","doi":"10.1177/00037028251325553","DOIUrl":"10.1177/00037028251325553","url":null,"abstract":"<p><p>Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUC). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like principal component analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating the acquisition of more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets, and additional computational overhead. In this work, we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly six-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution and subsequently using the upscaled spectra to accurately predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1465-1477"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12353105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual Mode Fusion Based on Rock Images and Laser-Induced Breakdown Spectroscopy to Improve the Accuracy of Discriminant Analysis.","authors":"Saifullah Jamali, Hongbo Fu, Mengyang Zhang, Huadong Wang, Nek Muhammad Shaikh, Bian Wu, Baddar Ul Ddin Jamali, Feifan Shi, Zongling Ding, Yuzhu Liu, Zhirong Zhang","doi":"10.1177/00037028251349524","DOIUrl":"10.1177/00037028251349524","url":null,"abstract":"<p><p>Rocks are an extremely important and indispensable part of the Earth's crust, with wide applications in various fields such as geology, environmental monitoring, and industry. Traditional methods often rely on a single analytical technique or visual inspection, but this may not achieve the accuracy required for thorough classification. Laser-induced breakdown spectroscopy (LIBS) technology mainly provides information on the composition and content of rock elements, while images can provide appearance information such as color and texture. The multilayer perceptron (MLP) and DenseNet121 models were selected for processing preprocessed LIBS and image data, respectively. When using LIBS and images separately for classification, the accuracy rates were 93.63% and 90.90%, respectively. However, after fusing the bimodal data using LIBS and images, we achieved a significant performance improvement of 97.27% in accuracy. This study indicates that advanced neural network models can effectively integrate LIBS and image data and improve the performance of rock classification.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1455-1464"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Secondary Structure Content of Proteins Using Raman Spectroscopy and Self-Organizing Maps.","authors":"Marco Pinto Corujo, Pavel Michal, Dale Ang, Lindo Vivian, Nikola Chmel, Alison Rodger","doi":"10.1177/00037028251335051","DOIUrl":"10.1177/00037028251335051","url":null,"abstract":"<p><p>Proteins are biomolecules with characteristic three-dimensional (3D) arrangements that render them different vital functions. In the last 20 years, there has been a growing interest in biopharmaceutical proteins, especially antibodies, due to their therapeutic application^. The functionality of a protein depends on the preservation of its native form, which under certain stressing conditions can undergo changes at different structural levels that cause them to lose their activity.¹ Although mass spectrometry is a powerful technique for primary structure determination, it often fails to give information at higher order levels. Like infrared (IR), Raman spectra are well known to contain bands (especially the amide I from 1625-1725cm^-1) that correlate with secondary structure (SS) content. However, unlike circular dichroism (CD), the most well-established technique for SS analysis, Raman spectroscopy allows a much wider ranges of optical density, making possible the analysis of highly concentrated samples with no prior dilution. Moreover, water is a weak scatterer below 3000 cm^-1, which confers Raman an advantage over IR for the analysis of complex aqueous pharmaceutical samples as the signal from water dominates the amide I region. The most traditional procedure to extract information on SS content is band-fitting. However, in most cases, we found the method to be ambiguous, limited by spectral noise and subjected to the judgment of the analyzer. Self-organizing maps (SOM) is a type of self-learning algorithm that organizes data in a two-dimensional (2D) space based on spectral similarity and class with no bias from the analyzer and very little effect from noise. In this work, a set of protein spectra with known SS content were collected in both solid and aqueous state with back-scatter Raman spectroscopy and used to train a SOM algorithm for SS prediction. The results were compared with those by partial least squares (PLS) regression, band-fitting, and X-ray data in the literature. The prediction errors observed by SOM were comparable to those by PLS and far from those obtained by band-fitting, proving Raman-SOM as viable alternative to the aforementioned methods.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1497-1507"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasmonic Hybrid Heterostructure Based on Reduced Graphene Oxide-Gold Nanostars Composite for Sensitive Surface-Enhanced Raman Spectroscopy Sensing.","authors":"Supriya Atta, Tamer Sharaf, Tuan Vo-Dinh","doi":"10.1177/00037028251344628","DOIUrl":"10.1177/00037028251344628","url":null,"abstract":"<p><p>In this study, we have developed a plasmonic hybrid heterostructure integrating two elements: Two-dimensional (2D) reduced graphene oxide-gold nanostars composite (rGO-GNS), and gold nanostars (GNS) substrate. By harnessing the unique plasmonic properties of rGO in chemical enhancement and that of GNS in electromagnetic enhancement, the hybrid heterostructure offers synergistic enhancement effects that enable ultra-low sensitivity and accurate identification and analysis of trace quantities of target substances. It is noteworthy that the high-density hotspots generated by strong plasmonic coupling of rGO-GNS and GNS results in ultra-high surface-enhanced Raman spectroscopy (SERS) enhancement compared to individual substrate either GNS or rGO-GNS substrate. Moreover, the uniformity and reproducibility of the GNS@rGO-GNS substrate were studied by using thiophenol (TP) as a model analyte, which indicates that the SERS sensor exhibited superior signal reproducibility with an RSD value 5% and long-term stability with a minimal signal loss after 30 days. To demonstrate a potential application of our SERS substrate, SERS detection of the pesticide thiram in river water was realized with a limit of detection (LOD) up to 50 pM, showing the potential for new opportunities for efficient chemical and biological sensing applications.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1445-1454"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12768889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Pulsing a Radio Frequency Glow Discharge Lamp on Emission Yields and Analytical Figures of Merit in Glow Discharge Optical Emission Spectroscopy.","authors":"Arne Bengtson, David Malmström, Rebecca Quardokus, Jessica Russell","doi":"10.1177/00037028251356458","DOIUrl":"10.1177/00037028251356458","url":null,"abstract":"<p><p>The impact of pulsing a radio frequency (RF) glow discharge lamp on analytical aspects in glow discharge optical emission spectrometry (GD-OES) was investigated. The experiments were done with a LECO GDS950 spectrometer. This instrument has a fixed pulse frequency of 320  Hz and adjustable pulse duty cycles (PDC) 100%-6.4%. In the first part, emission yields (EY) were studied by measuring coated steel samples in compositional depth profiling (CDP) mode. Variations in EY were measured by integrating the intensity of emission lines from several elements through the entire coatings at several PDC settings. The results show generally small EY variations. For improved accuracy, a set of correction constants is suggested. In the second part, the impact on signal-to-background (S/B), signal-to-noise-noise (S/N), and precision was investigated using \"high current\" pulsing. This means increased pulse power leaving the average power constant at the different PDC settings. The samples were a low alloy steel and a high purity iron blank (background) sample. The results showed significant increase of the S/B and S/N for four out of six spectral lines investigated at increasing pulse power, showing potential for improved detection limits (DL). Furthermore, there was a tendency towards improved precision with higher pulse power. Finally, the effect on depth resolution in CDP was investigated by running a ZnNi coated steel using \"high current\" pulsing. It was found that the depth resolution was unaffected up to 30% PDC.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1549-1558"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advertising and Front Matter.","authors":"","doi":"10.1177/00037028251385567","DOIUrl":"https://doi.org/10.1177/00037028251385567","url":null,"abstract":"","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":"79 10","pages":"1441-1444"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145249468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using LEGO Blocks for the Evaluation of Fluorescence Avoidance and Mitigation in Handheld Raman Spectrometers.","authors":"Richard A Crocombe, Pauline E Leary, Brooke W Kammrath, Thomas J Tague, William D P Costa, Michael D Hargreaves","doi":"10.1177/00037028251348481","DOIUrl":"10.1177/00037028251348481","url":null,"abstract":"<p><p>In a previous paper, we proposed the use of a set of colored LEGO blocks as \"standard\" samples for the evaluation of fluorescence avoidance and mitigation schemes in Raman spectroscopy, as well as for use to evaluate the instruments' performance on dark samples. The purpose of this paper is to establish that this set of LEGO blocks does represent a good test case for fluorescence avoidance and mitigation when using handheld Raman spectrometers, and for the ability to record Raman spectra from dark samples. The performance of ten different instruments, operating using different exciting lines (785, 830/852, and 1064 nm), and different data processing schemes, are compared. The combination of a series of colored blocks (white, yellow, red, and blue), and successively darker tone blocks (white, gray, and black) do challenge these instruments, and shed light on the ways that their manufacturers have optimized these instruments in specific areas and for different purposes.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1527-1548"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Mapping of Polymer Film Thickness Using Near-Infrared Hyperspectral Imaging.","authors":"Xiaoyun Chen, Jin Wang, Christopher Thurber, Matthew Benedict, Kurt Olson, Eric Marchbanks, Hyunwoo Kim, Michael Bishop","doi":"10.1177/00037028251323634","DOIUrl":"10.1177/00037028251323634","url":null,"abstract":"<p><p>A new method based on near-infrared (NIR) hyperspectral imaging (HSI) has been developed for online polymer film thickness mapping. Traditional online methods, including X-ray, capacitance, and physical gauging (micrometers), can only determine film thickness for a point with each measurement. The NIR-HIS method allows the determination of film thickness for a line based on each image, thus enabling true real-time two-dimensional (2D) mapping of film thickness as the film translates in front of the instrument. A Specim NIR camera, 1000-2500 nm, 384 (spatial) × 288 (spatial) pixels, was used in this study for various low-density polyethylene (LDPE), and high-density polyethylene (HDPE) films. Sample thickness between μm to mm can be mapped based on the myriad NIR absorbance bands with various molar absorptivity. The 2310 nm NIR peak was found to be the most effective feature for determining film thickness over the range of polyethylene film studied in this project: 10∼100 μm. A good correlation was found between the 2310 nm absorbance and the incumbent X-ray thickness scanner results. Interference fringes were found to be a potential source of error for quantitative analysis of thin films, and a classical least squares (CLS) analysis was found to be effective in removing fringes. This method was implemented to map out film thickness in real-time in an industrial blown film process.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1518-1526"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing Molybdenum Pentachloride Flow During Vapor Deposition Processes Using Absorption Imaging.","authors":"James E Maslar, Berc Kalanyan","doi":"10.1177/00037028251325565","DOIUrl":"10.1177/00037028251325565","url":null,"abstract":"<p><p>An absorption imaging technique was described for visualizing molybdenum pentachloride (MoCl₅) flow during an atomic layer deposition/pulsed chemical vapor deposition process. The imaging system was composed of a telecentric lens and a commercial 7.1-megapixels (MP) complementary metal oxide semiconductor (CMOS) camera. The light source was a fiber-coupled light emitting diode operating at a peak emission wavelength of 443  nm. Flow images of MoCl₅ vapor entrained in a carrier gas were recorded at approximately 93 frames per second in a research-grade vapor deposition chamber. The utility of this technique was illustrated by comparing the MoCl₅ flow patterns for two precursor injection conditions, conditions consisting of different argon carrier gas flow rate and chamber pressure. For a low flow rate and chamber pressure, the flow images showed a gradual expansion of the MoCl₅ concentration front through the field of view with a relatively short MoCl₅ residence time. These flow patterns result in a relatively uniform precursor concentration front impinging on the wafer surface with the precursor being efficiently exhausted from the chamber, making these conditions desirable for thin film deposition in this chamber. For a high carrier gas flow rate and elevated chamber pressure, the flow images showed a high gas velocity jet impinging on the wafer chuck surface and the formation of gas recirculation zones, resulting in a relatively long residence time. These flow conditions would make it difficult to reproducibly deposit uniform thin films in this chamber. This comparison demonstrated the utility of this technique for qualitative characterization of precursor flow fields with minimal data processing. However, the two-dimensional data obtained from this technique can also provide the basis for training and validating computational fluid dynamics models. Furthermore, the addition of duplicate optical systems would provide the basis for determining the three-dimensional precursor distribution through tomographic analysis.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1487-1496"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes.","authors":"Imran Rehan, Kamran Rehan, Sabiha Sultana, Mujeeb Ur Rehman","doi":"10.1177/00037028251334383","DOIUrl":"10.1177/00037028251334383","url":null,"abstract":"<p><p>Diabetes, a chronic metabolic disorder affecting millions worldwide, presents a persistent need for reliable and non-invasive diagnostic techniques. Here, we suggest a highly effective approach for differentiating between fingernails from diabetic individuals and those from healthy controls using laser-induced breakdown spectroscopy (LIBS). The excitation source employed was a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser emitting light with a wavelength of 1064  nm. The initial differentiation between individuals with and without diabetes was achieved by applying principal component analysis (PCA) to LIBS spectral data, which was then incorporated into a novel machine-learning model. The classification model designed for a non-invasive system included random forest (RF), an extreme learning machine (ELM) classifier, and a hybrid classification model incorporating cross-validation techniques to evaluate the outcomes. The algorithm analyses the complete spectrum of both healthy and diseased samples, categorizing them according to differences in LIBS spectral intensity. The classification performance of the model was assessed using a <i>k</i>-fold cross-validation method. Seven parameters, i.e., specificity, sensitivity, area under curve (AUC), accuracy, precision, recall, and F-score, were used to evaluate the model's overall performance. The findings affirmed that the suggested non-invasive model could predict diabetic diseases with an accuracy of 95%.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1478-1486"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}