Combined shifted-excitation Raman difference spectroscopy and support vector regression for monitoring the algal production of complex polysaccharides

Raman spectroscopy and U-Net deep neural network in antiresorptive drug-related osteonecrosis of the jaw

OBJECTIVE

Application of an optical method for the identification of antiresorptive drug-related osteonecrosis of the jaw (ARONJ).

METHODS

We introduce shifted-excitation Raman difference spectroscopy followed by U-Net deep neural network refinement to determine bone tissue viability. The obtained results are validated through established histological methods.

RESULTS

Discrimination of osteonecrosis from physiological tissues was evaluated at 119 distinct measurement loci in 40 surgical specimens from 28 patients. Mean Raman spectra were refined from 11,900 raw spectra, and characteristic peaks were assigned to their respective molecular origin. Then, following principal component and linear discriminant analyses, osteonecrotic lesions were distinguished from physiological tissue entities, such as viable bone, with a sensitivity, specificity, and overall accuracy of 100%. Moreover, bone mineral content, quality, maturity, and crystallinity were quantified, revealing an increased mineral-to-matrix ratio and decreased carbonate-to-phosphate ratio in ARONJ lesions compared to physiological bone.

CONCLUSION

The results demonstrate feasibility with high classification accuracy in this collective. The differentiation was determined by the spectral features of the organic and mineral composition of bone. This merely optical, noninvasive technique is a promising candidate to ameliorate both the diagnosis and treatment of ARONJ in the future.

Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma

In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.

A rapid and non-destructive identification for paper cup evidence based on shifted-excitation Raman difference spectroscopy and SOM clustering

In order to achieve rapid, non-destructive, efficient, and accurate classification of paper cup samples, we propose a classification model that integrates shifted-excitation Raman difference spectroscopy (SERDS) with self-organizing map (SOM) and Bayesian optimization-support vector machine (BO-SVM). We collected differential Raman data from 52 paper cup samples using SERDS, with an excitation wavelength range of 784-785 nm, a laser power of 440 mW, an integration time of 10 s, and a spectral range spanning from 280 to 2700 cm-1 . Subsequently, principal component analysis (PCA) was applied to reduce the dimensionality of the data. The SOM clustering outcomes were utilized as the foundation for constructing the discriminant analysis (FDA) and BO-SVM classification models. The primary constituent of the paper cup samples was identified as cellulose, while additional fillers such as talc, calcium carbonate, and kaolin were also present. The SOM clustering categorized the samples into seven distinct groups. The FDA model achieved a classification accuracy of 92.3%, and the BO-SVM model reached a classification accuracy of 96.2%. The SOM clustering effectively discerned samples with different fillers, as evidenced by distinct peak numbers and shapes in the differential Raman spectra, thereby underscoring the practical significance of SOM clustering. In comparison with FDA, BO-SVM exhibited enhanced classification accuracy and exceptional performance in handling outliers and linearly inseparable data, indicating its superior generalization capabilities.

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp

Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R², RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp.

Assessment of shifted excitation Raman difference spectroscopy in highly fluorescent biological samples

Shifted excitation Raman difference spectroscopy (SERDS) can be used as an instrumental baseline correction technique to retrieve Raman bands in highly fluorescent samples. Genipin (GE) cross-linked equine pericardium (EP) was used as a model system since a blue pigment is formed upon cross-linking, which results in a strong fluorescent background in the Raman spectra. EP was cross-linked with 0.25% GE solution for 0.5 h, 2 h, 4 h, 6 h, 12 h, and 24 h, and compared with corresponding untreated EP. Raman spectra were collected with three different excitation wavelengths. For the assessment of the SERDS technique, the preprocessed SERDS spectra of two excitation wavelengths (784 nm-786 nm) were compared with the mathematical baseline-corrected Raman spectra at 785 nm excitation using extended multiplicative signal correction, rubberband, the sensitive nonlinear iterative peak and polynomial fitting algorithms. Whereas each baseline correction gave poor quality spectra beyond 6 h GE crosslinking with wave-like artefacts, the SERDS technique resulted in difference spectra, that gave superior reconstructed spectra with clear collagen and resonance enhanced GE pigment bands with lower standard deviation. Key for this progress was an advanced difference optimization approach that is described here. Furthermore, the results of the SERDS technique were independent of the intensity calibration because the system transfer response was compensated by calculating the difference spectrum. We conclude that this SERDS strategy can be transferred to Raman studies on biological and non-biological samples with a strong fluorescence background at 785 nm and also shorter excitation wavelengths which benefit from more intense scattering intensities and higher quantum efficiencies of CCD detectors.

A rapid and nondestructive approach for forensic identification of cigarette inner liner papers using shift-excitation Raman difference spectroscopy and chemometrics

In forensic science, cigarettes are considered as crucial physical evidence because it helps to establish the connection between the criminal and the crime scene. In the present study, SERDS has been used for the examination of 25 different brands or series of cigarette inner liner paper. The discrimination power is calculated by using three methods, i.e., visual discrimination of the spectra, hierarchical cluster analysis (HCA) and principal component analysis (PCA). They are 100.00%, 92.42% and 100.00%, respectively. Cigarette inner liner paper samples were divided into four categories based on HCA and assignment of Raman special peaks: (1) talcum powder, (2) zinc oxide, (3) talcum powder and zinc oxide and (4) zinc oxide and barium sulfate. The PCA-FDA model was constructed for identifying the unknown samples, it delivered 100.00% calibration accuracy and validation accuracy. The results suggest that SERDS combined with the chemometric methods is a rapid, nondestructive and accurate method for the differentiation of cigarette inner liner papers.

Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.

Wide Field Spectral Imaging with Shifted Excitation Raman Difference Spectroscopy Using the Nod and Shuffle Technique

Wide field Raman imaging using the integral field spectroscopy approach was used as a fast, one shot imaging method for the simultaneous collection of all spectra composing a Raman image. For the suppression of autofluorescence and background signals such as room light, shifted excitation Raman difference spectroscopy (SERDS) was applied to remove background artifacts in Raman spectra. To reduce acquisition times in wide field SERDS imaging, we adapted the nod and shuffle technique from astrophysics and implemented it into a wide field SERDS imaging setup. In our adapted version, the nod corresponds to the change in excitation wavelength, whereas the shuffle corresponds to the shifting of charges up and down on a Charge-Coupled Device (CCD) chip synchronous to the change in excitation wavelength. We coupled this improved wide field SERDS imaging setup to diode lasers with 784.4/785.5 and 457.7/458.9 nm excitation and applied it to samples such as paracetamol and aspirin tablets, polystyrene and polymethyl methacrylate beads, as well as pork meat using multiple accumulations with acquisition times in the range of 50 to 200 ms. The results tackle two main challenges of SERDS imaging: gradual photobleaching changes the autofluorescence background, and multiple readouts of CCD detector prolong the acquisition time.

New methodology to process shifted excitation Raman difference spectroscopy data: a case study of pollen classification

Shifted excitation Raman difference spectroscopy (SERDS) is a background correction method for Raman spectroscopy. Here, the difference spectra were directly used as input for SERDS-based classification after an optimization procedure to correct for photobleaching of the autofluorescence. Further processing included a principal component analysis to compensate for the reduced signal to noise ratio of the difference spectra and subsequent classification by linear discriminant analysis. As a case study 6,028 Raman spectra of single pollen originating from plants of eight different genera and four different growth habits were automatically recorded at excitation wavelengths 784 and 786 nm using a high-throughput screening Raman system. Different pollen were distinguished according to their growth habit, i.e. tree versus non-tree with an accuracy of 95.9%. Furthermore, all pollen were separated according to their genus, providing also insight into similarities based on their families. Classification results were compared using spectra reconstructed from the differences and raw spectra after state-of-art baseline correction as input. Similar sensitivities, specificities, accuracies and precisions were found for all spectra with moderately background. Advantages of SERDS are expected in scenarios where Raman spectra are affected by variations due to detector etaloning, ambient light, and high background.

Shifted Excitation Raman Difference Spectroscopy with Charge-Shifting Charge-Coupled Device (CCD) Lock-In Detection

Shifted excitation Raman difference spectroscopy (SERDS) can provide effective, chemically specific information on fluorescent samples. However, the restricted ability for fast alternating detection (usually < 10 Hz) of spectra excited at two shifted laser wavelengths can limit its effectiveness when rapidly varying emission backgrounds are present. This paper presents a novel charge-shifting lock-in approach permitting fast SERDS operation (exemplarily demonstrated at 1000 Hz) using a specialized dual-wavelength diode laser (emitting at 829.40 nm and 828.85 nm) and a custom-built charge-coupled device (CCD) enabling charge retention and shifting back and forth on the CCD chip. For six selected mineral samples (moved irregularly during spectral acquisition), results demonstrate superior reproducibility of the fast charge-shifting read-out over the conventional read-out (operated at 5.4 Hz). Partial least squares discriminant analysis revealed improved classification performance of charge-shifting (four latent variables, sensitivity: 99%, specificity: 94%) versus conventional read-out (six latent variables, sensitivity: 90%, specificity: 92%). The charge-shifting concept was also successfully translated to sub-surface analysis using spatially offset Raman spectroscopy (SORS). Charge-shifting SERDS-SORS spectra recorded from a polytetrafluoroethylene layer, concealed behind a 0.25 mm thick, opaque, heterogeneous layer, matched reference spectra much more closely and exhibited a signal-to-background-noise (S/N_B) ratio two times higher than that achieved with conventional CCD read-out SERDS-SORS. The novel approach overcomes fundamental limitations of conventional CCDs. In conjunction with the inherent capability of the charge-shifting lock-in technique to suppress rapidly varying ambient light interference demonstrated by us earlier it is expected to be particularly beneficial with heterogeneous fluorescent samples in field applications.

Identification of Passion Fruit Oil Adulteration by Chemometric Analysis of FTIR Spectra

Passion fruit oil is a high-value product with applications in the food and cosmetic sectors. It is frequently diluted with sunflower oil. Sunflower oil is also a potential adulterant as its addition does not notably alter the appearance of the passion fruit oil. In this paper, we show that this is also true for the FTIR spectrum. However, the chemometric analysis of the data changes this situation. Principal component analysis (PCA) enables not only the straightforward discrimination of pure passion fruit oil and adulterated samples but also the unambiguous classification of passion fruit oil products from five different manufacturers. Even small amounts—significantly below 1%—of the adulterant can be detected. Furthermore, partial least-squares regression (PLSR) facilitates the quantification of the amount of sunflower oil added to the passion fruit oil. The results demonstrate that the combination of FTIR spectroscopy and chemometric data analysis is a very powerful tool to analyze passion fruit oil.

Principal component analysis to enhance enantioselective Raman spectroscopy

Enantioselective Raman (esR) spectroscopy is an innovative technique with a high potential for online process monitoring in chiral media, e.g. in the pharmaceutical industry. A prerequisite for an effective application is to combine the experimental approach with suitable concepts for data analysis. In this work, we present a chemometric approach to analyze the esR spectra recorded in an automatized polarization-resolved Raman set-up. It is demonstrated that the proposed method is capable of distinguishing between the enantiomers of the chiral alcohol 4-methylpentan-2-ol in a fully unsupervised fashion. Furthermore, it is shown that the difficulty of facing only small intensity differences between the esR spectra of the enantiomers can be overcome by feeding difference spectra between the pure enantiomers and the racemate into the principal component analysis (PCA) algorithm. The enantiomers are clearly discriminable along the first principal component.

Dual-Wavelength Raman Fusion Spectroscopy

Spatially compressed dual-wavelength Raman spectroscopy allows recording the full Raman spectrum using a detection system with limited spectral range. The common approach is to record the spectra with the two excitation lasers consecutively and then concatenate the full spectrum. However, with this approach, quantitative analysis for process monitoring is not possible as the investigated object may change between the two acquisitions. In this Note, spectral fusion is proposed as a concept to overcome this problem. The sample is illuminated by the two lasers simultaneously, hence leading to an on-chip fusion of the different parts of the Raman spectrum. It is shown that the resulting data are suitable for quantitative evaluation using univariate and multivariate methods. Dual-wavelength Raman fusion spectroscopy offers new opportunities for building highly compact devices for analytical chemistry.

Optical Spectroscopy for Analysis and Monitoring of Metalworking Fluids

For various industrial manufacturing processes, water-based metalworking fluids (MWFs) are of high relevance due to their cooling and lubricating ability. They commonly form oil-in-water emulsions or solutions and hence their composition and stability is crucial for their performance in the metalworking process. To ensure a long service life of the MWF, intense monitoring is obligatory. However, examination techniques which display comprehensive and precise information about the actual state of the cooling lubricant in use are currently not available. The present study aims at testing the suitability of spectroscopic methods in terms of Fourier transform infrared, Raman, and laser-induced fluorescence spectroscopy for analyzing and monitoring MWFs. It is shown that all three techniques are capable of determining the initial composition, i.e., the ratio of water and concentrate. Fourier transform infrared provides the best performance regarding monitoring the state of the fluid over an extended period of time. The spectral signatures show distinct changes during a five-month service life in a technical environment.

Breast Tumor Analysis Using Shifted-Excitation Raman Difference Spectroscopy (SERDS)

We used a shifted-excitation Raman difference spectroscopy method for the ex vivo classification of resected and formalin-fixed breast tissue samples as normal (healthy) tissue, fibroadenoma, or invasive carcinoma. We analyzed 8 tissue samples containing invasive carcinoma that were surrounded by normal tissue and 3 tissue samples with fibroadenoma only. We made various measurement sites on various tissue samples, in total 240 measurements for each type of tissue. Although the acquired raw spectra contain enough information to clearly differentiate between normal and tumor (fibroadenoma and invasive carcinoma) tissue, the differentiation between fibroadenoma and invasive carcinoma was possible only after the shifted-excitation Raman difference spectroscopy isolation of pure Raman spectra from the heavily fluorescence interfered raw spectra. We used 784 and 785 nm as excitation wavelengths for the shifted-excitation Raman difference spectroscopy method. The differences in the obtained pure Raman spectra are assigned to the different chemical compositions of normal breast tissue, fibroadenoma, and invasive breast carcinoma. Principal component analysis and linear discriminant analysis showed excellent classification results in the Raman shift range between 1000 and 1800 cm-1. Invasive breast carcinoma was identified with 99.15% sensitivity, and the absence of invasive carcinoma was identified with 90.40% specificity. Tumor tissue in tumor-containing tissue was identified with 100% sensitivity, and the absence of tumor in no-tumor containing tissue was identified with 100% specificity. As gold standard for the determination of the sensitivity and the specificity, we considered the conventional histopathological classification. In summary, shifted-excitation Raman difference spectroscopy could be potentially very useful to support histopathological diagnosis in breast pathology.

Simultaneous Acquisition of the Polarized and Depolarized Raman Signal with a Single Detector

Polarization-resolved Raman spectroscopy provides much more information than its conventional counterpart. However, it usually either requires a complicated setup with two spectrographs and detectors or two measurements must be performed sequentially. This study presents a simple and straightforward approach to recording both polarization components simultaneously with a single spectrograph and detector. The vertically and a horizontally polarized laser beam exiting a Wollaston prism are focused into the sample with a small spatial separation. The scattered light from both beams is imaged onto the slit of an imaging spectrograph as two spatially separated signals, i.e., the polarized and the depolarized Raman signal. Eventually, both spectra are acquired on a single CCD chip simultaneously. Experimental data of ethanol and dimethyl sulfoxide are shown as proof-of-concept. The new method has a number of advantages, for example, laser intensity fluctuations and the polarization dependence of the diffraction grating do not play a role. The proposed approach will be useful for an improved structural analysis and it will be the enabling technology for temporally resolved enantioselective Raman (esR) spectroscopy.

Universal enantioselective discrimination by Raman spectroscopy

A novel Raman spectroscopy approach allows universal enantioselective discrimination, structural and compositional analysis, as well as investigating molecular interactions.

Monitoring of Microalgal Processes

Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

Quantitative enantioselective Raman spectroscopy

Analytical methods for quantitative enantioselective measurements are highly desirable in the life sciences. A Raman spectroscopy approach for the determination of concentration and enantiomer ratio is proposed.

Light-emitting diode based shifted-excitation Raman difference spectroscopy (LED-SERDS)

Fluorescence interference is a common problem in Raman spectroscopy. A low-cost solution using shifted-excitation Raman difference spectroscopy (SERDS) with a light-emitting diode (LED) is presented. A conventional dielectric bandpass filter is employed to narrow the spectral bandwidth of the LED radiation and to stabilize its wavelength. Moreover, angle-tuning of the filter allows the wavelength to be controlled and shifted in order to record SERDS spectra.