Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

A Flexible Chamber for Time-Lapse Live-Cell Imaging with Stimulated Raman Scattering Microscopy.

Stimulated Raman scattering (SRS) microscopy is a label-free chemical imaging technology. Live-cell imaging with SRS has been demonstrated for many biological and biomedical applications. However, long-term time-lapse SRS imaging of live cells has not been widely adopted. SRS microscopy often uses a high numerical aperture (NA) water-immersion objective and a high NA oil-immersion condenser to achieve high-resolution imaging. In this case, the gap between the objective and the condenser is only a few millimeters. Therefore, most commercial stage-top environmental chambers cannot be used for SRS imaging because of their large thickness with a rigid glass cover. This paper describes the design and fabrication of a flexible chamber that can be used for time-lapse live-cell imaging with transmitted SRS signal detection on an upright microscope frame. The flexibility of the chamber is achieved by using a soft material - a thin natural rubber film. The new enclosure and chamber design can be easily added to an existing SRS imaging setup. The testing and preliminary results demonstrate that the flexible chamber system enables stable, long-term, time-lapse SRS imaging of live cells, which can be used for various bioimaging applications in the future.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling.

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2-94.6% accuracy, 89.8-91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600-1800 cm-1) and global loss of high wavenumber signal (2800-3200 cm-1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

The heritable landscape of near-infrared and Raman spectroscopic measurements to improve lipid content in Atlantic salmon fillets.

BACKGROUND: Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. RESULTS: Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27-0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ([Formula: see text] = 0.91-0.98) obtained with the reference method using Lin's concordance correlation coefficient (CCC = 0.63-0.90), and were significantly heritable ([Formula: see text] = 0.52-0.67). CONCLUSIONS: Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.

Diagnosis of intrahepatic cholangiocarcinoma by Raman spectroscopy provides high efficiency: A protocol for systematic review and meta-analysis.

BACKGROUND: We aim to evaluate the efficiency of Raman spectroscopy (RS) in diagnosing suspected patients with intrahepatic cholangiocarcinoma (ICC), manifested by diagnostic sensitivity, specificity, and accuracy. METHODS: We will research widely the articles concerning the use of RS in ICC through authenticated database including PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library, and CNKI between January 2012 and November 2020, retrieving at least 1500 spectra with strict criteria. This study will be carried out in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We are going to summarize the test performance using random effects models. RESULTS: Based on the pooled sensitivity, specificity, and diagnostic accuracy, we intend to provide the relative diagnostic efficiency in ICC through RS. CONCLUSION: Through this systematic review and meta-analysis, we intend to provide the pooled sensitivity, specificity and diagnostic accuracy of RS in the diagnosis of suspected ICC. Other parameters like positive likelihood ratios (LR), negative LR, diagnostic odds ratio (DOR), and area under curve (AUC) of the summary receiver operating characteristics (SROC) curve will also be calculated and related figures will be drawn to help illustrate the efficacy of RS in the diagnosis of ICC.

Raman spectroscopy accurately classifies burn severity in an ex vivo model.

Accurate classification of burn severities is of vital importance for proper burn treatments. A recent article reported that using the combination of Raman spectroscopy and optical coherence tomography (OCT) classifies different degrees of burns with an overall accuracy of 85% [1]. In this study, we demonstrate the feasibility of using Raman spectroscopy alone to classify burn severities on ex vivo porcine skin tissues. To create different levels of burns, four burn conditions were designed: (i) 200°F for 10s, (ii) 200°F for 30s, (iii) 450°F for 10s and (iv) 450°F for 30s. Raman spectra from 500-2000cm-1 were collected from samples of the four burn conditions as well as the unburnt condition. Classifications were performed using kernel support vector machine (KSVM) with features extracted from the spectra by principal component analysis (PCA), and partial least-square (PLS). Both techniques yielded an average accuracy of approximately 92%, which was independently evaluated by leave-one-out cross-validation (LOOCV). By comparison, PCA+KSVM provides higher accuracy in classifying severe burns, while PLS performs better in classifying mild burns. Variable importance in the projection (VIP) scores from the PLS models reveal that proteins and lipids, amide III, and amino acids are important indicators in separating unburnt or mild burns (200°F), while amide I has a more pronounced impact in separating severe burns (450°F).

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors.

In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 µm2 to approximately 200×200 µm2, respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.

Highly Sensitive Detection of the Antibiotic Ciprofloxacin by Means of Fiber Enhanced Raman Spectroscopy.

Sepsis and septic shock exhibit a rapid course and a high fatality rate. Antibiotic treatment is time-critical and precise knowledge of the antibiotic concentration during the patients' treatment would allow individual dose adaption. Over- and underdosing will increase the antimicrobial efficacy and reduce toxicity. We demonstrated that fiber enhanced Raman spectroscopy (FERS) can be used to detect very low concentrations of ciprofloxacin in clinically relevant doses, down to 1.5 µM. Fiber enhancement was achieved in bandgap shifted photonic crystal fibers. The high linearity between the Raman signals and the drug concentrations allows a robust calibration for drug quantification. The needed sample volume was very low (0.58 µL) and an acquisition time of 30 s allowed the rapid monitoring of ciprofloxacin levels in a less invasive way than conventional techniques. These results demonstrate that FERS has a high potential for clinical in-situ monitoring of ciprofloxacin levels.

Paper-Based SERS Platform for One-Step Screening of Tetracycline in Milk.

Throughout the last decade, the expansion of food testing has been gradually moving towards ordinary high throughput screening methods performed on-site. The demand for point-of-care testing, able to distinguish molecular signatures with high accuracy, sensitivity and specificity has been significantly increasing. This new requirement relies on the on-site detection and monitorization of molecular signatures suitable for the surveillance of food production and processing. The widespread use of antibiotics has contributed to disease control of livestock but has also created problems for the dairy industry and consumers. Its therapeutic and subtherapeutic use has increased the risk of contamination in milk in enough concentrations to cause economic losses to the dairy industry and have a health impact in highly sensitive individuals. This study focuses on the development of a simple Surface-Enhanced Raman Spectroscopy (SERS) method for fast high throughput screening of tetracycline (TET) in milk. For this, we integrate a paper-based low-cost, fully recyclable and highly stable SERS platform, with a minimal sample preparation protocol. A two-microliter sample of milk solutions spiked with TET (from 0.01 to 1000 ppm) is dried on a silver nanoparticle coated cardboard substrate and measured via a Raman spectrophotometer. The SERS substrate showed to be extremely stable with a shelf life of several months. A global spectrum principal component analysis approach was used to test all the detected vibrational modes and their correlation with TET concentration. Peak intensity ratios (455 cm-1/1280 cm-1 and 874 cm-1/1397 cm-1) were found to be correlated with TET concentrations in milk, achieving a sensitivity as low as 0.1 ppm. Results indicate that this SERS method combined with portable Raman spectrometer is a potential tool that can be used on-site for the monitoring of TET residues and other antibiotics.

Real-Time Measurement of Airborne Carbon Nanotubes in Workplace Atmospheres.

With growing applications of carbon nanomaterials, there is a concern over health risks presented by inhalation of carbon nanotube (CNT) aerosol in workplace atmospheres. Current methods used for CNT aerosol measurement lack selectivity to specific form of carbonaceous component or allotrope of interest. Moreover, the detection limits of these methods are also inadequate for short-term monitoring. Here, we describe, for the first time, a near real-time, field-portable instrument for selective quantification of airborne CNT concentration. The approach uses an automated cyclical scheme involving collect-analyze-ablate steps to obtain continuous near real-time measurement using Raman spectroscopy. The method achieves significantly lower detection limits by employing corona-assisted particle microconcentration for efficient coupling with laser Raman spectroscopy. A combination of techniques involving (i) use of characteristic Raman peaks, (ii) distinct ratio of disordered and graphitic peaks, and (iii) principal component classification and regression is employed to identify and quantify the specific form of the aerosolized carbonaceous nanomaterial. We show that the approach is capable of selectively quantifying trace single-walled CNT in the presence of interfering agents such as diesel particulate matter. The detection limit of the method for the single-walled CNT studied in this work was 60 ng m-3, corresponding to a 10 min aerosol collection period, which is significantly lower than that for the NIOSH Method 5040 (∼0.15 µg m-3 for an 8-h collection on a 25 mm filter at 4 L min-1), a commonly used method for elemental carbon. We demonstrate the automated real-time capability of this field-portable method by continuously measuring a transient single-walled CNT aerosol.

Rapid and Ultrasensitive Quantification of Multiplex Respiratory Tract Infection Pathogen via Lateral Flow Microarray based on SERS Nanotags.

Respiratory tract infections (RTIs) are severe acute infectious diseases, which require the timely and accurate identification of the pathogens involved so that the individual treatment plan can be selected, including optimized use of antibiotics. However, high throughput and ultrasensitive quantification of multiple nucleic acids is a challenge in a point of care testing (POCT) device. Methods: Herein, we developed a 2×3 microarray on a lateral flow strip with surface enhanced Raman scattering (SERS) nanotags encoding the nucleic acids of 11 common RTI pathogens. On account of the signal magnification of encoded SERS nanotags in addition to the high surface area to volume ratio of the nitrocellulose (NC) membrane, rapid quantification of the 11 pathogens with a broad linear dynamic range (LDR) and ultra-high sensitivity was achieved on one lateral flow microarray. Results: The limit of detection (LOD) for influenza A, parainfluenza 1, parainfluenza 3, respiratory syncytial virus, coxiella burnetii, legionella pneumophila, influenza B, parainfluenza 2, adenovirus, chlamydophila pneumoniae, and mycoplasma pneumoniae were calculated to be 0.031 pM, 0.030 pM, 0.038 pM, 0.038 pM, 0.040 pM, 0.039 pM, 0.035 pM, 0.032 pM, 0.040 pM, 0.039 pM, and 0.041 pM, respectively. The LDR of measurement of the target nucleic acids of the eleven RTI pathogens were 1 pM-50 nM, which span 5 orders of magnitude. Conclusions: We anticipate this novel approach could be widely adopted in the early and precise diagnosis of RTI and other diseases.

Gold Nanoparticles with Different Particle Sizes for the Quantitative Determination of Chlorpyrifos Residues in Soil by SERS.

Chlorpyrifos (CPF) is widely used in the prevention and control of crop pests and diseases in agriculture. However, the irrational utilization of pesticides not only causes environmental pollution but also threatens human health. Compared with the conventional techniques for the determination of pesticides in soil, surface-enhanced Raman spectroscopy (SERS) has shown great potential in ultrasensitive and chemical analysis. Therefore, this paper reported a simple method for synthesizing gold nanoparticles (AuNPs) with different sizes used as a SERS substrate for the determination of CPF residues in soil for the first time. The results showed that there was a good linear correlation between the SERS characteristic peak intensity of CPF and particle size of the AuNPs with an R2 of 0.9973. Moreover, the prepared AuNPs performed great ultrasensitivity, reproducibility and chemical stability, and the limit of detection (LOD) of the CPF was found to be as low as 10 µg/L. Furthermore, the concentrations ranging from 0.01 to 10 mg/L were easily observed by SERS with the prepared AuNPs and the SERS intensity showed a good linear relationship with an R2 of 0.985. The determination coefficient (Rp2) reached 0.977 for CPF prediction using the partial least squares regression (PLSR) model and the LOD of CPF residues in soil was found to be as low as 0.025 mg/kg. The relative standard deviation (RSD) was less than 3.69% and the recovery ranged from 97.5 to 103.3%. In summary, this simple method for AuNPs fabrication with ultrasensitivity and reproducibility confirms that the SERS is highly promising for the determination of soil pesticide residues.

Improving reliability of Raman spectroscopy for mAb production by upstream processes during bioprocess development stages.

The use of Raman in Bioprocess development have shown great potential for process understanding and monitoring although there are still some challenges and limitations in performance when conditions such as clone, media or scale are changed during bioprocess development. This study proposes different strategies to balance the different information content of multiple mammalian cell cultivations produced during a bioprocess development program, when several conditions are investigated. The result is a pragmatic approach to PAT monitoring that serves both development and manufacturing stages. Combining risk-assessment techniques with two ways of developing monitoring calibrations (local or general models), we were able to obtain good predictive power from Raman spectroscopy used as PAT tool, when multiple cultivation conditions vary. As an example, the effects of process scale, base powder media and cell-line on Raman spectra are discussed and how using local models specific to some of these cultivation conditions, has a positive impact on calibration performance. It is shown how more accurate calibrations can be obtained using Clone-based local models, which requires less batches than usual approaches (up to 3-9). This study uses thirty-five cultivations of four different types of CHO cell lines, eight different clones, and four different scales - 2 L, 7 L, 15 L and 10,000 L - in two Cultivation Modes - fed-batch and perfusion. The aim is to serve as blueprint to how can PAT approaches be best developed in parallel to bioprocess development.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy.

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (Rp) of 0.9260 and RMSEP of 87.9132 µg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

Dual-Amplification Strategy-Based SERS Chip for Sensitive and Reproducible Detection of DNA Methyltransferase Activity in Human Serum.

Herein, we present a dual-amplification sensing strategy-based surface-enhanced Raman scattering (SERS) chip, which combines rolling circle amplification (RCA) and polyadenine (PolyA) assembly for sensitive and reproducible determination of the activity of M.SssI, a cytosine-guanine dinucleotide (CpG) methyltransferase (MTase). Typically, in the presence of M.SssI, RCA process is triggered, resulting in long, single-stranded DNA (ssDNA) fragments that are hybridized with thousands of Raman reporters of Cy3. Afterward, the resultant ssDNA fragments are conjugated to SERS-active substrates made of silver core-gold satellite nanocomposites-modified silicon wafer (Ag-Au NPs@Si), with the SERS enhancement factor of ∼5 × 106. The core-satellite nanostructures are assembled relied on the strong affinity of PolyA toward gold/silver surface. Of particular significance, the developed SERS chip displays an ultrahigh sensitivity with a low limit of detection (LOD) of 2.8 × 10-3 U/mL, which is around 2 orders of magnitude higher than most reported methods. In addition, the constructed chip features a broad detection range covering from 0.05 to 50 U/mL. Besides for the ultrahigh sensitivity and broad dynamic range, the chip also features good reproducibility (e.g., the relative standard deviation (RSD) is less than ∼12%). Taking advantages of these merits, the developed chip is feasible for accurate discrimination of M.SssI with various concentrations spiked in human serum samples with good recoveries ranging from 99.6% to 107%.

Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses.

To develop a robust quantitative calibration model for spectroscopy, different sources of variability that are not directly related to the components of interest should be included in the calibration samples; this variability should be similar to that which is anticipated during validation and routine operation. Moisture content of pharmaceutical samples can vary as a function of supplier, storage conditions, geographic origin or seasonal variation. Additionally, some pharmaceutical operations (e. g., wet granulation) cause exposure of excipients and API to water. Although water is a weak Raman scatterer, moisture variability has an indirect effect on analytical model performance. Because many pharmaceutical components have intrinsic fluorescent characteristics (with broad spectral features), moisture variability may cause spectral artifacts in the form of baseline variation associated with fluorescence quenching. This work investigates the deleterious effects of water quenching on quantitative prediction accuracy of a multivariate calibration algorithm for Raman spectroscopy. To demonstrate this, a formulation composed of acetaminophen, lactose, microcrystalline cellulose, HPMC and magnesium stearate was used. Tablets were manufactured using laboratory scale equipment. A full-factorial design was used to vary acetaminophen (5 levels), and excipient ratios (3 levels) to generate tablets for calibration and testing. Tablet moisture variation was introduced by placing samples in different humidity chambers. Significant spectral effects arising from fluorescence were identified in the Raman spectra and due to moisture variation: the fluorescence related spectral variability caused substantial degradation of the prediction performance for API. The work demonstrated that accounting for moisture variation during method development reduced the prediction error of the multivariate prediction model.

Quantification of active ingredients in Potentilla tormentilla by Raman and infrared spectroscopy.

The most important active compounds present in Potentilla tormentilla rhizomes were quantitatively determined on the basis of Raman, attenuated total reflection (ATR) and diffuse reflectance mid- and near- infrared spectra (DRIFTS) collected for the untreated plant material. Partial least-squares (PLS) models were constructed utilizing vibrational spectra and the results of reference high-performance liquid chromatography analyses. Applying Raman spectroscopy, total polyphenols, tannins, ellagitannins, procyanidins, agrimoniin, 3-O-galloylquininc acid and catechin were simultaneously quantified in tormentil rhizomes, with the relative standard errors of prediction in the 2.0-4.9% range for both calibration and validation sets. These error ranges were found to be slightly higher for infrared techniques and amounted to 2.7-6.5%.

Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing.

Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements. Graphical abstract.

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples.

Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.