Hand-held Spectrometer Research Articles

Monitoring of volatile fatty acids during anaerobic digestion of olive pomace by means of a hand held near infrared spectrometer

The accumulation of volatile fatty acids (VFAs) over anaerobic digestion (AD) leads to malfunctioning of industrial reactors, hence decreasing biogas production. Real-time monitoring of VFAs is a challenge due to the complexity and high cost of current methods for their quantification. For this reason, this research evaluated the application of near infrared (NIR) spectroscopy to quantify volatile fatty acids as a tool for AD reactors monitoring. To do that, 129 samples from various AD reactors fed with olive oil pomace were taken and their NIR spectra were acquired with a hand-held spectrometer. After performing grid search, three spectral variable selection methods, namely competitive adaptive reweighted sampling, uninformative variable elimination (UVE) and successive projections algorithm, were assayed before developing PLRS models to correlate the NIR light transmittance through the samples at the wavelengths selected by those methods with their VFAs concentrations. UVE led to the best performance for all the VFAs assayed. Thus, R2 of validation of UVE-PLSR models for acetic, propionic, butyric, valeric and total VFAs were 0.895, 0.622, 0.866, 0.898 and 0.871, respectively. The predictive model for total VFAs achieved the highest accuracy (RMSEV = 539.5 mg/L), explained by the correlation between the light absorption at the wavelengths selected by UVE and the chemical characteristics of VFAs. All in all, the prediction errors achieved suggest that a portable near infrared spectrometer can be used for monitoring VFAs in AD processes.

Hyperspectral Leaf Reflectance Detects Interactive Genetic and Environmental Effects on Tree Phenotypes, Enabling Large-Scale Monitoring and Restoration Planning Under Climate Change.

Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf-level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6-year-old experimental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%-98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%-23% of spectral variation within populations and 14%-48% of the variation across all populations. Second, we found gene by environment interactions leading to population-specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjustments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change.

Do spectral reflectance indices distinguish between the greenness in three different moss species in moss banks on Galindez Island (Argentine Islands)?

Spectral reflectance indices of green state of Warnstorfia fontinaliopsis, Chorisodontium aciphyllum and Sanionia georgicouncinata on moss bank in the Galindez Island (Argentine Islands) were measured using a handheld spectrometer PolyPen RP 410 UVIS (Photon Systems Instruments, Drásov, Czech Republic) within the range of 380–790 nm in order to find suitable ones for effective classification of moss species within the same colour state (green). Among altogether 19 indices tested, there were some which did not differ significantly between the studied species (subgroup 1). Other indices (subgroup 2) were sensitive enough to distinguish one of the studied species from the others, and finally (subgroup 3), they were found statistically significantly different for all studied moss species. Also, the indices calculated at wavelengths typical for UAV spectral cameras (green, red and red edge channels) showed species-specific differences and can be potentially used to distinguish between different mosses within the same green physiological state indicating a good vigor.

Facile SERS screening of melamine in bovine milk with 2D printed AgNPs/glass fabric filter paper as the flexible substrate

Melamine is one of the most frequently detected adulterants in dairy products. The current study proposes a surface-enhanced Raman spectroscopy (SERS)-based analytical tool for fast and reliable screening of melamine in bovine milk. A hand-held Raman spectrometer was used in conjunction with a substrate composed of silver nanoparticles (AgNPs) that was 2D printed onto glass fiber (GF) filter paper. Under optimized conditions, a sensitive and fingerprint-like signal at 674 cm−1 was obtained. The AgNPs/GF substrate exhibited high sensitivity to melamine in milk down to 1.9498 × 10−5 mg/mL, which is well below the USA and EU safety limits (2.5 × 10−3 mg/mL). Remarkably, the proposed technology was also highly reproducible, showing spot-to-spot and block-to-block variations below 3.3 % and 4.9 % at 674 cm−1 in Raman intensity, respectively. The characteristic peak intensity and concentration of melamine showed an acceptable linear relationship (R2 = 0.9909) within the range of 0.0001–1 mg/mL. Overall, the method established in this study can provide an efficient and effective method for the quantitative target screening and detection of melamine in dairy products.

Discrimination of Fungicide-Contaminated Lettuces Based on Maximum Residue Limits Using Spectroscopy and Chemometrics

The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making.

Comparing the potential of benchtop and handheld mid-infrared spectrometers for predicting soil phosphorus (P) sorption capacity and evaluating the influence of sample preparation

Traditional soil phosphorus (P) sorption capacity is examined from a Langmuir isotherm batch technique, which is time-consuming, labour intensive and generates chemical waste. In this work, we provide an efficient and convenient technique with MIR spectroscopy to predict the Langmuir parameter of soil P sorption maximum capacity (Smax, mg·kg−1).Four spectral libraries from benchtop (Bruker) and handheld (Agilent) MIR spectrometers were built with samples in two particle size ranges, <0.100 mm (ball-milled) and <2 mm. respectively. Using an archive of samples with a database of sorption parameters, soils were classified into ‘low’ and ‘high’ sorption capacities. Chemometric regression models of partial least squares (PLS), Cubist, support vector machine (SVM) regression and random forest (RF) were evaluated for Smax prediction. Bruker spectral libraries with both soil particle sizes yielded ‘excellent models’, with SVM predicting Smax values with high accuracy (RPIQV = 4.50 and 4.25 for the spectral libraries of the ball-milled and <2 mm samples, respectively). In comparison, the Agilent handheld spectral libraries contained more noise and less resolution. For Agilent MIR spectroscopy, more homogeneous samples after ball milling resulted in a higher accurate Smax prediction. For Agilent libraries of ball-milled samples, an ‘approximate quantitative model’ (RPIQV = 2.74) was obtained from the raw spectra using the Cubist algorithm. However, for Agilent spectroscopy of <2 mm samples, the best performing Cubist algorithm can only achieve a ‘fair model’ (RPIQV=2.23) with the potential to discriminate between ‘low’ and ‘high’ Smax values.The results suggest that the benchtop spectrometer can predict the Langmuir Smax value with high accuracy without the need to ball mill samples. However, the handheld spectrometer can only make approximate quantitative predictions of Smax for ball-milled samples. For <2 mm samples, Agilent can only be used to classify ‘low’ and ‘high’ sorption capacity soils.

Relationship among intramuscular fat, crude protein, and moisture in porcine longissimus thoracis muscle and their prediction by a handheld fiber-optic near-infrared spectrometer

The handheld fiber-optic near-infrared (NIR) spectrometer with special probe, which is widely used for Wagyu beef carcass in all Japan, was determined to predict the chemical composition in pork. Since intramuscular fat (IMF) is related to eating quality and the relationships among chemical compositions were examined. The intact longissimus dorsi muscles collected from meat markets in Japan (N = 914) were scanned at three sites in the same cut, and analyzed chemically. The data were used for calibration (N = 553 out of 605), prediction (N = 300) and repeatability (N = 9). One-point increase in IMF resulted in linear decreases in moisture and crude protein at approximate rate of 0.7- and 0.3- points, respectively (N = 905). The spectrometer had high repeatability, and the optical values had interaction between the individual pork and the measurement site. The coefficients of determination in the prediction were 0.81, 0.91 and 0.66 for moisture, IMF and crude protein, respectively. The agreements between chemical and optical methods was strong for IMF (intraclass correlation coefficients 0.92) and moisture (0.81), and moderate for crude protein (0.64). These results showed that the handheld fiber-optic NIR spectrometer can quickly evaluate chemical composition for intact pork.

Integrated Resonant Electro‐Optic Comb Enabled by Platform‐Agnostic Laser Integration

AbstractThe field of integrated photonics has significantly impacted numerous fields including communication, sensing, and quantum physics owing to the efficiency, speed, and compactness of its devices. However, the reliance on off‐chip bulk lasers compromises the compact nature of these systems. While silicon photonics and III‐V platforms have established integrated laser technologies, emerging demands for ultra‐low optical loss, wider bandgaps, and optical nonlinearities necessitate other platforms. Developing integrated lasers on less mature platforms is arduous and costly due to limited throughput or unconventional process requirements. In response, a novel platform‐agnostic laser integration technique is proposed, utilizing a singular design and process flow, applicable without modification to a diverse range of platforms. Leveraging a two‐step micro‐transfer printing method, nearly identical laser performance is achieved across platforms with refractive indices between 1.7 and 2.5. Experimental validation demonstrates strikingly similar laser characteristics between devices processed on lithium niobate and silicon nitride platforms. Furthermore, the integration of a laser with a resonant electro‐optic comb generator on the thin‐film lithium niobate platform is showcased, producing over 80 comb lines spanning 12 nm. This versatile technique transcends platform‐specific limitations, facilitating applications like microwave photonics, handheld spectrometers, and cost‐effective Lidar systems, across multiple platforms.

A non-destructive, low cost and high throughput colorimetric method for chlorophyll estimation in rice (Oryza sativa L.)

Leaf chlorophyll is an important biochemical parameter used to assess plant health under stress. Chlorophyll is invariably estimated using destructive method of acetone or ethanol extraction. Reflectance spectroscopy such as IR, VIS–NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. Here we report a cheap and reliable spectroscopic method of chlorophyll estimation using a low-cost handheld spectrometer. We standardized the method in a rice diversity panel of 264 genotypes using L*, a* and b* parameters using a randomly taken calibration and validation sample. The parameters used for fitting the regression analysis were L*, a*, b* and δ E that indicates deviation of L*, a* and b* values of sample from the chlorophyll solution of a known concentration. We fitted the regression of total chlorophyll in calibration sample with L*, a*, b* and δ E separately as well as multiple regression with all parameters. The best R2 value (0.671) was observed for a* and the lowest for L* (0.436). Using multiple regression, the R2 value increased to 0.778 (p < 0.005). We used the regression equation for validation sample and obtained R2 value of 0.724 indicating reliable explanatory potential of variables. The parametric test for model reliability indicates good model fit of the observed relationship between chlorophyll content and L*, a*, b* and δ E. The method is cost effective, non-destructive and takes less time (< 10 s) and can be conveniently used for screening large diversity panels especially in case of repeated measurements across growth stages under stress.

Non-destructive identification of varieties of Hawaii-grown avocados using near-infrared spectroscopy: Feasibility studies using bench-top and handheld spectrometers

Non-destructive identification of varieties of Hawaii-grown avocados using near-infrared spectroscopy: Feasibility studies using bench-top and handheld spectrometers

Primary investigation of an innovative method for milk authenticity using two handheld spectrometers and chemometrics: Comparison with portable ATR-FTIR

This study advocates the use of handheld spectrometers, spanning the visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR, 900–1700 nm) ranges, alongside a portable attenuated total reflection-Fourier transform infrared (ATR-FTIR) instrument, for discerning water adulteration in raw bovine milk. Samples with water adulteration levels ranging from 0 % to 30 % underwent direct analysis without additional preparation. Principal component analysis was utilized for differentiation, while quantification was achieved using partial least squares regression (PLSR) and radial basis function-artificial neural network (ANN) models. The ATR-FTIR instrument displayed remarkable performance, achieving regression coefficients of prediction (R2p) of 0.99 and 0.99, with root mean square error of prediction (RMSEP) values of 0.20 % and 0.53 % in PLS and ANN regression models, respectively. Similarly, handheld NIR spectrometer yielded robust results, with R2p values of 0.99 and 0.99 and RMSEP values of 1.28 % and 1.12 % in PLS and ANN models, respectively. Vis-NIR spectrometer also demonstrated commendable performance, with R2p values of 0.92 and 0.94 and RMSEP values of 4.53 % and 4.83 % in PLS and ANN models, respectively. ATR-FTIR showed the best performance followed by handheld NIR and Vis-NIR spectrometers. All instruments can be used potentially for screening and/or quality control in the dairy industry.

Measurements of 131I activity in thyroid of workers at the place of radioiodine therapy in 38 hospitals in China.

To investigate the levels of 131I activity in thyroid of workers at the place of radioiodine therapy and their main influential factors in China, 341 workers at 38 hospitals performing radioiodine therapy procedure in five provinces were recruited to be measured in 2021. A hand-held gamma spectrometer with NaI(Tl) probe was plastered to the thyroids and thighs of the subjects during the measurement, and each measurement time was 120s. The internal exposure dose was calculated, and the committed effective dose was estimated. In 86 (25.22%) of the 341 examined workers, 131I thyroid activity was above minimum detectable activity (MDA, 26.6Bq). The maximum activity was 4.9 × 103Bq. The detection results above MDA were at 22 (57.89%) different hospitals. The detectable rate for private hospitals (4.8%) was significantly lower than that for public hospitals (26.6%), P < 0.05. The detectable rate for hospitals in provincial capital cities (15.4%) was significantly lower than in nonprovincial capital cities (41.7%), P < 0.001. The detectable rate for hospitals engaged in 131I therapy for thyroid cancer (31.2%) was significantly higher than only for hyperthyroidism (10.3%), P < 0.001. A total of 32 subjects' committed effective dose might exceed 1mSv. Results indicated the 131I activity in the thyroid of workers at the place of radioiodine varied considerably in China, and mainly related to ownership, location and therapy program of the hospitals.

Measurement of Background Ionizing Radiation Around Beta Glass Plc and Its Environs, Ughelli, Delta State, Nigeria

A study of the background ionizing radiation levels to helps monitor the impact of human activities on the environment, such as mining, farming, glass bottle production and power generation has been carried out around Beta glass Plc and Transcorp Power LTD using a hand-held gamma spectrometer (GAMMA-SCOUT; Geiger counter Radiation meter) and GPS (Oregon-450 Garmin). The study of the BIR levels was carried out between June to July, 2022. The average measured exposure rates (AV. BIR) ranged from 0.007 mRh-1 to 0.013 mRh-1, with 0.010±0.0002 mRh-1 as the average. The estimated mean outdoor absorbed dose rate (ADR) for the study region was 113.10 Gyh-1 to 87.153±2.091 Gyh-1, with an average annual effective dose equivalent (AEDE) of 106.884±2.565 mSvy-1 and an average excess lifetime cancer risk (ELCR) of (0.374±0.009) × 10-3. The measured values of radiological parameters, such as annual dose rate, annual effective dose rate, and excess lifetime cancer, were higher than background radiation levels. The average BIR is in line with the ICRP recommendation of 0.013 (mRh-1), while the effective dose rate (EDR) is somewhat below the ICRP recommendation of 1.00 (mSvy-1) for a normal environment. The obtained values of the radiological indicators, such as ADR, AEDE, and ELCR, are greater than the background radiation norm of 59 (?Gyh-1), 0. 07 (mSvy-1), and 0.29×10-3. The study found that radiation levels and doses in the study location do not immediately harm personnel or residents, but increased lifetime cancer risk values suggest that cumulative dosages may cause cancer.

Colorimetric Detection of Fentanyl Powder on Surfaces Using a Supramolecular Displacement Assay.

Fentanyl is a potent synthetic opioid with an alarmingly low lethal dosage of 2 mg. The equipment necessary to detect fentanyl in field settings (e.g., hand-held spectrometers) is restricted to highly trained, well-funded, and specialized personnel. Established point-of-need technologies, such as lateral flow immunochromatographic strips, are available; however, they often involve multiple contact-based steps (e.g., collection, mixing) that pose a higher risk to users handling unknown substances. Herein, we developed a colorimetric displacement assay capable of contactless detection of fentanyl in liquid or solid samples. The basis of our assay relies on the presence of fentanyl to displace a redox mediator, ferrocene carboxylic acid, inclusively bound in the cavity of a supramolecular host, CB[7]. The displacement is only possible in the presence of high affinity binding guests, like fentanyl (KA ∼ 106 M-1). The liberated redox guest can then react with indicator reagents that are free in solution, producing either: (i) a distinct blue color to indicate the presence of fentanyl or (ii) a pale blue tint in the absence of fentanyl. We demonstrate rapid and specific detection of fentanyl free base and fentanyl derivatives (e.g., acetyl fentanyl and furanyl fentanyl) against a panel of 9 other common drugs of abuse (e.g., morphine, cocaine, and heroin). Furthermore, we highlight the intended use of this assay by testing grains of fentanyl derivatives on a surface with a drop (i.e., 25 μL) of the assay reagent. We anticipate that this approach can be applied broadly to identify the presence of fentanyl at the point of need.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System.

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

Use of remote sensing and image processing for identification of wild orchids

A novel multi-technique approach has been applied for the identification and mapping of wild orchids using a combination of remote sensing and spectral image analysis. The five orchid species identified were the common spotted-orchid (Dactylorhiza fuchsia), heath spotted-orchid (Dactylorhiza maculata), pyramidal orchid (Anacamptis pyramidalis), heath fragrant-orchid (Gymnadenia borealis), and the dark-red helleborine (Epipactis atrorubens). Field studies have been done using a hand-held spectrometer operating in the 400–700 nm visible spectrum, photogrammetry using a digital camera as well as a multispectral image camera operating at the specific spectral bands of 450 nm (blue), 560 nm (green), 650 nm (red), 730 nm (red edge) and 840 nm (near-infrared) attached to an unmanned aerial vehicle Data analysis, using the hand-held spectrometer, followed by pattern recognition using principal component analysis and partial least squares-discriminant analysis, have identified the key distinguishing wavelengths for identification of the 5 orchid types as 400, 410, 420 and 560 nm. The use of remote sensing, using the UAV-MSI, and application of a dedicated spectral index has enabled field identification of the orchids. Finally, object-based image analysis of field gathered photogrammetry imagery, has enabled use of shape, size, and color to identify and distinguish orchid species. The developed data analytic tool, using random forest classification, can be used to identify and characterize wild orchids across multiple sites within their short lifespan with an accuracy of 86%. Any longer-term study would provide invaluable information on the diversity and complexity of orchid habitat, population variation both intra- and inter-site location, as well as the impact of climate change.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Fabrication of Al/Au hybrid SERS substrate using laser engraving for rapid detection of melamine and its analogues by hand-held Raman spectrometer

Surface-enhanced Raman spectroscopy (SERS), combined with a handheld Raman spectrometer, was utilized for the rapid and highly sensitive detection of melamine and its analogues. The investigation focused on the hybrid micro-nano structures of SERS-active substrates, fabricated by laser engraving aluminum (Al) sheets and depositing gold (Au) nanoparticles using magnetron sputtering. The laser engraving frequency was varied to get optimum SERS substrate. The fabricated SERS substrates were tested with rhodamine 6G (R6G) to optimize the Raman signal and subsequently detect melamine and its analogues viz., cyanuric acid, ammeline, ammelide in milk samples. The results demonstrated that a laser frequency of 20 kHz was the optimal condition for fabricating a micro-nano Al template with a depth of 61.21 μm, providing the highest Raman signal for R6G. The limit of detection (LOD) for melamine in mild acid solution and milk samples were determined to be 1x10-5 M and 1x10-4 M, respectively. The laser-engraved Al sheet technique offers a cost-effective approach for SERS substrate fabrication. The integration of the Al/Au hybrid SERS substrate with a hand-held Raman spectrometer demonstrates significant potential for detecting melamine and its analogues in realistic environments.

Toward more efficient and effective color quality control for the large‐scale offset printing process

AbstractThis study illustrates at‐line application of hyperspectral imaging in the visible range for quality control of large‐scale offset printing. In particular, the measurement stability of a competitive device is assessed and compared to traditional handheld and desktop spectrophotometers. The performance of the commercially available instruments was assessed based on collected spectra and their corresponding L*, a*, and b* values. The printing process was described by hyperspectral images (in visible range) of selected regions from template color fields acquired at 17 sampling occasions. Spectra constituting hyperspectral images were visualized and evaluated in the space of significant principal components obtained from the principal component analysis. Furthermore, confidence ellipses were constructed for each set of spectra characterizing a specific moment of the printing process. Comparing their mutual locations, shapes, orientations, and sizes enabled effective visualization of process variability and was more comprehensive regarding the classic approach based on information provided by desktop and handheld spectrometers.