Low-cost Spectrometer Research Articles

Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis

Despite the increasing use of portable, low-cost spectrometers in estimating soil properties, there is lack of documentation regarding the factors contributing to the lower performance of these spectrometers when compared to conventional ones. This study investigates potential factors influencing performance of the Nanoquest, a low-cost spectrometer, in estimating soil organic carbon (SOC) and total nitrogen (TN). To conduct the study, five different models (cubist, partial least squares regression, support vector machines, random forest, and generalised boosted models) were tested for the estimation SOC and TN and a fivefold cross-validation analysis was conducted for model hyperparameter optimization. The Nanoquest achieved a Lin’s concordance correlation coefficient (CCC) value of 0.84 and an R2 value of 0.74 for SOC. For TN, CCC values of 0.86 and an R2 value of 0.78 were obtained. To understand the impact of the spectral range and spectral resolution on SOC and TN estimation, the ASD spectra were digitally resampled to match the Nanoquest spectral range and resolution. This resampling resulted in a slight decrease in model performance for the spectral range and a more pronounced decrease for the spectral resolution.

Low-cost high-resolution distributed optical fiber system for spectrophotometric analysis of liquid samples: Application to detection of azo dyes

A distributed optical fiber system for spectrophotometric analysis of liquid samples based on light diffusing fiber (LDF) is presented. The sensor is based on a high-density white light emitting diode (LED) strip, which is side coupled to a glass based light-diffusing fiber (LDF) that acts as distributed optical receiver. The light emitted from a single LED propagates through the sample medium, is collected by the LDF and is then detected at the end of the fiber by a mini spectrometer. By sequentially turning on one LED at a time, the system permits the spectrophotometric analysis of the sample medium along the entire fiber length. This approach is capable of a continuous monitoring of absorbance spatial profile of liquid sample in the whole visible spectrum with a single low-cost spectrometer. The experimental results confirm the possibility of distributed measurements with a spatial resolution of about 12 mm over 1 m of measurement range. The approach has been successfully employed to the distributed detection and localization of azo dyes in water with a limit of detection lower than 1 ppm.

A general-purpose spectroscopic camera for single-board computers

A low-cost consumer-grade spectroscopic system offers potential applications in many fields. Linear variable optical filters (LVOFs) placed in front of imaging sensors are one of the most promising avenues to realize this goal. In this work, we built a spectroscopic system by depositing an LVOF directly onto the protective window of OV5647 color and OV9281 monochrome imaging sensors for the Raspberry Pi. The ¼” sensors cost $15-$80 CAD at the time of writing and have linear dimensions of only 2.7 – 3.6 mm. To achieve a suitable gradient over such small sensors, an ultra-slow shutting system was built for the physical vapor deposition of an SiO2 gradient layer between two partially transparent silver mirrors to form a miniature LVOF. The resulting devices operated as effective low-cost spectrometers that were single-moded in the visible spectrum. These simple systems were finally used to measure spectra from sample light sources, clearly illustrating the advantages and limitations of this simple and economical approach towards everyday consumer spectroscopy.

Evaluating the Capability of Low-Configuration Spectrometers to Retrieve Far-Red Solar-Induced Chlorophyll Fluorescence and Its Application for Identifying Crop Growth Stages

The ground-based continuous hyperspectral remote sensing technique presents a unique opportunity to advance terrestrial ecosystem monitoring. Accurate retrieval of solar-induced chlorophyll fluorescence (SIF) relies heavily on commercially available spectrometers with high spectral resolution (SR). However, high-SR spectrometers are resource-intensive and challenging to maintain, limiting their widespread deployment at observational sites. Here, we explored an alternative low-configuration and low-cost spectrometer for SIF retrieval at 2 sites cultivated with wheat and rice. The results revealed the strong correlations between irradiance and radiance measurements in the selected SIF bands from the high- and low-configuration spectrometers ( R 2 > 0.90, average root mean square error <8.95 mW m −2 nm −1 sr −1 , relative root mean square error < 7%). Far-red SIF retrieved from both spectrometers exhibited marked correlations at both half-hourly and daily averaged scales ( R 2 > 0.90). Additionally, we utilized simulated data to examine the impact of SR and signal-to-noise ratio (SNR) on the far-red SIF retrieval from both high- and low-configuration spectrometers. Strong correlations ( R 2 > 0.80) were found between the simulated data with SNR ≥ 300 and SR < 2 nm. Furthermore, we investigated the relationships between crop gross primary productivity (GPP) and far-red SIF from both types of spectrometers and their ability to identify crop growth stages. SIF from both types of spectrometers exhibited high correlations with GPP ( R 2 > 0.70). The dates of various wheat-specific growth stages observed using the low-configuration spectrometer were consistent with those identified by the high-configuration spectrometer, with less than a 3-d differences for wheat. Our study confirms the capabilities of the low-configuration spectrometers for far-red SIF retrievals and promotes their deployment over large-scale ground observation networks, potentially advancing the large development of low-cost ground-based SIF measurements.

Determining nutrients, dry matter, and pH of liquid organic manures using visual and near-infrared spectrometry

In agriculture, overfertilization with liquid organic manures (LOM) is causing environmental issues including eutrophication of non-agricultural ecosystems and nitrate pollution of groundwater. To avoid such problems, a precise and demand-oriented fertilization with LOM is needed. This can only be achieved if the nutrient composition of the LOM is known. However, traditional chemical analysis is cost- and time-intensive and furthermore dependent on a representative sample. Optical spectrometry in the visible and near-infrared range could provide an efficient alternative, if a chemometric calibration assures sufficient accuracy. To improve chemometric calibration, this study investigated several spectral preprocessing and regression algorithms, and compared predictions based either on dry or wet weight concentration. In addition, the capability of low-cost spectrometers was evaluated by simulating low-resolution spectra with smaller wavelength ranges. The reflectance spectra of 391 pig manure, 155 cattle manure, and 89 biogas digestate samples were used to predict plant macronutrients (N, P, K, Mg, Ca, S), micronutrients (Mn, Fe, Cu, Zn, B), dry matter (DM) and pH. The experiments demonstrate the general aptness of optical spectrometry to accurately predict DM, pH and all nutrients except boron in pig, cattle, and digestate LOM, even with simulated low-cost spectrometers. Best results show r2 between 0.80 and 0.97, ratios of performance to interquartile distance (RPIQ) between 2.1 and 7.8, and mean absolute errors normalized by the median (nMAE) between 5 and 36 %. The regression methods PLSR, LASSO, and least angle regression predominantly performed best. The innovative preprocessing methods named simple ratios (SR) and normalized differences (ND) proved to be very useful algorithms, especially for N and P predictions, outperforming the accuracy of classical techniques in several cases. Concentrations on dry weight basis improved predictions of K, Mn, and pH.

Making exciton physics easy and affordable

We present a facile and low-cost undergraduate laboratory experiment to study exciton physics. Using a simple abrasion technique to create samples of thin-film van der Waals material samples and a low-cost spectrometer, we show that prominent excitonic features can be easily resolved in the optical transmission spectra of semiconducting transition metal dichalcogenides at ambient conditions. Our method brings interesting semiconductor quasiparticle physics to low-budget departments, hoping to expand their undergraduate laboratory curriculum.

Low-Cost Instrument to Monitor Sulphur Dioxide Emissions Based on The DOAS Method

Various techniques to measure SO2 concentration based on Differential Optical Absorption Spectroscopy (DOAS) have been widely developed and applied for various measurements. However, most of the applications are still relatively expensive. Some efforts have been made to reduce the cost by using Ultraviolet Light Emitting Diodes (LEDs) as light sources, showing promising results. Further reductions can be possibly made by providing an alternative to replace high spectral resolution spectrometers widely used in DOAS applications since those spectrometers are commercially expensive. This paper studies the feasibility of a DOAS instrument using a low-cost spectrometer and UV-LEDs as light sources. The resolution of the spectrometer is 0.7 nm. With this resolution, it is expected that the instrument hardly captures narrow band structures of SO2 optical absorption in the spectral range between 280 nm and 320 nm when measuring SO2 gas concentration lower than the limits of SO2 emissions regulated by the Indonesian government. To compensate for this drawback, narrow and broad bands of optical absorption structures are considered in the data analysis to achieve a detection limit far below the regulated limits. To capture the broadband structures, four UV-LEDs are used to cover spectral absorption from 250 nm to 320 nm. The instrument was calibrated using eight different standard concentrations of SO2. The correlation between the readings and the standard concentrations is high, indicated by the Pearson correlation coefficient of 0.9999. It was also found that the lowest concentration the instrument can distinguish from blank samples or the Limit of Detection is 16 ppm. However, the instrument can precisely measure concentrations higher than or equal to 25 ppm with a standard deviation of less than 10% of the mean concentration measured from five measurements. This is far below the required legal limits, below 229 ppm. After the calibration, the DOAS instrument was used to measure SO2 sampled from the emission of burning coals. To compare, a commercial SO2 sensor was used to measure the same gas. The results indicate that the difference in the readings between the two instruments is around 6% of the concentration.

BabyNutri

The physical and physiological development of infants and toddlers requires the proper amount of macronutrient intake, making it an essential problem to estimate the macronutrient in baby food. Nevertheless, existing solutions are either too expensive or poor performing, preventing the widespread use of automatic baby nutrient intake logging. To narrow this gap, this paper proposes a cost-effective and portable baby food macronutrient estimation system, BabyNutri. BabyNutri exploits a novel spectral reconstruction algorithm to reconstruct high-dimensional informative spectra from low-dimensional spectra, which are available from low-cost spectrometers. We propose a denoising autoencoder for the reconstruction process, by which BabyNutri can reconstruct a 160-dimensional spectrum from a 5-dimensional spectrum. Since the high-dimensional spectrum is rich in light absorption features of macronutrients, it can achieve more accurate macronutrient estimation. In addition, considering that baby food contains complex ingredients, we also design a CNN nutrition estimation model with good generalization performance over various types of baby food. Our extensive experiments over 88 types of baby food show that the spectral reconstruction error of BabyNutri is only 5.91%, reducing 33% than the state-of-the-art baseline with the same time complexity. In addition, the nutrient estimation performance of BabyNutri not only obviously outperforms state-of-the-art and cost-effective solutions but also is highly correlated with the professional spectrometer, with the correlation coefficients of 0.81, 0.88, 0.82 for protein, fat, and carbohydrate, respectively. However the price of our system is only one percent of the commercial solution. We also validate that BabyNutri is robust regarding various factors, e.g., ambient light, food volume, and even unseen baby food samples.

On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries

In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R2 of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R2 value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R2 = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection.

Paper-Based Multiplex Sensors for the Optical Detection of Plant Stress.

The rising population and the ongoing climate crisis call for improved means to monitor and optimise agriculture. A promising approach to tackle current challenges in food production is the early diagnosis of plant diseases through non-invasive methods, such as the detection of volatiles. However, current devices for detection of multiple volatiles are based on electronic noses, which are expensive, require complex circuit assembly, may involve metal oxides with heating elements, and cannot easily be adapted for some applications that require miniaturisation or limit front-end use of electronic components. To address these challenges, a low-cost optoelectronic nose using chemo-responsive colorimetric dyes drop-casted onto filter paper has been developed in the current work. The final sensors could be used for the quantitative detection of up to six plant volatiles through changes in colour intensities with a sub-ppm level limit of detection, one of the lowest limits of detection reported so far using colorimetric gas sensors. Sensor colouration could be analysed using a low-cost spectrometer and the results could be processed using a microcontroller. The measured volatiles could be used for the early detection of plant abiotic stress as early as two days after exposure to two different stresses: high salinity and starvation. This approach allowed a lowering of costs to GBP 1 per diagnostic sensing paper. Furthermore, the small size of the paper sensors allows for their use in confined settings, such as Petri dishes. This detection of abiotic stress could be easily achieved by exposing the devices to living plants for 1 h. This technology has the potential to be used for monitoring of plant development in field applications, early recognition of stress, implementation of preventative measures, and mitigation of harvest losses.

Deep Learning Assisted Pressure Sensing Based on Sagnac Interferometry Realized by Side-Hole Fiber

In this paper, a novel deep learning characterization approach is proposed and demonstrated experimentally using a pressure sensor based on a Sagnac interferometer realized by a side-hole fiber. To enlarge the measurement range limited by the free spectral range, a long short-term memory (LSTM) neural network was proposed. The original spectra were recorded by a low-cost spectrometer, and the intensity of scaled spectra was used to construct one-dimension (1D) spectral data. The results show that the coefficient of determination ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R²</i> ) for the pressure prediction can reach 0.9996148 with the root mean square error (RMSE) equal to 2.559 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> MPa. Moreover, two-dimension (2D) data were obtained with the ascension algorithm of the Gramian angle field (GAF). A better <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R²</i> of 0.9999908 and a lower RMSE of 4.365×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> MPa can be obtained since the ascension algorithm could retrieve deeper features among the spectral data. The proposed approach can be adopted for a similar sensor structure, showing great potential in sensing applications requiring low cost and robustness.

Low-cost spectrometer design for ultra-high resolution spectral domain optical coherence tomography

Low-cost spectrometer design for ultra-high resolution spectral domain optical coherence tomography

Innovation in Teaching and Learning During Covid-19 Pandemic and Beyond: Absorption and Reflection Spectra with Low Cost Spectrometer

The work demonstrated that a low-cost spectrometer and ImageJ serve as an accurate, reliable, and robust model and tool for learners to grasp abstract concepts in the Introductory Physics Lab Course context. The model design was self-paced while promoting active and peer learning for students. Image analysis within the experimental activity adds to the novelty of the lesson. Within the experimental accuracy, the measurement was shown to correctly identify the absorption spectra of Potassium Dichromate, Methylene Blue, and Potassium Permanganate and to describe the subtractive color-mixing model where Cyan absorbs Red accurately, Magenta absorbs Green, and Yellow absorbs Blue.

Fast measurement of the thickness and group refractive index distribution of solid plates by line-field dispersive interferometry

Real-time measurement of the thickness and group refractive index is crucial for semiconductor devices. In this paper, we proposed a fast synchronous method for measuring the thickness and group refractive index distribution of solid plates based on line-field dispersive interferometry. The proposed method measured the line-field distribution in an illuminated region through a single step. A low-cost spectrometer calibration method using an eight-channel dense wavelength division multiplexer was developed for verification. The line-field distribution of a three-step silicon wafer was successfully measured within 3.3 ms. The combined uncertainties for the geometrical thickness and group refractive index were <50 nm and 4 × 10-4, respectively.

UV spectral analysis coupled with PCA-LDA to classify organic and conventional Lampung robusta coffee from different origins

This current research presents a simple analytical method for classifying organic and conventional coffee samples from different origins. UV pre-processed spectral data in the range of 250-400 nm was used to discriminate between organic Lampung robusta coffee from Lampung Barat (n=50) and two conventional Lampung robusta coffees from Lampung Barat (n=50) and Tanggamus (n=50). Ground roasted coffee samples with 50 mesh were used for samples. UV-vis spectrometer was utilized to acquire UV spectral data from an aqueous coffee sample. A chemometric method based on PCA and PCA-LDA algorithm was used to classify the samples. The PCA result shows all organic coffee samples were clustered on the negative of PC1 while all conventional coffee samples were on the positive of PC1. The conventional coffee samples from Lampung Barat and Tanggamus were grouped in different clusters according to their origin. The PCA-LDA resulted in a 100% accuracy in classification both for calibration and prediction. This method is a promising approach for organic Lampung robusta authentication with a relatively low-cost spectrometer and simple sample preparation.

Detection of Falsified Antimalarial Sulfadoxine-Pyrimethamine and Dihydroartemisinin-Piperaquine Drugs Using a Low-Cost Handheld Near-Infrared Spectrometer.

Falsified drugs are of serious concern to public health worldwide, particularly for developing countries where quality control of drugs is inefficient. In law enforcement against such fake medicines, there is a need to develop reliable, fast, and inexpensive screening methods. In this work, the ability of an innovative low-cost handheld near-infrared spectrometer to identify falsifications among two antimalarial fixed dose combination tablets, dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine, has been investigated. Analyzed samples were collected in Burkina Faso mainly in rural transborder areas that could be infiltrated by illicit drugs. A principal component analysis was applied on the acquired near-infrared spectra to identify trends, similarities, and differences between collected samples. This allowed to detect some samples of dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine which seemed to be falsified. These suspicious samples were semiquantitatively analyzed by thin-layer chromatography using Minalab® kits. Obtained results allowed to confirm the falsifications since the suspected samples did not contain any of the expected active pharmaceutical ingredients. The capacity of the low-cost near-infrared device to identify specifically a brand name of dihydroartemisinin/piperaquine or sulfadoxine/pyrimethamine has been also studied using soft independent modelling of class analogy (SIMCA) in the classical and data driven versions. The built models allowed a clear brand identification with 100% of both sensitivity and specificity in the studied cases. All these results demonstrate the potential of these low-cost near-infrared spectrometers to be used as first line screening tools, particularly in resource limited laboratories, for the detection of falsified antimalarial drugs.

A low-cost spectrometer to analyse the purity of honey

Light spectrum dispersion is an exciting subjectin science because of its beautiful atmospheric colour phenomenon which attracts students. However, to see the phenomenon is not easy since it needs a spectrometer, which is commonly expensive. Therefore, the present study aims to describe a low-cost spectrometer for investigating lighting spectrum and analysing the purity of honey as a pedagogical students’ project. The spectrometer was constructed from a webcam connected to a laptop, a free spectrometer software, DVD disk, and black cardboard. The calibration of the developed spectrometer used an Argon (Ar) lamp. Afterwards, measurement tests were carried out by using Neon (Ne) and Xenon (Xe) lamps. A white light-emitting diode was used as a light source to measure several types of honey wavelengths. The results from the experiment show that the wavelength of Ar, Ne, and Xe are (503 ± 4) nm, (463 ± 3) nm, and (451 ± 3) nm respectively. The measurement accuracy of the spectrometer is 99.5%. In addition, the commercial honey characterisations show that pure honey tends to have an excellent and smooth spectrum with one peak. On the contrary, the adulteries honeys show a rough wave with many peaks representing the inhomogeneous ingredients. It is found that the average accuracy for honey spectrum is 99.8%. Moreover, the low-cost spectrometer can be built and used easily by students for educational purposes.

Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%.

Thickness and birefringence of thin films assessed by interferometry using a low-cost spectrometer

The interference has significant importance from the viewpoints of fundamental science and applications. Though the experimental exercise and assessment based on numerical data are inevitable to understand the interference, it is challenging to perform classwork using spectrometers in a large teaching room due to the high-cost problems. In this work, the authors assessed low-cost spectrometers' availability and analysis methods for transmittance/reflectivity measurements for polymer films and their educational applications. The results show the accuracy and convenience of the apparatus and the methods.

Optical Biopsy of Amelanotic Melanoma with Raman and Autofluorescence Spectra Stimulated by 785 nm Laser Excitation

In this work, Raman and autofluorescence spectroscopy in the near-infrared region has been used for examining amelanotic melanoma as the most dangerous type of malignant melanoma. There were 9 patients with amelanotic melanoma, 60 with pigmented melanoma and 120 with basal cell carcinoma enrolled in this study. We studied 9 amelanotic melanoma cases to differentiate them from basal cell carcinoma (n = 120) and pigmented malignant melanoma (n = 60) using portable spectroscopy setup with laser excitation source at 785 nm and low-cost spectrometer. The spectra of the different tumor type were classified using projection on the latent structure analysis with 10-Fold cross-validation. The results of the tumor classification were presented using box-plot diagrams and ROC analysis. We obtained 0.53 and 0.88 ROC AUCs for distinguishing amelanotic melanoma versus (1) pigmented melanoma and (2) basal cell carcinoma respectively based on the joint autofluorescence and Raman spectroscopy analysis that allowed one to diagnose amelanotic melanoma as true melanoma but no basal cell carcinoma.