Search Results (36)

Computational imaging spectrometers using broad-bandpass filter arrays with distinct transmission functions are promising implementations of miniaturization. The number of filters is limited by the practical factors. Compressed sensing is used to model the system as linear underdetermined equations for hyperspectral imaging. This paper proposes the following method: parallel dictionary reconstruction and fusion for spectral recovery in computational imaging spectrometers. Orthogonal systems are the dictionary candidates for reconstruction. According to observation of ground objects, the dictionaries are selected from the candidates using the criterion of incoherence. Parallel computations are performed with the selected dictionaries, and spectral recovery is achieved by fusion of the computational results. The method is verified by simulating visible-NIR spectral recovery of typical ground objects. The proposed method has a mean square recovery error of ≤1.73 × 10⁻⁴ and recovery accuracy of ≥0.98 and is both more universal and more stable than those of traditional sparse representation methods. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

This review explores optical technologies for non-invasive glucose monitoring (NIGM) using aqueous humor (AH) as media, addressing the limitations of traditional invasive methods in diabetes management. It analyzes key techniques such as Raman spectroscopy, polarimetry, and mid- and near-infrared spectral methods, highlighting their respective challenges, alongside emerging hybrid approaches like photoacoustic spectroscopy and optical coherence tomography. Crucially, the practical realization of these optical methods for portable NIGM hinges on advanced instrumentation. Therefore, this review also details progress in compact NIR spectrometers. While conventional systems often lack suitability, significant advancements in on-chip technologies—including miniaturized dispersive spectrometers and various on-chip Fourier transform systems (e.g., spatial heterodyne, stationary wave integral, and temporally modulated FT systems)—utilizing integration platforms like SOI and SiN are promising. Such innovations offer the potential for high spectral resolution, large bandwidth, and miniaturization, which are essential for developing practical AH-based NIGM systems to improve diabetes care. Full article

In this investigation, we established an intelligent computational framework comprising a novel starfish-optimization-algorithm-optimized support vector regression (SOA-SVR) model and a multi-algorithm joint strategy to evaluate the processing applicability of wheat flour in terms of sedimentation value (SV) and falling number (FN) using near-infrared (NIR) data (900–1700 nm) obtained using a miniaturized NIR spectrometer. By employing an improved whale optimization algorithm (iWOA) coupled with a successive projections algorithm (SPA), we selected the 20 most informative wavelengths (MIWs) from the full range spectra, allowing the iWOA/SPA-SOA-SVR model to predict SV with correlation coefficient and root-mean-square errors in prediction (R_P and RMSE_P) of 0.9605 and 0.2681 mL. Additionally, RFE, in combination with the iWOA, identified 30 MIWs and enabled the RFE/iWOA-SOA-SVR model to predict the FN with an R_P and RMSE_P of 0.9224 and 0.3615 s. The robustness and reliability of the two SOA-SVR models were further validated using 50 independent samples per index, a statistical two-sample F-test, and a t-test. In conclusion, the combination of a portable miniaturized NIR spectrometer and an SOA-driven SVR algorithm demonstrated technical feasibility in quantifying the SV and FN of wheat flour, thus providing a novel strategy for the on-site assessment of wheat flour processing applicability. Full article

Miniaturized near-infrared (NIR) spectrometers are revolutionizing the agri-food industry thanks to their compact size and ultra-fast analysis capabilities. This work compares the analytical performance of a handheld NIR spectrometer and a benchtop FT-NIR for the determination of several parameters, namely, pH, electrical conductivity (EC₂₅), C/N ratio, and organic matter as LOI (loss-on-ignition) in compost. Samples were collected at different stages of maturity from a full-scale facility that processes olive mill semi-solid residue together with olive tree pruning residue and animal manure. Using an FT-NIR spectrometer, satisfactory predictions (RPD > 2.0) were obtained with both partial least squares (PLS) and support vector machine (SVM) regression, SVM clearly being superior in the case of pH (RMSEP = 0.26; RPD = 3.8). The superior performance of the FT-NIR spectrometer in comparison with the handheld spectrometer was essentially due to the extended spectral range, especially for pH. In general, when analyzing intact samples with the miniaturized spectrometer, sample rotation decreased RMSEP values (~20%). Nevertheless, a fast and simple assessment of compost quality with reasonable prediction performance can also be achieved on intact samples by averaging static measurements acquired at different sample positions. Full article

In the petroleum industry, accurately identifying downhole fluid is crucial for understanding fluid composition and estimating crude oil contamination and other properties. Near-infrared (NIR) spectrum analysis technology has achieved successful fluid identification applications due to its non-destructive nature and high efficiency. However, for real-time downhole fluid analysis, the NIR spectrometer faces challenges such as miniaturization and cost effectiveness. To address these issues, we construct a real-time downhole fluid identification system in this work. First, we propose a lightweight and deployable fluid identification model by integrating the successive projections algorithm (SPA) and a quadratic neural network (QNN). The SPA allows for spectral feature selection, and the QNN acts as an efficient identification model. Consequently, we use only four specific wavelengths with a one-hidden-layer QNN to achieve high identification accuracy. Second, we devise a hardware deployment scheme for real-time identification. We use four laser diodes to replace conventional light sources, further saving space. The QNN is then deployed to the STM32 MCU to implement real-time identification. Computational and online experiments demonstrate that our system functions well in real-time fluid identification and can further estimate the oil contamination rate with acceptable error. Full article

Iberian ham is a highly valued product, and considerable efforts have been made to characterize it quickly and accurately. In this scenario, portable NIR devices could provide an effective solution for the assessment of its attributes. However, the calibration quality of NIR equipment is directly influenced by the relevance of the used spectral region. Therefore, this study aims to evaluate the suitability of different NIR spectrometers, including four portable and one benchtop instrument, with varying spectral working ranges for quantifying the fatty acid composition of Iberian ham. Spectral measurements were carried out on both the muscle and the fat of the ham slices. The results showed that 24 equations with an RSQ > 0.5 were obtained for both the muscle and fat for the NIRFlex N-500 benchtop instrument, while 19 and 14 equations were obtained in the muscle and 16 and 10 equations in the fat for the Enterprise Sensor and MicroNIR, respectively. In general, more fatty acids could be calibrated when the spectra were taken from lean meat, except with the SCiO Sensor. Measurements performed in the lean and fat zones delivered complementary information. These initial findings indicate the suitability of using miniaturized NIR sensors, which are faster, are less expensive, and enable on-site measurements, for analyzing fatty acids in Iberian ham. Full article

Uvsq-Sat NG is a French 6U CubeSat (10 × 20 × 30 cm) of the International Satellite Program in Research and Education (INSPIRE) designed primarily for observing greenhouse gases (GHG) such as CO₂ and CH₄, measuring the Earth’s radiation budget (ERB), and monitoring solar spectral irradiance (SSI) at the top-of-atmosphere (TOA). It epitomizes an advancement in CubeSat technology, showcasing its enhanced capabilities for comprehensive Earth observation. Scheduled for launch in 2025, the satellite carries a compact and miniaturized near-infrared (NIR) spectrometer capable of performing observations in both nadir and solar directions within the wavelength range of 1100 to 2000 nm, with a spectral resolution of 7 nm and a 0.15° field of view. This study outlines the preflight calibration process of the Uvsq-Sat NG NIR spectrometer (UNIS), with a focus on the spectral response function and the absolute calibration of the instrument. The absolute scale of the UNIS spectrometer was accurately calibrated with a quartz-halogen lamp featuring a coiled-coil tungsten filament, certified by the National Institute of Standards and Technology (NIST) as a standard of spectral irradiance. Furthermore, this study details the ground-based measurements of direct SSI through atmospheric NIR windows conducted with the UNIS spectrometer. The measurements were obtained at the Pommier site (45.54°N, 0.83°W) in Charentes–Maritimes (France) on 9 May 2024. The objective of these measurements was to verify the absolute calibration of the UNIS spectrometer conducted in the laboratory and to provide an extraterrestrial solar spectrum using the Langley-plot technique. By extrapolating the data to AirMass Zero (AM0), we obtained high-precision results that show excellent agreement with SOLAR-HRS and TSIS-1 HSRS solar spectra. At 1.6 μm, the SSI was determined to be 238.59 ± 3.39 mW.m⁻².nm⁻¹ (k = 2). These results demonstrate the accuracy and reliability of the UNIS spectrometer for both SSI observations and GHG measurements, providing a solid foundation for future orbital data collection and analysis. Full article

The analytical study described in this work, based on NIR spectroscopy with a handheld device, allowed the development of a chemometric prediction model that has been validated for the objective evaluation of the ripening of olive drupes. The miniaturized, portable NIR spectrometer is proposed here as an easy-to-use sensor able to estimate the best harvesting time for ripening of olive drupes. The MicroNIR/chemometrics approach was developed for on-site identification of olive drupe ripening directly on plants, avoiding collection and successive laboratory analysis steps. A supporting parallel characterization by chromatographic techniques validated the spectroscopic prediction. The novelty of this approach consists in the possibility of investigating the olive drupe maturation point by collecting spectra in the near-infrared region and processing them using a chemometric model. The fast and accurate device allows one to easily follow the spectrum profile changes of olive drupes during ripening, thus preserving the fruits from being harvested too early or too late. The results of this study demonstrate the possibility of using the MicroNIR/chemometrics approach to determine the optimal ripening time of olives regardless of the plant variety, age and cultivation location. The results consequently demonstrated that the MicroNIR/chemometrics approach can be proposed as a new method to perform on-site evaluation of ripening by a single-click device. It can be conveniently used by any operator, who does not necessarily have to be expert but must simply be trained to use spectroscopy and a prediction model. Full article

Food safety and quality are the first steps in the food chain. This work reports a miniaturized, low-cost, and easy-to-use near-infrared spectroscopy (NIRS) measurement system for alfalfa quality control. This is a significant challenge for dairy farm technicians and producers who need rapid and reliable knowledge of the forage quality on their farms. In most cases, the instrumentation suitable for these specifications is expensive and difficult to operate. The core of the proposed NIR spectroscopy measurement system is Texas Instruments’ NIRscan Nano evaluation module (EVM) spectrometer. This module has a large sensing area and high resolution, suitable for forage samples. To evaluate the feasibility of the prototype for analyzing agrifood samples, different ways of presenting the sample, intact or ground, were tested. The final objective of the research is not just to check the efficiency of the proposed system. It is also to determine the characteristics of the measurement system, and how to improve them for alfalfa quality control. Full article

The transmissive mode laser micro-ablation performance of near-infrared (NIR) dye-optimized ammonium dinitramide (ADN)-based liquid propellant was investigated in laser plasma propulsion using a pulse YAG laser with 5 ns pulse width and 1064 nm wavelength. Miniature fiber optic near-infrared spectrometer, differential scanning calorimeter (DSC) and high-speed camera were used to study laser energy deposition, thermal analysis of ADN-based liquid propellants and the flow field evolution process, respectively. Experimental results indicate that two important factors, laser energy deposition efficiency and heat release from energetic liquid propellants, obviously affect the ablation performance. The results showed that the best ablation effect of 0.4 mL ADN solution dissolved in 0.6 mL dye solution (40%-AAD) liquid propellant was obtained with the ADN liquid propellant content increasing in the combustion chamber. Furthermore, adding 2% ammonium perchlorate (AP) solid powder gave rise to variations in the ablation volume and energetic properties of propellants, which enhanced the propellant enthalpy variable and burn rate. Based on the AP optimized laser ablation, the optimal single-pulse impulse (I)~9.8 μN·s, specific impulse (I_sp)~234.9 s, impulse coupling coefficient (C_m)~62.43 dyne/W and energy factor (η)~71.2% were obtained in 200 µm scale combustion chamber. This work would enable further improvements in the small volume and high integration of liquid propellant laser micro-thruster. Full article

The “Dangshan” pear woolliness response is a physiological disease that mostly occurs in the pear growth process. The appearance of the disease is not obvious, and it is difficult to detect with the naked eye. Therefore, finding a way to quickly and nondestructively identify “Dangshan” pear woolliness disease is of great significance. In this paper, the near-infrared spectral (NIR) data of “Dangshan” pear samples were collected at 900–1700 nm reflectance spectra using a handheld miniature NIR spectrometer, and the data were modelled and analysed using random forest (RF), support vector machine (SVM) and boosting algorithms under the processing of 24 pretreatment methods. Considering the variations between different pretreatment methods, this work determined the relative optimality index of different pretreatment methods by evaluating their effects on model accuracy and Kappa and selected the best-performing first derivative with standard normal variate and Savitzky–Golay and first derivative with multiplicative scatter correction and Savitzky–Golay as the best pretreatment methods. With the best pretreatment method, all five models in the three categories showed good accuracy and stability after parameter debugging, with accuracy and F1 greater than 0.8 and Kappa floating at approximately 0.7, reflecting the good classification ability of the models and proving that near-infrared spectroscopy (NIRS) in the rapid identification of “Dangshan” pear woolliness response disease was feasible. By comparing the performance differences of the models before and after the pretreatment methods, it was found that the ensemble-learning models such as RF and boosting were more stringent on pretreatment methods in identifying “Dangshan” pear woolliness response disease than support vector machines, and the performance of the ensemble learning models was significantly improved under appropriate pretreatment methods. This experiment provided a relatively stable detection method for “Dangshan” pear woolliness response disease under nonideal detection conditions by analysing the impact of pretreatment methods and models on the prediction result. Full article

This present review article considers the rapid development of miniaturized handheld near-infrared spectrometers over the last decade and provides an overview of current instrumental developments and exemplary applications in the fields of material and food control as well as environmentally relevant investigations. Care is taken, however, not to fall into the exaggerated and sometimes unrealistic narrative of some direct-to-consumer companies, which has raised unrealistic expectations with full-bodied promises but has harmed the very valuable technology of NIR spectroscopy, rather than promoting its further development. Special attention will also be paid to possible applications that will allow a clientele that is not necessarily scientifically trained to solve quality control and authentication problems with this technology in everyday life. Full article

A methodology such as near-infrared (NIR) spectroscopy, which enables in situ and in real-time analysis, is crucial to perform quality control of biodiesel, since it is blended into diesel fuel and the presence of contaminants can hinder its performance. This work aimed to compare the performance of a benchtop Fourier Transform (FT) NIR spectrometer with a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) to detect and quantify contaminants in biodiesel and biodiesel in diesel. In general, good models based on principal component analysis-linear discriminant analysis (PCA-LDA) of FT-NIR spectra were obtained, predicting with high accuracies biodiesel contaminants and biodiesel in diesel (between 75% to 95%), as well as good partial least square (PLS) regression models to predict contaminants concentration in biodiesel and biodiesel concentration in diesel/biodiesel blends, with high coefficients of determination (between 0.83 and 0.99) and low prediction errors. The miniNIR prototype’s PCA-LDA models enabled the prediction of target contaminants with good accuracies (between 66% and 86%), and a PLS model enabled the prediction of biodiesel concentration in diesel with a reasonable coefficient of determination (0.68), pointing to the device’s potential for preliminary analysis of biodiesel which, associated with its potential low cost and portability, could increase biodiesel quality control. Full article

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments. Full article