Spectral Analysis System Research Articles

A Step Forward in Hybrid Soil Laboratory Analysis: Merging Chemometric Corrections, Protocols and Data-Driven Methods

The need to maintain soil health and produce more food worldwide has increased, and soil analysis is essential for its management. Although spectroscopy has emerged as an important tool, it is important to focus primarily on predictive modeling procedures rather than specific protocols. This article aims to contribute to a routine work sequence in a hybrid laboratory that seeks to provide the best data for its users. In this study, 18,730 soil samples from the state of Paraná, Brazil, were analyzed using three different laboratories, sensors and geometries for data acquisition. Thirty soil properties were analyzed, some using different chemical methodologies for comparison purposes. After a spectral reading, two literary protocols were applied, and the final prediction results were observed. We applied cubist models, which were the best for our population. The combination of different spectral analysis systems, with a standardized protocol using LB for the ISS detection of discrepant samples, was shown to significantly improve the accuracy of predictions for 21 of the 30 soil properties analyzed, highlighting the importance of choosing the extraction methodology and improving data quality, which have a significant impact on laboratory analyses, reaffirming spectroscopy as an essential tool for the efficient and sustainable management of soil resources.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

Solar Wind Charge-Exchange X-ray Emissions from the O5+ Ions in the Earth’s Magnetosheath

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. Solar wind O6+ ions, which are the primary charge state for oxygen ions in solar wind, are considered. The line emissivity of the charge-exchange-borne O5+ ions is calculated by the Spectral Analysis System for Astrophysical and Laboratory (SASAL). It is found that the emission lines from O5+ range from 105.607 to 118.291 eV with a strong line at 107.047 eV. We then simulate the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of the north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm−2 s−1 sr−1 on the dayside, which is 4.5 times that before the solar wind structure reached the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at the north pole.

Fiber-optic spectrum monitoring of wavelength-division-multiplexed telecommunication signals with MHz update rates.

We propose a novel (to our knowledge) and simple real-time optical monitoring (RTOM) system for dynamic spectral analysis of telecommunication signals, involving electro-optic (EO) temporal sampling followed by dispersion-induced frequency-to-time mapping and high-speed photodetection. This system enables tracking of the presence and relative intensity of multiple wavelength-division-multiplexed (WDM) data streams that span over a broad frequency band with high resolution, accuracy, and fast measurement update rates. We derive the design conditions and trade-offs of the proposed scheme and report proof-of-concept experiments and a numerical result that demonstrate successful spectral monitoring of dense-WDM signals with different modulation formats and bit rates, over the full C-band, with the needed resolution to discern channels separated by a few tens of GHz, and with an unprecedented fast measurement update rate in the MHz range.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Machine Learning Approach to Enable Spectral Imaging Analysis for Particularly Complex Nanomaterial Systems.

Scanning transmission electron microscopy-based electron energy loss spectroscopy spectral imaging (STEM-EELS-SI) has been widely used in material research to capture a wealth of information, including elemental, electron density, and bonding state distributions. However, its exploitation still faces many challenges due to the difficulty of extracting information from noisy and overlapping edges in the convoluted spatial and spectroscopic data set. A traditional EELS spectral imaging analysis lacks the capability to isolate noise and deconvolute such overlapping edges, which either limits the resolution or the signal-to-noise ratio of the maps generated by EELS-SI. Existing machine learning (ML) algorithms can achieve denoising and deconvolution to a certain extent, but the extracted spectra lack physical meaning. To address these challenges, we have developed a ML method tailored to a spectral imaging analysis system and based on a non-negative robust principal component analysis. This approach offers an effective way to analyze EELS spectral images with improved space-time resolution, signal-to-noise ratio, and the capability to separate subtle differences in the spectrum. We apply this algorithm to 13 nanomaterial systems to show that ML can greatly improve image quality compared to a traditional approach, especially for more challenging systems. This will expand the type of nanomaterial systems that can be characterized by EELS-SI, and aid the analysis of structural, chemical, and electronic properties that are otherwise difficult to obtain.

Non-Destructive Detection of Abnormal Chicken Eggs by Using an Optimized Spectral Analysis System.

Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. However, there are very few studies on the optimization of measurement systems. This study aimed to establish optimum parameters for detecting of internal egg abnormalities (bloody and damaged-yolk eggs) using visible and near-infrared (Vis/NIR) spectrometry (192-1110 nm range) and multivariate data analysis. The detection performance using various system parameters, such as the types of light sources, the configuration of the light, and sensor positions, was investigated. With the help of collected data, a partial least-squares discriminant analysis (PLS-DA) model was developed to classify normal and abnormal eggs. The highest classification accuracy for the various system parameters was 98.7%. Three band selection methods, such as weighted regression coefficient (WRC), sequential feature selection (SFS), and successive projection algorithm (SPA) were used for further model optimization, to reduce the spectral bands from 1028 to less than 7. In conclusion the results indicate that the types of light sources and the configuration design of the sensor and illumination affect the detection accuracy for abnormal eggs.

Soft- and hardware platform for spectral analysis systems based on tunable semiconductor lasers

A soft- and hardware platform is described for supporting the operation of spectral analysis devices and systems based on tunable semiconductor lasers. The wide spectral range covered by lasers of this type and the versatility of methods for controlling their spectral parameters make it possible to solve various problems within the framework of a single instrumental approach. This includes the most complex tasks of spectral analysis of multicomponent gas mixtures including atmospheric and exhaled air. Main areas of the platform application include IR molecular spectroscopy, chemical technologies, atmospheric pollution control, diagnostics of low-temperature plasma components, biomedical diagnostics.

Abstract 629: Benefit of using a spectral flow analyzer for the analysis of immune cell populations in tumors

Abstract Flow cytometry is a widely applied approach for exploratory immune profiling and enables the identification of individual cells in a cell population, such as a tumor cell suspension, by staining cell markers with antibodies conjugated with a fluorophore. The markers allow for the differentiation of a large variety of immune cell populations, their change in frequency, and activation status upon treatment. Conventional flow cytometers, which use bandpass filters and need conventional compensation matrices, are limited by the number of parameters that can be simultaneously analyzed, restricting their utility. Spectral analysis technology promises a greater flexibility for panel design, a higher number of analyzed parameters, and more accurate visualization of results. Our standard all-in-one flow cytometry panel uses all possible fluorescent detectors of the flow analyzer BD Fortessa, which permits the differentiation of the main immune cell populations in the tumor, such as T cells (CD4+, CD8+, Treg), B and NK cells, macrophages (M1/M2), MDSCs (granulocytic and monocytic), and dendritic cells. However, in addition to immune cell frequency, it would also be interesting to investigate cell surface activation and memory markers like PD-1, CD40, CD44, or CD62L or intracellular activation markers like IFN-gamma, perforin, granzyme B, or Ki67. The 32-channel spectral flow analyzer SP6800 ZE from Sony allows the combination of both. Flow cytometry data of different tumor samples will be presented, as well as the comparison of the results and resolution of both flow analyzer systems. Auto-fluorescence of cells, which causes fluorescent signal contamination of other fluorescent markers in the conventional flow cytometer, can be subtracted using spectral technology to see the true fluorescent population, thereby allowing dim signal detection in rare populations. A comprehensive overview will be given regarding the pros and cons of the spectral analyzer system and difficulties switching from the one system to the other. Citation Format: Philipp Metzger, Muriel Malaisé, Cynthia Obodozie, Holger Weber. Benefit of using a spectral flow analyzer for the analysis of immune cell populations in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 629.

The Obtainable Uncertainty for the Frequency Evaluation of Tones with Different Spectral Analysis Techniques

Spectral analysis is successfully adopted in several fields. However, the requirements and the constraints of the different cases may be so varied that not only the tuning of the analysis parameters but also the choice of the most suitable technique can be a difficult task. For this reason, it is important that a designer of a measurement system for spectral analysis has knowledge about the behaviour of the different techniques with respect to the operating conditions. The case that will be considered is the realization of a numerical instrument for the real-time measurement of the spectral characteristics of a multi-tone signal (amplitude, frequency, and phase). For this purpose, different signal processing techniques can be used, that can be classified as parametric or non-parametric methods. The first class includes those methods that exploit the a priori knowledge about signal parameters, such as the spectral shape of the signal to be processed. Thus, a self-configuring procedure based on a parametric algorithm should include a preliminary evaluation of the number of components. The choice of the right method among several proposals in the literature is fundamental for any designer and, in particular, for the developers of spectral analysis software, for real-time applications and embedded devices where time and reliability constrains are arduous to fulfil. Different aspects should be considered: the desired level of accuracy, the available elaboration resources (memory depth and processing speed), and the signal parameters. The present paper details a comparison of some of the most effective methods available in the literature for the spectral analysis of signals (IFFT-2p, IFFT-3p, and IFFTc, all based on the use of an FFT algorithm, while improving the spectral resolution of the DFT with interpolation techniques and three parametric algorithms—MUSIC, ESPRIT, and IWPA). The methods considered for the comparison will be briefly described, and references to literature will be given for each one of them. Then, their behaviour will be analysed in terms of systematic contribution and uncertainty on the evaluated frequencies of the spectral tones of signals created from superimposed sinusoids and white Gaussian noise.

Existence and uniqueness criteria for nonlinear quantum difference equations with $ p $-Laplacian

&lt;abstract&gt;&lt;p&gt;Q-calculus plays an extremely important role in mathematics and physics, especially in quantum physics, spectral analysis and dynamical systems. In recent years, many scholars are committed to the research of nonlinear quantum difference equations. However, there are few works about the nonlinear $ q- $difference equations with $ p $-Laplacian. In this paper, we investigate the solvability for nonlinear second-order quantum difference equation boundary value problem with one-dimensional $ p $-Laplacian via the Leray-Schauder nonlinear alternative and some standard fixed point theorems. The obtained theorems are well illustrated with the aid of two examples.&lt;/p&gt;&lt;/abstract&gt;

Justification of the geometric dimensions of drum sorting machine

This article presents the results of research to determine the basic geometric dimensions of a drum sorting machine designed for potato sorting. A critical analysis of the current situation in this area has been carried out. A number of research works have been studied aimed at developing special methods to reduce manual labour and improve the quality of sizing and sorting potatoes. At the same time, it is important to sort by size with a low level of product damage, with high productivity. Known mechanical and robotic machines used for sorting potatoes: roller, drum, conveyor (belt) and combined. And also, with the help of machine and computer vision, laser backscattering of light, ultrasonic, visual and spectral analysis systems, optical, acoustic intelligent sorting systems. Among the mechanical ones, the drum sorting machine is the simplest in design. The disadvantage of this machine is that during operation the product to be sorted is only in the lower part of the drum, i.e. only part of the work surface is used. To eliminate the abovementioned disadvantage, a new design of the machine is recommended. Where the sorting surface is made of elastic mesh, the size of the holes increased in the direction of movement of the ends along the sorting surface. The ends are connected and pulled together on two drums. The holes of the elastic mesh material vary in size, the size of the holes increases from the beginning to the end of the sorting surface. The drum can be in a truncated cone or a cylinder shape. To ensure the efficient operation of the proposed machine, mathematical calculations are derived, including geometric and kinematic parameters.

Energy efficiency of lighting systems based on acousto-optic filtration

Designing systems with an acousto-optical filter requires considering its small numerical aperture and crystal size. These features lead to the low efficiency of using the luminous flux, which may be crucial in practical applications of such systems for spectral analysis. The article discusses ways to increase the energy efficiency in different types lighting systems based on acousto-optic filtration.

The distribution and retained amount of benzo[a]pyrene at the micro-zones of mangrove leaf cuticles: Results from a novel analytical method

Plant leaf cuticles play a critical role in the accumulation and transport of atmospheric polycyclic aromatic hydrocarbons (PAHs). The relationship between the distribution and retained amount of PAHs on the leaf cuticles and the leaves micro-zone structures is still unclear. In this study, a confocal microscopic fluorescence spectral analysis (CMFSA) system with a spatial resolution of 200 nm was established as a direct and noninvasive means to determine the microscopic distribution and quantify the retained amount of benzo[a]pyrene (B[a]P) at Aegiceras corniculatum (Ac), Kandelia obovata (Ko) and Avicennia marina (Am) leaf cuticle micro-zones (0.096 mm2). The linear ranges for the established method were 10–1900 ng spot−1 for Ac, 15–1700 ng spot−1 for Ko and 30–1800 ng spot−1 for Am, and the detection limits were 0.06 ng spot−1 for Ac, 0.06 ng spot−1 for Ko and 0.07 ng spot−1 for Am. Notably, B[a]P formed clusters and unevenly distributed at the leaf cuticles. On the adaxial cuticles, B[a]P was mainly accumulated unevenly along the epidermis cell wall, and it was also distinctively distributed in the secretory cells around salt glands for Ac and Am. On the abaxial leaf cuticles, B[a]P was concentrated in the salt glands and stomata apart from being unevenly distributed in the epidermis cell wall. Moreover, the amount of B[a]P retained presented a negative correlation with the polarity of leaf cuticles, which resulted in the amount of B[a]P retained on the adaxial leaf cuticles being significantly higher than that on abaxial leaf cuticles. Our results provide a potential in situ method for investigating the distribution and retained amount of PAHs at plant leaf surface micro-zones, which would contribute to further studying and understanding the mechanism of migration and transformation of PAHs by plant leaves from a microscopic perspective.

Nonlinear elasticity and damping govern ultrafast dynamics in click beetles

Many small animals use springs and latches to overcome the mechanical power output limitations of their muscles. Click beetles use springs and latches to bend their bodies at the thoracic hinge and then unbend extremely quickly, resulting in a clicking motion. When unconstrained, this quick clicking motion results in a jump. While the jumping motion has been studied in depth, the physical mechanisms enabling fast unbending have not. Here, we first identify and quantify the phases of the clicking motion: latching, loading, and energy release. We detail the motion kinematics and investigate the governing dynamics (forces) of the energy release. We use high-speed synchrotron X-ray imaging to observe and analyze the motion of the hinge's internal structures of four Elater abruptus specimens. We show evidence that soft cuticle in the hinge contributes to the spring mechanism through rapid recoil. Using spectral analysis and nonlinear system identification, we determine the equation of motion and model the beetle as a nonlinear single-degree-of-freedom oscillator. Quadratic damping and snap-through buckling are identified to be the dominant damping and elastic forces, respectively, driving the angular position during the energy release phase. The methods used in this study provide experimental and analytical guidelines for the analysis of extreme motion, starting from motion observation to identifying the forces causing the movement. The tools demonstrated here can be applied to other organisms to enhance our understanding of the energy storage and release strategies small animals use to achieve extreme accelerations repeatedly.

Spectrometers with MAES analyzers based on new photodetector arrays

Multichannel analyzers of emission spectra (MAES) are operated as part of more than six hundred systems for atomic emission spectral analysis at enterprises in Russia, the CIS countries, and others. Until recently, MAES analyzers used only BLPP-369M1 photodetector arrays, whose performance is sufficient for integral atomic emission spectrometry applications using bright sources of spectral excitation based, for example, on an arc or spark electric discharge. To solve problems where high temporal spatial resolution and high sensitivity are required, high-speed MAES analyzers have been developed based on two new photodetector arrays—BLPP-2000 and BLPP-4000. A number of optical spectrometers based on these arrays have been developed and put into production by the VMK-Optoelektronika company: Atomic emission spectrometers: Grand Globula and Ekspress arc and spark spectrometers for the direct analysis of powders by evaporation from the channel of a graphite electrode and for the analysis of metals; Grand-Potok arc spectrometers for the rapid analysis of powders by the spill-injection method; Grand-Ekspert and Favorit argon-spark vacuum spectrometers for the rapid analysis of metals and alloys; Grand-SVCh microwave-induced nitrogen plasma spectrometers and Grand-ISP inductively-coupled argon plasma spectrometers for the analysis of liquids; Kolibri-SVCh microwave-induced air plasma spectrometers and Pavlin flame atomic emission spectrometers for the determination of alkaline and alkaline earth elements in solutions. Grand-AAS high-resolution continuous-source electrothermal atomic absorption spectrometers for the simultaneous determination of elements in liquids.

ИССЛЕДОВАНИЕ ФИЗИКО-МЕХАНИЧЕСКИХ СВОЙСТВ БРОНЗОВЫХ ПОРОШКОВ, ПОЛУЧЕННЫХ МЕТОДОМ ЭЛЕКТРОЭРОЗИОННОГО ДИСПЕРГИРОВАНИЯ

Among the well-known repair technologies for restoring bronze parts, there are practically no technologies using metal powders from non-ferrous metals. Electroerosive dispersion is an energy-efficient process that makes it possible to obtain powdered copper and bronze from machine-building waste with lower costs and environmental impact. (Research purpose) The research purpose is in evaluating the physical and mechanical properties of bronze powders obtained by electroerosive dispersion from machine-building waste, and justifying the possibility of their application in the technologies of repair and restoration of parts. (Materials and methods) Authors received material for research on their own experimental facilities of the Nano-Center Central Research Center. Authors used an inverted metallographic microscope Olympus GX-51, a TESCAN VEGA II scanning electron microscope with an INCA Energy 350 X-ray spectral analysis system, a Microtrac Bluewave laser particle size analyzer, a Niton XL3t GOLDD+ X-ray fluorescence spectrometer, and a KMT-1 microhardness meter. (Results and discussion) The article presents qualitative and quantitative indicators of the bronze powder obtained by electroerosive dispersion, and the properties of this powder were evaluated for a specific method of production. The article presents granulometric composition of the powder, the shape and morphology of the particles, the fractional composition, microhardness and chemical composition. (Conclusions) In the studied powder, particles with a regular spherical shape prevail. The decrease in microhardness is explained by the recrystallization process. Differences in the chemical composition are explained by the interaction with the working fluid and various elements of the installation structure. Powder material obtained from bronze waste by electroerosive dispersion mostly corresponds to the parameters of the source metals and is suitable for the restoration and repair of parts.

Spectral analysis and optimization of the condition number problem

Spectral analysis systems using gamma radiation to determine the percentage fraction of several compounds in a sample need several stages. The conditioning number of covariance matrices associated with the least-squares system needs to be very close to one so that the determined values are as close as possible to the actual values. Scientific evidence gives us reason to believe that these matrices make it possible to obtain fractions of compounds in the sample close to the true values. This work focuses on the use of the metaheuristic Greedy Randomized Adaptation Search Procedure (GRASP) to estimate percent counts of constituents of a compound represented by prompt gamma ray spectra, based on the minimization of the condition number of the covariance matrix derived from the underlying linear system. For this purpose, steps of GRASP were modified: Algorithms are used for building a solution and for searching in the vicinity of that built solution. Following the stages of building a solution and searching around the built solution, carried out by GRASP, there are attempts to achieve a better quality solution than determined by local search by executing the linking algorithm between two solutions. The results presented in our work improve the condition numbers of the covariance matrices found in articles that are currently published, observing the characteristics of the application data under study. The proposed algorithm obtained the spectral count matrices as input, obtaining average improvements of 31.30% in the condition number of the covariance matrices in the execution of GRASP in the partitioned data. The objective was reached in 7 of the 9 instances. The improvement of the values in condition number was on average of 84.62% in cases of orthogonalized covariance matrices. The average error of the spectral count percentages for each element was 7.2×10−3%. Results lead us to the conclusion that it is possible to obtain covariance matrices that minimize numerical instability in the least squares solution.

In situ prediction of phenolic compounds in puff dried Ziziphus jujuba Mill. using hand-held spectral analytical system.

A low cost hand-held spectral analytical system was developed for in situ screening of phenolics and flavonoids in puff dried Ziziphus jujuba (Z. Jujuba) samples. Standards of gallic acid, caffeic acid, l-epicatechin, phloridzin and cianidanol were used to quantify the individual phenolics and flavonoids using high performance liquid chromatography-diode array detector (HPLC-DAD). The synergy interval partial least square with ant colony optimization (Si-ACO-PLS) was attempted to optimize and capture informative variables for the prediction of target compounds. The model performance was evaluated using correlation coefficients of prediction (Rp); root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). The Si-ACO-PLS yielded optimal performance, 0.8540≤Rc≤0.9250, 0.8360≤Rp≤0.9056, 0.84≤RMSEP≤16.30 and 2.03≤RPD≤2.26. The hand-held spectral analytical system coupled with Si-ACO-PLS proved to the reliable, rapid and cost-effective method to quantify the phenolics and flavonoids in Z. Jujuba.

High Resolution and Ultra-Compact On-Chip Spectrometer Using Bidirectional Edge-Input Arrayed Waveguide Grating

Two Edge-input arrayed waveguide gratings (AWGs) with star couplers of Confocal and Rowland configurations, respectively, are experimentally investigated and the one with the Rowland configuration shows better performance in terms of insertion loss and crosstalk. Based on the optimized Rowland configuration, two 64 × 0.4 nm ultra-compact on-chip spectrometers using bidirectional AWGs with two side inputs and different lengths of free propagation region (FPR) are designed and fabricated. Due to the utilization of two side inputs, the footprint of AWG spectrometer is significantly reduced compared to the conventional one center-input AWG spectrometer with the same resolution. The reduced footprint contributes to the performance improvement for an AWG spectrometer on high index contrast platforms, which is verified in our fabricated spectrometers on the silicon-on-insulator platform. The demonstrated ultra-compact on-chip spectrometer has large potential applications in miniaturized spectral analysis systems on a chip.