A Wavelet Derivative Spectrum Length Method of TFBG Sensor Demodulation.

Fibre optic sensors using tilted fibre Bragg grating (TFBG) have high sensitivity for refractive index measurements. In order to achieve good metrological parameters of the measurement, an appropriate method of spectrum demodulation must be used. The method proposed in the article is an improvement of the spectral length algorithm. The spectral length parameter is treated as the sum of the derivative filter responses. In the original version, the first difference of spectrum elements was used, while this article proposes to use the wavelet transform to calculate the numerical derivative approximation. The advantage of this solution is an easy way to select the level of smoothing filtration by changing the scale parameter. The derivation is appropriate even for a relatively low signal-to-noise level. The approximation of the spectral length by the derivative calculated using the wavelet transform eliminates the high-frequency noise of the optical signal. The absolute value of determined spectral derivatives after significant smoothing can be used to estimate the wavelength of the decay of modes. After analyzing experimental data and performing calculations, it turns out that this is a linear method with better resolution than the contour length algorithm.

Experimental Analysis on the Optimal Excitation Wavelength for Fine-Grained Identification of Refined Oil Pollutants on Water Surface Based on Laser-Induced Fluorescence.

Laser-induced fluorescence (LIF) is an effective and all-weather oil spill identification method that has been widely applied for oil spill monitoring. However, the distinguishability on oil types was seldom considered while selecting the excitation wavelengths. This study is intended to find the optimal excitation wavelength for fine-grained classification of refined oil pollutants using LIF by comparing the distinguishability of fluorometric spectra under various excitation wavelengths on some typical types of refined oil samples. The results show that the fluorometric spectra of oil samples significantly vary under different excitation wavelengths, and the four types of oil applied in this study are most likely to be distinguished under the excitation wavelengths of 395 nm and 420 nm. This study is expected to improve the ability of oil types identification using LIF method without increasing time or other cost, and also provide theoretical basis for the development of portable LIF devices for oil spill types identification.

Assessment of Repeatability in Supercritical Fluid Chromatography with Electrochemical Detection Based on the ISO 11843 Part 7.

The present study proposes a method for the assessment of repeatability in supercritical fluid chromatography with electrochemical detection (SFC-ECD), based on the ISO 11843 part 7 (ISO 11843-7:2018) which can theoretically provide detection limits and standard deviation (S.D.) through the stochastic properties of baseline noise without repetitive measurements of real samples. On the baseline noise of SFC-ECD, large-amplitude and periodic noises with less than 0.05 Hz were observed, and the power spectrum of the baseline noise showed 1/f fluctuation (f = frequency). It was found that the present power spectrum analysis, according to the law of error propagation, can provide suitable noise parameters to calculate S.D. of baseline noise and a relative S.D. (RSD) of peak area by ISO 11843-7. The chromatographic determinations of α-, ß-, γ- and δ-tocopherol have been taken as examples. In the present SFC-ECD, the RSDs of peak areas for α-, ß-, γ- and δ-tocopherol obtained by ISO 11843-7 were within 95% confidence intervals of the RSD of them obtained by repetitive measurements (n = 6). Thus, we found that ISO 11843-7 is applicable to the assessment of repeatability in SFC-ECD for determining tocopherols without repetitive measurements.

Predicting chemical, enzymatic and nutritional properties of fermented barley (Hordeum vulgare L.) by second derivate spectra analysis from attenuated total reflectance-Fourier transform infrared data and its nutritional value in Japanese quails.

Nutrient concentrations, enzyme activities, viable probiotic counts and organic acid levels of fermented barley (FB) produced by solid state fermentation (SSF) and of its unfermented counterpart (unfermented barley (UFB)) were determined by quantitative methods, and compared with the second derivate spectra analysis (SDSA) of their attenuated total reflectance-Fourier transform infrared measurements. Moreover, in order to test the nutritional value of FB and UFB feeds, 3-d old Japanese quails were fed FB or UFB at 400 g/kg diet for 4 weeks. Chemical analysis indicated that FB had significantly increased crude protein, ether extract and ash contents, associated with lowered fibre and sugar contents, as compared with UFB. IR spectra pattern of FB and UFB samples were similar, but their SDSA parameters, particularly peak areas, differed significantly. The differences between FB and UFB in peak areas were highly correlated with the differences between two feeds in lipid, protein, total fibre, starch and sugar contents (p < 0.05). Furthermore, SDSA analysis found new peak areas only available in FB samples, indicating fortification with new biomolecules due to the effect of SSF. Feeding the FB containing diet for 3 or 4 weeks enhanced growth performance significantly and increased the carcass yield of quails, indicating its nutritional benefits in young growing quails. The results indicated that the further analysis of IR spectra data revealed important information on the chemical and biological properties of feed materials, which are found to be highly correlated with the results of compositional analysis and their biological value in animal model.

Does Laser Surgery Interfere with Optical Nerve Identification in Maxillofacial Hard and Soft Tissue?--An Experimental Ex Vivo Study.

The protection of sensitive structures (e.g., nerves) from iatrogenic damage is of major importance when performing laser surgical procedures. Especially in the head and neck area both function and esthetics can be affected to a great extent. Despite its many benefits, the surgical utilization of a laser is therefore still limited to superficial tissue ablation. A remote feedback system which guides the laser in a tissue-specific way would provide a remedy. In this context, it has been shown that nerval structures can be specifically recognized by their optical diffuse reflectance spectra both before and after laser ablation. However, for a translation of these findings to the actual laser ablation process, a nerve protection within the laser pulse is of utmost significance. Thus, it was the aim of the study to evaluate, if the process of Er:YAG laser surgery--which comes with spray water cooling, angulation of the probe (60°) and optical process emissions--interferes with optical tissue differentiation. For the first time, no stable conditions but the ongoing process of laser tissue ablation was examined. Therefore, six different tissue types (nerve, skin, muscle, fat, cortical and cancellous bone) were acquired from 15 pig heads. Measurements were performed during Er:YAG laser ablation. Diffuse reflectance spectra (4500, wavelength range: 350-650 nm) where acquired. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) were calculated for classification purposes. The clinical highly relevant differentiation between nerve and bone was performed correctly with an AUC of 95.3% (cortial bone) respectively 92.4% (cancellous bone). The identification of nerve tissue against the biological very similar fat tissue yielded good results with an AUC value of 83.4% (sensitivity: 72.3%, specificity: of 82.3%). This clearly demonstrates that nerve identification by diffuse reflectance spectroscopy works reliably in the ongoing process of laser ablation in spite of the laser beam, spray water cooling and the tissue alterations entailed by tissue laser ablation. This is an essential step towards a clinical utilization.

Intellectual disabilities and power spectra analysis during sleep: a new perspective on borderline intellectual functioning.

BACKGROUND: The role of sleep in cognitive processes has been confirmed by a growing number of reports for all ages of life. Analysing sleep electroencephalogram (EEG) spectra may be useful to study cortical organisation in individuals with Borderline Intellectual Functioning (BIF), as seen in other disturbances even if it is not considered a disease. The aim of this study was to determine if the sleep EEG power spectra in children with BIF could be different from typically developing children. METHODS: Eighteen BIF (12 males) (mean age 11.04; SD ± 1.07) and 24 typical developing children (14 men) (mean age 10.98; SD ± 1.76; P = 0.899) underwent an overnight polysomnography (PSG) recording in the Sleep Laboratory of the Clinic of Child and Adolescent Neuropsychiatry, after one adaptation night. Sleep was subdivided into 30-s epochs and sleep stages were scored according to the standard criteria and the power spectra were calculated for the Cz-A2 channel using the sleep analysis software Hypnolab 1.2 (SWS Soft, Italy) by means of the Fast Fourier Transform and the power spectrum was calculated for frequencies between 0.5 and 60 Hz with a frequency step of 1 Hz and then averaged across the following bands delta (0.5-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (11-15 Hz), and beta (16-30 Hz), gamma (30-60 Hz) for S2, SWS and REM (Rapid Eye Movement) sleep stages. RESULTS: BIF have a reduced sleep duration (total sleep time; P < 0.001), and an increased rate of stage shifts (P < 0.001) and awakenings (P < 0.001) and WASO (wakefulness after sleep onset) % (P < 0.001); the stage 2% (P < 0.001), and REM% (P < 0.001) were lower and slow-wave sleep percentage was slightly higher (P < 0.001). All children with BIF had an AHI (apnoea-hypopnea index) less than 1 (mean AHI = 0.691 ± 0.236) with a mean oxygen saturation of 97.6% and a periodic leg movement index (PLMI) less than 5 (mean PLMI = 2.94 ± 1.56). All sleep stages had a significant reduction in gamma frequency (30-60 Hz) (P < 0.001) and an increased delta frequency (0.5-4.0 Hz) (P < 0.001) power in BIF subjects compared with typically developing children. CONCLUSION: Our findings shed light on the importance of sleep for cognition processes particularly in cognitive borderline dysfunction and the role of EEG spectral power analysis to recognize sleep characteristics in BIF children.

A Novel Accurate Peak Extraction Algorithm of Mass Spectrometry Based on Iterative Adaptive Curve Fitting.

In the analysis of mass spectrometry, the peak identification from the overlapped region is necessary yet difficult. Although various methods have been developed to identify these peaks, especially the continuous wavelet transformation, their applications are still limited and it is hard to deal with the complex overlapped peaks. In this study, a novel peak extraction algorithm of mass spectrometry based on iterative adaptive curve fitting is proposed to address these challenges. It fully utilizes the global optimization characteristics of adaptive curve fitting. Initial peak parameters are obtained using a window searching method, and the residuals between the adaptive fitting peak and the original data indicate the fit's effectiveness and provide information about the peaks in overlap. Using this information, we performed iterative adaptive fitting, continuously updating the overlapped peaks until the residuals met the completion criteria. All of the peaks within the overlapped region can be successfully extracted by the final fitting. The proposed method is evaluated by the simulated data, the real signal from a public data set, and the spectra of two different mass spectrometry instruments. The results demonstrate that this method can more effectively extract peaks with severe overlap and multiple overlapped peaks, resist noise interference, and offer the potential to process peaks with a high dynamic range. More importantly, the proposed method accurately identifies overlapped peaks in the actual spectra from various mass spectrometry instruments, which helps the qualitative and quantitative analyses to a great extent.

Exploring secular variation of the gravitational constant from high-resolution quasar spectra.

The exploration of potential variations in fundamental physical constants is crucial for testing of Grand Unification Theories (GUTs), which aim to unify the fundamental forces of nature. This study utilizes direct observational tests to explore these variations, offering a deep-look into the universe's distant past. By analyzing high-resolution quasar spectra of HE 0515-4414* and comparing them with laboratory-calibrated Ritz wavelengths, we establish an upper limit on the possible cosmological deviation of the gravitational constant: G ˙ / G = ( 0.918 ± 2.830 ) × 10 - 15 yr - 1 over cosmic timescales. Our findings provide a novel tool for probing the physical implications of GUTs, contributing to our understanding of fundamental physics.

Analysis of Local Properties and Performance of Bilayer Epitaxial Graphene Field Effect Transistors on SiC.

Epitaxial bilayer graphene, grown by chemical vapor deposition on SiC substrates without silicon sublimation, is crucial material for graphene field effect transistors (GFETs). Rigorous characterization methods, such as atomic force microscopy and Raman spectroscopy, confirm the exceptional quality of this graphene. Post-nanofabrication, extensive evaluation of DC and high-frequency properties enable the extraction of critical parameters such as the current gain (fmax) and cut-off frequency (ft) of hundred transistors. The Raman spectra analysis provides insights into material property, which correlate with Hall mobilities, carrier densities, contact resistance and sheet resistance and highlights graphene's intrinsic properties. The GFETs' performance displays dispersion, as confirmed through the characterization of multiple transistors. Since the Raman analysis shows relatively homogeneous surface, the variation in Hall mobility, carrier densities and contact resistance cross the wafer suggest that the dispersion of GFET transistor's performance could be related to the process of fabrication. Such insights are especially critical in integrated circuits, where consistent transistor performance is vital due to the presence of circuit elements like inductance, capacitance and coplanar waveguides often distributed across the same wafer.

Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA.

Chemical shift transfer (CST) is a well-established technique in NMR spectroscopy that utilizes the chemical shift assignment of one protein (source) to identify chemical shifts of another (target). Given similarity between source and target systems (e.g., using homologs), CST allows the chemical shifts of the target system to be assigned using a limited amount of experimental data. In this study, we propose a deep-learning based workflow, ARTINA-CST, that automates this procedure, allowing CST to be carried out within minutes or hours of computational time and strictly without any human supervision. We characterize the efficacy of our method using three distinct synthetic and experimental datasets, demonstrating its effectiveness and robustness even when substantial differences exist between the source and target proteins. With its potential applications spanning a wide range of NMR projects, including drug discovery and protein interaction studies, ARTINA-CST is anticipated to be a valuable method that facilitates research in the field.

Investigating the muti-scaling properties and connectedness of the sovereign bond yields: Hurst exponent and network analysis approach.

Using daily yield data of 14 sovereign bond markets from emerging and developed economies from July 10, 2000, to July 10, 2022, we examine their scaling properties using generalized Hurst exponent and spectral density analysis and investigate the connectedness based on a network analysis approach. We consider the yields of 2-year and 10-year bond yields to investigate the scaling properties for short- and long-term sovereign bonds. This selection also allows us to examine sovereign bond spreads with respect to the USA. We also use regularized partial correlation network analysis to connect different countries in communities based on yields. We find that the scaling behavior of the bond yields for both terms fits well using the Hurst exponent and spectral analysis confirms this finding. Moreover, we also find that even though bonds in both cohorts show anti-persistent behavior except that of the USA, the developed economies' bond yields are relatively less anti-persistent as compared to those of emerging economies. The networks of both the 2-year and 10-year yields indicate community formation in various countries which provides diversification benefits to the investors. Most of the emerging countries are classified into one community in the long-tenure bonds as well but this concentration is more evident in the short-tenure bonds.

Intermolecular hydrogen bonding of alcohols with dinitrobenzene radical anion and dianion: A combined electrochemical and DFT study.

Hydrogen bonding is one of the most important inter-molecular interactions in the field of biochemistry and medicinal chemistry. Such non-covalent interactions play a vital role in self-assembly phenomena, chemical structures, material properties and enzymatic catalysis. Herein, we present hydrogen bonding phenomenon in alcohols-dinitrobenzene (DNB) radical anion/dianion systems using electrochemical and computational approaches. First, 1,3-DNB radical anion and dianion were generated through electrochemical method and then hydrogen bonding interactions with aliphatic alcohols in DMSO are studied through cyclic voltammetry (CV). CV results show that the cathodic peak potential of 1,3-Dinitrobenzene in Dimethyl sulfoxide is shifted catholically upon addition of alcohols which represent hydrogen bonding phenomenon. Theoretical investigations are performed to gain deep insight on hydrogen bonding interaction strength in DNB-alcohol systems. H-bonding interaction of all isomers of DNB (1,2-, 1,3-, 1,4-), its corresponding radical anion, and dianion with aliphatic alcohols is studied using density functional calculations. The strength of H-bonding is evaluated both qualitatively and quantitatively using interaction energies, vibrational and electronic spectroscopic analysis. Understanding of these interactions will be helpful in gaining insight into biological systems where these interactions play significant role.

A Simulation Independent Analysis of Single- and Multi-Component cw ESR Spectra.

The accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understanding their structure-function relationships and electrochemical properties. The current methods of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users' defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly/partially resolved and unresolved spectra, which is common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

The effects of white matter hyperintensities on MEG power spectra in population with mild cognitive impairment.

Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer's disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.

Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework.

Harmful algal blooms (HABs) are severe ecological disasters threatening aquatic systems throughout the World, which necessitate scientific efforts in detecting and monitoring them. Compared with traditional in situ point observations, satellite remote sensing is considered as a promising technique for studying HABs due to its advantages of large-scale, real-time, and long-term monitoring. The present review summarizes the suitability of current satellite data sources and different algorithms for detecting HABs. It also discusses the spatial scale issue of HABs. Based on the major problems identified from previous literature, including the unsystematic understanding of HABs, the insufficient incorporation of satellite remote sensing, and a lack of multiple oceanographic explanations of the mechanisms causing HABs, this review also attempts to provide a comprehensive understanding of the complicated mechanism of HABs impacted by multiple oceanographic factors. A potential synthesized framework can be established by combining multiple accessible satellite remote sensing approaches including visual interpretation, spectra analysis, parameters retrieval and spatial-temporal pattern analysis. This framework aims to lead to a systematic and comprehensive monitoring of HABs based on satellite remote sensing from multiple oceanographic perspectives.

Physicochemical properties and microstructural behaviors of rice starch/soy proteins mixtures at different proportions.

Rice starch (RS) and soy protein (SP) were mixed at various ratios and the physicochemical, rheological and tribological properties of the resulted pastes were analyzed. Microscopy and spectral techniques were applied to follow the structural changes during cooling and heating processes. Higher proportion of SP resulted in lower swelling power. According to DSC and RVA results, SP inhibited the gelatinization of starch, leading to higher pasting temperature; the peak, trough, final viscosities, set back and enthalpy changes were decreased with increasing SP concentration. All the samples presented a shear thinning behavior and the mixtures with soy proteins showed lower viscosity, yield stress and consistency coefficient than pure starch paste. The creep-recovery test showed that the addition of soy proteins resulted in more liquid-like mixtures, causing weakening of instantaneous elastic recovery. The combination of rice starch and soy proteins increased the friction, compared with their individual components, except RS/SP 9/1 at sliding speed >20 mm/s. According to the microstructure and spectra analysis, the soy protein adhered on the surface of starch granules, which might have hindered the leaching of amylopectin; and the association between starch and soy proteins mainly occurred when the starch granules were gelatinized, which could be reinforced during starch retrogradation.

Vertical ground reaction force oscillation during standing on hard and compliant surfaces: The "postural rhythm".

When a person stands upright quietly, the position of the Centre of Mass (CoM), the vertical force acting on the ground and the geometrical configuration of body segments is accurately controlled around to the direction of gravity by multiple feedback mechanisms and by integrative brain centres that coordinate multi-joint movements. This is not always easy and the postural muscles continuously produce appropriate torques, recorded as ground reaction force by a force platform. We studied 23 young adults during a 90 s period, standing at ease on a hard (Solid) and on a compliant support (Foam) with eyes open (EO) and with eyes closed (EC), focusing on the vertical component of the ground reaction force (VGRF). Analysis of VGRF time series gave the amplitude of their rhythmic oscillations (the root mean square, RMS) and of their frequency spectrum. Sway Area and Path Length of the Centre of Pressure (CoP) were also calculated. VGRF RMS (as well as CoP sway measures) increased in the order EO Solid ≈ EC Solid < EO Foam < EC Foam. The VGRF frequency spectra featured prevailing frequencies around 4-5 Hz under all tested conditions, slightly higher on Solid than Foam support. Around that value, the VGRF frequencies varied in a larger range on hard than on compliant support. Sway Area and Path Length were inversely related to the prevailing VGRF frequency. Vision compared to no-vision decreased Sway Area and Path Length and VGRF RMS on Foam support. However, no significant effect of vision was found on VGRF mean frequency for either base of support condition. A description of the VGRF, at the interface between balance control mechanisms and sway of the CoP, can contribute information on how upright balance is maintained. Analysis of the frequency pattern of VGRF oscillations and its role in the maintenance of upright stance should complement the traditional measures of CoP excursions in the horizontal plane.

Effect of dissolved organic matter and its fractions on disinfection by-products formation upon karst surface water.

In this study, disinfection by-products (DBP) formation from dissolved organic matter (DOM) and its fractions, including both hydrophilic and hydrophobic components, were investigated at a typical karst surface water. The subsequent DBP formation potential was evaluated by deducing chemical characteristics of DOM fractions and representative algal organic matter (Chlorella sp. AOM) under the influence of divalent ions (Ca2+ and Mg2+) via spectra analysis. Both terrigenous and autochthonous DOM performed as critical DBP precursors, and DBP formation patterns were tightly correlated to organic matter chemical variations. DBP formation was significantly higher in drought period compared to that in wet period (P < 0.05). Particularly, trichloromethane (TCM) and dichloroacetonitrile (DCAN) showed distinct formation patterns compared to the scenarios in non-karst water. For DOM fractions, hydrophobic components showed higher DBP formation compared to hydrophilic counterparts, hydrophilic neutral enriched more reactive organic nitrogen for N-DBPs production. It was preferable to enrich humic-like substances after Ca2+ and Mg2+complexation in Chlorella sp. AOM, TCM formation increased whereas DCAN production remained unchanged in the presence of divalent ions. This study innovatively provided a linkage between chemical characteristics of DOM and understanding of DBP formation in karst surface water.

Oil pollutant identification based on excitation-emission matrix of UV-induced fluorescence and deep convolutional neural network.

Identifying the types of oil pollutants in a spill event can help determine the source of spill and formulate the plan of emergency responses. Excitation-emission matrix (EEM), which is also called three-dimensional fluorometric spectra, includes abundant spectral information in the domain of excitation wavelength and can be potentially applied to identify oil types. UV-induced fluorometric experiments were conducted in this study to collect EEMs for five types of oil that are commonly used in maritime transportation. A deep convolutional neural network (CNN) model for oil types identification was built based on the classic VGG-16 model. According to the identification results, the model was able to provide a reasonable classification on the five types of oil used in the experiments. Additionally, a biased classification result was observed in the experiment: the model was able to provide the most accurate classification on 0W40 lubricant but encounters difficulty distinguishing between - 10# diesel and 92# gasoline. The potential reasons for this result and the approaches to improve the model were also discussed.

Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIP-Boruta.

Regression models are constructed to predict glucose and lactate concentrations from near-infrared spectra in culture media. The partial least-squares (PLS) regression technique is employed, and we investigate the improvement in the predictive ability of PLS models that can be achieved using wavelength selection and transfer learning. We combine Boruta, a nonlinear variable selection method based on random forests, with variable importance in projection (VIP) in PLS to produce the proposed variable selection method, VIP-Boruta. Furthermore, focusing on the situation where both culture medium samples and pseudo-culture medium samples can be used, we transfer pseudo media to culture media. Data analysis with an actual dataset of culture media and pseudo media confirms that VIP-Boruta can effectively select appropriate wavelengths and improves the prediction ability of PLS models, and that transfer learning with pseudo media enhances the predictive ability. The proposed method could reduce the prediction errors by about 61% for glucose and about 16% for lactate, compared to the traditional PLS model.